Progress in Advanced Computing and Intelligent Engineering

Secured data transmission is one of the real issues faced in the world of Web. As the measure of data on Web is expanding daily, the importance of data security is also increasing. Several techniques like cryptography, watermarking, steganography are used to enhance the data to be transmitted. This paper uses a novel steganographic algorithm in the spatial domain using the concept of pixel modulation which diminishes the changes that occur in the stego image generated from the cover image. Experimental results and analysis of the observations show the effectiveness of the proposed algorithm. Different metrics like mean square error (MSE), peak to signal ratio (PSNR), bit-plane analysis, and histogram analysis have been used to show the better results of the proposed algorithm over the existing ones.

Dynamic texture synthesis is the process of generating artificial frames from the original ones. Three different innovative approaches are proposed to analyse the chaotic behaviour of dynamic texture. In the proposed method, an operation on the core tensor by considering a fewer number of principal components requires less number of model coefficients. The proposed approach of separating the colour tensor and applying kernel principal component analysis (KPCA) on all the dimensions of different colour cuboids helps to reduce the time complexity with the best quality. The method of creating a tensor with a frame-wise approach is more dynamic. Each frame of the dynamic texture is being taken in 3-D tensors, and then, KPCA is applied on each dimension of the tensor. A frame-wise approach is more flexible in terms of time complexity, PSNR, model coefficients and compression ratio as compared to existing algorithms.

Biometric system used as a pattern recognition system helps in personal identification by considering specific physiological or behavioral characteristics of an individual. It is divided on the basis of the authentication medium used such as fingerprint, face, and iris. In our paper, we have considered fingerprint and face recognition process. Fingerprint is used as a medium of identification on account of its uniqueness, permanence, universal acceptability, and ease of acquisition. We have proposed a hybrid technique which combines minutia-based technique with the global features to obtain a better recognition rate especially for poor-quality fingerprints. Face being one of the unique, non-intrusive, and non-contact physical traits which can be captured without user cooperation finds its application in the recognition system. Every face is illustrated as a linear combination of singular vectors of set of faces. Hence, we have used principal component analysis (PCA) for the recognition. PCA using Eigen face approach has been used as it reduces the dimensionality of data set, thereby enhancing the computational efficiency.

Our brain can with ease recognize images despite size variance, a capability that has been attributed to the normalization of images and feature extraction by biological computational models. In this paper, we study the recognition and retrieval of ultra-low-resolution facial images using Hopfield neural network, a biologically inspired recurrent artificial neural network. We employ two variations of Hopfield, one, proposed by Hopfield and Tank, the asynchronous Hopfield neural network. Second, cloud Hopfield neural network proposed by Singh and Kapoor for its better performance both in terms of retrieval rate and convergence time. Our results show that even when the distortion in the presented facial images is up to 35%, cloud Hopfield neural network is able to give 75.4% successful retrievals, while the asynchronous Hopfield network gives 63.4% successful retrievals. The results for these very low-resolution face images show promise and can help to achieve size invariance for computer vision.

With the increase of communication over Internet, the issue of security has become an important factor. Steganography is a consequence of the increasing degradation of reliability. The roots of steganography lie in ancient Greek civilization. With time, steganography has moved a long way in the path of advancement. It started off with least significant bit (LSB) embedding which mainly focussed on data security. With time, many algorithms have been designed using multi-bit steganography which takes into account both security and capacity of data embedded. In this paper, a new technique has been introduced where various parameters determine the number of bits embedded. This helps to improve the robustness of this method. We show empirically that our method withstands the statistical attacks and benchmark.

The OCR innovative techniques are used to create digital formed text from the basic handwritten and printed text papers. Once it is converted then it can be reused for data processing and reprocessing purpose. OCR system provides the approach of reproducing the editable text-result which near to the approximated unique required digitalized page with same orientations and alignments. The OCR algorithm derives the huge set of learned letters, characters, symbols and their desirable properties. It is best suited for pattern-cum-symbolic image-based recognition and digitalizing the passive mode characters into active mode characters. These inventions are widely executing in private and public sector for various data processing purposes. In OCR, the normal page imprecise text can be processed through various recognition level stages, such as Text-Classification, Level of Pre-cum-Postprocessing, Segmented Processing and Characteristic Mining. Researchers invented the new ideas and approaches for solving critical problems in innovative OCR mechanism. This paper contains the detail descriptive assessment of proposed methods, methodologies, steps handled and invention outcomes in the discovery of optical character recognition approaches and also described graphical representation of OCR algorithmic variations with their handled steps for processing various levels of text and the flow of methodology. This descriptive graphical representation will be helpful to all upcoming researchers in the innovative OCR field. Graphical representation flow is easy to understand and simple to gain the basic important knowledge to focus in their OCR research for further innovations.

Feature extraction of medical images is a challenging task due to variation in modalities and imaged objects (organs, tissues and specific pathologies). This article presents a simple feature descriptor, local diagonal Laplacian pattern (LDLP), devised on the idea of local diagonal extrema pattern (LDEP). LDEP is implemented using first-order diagonal derivatives, whereas LDLP uses Laplacian operation. In order to reduce computational complexity, the intensities at each diagonal element are correlated to the rest of the three diagonal elements together with the centre pixel. Furthermore, the corner elements are compared with the centre pixel to improve the quality of feature description. In addition, the dimension of the pattern is lowered by a half. LDLP is applied to various MR, CT images of the TCIA database and CT images of the NEMA database that results in better feature description at less computational cost.

