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The software is changing rapidly with the invention of advanced technologies and methodologies. The ability to rapidly and successfully upgrade software in response to changing business requirements is more vital than ever. For the long‐term management of software products, measuring software maintainability is crucial. The use of soft computing techniques for software maintainability prediction has shown immense promise in software maintenance process by providing accurate prediction of software maintainability. To better understand the role of soft computing techniques for software maintainability prediction, we aim to provide a systematic literature review of soft computing techniques for predicting software maintainability. Firstly, we provide a detailed overview of software maintainability. Following this, we explore the fundamentals of software maintainability and the reasons for adopting soft computing methodologies for predicting software maintainability. Later, we examine the soft computing approaches employed in the process of software maintainability prediction. Furthermore, we discuss the difficulties and potential solutions associated with the use of soft computing techniques in predicting maintainability of software. Finally, we conclude the review with some promising future directions to drive further research innovations and developments in this promising area. This systematic literature review provides a comprehensive overview of the soft computing strategies utilized for software maintainability for future researchers. [ABSTRACT FROM AUTHOR]

In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into SVM algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of SVM algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation. [ABSTRACT FROM AUTHOR]

Healthcare is predominantly regarded as a crucial consideration in promoting the general physical and mental health and well-being of people around the world. The amount of data generated by healthcare systems is enormous, making it challenging to manage. Many machine learning (ML) approaches were implemented to develop dependable and robust solutions to handle the data. ML cannot fully utilize data due to privacy concerns. This primarily happens in the case of medical data. Due to a lack of precise clinical data, the application of ML for the same is challenging and may not yield desired results. Federated learning (FL), which is a recent development in ML where the computation is offloaded to the source of data, appears to be a promising solution to this problem. In this study, we present a detailed survey of applications of FL for healthcare informatics. We initiate a discussion on the need for FL in the healthcare domain, followed by a review of recent review papers. We focus on the fundamentals of FL and the major motivations behind FL for healthcare applications. We then present the applications of FL along with recent state of the art in several verticals of healthcare. Then, lessons learned, open issues, and challenges that are yet to be solved are also highlighted. This is followed by future directions that give directions to the prospective researchers willing to do their research in this domain. [ABSTRACT FROM AUTHOR]

Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it's important to assess software maintainability. Many software maintainability prediction models' compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms. [ABSTRACT FROM AUTHOR]

Most of the traditional athletic performance dynamic change prediction platforms use gray correlation and neural networks to establish prediction methods, and the prediction results are inaccurate and time-consuming. Therefore, the construction of a mobile prediction platform for dynamic changes in athletic performance based on screening factors is proposed to improve the accuracy of its prediction results and shorten the prediction time. On the basis of hardware, the main body of students is determined, the athletic performance data of students is collected by the physical education management system of colleges and universities, and the missing data is filled by the criterion function of K-average algorithm. The KNN algorithm is used to make decisions to obtain the most representative predictors of athletic performance data, and the characteristics of students' athletic performance are extracted. The effective factors are selected according to the tenfold cross-validation fold MSE minimum principle of SVM, and the mandatory screening based on SVM is used to assign weights to each reserved factor. According to the weighted result of the obtained factors, the influencing factors of athletic performance are analyzed, and the weighting process is completed for the factors, and the athletic performance prediction is realized by using support vector machine. In the platform established in this paper, the students' 1000-m running results are predicted. The simulation results show that, compared with the traditional platform, the constructed platform has higher prediction accuracy and takes less time. [ABSTRACT FROM AUTHOR]

In order to improve the effect of sports information mining effectively, a sports security information mining platform based on multimedia data sharing technology is designed. The hardware part of the platform includes teaching multimedia data sharing module, sharing server module, sharing client and Web server. In order to realize the information transmission with low time delay and low energy consumption, ZigBee technology is introduced into the software design to realize the communication function of information and complete the evaluation of mining quality. The experimental results show that the time of sports information mining is obviously shortened and the application effect of the platform is improved after the application of the mining platform designed in this study. [ABSTRACT FROM AUTHOR]

In order to realize the best matching search of mobile intelligent education system resources, a resource search method of mobile intelligent education system based on distributed hash table is proposed. Firstly, combine the chord system based on distributed hash table and vector space model to form a resource discovery mechanism. After locating multi-attribute resources, search the location resources based on the resource search model of chord and VSM, and then solve the similarity between query vectors and location resource vectors by establishing the vector relationship between location resources and user queries, Finally, according to the resource similarity solution results, the resources with the greatest relevance to the search content are obtained. The test results show that the value of search request blocking rate is far lower than its threshold, the search performance is good, and the matching degree of resource search results is high. [ABSTRACT FROM AUTHOR]

