Recent Advances in Computer Science and Communications - Volume 14, Issue 5, 2021

Aims: Propose an imputation measure for filling missing data values so as to make the incomplete medical datasets as complete datasets. Apply this imputation measure on imputed datasets to achieve improved classifier accuracies. Objective: The basic intention of the present study is to present an imputation measure to find the proximity between medical records and an approach for imputation of missing values in medical datasets to improve the accuracy of existing classifiers. Methods: The performance of proposed approach is compared to existing approaches with respect to classifier accuracy and also by performing non-parametric test called Wilcoxon test. Results & Conclusion: Experiments are conducted by considering three benchmark datasets CLEVALAND, PIMA, ECOLI and by applying proposed imputation technique with KNN, J48 and SMO classifiers and classifier accuracies are determined. The results obtained are then compared to thirteen existing benchmark imputation techniques available in KEEL repository. Experiment results proved the importance of the proposed imputation technique.

Background: E-mail is an efficient way to communicate. It is one of the most commonly used communication methods, and it can be used for achieving legitimate and illegitimate activities. Many features that can be effective in detecting email fraud attacks, are still under investigation. Methods: This paper proposes an improved classification accuracy for fraudulent emails that is implemented through feature extraction and hybrid Machine Learning (ML) classifier that combines Adaboost and Majority Voting. Eleven ML classifiers are evaluated experimentally within the hybrid classifier, and the performance of the email fraud filtering is evaluated by using WEKA and R tool on a data set of 9298 email messages. Results: The performance evaluation shows that the hybrid model of Voting using Adaboost outperforms all other classifiers, with the lowest Error Rate of 0.6991%, highest f1-measure of 99.30%, and highest Area Under the Curve (AUC) of 99.9%. Conclusion: The utilized proposed email features with the combination of Adaboost and Voting algorithms prove the efficiency of fraud email detection.

Introduction: Intrusion detection systems play a key role in system security by identifying potential attacks and giving appropriate responses. As new attacks are always emerging, intrusion detection systems must adapt to these attacks, and more work is continuously needed to develop and propose new methods and techniques that can improve efficient and effective adaptive intrusion systems. Feature selection is one of the challenging areas that need more work because of its importance and impact on the performance of intrusion detection systems. This paper applies an evolutionary search algorithm in feature subset selection for intrusion detection systems. Methods: The evolutionary search algorithm for the feature subset selection is applied and two classifiers are used, Naïve Bayes and decision tree J48, to evaluate system performance before and after features selection. NSL-KDD dataset and its subsets are used in all evaluation experiments. Results: The results show that feature selection using the evolutionary search algorithm enhances the intrusion detection system with respect to detection accuracy and detection of unknown attacks. Furthermore, time performance is achieved by reducing training time, which is reflected positively in overall system performance. Discussion: The evolutionary search applied to select IDS algorithm features can be developed by modifying and enhancing mutation and crossover operators and applying new enhanced techniques in the selection process, which can give better results and enhance the performance of intrusion detection for rare and complicated attacks. Conclusion: The evolutionary search algorithm is applied to find the best subset of features for the intrusion detection system. In conclusion, it is a promising approach to be used as a feature selection method for intrusion detection. The results showed better performance for the intrusion detection system in terms of accuracy and detection rate.

Background: The App Stores, for example, Google Play and Apple Play Store provide a platform that allows users to provide feedback on the apps in the form of reviews. An app review typically includes star rating followed by a comment. Recent studies have shown that these reviews possess a vital source of information that can be used by the app developers and the vendors for improving the future versions of an app. However, in most of the cases, these reviews are present in unstructured form and extracting useful information from them requires a great effort. Objective: This article provides an optimized classification approach that automatically classifies the reviews into a bug report, feature request, and shortcoming & improvement request relevant to Requirement Engineering. Methods: Our methodology used supervised Machine Learning techniques (Multinomial Naive Bayes, Linear SVC, CART Decision Tree) that have been first evaluated based on t h e conventional Bag of Words (BOW) feature extraction model along with other auxiliary features such as review rating, review length, review tense, and review sentiment score to automatically classify app reviews into their relevant categories, and subsequently, a feature selection method is used to provide an optimized classification approach for improving classifier performance using Particle Swarm Optimization (PSO) nature-inspired algorithm. Results: Result shows that we achieved best results with precision of 62.0 % and recall of 47.3 % with Linear SVC Machine Learning technique, which we further optimized with PSO nature-inspired algorithm, i.e., with PSO + Linear SVC, thus, resulting in a precision of 63.6 % and recall of 55.0 %. Conclusion: This optimized automatic classification improves the Requirement Engineering where developer straightforwardly knows what to improve further in the concerned app.