Biometric systems, which are being adopted as the most effective solution for security breaches, are pattern recognition systems which identify or verify the person based on their physical/behavioural traits. Multimodal biometric systems, being more reliable and accurate than unimodal systems, are gaining much popularity these days. This paper focuses on review of multimodal systems based on iris and palmprint. Also a new scheme is proposed for extraction of palmprint image from unconstrained background. Then, that image is fused with iris, and their combination is used for verification purpose. The approach mainly relies on IFCM technique for palmprint and RED algorithm for iris feature extraction. Score-level fusion is used for combining two modalities and utilizing hamming distance for generating matching scores for both the traits. Combination of iris and palmprint is a very powerful biometric trait because of the individual strengths and uniqueness of both the traits.

The aim and objective of the study are to segment the ultrasound image of thyroid gland using PCA-based segmentation method and to extract the geometric and statistical features and to classify the disease using feed forward back propagation network. Thirty patients with thyroid disorder and thirty age- and sex-matched normal were included in this study. Ultrasound image of thyroid gland for all the patients was obtained. Automated segmentation algorithm using interclass variance analysis method was used for segmentation of thyroid gland. The geometrical features such as area and volume and statistical texture features were obtained from the segmented output image. Finally, the classification of thyroid disease was performed using feed forward back propagation network. The thyroid parameters correlated significantly with the feature extracted parameters for normal and abnormal cases. The feature extracted parameters show increased value for abnormal thyroid gland compared to normal. The developed automated image segmentation algorithm provides a quantitative analysis for estimation of area and volume of thyroid gland. The feed forward back propagation network provided the sensitivity of 85.71%, specificity of 95%, and accuracy of 91% in classification of abnormal thyroid gland and normal.

Cancer is a lethal disease if not detected in an early stage. This paper presents an outline of different types of cancers and recent advancement in soft computing techniques for their detection. It focuses on how different image processing techniques are optimized using neural networks, fuzzy logic, and genetic algorithms to detect cancers.

It is proposed to develop a system for systematic evaluation process of human posture recognition in video sequences which is essential for many solicitations. Video frame has variant information such as different posture, gauge and brightness. This paper implements the algorithm Scale-Invariant Feature Transform (SIFT) to detect and recognize humans posture, in which image is invariant to scrabbling and rendition. This paper explores the application of the SIFT approach in the framework of human posture detection and can deal with the circumstantial noise disputes. The proposed detector is rotation invariant and achieves satisfactory human posture detection. Results are demonstrated based on the video frames, voice broadcast of weight lifting, footage from the movie and a cricket match, etc. To appraise the performance of the suggested algorithm, experiments have been conducted by employing an ANN classifier on a database.

Braille translation software translates electronic documents written in different writing system of the world into Braille code. From these files, a refreshable Braille display can present tactile Braille or a Braille embosser can produce a hard copy on special paper. Few products are available which are economically costly and they mainly focus on national and international languages. There is a need for such products for regional languages of India also. Several researches are carried out on different languages to convert it into Braille. Odia Braille is one of the regional language in which moderate research has been carried out. This paper is focused on translation of electronic document of Odia into Braille as well as Braille to text. The generated Braille code is compatible with Braille embosser of any printer. The mapping table of Unicode of Odia letters and Braille code has been developed for translation. The translation algorithm is also tested on the national language Hindi for its effectiveness. The inverse translation of Braille to text also verified and found the results are satisfied.

Classification of an image on the basis of independent patches selected from image has been effectively used due to its performance. These Image blocks information is collectively represented by Bag of features. Bag of features consist four major parts, block sampling, descriptor generation from collection, characterization of distributions extracted from descriptor and classification of images. In this paper we present GPU based bag of feature generation for effective activity detection in video. Proposed framework focuses on parallel implementation strategy to reduce time taken by traditional bag of visual feature approaches. In bag of visual feature approach clustering takes significant amount of time. We propose GPU based implementation of centroid calculation and dataset generation by distance calculation to reduce time taken by clustering. Feature extraction from video dataset is performed by using MHI with energy and further HOG descriptor. We have used two datasets UT interaction and ATM to validate our results. Our experiments demonstrate that calculations are reduced to almost ten times without affecting accuracy.

This article introduces an approximate rehabilitation of unidentifiable edge in medical images by settling demarcation line based on experimental evidence, by using the essence of classical discrepancy theoretic approach. The main intriguing aspect of this method is distributing the quality points over the ambiguous area by the virtue of jittered sampling method for validating the points in a deterministic way, after finding the decisive point. Before this textual enhancement on unidentifiable edge, a known image classification model is introduced by using wavelet transformation to classify the brain MRI in three categories as normal, benign, and malignant. Here, we are taking timestamp one snapshot of an image, not to consider the aliasing and anti-aliasing effect. We are concentrated on the edges those are not detected even after applying edge detection method.

The problem have been noticed of error in the images while sending, the noise affect in the image so that the result is, the distortion of the secret message embedded on the image in case of watermarking technique. We have seen that the LSB technique is very prone to the noise, so in order to remove this problem, this paper proposes a method for error detection and correction on images entitled as error detection and correction using parity and pixel values; in this, we are adding two extra information that is parity bit and pixel value in order to correct and detect the error bit.