In a broad spectrum, software metrics play a vital role in attribute assessment, which successively moves software projects. The metrics measure gives many crucial facets of the system, enhancing the system quality of software developed. Moreover, maintenance is the correction process that works out in the software system once the software is initially made. The noteworthy characteristic of any software is 'change,' and as a result, additional concern ought to be taken in developing software. So, the software is expected to be modified effortlessly (maintainable). Predicting software maintainability is still challenging, and accurate prediction models with low error rates are required. Since there are so many modern programming languages on the horizon. To accurately measure software maintainability, new techniques have to been introduced. This paper proposes a maintainability index (MI) by considering various software metrics by which the error gets minimized. It also intends to adopt a renowned optimization algorithm, namely Firefly (FF), for the optimum result. The proposed Base Model-FF is compared to other traditional models like BM-Differential Evolution (BM-DE), BM-Artificial Bee Colony (BM-ABC), BM-Particle Swarm Optimization (BM-PSO), and BM-Genetic Algorithm (BM- GA) in terms of performance metrics like Differential ratio, correlation coefficient, and Random Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

In order to relieve the pressure of medical resources and meet the demand of human health real-time detection, a remote health detection algorithm based on sensor signal purification was designed. In the wearable health device, health parameter sensors such as integrated temperature sensors were set up, and the results of human health sensing signals were collected by wireless communication technology and transmitted to a remote data processing center. The data processing center used the wavelet threshold method to denoise the human health sensing signal, and selected the joint time–frequency decomposition method to purify the human health sensing signal. The purified result of the sensor signal was set as the input sample of the hidden Markov model. The Baum-Welch local optimization algorithm was used to update the parameters of the hidden Markov model until the posterior probability of the model approached the maximum value, and the human health detection results were output. The experimental results show that the acquisition error of human physiological parameters is less than 0.5% when the sensor signal is purified by this algorithm, and the abnormal health state of testers such as falling, high body temperature and high blood pressure is accurately detected. [ABSTRACT FROM AUTHOR]

The number of vehicles travelling on the road has increased tremendously over the past few decades throughout the globe. The advent of Intelligent Transport System (ITS) enabled the vehicles to be managed intelligently. However, the ITS network can be intruded by malicious users to access the sensitive information. So a strong Intrusion Detection System (IDS) is required to safeguard the ITS network. Due to the huge volume of data generated by the sensors in the vehicles in ITS, there is a dire need to extract the most important features from the ITS data for classification phase in IDS. Even though, many researchers have worked on building an IDS system in ITS, not much work is done on addressing the aforementioned issue. To address this problem, we have utilized the Moth–Flame Optimization (MFO) in this work. In the proposed work, a MFO based ensemble machine learning model is proposed to classify the IDS dataset in ITS. Firstly the IDS dataset is normalized using the standard-scaler method. Then optimal features from the IDS dataset are chosen by MFO. These optimal features are trained by an ensemble classifier comprising of linear regression, random forest and XGBoost classification algorithms for intelligent decision making. The performance of the proposed model is compared against several state of the art meta-heuristic algorithms based on ensemble models which achieved a superior accuracy and recall of 100%, and precision of 99.5% on shellcode attacks. [ABSTRACT FROM AUTHOR]

The rampant spread of explicit content across social media can leave a damaging mark on our society. Hence, the need to be vigilant in detecting and curtailing sexually explicit content cannot be overstated. As such, it becomes paramount to discern and manage sexually explicit material to curb its dissemination and safeguard our digital communities from its harmful effects. In this article, we propose a unique technique entitled attention‐enabled pooling (ABP) embedded Swin transformer‐based YOLOv3 (ASYv3) for the detection of obscene areas present in the images with a bounding box around the offensive regions. ASYv3 employs a unique two‐step approach for enhanced performance in obscene detection. In the first step, a scalable and efficient Swin transformer block is integrated, utilizing self‐attention and model parallelism to train massive models effectively. In the second phase, the embedding layer of the Swin transformer is replaced with ABP, mitigating disruption of feature context. ABP allows for the projection of raw‐valued features into linear form with proper attention to feature context information at specified locations, resulting in optimized feature extraction. The proposed ABP embedded Swin transformer‐based YOLOv3 (ASYv3) was trained with annotated obscene images (AOI) dataset. The proposed ASYv3 model surpassed the state‐of‐the‐art methods by achieving 97% testing accuracy, 96.62% precision, 97.40% sensitivity, 3.48% FPR rate, 97.37% NPV values, and 95.59% mAP values, respectively. [ABSTRACT FROM AUTHOR]

