Advances in Communication and Computational Technology

The remarkable technological advancements in the health care industry have improved recently for the betterment of patients’ life and providing better clinical decisions. Applications of machine learning and data mining can change the available data to valuable information that can be used for recommending appropriate drugs by analyzing symptoms of the disease. In this work, a machine learning approach for multi-disease with drug recommendation is proposed to provide accurate drug recommendations for the patients suffering from various diseases. This approach generates appropriate recommendations for the patients suffering from cardiac, common cold, fever, obesity, optical, and ortho. Supervised machine learning approaches such as Support Vector Machine (SVM), Random Forest, Decision Tree, and K-nearest neighbors were used for generating recommendations for patients. The experimentation and evaluation of the study was carried out on a sample dataset created only for testing purpose and is not obtained from any source (medical practitioner). This experimental evaluation shows that the Random Forest classifier approach yields a very good recommendation accuracy of 96.87% than the other classifiers under comparison. Thus, the proposed approach is considered as a promising tool for reliable recommendations to the patients in the health care industry.

In recent years, the mobile in one’s pocket has become a multifunctional device and is no longer a device used just for making calls or sending text messages. People make use of their mobiles either to take pictures and videos, or to play games or to listen to music. An increasing number of people today have an autonomous Internet connection and they get online using a mobile device to share text, image, games, or applications as new applications are constantly being available. Mobile computing devices including smart phones provide us the capability to access information/make calls and interact using communication methods that can be insecure also. Moreover, redesigning smart phones resulted in a technology that can keep us involved for most of the time and also oblivious to events around us. In this paper, we will cover how mobility in terms of smart phones evolved, its effects on our lives: both positive and negative, various users of mobility and its benefits to casual users, professional users, and transactional users. Then, we will cover effects of mobility on human wellness and existence & solutions for it, security threats to mobility along with future of mobilization with reference to need of mobile cloud. This paper investigates the usage patterns of various mobility devices. We have taken a dataset of 300 people of different countries from UCI repository and analyzed various important patterns of the available smart mobility devices. To sum up, smart phones are remarkably affecting in both positive and negative ways in our world. Since all smart phones are improvising in technical specifications, we have to redesign future smart phones by reducing the negatives and increasing the positive impacts on the globe.

In this paper, we investigate the time-fractional Cahn–Allen equation (CAE) with a novel homotopy-based numerical technique, namely homotopy perturbation transform technique in which homotopy perturbation method and Laplace transform (LT) are combined. In order to verify the reliability and accuracy of the proposed technique, the numerical results are also presented graphically.

Colorization, a task of coloring monochrome images or videos, plays an important role in the human perception of visual information, to black and white pictures or videos. Colorizing, when done manually in Photoshop, a single picture might take months to get exactly correct. Understanding the tediousness of the task and inspired by the benefits of artificial intelligence, we propose a mechanism to automate the coloring process with the help of convolutional neural networks (CNNs). Firstly, an Alpha version is developed which successfully works on trained images but fails to colorize images, and the network has never seen before. Subsequently, a Beta version is implemented which is able to overcome the limitations of Alpha version and works well for untrained images. To further enhance the network, we fused the deep CNN with a classifier called Inception ResNet V2 which is a pre-trained model. Finally, the training results are observed for all the versions followed by a comparative analysis for trained and untrained images.

EEG signals can be preferred as a biometric trait because of their uniqueness, robustness to spoof attacks, and many other advantages as compared to other commonly used identifiers such as finger print, palm print, and face recognition. A complete overview of biometric systems based on EEG signals, their acquisition, pre-processing, feature extraction, and classification at different frequency bands is presented in this paper. Comparison is made between various techniques and their efficiencies used in EEG-based biometric systems. Different signal acquisition methods (resting state, visual stimuli, cognitive activities) used in previous works have been discussed with their pros and cons. Nowaday’s researchers focus on low-cost EEG acquisition systems with a smaller number of electrodes with better accuracy. Databases used in this area are also discussed, some of them are public and some authors acquire their personal data. A table is provided which compares the results of different signal acquisition methods, pre-processing techniques, feature extraction, and classification techniques.

The agriculture sector is essential for every country because it provides a basic income to a large number of people and food as well, which is a fundamental requirement to survive on this planet. We see as time passes, significant changes come in the present era, which begins with Green Revolution. Due to improper knowledge of plant diseases, farmers use fertilizers in excess, which ultimately degrade the quality of food. Earlier farmers use experts to determine the type of plant disease, which was expensive and time consuming. In today’s time, image processing is used to recognize and catalog plant diseases using the lesion region of plant leaf, and there are different modus-operandi for plant disease scent from leaf using neural networks (NN), support vector machine (SVM), and others. In this paper, we improve the architecture of the neural networking by working on ten different types of training algorithms and the proper choice of neurons in the concealed layer. Our proposed approach gives 98.30% accuracy on general plant leaf disease and 100% accuracy on specific plant leaf disease based on Bayesian regularization, automation of cluster, and without overfitting on considered plant diseases over various other implemented methods.

Maximum power point tracker (MPPT) controls the DC/DC converter for extracting maximum power from solar photovoltaic (SPV) array connected with power generation system. MPPT operates at its maximum power point (MPP) (Vmp, Imp) irrespective to load conditions and input weather conditions. Use of by-pass diodes in series-connected SPV modules under non-uniform insolation is a key cause for many power peaks in the power–voltage characteristics of SPV array. Henceforth the problem of MPPT under partial shading becomes a nonlinear optimization problem. A new quick and reliable MPPT technique is proposed in this paper to identify the global MPP under partial shadow conditions. The computation time and correctness in tracking global MPP are compared with standard soft computing techniques: modified binary (MB) search, differential evolution (DE) techniques, and particle swarm optimization (PSO). The results show correctness of the presented random binary search technique in tracking the global MPP in very less time than the conventional soft computing techniques. The technique is quick, simple, and oscillation-free for tracking global MPP in least iterations; hence, the computation (hardware) requirements are less than that using PSO and DE MPPT techniques.

Wireless mesh networks (WMNs) as compared to other conventional networks had emerged as the most popular choice in today’s era due to its distributed nature. WMNs allow multi-hop communication with the capability of self-healing. Due to its distributed nature, internal attacks such as blackhole, wormhole, and DOS attack could be easily launched to degrade the overall performance of WMNs. The attack disrupts the normal operation of routing protocols and causes a large amount of packet drop between mesh entities. Therefore, to ensure secure routing, it is essential to compute the trustworthiness of each node and to detect the malicious nodes within the WMNs. In this paper, we had proposed a trust-based detection algorithm that ensures the detection of malicious nodes in WMNs. The experimental result of our proposed protocol ensures an efficient detection of the malicious node with a higher detection rate.

Farming is the most significant portion of Indian economy. It contributes 17–18% in India’s gross domestic product (GDP) accordingly Indian economic survey of year 2018 and employs more than 50% of the workforce in India. This paper presented a microcontroller-based system to protect agriculture land from crop vandalization by designing a band-pass filter (BPF) with the help of a sound sensor. Presently most of our agriculturists follow traditional approaches to protect their agricultural area from birds attack. But these approaches are almost ineffective and time-consuming. This paper aims to solve the above-defined problem economically and effectively by delivering an automated surveillance system to our agriculturists. We examined this system in the campus of School of Agricultural Sciences & Rural Development Medziphema, Nagaland.

Cloud Federation is the coalition of multiple Cloud Providers (CPs) to surmount the constraint of finite physical resources of individual CPs. The collaboration enables them to share resources with each other at a certain price based on Federation Level Agreement (FLA). The pricing mechanism used for the resources requested by customer’s acts as an important factor in the calculation of their bills and in the generation of revenues for CPs, in such an environment. Consequently, it performs a key role in the selection of a CP by customers and in incentivizing the CPs to remain in Federation. Considering this, the present paper aims to propose mechanisms of pricing to charge internal users and peer CPs. The proposed mechanisms are evaluated in a simulated environment. The simulation results illustrate that the proposed pricing mechanisms ensure fair bills for customers and profitable share of revenues for the collaborating CPs.

Higher order squeezing and photon statistics in pump mode in five wave mixing process has been studied under short-time interaction in nonlinear medium. A comparison of squeezing in field amplitude and higher order amplitude has been investigated, and we have found that squeezing increases with higher order of field amplitude. Photon statistics has also been studied and found to be sub-Poissonian in nature. It is also observed that squeezing and photon statistics is directly related to number of photons present in the system prior to interaction in nonlinear medium.

Real power loss minimization is the key objective of this work, and it has been attained by applying chaotic-based adaptive butterfly mating optimization algorithm (CABM). Butterfly mating limitations and scattering in the exploration area are articulated in the CABM algorithm. Butterfly positioned near to the border may get puzzled regarding the direction to the border since UV augments amid of an increase in distance. Through indistinguishable unchanging step, acceleration of butterflies is restricted in iterations. Chaotic-based adaptive butterfly mating optimization (CABM) algorithm’s validity is verified by testing in IEEE 57 bus test system. Projected CABM algorithm reduced the power loss effectively.

We have presented here an online signature database. The main issue in signature verification is to acquire the reliable signature database for checking the performance of the developed system. In this work, we have collected a signature database, mobile-based signature database (MOBSIGN DB). We have followed the same protocols as followed in SVC2004 signature database. We have collected dynamic as well as offline images of the signatures. The dynamic properties include xy coordinates and actual time taken for doing the signature (which was arbitrarily taken in SVC2004) for each data point of the captured signatures.

Association rules are the strong rules which occur frequently in the dataset. Association Mining is the technique used to explore these rules with the help of various algorithms available in data mining. This paper discusses the use of apriori() to mine the strong rules which are helpful to find out the customer purchasing pattern and help to increase the sale. In this paper, R language is used and arules() package is used to mine the rules based on the value of support and confidence.

In a business organization, the data needs to be shared among different personnel, therefore chances of fraud are more. In order to protect the organization’s confidential and sensitive data among different levels of employees from theft or any other illegal activity that violates the company security policy, ABE known as Attribute-Based Encryption was introduced. ABE is a cryptographic parameter that plays a vital role in providing fine-grained access control of outsourcing data in data sharing system of the business organization but due to its non-efficiency and several other limitations, it was proved inefficient in outsourcing of data. However, Cipher-text Policy Attribute-Based Encryption (CP-ABE) was implemented to overcome the existing ABE issues and to protect the firm’s confidential data from theft or leakage. CP-ABE defines an access policy that covers all the attributes within the system. In this scheme, the user’s private key is associated with the set of attributes for encryption and decryption purposes. Our experimental simulations demonstrate the real encryption/decryption time over different types of files and their sizes and how they get affected after applying CP-ABE. The performance and security analysis stipulated that the used scheme is competent to securely manage the data in any organization.

In the field of business intelligence, we require the analysis of multidimensional data with the need for it being fast and interactive. Data warehousing and OLAP approaches have been developed for this purpose in which the data is viewed in the form of a multidimensional data cube which allows interactive analysis of the data in various levels of abstraction presented in a graphical manner. In data cube, there may arise a need to materialize a particular cuboid given that some other cuboid is presently materialized, in this paper, we propose an algorithm for cuboid materialization starting from a source cuboid to the target cuboid in an optimal way such that the intermediate cuboids consume less space and require lower time to generate by making sure those cuboids have the least number of rows compared to other valid cuboids available for selection, by sorting them based on the product of cardinalities of dimensions present in each cuboid.

