Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share agreat deal of information, which may also contain sensitive information,that may be subject to commercial or non-commercial privacy attacks. Asa result, guaranteeing various levels of privacy is critical while publishingdata by OSNs. The clustering-based solutions proved an effective mechanismto achieve the privacy notions in OSNs. But fixed clustering limits theperformance and scalability. Data utility degrades with increased privacy,so balancing the privacy utility trade-off is an open research issue. Theresearch has proposed a novel privacy preservation model using the enhancedclustering mechanism to overcome this issue. The proposed model includesphases like pre-processing, enhanced clustering, and ensuring privacy preservation.The enhanced clustering algorithm is the second phase where authorsmodified the existing fixed k-means clustering using the threshold approach.The threshold value is determined based on the supplied OSN data of edges,nodes, and user attributes. Clusters are k-anonymized with multiple graphproperties by a novel one-pass algorithm. After achieving the k-anonymityof clusters, optimization was performed to achieve all privacy models, suchas k-anonymity, t-closeness, and l-diversity. The proposed privacy frameworkachieves privacy of all three network components, i.e., link, node, and userattributes, with improved utility. The authors compare the proposed techniqueto underlying methods using OSN Yelp and Facebook datasets. The proposedapproach outperformed the underlying state of art methods for Degree ofAnonymization, computational efficiency, and information loss.

Content-Based Movie Recommendation System Using MBO with DBN

The content-basedfiltering technique has been used effectively in a variety of Recommender Systems(RS).The user explicitly or implicitly provides data in the Content-Based Recommender System.The system collects this data and creates a profile for all the users,and the recommendation is generated by the user profile.The recommendation generated via content-basedfiltering is provided by observing just a single user’s profile.The primary objective of this RS is to recommend a list of movies based on the user’s preferences.A con-tent-based movie recommendation model is proposed in this research,which recommends movies based on the user’s profile from the Facebook platform.The recommendation system is built with a hybrid model that combines the Mon-arch Butterfly Optimization(MBO)with the Deep Belief Network(DBN).For feature selection,the MBO is utilized,while DBN is used for classification.The datasets used in the experiment are collected from Facebook and MovieLens.The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations.Eachfile is com-pared with features that prove the features will support movie recommendations.The proposed model’s mean absolute error(MAE)and root-mean-square error(RMSE)values are 0.716 and 0.915,and its precision and recall are 97.35 and 96.60 percent,respectively.Extensive tests have demonstrated the advantages of the proposed method in terms of MAE,RMSE,Precision,and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm(FCM-BAT),Collaborativefiltering with k-NN and the normalized discounted cumulative gain method(CF-kNN+NDCG),User profile correlation-based similarity(UPCSim),and Deep Autoencoder.

A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.

Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.

Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars,stones,or leaves.Optical recognition systems can help in preserving,sharing,and accelerate the study of the ancient scripts,but lack of standard dataset for such scripts is a major constraint.Although many scholars and researchers have captured and uploaded inscription images on various websites,manual searching,downloading and extraction of these images is tedious and error prone.Web search queries return a vast number of irrelevant results,and manually extracting images for a specific script is not scalable.This paper proposes a novelmultistage system to identify the specific set of script images from a large set of images downloaded from web sources.The proposed system combines the two most important pattern matching techniques-Scale Invariant Feature Transform(SIFT)and Template matching,in a sequential pipeline,and by using the key strengths of each technique,the system can discard irrelevant images while retaining a specific type of images.

Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical data.But,the tra-ditional decision forest(DF)algorithms have lower classification accuracy and cannot handle high-dimensional feature space effectively.In this work,we pro-pose a bootstrap decision forest using penalizing attributes(BFPA)algorithm to predict heart disease with higher accuracy.This work integrates a significance-based attribute selection(SAS)algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac illness.The pro-posed SAS algorithm is used to determine the correlation among attributes and to select the optimum subset of feature space for learning and testing processes.BFPA selects the optimal number of learning and testing data points as well as the density of trees in the forest to realize higher prediction accuracy in classifying imbalanced datasets effectively.The effectiveness of the developed classifier is cautiously verified on the real-world database(i.e.,Heart disease dataset from UCI repository)by relating its enactment with many advanced approaches with respect to the accuracy,sensitivity,specificity,precision,and intersection over-union(IoU).The empirical results demonstrate that the intended classification approach outdoes other approaches with superior enactment regarding the accu-racy,precision,sensitivity,specificity,and IoU of 94.7%,99.2%,90.1%,91.1%,and 90.4%,correspondingly.Additionally,we carry out Wilcoxon’s rank-sum test to determine whether our proposed classifier with feature selection method enables a noteworthy enhancement related to other classifiers or not.From the experimental results,we can conclude that the integration of SAS and BFPA outperforms other classifiers recently reported in the literature.

An Interoperability Cross-Block Chain Framework for Secure Transactions in IoT

The purpose of this research is to deal with effective block chain framework for secure transactions.The rate of effective data transactions and the interoperability of the ledger are the two major obstacles involved in Blockchain and to tackle this issue,Cross-Chain based Transaction(CCT)is introduced.Traditional industries have been restructured by the introduction of Internet of Things(IoT)to become smart industries through the feature of data-driven decision-making.Still,there are a few limitations,like decentralization,security vulnerabilities,poor interoperability,as well as privacy concerns in IoTs.To overcome this limitation,Blockchain has been employed to assure a safer transaction process,especially in asset exchanges.In recent decades,scalable local ledgers implement Blockchains,simultaneously sustaining peer validations of transactions which can be at local or global levels.From the single Hyperledger-based blockchains system,the CCT takes the transaction amid various chains.In addition,the most significant factor for this registration processing strategy is the Signature to ensure security.The application of the Quantum cryptographic algorithm amplifies the proposed Hyperledger-based blockchains,to strengthen the safety of the process.The key has been determined by restricting the number of transactions that reach the global Blockchain using the quantum-based hash function and accomplished by scalable local ledgers,and peer validations of transactions at local and global levels without any issues.The rate of transaction processing for entire peers has enhanced with the ancillary aid of the proposed solution,as it includes the procedure of load distribution.Without any boosted enhancement,the recommended solution utilizes the current transaction strategy,and also,it’s aimed at scalability,resource conservation,and interoperability.The experimental results of the system have been evaluated using the metrics like block weight,ledger memory,the usage of the central processing unit,and the communication overhead.

Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

An automated retinal disease detection system has long been in exis-tence and it provides a safe,no-contact and cost-effective solution for detecting this disease.This paper presents a game theory-based dynamic weighted ensem-ble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection.The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features,and XGBoost classifier for the classification.The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2.A novel ensemble classifier based on the game theory approach is proposed for the fusion of the outputs of the transfer learning model and the XGBoost classifier model.The ensemble approach significantly improves the accuracy of retinal disease pre-diction and results in an excellent performance when compared to the individual deep learning and feature-based models.

Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

The software engineering technique makes it possible to create high-quality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly software proce-dures isfinding andfixing bugs.Although it is impossible to eradicate all bugs,it is feasible to reduce the number of bugs and their negative effects.To broaden the scope of bug prediction techniques and increase software quality,numerous causes of software problems must be identified,and successful bug prediction models must be implemented.This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization(MFO)algorithm to forecast the number of bugs in software based on the program data itself,such as the line quantity in codes,methods characteristics,and other essential software aspects.Here,the MFO method is used to train the neural network to identify optimal weights.The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost(AB),Random Forest(RF),K-Nearest Neighbour(KNN),K-Means Clustering(KMC),Support Vector Machine(SVM)and Bagging Clas-sifier(BC)are examples of machine learning(ML)techniques.The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy.The obtained data revealed that the proposed strategy outperforms the traditional approach.