Countless cybercrime instances have shown the need for detecting and blocking obscene material from social media sites. Deep learning methods (DLMs) outperformed in recognizing obscene content flooded on many online platforms. However, these contemporary DLMs primarily treat the recognition of obscene content as a simple task of binary classification, rather than focusing on the labelling of obscene areas. Hence, many of these methods could not pay attention to the fact that misclassification samples are so diverse. Therefore, this paper focuses on two aspects (i) developing a deep learning model that could classify and label the obscene portion, and (ii) generating a labelled obscene image dataset with a wide variety of obscene samples to minimize the risks of inaccurate recognition. We have proposed a method named S3Pooling based bottleneck attention module (BAM) embedded MobileNetV2‐YOLOv3 (SBMYv3) for automatic detection of obscene content using an attention mechanism and a suitable pooling strategy. The key contributions of our article are: (i) generation of a well‐labelled obscene image dataset with a variety of augmentation strategies using Pix‐2‐Pix GAN (ii) modifications to the backend architecture of YOLOv3 using MobileNetV2 and BAM to ensure focused and accurate feature extraction, and (iii) selection of an optimal pooling strategy, that is, S3Pooling strategy, while taking the design of the feature extractor into account. The proposed SBMYv3 model outperformed other state‐of‐the‐art models with 99.26% testing accuracy, 99.39% recall, 99.13% precision, and 99.13% IoU values respectively. [ABSTRACT FROM AUTHOR]

Hand gestures are an effective method of communication, especially when we are communicating with people who cannot understand our spoken language. Furthermore, it is a key aspect to human–computer interaction. Understanding hand gestures is very important to ensure that listeners understand what speakers are attempting to communicate. Even though several researchers have proposed deep learning-based models for hand gesture recognition, the hyper-parameter tuning of these models is a relatively unexplored area. In this work, Convolutional Neural Networks (CNN) are used to classify hand gesture images. To tune the hyper-parameters of the CNN, a recently developed metaheuristic algorithm, namely, the Harris Hawks Optimization (HHO) algorithm, is used. Our in-depth comparative analysis proves that the proposed HHO-CNN hybrid model outperforms the existing models by attaining an Accuracy of 100%. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection. [ABSTRACT FROM AUTHOR]

Dry eye disease (DED) is a chronic eye disease and a common complication among the world's population. Evaporation of moisture from tear film or a decrease in tear production leads to an unstable tear film which causes DED. The tear film breakup time (TBUT) test is a common clinical test used to diagnose DED. In this test, DED is diagnosed by measuring the time at which the first breakup pattern appears on the tear film. TBUT test is subjective, labour-intensive and time-consuming. These weaknesses make a computer-aided diagnosis of DED highly desirable. The existing computer-aided DED detection techniques use expensive instruments for image acquisition which may not be available in all eye clinics. Moreover, among these techniques, TBUT-based DED detection techniques are limited to finding only tear film breakup area/time and do not identify the severity of DED, which can essentially be helpful to ophthalmologists in prescribing the right treatment. Additionally, a few challenges in developing a DED detection approach are less illuminated video, constant blinking of eyes in the videos, blurred video, and lack of public datasets. This paper presents a novel TBUT-based DED detection approach that detects the presence/absence of DED from TBUT video. In addition, the proposed approach accurately identifies the severity level of DED and further categorizes it as normal, moderate or severe based on the TBUT. The proposed approach exhibits high performance in classifying TBUT frames, detecting DED, and severity grading of TBUT video with an accuracy of 83%. Also, the correlation computed between the proposed approach and the Ophthalmologist's opinion is 90%, which reflects the noteworthy contribution of our proposed approach. [ABSTRACT FROM AUTHOR]

Predicting global solar activity is crucial for smart cities, especially for space activities, communication industries, and climate change monitoring. The recently developed models to predict solar activity based on stand-alone artificial intelligence, using machine and deep learning models, and hybrid models are promising. Yet they may not be effective at capturing simpler linear patterns in the data and often fail to provide reliable predictions due to their computational cost and complexity. This article proposed a novel residual hybrid machine learning method integrating linear regression machine learning, and deep learning neural networks for solving predictive accuracy in individual machine learning models that reduces complexity. The residual hybrid model leverages the capacities of the support vector machine (SVM) and long short-term memory neural network (LSTM) for hybrid SVM-LSTM model. The performance of the model is evaluated using the correlation coefficient, determination coefficient, root-mean-squared error (RMSE) and mean-absolute error. The simulation results indicated that compared to the SVM-LSTM, the training and testing RMSE of the LSTM is reduced by 76.62% and 71.18%, respectively. It also decreases the training and testing RMSE of the SVM by 77.06% and 71.81%, respectively. The proposed model can be implemented as reliable solution for accurately predicting solar activities in smart cities. [ABSTRACT FROM AUTHOR]