Travelling to different places depends on lots of factors such as hotels, restaurants, nearby hospitals, places to visit in cities, etc. All this information is available on different websites in an unstructured manner thus people do not get information as per their queries in organized format. People search for these factors on search engines which use keyword matching mechanism. Therefore, this paper presents full-text queries searching mechanism which gives precise results in a structured format. Here, our system scraps data from websites to collect information about cities, hotels and hospitals. Concepts of linked data using ontology are implied which has the capability to relate multiple sources of data available on different websites and infer new knowledge from it. Natural Language processing methods such as co-reference resolution is used, which forms a relationship between sentences scrapped from web, which helps to perform better search query without losing meaning of sentences during the processing. In our work, we have also used the Named entity recognition mechanism which applies tags on words with the real-world concepts that they represent. These tags are further utilized by Python library named RDFLib to match the tags which form a relationship between classes within ontology. This relationship between classes and tags are further used to insert and extract data from ontology.

This paper recognizes online handwritten Gurmukhi characters and words. The neural network-based recognition for online handwritten Gurmukhi characters and words has been observed first time in this study. In this work, a scheme is proposed to develop a feature vector and its use as an input to neural network recognition engine. A set of low-level, high-level, and Gabor features are extracted, and a feed-forward neural network is trained to recognize 40 classes of Gurmukhi characters. This work implements rearrangement of strokes stage after recognition and post-processing stages. The results have been achieved as 93.53% and 83.69% for 4511 Gurmukhi characters and 2576 Gurmukhi words, respectively.

Classification of online stream data is must for network analysis and providing Quality of Service (QoS). Stream data has properties which requires the algorithm to be incremental and should handle concept drift. Traffic classification is the prominent solution to handle bulk data streams to provide services like packet filtering, routing policies, traffic shaping, limiting traffic, etc. Many stream-based classification algorithms exists in literature to meet the requirements like scanning the data only once, any time analysis and fast response, and limited memory utilization. Further, more accurate, fast, and limited memory supporting algorithms and concepts are required to handle everyday increasing data over Internet. This research work proposes an improvement in accuracy of the classification performed using lesser number of training instances to decide a split during induction of the decision tree (Hoeffding tree). Jensens’s inequality concept is used, and the Hoeffding bound reduces to minimize the bound for the bad events (i.e., it limits the margin of error of the algorithm). Number of examples reduced results in fast execution and decrease the memory used.

Additive manufacturing technologies possess the capability to produce products according to customer demands, which make these technologies as the best option for lightweight technology. Another advantage is the design complex intricate shapes can be easily manufactured by rapid prototyping technologies to include lattice structures in automobile applications like the body panels of the car, the engine’s intercooler, or gas tank with the aim of weight and strength optimizations. There are various techniques available for manufacturing lattices. This paper presents an outline of lattice structure with respect to different morphologies, various manufacturing methods for it, and different materials available to produce lattice structures. It discusses how the variation of characteristics can improve the lattices’ performance significantly, from a mechanical and application point of view. The characterization of lattice structures and the recent developments in finite element analysis models are studied. Many authors compared conventional techniques with additive manufacturing; it was found that manufacturing defects induced in Micro-Lattice Structure manufactured via additive manufacturing were less. With the help of additive manufacturing, complex morphologies can be easily developed by using CAD software. The simple, rapid, and scalable fabrication of MLS are achieved using additive manufacturing. This paper gives an overall idea about how research progressed by different researchers regarding lattices in terms of materials FE analysis and suggests future research directions required to improve their use in lightweight applications.

Image retrieval is broadly classified into two parts—Description-based image retrieval and content-based image retrieval. DBIR involves annotating the images and then using text retrieval techniques to retrieve them. This requires vast amount of labor and is expensive for large image databases. Semantic gap is another major issue in DBIR. Different people may perceive the contents of the image differently and hence annotate it differently, e.g., a bright shiny silver car for one person can be boring dull gray car for another. Thus, no objective keywords for search can be defined, whereas CBIR aims at indexing and retrieving images based on their visual contents like color, texture, and shape features. For a content-based image retrieval system, the performance is dependent on how the feature vector is represented and what similarity metrics is chosen. The disadvantage with visual content descriptor is that it is very domain specific, e.g., color correlogram and color histogram for color feature extraction, Tamura and GLCM for texture feature extraction, local binary pattern and HOG for face recognition, SIFT and SURF for object detection, etc. Hence, convolution neural network which is not domain specific would be a better approach.

Link availability, mobility, scalability, transmitting and receiving power, path loss model, signal quality and intelligent resource utilization heuristics are numerous factors for enhancing the network performance of HANETs. Quality of service and quality of experience can be improved with cross-layer, cross-domain and cross-network design which extracts the critical information from different platforms as required by dynamic behavior and heterogeneity of the network. Distance between the nodes, packet size, link availability, mobility model, and transmission range are simulation parameters which can degrade the heuristic performance of the network. In this paper, we worked on optimized link heuristic under heterogeneous network where handover plays an important role. We observed that in ad hoc network, the performance of optimized link heuristic is stable as compared to reactive protocols. 6LoWPAN with congestion control policy performs efficiently for IoT scalable architecture, and protocol stack handling is suitable for energy-efficient device.

With the advancement of biomedical technology, human brain signals are easy to measure and which are known as electroencephalogram (EEG) signals. These signals are used in different applications. One of the applications for brain waves is biometric authentication. For any signal to use as biometric parameter, it must possess some biometric characteristics such as universality, uniqueness, permanence, collectability, performance, acceptance, and circumvention. EEG has several characteristic to use as biometric parameter. This paper shows the uniqueness of EEG signal using some statistical parameters that support the uniqueness property of EEG.

According to IUCN’s global analysis of extinction risk for plant species, there is a true extent of threat to 380,000 plant species worldwide (Amlekar et al. in IBMRD J Manage Res 3:224–232, [1]). For future concerns, it is important to spread more awareness about conservation of plants. Thus, to save plants, it is first important to identify them. It is important to identify plants in that case. This shows that recognition and classification of plant leaves have become an essential field of research. Plants can be classified on various bases like flowers or on cellular structures, but these can be done only by botanists. Thus, as plant leaves are easily available, they can be taken as the desired option for classification. Now, to classify a leaf image into a particular class of species, it is necessary to select those features which differentiate the classes as much as possible. To select these features, discriminant analysis is done (Wu et al. in IEEE, pp 11–16, [2]). This paper focuses on two discriminant analysis approaches—linear and quadratic discriminant analysis to reduce the number of features in plant leaf images, and then, various classifiers including SVM (SVC), KNN and logistic regression [21] are used to classify a particular image into a particular class of plant species. The other two phases include feature extraction and classification (Rahmani et al. in Int J Organ Collective Intell (IJOCI) 6:15–28, [3]). All the experiments are conducted on Flavia dataset, and the highest accuracy of 87% is obtained on KNN classifier using quadratic discriminant analysis. The takeaway of our experiment is that QDA performed better as compared to LDA in case classes that have different variances.

Isomorphism has been a long-standing research problem, an instance of which is to determine if two graphs are structurally the same. Verification of isomorphism gets computationally intensive for huge graphs with nodes in the order of millions. The computational complexity is high because the current forms of graph representations require complex data structures for processing. This paper presents an experimental proof of an expedient means to detect isomorphism, using Locality Sensitive Hashing (LSH). LSH was originally designed to find similar document pairs, within massive datasets, in polynomial time. The graphs are modeled as simple bag-of-words documents, using the proposed Node-Neighbor-Degree sequence approach. For huge graphs, this representation facilitates in-memory computation. This approach was validated on graphs, with nodes in the order of thousands. The proffered method was found to be significantly faster than conventional graphic tools.

Image segmentation is a prominent task done in computer vision. Image thresholding is one such technique in image segmentation. Thresholding is a method of categorizing image intensities into two classes and on the basis of that yielding an image which is a binary image, and ideally also has all the fine details of region of interest which an image should have for analysis. Image thresholding is widely used as it reduces the computational cost of processing the image and makes processing feasible in real-world applications like medical imaging, object detection, recognition task, character recognition, etc. This paper dwells into the depth of thresholding techniques to know which technique can perform better on all kinds of images so as to extract region of interest. We found out that not every technique is good for all cases, Otsu’s global thresholding is a promising and faster way to segment and generate a binary image, but works well with images having negligible noise and region of interest already being very much clear in the original image, whereas applying methods like Otsu’s thresholding on sliced blocks of images and then merging them or applying moving averages (sliding windows) on images having noise which is distributed in a specific region of image, moving averages gave result better on images which have distributed gradient noise. Whereas the hybrid technique used are a combination of global and local thresholding.

Sentimental Analysis (SA) is a process by which one can examine the feelings towards services, products, movies with the help of reviews. SA is a computing treatment of feeling, opinion, and subjectivity of contents. In this survey paper, we explain the overview of the sentiment analysis. For finding the sentiment analysis of reviews, different types of levels and classification of text data are explained. Three types of levels are explained and for classification two approaches machine learning approach and lexicon-based approach are explained. Some latest articles are used to show the accuracy of the classifiers.

There are numerous toxic and lethal substances that are available in the environment due to the rapid industrial growth and excessive application of pesticides. These substances get into the human food chain majorly through air, water, and soil. This paper presents a case study investigating the organochlorine pesticide levels in women experiencing the malignant and benign growth of breast cancer disease in order to evaluate the exposure against the chemicals and its association with the risk of breast cancer among women. After obtaining the blood and adipose tissue samples, levels of 51 chemicals including DDT, its metabolites, and isomers of HCH among 50 women each with the malignant and benign growth of breast cancer disease are measured. The levels of the chemicals in women with malignant growth of breast cancer are compared with benign cases and a prediction model is built using popular ensemble machine learning framework. The proposed framework is an optimized version of Random Forest algorithm in which feature selection is implemented and the process is incorporated with the preprocessing filters. The proposed framework for breast cancer prediction successfully achieved a prediction accuracy of 90.47%, which is found to be better than the standard classifiers like SVM, neural network, etc.

The paper presents the realization of a lossless grounded inductance circuit that is capable of low-voltage (LV) operations. This has been possible by incorporating an important non-conventional circuit design technique known as bulk-driven (BD) for implementing a second-generation current conveyor (CCII) cell. The bulk-driven second-generation current conveyor (BDCCII) nearly allows rail-to-rail input-output operations while operating at supply voltage levels as low as ±0.5 V. Further three voltage-mode filter configurations namely: a second-order band-pass (BP), band-elimination (BE) and high-pass (HP) have been implemented to verify the workability of the simulated high-valued inductance. These filters have been specifically designed for applications which involve extremely low-frequency operation like biosensors, bio-signal processing, etc. PSPICE simulations have been performed using 0.18 µm CMOS standard technology and obtained results confirm the theoretical analysis.

During the diagnosis of breast cancer, neoadjuvant chemotherapy is supplied intravenously. Physicians recommend chemotherapy before surgery to reduce the invasive tumor’s large size. This research work suggests a model for Neural Network Ensemble Machine Learning, Implementation of a series of machine learning algorithms to create an enhanced and efficient predictable solution patients ‘maximum pathological response after Neoadjuvant Chemotherapy. The quality of the neural network ensemble framework for machine learning is measured using multicriteria technique of decision making known as simple weighted additive (WSAW). The performance score for WSAW is calculated by taking into account ten measurements, namely accuracy, mean absolute error, root mean square error, TP rate, FP rate, accuracy, recall, F-measure, MCC, and ROC. The results are verified using the technique of cross-validation K-fold to achieve 97.20% accuracy. The findings are quite positive when the execution of the proposed system is coupled with the output of state-of-the-art classificators, for example, Bayes Net, Naïve Bayes, logistic, multilayer perceptron, SMO, voted perceptron, etc. With the increasing trend of artificial intelligence applications in cancer research, machine learning has a great future in forecasting and decision making.