Type 2 diabetes mellitus (DM) is one of the most chronic diseases worldwide. Presently, there are no specific symptoms for this disorder. Hence, an early diagnosis is very critical to prevent the long-lasting complication caused by the disease. In this paper, the efficiency of thermogram was investigated for the evaluation of this disease when compared with biochemical standard, i.e., glycated hemoglobin (HbA_1c). The various gray-level co-occurrence matrix (GLCM) and statistical features were extracted from the thermograms of open mouth with the help of MATLAB software. The best result obtained from classification using artificial neural network had an accuracy level of 86.7%, sensitivity of 80%, and specificity of 93.3%. The computer-aided diagnosis (CAD) model was developed for the evaluation of type 2 DM from thermogram. This study proved that the thermogram of open mouth can be an efficient, faster, and powerful diagnostic tool for type 2 DM when compared with the HbA_1c.

Smaller objects reconstruction using three-dimensional techniques is one among the challenging tasks from the decade. Researchers in graphical designing and professionals of photography are continuously working on the reconstruction of 3D object techniques to meet the demand of real-time applications of almost all in every walk of real life. Reconstruction of 3D objects has a major role in the reverse engineering applications too. The major challenges in successful 3D object reconstruction are high computational costs and lack of accuracy. Fringe projection has come into view as a propitious 3D reconstruction mechanism with low computational cost for high precision and resolutions. It makes use of digital projection, structured light systems, and phase analysis on fringed images. Its performance is shown as acceptable in the research analysis carried out on the implementation of it and its insensitiveness to ambient light. An overview of some of the fringe projection techniques are presented in this paper and also propose a new simple fringe projection system, which can yield the more accurate and acceptable results with different objects.

In this paper, we propose a new approach for Odia handwritten character recognition based on discrete wavelet transform (DWT) and principal component analysis (PCA). Statistical feature descriptors like mean, standard deviation, energy have been computed from each sub-band of the second level DWT and are served as the primary features. To find the most significant features, PCA is applied. Subsequently, back-propagation neural network (BPNN) is harnessed to perform the classification task. The proposed method is validated on a standard Odia dataset, containing 150 samples from each of the 47 categories. The simulation results offer a recognition rate of 94.8%.

Traumatic brain injury (TBI) is a major problem affecting millions of people around the world every year. Usually, TBI results from any direct or indirect physical impact, sudden jerks, or blunt impacts to the head, leading to damage to the brain. Current research in TBI is focused on analyzing the biological and behavioral states of patients prone to such injuries. This paper presents a technique applied on MRI images in estimation of lesion volumes in brain tissues of traumatic brain-injured laboratory rats that were subjected to controlled cortical impacts. The lesion region in the brain tissue is estimated using segmentation of the brain, diffusion, and the damage regions. After the segmentation, the area of the damaged portion is estimated across each slice of MRI and the combined volume of damage is estimated through 3D reconstruction.

Cloud computing is the budding paradigm nowadays in the world of computer. It provides a variety of services for the users through the Internet and is highly cost-efficient and flexible. However, despite all its advantages, security and privacy have evolved to be of significant concern in Cloud computing. Online computing is preferred by consumers and businesses only if their data are assured to remain private and secure. This paper aims to study several key concepts, namely Cloud characteristics, delivery models, deployment models, and security issues. The document also discusses the work done on Cloud security and privacy issues.

Design of cryptographic algorithms has ever been meant for making messages confidential strong enough for cryptanalysts. This has resulted into different categories of encryption/decryption algorithms while they have their own pros and cons. The cryptanalysts have played roles in observing confidential messages, identifying used algorithms to encrypt messages, and they have been successful in exploiting the weak points, if found any, in the algorithms’ designs. But Hybrid cryptographic algorithms, which bring mixture of randomness in the ciphered text, are ones of some obstacles for them. It is more difficult to break the hybrid ciphered text than that of block or stream cipher text. The current work presents design of a hybrid algorithm that makes uses of content replication removal in input data, block cipher and stream cipher cryptographic algorithms.

To create pursuit in the multimedia information along with protection, perceptual security techniques are gaining more attention. A novel approach for perceptual encryption of images based on moves of knight pieces is proposed. The novelty of the approach lies in simulating unpredictable key sequence generation based on crossover operation and knight piece moves. Three stages are carried out in the proposed method. In the first stage, the initial population of a genetic process is selected based on the amount of perceptual information. In the second stage, random keys are generated based on pseudorandom number generator for crossover operation. Two different cases are considered in the proposed approach for crossover operation. Crossover of one-point and two-point operations is applied to the random positions of the knight piece. In the third stage, movement of knight pieces is carried out iteratively for fixed number of times, and crossover operation is performed according to one and two points on selected pixels. Structural similarity index, PSNR, entropy, and correlation coefficient are analyzed to measure the performance of the proposed approach. The comparative study with existing perceptual method reveals that the proposed work provides better perceptual security for images.

Security has become a potential issue among the Internet users all over the globe. IP spoofing is one of the major security threats to the Internet. Spoofing is a technique in which an attacker does forgery of ID’s of a legitimate host in the network. Spoofing is a serious problem to the present Internet world. It creates serious security problems such as packet authenticity, launching DDOS attacks, etc. In this paper, we propose a flow marking based anti-spoofing (FMAS) mechanism for wide area networks (WAN) which uses marking of source IP address field of a flow for encoding. We have tested our anti-spoofing mechanism using software-defined networking (SDN) approach by dynamically marking network traffic flows for enhanced security.

One of the drawbacks in visual cryptography is cheating attacks, where the malicious adversaries can cheat the honest participant by submitting fake shares during reconstruction phase. Cheating immune visual cryptographic schemes are used for mitigating cheating attacks in visual cryptography. There are two types of cheating immune schemes: One is share authentication-based schemes, and the other is blind authentication-based schemes. For the existing blind authentication-based schemes, the pixel expansion value will increase in the order of O(n). In this paper, a blind authentication-based cheating immune visual cryptographic scheme is proposed by modifying the existing scheme based on uniform codes where the pixel expansion value will increase in the order of O(log n).

