Intelligent Communication Technologies and Virtual Mobile Networks

Densely deployed Wireless Sensor Network (WSN) generates massive amount of data, which is processed and transmitted by resource constrained sensor nodes. The challenge of reducing high transmissions with low cost on-node processing can be achieved using Compressive Sensing (CS) CS data gathering from these nodes is designed under various routing mechanisms. Cluster-based routing integrated with CS, reduce transportation cost, but overall transmissions in the network are not reduced. When the density of nodes is high, advantage can be taken of their spatial closeness to form clusters. Motivated with this, we propose a novel Spatially Correlated Cluster (SCC) and integrate CS at cluster head. Different from other spatially correlated clusters, clusters with radius equal to sensing range of sensors is formed. Comparing our work with the state of the art methods, the proposed system reduces overall number of transmissions, hence reducing energy consumption and prolonging the network lifetime.

In recent years, Fires and explosion in coal mines imposes number of life threats for mine workers along with a rapid increase in environmental air pollution. By using various risk assessment methodologies, coal miners can easily predict the potential risks of forthcoming hazards in advance. In this work, a novel approach is proposed for monitoring the fire-resistant hydraulic fluids (HFA) contamination level. Fire resistance property of HFA fluids varies with the viscosity. Water content. By monitoring the water content in HFA fluids, fire resistance can be easily predicted. Fire resistance hydraulic fluid properties are trained in Ensemble Boosted Regression Tree (EBRT) to predict the potential risk in coal mines. EBRT is the supervised training algorithm which is proposed for leveraging an efficacious coal mine monitoring into existence. EBRT model estimates stronger prediction by linearly integrating the weaker estimations. Threshold rule-based decision making is adopted for the effective mitigation of risks. EBRT is optimized to minimize the cross-validation loss. Furthermore, Bayesian optimizer is used to minimize the objective function to 7.81 with regularized parameter lambda is chosen as 0.34 to minimize the ensemble trees. The root Mean square error is optimized to 31.68.

Image compression is the most important feature for acheiving an efficient and secure data transfer. One of the main challenges in compression is developing an effective decompression. The input images that is compressed may not be more effectively restored in the decompression process that is based on quantization using Cosine Transformations or Wavelet transformations where the pixel information will be lost. To overcome these challenges, encoding process were employed. In the encoding process the pixel information were well protected but the compression efficiency is not improved. In order to overcome this challenge Lossless Patch Wise Code Formation (LPWCF) is employed. In the patch wise code generation the compression process is based on the pixel grouping and removing the relevant and recurrent pixels. In the proposed method, the images were first reduced in size by combining the current pixel with the previous pixel. The resulting image size is nearly the half of the size of the input image. The resulting image is then divided into small patches. In the patch recurrent pixels and their locations were identified. The identified pixel locations were placed prior to the pixel value and then the process is repeated for the complete image. The result of each patch acts as a code. In the receiver side the same process is reversed inorder to obtain a decompressed image. The process is completely reversible and hence the process can be employed in the process of transmission of the images. The performance of the process is measured in terms of the compression ratio, the image quality analysis of the input and the decompressed image based on PSNR, MSE and SSIM.

A face search system which merges a live search strategy along with a state-of-the-art commercial-off the shelf (COTS) matcher, one cascaded framework. In this first sort massive album of photos, to figure out the top-k most alike faces. The k retrieved prospect is re-ranked by emerging equalities depending on deep features and those results by the COTS matcher. The software based technique is complex and large. It analysis unique shape, pattern and positioning to the respective facial features. It estimates with the records consists of images present in central or local database, the deep network representation combines with a state-of-the-art as well as COTS face matcher in large-scale face search system. According to study on the face datasets leads to complexity: LFW dataset (consist of face detectable). In this project Viola-Jones face detector algorithm is used. The Viola-Jones Technique use to perform feature extraction and evaluation the Rectangular features measures with a new image representation their calculation is very fast.

One of the dreaded diseases that adversely affects human eye in the world is the Glaucoma. It should be remembered that without eyes, nothing is possible in this world. Further, as per the WHO, it is a well-known fact that the glaucoma is determined as the second largest disease across the globe. Proper care should be taken to avoid this at an early stage, as this would later result in the loss of human vision. The glaucoma disease occurs in the human eyes because of the increase in the intraocular pressure of the fluid flow in the drainage canal of human eyes. To reduce the glaucoma treatment expenses, we are devising a low cost module method for detecting the primary glaucoma in the humans using their fundus images. The images of the infected eye will be captured by the fundus camera, analyzed & a info is given to the patient that he/she is affected with the glaucoma disease. Once the person comes to know that they are affected, then proper diagnosis can be done by gathering consultations from the medical experts. The method of detecting the primary glaucoma is being presented in this section using a revised artificial neural network along with a back propagation algorithm concepts. The morphological operators concepts are being used for the processing the cup and the disc & finally the region of interest, i.e., the cup and the disc areas are detected from which the infected ratio is computed. Using a revised feature extraction process, the features of the captured disc & the cup can be efficiently detected with a concept of CDR detection and the result will declare whether the input test image is glucomatic or not. Simulations have been done in the Matlab environment. Databases have been taken from the hospitals & online. The simulation results have shown the effectivity of the method proposed in this extensive research work.

In today’s world, almost everything has been digitised and hence the cloud computing is emerging as a boom in its field. But the most disturbing fact is that the cloud computing also appears as a vulnerable platform and therefore the security becomes the most important aspect in the cloud computing applications. Security issues are mainly dealt with known and unknown threats, malware and network access by unknown or unauthorised users. Hence, it has become a fundamental feature to create a secure platform for the cloud computing technologies. In this scenario, we proposed a RTMDC (Reputation based Trust Management system with Data Colouring) systems, which builds and tests a secure platform using Reputation Based Trust Management technique. Reputation based trust factor is then calculated using the collective reviews given by the users. This research work focuses on the images that has been related to the cloud users.