The performance measure of M-ary phase shift keying and M-ary frequency shift keying for maximal ratio combiner (MRC) is numerically investigated on the channel with one-wave-diffused-power (OWDP) fading. The numerical evaluation of the performance parameters is done by the probability density function (pdf) based method. The pdf of the MRC receiver is evaluated using the characteristic function. The outcomes obtained are plotted to observe the effect of OWDP fading parameter K and MRC diversity with L branch. The outcomes are simulated by Monte Carlo simulation.

Face recognition is a challenging research problem with several critical applications. Incremental Learning is an approach which does not require all the training samples beforehand for model development. In this direction, a popular approach called incremental principal component analysis (IPCA) was suggested by Chandrashekara et al. Later, several variants of IPCA were suggested by various researchers from time to time. However, it is not clear which of these methods are better for face recognition. To determine this, in this paper, we have performed extensive experiments on two publicly available face datasets, i.e. AR and ORL. The performance of the methods is determined in terms of average classification accuracy and average training time. IPCA and its 11 variants as suggested in the literature are considered for comparison. It has been found that there is no clear winner among the compared methods. However, the performance of MCT-based incremental two-dimensional two-directional PCA (MI(2D) $$^2$$ 2 PCA), incremental indefinite kernel PCA (IIKPCA), incremental two-dimensional kernel PCA (I2DKPCA), and incremental two-dimensional PCA (I2DPCA) methods is comparable and better than rest of the methods.

Robot arm is one of the most buzzing words in industrial automation. The challenge of designing a robot arm is anticipating and controlling its system dynamics. This paper focuses on developing of robot arm with 3 degrees of freedom (DoF) with high speed and precision. The position of the robotic arm is determined from the accelerometer using closed-loop feedback system. The stepper motor in the arm of the robot decides the direction; the desired movements will be controlled by PWM pulses through the controller, as per the requirement of the user. A4988 stepper motor driver is used instead of rotary encoder to control the movements of robotic arm by stepper motor. The step angle of the arm is controlled by micro-stepping using 200 steps to 3200 steps resolution which provides a smooth movement and precision degree of accuracy. A graphical user interface is designed using LabVIEW to display and control the position of arm. Finally, 3-axis robot arm with micro-stepping is developed by integrating the electronic circuitry and mechanical parts.

This paper presents a new hybrid image watermarking method based on IWT, DCT, and SVD domains, to solve the problem of false-positive detection and scale down the impact of geometric attacks. Properties of IWT, DCT, and SVD enable in achieving higher imperceptibility and robustness. However, SVD-based watermarking method suffers from a major flaw of false-positive detection. Principal component of watermark is embedding in the cover image to overcome this problem. Attacker cannot extract watermark without the key (eigenvector) of the embedded watermark. To recover geometrical attacks, we use a synchronization technique based on corner detection of the image. Computer simulations show that the novel method has improved performance. A comparison with well-known schemes has been performed to show the leverage of the proposed method.

Cloud computing is a kind of large-scale distributed computing. It has recently emerged as a new technology for providing services over the Internet. It has gained a lot of attention in individual, industry, and academia. Cloud computing dynamically allocates virtual resources as per the demands of users. The rapid increase of data computation and storage in cloud results in uneven distribution of workload on its heterogeneous resources, which may violate SLAs and degrades system performance. Distributing balanced workload over the available hosts is a key challenge in cloud computing environment. VM placement is the process by which it selects the most suitable physical machine (PM) in cloud data centers to deploy newly created virtual machine (VM) during runtime. In this paper, we study VM placement problem with the goal of balanced resource utilization in cloud data centers. Several algorithms have been proposed to find a solution to this problem. Most of the existing algorithms balance the cloud resources based on its utilization which results in unbalanced and inefficient cloud resource utilization. We propose an algorithm, which minimizes the imbalance of resource usage across multiple dimensions, reduces resource leak and wastage, and maximizes the resource utilization. Our algorithm finds a suitable host for incoming VM from categorized host list based on remaining resources using cosine similarity measure. Simulation results show major improvements over the existing algorithms like Binary Search Tree-based Worst Fit and Best Fit, Round Robin, and default Worst Fit.

Vehicular cloud computing (VCC) has emerged as a new technology to present better and uninterrupted information to traffic management and road safety. However, the exchange of information among all different vehicles, device and roadside infrastructure over the public network presents a favorable scenario to the adversary to perform active and passive attacks. Therefore, there is an organizing demand for an efficient and secure architecture of the secure exchange of information in VCC. In this direction, one of the big challenges is to mutually authenticate the legitimate sources of the information provider and receiver, and then to manage secure session among them. To ensure secure and authorized communication in VCC, this paper discusses the construction of the authenticated session key agreement protocol for VCC. The proposed framework also maintains the anonymity of participating vehicles. The proposed protocol requires fewer computations from the user side as compare to the cloud server and roadside infrastructures. Moreover, the proposed scheme is theoretically proved secure in the random oracle model. Proposed scheme’s performance analysis and comparison with related results on security and performance attributes keep it ahead.

The purpose of this paper is to describe recent trends in image using granular computing. Granular computing is used to solve any type of problem with the help of granules. Granules are the key of granular computing. This paper presents granular computing view to solve problem of image processing. Hence, it discusses about different parameters of granular computing, role of granular computing in image processing, different techniques of image processing and how granular computing helps to solve image processing problems, granular computing achievements, literature survey of granular computing and image processing techniques, how granular computing is better than other image processing techniques, and at last discusses about problem or challenges in granular computing and image processing.

Security and performance are two of the most important concerns for cryptographic hashing algorithms, presenting a compelling challenge, since there seems to be a trade-off between achieving high speed on one hand and robust security on the other. However, with the advances in computer architecture and semiconductor technology, it is possible to achieve both by adopting parallelism. This paper presents a novel transformation based on the recursive tree hashing to parallelize and speed up typical hashing algorithms. The proposed transformation, called Enveloped Inverted Tree Recursive Hashing (EITRH), has three steps: “message expansion,” “parallel reduction,” and “hash value generation.” It improves upon the accuracy and the speed of hash code generation. Also proposed are some algorithms using the EITRH transformation for high-speed hashing on multiple cores. The security analysis of EITRH framework demonstrates its multi-property preservation capabilities. Discussion of EITRH w.r.t. performance benchmarks suggests its potential to achieve high speed in practical implementation.

An analysis is presented to the study, the proficient implementation of ancient mathematics formulae for multiplications and squares in the cryptographic system. In this approach, we have used ancient mathematics techniques and algorithms in the different projective coordinates system (Jacobian, Chudnovsky-Jacobian, Modified Jacobian coordinates system) to get minimum steps in the calculation of addition algorithm, doubling algorithm and for improving the speed of processing time in the operations of ECC (points addition, points doubling). The coding and synthesis are done in MATLAB for 16-bit digit multiplications and squares. The results proved that the Vedic mathematics-based scheme shows better performance compared to the conventional method and total delay in computation is reduced by Vedic mathematics Sutras (Urdhva-tiryagbhyam, Dvandva-yoga). The results of some AVIM techniques over ECC were obtained and discussed in the form of tables and graphs.

This paper proposes a new energy-efficient hierarchical cluster-based routing protocol (EEHCR). It assures for inter-cluster communication between cluster head to cluster head toward the base station irrespective of the deployment strategy. The proposed protocol works in two phases, namely cluster-formation phase and hierarchy-formation phase. Intra-cluster communication may take place between either member to cluster head directly or member to cluster head indirectly, through proxy cluster head. Depending on the deployment of sensor nodes, inter-cluster communications between clusters may take place in either of four ways: (i) cluster head to cluster head or (ii) cluster head to a member or (iii) member to cluster head or (iv) member to member, during the formation of hierarchy. The proposed protocol provides a solution for all four cases of inter-cluster communication in order to implement the proposed network model. The EEHCR is implemented in OMNeT++ network simulator. The result is compared with TL-LEACH and MR-LEACH hierarchical routing protocols. Simulation results demonstrate that the proposed protocol is effective in prolonging the network lifetime.

A simple electronically tunable current-controlled chaotic Chua’s circuit using second-generation current-controlled current conveyor (CCCII) with 0.25 µm CMOS technology is presented. An electronically control behavior of nonlinear resistance (NR) produces significant change in voltage–current characteristics due to the bias current present in CCCII. The nature of the chaotic circuit in terms of different attractor can be achieved by controlling the variable resistor. The phase portraits of the proposed design are well simulated in PSPICE. The proposed circuit has several advantages viz. uses minimum number of passive components, low frequency operation, and tunable chaotic nature. Finally, a comparative study in terms of physical parameter is tabulated in this scientific literature.

Phishing attack is used to obtain the information like username, password, bank account details, and credit card details. It is a most popular cybercrime today. Phishing attacks also affect the online payment sector financial institution, file hosting or cloud storage, and many others. Phishing attack always targets to these Web sites which are related to the online payment sector and Web mail. Many techniques are used to prevent the phishing attack like blacklist, Heuristic, visual similarity, and machine learning. Blacklist technique is most commonly used because it is easy to implement, but this technique cannot detect a new phishing attack. So, now, machine learning is most efficient technique to detect the phishing attack and this technique is able to detect all drawback of other phishing detect techniques. So this research work is completely based on machine learning algorithms which are logistic regression, decision tree, random forest, and SVM to detect phishing Web site. In this research work, select the best model on the basis of six analysis factors.

Applications of wireless sensor networks such as target tracking, estimating the location of the fire in forests, tracking the enemy tanks in the battlefield and geographical routing require the location of sensor nodes. There are various existing range-free localization approaches to localize the sensor nodes like APIT, DV-hop, centroid method, etc. But because of the wireless transmission medium of sensor networks, security threats like Sybil attack, wormhole attack, selective forwarding, sinkhole attack, etc., could happen more easily. Sybil attack is the most common and vital security threat in WSN. Sybil attack-affected node can have multiple identities which can affect the localization process and increase localization error. Recently, Sybil-free localization scheme SF-APIT has been proposed to detect Sybil attack but because of the exclusion of suspected nodes in the localization process, the number of blind nodes or unresolved sensor nodes is increased significantly. In this paper, we have proposed a novel lightweight secure hybrid localization scheme against Sybil attack, named Sybil-Free Hybrid Localization Scheme (SFHL), to address Sybil attack more efficiently and to localize blind or ghost nodes using a hybrid approach. Firstly, the Sybil attack is detected while using the APIT localization scheme, and then, Sybil nodes and blind nodes are identified. At last, unresolved nodes are localized using a hybrid localization scheme. Simulation results depict that SFHL performed better than existing localization approaches in the presence of Sybil attack, in terms of minimizing the number of blind nodes and localization error.

The complete realization of Internet of Things (IoT) paradigm comes with its own share of challenges. Scalability, which is one among these challenges, can be addressed through efficient routing protocols developed for low power and lossy networks (LLNs). The paper proposes an LLN routing protocol, hybrid-RPL (H-RPL), which allows the nodes to efficiently and adaptively switch\storing and non-storing modes of operation. H-RPL takes into account the network resources like node memory and node battery while performing the switch operations. Also, the proposed routing protocol performs the dual role of reducing traffic surrounding the root node, as well as preventing the routing table overflow. H-RPL has been simulated and tested, and the results show considerable improvements compared to the storing and non-storing modes of traditional RPL.

