The use of data mining algorithm in different domains like marketing, finance, retail, cyber security, fraud detection, medical science, etc…is well known. In recent times, the data mining is also implemented for e-governance data. Normally, egovernance data are only used for Online Transaction Processing System (OLTP). However, one can use data mining algorithm to uncover hidden and new trends from e-governance data. In this paper, Naïve Bayes data mining algorithm is applied on Deceased Registration data to find hidden trends.

In recent years it is witnessed that the Wireless Mesh Networks (WMNs) are becoming the most promising technology as they offer low cost broadband wireless connectivity, larger coverage area, high flexibility and easy deployment. WMNs are an extension of existing wireless networks. WMN is an emerging technology; however, there are certain challenges that still exist in the network such as scalability, load balancing, mobility, power management etc. Here we have proposed a novel routing protocol which considers buffer occupancy of intermediate nodes for route selection. Simulation results convey that the proposed protocol outstandingly enhances the performance of the network by balancing the traffic load among less congested nodes compared to the standard protocol.

Fuzzy Logic System (FLS) is an efficient method to solve engineering problems. However, the training of a Fuzzy Logic System is a time-consuming task. Optimization Algorithm can be used to optimize the rule base of any FLS. Out of Type-1 FLS and Type-2 FLS, the type-2 found to be more effective to deal with noisy data. Due to their computational requirements Interval Type-2 (IT2) has been preferred over General Type-2. Whale Optimization Algorithm (WOA)has been introduced recently. The algorithm has been tested on different engineering problems and is found to be more effective. General Purpose Computing using graphics Processing Unit (GPGPU) is a new way to solve compute intensive problems on Graphics Processing Unit (GPU). CUDA-C is a parallel language that can be used to execute parallel code NVIDIA GPU. This paper integrates IT2 FLS, WOA and Processing Power of GPU. A toolbox is proposed that can be used to optimize the rule base in parallel. The toolbox provides both the implementations, i.e. serial and Parallel. FLS WOA Toolbox is design in such a way that user can pass parameter dynamically according to their need without interfacing with the code.

Skin cancer accounts to be a standout amongst the most prevalent types of carcinoma ailments, particularly among Caucasian offspring and pale-skinned persons. Specifically, the melanocytic dermis lesion are conjectured as the most lethal among three pervasive skin carcinoma ailments and the second most communal type amongst youthful grown-ups who are 15-29 years old. These apprehensions have impelled the requirement of automated systems for the diagnosis of skin carcinomas within a limited time frame to reduce unnecessary biopsy, proliferating the momentum of diagnosis and giving reproducibility of indicative outcomes. In this survey paper a brief overview of automated detection and segmentation of vascular structures of skin lesions is presented

The scope of different applications of Artificial Neural Network is immense. In recent times, Artificial Neural Network has been used in different type of applications. The use of Artificial Neural Network in Medical Science, Financial Markets, Employee Hiring, Fraud Detection, Cyber security are well know. In this paper, the use of Artificial Neural Network is proposed for Birth Registration e-governance data. The practical implementation was done using Microsoft Analysis Service and results indicates that interesting relationship can be obtained using Microsoft Neural Network data mining algorithm

In today Internet has grown to be persistent in daily livelihood furthermore Cloud computing is a rising model where computing resources offered over the Internet as scalable, on-demand (Web) services. An association deploy internet service needs to use enormous amounts of money on infrastructure needs to serve feasible users which is not a problem for large venture but when it comes to Small and Medium Enterprises or Enterprises affordability becomes a huge factor with the huge infrastructure come problems like machines failure, hard drive noises, software bugs, etc. Here might be a big problem for such a community. Cloud Computing is the ultimate solution to this problem. Rather than buying, installing and operating its own systems, an organization can rely on a cloud provider to do this for them. Cloud Computing key market leaders like Google, Amazon and Microsoft etc, these providers introduce new operating and business models that allow customers to pay for the resources they completely use, instead of making tremendous upfront investments. The purpose of this paper is to analyze most popular platforms, The Google App Engine, Amazon Web Services, and Windows Azure Platform.