In recent times, human tracking plays a crucial role in several applications like surveillance, free biometry, realistic world etc. In this research work, we suggest a new method to track the objects like humans using the motion obtained from color images. This algorithm does not use the object characteristics which is tracked and hence it resembles human eyes that uses the process of tracking in all the available images in RGB. Spatial and temporal association of motions are considered for motion association, which is the proposed plan of action to decrease the undesired selection process. Furthermore, for different images the Wigner distribution has been used which is less dependent on the fluctuation in threshold frame and thus reduces the untrue object detections. The results acquired with this algorithm is identical and consistent which in turn provides the reduction in computational complexity of this algorithm.

IoT comprising heterogenous devices with varied and constrained resources imposes challenge in managing the available network resources. The new technology raised to solve these challenges is fog computing. In this research work, Agent technology for Fog enhanced vehicular services model is proposed. For managing the resources at the edge of the network fog is used and cloud agency is used for providing services to the tasks that are not given by the fog. The proposed work is designed and simulated using cloudsim tool and analysed using cloud analyst tool. Performance measures such as resource utilization, allocation time and congestion rate is measured and resulted with better resource utilization, less allocation time and reduced congestion rate.

Basic idea behind the Face Annotation is to detect the facial expression and process further on it for various applications. Techniques of face annotation are used to give an appropriate name to the face image. In this research work, first the face notations are saved in the database and it can be retrieved any time for further processing and then it compares two different images of a same person and finds out whether those images belongs to the same person only. In this paper we described various techniques of face annotation such as Content based, Retrieval based, Search Based, Cluster Based and Caption Based face annotation. Based on the study we present the parametric evaluation of existing techniques.

Cyber Security assumes a critical job in the field of Information and Communication Technology [ICT]. Securing the information has become one of the greatest difficulties in the present day. When we consider cyber security, the main aspect that strikes in our mind is ‘cyber crimes’ which are expanding enormously day by day. Different Governments and organizations are taking numerous measures so as to keep these cyber crimes. Other than different measures cyber security is as yet a big concern to many. This paper for the most part centers around difficulties looked by cyber security on the most recent technologies. It also focuses around most recent about the cyber security methods, ethics and the trends changing the appearance of cyber security. Governments, military, associations, budgetary foundations, colleges and different organizations gather, process and store a great deal of private data on PCs and transmit that data over systems to various PCs. With the tenacious quick advancement of volume and enhancement of cyberattacks, incite attempts are required to anchor touchy business and individual data, and to anchor national security. The paper talk with respect to the idea of the internet and shows how the web is unbound to transmit the mystery and budgetary data. We demonstrate that hacking is as of now typical and dangerous for overall economy and security and exhibited the diverse techniques for Cyber attacks in India and around the globe.

Algorithms are the crucial and important part for any research and developments. Algorithms are usually published in the scientific publications, journals, conference papers or thesis. Algorithms plays important role especially in the computational and research areas where the researchers and developers look for the innovations. Therefore there is need for a search system which automatically searches for algorithms from the scholarly big data. Algo_Seer is been proposed as part of CiteSeer system which automatically searches for pseudo codes and algorithmic procedures and performs indexing, analysis and ranking to extract the algorithms. This work proposes a search system Algo_Seer which utilizes a novel arrangement of procedures such as rule based method, machine learning methods to recognize, separate and extract the calculated algorithms from the academic reports. Particularly mixture troupe machine learning systems are utilized to obtain the efficient results.

The term fusion means in moderate approach to extract the information acquired in several domains. The term infrared and visible image fusion has been intended to find compatible fused image with detailed textures of visible images and an impressive infrared object area. We therefore combine infrared and visible images to create solitary image. Current real - time applications that encourage image fusion including military surveillance, automate agricultural, object recognition, remote sensing, and medical applications. The concept of merging two or more than two images using the various image fusion schemes. This paper begins with the background information on the image fusion. Secondly, infrared and visible image fusion rest on multi-scale transformation of existing techniques are reviewed with all the merits and demerits of the same table lists. Further section elaborates fusion strategies and fusion performance evaluation metrics are summarized.

This work concentrates mainly for the implementation of Standard DWT using IEEE 754 floating point format. Currently, in the signal processing, for audio purpose the fixed point DWT is used as audio CODEC [1]. The main bottleneck of the fixed point DWT or the traditional DWT is the speed because at the input of the fixed point DWT the over-sampled ADC which is the Sigma-Delta ADC is used. The Sigma-Delta ADC can’t give the speed more than 1 MHz because as the sampling rate increases, the step size decreases so that it takes more time to follow the analog signal which causes the limitation of the speed. Due to the speed limitation of ADC, the whole audio CODEC system which was designed by the fixed point DWT becomes slow even it has the capability to operate with a better speed. Hence, to optimize the system the FIR filters which are used to constitute the standard floating point DWT have been implemented in VLSI.

IoT evolution plays an important role in healthcare applications for data sharing and patient monitoring systems. Patient related information is to be stored securely in health care data management system. By using this database management systems, patient status can be estimated. To identify the quality of analytical data, which is collected from healthcare sensors based on the trail sets. In our proposed system, we used a PI sensor for better results and it will continuously update the information about the patient to the server by using Message Queue Telemetry Transfer (MQTT) protocol. Massive sensors receives the condition of both current health conditions and previous health condition and that will be compared. If any changes found, it will immediately give the alert to the doctor and to the care taker. Machine to Machine (M2M) communication has been established to transfer the data with very high speed. Due to high speed connectivity, the result of patient monitoring will be very accurate and the patient’s life can be saved without any delay.

In Mobile ad hoc Networks (MANETs), discovering the stable, reliable and secure routes is a difficult analysis downside because of the open medium and dynamic topology. Most of the recent ways didn’t solve the entire downside of data loss in MANETs as they targeted on distinguishing the malicious nodes and forestall them from electronic communication method solely. There are many different reasons corresponding to mobility and congestion of mobile nodes due to which the data could loss in MANETS. Thus planned theme ETSR is meant by considering these parameters so as to provide a more stable, reliable and secure routes.