Mining mainly aims toward extracting hidden useful patterns from the large amount of data which is exploited by various organizations for their business planning. The organizations generally used to store sensitive data, during their mining processes, which they do not want to disclose either due to legal constraints or competition among themselves. Privacy preserving data mining is focused on hiding this sensitive information while still getting accurate mining results. This paper deals with UML modeling of multiplicative perturbation-based approach applied in privacy preserving data mining system. Multiplicative perturbation is the most popular technique nowadays, which mainly aimed to preserve multidimensional information while performing different mining operations. Authors have modeled the entire approach by using IBM Rational Software Architect tool. This standard model will facilitate researchers and organizations to design more specific and aligned business solutions in order to secure sensitive data by proposed UML models based on multiplicative perturbation approaches.

Recently, many applications are Internet-based where confidentiality is maintained by secret communication. Cryptography and steganography are two popular methods used for the purpose. The proposed method develops a new data hiding scheme combining the concepts of cryptography and steganography to enhance the security of communication. In cryptography, basic concept of differential and integral calculus is used during encryption and decryption, respectively, and in steganography, data is embedded in an image file and extracting the original message using discrete cosine transformation. This hybrid technique is sound as the number of secret keys used for encryption varies with the size of message, and at the same time generated keys are independent of each other which prevent hacking of all keys together. As a result, more security is enforced in the communication channel. The method is evaluated by measuring the distortion of originality of image file computing peak signal-to-noise ratio.

Wireless sensor networks (WSNs) are a new and most recent technology which is used in many critical applications. WSN is a network which is deployable anywhere in the environment. Due to this nature of WSN, an unattended network is more probable for vulnerable and unavoidable attacks. WSN is comprised of huge different types of nodes which are distributed within the network. Some of the nodes have sensing technique for detecting unusual behaviour in the network. Detection and prevention mechanisms are used for securing the network, with the consideration of energy efficiency. Biometric-based system is most efficient way of detecting an intruder which is one of the major techniques of pattern matching. Since WSN is more probable vulnerable to various harmful security attacks as it has the nature of broadcasting the transmission data and has very low capacity of computation, an Intrusion detection system (IDS) is used to detect such mobile attackers or intruders. Intrusion prevention system (IPS) uses a mechanism which enables the reduction of consumption of energy in sensor nodes of WSN.

The cyberattack happens to be a nation concern—as the target can hold the confidential matter of military or a commercial business with the intellectual assets and viable secrets that pave the way to challenge. The majorities of the professionals are aware of the cyberattacks on the data center space but are helpless and have a severe impact on the two domains. The prime domain, information technology which refers to the information processing—servers, storage arrays. The other one is about the physical infrastructure that controls the first domain. The cyberattacks on the network can be mitigated through the educative programs for the employees and training the staffs with the certain basic security policies. Introducing the preventive techniques can defend the sensitive data from the hackers.

Renewable energy hybrid system is one of the most promising, economical, and reliable options for electrifying rural areas. Hybrid energy systems are being utilized for minimizing usage of fossil fuels to reduce environmental effects. But analysis of hybrid system is quite complex; therefore, it requires software tools for design, analyze, and optimization of software. Motive of the paper is to introduce various major software tools required for design, analyze, and optimization of hybrid system. The software discussed are HOMER, RET Screen, PVsyst, iHOGA.

A MES is developed which simulates the methodology of a medical practitioner’s way of detecting Cardiovascular diseases. The challenge of missing data has been solved by most frequent value imputation method. The issue of continuous attributed data is solved by entropy-based discretization method. The model seems to predict the presence or absence of heart disease from minimal attribute set thus minimizing redundancy. Reducts has been extracted from the features by considering quality of approximation. Learning by Example Module, Version 2 (LEM2) algorithm based on Rough Set has been used for rule induction and generation. Two Rough Set classifiers are designed, namely RSC-1 and RSC-2. Each classifier is fed with all features and then with reducts separately as inputs. A comparative study is performed between the classifiers, and result shows that RSC-1 whose input is locally discretized reduct performed better with an accuracy of $$84.46 \pm 5.24$$84.46±5.24 percentage.

We propose an approach for computing relevant slices for feature-oriented programs. Our approach is based on an intermediate representation of the program called dynamic feature dependence graph (DFDG). We have named our proposed approach feature relevant slicing (FRS) algorithm. Our approach first computes the dynamic slice for a test case corresponding to the desired program location. Then, it determines the potential dependency for the desired program location, and eventually, it computes relevant slice using FRS algorithm.

The software effort estimation phase is especially critical in the software development phase. This phase is principally oriented on manipulation of the values of the cost drivers and scale factors. Also, most of the models depend on the size of the project, and a diminutive alteration in the size directs to the in proportion alterations in the effort. Miscalculations of the cost drivers have even additional ear-splitting data as a result too. In this paper, the approach of generalized reduced gradient (GRG) nonlinear optimization with best-fit analysis has been applied to tune the COCOMO model cost drivers so that level of accuracy can be achieved. This proposed methodology has been observed more efficiently in providing the software effort estimation through the help of minimizing MRE value. We have applied this methodology on NASA 63 data sets. We have shown the comparison between the estimated MRE and actual MRE of the data sets. We have also exposed the evaluation between the estimated MMRE and actual MMRE.