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detectionfor transmitting and creating new messages or existing message modifications.The attacker on another node evaluates the forging activity in themessage directly or indirectly. Every node sends short packets in a MANETenvironment with its identifier, location on the map, and time through beacons.The attackers on the network broadcast the warning message usingfaked coordinates, providing the appearance of a network collision. Similarly,MANET degrades the channel utilization performance. Performancehighly affects network performance through security algorithms. This paperdeveloped a trust management technique called Enhanced Beacon TrustManagement with Hybrid Optimization (EBTM-Hyopt) for efficient clusterhead selection and malicious node detection. It tries to build trust amongconnected nodes and may improve security by requiring every participatingnode to develop and distribute genuine, accurate, and trustworthy materialacross the network. Specifically, optimized cluster head election is done periodicallyto reduce and balance the energy consumption to improve the lifetimenetwork. The cluster head election optimization is based on hybridizingParticle Swarm Optimization (PSO) and Gravitational Search OptimizationAlgorithm (GSOA) concepts to enable and ensure reliable routing. Simulationresults show that the proposed EBTM-HYOPT outperforms the state-of-thearttrust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13%of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay,and 16.34% of packet length.

Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

In spite of the advancement in computerized imaging,many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way,the research in the zone of image denoising is very dynamic.Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise sup-pression is a basic and productive methodology.In this paper,Bilateral Filter(BF)based Modular Neural Networks(MNN)has been utilized for speckle noise sup-pression in the ultrasound image.Initial step the BFfilter is used tofilter the input image.From the output of BF,statistical features such as mean,standard devia-tion,median and kurtosis have been extracted and these features are used to train the MNN.Then,thefiltered images from the BF are again denoised using MNN.The ultrasound dataset from the Kaggle site is used for the training and testing process.The simulation outcomes demonstrate that the BF-MNNfiltering method performs better for the multiplicative noise concealment in UltraSound(US)images.From the simulation results,it has been observed that BF-MNN performs better than the existing techniques in terms of peak signal to noise ratio(34.89),Structural Similarity Index(0.89)and Edge Preservation Index(0.67).

Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

In recent years,Peripheral blood smear is a generic analysis to assess the person’s health status.Manual testing of Peripheral blood smear images are difficult,time-consuming and is subject to human intervention and visual error.This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques.Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images.In order to mitigate this issue Deep Convolution Neural Network(DCNN)based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil,eosinophil,lymphocyte,monocyte,neutrophil,erythroblast,platelet,myocyte,promyocyte and metamyocyte.The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing,feature extraction and classification.Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization(HE).It is enclosed to improve an image contrast.In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means(FCM)model whereas its centroid point optimality method with Slap Swarm based optimization strategy.Moreover some specific set of Gray Level Co-occurrence Matrix(GLCM)features of the segmented images are extracted to augment the performance of proposed detection algorithm.Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features.Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.

Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing information.This is applied greatly in business enterprises and government sectors for delivering the services to their customers,clients and citizens.But,the interaction is success-ful only based on the trust that each device has on another.Thus trust is very much essential for a social network.As Internet of Things have access over sen-sitive information,it urges to many threats that lead data management to risk.This issue is addressed by trust management that help to take decision about trust-worthiness of requestor and provider before communication and sharing.Several trust-based systems are existing for different domain using Dynamic weight meth-od,Fuzzy classification,Bayes inference and very few Regression analysis for IoT.The proposed algorithm is based on Logistic Regression,which provide strong statistical background to trust prediction.To make our stand strong on regression support to trust,we have compared the performance with equivalent sound Bayes analysis using Beta distribution.The performance is studied in simu-lated IoT setup with Quality of Service(QoS)and Social parameters for the nodes.The proposed model performs better in terms of various metrics.An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services.The most salient features of IoT system is to design it with scalability,extendibility,compatibility and resiliency against attack.The existing worksfinds a way to integrate direct and indirect trust to con-verge quickly and estimate the bias due to attacks in addition to the above features.