Present studies, development of genomic technologies are highly concentrated on galactic scale gene data. In Bioinformatics community, the sizable volume of gene data investigation and distinguishing the behavior of genes in antithetical conditions are the intriguing task. This cognitive factor can be deal by the clustering technique, its groups the similarity patterns at various features. Moreover, gene expression data indicates the contrastive levels of gene behaviors in various tissue cells and it does provide the feature information effectively. This gene clustering investigation is precise and accommodating in cancer uncovering because of its easiness to detect the cancerous and non-cancerous genes. The precautionary measures cancer diagnostic is precise crucial for cancer prevention and treatment. The existing cancer gene clustering techniques includes several limitations such as time complexities in training and testing samples, maximum redundant features and high dimensional data. These issues are severely influences the data clustering accuracy. This paper focuses on survey of various clustering techniques of cancer gene clustering with respect to cancer gene benchmark datasets. Furthermore, review of existing cancer gene clustering technique describes the advantages and limitations comprehensively.

With growing digitization, the challenge for marketers is to understand how consumers consuming Over-The –Top (OTT) content adopt and consume messages in this format effectively. Superimposing the theoretical framework of Uses and Gratification meant for television to internet platform, this paper in a novel approach tries to understand the consumption patterns and adoptability factors of OTT among consumers. The qualitative methodology adopted for this research, brought out four themes enabling the success of this platform: Convenience, mobility, content and subscription strategies. These strategic parameters will ensure higher engagement levels of the consumers for the OTT content.

Cloud computing offers many services among one is Storage as a service. Using this service user can outsource his data and whenever required he can download or he can share with others. Using cloud computing PHR owner can share his documents, but due to security challenges in cloud computing the health records of owner must be encrypted before outsourcing. In order to provide security for owner health records ABE (attribute based encryption) is best suitable for other encryption techniques. Using ABE the PHR owner can define access control over his encrypted cloud data. But using ABE dynamic polices over encrypted cloud data is big challenge. In this paper for achieving grant and revoke privileges over encrypted data Timestamp server is introduced.

In software industry estimation of effort, cost (EDC) and duration is a troublesome procedure. The exertion itself is in charge of experiencing trouble in evaluating EDC. In any software estimation process, the preeminent advance is to characterize and comprehends the framework to be assessed. Software cost estimation algorithmic methods are estimating by analogy, expert judgment method, price to win method, topdown method, bottom-up method are developed by researchers in the field of software engineering. Any methods are not superior to the other methods. In fact, the qualities and shortcomings of those strategies are differentiating to one another. Presently there are two types of software EDC models to estimate the software those are statistical approaches and machine learning approaches. Many of the software cost estimation methods follows statistical approaches, which are not having the capability to present causes and strong or accurate results. Machine learning techniques are suitable in software engineering because it produces accurate results, which estimates by training rules and iterations. Machine learning techniques resolve the challenges like developing programs in a computer and to give the effective outputs by using the experience

Face recognition systems have been emerging as acceptable approaches for human authorization. Face recognition help in searching and classifying a face database and at a higher level help in identification of possible threats to security. In face recognition problem, the objective is to search a face in the reference face database that matches a given subject. The task of face recognition involves the extraction of feature vectors of the human face from the face image for differentiating it from other persons [6]. In this work, the comparative analysis is done based on the varying number of highly significant principal components (Eigenvectors) of PCA for face recognition. Experimental results show a small number of principal components of PCA are required for matching. PCA technique is a statistical technique, it reduces the dimension of the search space that best describes the images

The objective of this paper is to propose a secure, user friendly and economical multi-level authentication scheme that uses multiple factors for gaining access to resource on insecure platforms and for financial transactions. The proposed study is based on a premise that when multiple levels and multiple factors are incorporated in an authentication scheme it not only becomes difficult to break but also resistant to different forms of attacks. This work purposes a scheme where authentication process is carried out in three levels using multiple factors and is called 3L-MFA. The scheme also uses Out of Band (OOB) authentication as one of the factors that offers credible security against man-in-the-middle (MIM) attack. The first level uses username password based on double encryption. Second level uses OTP verification based on Out of Band (OOB) authentication using email id and mobile number. Third level involves user’s interaction on graphical screen in terms of predetermined number of clicks on images, buttons and selection of predetermined number of menu items. The security of proposed system depends upon double encryption using SHA-1 and AES128-CBC, out of band authentication using OTP and user interaction on a graphical screen that uses probability combination of various numbers.