Background: Decision making requires a rigorous process of evaluation, which is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a polity. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched/constituted by government in view of the citizen welfare. Although, the governmental policies intend to better shape up the quality of life of people but may also impact their everyday's life. Objective: The contemplation of public opinion plays a very significant role in the process of policy evaluation. Therefore, the aim of this paper is to incorporate the concept of opinion mining in policy evaluation. An attempt has been made to elevate the process of policy evaluation by analyzing public opinion. Methods: A latest governmental scheme Saubhagya launched by the Indian government in 2017 has been selected for evaluation by applying supervised learning based opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. Results: The result validates that the proposed methodology supports the optimizing process of policy evaluation and provides a more accurate and actual status of policy's effect among Indian citizen. As a result, this would aid in identifying and implementing the preventive and corrective measures required to be taken for a successful policy. Conclusion: The proposed methodology will stabilize and strengthen the process of policy evaluation which target towards favourable and flourishing future prospects concerning the socio-economic status of a nation. The results are quite exciting and further extension of work will be performed in order to develop and design a patent framework in the area of social big data analytics.

Introduction: Cloud computing involves the use of maximum remote services through a network using minimum resources via internet. There are various issues associated with cloud computing, such as privacy, security and reliability. Due to rapidly increasing information on the cloud, it is important to ensure security of user information. Biometric template security over cloud is one such concern. Leakage of unprotected biometric data can serve as a major risk for the privacy of individuals and security of real-world applications. Methods: In this paper, we improvise a secure framework named DeepCrypt that can be applied to protect biometric templates during the authentication of biometric templates. We use deep Convolutional Neural Networks to extract features from these modalities. The resulting features are hashed using a secure combination of Blowcrypt (Bcrypt) and SHA-256 algorithm, which salts the templates by default before storing it on the server. Results: Experiments conducted on the CASIA-Iris-M1-S1, CMU-PIE and FVC-2006 datasets achieve around 99% Genuine accept rates, proving that this technique helps to achieve better performance along with high template security. Conclusion: The proposed method is robust and provides cancellable biometric templates, high security and better matching performance as compared to traditional techniques used to protect the biometric template.

Background: Speech Recognition is the most effective and suitable way of communication. Extracted features play an important role in speech recognition. Previous research works for Hindi speech recognition lack detailed comparative analysis of the feature extraction methods using dynamic and energy parameters. Objective: The research work presents experimental work done to explore the effects of integrating dynamic coefficients and energy parameters with different feature extraction techniques on Connected word Hindi Speech recognition. As extracted features play a significant role in speech recognition, a comparative analysis is presented to show the effects of integration of dynamic and energy parameters to basic extracted features. Methods: Speaker dependent system was proposed with monophones based five states Hidden Markov Model (HMM) using HTK Tool kit. Speech data set of connected words in Hindi was created. The feature extraction techniques such as Linear Predictive Coding Cepstral coefficients (LPCCs), Mel Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Prediction (PLPs) coefficients were applied integrating delta, delta2, and energy parameters to evaluate the performance of the proposed methodology for speaker dependent recognition. Results: Experimental results show that the system achieved the highest recognition word accuracy of 89.97% using PLP coefficients. The PLP coefficients achieved 4% increment in word accuracy than original MFCCs and a 16% increment in word accuracy than LPCCs. Adding energy parameters to original MFCCs increased word accuracy by 1.5% only while adding dynamic coefficients delta and double delta had no significant effect on speech recognition accuracy. Conclusion: Research findings reveal that PLP coefficients outperformed. Explorations reveal that the integration of energy parameters are better than original MFCCs. Investgations also reveal that adding energy parametres improved recognition score while adding delta and delta2 coefficients to basic features did not improve the recognition scores. Research findings could be used to enhance the performance of a speech recognition system by using a suitable feature extraction technique and combining the different feature extraction techniques. Further, investigations can be used to develop language resources for refining speech recognition. The work can be extended to develop a continuous Hindi speech recognition system.