At some point, the understudies have insufficient information to proceed further in their career, so getting right exhortation from experienced individual is vital which will be accomplished by establishing an online alumni interface. With this the alumni can communicate to the students regarding job opportunities and the students can share their department activities to the alumni. The proposed system has the dynamic architecture and less static content, which can empower the full duplex association between all graduated students and understudies. This paper portrays how the system will function and collaborate the graduated class with the present understudies.

Image security is emerging as a major problem with the exponential growth of data stored and transformed through the network around the world. Many cryptographic techniques are used for securing the data like images, audio and text files. A new technique of DNA cryptography provides high security based on DNA nucleotides bases A-Adenine, C-Cytosine, G-Guanine and T-Thymine. These alphabets can be easily assigned to binary values (A-00,C-01,G-10,T-11). In this proposed model Polymerase Chain Reaction encoding technique is used in which the image to be encoded is flanked between primer keys. The DNA codons are encoded by the base of four provides keys of 256 combinations for high security, and it reduces the size of cipher text. Primer keys are generated by pseudo random sequence generator. Deciphering the image is possible with encryption key and primer sequence key. The HDL synthesis report for hardware design is implemented for encryption using verilog code on a device Virtex VII.

Data collection by public and private organizations is increasing for extracting hidden knowledge from it that may be used for assisting decision making process. Moreover, availability of high speed internet and sophisticated data mining tools make sharing of this collected data across various organizations possible. As a consequence, these organizations may share and combine their datasets to retrieve the improved result from the combined data using collaborative data processing. Sharing of such data as it is between collaborative organizations may compromise individual’s privacy as the collected data may contain sensitive information about individuals in its original form. To address this challenge, two main categories of privacy preserving approaches viz. the non-cryptography based approach and the cryptography based approach can be utilized. This paper aims to discuss an insight of these approaches and to highlight the parametric comparison.

Structural Health Monitoring (SHM) helps to estimate the health of the structures to detect the damage. A continuous monitoring is provided through wireless sensor Network (WSN). As an enabling technology, WSN along with SHM helps to achieve a low cost estimate. The damage detection is achieved through 2 phases (i) Training phase (ii) Operational phase. The training phase collects the data for the formation of data points. The Data point now forms the boundary region to detect the damaged areas. The operational phase contains three sub processes. They are data collection, transmission and damage evaluation. The clusters are formed and cluster head passes the details to detect the damage. The simulation shows the efficiency of these processes.

Identifying web user activity and interest of the users helps to improve the web access performance. Web usage mining applications like website enhancement, web personalization, prediction and prefetching etc. are used to improve the web performance. Increasing web usage in internet leads to network traffic, user latency, and server burden. Proxy server acts as an intermediate between the web user and web server to reduce the server burden. Updating dynamic content in a proxy cache is the major drawback in proxy server. In recent days various new add-on algorithms are given to server to reduce user latency but then it has become additional overload of the server. In this paper, the work is organised with three portions; the first portion focused in optimized way of running Monte Carlo prediction algorithm to reduce the server load. Second portion works on dynamic content to get update in the proxy cache to improve the performance of the website and finally the third portion deals with the prefetching engine in proxy server which maintains two caches to reduce server load and also to reduce user latency. The successful implementation shows the optimized way of reducing server load for add-on programs.

Today’s databases of corporations are so huge, that they can only be approached by experienced programmers. Accessing data from a database usually needs notable skills such as knowledge of SQL; however, the most of us who interact with databases every day don’t have that background. Hence it’s an increase demand for non-technical user to be able to redeem data from databases without having to list SQL queries. And this problem is solved by using approach of Natural Language Processing. This research work presents an approach for querying system for natural language processing. Hence it will dramatically simplify the process of handling with large data and making data available for everyone.

Recently, the conversational agents like Chatbots are widely employed for achieving a better Human-Computer Interaction (HCI). In this paper, a retrieval based chatbot is designed using Natural Language Processing (NLP) techniques and a Multilayer Perceptron (MLP) neural network. The purpose of the chatbot is to extract user’s feedback based on the services provided to them. User feedback is a very essential component for the betterment of the service. Chatbot serves as a better interface for obtaining an appropriate user feedback. Furthermore, sentiment analysis is done on the feedback as a result a suitable response is delivered to the user. A Long Short Term Neural Network (LSTM) is used to classify the sentiment of the feedback.

Digital evolution in capturing video, advances in compression technology and internet leads to availability of large videos on the web. There is growing need for efficiently retrieving relevant videos. Semantic Concept detection assigns multiple labels to segmented shots or entire video which facilitates many applications like multimedia indexing and retrieval. This paper presents the semantic concept detector architecture for unbalanced dataset which assigns multiple labels with probability to input video. The proposed architecture uses visual features extracted on global scale. The unbalanced dataset problem is handled by partitioning dataset into segments further evaluating classifiers on these dataset. Feature fusion and decision fusion is evaluated using machine learning algorithm for all segments. Performance of the concept detection architecture for above fusion methods are reported with Mean Average Precision. The proposed method for multilabel concept detection is evaluated on TRECVID 2007 dataset and performance is better than existing early and late fusion.

In recent years, cloud services are emerging popular among the public and business ventures. Most of companies are trusting on cloud computing technology for production tasks. Distributed Denial of Service (DDoS) attack is a major general and critical type of attack on the cloud that proved extremely damaging the services. In current years, several efforts have been taken to identify the numerous types of DDoS attacks. This paper explains the various types of DDoS attack and its consequence in cloud computing. Also, this paper provides the various impacts of DDoS attack on cloud environment. The main goal of this paper is to discuss about prevention, detection and mitigation approaches of DDoS attacks on cloud environment with strengths, challenges and limitations of each approach. So that researchers can gets completely novel intuitive understanding into how to alleviate DDoS attacks in the field of cloud computing.