Our country has developed rapidly for decades with lot of commercial buildings and well contacted roads with a growing number of automobiles. The transportation industry has become the backbone of economy because of its widespread use in trade and commerce. Therefore, parking the vehicles has become a consideration. For parking the vehicles in a parking space, old parking system is still used which is maintained in an unplanned mode and without any discipline. Because of this, people usually park their cars wherever they want, which can be a mess because most people don’t follow any discipline. There should be some mechanism to park the vehicles appropriately; therefore, this research has introduced an optimized vehicle parking mechanism with the concept of Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) which helps to get rid of the chaos usually faced.

Vehicular Ad hoc Network (VANET) is known as an infrastructure less network having dynamic nodes with Road Side Units (RSUs). Data Broadcasting becomes a very difficult task because of more density, scalability, randomness, mobility of vehicles. VANET has an ability to prevent accidents by transmitting data on-time on the network and this has raised an attention for number of researchers. Therefore, in this paper a realistic mechanism has been proposed to avoid the fatal accidents on road using clustering approach with the concept of Artificial Intelligence. Hybridization of Artificial Neural Network (ANN) and Support Vector Machine (SVM) is conducted to speed up the data transmission process that assists in providing information accurately and on-time. To demonstrate the efficacy of the novel mechanism, parameters such as Throughput, Packet Delivery ratio (PDR) are considered.

The recent years have seen an exponential growth of data generation this enormous amount of data has brought new kind of problem. The existing data mining techniques are unable to process the Big Data or they are not efficient in handling it. The major problems appeared with the Big Data are storage and processing, and how big data process efficiently in order to this high computation platforms required. To extract valuable information from big data appropriate algorithms required. To deeply understand this issues and to find recommendations in this paper taking review from previous studies. First part of review explores how data analysis done with data mining algorithms and its limitations. Second part of review explores data analysis with big data and with different algorithms and framework.

Wireless Sensor Networks (WSNs) are utilized for a plethora of applications such as weather forecasting, monitoring systems, surveillance, and so on. The critical issues of the WSN are energy constraints, limited memory, and computation time. This spectrum of criticality takes a deep dive with large-scale WSNs. In such scenario, the network lifetime has to be efficiently utilized with the available resources by organizing into clusters. Even though the technique of clustering has proven to be highly effective in minimizing the energy, the tradition cluster based WSNs, the protocol overhead is high for Cluster Heads (CHs) as it receives and aggregates the data from its cluster members. Therefore, efficient management of CH along with routing behavior is vital in prolonging the network lifetime. In this paper, an enhanced CH-Management technique is proposed which efficiently elects its CH using Particle Swarm Optimization (PSO), hereafter referred to as PSO_DDE. The PSO_DDE approach considers various parameters such as within-cluster distance between nodes (intra-cluster distance), neighbor density, and residual energy of nodes for the best candidate selection of CH. Also, the cluster formation is defined by the k-means based on the Euclidian distance. The PSO_DDE approach is integrated with the Dynamic Source Routing (DSR) for efficiently traversing the data packet to the sink node. The performance metrics are compared with the existing approaches using NS-2 simulator, and the proposed approach shows superiority of results.