Background: With the complexity of the network structure and the diversity of access, the access control model of distributed permissions management came into being. Aims: The central idea of the model is to store the access permissions of all nodes in the model to the network nodes covered by the access control model, and let the neighboring nodes act as access control agents. That is, each node in the model is a routing node in the execution of a certain access control. Methods: Considering the execution parameters of distributed access control model, the evaluation index of key node identification technology in distributed authorized access control model is established. Access control to achieve the management of distributed permissions. It is necessary to protect the key nodes in the model, increase the robustness of the access control model and support the smooth implementation of distributed access. Results: This paper presents a key node recognition algorithm based on distributed access control magic. The application of key node recognition algorithm in distributed access control model in traditional network is compared. Conclusion: Compared with the traditional key node recognition algorithm in the distributed access control model, the algorithm proposed in this paper is more accurate.

Background: The paper discussed about the malware propagation dynamics in wireless sensor network. Malware attack is harmful for network stability as well as battery consumption of sensor nodes. Objective: The objective of proposed study is to develop a model that describes the dynamic propagation behavior of malware in wireless sensor network and suggest corrective measure through which breakout from the network. Methods: Use the concept of epidemic modeling and spatial correlation to describe the malware dynamics in wireless sensor network. Write the sets of differential equation to study its behavior. Results: Methodically find results have been verified with the help of simulation using MATLAB. The effect of spatial correlation on malware propagation in wireless sensor network has been analyzed. Conclusion: In this paper, an epidemic based model with spatial correlation is presented for the study of malware propagation characteristics in WSN. The equilibrium points of the system have been obtained. The expression of basic reproduction number and threshold value of correlation coefficient have been obtained. The basics reproduction (R 0 ) helps in the analysis of network stability. On the basis of their value we found that if R 0 is less than one the system will be stable and malware-free, and when R 0 is greater than one the system exists in endemic state and malware persists in the network. The spatial correlation helps to control the malware propagation in the network.

Background: In daily life, one may encounter several problems whose solution is based on the consideration of several criteria altogether. To generate a solution for such problems is, in fact, a complex task. If one generates a solution, then there is no guarantee that the solution is optimal. It is really challenging to take into account all the criteria at the same time and generate a solution that is optimal. Several techniques have been proposed to solve multi-criteria decision- making problems. Methods: In this paper, we propose an intelligent solution to multi-criteria decision-making problems by means of fuzzy aggregation using the matrix method. The proposed method finds application in solving such problems where the criteria are defined in the qualitative form (fuzzy linguistic variables) rather than in quantitative form (crisp). A novel application of the proposed approach to rank the employees in an organization is presented in the paper. Results: The proposed method is empirically validated by applying it to find the best employee in an institute of repute. Data collection is done in real-time to prove the utilization of the proposed approach in an efficient manner. Conclusion: The results obtained after the application of the proposed method to rank the employees are realistic as it takes into consideration the ambiguity involved in the human thought process.

Background: The spam message aimed at mobile devices has become a prevalent activity in recent years. About 76% of mobile users received spam messages of Ads, fraud messages and phishing links. It has led to a significant financial loss of 91.5 billion Yuan per year and this problem will be more severe in the coming future. Although the operators are making much effort against spam message, the number of spams are increasing day by day. Aim: Many schemes designed to detect and filter spam have been proposed in the research area. The Bayesian classification algorithm is one of the most popular schemes. Most of the spam detection schemes based on Bayesian are designed for communication providers. The scalability of filter policy is hard to control because the strict policy might filter the normal messages while some spam messages would not be detected due to the flexibility in the policy. Methods: The current mobile device has a robust computational capability so it can execute complex jobs. A spam detection framework is designed in the present study. Results: It can sniffer the coming messages in mobile device by hooking the Android SMS API, and send them to the filter module, -which is responsible to filter messages into normal or spam based on Bayesian classification. This is a light-weight framework of consuming low power, which is suitable for mobile devices. Extra experiments are conducted to prove their accuracy and efficiency. Conclusion: The results showed that it could filter the spam among receiving the messages in realtime and become more accurate by learning the users' feedback.