In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain technology offers a promising solution to address these issues. The proposed system utilizes a blockchain network, where each academic certificate is stored as a digital asset on the blockchain. These digital certificates are cryptographically secured, timestamped, and associated with unique identifiers, such as hashes or public keys, ensuring their integrity and immutability. Anyone with access to the blockchain network can verify a certificate's authenticity, using the MetaMask extension and Ethereum network, eliminating the need for intermediaries and reducing the risk of fraudulent credentials. The main strength of the paper is that the data that are stored in the blockchain are unique identifiers of the encrypted data, which is encrypted by using an encryption technique that provides more security to the academic certificates. Furthermore, IPFS is also used to store large amounts of encrypted data. [ABSTRACT FROM AUTHOR]

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC's 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra. [ABSTRACT FROM AUTHOR]

Recently, machine learning methods have been successfully used for the prediction of cardiovascular disease. Early diagnosis and prediction is necessary for giving effective treatment to avoid higher mortality rates. Several classification algorithms have been developed recently which satisfy the need, but show limited accuracy while predicting the heart disease. Hence, the focus of this study is on early prediction of heart disease and to improve the accuracy of prediction using benchmark heart disease datasets such as UCI Cleveland dataset and Heart disease clinical dataset by implementing effective classification and optimization algorithms. Optimization algorithms generally exhibit the benefit of dealing with complex non‐linear issues with better adaptability and flexibility. The Emperor penguin optimization algorithm, which can select the best features for classification has been utilized in this study to improve the efficiency, minimize reconstruction errors, and increase the quality of heart disease classification. Further, the newly developed stacked sparse convolutional neural network based auto encoder (SSC‐AE) classification algorithm has been employed for significant feature classification with higher robustness and efficacy. Accuracy, Area Under Curve (AUC), and F1 score are some of the measures used to compare the outcomes of several machine learning algorithms to those of the proposed model in this study. The results show that the proposed model, SSC‐AE, is superior to other classification models. [ABSTRACT FROM AUTHOR]

Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1–6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is vital for various remote sensing applications; however, it remains challenging due to the complex and high-dimensional nature of hyperspectral data. This paper introduces a novel approach to address this challenge by leveraging spectral and spatial features through a lightweight HResNeXt model. The proposed model is designed to overcome the limitations of traditional methods by combining residual connections and cardinality to enable efficient and effective feature extraction from hyperspectral images, capturing both spectral and spatial information simultaneously. Furthermore, the paper includes an in-depth analysis of the learned spectral–spatial features, providing valuable insights into the discriminative power of the proposed approach. The extracted features exhibit strong discriminative capabilities, enabling accurate classification even in challenging scenarios with limited training samples and complex spectral variations. Extensive experimental evaluations are conducted on four benchmark hyperspectral data sets, the Pavia university (PU), Kennedy Space Center (KSC), Salinas scene (SA), and Indian Pines (IP). The performance of the proposed method is compared with the state-of-the-art methods. The quantitative and visual results demonstrate the proposed approach's high classification accuracy, noise robustness, and computational efficiency superiority. The HResNeXt obtained an overall accuracy on PU, KSC, SA, and IP, 99.46%, 81.46%, 99.75%, and 98.64%, respectively. Notably, the lightweight HResNeXt model achieves competitive results while requiring fewer computational resources, making it well-suited for real-time applications. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments. [ABSTRACT FROM AUTHOR]

The advent of fog computing as an extension of cloud-enabling technology has moved from the hub of the internet framework to the unmanned aerial vehicle (UAV)-enabled control and management of drone-based data. The key objectives are to improve the computation and processing of drone-based resource constraints and deliver the data to the outsourced computational node to schedule, process, manage, optimize, and preserve it since they are located close to each other through a wireless sensor network. This collaborative technological approach to robustness is the standard of drone system design and implementation, reducing the computational power, resource consumption, and latency for the application that needs a fast response. However, recent research into fog-enabled drone-based data management and optimization poses a serious challenge in terms of privacy, security, and preservation. On the other hand, Blockchain Hyperledger Technology, which is mostly used in Bitcoin Cryptocurrencies, has been applied in a large number of distributed applications due to the ledger integrity, transparency, trustworthiness, provenance, reliability, availability, protection, and security-related distributed features that are provided. Therefore, the distributed application of drone control has received more attention because of its ability to handle collaborative procedures to capture, schedule, process, optimize, manage, and preserve drone-based data using fog nodes and blockchain hyperledger technology. In this paper, we proposed a collaborative approach using blockchain hyperledger fabric and a metaheuristic-enabled genetic algorithm for fog node management called B-Drone. This approach handles drone-based data collection, scheduling, optimizing, processing, managing, and preservation in a secure manner in the fog node. The protection of each transaction between drone and fog node before exchange is private with the utilization of the hash-encryption (SHA-256) algorithm. The blockchain smart contracts are implemented and deployed to auto-manage all the connectivity and communication protocols between drones and fog nodes in the designed private permissioned network. The simulations of the proposed collaborative approach reduce the computing cost down to 12.03% and increase the performance up to 73.11%, thus decreasing the cost of drone-ledger preservation down to 30.13% and the robust usage of the network up to 54.29% as compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to the explosive increase of electric vehicles (EVs) and universal charging stations (CS), achieving fast authentication is an important topic in the vehicle-to-grid (V2G) network at present. Fast authentication cannot only forcefully defend potential adversaries but also can greatly speed up the charging process between EVs and CSs. Although many researchers have realized this problem and proposed some lightweight cryptographic protocols for fast EV charging, desired security features are not entirely satisfied, such as man-in-the-middle attacks, replay attacks, and impersonation attacks. In this article, we propose an ultra super fast authentication protocol for EV charging by utilizing the characteristics of extended chaotic maps. Furthermore, in view of the unsolved security issues mentioned previously, our proposed protocol can provide elaborate solutions to eliminate these possible attacks in a provable manner. Finally, compared with the relevant authentication protocols for V2G network, the communication and computation costs of our protocol are decreased a lot. [ABSTRACT FROM AUTHOR]