This research paper deals in digitally solving all the general problems of residents living in Indian apartments. It ensures the high standard of facilities which they get in their apartments. The application proposed in this paper will make the society more luxurious and standardized. The modules like REAP for praising the service providers, voting system for the selection process of members of the society, and community engagement module for spreading information/notice among all the residents will make the lifestyle of the people smarter and easy going. It ensures high comfort for the residents and promotes digitalization in the society.

Seismic data interpretation and subsurface mapping are key skills to analyze subsurface geology. They form the basis for the decision concerning hydrocarbons exploration and extraction. Interpreting a seismic graph, with perfection, needs expert knowledge. The knowledge of seismic data interpretation used in exploration industry is largely individualistic, with each human expert using his/her own set of mental database of interpretation rules developed over years of experience. For the lack of appropriate structure and formalization, this essential body of knowledge is unable to smoothly percolate to the next generation of seismologists, who are expected to deliver reasonable accuracy in their interpretations, almost immediate to their induction. Characterization of human knowledge is the process of structuring, formalizing and transforming the nature of the knowledge from tacit form to explicit form. Current work presents design and development of intelligent system to characterize this knowledge and deliver it using tutoring strategy exclusively devised as per the learner adjudged learning preference. This prototype additionally also measures learner’s performance and facilitates learning gain. The system has been tested with 16 participants, and the resultant performance is recorded.

Electronic commerce is the trading or facilitation of trading of products and services using computers and latest technology. Here, focus is on developing software for e-marketing to provide a new platform which will give benefit to the already existing way of e-marketing. In this, ways are given out to analyze the sales progress of a particular product in the market. Based on different customer’s demand studied, certain parameters are found which allows us to calculate the growth of a product. A model will improve the process of selling a product by managing customer’s demand along with the important parameters by integrating online and offline marketing skills and also improve e-marketing plan. The main objective is to anticipate the demand based on some factors. After implementing e-marketing plan and strategies, the sale is supposed to be enhanced.

Our aim is to use a machine-driven helping figure which can solve the problems of carrying heavy goods by following us. Keeping this conception in our mind, we built ‘MINION’ which could be the better viable answer to these problems of carrying goods when there is no one around. Our robot is built on chassis rover tank with four sonar sensors as receivers which are faced in four directions for forward, right, backward and left sides. It follows a transmitter which gives signal continuously to the receivers on the robot. A person carries the transmitter and makes the robot follow him/her. It moves itself with the help of transmitter–receiver using ultrasonic wave so that it has no need of manual input. An infrared sensor is set up on the robot to maintain a specific distance between them. Both MINION and the transmitter module use DC battery, and it is easy to operate and thus is useful in numerous applications.

In recent years, bioinformatics has become an essential subject for molecular biological study. The various available algorithms are used for analyzing and integrating biological data. Among many biological statistics RNA (ribonucleic acid) is one of the most important as it is used in protein synthesis. In computational molecular biology, the optimal secondary structure prediction of large RNA is a problem being faced today. RNA sequences of some virus are very large in number which requires a large amount of time for secondary structure prediction. Consequently, parallelization of algorithm is one of the solutions to diminish time consumption. This paper proposes the algorithm GAfold for predicting secondary structure of RNA on shared memory multicore architecture. The various RNA sequences as an input have been taken from Gutell database. For calculating minimum free energy, thermodynamic model is used and the outcomes are compared with existing algorithms.

Online shoppers get ample opportunity to buyers to shop online. The Web gives online shoppers extra channel for looking data of items and administrations, despite everything. But still, it has various issues to be addressed, basically the risk and fraud affecting the attitude and number of sales online. The paper explores the consumer perceptive model toward online shopping. It gives the insight of the factors affecting using structural equation modeling. The trust relationship is hugely affected between the customer and the online merchandiser if the risk to fraud vendor or product increases.

In this paper performance of the Massive MIMO is analyzed in terms of SER and SNR values. SER and high data rate is the current requirement for the 5G technology based on the observed result it may be concluded that Massive MIMO has lower SER values and this result is compared with the current MIMO technology. It is observed that SER values for Massive MIMO is comparatively very less than the MIMO system. Therefore, it will be beneficial to opt Massive MIMO concept for the next generation technology.

Concrete mix proportions proposed by empirical equations do not account for slight modifications. Mix design becomes primitive in case of introduction of new parameters as research progresses such as accounting for new kinds of admixtures, super-plasticizers or binders. In practice and theory, effect of age on compressive strength is correlated. Metric of concrete compressive strength with age is an important criterion in prediction problem. Prediction problem parameterisation as quantity or ratios is a controlling model choice decision. The existing codes of practice do not account for a standardised process of evaluating compressive strength. Considering the strength prediction problem here as a classification domain of input space, it is modelled using M5’ model tree algorithm. The study conducted shows the performance promised by such a model to be accurate within statistical error.

Global warming effects on earth are caused by several factors. To understand the overall effects of global warming on earth, we have to understand the contributions and effects of each component of the planet. The exhaust gases from vehicles, power plants, and other sources are building up in the atmosphere, acting like an unimagined thick blanket over our planet. It has been clearly identified that additional effective technologies are needed to control CO₂ in the atmosphere. In the current study, absorption of CO₂ is achieved in a petrol engine using modified ZSM5. The reduction of CO₂ by absorption on Cu-ZSM5 and Li-ZSM5 is compared.