Background: Recent advances in the World Wide Web and semantic networks have amplified social networking platforms, where the users share their photos, hobbies, location, interests, and experiences such as movie or restaurants. Social media platforms such as Facebook, twitter and LinkedIn are used to recommend the users the things of their interests such as movie, food, locations and friends. Objective: A novel method for the recommendation of movies to a friend using whale optimization has been introduced. Ratings given by friends of various movies are employed to recommend movies. Methods: Different evolutionary-based optimization methods have been applied for movie recommendation. The proposed method has been tested on movie-lense dataset and results are compared with 5 other methods namely, K-means, PCA K- means, SOM, PCA-SOM, PSO and ABC in terms of mean absolute error, precision and recall. Results: The experimental results demonstrate that proposed method outperformed all considered methods for 88.5% clusters centers in terms of precision, recall and mean absolute error. Conclusion: A novel recommendation system based on users rating has been designed to recommend movies to friends. It leverages the strengths of whale optimization to find the optimal solution.

Background: In this competing era, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching- Learning Process (TLP). An assessment is one way to identify the learner’s weak spot in the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. To generate questions is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Objective: The proposed system is to generate the test questions that are mapped with bloom’s taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest bloom’s cognitive levels. Methods: Proposed an Automatic Question Generation (AQG) system which, automatically generates the assessment questions from the input file Dynamically. Results & Conclusion: The outputs are dynamic to create a different set of questions at each execution. Here, the input paragraph is selected from the computer science domain and their output efficiency is measured using precision and recall.

Background: Genes expression is high dimensional data, so it is very difficult to classify high dimensional data through traditional machine learning approaches. In this work we have proposed a model based on combined approach of Convolutional Neural Network and Recurrent Neural Network, both belong to deep learning model. The prediction has shown improved result than other machine learning algorithms. Expressions are generated through histone modification. Methods: To improve the accuracy deep learning model is proposed i.e. based on Convolutional and Recurrent neural network. This proposed model uses filter, causal convolutional layers and Residual Block for predictions. Results: In this work we have implemented the machine learning algorithms and deep learning algorithms like Logistic Regression, SVM, CNN, Deep Chrome and the proposed Temporal Neural Network. The performance is measured on the basis of parameters like accuracy, precision and AUC on the training and testing set. Conclusion: The proposed Temporal Neural Network model has shown better performance than other machine learning and deep learning algorithms. Due to this proposed deep learning algorithm can be successfully applied on the genes expression dataset.

Background: The block-chain is a growing decentralized scheme applied in many applications, such as an auction, smart contract, Health, and banking sector. The cloud service providers can sell the resource to cloud consumers using an auction. The main challenge in resource allocation using auction is to provide reliability to the users. Methods: In this paper, a blockchain-based combinatorial grouping auction with a reserve price mechanism (BCGAWRP) was proposed. The proposed scheme maximizes the total revenue and resource utilization by assuring reliability. The proposed BCGAWRP performance was assessed by simulating the cloud environment. Results: The experimental result shows that the proposed BCGAWRP algorithm increases revenue more than the traditional combinatorial auction algorithm. Conclusion: Moreover, simulation studies show that reserve price is useful and provides a mechanism to achieve the trade-off between the seller's and the buyer’s virtual machines.

Background: Cloud computing is an emerging technique by which anyone can access the applications as utilities over the internet. Cloud computing is the technology which comprises of all the characteristics of the technologies like distributed computing, grid computing, and ubiquitous computing. Cloud computing allows everyone to create, to configure as well as to customize the business applications online. So the cloud computing techniques need security of information communicated between the sending and receiving entities. Objective: The secure data storage disadvantage of Cloud computing can be resolve up to some extent with the help of the implementation of the cryptographic algorithms during the storing and accessing of the data from the cloud servers. Methods: In this paper we have compared four different recently implemented Cryptographic Algorithms which are Modified RSA (SRNN), Elliptic Curve Cryptography Algorithm, Client Side Encryption Technique and Hybrid Encryption Technique. Conclusion: Client Side Encryption Technique and Hybrid Encryption Technique are better than Modified RSA and Elliptic Curve Cryptography because Client Side Encryption Technique has an advantage that in this data is encrypted prior to upload the data on the cloud i.e. encryption at the client side which provides an additional layer of security to the data on the cloud. On the other hand, Hybrid Encryption Technique has an advantage that it uses the rapidity of the processing time of Symmetric Key Cryptography and robustness in key length of the Asymmetric Key Cryptography.