Clustering of incomplete data set containing missing values is a common problem in the literature. Methods to handle this problem have vast variations, including several imputation as well as non-imputation techniques for clustering. In this work, we have described the analysis of different approaches explored for handling missing data in clustering. The aim of this paper is to compare several FCM clustering approaches based on imputation and non-imputation strategies. Experimental results on one artificial and four real-world data sets from UCI repository show that linear interpolation-based FCM clustering approach performs significantly better than other techniques for these data sets.

Dynamic Web applications, such as e-commerce Web application, extensively use databases as backend servers. These Web applications are highly dependent on the efficiency with which the Web databases can be accessed. A dynamic Web page consists of many small fragments of scripting languages, called Web views, which may require access to data in disparate databases. These Web views when materialized can enhance the efficiency of database access. An e-commerce Web application has large number of users involved in various personalized interactive activities. These generate a stream of Web page accesses and modification requests that necessitate taking dynamic decisions for selecting Web views for materialization, which minimize the average access time of Web views. In this paper, a Web view selection algorithm (WVSA) that selects Web views for materialization is proposed. Experimental results suggest that materializing Web views using WVSA increases the efficiency of Web view accesses.

The proposed model is a cluster-based multi-hop routing protocol which employs a time-division multiple access (TDMA)-based medium access method. The model employs a centralized cluster head (CH) selection scheme in which the base station (BS) appoints the node with the highest residual energy (RE) as the CH in each cluster. A centralized CH selection scheme ensures that all the CHs are evenly distributed in the network. The BS also sets up multi-hop paths to communicate with the CHs. This ensures that none of the CHs make long-range transmissions to communicate with the BS. If a non-cluster-head (non-CH) node wants to claim one or more data slots in a frame, it sends a control message to its CH in the allocated control slot. However, the node has to turn its radio ON from the beginning of the control period. This is so because before claiming any data slot(s), it should know which data slots have already been claimed. The experimental results show that the proposed model saves a considerable amount of energy in sensor nodes.

A Quadrotor comes under the rotor-type category of Unmanned Aerial Systems (UAS) which has four rotors. They are non-linear multivariable system that is highly underactuated. The Quadrotors have the capability to hover at one place during their flight and to take-off and land at any place vertically with the help of the vertical thrust produced by four rotors. They do not require a track to lift-off and land like fixed-wing UASs. They have three rotational motions, i.e. roll, pitch and yaw. These are obtained by changing the speed of the rotors. The model of a Quadrotor is simulated using MATLAB and Simulink and a PID control action is used to sustain its roll, pitch, yaw and altitude.

In this work, reactive power problem is solved by levy flight-based white wolf (LFW) algorithm. White wolf actions such as hunting, encircling the prey are imitated to design the algorithm. Mainly population of white wolf will be in stagnation mode in leader wolves. In the projected algorithm, levy flight has been applied in the white wolf algorithm to improve the global search very effectively. Predominantly stagnation of leader white wolf will be evaded, and candidate solution will be enhanced during all phases of the search system. In standard IEEE 14, 30 bus test system levy flight-based white wolf (LFW) algorithm is evaluated. LFW approach reduced the loss efficiently.

The present communication system demands high data rate, spectral efficiency, and reliability. By employing numerous antennas in transmitter and receiver sides of a wireless channel, the spatial multiplexing or diversity gains can be explored. The modern communication network can be designed to attain a high data rate, enhanced link reliability, and improved range. MIMO technique can increase spectral efficiency without using extra bandwidth. This paper reviews recently published results on MIMO—Multiple Input Multiple Output. This paper describes the BER performance using Alamouti Space-Time Block Code and Average Channel Capacity has been discussed for different antenna system, i.e., SISO—Single Input Single Output, SIMO—Single Input Multiple Output, MISO—Multiple Input Single Output, and MIMO—Multiple Input Multiple Output systems under Rayleigh and Rician fading conditions. The simulated BER of MIMO has been compared with its theoretical result and with all other antenna configuration systems. Finally, the Average Channel Capacity for all the systems is analyzed and simulated under both Rayleigh and Rician Fading Channels.

Face recognition has been a popular research area with several real-world applications. Linear discriminant analysis (LDA) is a well-known method for face recognition in literature. However, one of the requirements of LDA is the availability of all data samples available before training. In this paper, we have proposed a novel variant of LDA method based on incremental learning and is called incremental weighted linear discriminant analysis (IWLDA). In IWLDA, a weighted pairwise Fischer criterion is suggested to efficiently separate all class data homogeneously in the transformed subspace. IWLDA is developed in such a manner that the distance between nearby classes is increased and simultaneously the distance between farther classes is reduced and the overall distance is preserved. This results in improved classification accuracy. Experimental results on 5 publicly available datasets, viz. AR, CACD, YaleB, FERET, and ORL show that the proposed method outperforms the popular methods, i.e., principal component analysis (PCA), LDA, incremental principal component analysis (IPCA), and incremental linear discriminant analysis (ILDA) on all the datasets in terms of K-fold ( $$K = 2$$ K = 2 , 3, 4, 5) cross-validation. Further, it is also found that the training time of IWLDA is better than the batch methods, i.e., PCA and LDA.

Weibull distribution is a befitting prototype for explaining fading channels that consist of multipath environment in radio propagation surrounding it in indoor or outdoor. This work shows a review on the expression of channel capacity with L-branch maximal ratio combining system and over uncorrelated Weibull fading channel using optimum rate adaptation with constant transmit power scheme. An expression for the probability of outage has been also deduced in this paper. In this paper, an expression for a relation between the fading parameter of Rayleigh fading and Weibull fading has been also established.

In this paper, eigenvector-based moments are proposed for offline signature validation. Here, principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for dimension reduction and generated eigenvector which is calculated using Euclidean distance. It measured the distance between two vectors having an equal size in 2-D space. A newly suggested approach to generate the eigenvector from training and testing samples of signatures, which is calculated through Euclidean distance as a classifier. In which, it has shown high verification accuracy of 91.07% on the MCYT-75 corpus and GPDS synthetic signature database.

In this chapter, we have tried to compose a user-friendly method primarily for expansion of q-homotopy analysis method (q-HAM) among Laplace transform (LT), especially q-homotopy analysis transform method (q-HATM) to resolve fractional model of electrical circuits in equation with Caputo sense. Thereafter, the analytical solution of fractional model of electrical circuits is compared with the exact solution. The results attained by q-HATM may be hypothesized as a different and effective method for solving fractional model. Two test examples demonstrate the correctness as well as effectiveness of the present technique.

In the era of the Internet, many files (text, image, audio, and video) are shared through the Internet. Security is the main issue, during sharing information. To resolve the security problem, two techniques are used to make our information secure, i.e., cryptography and steganography. Cryptography is used for encryption but does not hide the information. So, it is not too much secure. The emerging Internet technology has led to the need for a high level of security which is provided by steganography. Steganography is used to hide the existence of a secret message. As a result, unauthorized users cannot access secret information in different multimedia files. This paper gives a brief overview of steganography, type of steganography, insertion technique used in image steganography search such as LSB, GLM, parity checking, and interpolation. It also includes the application and a set of parameters to test the imperceptibility of an algorithm. In image steganography, the cover medium used for hiding data is an image. The most common technique for image steganography involves the least significant bits (LSBs) of image pixels which are altered with the bits of secret message. There are a lot of limitations in the LSB techniques, due to which many new image steganography techniques are developed. The cyclic LSB technique is such a technique. In this paper, an altered version of the cyclic LSB technique is presented. The advantage of this method is that RGB channels of the image are rotated into the cycle, i.e., red channel is used for hiding first bit, the green channel is used for hiding the second bit, the blue channel is used for hiding third bits, and then this cycle is repeated. In this method, no separate key is used for encryption and decryption, thus avoiding any overhead of managing key. This technique provides enhanced capacity and security over any other image steganography techniques.

Food safety is a most highlighted area in today’s fast-food world as many health problems such as diarrhea, vomiting, typhoid, and food poisoning can be happened by consuming those contaminated packed foods. Hence, every possible detail of a packed food item must be placed in a supply chain management (SCM) to trace the path of the food item before delivery. Blockchain technology can be used to track the records of the journey of a packed food item from the manufactured time to the delivery time of the packet. In this paper, we have proposed a model based on blockchain technology (BT) to trace food items through various stages of supply chain management. Security analysis discussed in the paper proved that this chain will achieve the desired level of security in this area.

Digital PLL is a fundamental construction block for Bluetooth Low Energy (BLE) systems and several other wireless technologies like applications of Internet of Things (IoT), clock generation, recovery, and reconstruction of data. It is also used for ultra-low power operations and numerous digital signal processing techniques. The proposed Digital PLL is base on the loop filter that is observer-controlled, which helps to filter out entire additive noise and the output is improved in the terms of phase noise and transient time response. The main drawback of the previous PLLs is current transreceiver noise that modifies the overall phase noise output of the system is overcome by the new technique known as oscillator pulling technique. Here the output can be separate out without modifying the general performance of phase noise. The observer filter is fundamentally a second-order time-varying system having fundamental gain value. The proposed Digital PLL has been simulated using 0.35 μm CMOS technology. The proposed DPLL is having lesser phase jitter, high operating frequency, and less complexity of the circuit. The designed digital phase-locked loop is 1.5–3 times faster that of the conventional one.

In our modern era where the Internet is ubiquitous, everyone consumes various informations from the online resources. Along with the use of a huge amount of social media, news spread rapidly among the millions of users within a short interval of time. However, the quality of news on social media is lower than the traditional news outlets; the main reason behind that is the large amount of fake news. So in this paper, we have explored the application of machine learning techniques to identify the fake news. We have developed two models with the help of support vector machine, random forest, logistic regression, naive Bayes, and k-nearest neighbor machine learning algorithms, and this method is compared in terms of accuracy. A model focuses on identifying the fake news, based on multiple news articles (headline) and Facebook post data which gather informations about user social engagement. We achieved maximum classification accuracy of 98.25% (logistic regression) for a dataset A and 81.40% (KNN) accuracy for a dataset B.

The vehicle tracking system is a modern security and armada handling method. This module is an example of an embedded system that can be used by vehicles to track and know the position is known by global positioning system and global system in particular vehicle. The proposed system is embedded in the car that provides exact real-time location and position. Whenever there occurs any vehicle problem like robbery, accident, fire alarm, etc., in view of these issues, a message will be delivered from the vehicle about its position in view of latitude and longitude to the expected receiver who is asked for help. A program is coded to get the proper position of the vehicle which is on move or steady. In order to identify the location of the vehicle that includes Latitude and Longitude position and also receive continuous data from the two effective systems used which is a GSM and a GPS Modem identifying the position.

Phishing attacks are one of the most serious threats faced by the users on the internet the attackers try to steal sensitive information such as login details, credit card details, etc. by deceiving the users to enter sensitive information on the phishing websites and thus leading to huge financial losses. Many schemes have been proposed to detect phishing attacks but the amount of such attacks has not decreased. New attacks like Active Man-In-The-Middle (MITM) phishing attacks have emerged which include Real-Time Man-In-The-Middle (RT MITM) and Controlled Relay Man-In-The-Middle (CR MITM) phishing attacks. These attacks allow the attackers to obtain the users’ account details and relay them in real-time. Similarly, the attacker can lure the user to enter details on a spoofed app and thus gain access to the user’s account. The existing popular authentication schemes fail to address these attacks. In this paper, we propose a novel user authentication scheme that enables the user to log into his/her account without memorizing any password or any other authentication token. In the proposed scheme, the user has to scan a dynamically generated QR-code using the smartphone app and then verify the image, captured by the webcam and sent it on the smartphone via push notification. Thus, the complete authentication procedure requires minimal user involvement and implements automatically. We have implemented and evaluated the proposed scheme in terms of usability, deployability, and security parameters and the results depict that the proposed authentication scheme performs well and can be used as a secure user authentication scheme.