In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks. [ABSTRACT FROM AUTHOR]

With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing. Researchers are searching for alternative strategies to identify the author of an unknown text. There is a need to develop a system to identify the actual author of unknown texts based on a given set of writing samples. This study presents a novel approach based on ensemble learning, DistilBERT, and conventional machine learning techniques for authorship identification. The proposed approach extracts the valuable characteristics of the author using a count vectorizer and bi-gram Term frequency-inverse document frequency (TF-IDF). An extensive and detailed dataset, "All the news" is used in this study for experimentation. The dataset is divided into three subsets (article1, article2, and article3). We limit the scope of the dataset and selected ten authors in the first scope and 20 authors in the second scope for experimentation. The experimental results of proposed ensemble learning and DistilBERT provide better performance for all the three subsets of the "All the news" dataset. In the first scope, the experimental results prove that the proposed ensemble learning approach from 10 authors provides a better accuracy gain of 3.14% and from DistilBERT 2.44% from the article1 dataset. Similarly, in the second scope from 20 authors, the proposed ensemble learning approach provides a better accuracy gain of 5.25% and from DistilBERT 7.17% from the article1 dataset, which is better than previous state-of-the-art studies. [ABSTRACT FROM AUTHOR]

• A Novel ensemble/cooperative framework based on concept-based and clustering is proposed to perform Twitter sentiment Analysis. • It employs majority voting, tie breaker criteria, and linguistic rules in concept-based module. • Comparative analysis between clustering and classification is presented when integrated with concept based methods. • It presents the performance of feature representation methods (Boolean and TF-IDF). • Experimental results on Twitter Datasets revealed better performance of proposed framework. [Display omitted] Concept-based sentiment analysis (CBSA) methods have gained prominence in natural language processing in recent years. These methods consider the underlying semantic meanings of text to perform different tasks such as Twitter sentiment analysis (assigning positive, negative, or neutral sentiment to Tweets). CBSA is superior to traditional statistical methods for accurately discovering sentiment labels. Due to a limited knowledge base, these methods are unable to identify the sentiment polarity of all kinds of text. Therefore, supervised learning techniques are mostly ensembled with CBSA methods to classify the whole text. These techniques require labeled data. It is a tedious and time-consuming task due to the manually labeling of large datasets (Such as Twitter datasets). Therefore, an unsupervised learning mechanism can be a better alternative to solve this problem. In this paper, a novel unsupervised learning framework based on Concept-based and hierarchical clustering is proposed for Twitter sentiment analysis. Popular hierarchical clustering methods including single linkage, complete linkage, and average linkage algorithms are ensembled serially. Two different feature representation methods including Boolean and Term frequency-inverse document frequency (TF-IDF) are investigated. We have also experimented with Well-known classifiers (Naive Bayes, Neural Network) for a fair comparison. Accuracy measure (proportion of correct predictions) is used to evaluate the performance of understudied techniques. It is empirically shown that the performance of unsupervised learning techniques is comparable with supervised learning techniques. [ABSTRACT FROM AUTHOR]

Sign language plays a pivotal role in the lives of impaired people having speaking and hearing disabilities. They can convey messages using hand gesture movements. American Sign Language (ASL) recognition is challenging due to the increasing intra-class similarity and high complexity. This paper used a deep convolutional neural network for ASL alphabet recognition to overcome ASL recognition challenges. This paper presents an ASL recognition approach using a deep convolutional neural network. The performance of the DeepCNN model improves with the amount of given data; for this purpose, we applied the data augmentation technique to expand the size of training data from existing data artificially. According to the experiments, the proposed DeepCNN model provides consistent results for the ASL dataset. Experiments prove that the DeepCNN gives a better accuracy gain of 19.84%, 8.37%, 16.31%, 17.17%, 5.86%, and 3.26% as compared to various state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The classroom learning environment facilitates human tutors to interact with every learner and get the opportunity to understand the learner's psychology and then provide learning material (access learner prior knowledge and well align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in intelligent tutoring system is quite tricky. This research has focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum to the learners. The prime focus of this research article is to evaluate the proposed SeisTutor using Kirkpatrick's four-phase evaluation model. Experimental results depicting the enhanced learning gain through intelligence incorporated SeisTutor as against the intelligence absence are demonstrated. [ABSTRACT FROM AUTHOR]