Introduction: This manuscript is explicating the mutual compatibility of boot modes in a computer system and partitioning styles of Hard Disk Drive. Most of us are familiar with these terms and know a little about these. Methods: This manuscript contains ample information about the boot modes of a computer system and partitioning styles of Hard Disk Drive (HDD) and their related configuration through which we get to know about their configuration and endurability. It also contains some practically verified case studies of the problems which occur due to the wrong configuration of boot modes and partitioning styles, though there are a lot more, the most common ones are discussed along with their solutions. Results: In order to achieve the compatibility, it might require to convert the primary HDD into either GPT or MBR partitioning schemes. It should be marked that this interconversion wipes data on HDD, so cautiously, the data on the hard drive must be backed-up before any conversion process. Discussion: This is helpful when the system is equipped with the latest configuration, i.e., UEFI, and if there is a need to install an older operating system that does not support UEFI boot mode (for example, Windows XP), then CSM can help. In addition, some graphics cards (for example, GTX 680) do not support UEFI and hence, require CSM to boot. This means that the CSM can help as a runner for all that hardware configuration (in an updated system with new configuration), which can run only through legacy BIOS configuration. Conclusion: The information contained had been practically verified and is helpful in coping with the newer technology trends, which contains more features along with backward compatibility.

Background: Complex systems involved in biochemistry, neuroscience, physics, engineering and social science are primarily studied and modeled through network structures. The connectivity patterns within these interaction networks are discovered through clustering-like techniques. Community discovery is a related problem to find patterns in networks. Objectives: Existing algorithms either try to find few large communities in networks; or try to partition network into small strongly connected communities; that too is time consuming and parameterdependant. Methods/Results: This paper proposes a chromatic correlation clustering method to discover small strong communities in an interaction network in heuristic manner to have low time complexity and a parameter free method. Comparison with other methods over synthetic data is done. Conclusion: Interaction networks are very large, sparse containing few small dense communities that can be discovered only through method specifically designed for the purpose.

Background: Lacuna of the traditional stock market systems overtime has been bridled by BlockChain Technology. Used cases designed in BlockChain Technology has a flair to deal with the problems in the financial sector. Most vituperative outburst is related to stock markets where the centralization of the clearing house increases the dependency on the central system. Centralization of the clearing houses increases the risks and delay in response to the users of the system. Traditionally, stock market players need to endure the complex multi-layer process of pre-trading, trading and post-trading settlements. The processes in these systems were time consuming and cost inefficient in terms of resources utilized due to the role of intermediaries. Objective: The objective of this paper is to propose a system which will help embark the issues of processing, traceability, transparency, and availability of stocks using BlockChain Technology. Methods: We offer a decentralized system for the stock market users, and as a result, the intermediaries become expendable. The rules and regulations will execute within every smart contract for every trade transaction being regulatory. Results: This paper discusses various solutions to the problems that arise due to current centralized or decentralized systems. Conclusion: We will discuss aspects like security and transparency of the proposed system while concluding the paper.

Background: The MapReduce programming model was developed and designed for Google File System to efficiently process large distributed datasets. The open source implementation of the Google project was called Apache Hadoop. Hadoop architecture comprises of Hadoop Distributed File System (HDFS) and Hadoop MapReduce. HDFS provides support to Hadoop for effectively managing large datasets over the cluster and MapReduce helps in efficient large-scale distributed datasets processing. MapReduce incorporates strategies to re-executes speculative task on some other node in order to finish computation quickly, enhancing the overall Quality of Service (QoS). Several mechanisms were suggested over default Hadoop’s Scheduler, such as Longest Approximate Time to End (LATE), Self-Adaptive MapReduce scheduler (SAMR) and Enhanced Self-Adaptive MapReduce scheduler (ESAMR), to improve speculative re-execution of tasks over the cluster. Objective: The aim of this research is to develop an efficient speculative task detection mechanism to improve the overall QoS offered over Hadoop cluster. Methods: Our studies suggest the importance of keeping a regular track of node’s performance in order to re-execute speculative tasks more efficiently. Results: We have successfully reduced the detection time of speculative tasks (∼ 15%) and improved accuracy of correct speculative task detection (~10%) as compared to existing mechanisms. Conclusion: This paper presents an efficient speculative task detection algorithm for MapReduce schedulers to improve the QoS offered by Hadoop clusters.