Research-oriented work in speech recognition has garnered a lot of interest since last two decades. Emotions derived from speech have drawn considerable interest of researchers especially for analysis of human behavior. Emotions from a speech are extracted and identified by classifiers and systems being developed and improved over a period of time. This paper attempts to discuss the process of speech emotion recognition, different methods of pre-processing techniques, feature extraction methods, and classifiers used for speech emotion recognition.

The sensors are positioned below the water to monitor the underwater environment in an Underwater Wireless Sensor Network. This network is vulnerable to antagonistic environmental conditions and is more prone to attacks. Since the underwater environment begets a fluctuating nature in reference to temperature, speed, it is necessary to make the network reliable to numerous deviations. This paper suggests a detection and prevention technique for wormhole attack in Underwater Wireless Sensor Networks. Using the suggested technique, unwanted packets can be avoided at the destination node, hence decreasing the overall traffic on the network. This scenario does not consider the number of packets and also sustains against the wormhole attack. This lead to a considerable decrease in the accumulation of extra hardware at nodes. The counter function uses an easy algorithm. Therefore, to ensure the efficient functioning of the network, the error is adaptively controlled. The proposed method also improves the energy-efficiency as compared to the existing techniques.

Flexible ionic polymer metal composite sensors are desired for detecting pulse rate of the human body and underwater energy harvesting application as compared to the brittle inorganic piezoelectric material. The present research reports the sensing performance of ionic polymer metal nanocomposite (IPMNC) using Platinum (Pt) coated polyvinylidene fluoride (PVDF) (PVDF)/polyvinyl pyrrolidone (PVP)/polystyrene sulfonic acid (PSSA) ionic membrane. The Pt electrode was attached on PVDF/PVP/PSSA ionic membrane using electroless deposition method. The simple solvent casting method was used to fabricate the PVDF/PVP/PSSA ionic membrane. The PVDF/PVP/PSSA based IPMNC generated a sensing voltage of 0.65 V with an application of 0.6 N force. Our IPMNC sensor produces sensing signals with different metal electrolytes and produced highest sensing signal of 0.56 V with immersing the IPMNC sample in 1.5 N NaCl. The data was obtained using data acquisition system (DAS) consists of IPMNC sample on PCB that was connected amplifier, analog to digital convertor, and an Arduino Uno board. The DAS was connected to computer for obtaining data. Our proposed IPMNC sensor can be applicable to the pressure sensor and wearable technology for generation of energy. Our IPMNC can also be useful for detecting metal ions in water and generation of electricity from water medium.

Cloud computing is the latest paradigm which offers different cloud services by rearranging of resources and provides them on the bases of user’s demand. Users are using cheaper data storage and computation offered in the cloud environment, and they are also facing many problems about reliability, privacy-preserving, and optimized searching of their outsourced data. Proposed scheme “Privacy-preserving keyword search” allows users to encrypt their stored data while preserving some search capabilities and also seek few efforts to consider the reliability of the searchable encrypted data outsourced to the clouds. Client-side encryption and server-side searching of encrypted data without decryption in server-side enable more efficient and eliminate privacy issues between client and server.

A social graph is a network that shows the representation of how the nodes are connected. The classification of the network is done into different communities for data transmission. In the Bubble Rap routing protocol, the local and global centrality of a node is considered for message transmission. The global centrality tells how much the node is important in the entire network while the local centrality tells how much a node is important in its community. In Bubble Rap routing algorithm communities are formed with the help of K-Clique algorithm. The K-Clique algorithm is mainly designed for binary static graphs whereas Delay-Tolerant Network is highly dynamic in nature as the connections among the nodes keep on changing with time. In an Adaptive Approximate community detection algorithm, the information of the current network and previous community structure is followed to deduce the current structure of the community for the network. The community is decided using node degree and a frequency-duration utility. In this paper, we have implemented the algorithm for bubble rap protocol, and depending on the results of simulation it can be seen that performance-wise this algorithm is a better thank-clique community detection algorithm.

In today’s technical era, automation is the most important sector where unbelievable progress and growth is shown in all over the world. In all field including home, industry, domestics, agriculture, transport, etc., the automation is reducing the physical work of human being and also it reduces the error in executing the work/operation. This paper presents the automatic control of electrical loads based on the atmospheric condition where the load is located (smart home/office/conference room). The controlling of the electrical loads will be wireless, and microcontroller-based control methodology is adopted. In addition to the control of the loads, the surrounding atmospheric conditions will be monitored with the help of sensing devices which will be used as feed for controlling the devices. And based on the response obtained from the sensing devices, the microcontroller-based kit will send command to the electrical appliances concern so that operation of which will be controlled. Two-way supply will be given to the load that one from conventional (EB) and the other from solar system. The supply will be connected based on the availability of the source.

The vehicular ad hoc network (VANET) is an interesting research area in the domain of mobile ad hoc networks (MANET). The VANET is a subform of MANET which composed of several vehicles, wireless gadgets, and roadside base stations. Through a wireless strategy, they interact with each other to create a smart and secure transport system. Due to high mobility, dynamic topology, and no power constraints, VANET is much attracted to both researcher and business persons. Regardless of its facilities and advantages, it suffers from many security and privacy issues which needed more focused on. In this paper, we have discussed security and privacy aspect of VANET. In this paper, VANET architecture, VANET models, security and privacy requirements, application of VANET, and other aspects are investigated. Additionally, we also provide some open issues and future trends, which still create a barrier for the wide adoption of VANET. The given security and privacy challenges and solutions will provide directions to researchers for designing a more secure and reliable VANET system.

Various papers on the analysis of hybrid diversity combiner are available to improve the mean output SNR at receiver end. In this paper, we will provide the review on performance of hybrid diversity combiner over Nakagami-m fading channels. This paper also reviews a mathematical expression for analyzing mean SNR gain and outage probability of hybrid diversity combiner.

Epilepsy is a neurological disorder, which causes seizures. Detection of epilepsy is carried out by analyzing EEG signals. Detecting epileptic seizures from long-term EEG data is a time consuming and tedious task, which requires vast clinical expertise. Most of the epilepsy detection algorithms available today are highly patient dependent. In this paper, an efficient and patient independent epileptic seizure detection algorithm based on machine learning is proposed. We have developed a method to classify seizure and non-seizure data using different machine learning algorithms. Time-domain, frequency-domain, and wavelet-domain features are used in this work. Feed-forward neural network, anomaly detection using multivariate Gaussian distribution and long short-term memory network are employed to classify seizure and non-seizure data. CHB-MIT database is used in this study. Long short-term memory network has given the highest seizure detection accuracy (97.4%) and the lowest false positive rate (7.88%).

The purpose of this study is to predict nutrient removal from wastewater by using different soft computing classifiers. A comparison of different classifiers, e.g., linear regression, nonlinear regression and artificial neural network (ANN) is done. ANN shows promising results as compared to linear and nonlinear regression. In this study, the data is collected from previous research papers. Out of the collected data, 75% is used to train the models and residual 25% is used for the validation of the models. The model accuracy is depending upon three evaluation parameters which are coefficient of determination (R2), root mean square error (RMSE), and means absolute error (MAE). The result shows that the ANN model is more accurate to predict the nutrient removal from wastewater as compared to linear and nonlinear models.

The Internet has a plethora of text articles, and it has become a necessity to extract only the relevant information from all the sources. Automatic keyphrase extraction is an essential part of the process of information extraction as it is impossible to manually identify all the keyphrases in textual sources. Keyphrase extraction has thus become an indispensable component of contemporary world of Internet. Researchers have treated keyword extraction as a classification problem where the input candidate words are classified as keywords or non-keywords. The paper tries to address two major issues in keyphrase extraction process, namely candidate selection and extraction of relevant features. Noun phrases extracted using specified regular expressions are considered as candidate words. A supervised machine learning method based on statistical and linguistic features is proposed for keyword extraction using SVM. The experimental results compared with well-known methods, namely SingleRank, ExpandRank, baseline TF-IDF, and the latest work show considerable improvement over the previously achieved results.

In this work, in-line tunneling based dual metal double gate tunnel FET (DMDG-VTFET) is reported. A silicon epitaxial layer is present between the source and gate so as to align the carrier tunneling parallelly to the gate electric field. This proposed modulation in the design due to the introduction of silicon layer suppresses the parasitic tunneling paths which cause the depreciation of the subthreshold slope (SS). So, with this proposed device, a super-steep SS is achieved. The device parameters which are critical to the device characteristics are optimized such that the high-performance ON state current of 1.2 mA, low OFF state current nearly 3.53 fA and SS of 37 mV/decade are obtained. The reduction in SS of the device creates more room for the device scaling and makes it suitable for low-power and high switching speed applications. The accurate analysis of linearity of the device is also important hence linearity estimation of the device is done by investigating the linearity figures of merit such as VIP3, IMD3, IIP3, 1-dB compression point along with the temperature sensitivity analysis to get insight into the stability of the device in varying temperature environment.

An innovation of Renewable Energy (RE) sources promises to bring down costs and starts to deliver a clean energy future without compromising reliability. Renewable energy resource selection comes under the domain of multi-criteria decision making (MCDM) problem as it includes multi-conflicting criteria, namely social, technological, environmental, economic, and political. MCDM methodologies are used in order to select preferred alternative resources because of the presence of complexities in energy planning and energy projects. This paper presents an integrated TOPSIS-Z MCDM method for the selection of optimal RE. The pairwise decision matrix is formed by the decision-makers (DM), which is represented in the Z-number, and weights are evaluated. TOPSIS is used to evaluate and rank suitable RE sources. To validate the efficacy of the proposed methodology, Spearman’s Rank Correlation Coefficient (SRCC) is used and the proposed methodology is compared with various other MCDM methods such as ARAS, VIKOR, and COPRAS.

The prominent criteria for the Wireless Sensor Network are a lifetime of the network, stability, and the energy parameter. To enhance these crucial parameters, the paper introduces Routing Algorithms for hybrid nodes in sensor networks ‘RA-HNW’ protocol having four forms of sensor nodes varying in energy and capacities to improve the stability and lifespan of the sensor networks. The simulations for the proposed RA-HNW is simulated in MATLAB 2018b. It is observed that using the functionalities and capabilities of the different types of nodes taken in the work, the stability of the proposed schema increases by 49.12% as compared to the DCHRP4, twice and thrice as compared to ETSSEP, TSEP, and SEP. The lifetime criteria also upgrade by 10.49% over DCHRP4 with level four heterogeneity, 15.85% over DCHRP, 19.65% over ETSSEP, and about 64.87% over TSEP. The proposed RA-HNW methodology is well suited for working in the fixed sink environment.

With the rapid growth in the network, a demand for more flexible and secure technology of network programmability is increasing. With the introduction of technology like SDN, a new platform for communication evolved which is free from traditional network barriers like vendor lock-ins, limited scope for innovation, buggy software, etc. No doubt, SDN has solved these problems but SDN security issues are of major concern. SDN security is the key research area that is gaining popularity. Our paper discusses SDN security challenges. In our experiments, we tried to depict the network performance and utilisation of resources in case of security attacks. With the aim of securing SDN in the case of heterogeneous distributed SDN controllers, we suggested a routing protocol that manages routing tasks where TLS security has been embedded in the header for OpenFlow packet. We simulated the system with our testbed of virtual machine with different attacks scripted in python.