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions. [ABSTRACT FROM AUTHOR]

Human faces contain useful information that can be used in the identification of age, gender, weight etc. Among these biometrics, body mass index (BMI) and body weight are good indicators of a healthy person. Motivated by the recent health science studies, this work investigates ways to identify malnutrition affected people and obese people by analyzing body weight and BMI from facial images by proposing a regression method based on the 50‐layers Residual network architecture. For face detection, Multi‐task Cascaded Convolutional Neural Networks have been employed. A system is created to evaluate BMI along with age and gender from human facial real‐time images. Malnutrition and obesity are commonly determined with the help of BMI. In the previous works, height, weight, and BMI estimation through automatic means have predominantly focused on full‐body images and videos of humans. The usage of facial images for estimating such traits have been given less importance. In order to facilitate the analysis, the dataset is cleaned along with metadata containing information about the persons height, weight, age, and gender. Gender‐based analysis is performed for the prediction of BMI. Finally, an email containing the persons picture along with their details is sent to the concerned health officer. [ABSTRACT FROM AUTHOR]

Centralized Personal Health Records (PHR) are mutable with compromised security as it may lead to a single point of failure. Confidentiality, protection and security are the common issues in clinical record frameworks. Specific security and protection schemes are being used to secure clinical records. Accordingly, using the Interplanetary File System (IPFS), a decentralized PHR can be maintained to allow patients to access their records without delay. Moreover, a Kademlia-based distributed hash table provides fault tolerance and enables patients to keep track of their medical history. However, a significant issue in IPFS is data availability. It is only available on the web until users or hosts of the network request each peer, later it leads to a permanent loss of data. We propose an architecture that aims to provide faster retrieval and constant PHR availability using Blockchain and IPFS. The results show that an optimal node is selected in each iteration amongst all the available adjacent nodes. [ABSTRACT FROM AUTHOR]

A Brain–Computer Interface (BCI), integrated with the Internet of Medical Things (IoMT) and based on electroencephalogram (EEG) technology, allows users to control external devices by decoding brainwave patterns. Advanced deep learning-based BCIs, especially those utilizing sensorimotor rhythms (SMRs), have emerged as direct brain–device communication facilitators. SMRs involve users imagining limb motions to induce specific brain activity changes in the motor cortex. Despite progress, some users struggle with BCIs due to weak signals, individual variability, and limited task applicability. This study introduces an unsupervised EEG preprocessing pipeline for SMR-based BCIs. It evaluates an EEG dataset recorded during finger movements, employing two cleaning methods: an investigator-dependent pipeline and our proposed unsupervised method. Two distinct feature datasets are generated: one from cleaned EEG data processed into spectrogram images using supervised preprocessing, and another from data cleaned using our proposed unsupervised pipeline. The study extensively assesses five transfer learning convolutional neural network (TL-CNN) models for distinguishing Motor Imagery (MI) from finger movements (Mex) using the generated datasets. A novel probability fusion technique is developed to enhance TL-CNN classification in Mex versus MI finger-pinching actions. Comparative results show that the fusion-based method outperforms other state-of-the-art methods when applied to unsupervised EEG data. Specifically, our proposed approach achieves 97.9% accuracy, 93.4% precision, 95% recall, and an F1-score of 93.2%, demonstrating significant progress in distinguishing MI and Mex activities through the use of our unsupervised pre-processing pipeline and fusion-based CNN method. Our findings demonstrate the potential of our approach to serve as a benchmark for the global interdisciplinary research community and enable the development of future more effective and user-friendly real-time BCI systems. • Near real-time EEG preprocessing and probability-based fusion are proposed. • A novel approach to disentangle MI (mental motor acts) from Mex (motor execution). • Hand Movement Classification using a Fusion of CNN architectures in the IoMT. • 64 EEG channels have been used to perform ERS and ERD analyses. • Experiments show good performances on EEG time series and time–frequency datasets. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low-power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL-KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyperparameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR-10 dataset with only 10 training rounds. [ABSTRACT FROM AUTHOR]

Summary: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state‐of‐the‐art machine learning algorithms, namely Support Vector Machines (SVM), K‐Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy. [ABSTRACT FROM AUTHOR]