Aim: Cloud Security is a strong hindrance which discourages organisations to move toward cloud despite huge benefits. Distributed denial of service attacks operated via distributed systems compromise availability of cloud services which cause limited resources for authentic users and high expense for cloud service users and business owners. Objective: Techniques to identify distributed denial of service attacks with minimized false positives are highly required to ensure availability of cloud services to genuine users. Scarcity of solution which can detect denial of service attacks with minimum false positives and reduced detection delay has motivated us to compare classification algorithms for detection of distributed denial of service attacks with minimum false positive rate. Methods: Classification of incoming requests and outgoing responses using machine learning algorithms is a quite effective way of detection and prevention. We designed a performance tuned support vector machine algorithm with features of F-hold cross validation strategy. Results: F-hold crosses validation strategy, which can detect denial of service packets with 99.89% accuracy. Conclusion: This system ensures economic sustainability for business owners and limited resources mitigation for authenticated and valid cloud users.

Biometrics is a universally used automatic identification of persons, depending on their behavioral and biological quality. Biometric authentication represents a significant role in the identity management system. Protection of biometric authentication systems demands to be discussed as there are still some points related to the integrity and public receiving of biometric systems. Feature extraction module of biometric authentication systems, during the period of enrolment, scan the biometric information to determine a set of distinctive features. This set of totally distinct features is recognized as a biometric template. This biometric template is effective in distinguishing between different users. These templates are normally stored during the time of enrolment in a database arranged by the user’s identification data. For the templates of biometrics, protection from attackers is an essential issue, since the compromised template will not be canceled and reissued like a password or token. Template security is not an obvious task because of variations present within the extracted biometric features of users. This paper surveys about different existing approaches for designing a protection scheme for biometric templates with their strengths and boundaries existing in the literature. Some prospect information in designing template protection schemes have been elaborately explained in this paper.

Background: Technology in the field of education is permanently developing, growing and this raise will repeatedly offer new and unusual advances. The significant objective of this project is to encourage the understudies in participating in learning and enhancing their insight abilities. Methods: An Android application provides a new technique of developing a test or quiz using smart devices. This project implements a mobile quiz application based on face recognition as an authentication process to ascertain students' identity. The authentication process was implemented in two steps face detection using Mobile Vision APIs and face recognition using a Speed Up Robust Features (SURF) algorithm. Image classification and retrieval process is applied using SURF algorithm to extract features vector, then compare those vector with those of all stored images at server database and the matching process is applied based on RANSAC algorithm. A Wi-Fi ad hoc network in this project is established using jmdns java library to enable accessing the application by students. For training purposes, a data set containing 10 persons is added with 5 images per person. Results: A quiz environment has been arranged, in class with seven examiners each one separately accessed the quiz application with randomly chosen questions by the server. The achieved recognition rate was 85%, with a total average computation time 8.816 s per user login. Conclusion: This quiz application decreases manual intervention and brings adaptability to users with ease of use.

Background: Due to advancement in usage of Internet and pattern discovery from huge amount of data flowing through internet, personal information of an individual or organization can be traced. Hence, to protect the private information is becoming extremely crucial, which can be achieved through privacy preserving data mining. Objective: The main objective to preserve the privacy of data and to maintain the balance between privacy and accuracy by applying privacy preserving technique and optimization respectively. Methodology: The generation of class association rule is done by utilizing associative classification technique namely class based association due to its simplicity which serves the purpose of classifying the data. Furthermore, privacy of the data should be maintained and hence privacy preserved class association rules are produced by applying privacy preserved technique namely anonymization. Hence, optimization technique specifically genetic algorithm as well as neural network has been applied to maximize the accuracy. Results: (Four various real datasets has been utilized for different experimentation). Implemented Classification Based on Association (CBA) algorithm of Associative Classification technique and it provides virtuous accuracy as compared to other techniques by setting the support as 0.2 and confidence at 0.6. Privacy preserving techniques namely k-anonymization was implemented to preserve the privacy but it has been observed that as privacy (k-level) increases, accuracy (percentage) decreases due to data transformation. Conclusion: (Hence, optimization technique namely Genetic Algorithm (GA) and Neural Network (NN) has been implemented to increase the accuracy (probably 7-8%). Furthermore, on comparison of GA and NN considering the time parameter, GA outperforms well.