Single-Carrier Frequency Division Multiple-Access (SC-FDMA) is used in uplink data transmission in Long-Term Evaluation (LTE) due to its low Peak-to-Average Power Ratio (PAPR) properties. In this paper, SC-FDMA link has been modeled and proposed to design this link using LabVIEW. The real-time data transmission through the SC-FDMA link is carried out using Software Defined Radio (SDR) testbed which is implemented using Universal Software Radio Peripheral (USRP) devices. The data transmission and reception are carried out through SC-FDMA link and the performance evaluation is estimated for PAPR and Bit Error Rate (BER). The simulation results show that the PAPR and BER parameters of the proposed SC-FDMA link design are significantly lower than the OFDMA in downlink. Moreover, the simulation of BER with different modulation schemes is carried out for different Signal to Noise Ratio (SNR) values. The analysis of PAPR impacts the OFDMA waveform and benefits on SC-FDMA transmission link in any channel condition.

This paper presents a novel strategy for calculating Lead-Acid battery charging and discharging time with different cases for standalone PV-based DC microgrid systems. The strategy has been implemented for a PV module incorporating MPP tracker to control the Lead-Acid battery charging and discharging time. This technique allows for improved condition of Lead-Acid battery under charge and discharge cycles. In addition, a supercapacitor is connected to the proposed configuration. The supercapacitor charging time at constant solar irradiance is calculated and compared with Lead-Acid battery and further verified by Ragone Plot. The performance of the proposed strategy has been validated through implementation on OPAL-RT real-time simulator.

Coronary artery is a major reason of health ailments all over the world. Its detection and management incurred huge amount of deaths across the nation. Heart disease can be diagnosed using various invasive and non-invasive methods. One of the effective methods for detection of coronary artery disease is coronary angiography, which is expensive and also has side effects. This further requires high level of technical expertise. Due to improvement in technology and low-cost storage devices, storage of huge amount of data becomes easy. Even health sector has been untouched. Machine learning methods are being used to analyze the collected data due to its capability to predict the diseases. In this work, machine learning methods are implemented in order to achieve low-cost, reproducible, non-invasive, rapid, and precise identification of heart disease. This paper adopted ensemble method with multiple classifiers to construct and validate the model. For experiment purpose, Z-Alizadesh Sani coronary artery disease dataset is used. The ensemble method of prediction outperforms the other disease prediction methods.

Cloud computing is a newly emerged field, and it develops very quickly. In cloud computing, the focus is on delivering computing resources such as hardware and software to the end-user directly, no matter when and where. These services are charged from clients on the per-use bases. There is a need for a decent scheduling mechanism that can help in the appropriate use of the assets. There are various resources available on the cloud and needs an efficient algorithm for better utilization of resources. One way is task scheduling. In cloud environment, there are numerous tasks which are running on the cloud; if two tasks try to approach a common resource, then there may be a chance of deadlock, which leads to system failure and performance degradation. For the better experience of cloud resources and to eradicate the aforementioned situations, there is a need of proper mapping between tasks and resources. In this paper, we comprehensively survey the various existing scheduling algorithms and these algorithms are compared on the basis of various performance metrics which are important to consider for the proper utilization of the resource.

In current scenario, there is a need of such a structure with related devices, individuals, time, spots, and frameworks, which is completely participated in what is called as Internet of things (IoT). This IoT combines the whole world with the PC-based system and around the end, it results in a greater efficiency, accuracy, and advantage for the customer. Security and assurance are the basic two factors for monitoring the health of any patient. A system is required for the blend of confirmation show with an essentialness capable access control instrument. IoT has emerged as one of the most trending domain in the present scenario. Health care is one of the leading research areas where people are moving to provide better solutions efficiently. This paper demonstrates a comprehensive and comparative study portraying the related work done by the current writers dependent on the wireless body zone systems (WBANs). Through this paper, different correlations between a several parameters and strategies of using various IoT devices related with healthcare frameworks are being furnished. Various research challenges of IoT healthcare domain have been also discussed. The comparative study presented here is based on various parameters like techniques used in healthcare frameworks, data collection techniques, etc.

All over the world, injuries and deaths of wildlife and humans are increasing day by day due to the huge road accidents. Thus, animal–vehicle collision (AVC) has been a significant threat for road safety including wildlife species. A mitigation measure needs to be taken to reduce the number of collisions between vehicles and wildlife animals for the road safety and conservation of wildlife. This paper proposes a novel animal detection and collision avoidance system using object detection technique. The proposed method considers neural network architecture like SSD and faster R-CNN for detection of animals. In this work, a new dataset is developed by considering 25 classes of various animals which contains 31,774 images. Then, an animal detection model based on SSD and faster R-CNN object detection is designed. The achievement of the proposed and existing method is evaluated by considering the criteria namely mean average precision (mAP) and detection speed. The mAP and detection speed of the proposed method are 80.5% at 100 fps and 82.11% at 10 fps for SSD and faster R-CNN, respectively.

Sepsis is a major challenge in the field of medical science. It affects over a million patients annually and also increases the mortality rate. Generally, sepsis condition is not identified easily. Thus, an intensive analysis of patients is required for identifying sepsis in the Intensive Care Unit (ICU). In this research work, an outcome prediction based machine learning models for identifying different stages of sepsis is proposed. Machine Learning (ML) models can help to predict the current stage of sepsis using existing clinical measurements like clinical laboratory test values and crucial signs in which patients are at high risk. We explore four ML models namely XGBoost, Random Forest, Logistic Regression, and Support Vector Machine by utilizing clinical laboratory values and vital signs. The performance evaluation of the proposed and existing techniques is performed by considering the same dataset. These models achieve an AUC (Area under the Curve) 0.95, 0.91, 0.76, and 0.93, respectively, for recognition of sepsis. Experimental results demonstrate that the XGBoost model with 10-fold cross-validation performs well than other models across all the performance metrics.

The Mahgoub Adomian Decomposition Procedure is scouted to offer an analytic outcome of a nonlinear differential equation for HIV contamination of CD4+T-cells. This procedure is implemented as a model for HIV contamination of CD4+T-cells. Numerical consequences display this procedure is straightforward and unique when implemented to system of nonlinear differential equations.

Industry 4.0 is an energetic movement as of late offered by means of the German administration. The aim of the movement is the altering of modern assembling throughout digital technology and the abuse of possibilities of new innovations. Our nations pressed towards “Make in India” have taken comprehension of Industry 4.0 and ongoing it’s situating in this space. First shrewd processing plant—touching from mechanization to independence—where machines make conversation with one another is being laid down up in Bangalore at the Indian Institute of Science’s Centre for Product Design and Manufacturing with seed financing from the Boeing Corporation. An Industry 4.0 creation framework is in this manner adjustable and empowers individualized and modified items. This paper introduces the writing study on Internet of thing based assembling and arranging process in keen ventures. This paper gives an itemized depiction of things to come challenge, remote sensor observing, and efficient strategies over the IoT.

The glandular morphology analysis done within the colon histopathological images is an imperative step for grade determination of colon cancer. But the manual segmentation is quite laborious as well as time-consuming. It also suffers from the subjectivity among pathologists. Thus, the rising computational pathology has escorted to the development of various automated methods for the gland segmentation task. However, automated gland segmentation remains an exigent task due to numerous factors like the need for high-level resolution for precise delineation of glandular boundaries, etc. Thus, in order to alleviate the development of automated gland segmentation techniques, various image enhancement techniques are applied on colon cancer images for preprocessing them in order to get an enhanced image in which all the critical elements are easily detectable. The enhancement results are analyzed based on both objective qualitative assessment as well as subjective assessment given in the form of scores by the pathologists. And thus based on the qualitative analysis, a new combined technique, i.e., colormap-enhanced image sharpening, is proposed in order to get an enhanced image in which all the critical elements are easily detectable. These techniques’ results will thus help pathologists in better colon histopathology image analysis.

This work focuses on one of the most important stages of hadrontherapy treatment planning. The simulation is performed using Monte Carlo (MC) Treatment Planning toolkit GEANT4 which is considered as the most accurate three-dimensional (3-D) dose calculation algorithm for studying Bragg peaks in tissue-equivalent material and dosimetric validation. In hadrontherapy, the study of a significant difference in the position of the Bragg peak is important to get realistic results. Proton therapy treatment planning involves a wider and sensitive range of parameters to be evaluated carefully from studying stopping power, linear energy transfer, the energy imparted to secondary particles and their range. The technical parameters from scanning, modification, accuracy and time also need to be assessed precisely as these vary from patient to patient depending on the health condition. All these parameters vary with Monte Carlo inputs and a slight variation in input generates different results, where, hadrontherapy treatment demands high precision. In this preliminary work, we studied one of the crucial factors ‘Step limit’ which shows the maximum ‘step size’ and limited by the accuracy and time factor of simulation. The simulation in GEANT4 is executed by activating suitable Physics Model ‘QGSP_BIC’ with default electromagnetic physics model em_option_3 implemented in Geant4 taking proton as an incident particle with energies 70, 100 and 130 MeV. For simulation, the implemented step size is 0.5 and 0.05 mm step size. The step size value 0.05 mm is found superlative for simulation taking into account simulation time and accuracy.

Proton Exchange Membrane Fuel Cells (PEMFC) is now emerging an eco-friendly solution of the future to conventional energy systems. The cell performance is influenced by various parameters such as operating temperature and also reactant pressure, flow rates, and relative humidity. Maintaining the critical parameters in required levels is very essential for ensuring efficient performance from the PEMFC system. This paper presents the effect of optimizing cathode Relative Humidity (RHc) on stack performance. Here, the most popular optimization algorithms like Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are used for finding the optimum value of RHc. A performance evaluation between these algorithms is presented based on the voltage regulation obtained from the PEMFC stack for a PEMFC model in Simulink with intelligent controllers like fuzzy and neuro-fuzzy controllers.

Mortality rate because of breast cancer diminishes to a large extent if the categorization of breast lesions as malignant or benign is done properly. But this process is quite complicated owing to erroneous detection of noisy pixels as false positives. It can be reduced by proper enhancement of cancer indicating features present in breast cancer ultrasound images. Therefore, the technique proposed for denoising, edge correction, and enhancement is pivoted around two drastic issues. The first issue is related to the blurring of important details because of improper noise suppression, whereas the second issue is associated with poor contrast between background tissues and masses in ultrasound images of breast cancer. In this paper, we propose an ensemble hybrid filter that restores the noisy ultrasound breast cancer images, corrects the edges of restored ultrasound images without getting degraded with additional Gaussian noise which degrades the ultrasound images during edge correction and finally enhances its visual quality. It uses Weiner filtering to remove a small amount of noise, fuzzy derivatives, and smoothing for edge preservation and intensification membership function along with contrast limited adaptive histogram equalization for enhancement. Experimental results are obtained to demonstrate the feasibility of the proposed approach. These results are also compared to traditional Weiner filter by numerical measures and visual inspection.