Ion cyclotron resonance heating (ICRH) data acquisition control system (DAC) system for 100 kW, 45.6 MHz has been designed for RF ICRH system. This system is used for control and monitoring for 2, 20, and 100 kW RF amplifier stages, respectively. ICRH system consists of different power supplies for each RF amplifier stages. PLC-based DAC has been used for various power supplies monitor, control, and interlocks. Acquired data has been stored in MDSPLUS tree in binary format with defined signal list. Python is an open source scripting language which has been used for core development of software system. Python is very popular programming language for embedded as well as high-level programming. MDSPLUS is an open source package used for data archival and visualization. It is data archival and analysis tools used in various fusion experiments for data logging and visualization with configuration. It has standard developed program interface available with most of the popular languages like Fortan, C++, Python, Java, MATLAB, and IDL for graphical display of data and browsing. The paper introduces the interface of Python-based API for integration of Python and MDSPLUS for ICRH DAC software. Module allows user to read/write data directly in binary tree of MDSPLUS hierarchy. Java-based jScope tool is a part of MDSPLUS database which is used for data visualization.

When a gas is passed over a chemically sensitive layer, its electrical properties change due to adsorption of gas molecules. This phenomenon is implied in thin film sensors. To improve the sensitivity and selectivity of sensors, a parametric modeling approach is preferred. In this, the parameters affecting sensor response are varied, and the sensor response is acquired for developing a model based on variation of parameters. The developed model suggests changes in sensor design and operating parameters, enhancing sensor performance for detecting explosives. A system in which the parameters of temperature, cycle time, and gas concentration can be varied is implemented using an embedded system approach. To facilitate the development of model, the sensor response is made available in comma-separated values. Also, a real-time plot of the sensor response is made available for identification of presence of a nitroaromatic explosive.

Nowadays, Congestion is proving to be a major issue in all kinds of network. Huge chunk of data transmission is the leading cause of congestion to take place. Hence, without increasing the capacity of the network our defined congestion control algorithms works dealing and managing efficient data transmission with minimum or no congestion through analysis. The algorithm is build on the basis of two parameters: The first parameter is the queue length, and the second parameter is the RTT [Round-Trip Time]. Before transmitting data to any destination, the source should check the available queue length of the destination based on some threshold. To understand the network congestion, RTT is calculated. Smaller the value of RTT, lesser is the congestion and vice versa. In this paper, two different algorithms based on the above parameters are defined, implemented, and analyzed.

We propose a distributed and transmission power efficient fault-tolerant topology management technique, known as transmission power efficient disjoint path (TEDP), designed for nonhomogeneous wireless sensor networks (WSNs). A nonhomogeneous WSN is consisting of a substantially large count of energy-constrained and low-computational capability low-cost sensors and a small number of resource-extensive sensors with unrestrained battery power. The aim of our proposed algorithm is to designate every sensor node’s communication range in order to ensure that every sensor node establishes k-vertex disjoint connectivity with resource-extensive nodes, and the overall transmission power utilization in the network is substantially minimized. With the employment of TEDP algorithm, the induced topologies can tolerate k − 1 node failures in the worst case. The experimental outcomes demonstrate that in contrast to existing solutions our algorithm attains the decline in overall transmission power substantially by 25% and the decline in utmost transmission power needed in a sensor node by 30%.

Localization is required to maintain real-time position for processing in indoor and outdoor conditions. This may be utilized to support mobile computing and networking among nodes in wireless sensor networks. In this paper, a dynamic algorithm for location estimation is proposed for the wireless sensor networks. The algorithm is tested on the TinyOS-based emulation test bed for validation of the approach in real-time environment.

Software Defined Networking (SDN) is a new approach of managing and programming networks enabled by OpenFlow. For load balancing, a migration among OpenFlowDevice (OFDevice) is needed from heavy-loaded controller to least-loaded controller. During migration of OFDevice, the response time, jitter and packet loss for the end user are high. To address this problem, we propose a method in which the response time, jitter and packet loss are minimized during device migration. In this approach for migrating the optimal OFSwitch, we use-liveness, safety, and serializability. The proposed approach focuses on selecting such a OFDevice which causes minimum load on the controller. The experimental results show that our proposed method improves response time, jitter and packet loss.

Mobile node having ubiquitous characteristics, collectively form a group, generally known as mobile ad hoc network (MANET). It has a wide range of applications due to its easy installation, no any central authority-based failure point, no fixed infrastructure and many more. Openness of ad hoc network leads to the wide range of security issues that need to be investigated as well as resolved. In this paper, we are proposing the solution to the blackhole problem that overcomes this issue in more fruitful way. In general, attacker sends a forged RREP with high DSN and low hop count to fraudulently showing the source node having shortest path. When data are transmitted through that route all packets have been dropped. As per our approach, destination sequence number (DSN) is compared with a threshold value and fake messages are discarded. The simulation work has been carried out to compare the proposed approach with the standard AODV routing protocol on the basis of packet delivery ratio (PDR), and it shows positive results.

FiWi is a fusion of optical network and wireless network. It gives high bandwidth, high data rates, high mobility, and low cost. The connection interface between the PON and WMN is established by the use of an optical network unit (ONU). ONU is an important component of communication as well as it affects the overall cost of system, hence its placement in the network becomes an important issue. Various research works have already been done to provide the solution. We aim to present a new optimizing algorithm for ONU placement and its minimization; this will result in the enhancement of network performance and reduction in the cost of overall network.