Network Traffic is one of the major issues in wireless Sensor Networks (WSNs). WSN is a self-constructed and organization less wireless networks which is used to observe and check the physical or environmental conditions and to cooperatively pass their data through the network to a sink where the data can be appropriately observed and examined. Number of research works in wireless sensor networks (WSNs) is primarily focused on improving the network performance along with enhancing the quality of service parameters such as the data arrival rate, available bandwidth, congestion, transmission rate, queue length and energy. Various natural computational algorithms have been proposed for overcoming these issues. In this paper we have discussed about some of the bio-based algorithms such as Genetic Algorithms, Simulated Annealing, Ant Colony Optimization, Particle Swarm Optimization, Firefly Algorithm, etc. to control congestion in wireless sensor networks.

Generalized frequency division multiplexing (GFDM) is an upcoming modulation method for fifth generation (5G) wireless communication systems with many advantages over conventional orthogonal frequency division multiplexing (OFDM). In this work, GFDM with clipping and filtering technique is proposed and its performance in terms of peak to average power ratio (PAPR) is presented. The proposed GFDM system along with Rapp’s solid state high power amplifier (SSPA) is simulated to evaluate its PAPR and BER performance. The performance of the present system is compared with the OFDM system for equal and unequal spectral efficiency conditions. The simulation results show that at equal spectral efficiency condition (ESE), the PAPR of clipped and filtered GFDM signal is reduced by 1.6 dB as compared to clipped and filtered OFDM signal with almost similar BER performance. In addition to that, the complexity of GFDM system is less than that of OFDM for equal spectral efficiency condition.

There are number of challenging research areas available in the field of medical technologies. Among them cardio-vascular disease prediction plays a vital role. By applying data mining techniques, valuable knowledge can be extracted from the health care system. In this proposed work heart disease can be detected by using a classifier algorithm. The world health organization has projected 17.7 million people died from CVDs in 2015, representing 31% of all global deaths. According to this survey, it is anticipated that nearly 7.4 million people will die due to coronary heart disease and 6.7 million were due to stroke. The proposed algorithm was Modified Multinomial Naïve Bayes algorithms (MMNB). This algorithm helps us to predict the heart disease more accurately compared to other supervised algorithm. The proposed algorithm provides 74.8% of accuracy which is better than the Naïve Bayes Algorithm.

The web access behaviour of the web users is influenced by customers’ state of mind. The influence of the customers’ psychology in web access behaviour is analysed in this paper. Positive emotion along with positive mood induces better attitude in the web users’ behaviour whereas negative emotion along with negative mood induces a negative attitude among web users. The state of human mind changes along with the temporal property based on the emotion and mood. The statistical study on the historical data is used to discover the influence of mental state which affects the web users’ behaviour. Various machine learning algorithms along with statistics and psychology are studied to discover the knowledge about the web users’ access behaviour.

The important component for complete blood count is haemoglobin. The normal Hemoglobin (Hb) concentration in blood is about 12–15 gm/dl for females, 13.5–17.5 gm/dl for males and 11 to 16 g/dl for children. The invasive methods are used to measure the Haemoglobin concentration by ejecting the blood from the patient and subsequently analyzed. The disadvantages of the invasive methods are it causes delay between the blood collection and its analysis causes pain while ejecting the blood and the temperature should be maintained for the blood samples during transportation. The non-invasive method overcomes these disadvantages by pain free analysis of the blood, real time analysis. Proposed technique has 96.56% accuracy compared to clinical measurements.

Internet has become a part of our daily life. Most of the communications or the transfer of the data across the globe is happening over the internet. With Internet of Things [IoT], the devices can transmit data with each other over the internet. As the internet play a crucial role, the security of the data also becomes important. Confidentiality is important in the healthcare sector where encryption plays an important role. There are many effective encryption algorithms available, but through this work we are proposing a novel method that aims at adding the randomness to the encryption algorithm. The random number generation technique used in this paper is Web Scraping, here the data is scraped from the web page where the data gets constantly updated which adds to the randomness. The random number generation module is added to the existing encryption algorithm and the encryption algorithm is tested on different file sizes to test the level of encryption.

Presently, the number of people affected by Diabetes Mellitus (DM) is significantly increased because of the presence of high blood sugar level because of the failure of pancreas to generate enough insulin. DM is one of the chronic diseases and is widely spread all over the word. In recent days, there is an exponential growth in the number of researches carried out in this field because of the DM leads to death causing disease like heart stroke, eye blindness, etc. So, the prediction of DM at the earlier stage is highly useful to prevent the increasing mortality rate. Numerous data mining and machine learning (ML) models has been developed to diagnose, and handle DM. Keeping this in mind, in this paper, we try to review the recently developed ML and data mining models to predict DM. The existing DM prediction techniques in different aspects have been reviewed and a detailed comparison is also made at the end of the survey.

Recent technological trends have sealed the method for rising and provides advanced services for the stake holders within the agricultural sector. A lucky shift is current from proprietary and tools to IoT-based, open systems which will change simpler collaboration between stakeholders. This approach includes the technological support of application developers to start specialized services which will seamlessly interoperate, therefore making a complicated and customizable operating atmosphere for the tip users. we tend to propose the implementation of AN design that instantiates such AN approach, supported set of domain freelance code application known as ‘‘generic enablers’’ that are developed within the context of the FI-WARE project.

Identifying important nodes in a network is an important area of research in network science. m-ranking method is a method proposed Reji Kumar et al. [18] for ranking the nodes in a network which avoids the chance of assigning same rank for two nodes with different physical characteristics. This ranking takes into account the degree of all nodes and weights of all edges in a network. As the network becomes bigger and bigger the m-ranking method takes more and more time to complete. To overcome this difficulty in this paper we propose an approximation to this method, which simplifies the calculations without undermining the ranking outcome. We illustrate the procedure in some example networks.

Cloud computing is an emerging paradigm where the user can benefit many efficient services. Since many services resemble the same functionality, the user faces relevant and un-relevant information as data burden. So, Recommender System (RS) is getting used to suggest the user only the information that suits their search. Here (CFC)-Collaborative Filtering Coefficient is used as RS which functions by analyzing user history and similar service from neighbor users. Pearson coefficient is used to calculate the association between the services. But, it works for existing users not for new users because the further user details are not sufficient to recommend a service. To overcome this, the KNN approach is utilized to classify the recommendation from a k-nearest neighbor by finding the resemblance between various client ratings using Euclidean Distance measure. Thus, the KNN-CFC hybrid novel approach can create a new efficient RS framework which supplies the client a most relevant service information with low execution time for various data densities and different users and services.