Salient object detection is a challenging research field in computer vision. The existing saliency detection methods generally focus on finding feature maps for saliency computation. However, the combination of these feature maps significantly improves salient region(s) detection. In this paper, we propose a novel feature integration approach called U-FIN in which final saliency map is obtained by a weighted combination of individual feature maps. The proposed approach works in three phases viz. (i) artifact reference (AR) map generation (ii) weight learning and (iii) final saliency map computation. Firstly, AR map is produced using majority voting on the individual feature maps extracted from the input image. Secondly, linear regression is employed for weight learning which is used in the next phase. Finally, the individual feature maps are linearly combined using weights learned in the second phase to generate the final saliency map. Extensive experiments are conducted on two benchmark datasets, i.e., ASD and ECSSD to validate the proposed feature integration approach. The performance is measured in terms of precision, recall, receiver operating characteristic (ROC) curve, F-measure and area under the curve (AUC). Extensive experiments demonstrate the superiority of the proposed U-FIN approach against nine state-of-the-art saliency methods on ASD dataset and comparable on ECSSD dataset with the best performing methods.

Real-time images are very much desirable in different real-life applications such as defense, medical, satellite, tv, Internet. Construction of relevant dictionary for the training of computer during the prediction of high-resolution image from low-resolution images in real-time requires high mathematical computations. This paper presents a new way of dictionary formation for learning-based super-resolution of real-time streaming of images. Here, the dictionary is formed with help of images that are similar to the input image in terms of structural similarity score. It helps in reducing memory requirement for the dictionary formation. Further, to speed up the process, a technique based on similarity score is proposed for updating of the dictionary. This involves the comparison of current image in the input sequence with the present reference image that was used for dictionary formation. Efficacy of the algorithm is shown through extensive simulations.

In healthcare sector, cancer is one of the most threatening and fast-growing diseases. The early diagnosis of this disease is very important as the success rate of its treatment depends upon how early and accurately it is diagnosed. The machine learning algorithms are helpful in detection and prediction of diseases. To improve efficiency of these algorithms, optimal features need to be selected. So, this research work uses genetic algorithm to select optimal features before applying k-nearest neighbor (KNN) and weighted k-nearest neighbor (WKNN) on Wisconsin Breast Cancer Prognosis dataset extracted from UCI repository. This approach helps in early prediction and the results show that WKNN performed better with 86.44% accuracy than KNN which gives 83.05% accuracy.

With the increase in patients suffering from diabetes, early detection and prediction of diabetes are the major area of concern. In this study, we propose a hybrid model using data mining techniques to analyze the available data to predict the occurrence of diabetes. This model is a combination of cluster and class-based approach which uses K-means and weighted K-means for clustering and logistic regression for classification. K-means is a simple and widely used technique, but it is highly sensitive toward initial centroids and outliers which further affect the prediction accuracy of logistic regression. The aim is to determine a way to improve the initial centroid selection for K-means and retain maximum original dataset to enhance the performance of logistic regression. Results show that accuracy of the classification model using K-means and weighted K-means is 96.97% and 97.84%, respectively. Further, using the classification results, this paper analyzes the risk associated with diabetic and non-diabetic patients.

Cardio Vascular Diseases (CVD) are a cluster of diseases of blood vessels and heart, which ranges from a small blood clot to severe heart failure. Machine learning classifiers help to forecast the plausibility of patients subjected to Cardio Vascular Disease (CVD) by analyzing various medical parameters such as heart rate, Cholesterol level, HbA1c, weight, ECG results. This paper focuses on the performance of various machine learning classifiers based on accuracy and execution time over a CVD dataset in predicting Cardio Vascular Disease (CVD). RandomForest, J48, Hoeffding tree, Logistic Model Trees (LMT), and RandomTree classifiers were used in the prediction. In the analysis, Hoeffding tree classifier achieved high accuracy of 85.1852% and execution time of 0.17 s in predicting patients subjected to CVD than the other classifiers under analysis.

Storage is the most important service provided by cloud computing technology helps many individuals from managing in-house storage infrastructure. To provide the availability of data at any time and anywhere, the cloud service provider will store the redundant copy of data in the cloud storage server. This will leads to the waste of storage space by duplicating the user’s data in the cloud. The data deduplication technique has attracted many cloud vendors to increase the storage space by deleting the same copy of data from the cloud storage server and reduce the storage cost for cloud users. The data deduplication comes with many security issues due to its exploration of metadata stored in the cloud. To deal with these security issues, many secure data deduplication techniques have been proposed. Hence, in this article, we present the taxonomy on the classification of data deduplication techniques for general data. And we discuss the secure data deduplication along with different security threat models during data deduplication process. In this article, we also discuss the various existing secure data deduplication schemes proposed by researchers and present in taxonomy. Finally, we identify some of the open research challenges and present for future research direction in the big data era.

Prediction of software faults using different machine learning techniques has been reported earlier by many researchers. However, these individual techniques suffered from performance problems as the software fault dataset changes. In the last few years, ensemble-based approaches gained popularity in the software fault prediction because of their improved performance over the single-fault prediction techniques. Motivated by this reason, an experimental study is performed on ensemble methods for predicting whether a software contains fault or not. The study includes random forest, bagging, random subspace, and boosting as ensemble methods, and decision trees, logistic regression, and k-nearest neighbors as base learning algorithms for the ensembles. The experiments are conducted on 15 software fault datasets corresponding to the Eclipse project and PROMISE Software Engineering repository. We evaluated the results using precision, accuracy, recall, and ROC-AUC score. Additionally, we performed the Friedman statistical test to see whether the performance difference of ensemble methods is statistically significant or not. We also performed the Wilcoxon signed-rank test for the paired samples to check which of the two techniques have performance differences.

eChronos is a formally verified real-time operating system (RTOS) designed for embedded microcontrollers. eChronos was targeted for tightly constrained devices without memory management units. Currently, eChronos is available on proprietary designs like ARM, PowerPC, and Intel architectures. eChronos is adopted in safety critical systems like aircraft control system and medical implant devices. eChronos is one of the very few system softwares not been ported to RISC-V. RISC-V is an open-source instruction set architecture (ISA) that enables a new era of processor development. Many standard operating systems, software toolchain has migrated to the RISC-V architecture. According to the latest trends [1], RISC-V is replacing many proprietary chips. As a secure RTOS, it is attractive to port on an open-source ISA. SHAKTI and PicoRV32 are some of the proven open-source RISC-V designs available. Now having a secure RTOS on an open-source hardware design, designed based on an open-source ISA makes it more interesting. In addition to this, the current architectures supported by eChronos are all proprietary designs [2], and porting eChronos to the RISC-V architecture increases the secure system development as a whole. This paper presents an idea of porting eChronos on a chip which is open-source and effective, thus reducing the cost of embedded systems. Designing a open-source system that is completely open-source reduces the overall cost, increased the security, and can be critically reviewed. This paper explores the design and architecture aspect involved in porting eChronos to RISC-V. The authors have successfully ported eChronos to RISC-V architecture and verified it on spike [3]. The port of RISC-V to eChronos is made available open-source by authors [4]. Along with that, the safe removal of architectural dependencies and subsequent changes in eChronos is also analyzed.

This paper presents the proposed design of a multi-band MIMO antenna for the Wi-Max technology having a frequency range is 2–11 GHz. The MIMO antenna was designed by using two rectangular patches having width 19.76 mm and length 15.67 mm with defected ground structure. The inset feed method is used in phase for the exciting the antenna. This antenna has resonated on multiple bands with the enhancement of specifications like VSWR, return loss, efficiency, gain, and directivity. The Directivity and Gain of MIMO antenna have obtained 6.09 dB and 6.03 dB, respectively. Also, the efficiency of the MIMO antenna is 99.06%. This MIMO antenna is provided better return loss and VSWR for higher efficiency. The objective of this paper is providing a novel approach with multi-band operation by using the defective ground.

Mobile ad hoc network due to its inherent capabilities received a significant researcher’s attention recently. Wireless networks like mobile ad hoc networks are application-specific, infrastructure-less, short-duration networks with many features like arbitrary or dynamic topology, homogeneity/heterogeneity, self-organizing, decentralization, high flexibility, routing, etc. The main purpose of mobile ad hoc networking is to extend its short-duration services for specific applications into the realm of autonomous, mobile, wireless domains, where the mobile nodes form the network topology in an arbitrary fashion. The vulnerabilities in a wireless environment, viz., temperature, noise, pressure, magnetic effects, have significant impact on the connectivity. Connectivity is related to node mobility, link formation and hence failure of either one or both results in connectivity loss. When connectivity is lost, the network becomes unreliable. In this paper, a comprehensive review of the effects of wireless environment on mobile ad hoc network reliability is proposed. From the results, it is understandable that in the absence of noise the network is highly reliable and as the environmental noise increases, the reliability falls down by 32%.

In this paper, a foldable miniaturized higher-order FSS with a tunable characteristic which acts as the band-stop filter is studied. The miniaturized configuration is achieved by bending the arms of the rotated cross-shaped structure. The proposed parallelogram facet is reduced by 75 $$\%$$ % as compared to the facet of the rotated cross-shaped structure. The proposed Miura-ori pattern design demonstrates the most stable angular performance at large angles of incidence and the frequency deviation obtained is 0 $$\%$$ % when the angles of incidence ( $$\theta $$ θ ) are varied from 0 $$^\circ $$ ∘ to 60 $$^\circ $$ ∘ at different TE and TM polarization. The folding and unfolding in the FSS structure occur due to the variation of different folding angles $$\alpha $$ α = 0 $$^\circ $$ ∘ , 30 $$^\circ $$ ∘ , 60 $$^\circ $$ ∘ . This causes the interaction between the incident waves and the movement of the interface of the foldable FSS which results into shift of the resonant frequency up to 20 $$\%$$ % as per the desired performance. Thus, the kinematics deformation makes the design tunable. The proposed structure of FSS is simulated in HFSS 15.0. The corresponding higher-order transfer function of the twisted and foldable FSS is obtained from the MATLAB R2014a using the HFSS simulation data. The simulation results are presented which shows the viability of the proposed design.

Artificial intelligence is the future of technology and due to the presence of artificial intelligence in the vehicle industry the growth rate has increased exponentially. With this enormous growth in the technology, various vulnerabilities also emerged that are generally neglected by the manufacturers during the manufacturing stage. The vulnerabilities are not only from the manufacturer’s side but also it can be from the developer’s side who is developing the hardware for Internet connectivity and also the software which is being used to operate the hardware which will ultimately control the vehicle. These vulnerabilities can prove to be a hacker’s paradise that is looking to infiltrate into a system like this and it will make things complicated as human lives will be at risk. This paper deals with a discussion on various security features and challenges that are emerging with the development of this technology. The paper also discusses the various risk associated with the vulnerabilities of automated vehicles and it also discuss the possible security and safety measures that can be opted to produce a safe automated vehicle. This paper also discusses the security models that are implemented by various manufacturers of the automated vehicles and it also measures the precision of those security models. The paper also deals with various intrusion detection and prevention methodologies that can be followed to control the attack on the target machine.

Recently reported technological growth in wireless sensor network (WSN) has extended its application in various disastrous applications. One of the most concerned issues is the forest fires occurring across the globe. Every year thousands of hectares of forest are burnt in the forest fires occurring due to one or the other reasons. Although numerous attempts have been made for the detection of forest fires at the earliest, there is still scope for the utilization of optimum technique for the same. This paper aims to report a review of taxonomy of some of the significant forest fire detection techniques encountered in the literature so far. Moreover, scenario of the forest fires prevailing in India is also discussed. In this paper, the comprehensive tabular study of the state-of-art techniques is given which will help in the appropriate selection of methods to be employed for the real-time detection of forest fire.