This paper analyzes the performance of 10 GPON for 50 km bidirectional fiber link using travelling wave semiconductor optical amplifier (TSOA) with square root module (SRM). In the proposed network, the system utilizes 1550 and 1300 nm wavelength for downlink and uplink transmission, respectively. With the use of TSOA-SRM combination in the architecture, we have observed the improvement in bit error rate, quality factor and SNR for data rates up to 10 Gbps with 64 users in downlink and 40 users in uplink, improvement in SNR further helps us to reach extended GPON.

Fiber-wireless (FiWi) access network is a category of networks which utilize optical network’s bandwidth and wireless network’s flexibility. This last mile technology will serve all needs of access networks for many years to come. However, its energy consumption has also been increased rapidly due to the development of high bandwidth application like Internet of Things (IoT). Therefore, it has become critically important to discuss the energy-related issue for FiWi. Based on the literature, this chapter provides an exhaustive survey of recent progress on energy efficiency mechanisms such as ONU sleeping mechanisms, power saving modes (PSM), and some cooperative-based energy saving schemes. Furthermore, it also provides future directions for better energy efficiency in FiWi network.

A sustainable grid computing infrastructure is characterized by high-speed computer network, a suitable number of computing devices, and the software modules that are capable of establishing a reliable communication framework between the networked components. The number of issues and their extent of impact in the low and lower-middle income countries vary significantly depending upon the type of the grid application and economic modalities of the nation. This paper rectifies such major and minor pitfalls arriving in the fabrication process of a typical grid architecture and their extent of hurdles in such countries. The pitfalls after a deep analysis also result the paper in suggesting worthy resolution methods possible to overcome these issues. An efficient grid thus consists of a proper cache of suitable mechanisms to expound the problems listed in this paper to achieve a goal of a novel grid infrastructure.

The routing is one of the major operations in WSNs. In our proposed work distance, energy and the angle formed by the node with the base station are taken as input parameters. The Mamdani fuzzy inference system is used to select the chance of a sensor node to become a cluster head (CH). A-Star search algorithm is used to locate an optimum route from source to sink node. The data packets are routed on the selected path.

Fiber wireless FiWi access network is an integration of two networks. The first part of network is called passive optical network (PON), used at backend. The second part of network, wireless mesh network (WMN) is used at front end. This combination facilitates high data rates, stability, and flexibility. Routing is very important part of wireless mesh network (WMN), and therefore of FiWi access network. It directly affects the network parameters such as throughput, delay, packet delivery ratio which in turn affects the network performance. In this paper, a routing algorithm is proposed for FiWi network, on the basis of minimum angle criteria. The algorithm results in lesser delay as compared to standard routing algorithm.

Performance of mobile ad hoc networks (MANETs) increases by the cooperation of mobile nodes among themselves. In MANETs, mobile nodes share their resources such as battery power, memory, and bandwidth. But, in the real world some nodes do not want to contribute their resources either fully or not at all and thereby storing their resources for future use. So these nodes behave as selfish due to their resource constraint. In this work, we propose an approach for detection of the selfish nodes in MANET and avoid them during the data distribution process when there is sybil attack by considering the routing optimality, checking optimality, and forwarding optimality. The analysis of the simulation results indicate that the network performance has been increased as compared to the traditional network in terms of data accessibility and hence decreasing overall selfishness alarm after detecting the selfish nodes.

Data security is a major concern in cloud computing. It must satisfy the three goals of security in computing—integrity, confidentiality, and availability. Homomorphic encryption is a technique in which user or cloud service provider (CSP) can perform operations on cloud data without performing decryption. Many algorithms are available for homomorphic encryption. But these algorithms generate large size ciphertext. This paper focuses on homomorphic encryption which generates small size ciphertext. It is a variant of scheme proposed by Dijk et al. In an experimentation of this scheme, encrypted data are stored in DynamoDB of Amazon Web service (AWS) public cloud. When user requires data, it can be downloaded on users machine and then decrypted.

People have different opinion on Cloud Computing. Cloud Computing is changing the way of work by deploying the applications and files in the Cloud. People can easily access the applications and documents from anywhere in the world by just assembling the number of computers and servers via the Internet. So hosting the applications in the Cloud makes easy for the group members to cooperate. We can define Cloud Computing as the utility computing, which delivers the rental services on demand and scalable infrastructure over the Internet, and the virtualized datacenter that supports those services. Cloud Computing is the big change for the professionals and academia in the computing world. Cloud is different from the desktop computing since it provides online tools, guidance, and advancement to the customers. Cloud Computing does not bind the software to our personal computer rather it provides the efficient way to store them on the servers which can be accessed via the Internet. In case our PC fails to deliver the services, nothing to worry because you have your software in the Cloud. Cloud not only provides the above service, but also bears flexibility, better quality, long-term deployment, and many more.

In a distributed system, multiple processes on multiple nodes can execute at same instance of time and may want to enter into the critical section. Several distributed algorithms require that there be a node with the coordinator responsibilities that perform some kind of coordination activities within the system. In election algorithm, two or more processes may identify coordinator had crashed simultaneously but actually this does not cause any issue to the election process except waste of network bandwidth. In this paper, I have proposed a solution for electing a coordinator in the distributed system simultaneously to help in reducing the network bandwidth by dropping out the extra network messages generated during election process.

Unified communication allows the users to share data between multiple devices having different platform. Previously, data was stored on local servers and accessed by employees through their desktops that have security policies inbuilt. In the age of integrated communication, users can access stored data from anywhere, anytime via smart devices and 4G wireless technologies. Traditional security protocols like access control are not well suited for mobile environment. Dynamic features of mobile devices can incorporate the conventional access control scheme to escalate the security of traditional protocols. In this work, dynamic attributes like spatial or temporal attribute, application usage, unlock failures, location and proximity of mobile devices are employed to curb the access of data. Using pairs of mobile agents, the work deals with the constraints of mobile devices and provides an efficacious solution. The proposed scheme can identify the authenticity of data owner and preserve the anonymity of the user.