Background: Human Classification in public places is an emerging area in the applications of Computational Intelligence. Therefore, modeling of an optimal architecture of the neural network is required to classify them. Methods: In this work for this purpose, blob dataset has been used to train the neural network. This dataset consists of 2408 features of a human blob. Results: Further, analysis of this blob dataset has been done on the basis of various characteristic parameters for affirmation of actual training. During training and testing of this dataset, it has been observed that when nodes at hidden layer are below and above 10 then training of neural network is under fitted and overfitted respectively and works effectively when the nodes are 10 at the hidden layer. Conclusion: From the experimental work performed in this study, an optimal neural network has been obtained to classify human using blob dataset.

Background: Reverse nearest neighbors query is an important means to solve many practical applications based on the concept of Influence Sets. It is widely used in various fields such as data mining, decision support, resources allocation, knowledge discovery, data flow, bioinformatics and so on. Objective: This work aims to improve time efficiency of Reverse Nearest Neighbors query of moving objects with large data scale. Methods: A new spatio - temporal index HRNN-tree is developed.Then an algorithm for reverse nearest neighbors query based on HRNN-tree is developed. Results: Our algorithm is superior to the existing method in execution time. The performance of our algorithm is excellent especially for the queries with large data scale and small values of k. Conclusion: This study devises a new spatio - temporal index HRNN-tree. Then an algorithm for reverse nearest neighbor search of moving objects is developed based on this index. This algorithm avoids that the query performance deteriorates rapidly as the data space grows and has a better performance fort the large data space. This work will be helpful to enrich and improve the abilities of intelligent analysis, mobile computing and quantitative query based on distance for spatio - temporal database.

Background: The increasing need for various data publishers to release or share the healthcare datasets has imparted a threat for the privacy and confidentiality of the Electronic Medical Records. However, the main goal is to share useful information thereby maximizing utility as well as ensuring that sensitive information is not disclosed. There always exist utility-privacy tradeoff which needs to be handled properly for the researchers to learn statistical properties of the datasets. Objective: The objective of the research article is to introduce a novel SK-Clustering algorithm that overcomes identity disclosure, attribute disclosure and similarity attacks. The algorithm is evaluated using metrics such as discernability measure and relative error so as to show its performance compared with other clustering algorithms. Methodology: The SK-Clustering algorithm flexibly adjusts the level of protection for high utility. Also the size of the clusters is minimized dynamically based on the requirements of the protection required and we add extra tuples accordingly. This will drastically reduce information loss thereby increasing utilization. Results: For a k-value of 50 the discernabilty measure of SK algorithm is 65000 whereas the Mondrian algorithm exhibits 70000 discernability measure and the Anatomy algorithm has a discernability measure of 150000. Similarly, the relative error of our algorithm is less than 10% for a tuple count of 35000 when compared to other k-anonymity algorithms. Conclusion: The proposed algorithm executes more competently in terms of minimal discernability measure as well as relative error, thereby proving higher data utility compared with traditionally available algorithms.

Background: In the recent era, the prediction of enzyme class from an unknown protein is one of the challenging tasks in bioinformatics. Day-to-day, the number of proteins increases which causes difficulties in clinical verification and classification; as a result, the prediction of enzyme class gives a new opportunity to bioinformatics scholars. The machine learning classification technique helps in protein classification and predictions. But it is imperative to know which classification technique is more suited for protein classification. This study used human proteins data that is extracted from the UniProtKB databank. A total of 4368 protein data with 45 identified features were used for experimental analysis. Objective: The prime objective of this article is to find an appropriate classification technique to classify the reviewed as well as un-reviewed human enzyme class of protein data. Also, find the significance of different features in protein classification and prediction. Methods: In this article, the ten most significant classification techniques such as CRT, QUEST, CHAID, C5.0, ANN, SVM, Bayesian, Random Forest, XgBoost, and CatBoost have been used to classify the data and discover the importance of features. To validate the result of different classification techniques, accuracy, precision, recall, F-measures, sensitivity, specificity, MCC, ROC, and AUROC were used. All experiments were done with the help of SPSS Clementine and Python. Results: Above discussed classification techniques give different results and found that the data are imbalanced for class C4, C5, and C6. As a result, all of the classification techniques give acceptable accuracy above 60% for these classes of data, but their precision value is very less or negligible. The experimental results highlight that the Random forest gives the highest accuracy as well as AUROC among all, i.e., 96.84% and 0.945, respectively, and also has high precision and recall value. Conclusion: The experiment conducted and analyzed in this article highlights that the Random Forest classification technique can be used for protein of human enzyme classification and predictions.