Cancer (tumors) is the cause of every sixth death around the world. This makes cancer a second leading cause of death. Globally 42 million people across the world suffer from cancer and this figure is continuously increasing. In India around 2.5 million people are suffering from different types of cancer. If detected in early stage, then with proper treatment it can be cured. This paper presents details of a few methods used for detection of diseases like Breast cancer, brain tumor, liver cancer, lung cancer and Spine tumor. This paper also speaks about the different machine learning techniques used to classify the diseases into malignant & benign.

This paper tries to analyze Uber data set, and would implement business intelligence using Hadoop framework. This means by reducing the overhead and focusing on the routing optimization paths for a popular destination. However, there is dependably absence of straightforward and realistic techniques for choosing prominent destination. The paper tries to solve this problem by defining a threshold mechanism in order search the popular places. By, finding the days on which each place has more trips and more number of active vehicles by performing analysis on the Uber dataset in Hadoop using MapReduce in Java. Based on the data, find the top 20 destinations people travel the most, top 20 cities that generate high revenues for travel, based on booked trip count.

Crying is an infant behavior, a part of behavioral system in human which assures persuade of the helpless neonate by eliciting others to meet their basic needs. It is one of the way of communications and a positive sign of healthy life for the infant. The reasons involved for infant’s cry includes hungry, unhappy, discomfort, sadness, stomach pain, has colic or any other diseased conditions. The health of new born babies are effectively identified by the analysis of infant cry. Researchers made a huge analysis of infants by using methods like spectrography, melody shape method, and inverse filtering etc. The paper proposes a procedure to detect the emotion of infant cry by using Feature Extraction techniques including Mel-frequency and Linear predictive coding methods. A statistical tool is used to compare the efficiency of the two techniques (Mel-frequency and linear predictive coding). Present work is carried out mainly for five reasons which includes infant crying, has colic, hungry, sad, stomach pain, unhappy.

Grid computing the job scheduling is the major issue that needs to be addressed prior to the development of a grid system or architecture. Scheduling is the user’s job to apropos resources in the grid environment. Grid computing has got a very wide domain in its application and thus induces various research opportunities that are generally spread over many areas of distributed computing and computer science. The cardinal point of scheduling is being attaining apex attainable performance and to satisfy the application requirements with computing resources at exposure. This paper posits techniques of using different scheduling techniques for increasing the efficacy of the grid system. This hybrid scheduler could enable the grid system to reduce the execution time. This paper also proposes an architecture which could be implemented ensuring the optimal results in the grid environment. This adaptive scheduler would possibly combine the pros of two scheduling strategies to produce a hybrid scheduling strategy which could cater the ever changing workload encountered by the gird system. The main objective of the proposed system is to reduce to overall job execution time and processor utilization time.

Remote sensor system (RSS) is a framework formed with an extensive number of minimal effort micro-sensors. Number of messages can be sent to the base station (BS) by using this system. RSS includes ease hubs with affected battery power, additionally the battery swap isn’t easy for WSN with thousands of physically inserted hubs, which suggests vitality productive steering convention to supply a long-labor of affection time. To accomplish the point, we require not just minimizing absolute vitality utilization additionally to adjust WSN load. Scientists have proposed numerous conventions, for example, LEACH, TEEN, SEP, DEEC. The elective Cluster Heads (CHs) communicate the base Station (BS) through beta elective nodes, by exploitation multi-hopping. We tend to logically divide the network into two elements, on the idea of the residual energy of nodes. The normal nodes with high initial and residual energy are going to be extremely probable to be CHs than the nodes with minor energy. The algorithms applied in a situation where initial energies of nodes are different from each other are called as mixed clustering schemes. It is difficult to implement an energy aware mixed clustering algorithm due to the complex energy design of the network.

In this paper, the current consumption of microcontroller with varying frequency and voltage has been experimented in an attempt to understand the power consumption of microcontroller. The power consumption of microcontroller, when connected to an active load, is investigated. The microcontroller is experimented in sleep mode and analyzed the current consumption in its deepest sleep mode and active mode. Among the various wake-up sources, we have used an external input pin and watchdog timer method for the experimental setup. Using the obtained values the battery life is estimated.

Hyperledger Fabric is one of the most popular blockchain framework which is hosted by the Linux Foundation. As a basis for the development of applications or solutions with a modular architecture, Hyperledger Fabric offers plug - and - play components such as consensus and membership services. Ethereum is a decentralized platform running smart contracts, applications running exactly as programmed without any possibility of downtime, censorship, fraud or interference from third parties. These apps are based on a custom built blockchain, an enormously powerful global shared infrastructure that can change value and represent property ownership. This study deals with comparison of Hyperledger Fabric and Ethereum platforms while covering various factors like architecture, performance, use cases, ease of algorithm etc.

Traffic light control systems are widely used to control the flow of the traffic in smart city. Due to rapid increase and continuous flow of the vehicles there is a lot of traffic congestion and people tend to break the traffic rules risking their life. Pedestrians are meant for the people to cross the road who are on foot but the vehicles are there on the pedestrians. At times breaking the traffic rule might lead to accidents. At night street lights are on even though the street is empty, which is a waste of energy and is a serious issue. So as to reduce the traffic congestion and make the people follow the traffic rules we propose “Smart City Traffic Control System”. It consists of a dynamic traffic light control system (based upon the density), a laptop camera (for monitoring and it will record the video in case if traffic rule has been broken, this recording will be stored and mailed to the traffic police department), a GSM module (to notify the traffic police department via SMS that a traffic rule has been broken, TSOP sensor, Arduino Mega Microcontroller, LEDs.

This article presents an emerging methodology in research and optimization called hype heuristics. The new approach will increase the extent of generality within which the optimization systems operate. Compared to heuristics (Meta) technology that works in a particular class of problems, hyper heuristics leads to general systems that manage extensive variety of issue area. Hype heuristics make an intelligent choice of the correct heuristic algorithm in a given situation. The article analyzes the absolute most recent works distributed in different fields.