Alzheimer disease is a form of dementia that results in memory-related problems in human beings. An accurate detection and classification of Alzheimer disease and its stages plays a crucial role in human health monitoring system. In this research paper, Alzheimer disease classification was assessed by Alzheimer’s disease Neuro-Imaging Initiative (ADNI) dataset. After performing histogram equalization and skull removal of the collected brain images, segmentation was carried-out using Fuzzy C-Means (FCM) for segmenting the white matter, Cerebro-Spinal Fluid (CSF), and grey matter from the pre-processed brain images. Then, hybrid feature extraction (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Gray-Level CoOccurrence Matrix (GLCM)) was performed for extracting the feature values from the segmented brain images. After hybrid feature extraction, reliefF feature selection was used for selecting the optimal feature subsets or to reject the irrelevant feature vectors. Then, the selected optimal feature vectors were given as the input to a supervised classifier Support Vector Machine (SVM) to classify three Alzheimer classes of subjects; those are normal, Alzheimer disease and Mild Cognitive Impairment (MCI). The experimental outcome showed that the proposed methodology performed effectively by means of sensitivity, accuracy, specificity, and f-score. The proposed methodology enhanced the classification accuracy up to 2-20% compared to the existing methodologies.

The proposed system attempts to implement various new technologies to an app for enabling users to place orders and bargain the prices with a machine learning powered bot. Such a system can provide new experience to the customers while increasing business profitability and efficiency with the help of collected data. This system comprises of 4 parts. The Android app at the frontend, and the database, natural language processing platform and the machine learning model to predict price as the backend deployed on the cloud. The app is designed with modular architecture pattern so that changes can be made easily to fit any business model. Since the predictions for bargaining are provided by a machine learning algorithm, the system performance and accuracy is likely to improve with additional data

Exponentially growing data, big data, dark data and data trash are throwing excellent opportunities in the world. But associated costs and risks are also significant. “Big Garbage in, Big Garbage out” seems new phrase in computing. This paper is motivated by risk and cost stresses on non-IT firms. Literature review, from management perspective, reveals lack of world’s attention towards immortality of data in the data stores of the world and rising risks & costs. Progressive digitization calls for regular elimination of data trash and consequent avoidable costs. Existing concept of Time to Live (TTL) or hop limit is eliminating huge data in transit on the internet system in real time. Similar concepts and tools could be potential help in reduction of size of data inventory. Paper presents a rudimentary model for expansion of concept of TTL with the assistance of user defined shelf life of data.

Harmony Search (HS) a meta heuristic algorithm inspired by music improvisation process in which the musician searches for the best harmony and continues to polish the harmony in order to improve its aesthetics. The HS algorithm was introduced in the year 2001 and has found applications in diverse fields. This manuscript reviews the recent significant developments in the structure of Harmony Search algorithm and also describes its recent state-of-the-art applications. As evidenced by a number of studies, this algorithm features several innovative aspects in its operational procedure that foster its utilization in diverse fields such as engineering, construction, telecommunications, robotics, health and energy

Harmony Search (HS) a meta heuristic algorithm inspired by music improvisation process in which the musician searches for the best harmony and continues to polish the harmony in order to improve its aesthetics. The HS algorithm was introduced in the year 2001 and has found applications in diverse fields. This manuscript reviews state-of-the-art applications of Harmony Search algorithm. As evidenced by a number of studies, this algorithm features several innovative aspects in its operational procedure that foster its utilization in diverse fields such as engineering, construction, telecommunications, robotics, health and energy

Cowry game is an ancient board game from India, also known as Chowka Bhara. It is a race game of chance and strategy for 2-4 players, in which playing pieces are moved around a square board according to the throw of special dice (cowry shells). This game involves decision-making under uncertainty and imprecision with multiple players, and therefore can be considered as an appropriate model for real-life problems that contain stochastic components. In this research, we propose and analyze a few basic playing strategies for Cowry game, and describe the framework for the implementation of these strategies. We also provide an experimental comparison of the proposed strategies to evaluate their performances. The comprehensive study of Cowry game presented in this work can be used to gain a better understanding of the game, and may result in the formulation and implementation of more advanced strategies. It can also serve as a basis for producing better artificial players in similar strategic race games.

Wireless Sensor Network is a combination of one and more nodes basically used for data collection and data aggregation. The collected data is aggregated through a data aggregator and it will be sent to bases station. While data transfer takes place, the intruders sniff the data that are being transmitted from one node to another. In order to prevent data sniffing by the intruder a study is made to prevent data sniffing and data breaching its boundaries. A two-fold cryptographic method is proposed using Fibonacci number and Unicode systems. The proposed method is two-fold viz, converting the normal simple text to secret message ii) convert secret message to Unicode text. The proposed method uses a two-fold security key encryption algorithm for transfer of data in the Wireless sensor networks and a decryption algorithm is also provided to convert back to its normal plain text.