Copyright infringement adversely affects the interest of copyright holders of images and videos which are uploaded to different websites and peer-to-peer image sharing systems. This paper addresses the problem of detecting copyright infringement so that copyright holders are given due credit for their work. There are several images and videos that are shared every day by millions of users with some amount of modification in images and videos originally uploaded by the copyright holders such as photographers, graphic designers, and video providers. Copyright violators, who are not the original creators of multimedia content modify them using image processing and frame modification techniques such as grayscale conversion, cropping, rotation, frame compression, and frame speed manipulation. Then, upload the tampered images and videos. To address this problem, we propose an IPFS-based (InterPlanetary File System-based) decentralized peer-to-peer image and video sharing platform built on blockchain technology. We use a perceptual hash (pHash) technique to detect copyright violations of multimedia. When multimedia is to be uploaded to the IPFS, the pHash of the same content is determined and checked against existing pHash values in the blockchain network. Similarity with existing pHash values would result in the multimedia being detected as tampered with. Blockchain technology offers the advantage of non-involvement of a third party and consequently the avoidance of a single point of failure. • We present a platform of blockchain and IPFS to store multimedia objects as transactions. • The proposed framework ensures multimedia availability, immutability, and transparency. • The proposed approach is fully distributed where copyright information can be verified. [ABSTRACT FROM AUTHOR]

Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an artificial intelligence-based intelligent system for earlier prediction of the disease using Ridge-Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely least absolute shrinkage and selection operator and ridge regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent Classifier is integrated with ridge regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-the-art machine learning algorithms such as support vector machine and logistic regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers. [ABSTRACT FROM AUTHOR]

The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The "thing" in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such "thing"s generates data, there is a chance of huge data traffic in the internet. This could be handled only by integrating "Internet of Everything (IoE)" paradigm with a completely diversified technology — Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy Efficient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data traffic in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased life-time and reduction in traffic. • Optimization of Energy in IoE using Wind Driven Optimization Algorithm. • Optimization of Cluster Head (CH) using Firefly Algorithm. • Reduction of data traffic to the Energy cloud. [ABSTRACT FROM AUTHOR]

Supporting visually impaired people during their navigation is a challenging task that involves localization, tracking, navigation, obstacle avoidance, and path guidance. Many researchers have experimented with Sonar, RFID, GPS, NFC, walking sticks, waist-based devices, and even computer vision modules for blind navigation. Using five sonar sensors for obstacle detection with direction and timestamp, we developed Sonar Glass. As humans see in left, right, front, top, and bottom directions based on eye angle and the head pose, the sonar glass is designed to provide obstacle information at the same angle. The head movement of the visually impaired person activates a pair of sensors for each module. Understanding human eye movement mechanisms and developing a synchronization protocol for each pair of visual sensors on sonar glass sitting on both eyes is the main goal of this paper. The major challenge lies in understanding and simulating the human vision mechanism and to realize how the field of Artificial Intelligence can be a contributor in producing technologies for the visually impaired. We are using log-polar transform to simulate human retinal image mapping. The Scale Invariant Feature Transform (SIFT) algorithm has also been implemented. It is the first time that both human eyes can be replaced by vision sensors. After comparing the estimated obstacle information from one sensor pair with that of the other sensors, the voice track is activated. The blind person uses the nearest obstacle information to avoid the obstacle and to extract spatial information about obstacles ahead of the user and provide an early warning. Unique in its design, the sonar Glass's synchronization protocol for a pair of related sensors provides possible object information in that direction. The executions were simulated in MATLAB and the results obtained in real time are found to be promising as the tests were carried out both indoor and outdoor. The sonar Glass design is unique of its kind and the synchronization protocol for the pair of related sensors provides the possible vision about the object information at that particular direction. [Display omitted] • Describes the need for a human-eye-based synchronisation protocol. • Human visual system is analyzed with the biological aspects of a normal human eye. • The concepts and technologies of eye tracker development are explained. • Includes experimental results that attempt to estimate human eye fixation duration. • Synchronize two sensors to produce artificial vision while human eyes observe an item [ABSTRACT FROM AUTHOR]

• Identified Blockchain implementation challenges are grouped into five main categories. • A two-phase fuzzy Delphi method is applied for challenges identification. • Fuzzy group-based BWM has been used to prioritize the considered challenges. • Various practical managerial insights are presented based on the results obtained. Blockchain as the trust‐enabling technology has supported and transformed cyber-physical systems in recent years. Therefore, many organizations have adopted blockchain-based technology to develop industrial software-product-service systems. However, the implementation of blockchain at the organizational levels can be related with several technical, organizational, legal, financial, and environmental challenges (intra-organizational and inter-organizational). Analysis of these challenges will play a significant role in organizational performance. This study has investigated an extensive list of challenges of software blockchain-based implementation in cyber-physical systems. The proposed comprehensive model is applied to the software development industry in Iran as a case study. Based on the experts' opinions, challenge identification was conducted via the two-phase fuzzy Delphi method for medium and large organizations. Extensive quantitative and qualitative challenges are evaluated via the efficient best-worst fuzzy method. Obtained results indicate that smart contracts vulnerabilities and deterministic execution of the technical challenge group have the most critical importance and governance of the legal challenge group has the least vital importance. It is mentionable that most technical challenges have higher priority than other considered challenges. [Display omitted] [ABSTRACT FROM AUTHOR]