Background: The conventional text passwords are by far the main means of authentication and they will continue to be popular as they are easy to deploy and to use. However, they have been largely exposed to different kind of attacks like guessing, phishing and key-logger attacks. Objective: It is efficient to use both CAPTCHA and text password in a user authentication to create an additional layer of security over the passwords. Captcha and password work side by side and independently in such captcha-assisted systems. Captcha filters out the suspicious programs from the human beings and password recognizes the legitimate user among the human beings. Methods: In this paper, we have suggested a dynamic password scheme combining the traditional text password and captcha. User authentication will be verified by different passwords at different login session based on the captcha presentations. Results: Suggested method does not replace the conventional password process rather than modifying it. Therefore, users’ current sign-in experience is largely preserved. It will be implemented in software alone, increasing the potential for large-scale adoption on the Internet. Conclusion: The scheme is easy to implement and will be useful to improve the security to a great extent.

Background: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. Aim and Objective: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. Methods: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Results and Conclusion: Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Background: In recent years, lung cancer is a common cancer across the globe. For the early prediction of lung cancer, medical practitioners and researchers require an efficient predictive model, which will reduce the number of deaths. This paper proposes a lung cancer prediction model by using the Random Forest Classifier, which aims at analyzing symptoms (gender, age, air pollution, weight loss, etc.). Objective: This work addresses the problem of classification of lung cancer data using the Random Forest Algorithm. Random Forest is the most accurate learning algorithm and many researchers in the healthcare domain use it. Methods: This paper deals with the prediction of lung cancer by using the Random Forest Classifier. Results: The proposed method (Random Forest Classifier) applied on two lung cancer datasets achieved an accuracy of 100% for the lung cancer dataset-1 and 96.31 on dataset-2. In the prediction of lung cancer, the Random Forest Algorithm showed improved accuracy compared with other methods. Conclusion: This predictive model will help health professionals in predicting lung cancer at an early stage.

Background: Several approaches were proposed earlier to provide a secure infrastructure dependent communication in a smart home network. Some used overlay networks, some used lightweight encryption techniques, and some used honey pot techniques. However, all the approaches are vulnerable to network attacks due to the dependency on the device and server, and due to centralization, there exists a higher chance of attacks. Objective: To develop a security architecture that is more resilient to cyber-attacks and less dependent on any complex network parameter, i.e. an encryption algorithm or an overlay network. Methods: Authentication module along with squid performs token generation, and monitoring module helps devices to communicate with each other. The integrity protection module performs data integrity and the expiration of token is performed by the access module with the clock. Our approach meets with all the security aspects of a smart home network. Results: The analysis of our secure architecture showed that this architecture provides more flexibility, robustness in terms of Load Balancing, Network Lifetime maximization, Failure Management, Energy efficiency, Link quality, and heterogeneity of the network as compared to other existing security policies or architecture. Conclusion: The proposed framework ensures and improves all the security requirements for a smart home network. Token-based authentication is much secure and robust as compared to traditional approaches. This framework is suited for secure communication in a smart home environment, but it lacks for controlling zero-day attacks. In the future, we will improve its resilience against the zero-day attacks and also enhance security features in the current architecture.

Background: Electronic circuits testing and verification are performed to determine faulty devices after IC fabrication and to ensure that the circuit performs its designed functions. The verification process is considered as a test for both sequential and combinational logic circuits. The sequential circuits test is a more complex task than the combinational circuits test. However, dedicated algorithms can be used to test any type of sequential circuit regardless of its complexity. Objective: This paper presents a new Design Under Test (DUT) algorithm for 4-and 8-tap Finite Impulse Response (FIR) filters sequential circuits. The FIR filter and the proposed DUT algorithm are implemented using field Programmable Gate Arrays (FPGA) platform. Methods: The proposed DUT test generation algorithm is implemented using VHDL and Xilinx ISE V14.5 design suite. The proposed test generation algorithm for the FIR filter utilizes filtering redundant faults to obtain a set of target faults for the DUT. Results: The proposed algorithm reduces time delay for up to 50 % with power consumption reduction of up to 70 % in comparison with the most recent similar work. Conclusion: The implementation results ensured that a high speed and low power consumption architecture can be achieved. Also, the proposed architecture performance is faster than that of the existing techniques.