IOT: Internet of Things is a developing technique, it is the system of vehicles, home apparatuses, physical gadgets, and different things installed with hardware, programming, sensors, actuators, and system availability which empower these items to associate and trade data. IOT is made out of vast number of various end frameworks associated with web. Physical gadgets installed with RFID, sensor, etc. which enables item to communicate with one another. Security is a serious issue because all the heterogeneous end systems are communicated with each other through internet.

Wireless Sensor Networks (WSNs) have a large amount of real-time applications with tiny and energy utilized sensors. The network topology separates the sensors into several clusters. In this paper we propose an Impartial Clustering Algorithm, which enables the cluster formation without any requirement of set-up overhead. The proposed algorithm nominates one node called as Adjacent node to gather data from other sensor nodes. It also maintains the load balancing technique to ensure that all the sensor nodes will die only when they transmit all their data in the network within the time to live. The experimental results proved that the proposed work performed well when compared with all the other related works in terms of energy utilization and load balancing.

MANET is a set of non-identical mobile nodes, where all the nodes are under energy constrained environment. Here reduction in energy consumption is the main issue. This paper consists Energy Efficient Analysis Cluster based routing Protocol (EECP), in which the network coding is used along with cluster heads for reducing the number of transmissions and increasing the network lifetime. As the number of transmission is reduced, energy consumption is also reduced. Through simulation using NS-2, it has been shown that the performance of EECP is better than CBRP.

In the contemporary digital environment, secure search over encrypted data is necessary to prevent the unauthorized data usage practices. To provide data privacy and security fine grained access control is important. For having data privacy data have to be stored onto the cloud in an encrypted form and have to be decrypted before retrieving the message. Encrypting the information is a tedious process for mobiles and also its recovery is a significantly challenging task since the mobile devices have limited bandwidth and battery life. Searching the encrypted data over mobile cloud is a tedious process. To tackle this issue, Cipher text Policy Attribute Based Encryption (CP-ABE) mechanism is proposed. The proposed system will perform computation in cloud rather than the mobile device. This mechanism could effectively prevent malicious searching and decrypting of files. Also, the proposed system support many number of attributes and flexible multiple keyword search patterns in which query file order will not affect the search result. This indicates CP-ABE scheme to improve the efficiency and security.

This paper proposes an approach to estimate the possible built-up areas from high-resolution remote sensing images covering different scenes for monitoring the built-up areas within limited time and minimal cost. The motivation behind this work is that the frequently recurring patterns or repeated textures corresponding to common objects of interest (e.g., built-up areas) in the input image data can help in discriminating the built-up areas from others. The proposed method consists of two steps. First step involves extracting a large set of corners from the input image by employing an improved Harris Corner detector. The improved Harris Corner selects the local maxima from the extracted corners by performing the gray scale morphological dilation operation. It then finds those points in the corner strength image that matches the dilated image and is greater than the threshold value. In the second step, an adaptive global thresholding is applied to the corner response image and binary morphological operations are performed to obtain the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in the literature in terms of detection accuracy.

Sentiment basically represents a person’s attitude, expressing thoughts or an expression triggered by a feeling. Sentiment analysis is the study of sentiments on a given piece of text. Users can express their sentiment/thoughts on internet which may have impact on the user reading it [7]. This expressed sentiment are usually available in unstructured format which needs to be converted. Sentiment analysis is referred to as organizing text into a structured format [7]. The challenge for sentiment analysis is insufficient labelled information, this can be overcome by using machine learning algorithms. Therefore, to perform sentiment analysis we have employed Deep Neural Network.

A Vehicular Adhoc Network (VANET) is a sub-class of Mobile Adhoc Network (MANET) which provides vehicle to vehicle communication and vehicle to infrastructure communication, i.e., between vehicles and Road-Side base stations. The presence of Vehicular technology in the present automotive industry owes to its increasing number of applications with a good resource for the Intelligent Transportation System. The designed system uses the road side unit (RSU) to ensure the originality of the vehicles which is sending messages and also to prevent redundant messages. With large computing power, it stores all information about the vehicle and provides a pseudo id to the vehicle for communication, thereby anonymity is preserved. The RSU verifies the vehicle id, and the freshness of the message is verified by using timestamp. Once the details are verified, it finally broadcast the message to all vehicles and other RSUs that may need the information, thereby arresting redundant messages. Cryptographic algorithm is applied for encryption and decryption of messages, ensuring secure messages.

Distributed storage stages guarantee advantageous applications to clients for sharing records and cooperate with coordinated efforts. The main challenge in a shared cloud-based application is the security and privacy of sensitive information stored by a particular user. For instance, Cloud users has the ability to erase documents and disavow them without counselling the alternate cloud associates. To handle this drawback, Proof of Shared belonging (PoSW) is proposed, a particular PoSW is subjected to execute information protection mechanism, confirmation and information reduplication. Furthermore, PoSW tends to utilize the diagonal cryptography equation to ensure the security of the mutual data records in order to provide a common information sharing approval, document ownership, and develop an absolutely particular relation between the document holders. Hence, integrating PoSW into the cloud server assists it to check the common belonging and overcome the data reduplication challenges in the common cloud-based data records.

Internet of Things is the network of physically connected devices, which enables these devices to communicate with real world appliances. Home Automation is a beneficial technology for automatically controlling the activities done at home. The different technologies like Bluetooth, WIFI and Zigbee are used to implement the Home Automation System (HAS) where various devices like smart phone, tablet and laptops are used for controlling various household appliances. In this paper we evaluated the capabilities and behaviours of the WSN technology on home automation systems mainly regarding the power consumption and also the advantages of integrating solar panel to the system to enhance the power consumption ratio.