The energy-related issues in distributed systems that may be energy conservation or energy utilization have turned out to be a critical one. Researchers worked for this energy issue and most of them used Dynamic Voltage and Frequency Scaling (DVFS) as a power management technique where less voltage supply is allowed due to a reduction of the clock frequency of processors. The cloud environment has multiple physical hosts, and each host has several numbers of virtual machines (VMs). All online tasks or service requests are scheduled to different VMs. In this paper, an energy-optimized allocation algorithm is proposed where DVFS technique is used for virtual machines. The fundamental idea behind this is to make a compromise balance in between energy consumption and the set up time of different modes of hosts or VMs. Here, the system model that includes different sub-system models is explained formally and the implementation of algorithms in homogeneous as well as heterogeneous environment is evaluated.

Task scheduling is an NP-hard problem. In earlier, different task scheduling algorithms are proposed for cloud computing environment. By adapting new and modified task scheduling algorithms, better resource utilization can be obtained. In real time, most of the tasks are deadline-based tasks. The deadline-based task has different parameters. The backfilling algorithm is used to schedule these types of tasks in Haizea. In this paper, we modeled the existing backfilling algorithm for scheduling deadline-based task using Petri-Net. The paper presents the design model of the existing backfilling algorithm. The model specifies real-time challenges of backfilling algorithm using Petri-Net. The work also comes forward with some design issues of backfilling algorithm using Petri-Net.

This paper presents a new version of discrete cuckoo search algorithm to minimize makespan on parallel batch processing machines. In the proposed algorithm, we consider a modified Lévy flight based on job interchange and job insertion techniques to generate new cuckoos by random walk. The initial population of the algorithm is generated using best-fit heuristic approach. Results of computational experimentation with a large set of random instances of non-sparse parallel batch processing with unequal job ready times show that the proposed algorithm significantly performs better than some state-of-the-art algorithms.

The data centers are the world’s biggest consumers of electricity. The consumption of energy in the cloud is proportional to the CPU utilization of the virtual machines (VMs). As the size of the cloud infrastructure increases the complexity of the resource allocation problem increases and becomes very difficult to solve it efficiently. This is an NP-Hard problem. There are several heuristics that may be used to solve the problem. Through task consolidation, we can get many benefits such as maximizing cloud computing resource, utilization of resources in a better way, efficient use of power, customization of IT services, Quality of Service, and other reliable services, etc. We find from the literature review that there is a high level of coupling between energy consumption and resource utilization. This paper presents the resource allocation problem in cloud computing with the objective to minimize energy consumed in computation. The simulation results show that a 70% principle of CPU utilization is the most energy efficient threshold for task consolidation in a virtual cluster. It has been verified with MaxUtil and ECTC (Energy Conscious Task Consolidation) algorithms.

Cloud computing has been gaining popularity in the recent years. Several studies are being proceeded to build cloud applications with exquisite quality based on user’s demands. In achieving the same, one of the applied criteria is checkpoint-based load balancing in real-time scheduling through which suitable cloud service is chosen from a group of cloud services candidates. Valuable information can be collected to rank the services within this checkpoint-based load balancing. In order to attain ranking, different services are needed to be invoked in the cloud, which is time-consuming and wastage of services invocation. To avoid the same, this chapter proposes an algorithm for predicting the ranks of different cloud services by using the values from previously offered services.

The mobile users in mobile cloud computing (MCC) environment suffer due to less energy and resources of mobile devices. Although there are several works have been proposed, those are not sufficient to overcome these problems. To accommodate this claim, in this work, a game theory-based energy efficient transmission policy for MCC has been proposed named as E2G. E2G works based on the subgame perfect Nash equilibrium (SPNE) where each mobile device works as a player and selects a best mobile device by choosing best strategy among all players. The mobile device which is chosen based on SPNE decision will be responsible for communication with the cloud. The simulation results show that E2G helps to minimize the energy consumption of mobile devices as compared to the existing approaches.

Open source software (OSS) can be used for the development of geospatial database in cloud computing environment. Open source GIS (OSGIS) yields strong spatial solutions in a cost-effective manner and used for work connected to geospatial database creation, geospatial Web services, geospatial database storage, etc. The present work critically analyzes two popular OSGIS software i.e., Quantum GIS and Map Window GIS for geospatial database creation to give better comprehension of procedure and execution in education sector with cloud SDI. Presently, geospatial database provides detailed information about the entire technical institute of Odisha in test case. The objective is to deliver spatial statistics at reasonable costs and anticipated to be advantageous for stake holders such as students, parents, faculty members, government organizations. The same may be scaled up in future for technical institutes of other states in India.

Recently, the development of mobile devices creates lots of interest to the users, where a mobile user can run various kinds of applications. In mobile cloud computing (MCC) environment, mobile devices suffer from less battery power when performing operation for longer duration. To overcome this problem, in this work, an energy-efficient mechanism for computation in mobile devices named as Mobile to Cloud (M2C) is proposed. In this approach, offloading the resource-intensive computation is done by the cloud. M2C can reduce the overall latency for processing various kinds of data from the mobile devices by considering a cloudlet layer. The simulation results indicate that the execution time of applications is improved by M2C as compared to without using cloudlet layer.