Physiological measurements are an important parameter of a person's health. Therefore, we must take the information about our body's physiological measurements to remain updated with our health status. There are a huge number of patients and few physicians in this world. This is creating an imbalance in the health industry. To cope with the imbalance, there is a high need for an application that allows users to monitor their health in case of an emergency. This paper proposes novel algorithms and methods to measure a person's heart rate and blood pressure using photoplethysmography (PPG) signals. PPG signals are captured through the Android cell phone flashlight camera. The heart rates calculated from the android application are compared with the digital blood pressure and heart rate monitor medical device, we achieved an encouraging accuracy of nearly 98%. [Display omitted] • We have proposed a sliding window average based on heartbeat detection algorithms and architecture. • FFT-based decomposition of signals and finding local and global amplitude to detect the correct heartbeat signal. • Our proposed soluation is more accurate and faster than previous techniques to measure heartbeat and blood pressure. [ABSTRACT FROM AUTHOR]

Concrete privacy endeavours to give Confidentiality, Integrity, and Availability (CIA) measures to secure traffic streams in sensitive healthcare applications are a necessity. When talking about the access of sensitive healthcare data or the confidentiality of highly esteemed data, the first thing that comes to mind is that it should be secured. Sensitive healthcare data needs to be protected to restrict illegal access, exposure, and/or manipulation. As there is no such protection by which we can make our systems fully secure, the most acceptable methodology is to perform layered modelling of safety measures. This paper intends to provide a mathematical description of the Modular Encryption Standard (MES) and the augmentation of condition-centric risk monitoring of confidential information to provide a layered model for securing healthcare data confidentiality. Decision-making regarding the risk monitoring strategy of MES is augmented using a machine learning approach based on a Fuzzy Inference System amalgamated with Neural Networks. Result analysis shows that MES has less than a 0.005 error-rate and a 97% precision-rate, which elucidates its desideratum towards increasing security risks. [Display omitted] • Propose an approach for securing healthcare data confidentiality. • Provide mathematical modelling for the entire working of Modular Encryption Standard. • Enhance the decision-making strategy of Modular Encryption Standard using fuzzy logic. [ABSTRACT FROM AUTHOR]

Presently, lightweight devices such as mobile phones, notepads, and laptops are widely used to access the Internet throughout the world; however, a problem of privacy preservation and authentication delay occurs during handover operation when these devices change their position from a home mesh access point (HMAP) to a foreign mesh access point (FMAP). Authentication during handover is mostly performed through ticket-based techniques, which permit the user to authenticate itself to the foreign mesh access point; therefore, a secure communication method should be formed between the mesh entities to exchange the tickets. In two existing protocols, this ticket was not secured at all and exchanged in a plaintext format. We propose a protocol for handover authentication with privacy preservation of the transfer ticket via the Diffie–Hellman method. Through experimental results, our proposed protocol achieves privacy preservation with minimum authentication delay during handover operation. [ABSTRACT FROM AUTHOR]

Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person's movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%. [ABSTRACT FROM AUTHOR]

With the explosive advancements in smartphone technology, video uploading/downloading has become a routine part of digital social networking. Video contents contain valuable information as more incidents are being recorded now than ever before. In this paper, we present a comprehensive survey on information extraction from video contents and forgery detection. In this context, we review various modern techniques such as computer vision and different machine learning (ML) algorithms including deep learning (DL) proposed for video forgery detection. Furthermore, we discuss the persistent general, resource, legal, and technical challenges, as well as challenges in using DL for the problem at hand, such as the theory behind DL, CV, limited datasets, real-time processing, and the challenges with the emergence of ML techniques used with the Internet of Things (IoT)-based heterogeneous devices. Moreover, this survey presents prominent video analysis products used for video forensics investigation and analysis. In summary, this survey provides a detailed and broader investigation about information extraction and forgery detection in video contents under one umbrella, which was not presented yet to the best of our knowledge. [ABSTRACT FROM AUTHOR]

• To develop an efficient network slicing with three phases. • To collect the relevant data attributes of network slicing. • To optimize the weight function GS-DHOA algorithm. Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like "user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type." Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like "eMBB, mMTC, and URLLC" for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing. [ABSTRACT FROM AUTHOR]