The headway of overall arranging development, sensor systems and versatile terminal, an extensive number obviously information are amassed. Bearing information contains an abundance of information, including directionality, time game-plan, and other outside expressive qualities. The examination obviously likeness estimation is the prelude of heading information the board and excavation, which acknowledge a fundamental occupation in bearing getting ready. Most course likeness work just spotlights on the dimensional-normal highlights. The augmentation of multi-credits to the heading changes the course furtiveness. MELD (Most extraordinary Least Direction Separation) and TLDS (Total of least Direction Separation) and inspect the association among the direction-common furtiveness and scholarly similarity. The headings including the zones, accurate location, and obvious characters are called multi-qualities bearings.

Virtual Machines (VM) are considered as the fundamental components to cloud computing systems. Though VMs provide efficient computing resources, they are also exposed to several security threats. While some threats are easy to block, some attacks such as co-resident attacks are much harder even to detect. This paper proposes Defender Vs Attacker Security Game Model otherwise called Two-Player security game approach based defense mechanism for minimizing the Co-resistance DOS attacks by making it hard for intruders to initiate attacks. The proposed defense mechanism first analyzes the attacker behavior difference between attacker and normal users under PSSF VM allocation policy. Then the clustering analysis is performed by EDBSCAN (Enhanced Density-based Spatial Clustering of Applications with Noise). The partial labeling is done depending on the clustering algorithm to partially distinguish the users as legal or malicious. Then the semi-supervised learning using Deterministic Annealing Semi-supervised SVM (DAS3VM) optimized by branch and bounds method is done to classify the nodes. Once the user accounts are classified, the two-player security game approach is utilized to increase the cost of launching new VMs thus minimizing the probability of initiating co-resident DOS attack.

The term “Fuzzy” means vague or imprecise or uncertain or inexact. Fuzzy Sets enable us to accept the vagueness and lack of precision. Fuzzy Sets are used when classical/crisp representation cannot make the decision for a problem. Fuzzy Set Theory is a vast field and relies heavily on mathematical equations. This paper is an attempt to capture its essence without getting overwhelmed by the complexity of details. In this paper, we have confined our discussions about Fuzzy Sets and its operations in contrast to Classical Sets. We have started with Classical Sets followed by a discussion on how a Classical Set fails in some of the problems and how Fuzzy Sets overcome those issues. Then we have briefly discussed the Linguistic Variables and how it is more practical and realistic than a binary reasoning. For the sake of simplicity and understanding, we have tried to avoid mathematical equations wherever possible.

In this big data era, the data storing methods are vary based upon the data type and the technologies upgradation. Due to the increase of voluminous data, the traditional Relational Database Management Systems (RDBMS) are immature to handle the unstructured data. To overcome this issue, NoSQL databases are used to store and process the unstructured data. The big data migration from SQL to NoSQL database is more complex. The SQL databases are well-normalized database. Denormalization plays a major role in retrieving the data more efficiently. This work is carried on migrating the big data from SQL to NoSQL database using the Graph-based Denormalization method. The proposed method is more efficient for big data migration and post-migration process.

Wireless Sensor Networks (WSNs) are key enablers for IoT and pervasive computing paradigm. While devices are being seamlessly enabled with connection and communication capabilities, exploring techniques to quantify and improve quality has gathered significance. This work explores quality of a Data Aggregation Tree (DAT) in sensor networks. DATs are building blocks for data collection in WSNs. In this work Quality of Experience (QoE) and Quality of Service (QoS) of DATs is evaluated using data aggregation ratio $$\alpha $$ and generated data $$\delta $$ respectively. An algorithm Quality Aware Data Aggregation Tree (QADAT) to construct a quality aware DAT is proposed. QADAT adapts the DAT to network and user expectation dynamics. Simulation results show the effectiveness of the proposed algorithm and demonstrates quality awareness through DAT adaptability.

Controlling a bot machine that uses non-conventional energy form, i.e. light is said to have an upper hand in pioneering transportation system. The expanding request of making the streets more secure has persuaded a ton of organizations to create finest autonomous vehicles. This paper will concentrate on the potential outcomes of utilizing just light-sensing gadgets alone for the light tracking bot using advanced color detection algorithm. The algorithm would help the bot in sensing the color of light and act accordingly, for instance green color to proceed, red color to stop. This particular requisition has high scope in real time application over the emergent underground transportation system; speculating on how the emerging innovative advances fit to the fiddle urban areas of the 21st century.

For Detecting Arrhythmia, the commonly used Medical test is an Electrocardiogram (ECG) which is widely used by medical practitioners to measure the electrical activity of heart. By Analysing ECG signal’s each heart beat we can find the abnormalities present in heart rhythm. In this work we survey different methods used for classifying ECG arrhythmia using Support Vector Machine and also discussed about the challenges associated with the classification of ECG signal. For classification we require Pre-Processing of ECG signal, Preparation Method, Feature Extraction or Feature Selection Methods, Multi class classification strategy and kernel method for SVM classifier. Recently, for the classification we have several datasets available which have been clinically detected arrhythmia present in each ECG recordings. By initiating this research survey we aim to explore current methodology for diagnosing arrhythmia and classifying ECG signal using SVM.

Mobile Ad-Hoc Network (MANET) is a relationship of the mobile nodes with constrained transmission range and asset with no fixed infrastructure. But, malicious attack of node reduce the trust-level nodes that lead to insecure in delivering data. The increments in attacks cause extreme energy consumption that tends to a decrease in network-lifetime. The security and routing issues are concentrated by introducing trust aware ad-hoc protocols. In this research proposal, Trust-Aware ad-hoc Routing (T2AR) with Ant Colony Optimization (ACO) is used for maximizing the trust level based on trust-rate, energy, mobility based malicious behavior prediction. Ad-hoc On-Demand Distance Vector (AODV) uses two processes to find and maintain routes: the route detection process and the route maintenance. Hence, the T2AR-AODV-ACO methodology precisely transmits data from source to destination (S-D) by executing better throughput, routing overhead, end-to-end delay and energy consumption in trust aware ad-hoc routing.