In early days, industries used physical role-based servers but as they were hard to scale according to the load they are getting and it was hard to manage the infrastructure; if any server is failed, then the service that corresponds to that server also gets down and a plausible solution to all these problems was solved by virtualization. Different researchers have been contributing and showing their effort to make this worth in the past. In this work, the server has been deployed and Datadog tool has been used to record different parameters to check the performance. Different parameters which have been considered are CPU utilization, disk usage, disk latency, memory breakdown. The aim of this work is to show how servers are reacting when the servers are at full load and when the load is lesser. Further, the virtualized environment also provides advanced features like load management on servers in real time and it also enables the organization to make their environment more efficient and robust with more efficient backups and security.

The online handwriting recognition is recognition of handwritten data through the machine using a digital pen. The online learning includes training of the classifier with test data and the test data becomes part of a training model for next test data. We have done a novel study first in this direction to experiment online learning with online handwritten strokes. The experimentation carried out with benchmarked datasets as unipen and online handwritten Gurmukhi script strokes including 12,477 and 26,572 samples, respectively. The tool used in experimentation is Libol which includes all the state of art algorithms for online learning. The results indicate that online learning could be a suitable choice for online handwriting recognition. The online learning is popular today for its use with large data and less computation time. The present study could be benefited for online handwriting recognition like applications in online learning environments.

Breast cancer is the most routinely identified carcinoma among women in India, and it is one of the foremost causes of cancer death in women. Radiologists prefer mammograms for visualizing breast cancer. Different types of noises including Gaussian noise and salt-and-pepper noise affect the mammograms leading to inaccurate classification. Mammograms consist of numerous artifacts too, which depressingly affect the finding of breast cancer. The existence of pectoral muscles makes anomaly finding a cumbersome task. The recognition of glandular tissue in mammograms is vital in assessing asymmetry between left and right breasts and in conjecturing the radiation risk associated with screening. Thus, the proposed method focuses on improving the segmentation accuracy of noisy mammograms. It involves preprocessing which includes denoising using a pretrained convolutional neural network, artifacts removal using thresholding, and modified region growing and enhancement using two-stage adaptive histogram equalization along with segmentation of mammogram images into sections conforming to different densities using K-means clustering. The projected method has been confirmed on the Mini-MIAS database with ground truth provided by expert radiologists. The results illustrate that the proposed method is competent in eradicating noise and pectoral muscles without degrading quality and contrast and in fragmenting different denoised mammograms into different mammographic densities with high accuracy.

Generative adversarial network (GAN) is the most potent unsupervised learning generative model in deep learning. Though numerous impressive results have been published in computer vision tasks, it is still complex to compare and validate the performance of GAN algorithms. In this work, the review of quantitative evaluation measures of GAN is done with the performance of Frechet inception distance (FID) and the inception score (IS). Evaluations of several recently proposed GAN approaches are based on these two metrics. These evaluations demonstrate an evident variation in their performance based on key factors like training model and hyperparameters such as dimensions of the latent space, learning rate, and gradient penalty. This work discovers the proper dimension of latent space and compares FID and IS that are implemented for evaluation of generated data distribution. FID and IS are the best metrics for evaluating generated data distribution. This work gives an emphasis on appropriate dimension of the latent space and compares these two metrics concerning the improved GAN models. The experimental analysis shows that FID gives better performance compared to IS. NS-GAN and LS-GAN perform more precise. The generator generates better results for 10-dimensional latent spaces, which are not really distinct from the consequence of the normal 100-dimensions. It is recommended to use LS-GAN foe better performance and understanding of algorithms.

In a complex process like face recognition, the type of the features embedded in human face plays a dominant role in the recognition process. The global features describe the whole face image, while the local features are used to describe the local areas in the face. Therefore, combining these features together in the same feature vectors leads to provide attractive features, which have the ability to distinguishing between similar face images. In this paper, we propose a combined local and global face recognition technique. The local features are captured using radon transform descriptor, while the global features are provided using Chebyshev–Fourier moments. Extensive experiments have been performed on four different face databases, namely JAFFE, FERET, YALE, and UMIST, to evaluate the accuracy of the suggested approach under different face variations. Results of these experiments indicate that the presented technique achieved high recognition accuracy in the presence of illumination, pose, and facial expression variations.

Ultra-reliable low-latency communications (uRRLC) and enhanced mobile broadband (eMBB) traffics are needed to be supported expeditiously in the emerging 5G networks. To achieve ultra-reliability as high as 1 − 10−7 in uRLLC, ongoing eMBB packets should be stopped instantly, which results in reduced quality of services (QoS) of eMBB services. This difficulty, known as co-existence problem, is a serious concern in 5G cellular networks and requires a proper mechanism to protect the ongoing services. This paper introduces ANFIS-based reactive strategy for a flexible frame structure that can provide high priority to the uRLLC traffic while ensuring the reliability to other eMBB traffic in the 5G cellular network scenario. Proposed flexible frame structure approach can be a possible solution to the co-existence problem by providing improved QoS for eMBB and reduced delay for uRLLC. The experimental results prove that the proposed approach contributes to the critical advancement for forecasting performance in accordance with the error analysis results.

The sentiment analysis is an emerging field of natural language processing which is based on human–computer interaction, information retrieval and distilling sentiments from the ever-increasing online social data. It involves identifying the words or phrases in the underlying text express positive, negative or neutral attitude. The objective of this paper is to extract the editorial text of a leading newspaper and classify the sentiments expressed at different levels, namely paragraph level, sentence level and word level into positive, negative or neutral.

The aim of this research work is the early prediction of childhood obesity after the age of three years from available clinical records of patients. Nowadays, child obesity is a highlighted research area as excessive body fat harmfully affects a child’s health. Obese children have more risk of suffering from health problems such as heart diseases, type 2 diabetes, cancer, and osteoarthritis in their adulthood. Thus, early prediction of childhood obesity is essential for fat and overweight babies. In this paper, we have proposed a prediction model for this purpose. Analyses of three different machine learning methods: SVM, KNN, and ANN for establishing accuracy in the prediction model have been done. From the result analysis, it can be established that a prediction model based on machine learning techniques can be used to predict obesity in children after the age of two years.

Epilepsy is a well-known neurological disorder which affects moreover 2% of the World’s population. Irregular excessive neuronal activities to the human brain cause epileptic seizures onset. Electroencephalograph (EEG) signals are mostly examined for the detection of epileptic seizure onsets. But an EEG signal consists of a huge amount of complicated information and it is very difficult to analyze it manually. Over the decades, a lot of research has been focused on the development of automated epilepsy diagnosis systems. These systems are dependent on sophisticated feature captureization and classification techniques. The paper aims to present a generalized review and performance comparison of the work reported over a decade in the area of automated epilepsy diagnosis systems that will help future researchers lead a better direction.

Cloud datacenter offers economic and elastic computing benefits to the customers, where on-demand virtual machine (VM) allocation plays a significant role. Inefficient VM placement leads to resource wastage and high power consumption that raises the requirement of server consolidation. The feasibly optimal placement of VMs with the objectives of minimum power consumption and maximum resource utilization is the key to server consolidation. Though many multi-objective VM placement schemes are available in the literature, it mostly works on weighted sum approaches that transforms multi-objective problem (where some objectives maximize and others minimize) into single objective to give optimized solution. Hence, the existing approaches do not correctly justify the multi-objective VM placement problem. To provide correct solution of multi-objective and multi-constrained VM allocation problem, this work presents GA based evolutionary server consolidation framework by applying rank based non-dominated sorting for multiple objectives to generate pareto-optimal solution. It enables maximum resource utilization and minimum power consumption to accomplish effective server consolidation. The VM placement is done using genetic algorithm (GA) which encodes VM allocation information into chromosomes. The performance evaluation of the proposed work is carried out by execution of numerous experiments in simulated datacenter environment. The experimental outcome reveals that the proposed VM allocation framework improves resource utilization upto 38.54, 41.67, and 44.8% and minimize power consumption upto 11.32, 12, and 13.7% over random, best-fit, and first-fit heuristic-based approaches.

This paper focuses on the monitoring of air pollution, sound level, and ambient environment on small-scale areas such as home supplies and organization, etc., or rural area-based cost-effective and user-friendly with software-based metering infrastructure. It is an integrated system that monitors air pollution and AQI-based upon NAMP, sound level, ambient temperature, and smoke concentration in an environment with high level of accuracy. A dedicated VI panel is designed and developed to analyze AQI with and without temperature compensation. It involves the use of various gas sensors for sensing various gases that acquire through NI my-RIO DAQ module via Wi-Fi standard FPGA technology. It has a no. of smart features such as data logging, alert mechanism, and comparison of accuracy of AQI with and without temperature compensation. The whole setup is linked to a virtual panel for monitoring the parameters and checks the status of air quality index, sound level, and different gases concentration using LabVIEW software.

Credit card fraudulent transactions are cost-sensitive in nature, where the cost differs in each misclassification transaction. Generally, the classification methods do not work on the cost factor. It considers a constant cost factor for each misclassification. In this paper, it proposes a modified instance-based cost-sensitive decision tree algorithm which reflects on different cost factor for each misclassified transactions. In the proposed algorithm, it implements different instance-based costs into the cost-based impurity measure as well as cost-based pruning approach. Experimentally, it shows that the proposed algorithm performs better in terms of cost savings as compared against classical decision tree algorithms. Additionally, it observes that the smaller trees are generated in minimum computational time.

Parameter variations can effectively be defined by interval systems. Due to this, several systems are modeled as interval systems. In this work, Doha water treatment plant is modeled as interval system. Then, reduced-order modeling is also proposed for such interval modeled Doha water treatment plant. Firstly, for obtaining the interval system of Doha water treatment plant, uncertainty is considered in all coefficients of systems. Secondly, reduced-order modeling for interval model obtained is proposed by minimizing error in between time moments and Markov parameters. For minimization, Jaya algorithm is utilized due to being simple in implementation. It is established from the results obtained that the reduced-order model is effectively approximating the system.

Extreme event forecasting helps in predicting the user demands during the peak travel times. The application of extreme event forecasting lies in predicting an increased demand for resources and hence can aid in effective resource allocation. The statistical approaches are used for the analysis of time series forecasting but for extreme events, it becomes difficult to predict the actual nature by using only the historical data. These methods alone are not sufficient to accurately predict user demands. Time series forecasting techniques along with machine learning algorithms are used to perform the extreme event forecasting. Here, in the paper, we have created the ensemble of two machine learning models, viz. recurrent neural networks (RNN) and Bayesian neural networks which remove the anomaly and improve the accuracy. Automatic feature extraction module long short-term memory (LSTM) has been used to extract the features. The proposed model enhances the accuracy by an extensive margin.

Exponential growth and enormous development have made faster and seamless communication possible in the field of information and communication technology between several devices amongst each other. New technological innovations brought up new opportunities over several disciplines such as individual well-being and customized healthcare services. Internet-of-Healthcare Things (IoHT) improved consistently and developed in a steady manner. However, according to unstructured and critical healthcare data nature, higher Quality of Service (QoS) is considered a major challenge over designing such systems for providing faster responses and data-specific complicated analytics services. Considering the mentioned issues, this particular paper aims to provide agenda of a five-layered heterogeneous model with IoHT framework based on cloud, fog, and mist along with the capability of routing offline/batch mode data and efficiently handling either instantaneously or real-time.

Operating system is the heart and soul of any computer system. It is solely responsible for the communication between user and hardware. Many important functions of computer system are assigned to the operating system. It not only helps to perform the assigned task but also plays an important role in management. Affiliate marketing is going through a major change in this era. The opportunities are increasing and so is the scope of affiliate marketing. In this paper, we will describe how the different operating systems help to improve the performance of affiliate marketing mechanism. It will also analyze the performance of affiliate marketing under different operating system conditions.