Domain-ontology based Particle Swarm Optimization (PSO)-inspired Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) and Improved Bipartite graph is an efficient web service composition approach. It composed a number of web services to achieve high customer satisfaction. It also improves the clustering quality and reduced the processing time of web service composition. The most verification problems are easily verified by using Petri Net in planning, verification and execution phase. In order to provide more efficient automatic web service composition, in this paper multiple Quality of Service (QoS) parameters like cost, accuracy, accessibility, robustness, scalability, modifiability and security are included in Petri net-based algebra approach. While adding a greater number of QoS parameters there will be multi-objective services composition optimization problem. It is solved by introducing a Multi-Objective Genetic Algorithm (MOGA) and Multi-Objective PSO (MOPSO) algorithm. It selects qualitative different services from multiple functionally identical web services which achieve the best reliability model. Thus, the automatic web service composition is performed for discovering the appropriate services by satisfying the customer requirements. Experimental results show that the proposed method performs better than the existing method.

The use and dependence on software in various fields has been the reason why researchers for past decades have spent their efforts on finding better methods to predict software quality and reliability. Soft computing methods have been used to bring efficient improvement in prediction of software reliability. This study proposed a novel method called Fuzzy Greedy Recurrent Neural Network (FGRNN) to assess software reliability by detecting the faults in the software. A deep learning model based on the Recurrent Neural Network (RNN) has been used to predict the number of faults in software. The proposed model consists of four modules. The first module, attribute selection pre-processing, selects the relevant attributes and improves generalization that improves the prediction on unknown data. Second module called, Fuzzy conversion using membership function, smoothly collects the linear sub-models, joined together to provide results. Next, Greedy selection deals with the attribute subset selection problem. Finally, RNN technique is used to predict software failure using previously recorded failure data. To attest the performance of the software, the popular NASA Metric Data Program datasets are used. Experimental results show that the proposed FGRNN model has better performance in reliability prediction compared with existing other parameter based and NN based models.

Traditional1call tree classifiers work1with information whose values1area unitcelebrated and precise. We1have a tendency to extend1such classifiers to handle information1with unsureinfo. Worth uncertainty1arises in several applications throughoutthe1informationassortmentmethod.Example1source of uncertainty embrace1measurement/quantization errors, information staleness, and multiple recurrent measurements. With1uncertainty, the worth of a knowledge item1is commonlydepicted not by one1single worth, however by1multiple values forming a probability distribution. Instead of1abstracting unsureinformation by applied1math derivatives (such as1mean and median), we have a1tendency to discover that1the accuracy of a call1tree classifier will bea lot1of improved if the “complete1information” of a knowledge item is utilized. Since1process pdf’sis computationall1 a lot of pricey than1process single values (e.g., averages), call tree1construction on unsure information1is more electronic equipment1demanding than that sure information. To1tackle this problem, we have a tendency1to propose a series of pruning1techniques which will greatly improve1construction potency.

The analysis of electroencephalogram(EEG) signals, for implementation of brain-computer interface (BCI), has enticed a lot of interest in the research community. It can be used in a variety of applications ranging from medical rehabilitation to pleasure and entertainment. BCI is very promising research domain even in the face of a number of challenges, especially in domain of signal processing, feature extraction and that of classification techniques, as these EEG signals contain considerable amount of noise and artifacts. In this paper, various feature extraction algorithms used in BCI are investigated and compared. Common spatial pattern(CSP) is a popularly applied algorithm for extracting features from EEG signals in an implementation of BCI. Filter bank spatial pattern (FBCSP) and spectrally weighted common spatial pattern (SWCSP) are further extensions of CSP. The performance of these methods is evaluated and calculated on data set 2a of BCI competition IV, on the basis of standardized mean squared error (SMSE) . Results shows that FBCSP considerably outperforms the performance of the other methods under consideration.