A MIMO–UWB microstrip feed monopole antenna with C band and lower WLAN notch band characteristics is proposed. UWB frequency range is obtained by two octagonal shaped monopole radiating patches etched on FR-4 dielectric and with a partial ground plane. A T-shaped stub is used to decouple energy between the two radiating patches. Two Notched bands, one at 3.7 to 4.2 GHz, and the other at 5.15–5.35 GHz are achieved with two U–shaped slots etched out on two radiating patches. The dimension of antenna is 60 × 35 × 1.6 mm3, that covers –10 dB bandwidth of 7.5 GHz, <−15 dB mutual coupling, and <0.01 envelope correlation coefficient over 3.1 to 10.6 GHz frequency range except the two notch bands, makes it a suitable for portable UWB applications.

The Mobile Ad-hoc Network (MANET) is defined as the grouping of different wireless mobility nodes that tends to create provisional networks without having any wired network connections. There are no static topology in the network because of the nodes mobility. Here the topology of the network has the ability to change their connection at any time and more often too, which can’t be guessable. This paper focused for evaluates the various mobility models impacts [3] in Mobile Ad-Hoc Network. Here we present about the independent metrices of multicast routing protocol for capturing the characteristics of mobility which includes the resolution of temporal and spatial dependence, restrictions, geographic resolution, Pause time and hybrid networks.

In this paper, we propose a new secure routing protocol for mobile Ad-hoc networks. The proposed new secure routing protocol works by evaluating the trustworthiness of each node to reduce the hazards from malicious mode and enhance the security of network. A new trust computation model is used to calculate the trustworthiness of a node based on the historical behavior. The trust value is evaluated by including the intruder node using Intrusion Detection System (IDS). In this paper, intruder nodes are detected based on the packet delivery mode during the communication. The Ad-hoc on demand multipath distance vector routing protocol is used in this paper which is based on the trust model that computes the trust of nodes and makes decision accordingly. A new dynamic and trust-based routing protocol named Ad-hoc on demand Dynamic Trusted Multipath Distance Vector (AODTMDV) has been proposed for performing effective and secured multi-path routing in MANETs.

Steganography is the technique in which the secret messages are hidden within the data. It keeps both the data and their existence in a secure manner. It is used in various real time applications and also it enables a secure communication. Text files, images, audios, and videos are used in steganography to conceal the communication. The main objective of this paper is to provide a general analysis on various approaches in video steganography. It covers related works, the strength of steganography, types of steganography and different techniques of video steganography. The absolute study of various techniques of video steganography is also highlighted.

Cross-Site Scripting (XSS) is one of the huge issues of any Web-based or Online applications. In this attack, the attacker uses malicious code to intercept the information through users web application and sends it to the corresponding web server. This is possible because web browsers are capable of executing the instructions stored in Web pages. This enables the attackers to make use of this feature, so as to execute the malicious code in a user’s Web browsing application. This attack if happened, may result in very slow and poor web surfing. It is also capable of stealing the cookies, passwords and other personal information of the user. These kind of attacks are very easy in terms of implementation but the prevention or detection of this attack is a challenging task. In this paper firstly the existing research on the prevention of XSS is presented. Then a framework is proposed to detect the XSS, which can provide a legitimate solution for the mitigation of the attack.

Wireless sensor networks (WSNs) is a structure whose construction and design typically consist of the distributed sensor nodes. This type of networks is applicable to variety of domains. The application of WSN consists of Military and security forces, disaster management, health monitoring, agriculture and irrigation sector, etc. The biggest hurdle that comes across in WSN is its intrinsic nature of limited power. It is the most challenging thing which affects the lifetime of the sensor network. That is the main reason due to which there is a need to develop the systems for saving the power utilization in WSN. For optimal use of the power in WSN so as to improvise the network lifetime data transfer path are selected in such a way that the total energy requirement for transferring the data along the path is minimized. Cluster-Based data aggregation in WSN is one such way that plays a vital role in minimizing energy consumption. In the clustering, the cluster heads are selected that gathers data from sensor nodes. This process is called as data aggregation. The aggregated data is then transferred to the base station. Using this way the sensor nodes overhead of transferring the data to BS will be reduced, thus reducing the energy consumption of the network. In this paper, we present the various existing researches conducted for minimum energy utilization and improvising network lifetime. Wireless sensor networks (WSNs) is a structure whose construction and design consist of the distributed sensor nodes. This type of networks is applicable to a variety of domain. The application of WSN consists of Military and security forces, disaster management, health monitoring, agriculture and irrigation sector, etc. The biggest hurdle that comes across in WSN is its intrinsic nature of limited power. It is the most challenging thing which affects the lifetime of the sensor network. That is the main reason due to which there is a need to develop the systems for saving the power utilization in WSN. For optimal use of the power in WSN so as to improvise the network lifetime data transfer path are selected in such a way that the total energy requirement for transferring the data along the path is minimized. Cluster-Based data aggregation in WSN is one such way that plays a vital role in minimizing energy consumption. In the clustering, the cluster heads are selected that gathers data from sensor nodes. This process is called as data aggregation. The aggregated data is then transferred to the base station. Using this way the sensor nodes overhead of transferring the data to BS will be reduced, thus reducing the energy consumption of the network. In this paper, we present the various existing researches conducted for minimum energy utilization and improvising network lifetime.

RF subcarrier tweak in FSO correspondence framework end up well known step by step because of the enhancement in the framework execution, which are for the most part affected by the robust environmental disturbance due to turbulence. In this paper, we infer error execution limits for RF subcarrier adjusted FSO correspondence frameworks with synchronous RF demodulator working over solid environmental disturbance channel which is demonstrated by gamma-gamma. Execution results are assessed as far as normal CNR, BER and penalty of power endured by the framework because of turbulence impacts.

Biometrics is a term which is used to identify the person by using their body characteristics. This paper describes a method to recognize and identify the persons by their gait cycle. This paper focuses on identifying a human by using neural network which is used to train the dataset. In order to identify an individual, various algorithms are used like background subtraction, frame differencing etc. Human identification is an increasing approach for a security reason and promising technology for military services, banks and colleges. Gait is the new biometric authorization system which does not need to contact any device for authentication. This is the promising research area because it is difficult to hide.