Resilient Service Authentication for Smart City Application Using IoT

Internet of Things(IoT)support for smart city systems improves ser-vice scales by ignoring various user congestion.People are looking for different security features for reliable and robust applications.Here,the Permanent Denial of Service(PDoS)problem arises from improper user identification.This article introduces the Service-Reliant Application Authentication(SRAA)to prevent PDoS attacks in a smart area of the city.In this authentication method,the security of the application is ensured through the distribution of guarded access.The supervised access distribution uses user interface features and sync with the user device.Abnormality in linking user device,application,and authentication is seen in Back Propagation(BP)readings.BP learning reduces given weights based on abnormalities trained during the access distribution process.The oddity is reflected in the sequence from previous training sessions to ensure consistent syn-chronization of distributed services.From PDoS,the web device displays a few unattended loads on the service,which reduces service failure.The effectiveness of the proposed verification method is verified using delays to verify metric accu-racy,false standard,sync failure,and bit rate.

Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

Suspicious fall events are particularly significant hazards for the safety of patients and elders.Recently,suspicious fall event detection has become a robust research case in real-time monitoring.This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving back-grounds in an indoor environment;it is further proposed to use a deep learning method known as Long Short Term Memory(LSTM)by introducing visual atten-tion-guided mechanism along with a bi-directional LSTM model.This method contributes essential information on the temporal and spatial locations of‘suspi-cious fall’events in learning the video frame in both forward and backward direc-tions.The effective“You only look once V4”(YOLO V4)–a real-time people detection system illustrates the detection of people in videos,followed by a track-ing module to get their trajectories.Convolutional Neural Network(CNN)fea-tures are extracted for each person tracked through bounding boxes.Subsequently,a visual attention-guided Bi-directional LSTM model is proposed for the final suspicious fall event detection.The proposed method is demonstrated using two different datasets to illustrate the efficiency.The proposed method is evaluated by comparing it with other state-of-the-art methods,showing that it achieves 96.9%accuracy,good performance,and robustness.Hence,it is accep-table to monitor and detect suspicious fall events.

Intelligent MRI Room Design Using Visible Light Communication with Range Augmentation

Radio waves and strong magneticfields are used by Magnetic Reso-nance Imaging(MRI)scanners to detect tumours,wounds and visualize detailed images of the human body.Wi-Fi and other medical devices placed in the MRI procedure room produces RF noise in MRI Images.The RF noise is the result of electromagnetic emissions produced by Wi-Fi and other medical devices that interfere with the operation of the MRI scanner.Existing techniques for RF noise mitigation involve RF shielding techniques which induce eddy currents that affect the MRI image quality.RF shielding techniques are complex and lead to RF leak-age.VLC(Visible light Communication)is an emerging and efficient technology to avoid RF interference near MRI scanners.Range augmentation with power conservation of the LED is a big challenge in existing VLC systems.The major objective of the proposed work is to develop an intelligent-MRI room design without RF interference using visible light communication and enhance the distance between VLC transmitter and VLC receiver.In this paper,it is proposed to implement VLC using On-Off keying modulation and enhance distance using large active area photodiodes with Automatic Gain Control Circuit(AGC)using software and hardware.The performance of the proposed intelligent MRI-VLC system is analyzed by calculating Bit Error Rate at an inclined distance of 50 cm away from line of sight of the LED.The Experimental results showed that the maximum distance achieved was 400 cm at Bit Error Rate(BER)of 1.5×10^(-5).

Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.

A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks

In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)protocols to have necessary communication among the nodes.Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered.The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots.The two methods are found to have certain drawbacks.Thefirst category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours.When second category is followed,it may have inefficient slots in the wake-up schedules leading to performance degradation.Therefore,the motivation behind the work in this paper is that by combining the two categories,it is possible to reap benefits of both and get rid of the limitations of the both.Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots.Thus a Hybrid Approach to Neighbour Discovery(HAND)protocol is realized in WSN.The simulation study revealed that HAND outperforms the existing indirect ND models.

Computation of PoA for Selfish Node Detection and Resource Allocation Using Game Theory

The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.