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Abstract This research tries to establish the emissions diffusion model of high-speed motor vehicles on the Capital Ring Expressway (CRE) around Beijing, China, applying the MOVES and the CALPUFF models. Data required for this study have been collected from different sources. The results show that small buses and large trucks have the highest pollutant emissions on the CRE. The pollutants like CO and HC mainly comes from gasoline vehicles, while NOx, PM10 and PM2.5 comes from diesel vehicles. The average concentrations of the diffusion of CO, NOx, CH4, PM10, and PM2.5 along with the CRE are 6.5 mg/m3, 2.3 mg/m3, 0.27 mg/m3, 0.055 mg/m3 and 0.056 mg/m3 respectively. The results of this study have practical significance and influence on the emission reduction and concentration control strategies under different circumstances, and put forward suggestions on changing transportation modes, strengthening supervision and so on. It has produced a number of recommendations for the policy makers including (1) active promotion of new energy vehicles on expressways, (2) strategic change of the mode of passenger and freight transportation on expressways, (3) strengthen traffic management and supervision of motor vehicles on expressways, and (4) strengthen vegetation protection in rural and ecologically sound areas along the route.

Abstract Background Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. Materials and methods The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. Results Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). Conclusion This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient’s CVD prognosis.

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network.

IntroductionGlobal Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.MethodsThis paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.ResultsOn the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.DiscussionThese findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model’s use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

As an eco-friendly land management strategy, agroforestry can combine farming, forestry, and livestock to provide an assortment of ecosystem services (ES). Piper Chaba- based agroforestry system (PCAFS), a prevalent agroforestry system in northern Bangladesh, shows promise for environmental protection and diverse ES, yet lacks sufficient literature-based support. Addressing this discrepancy, the study surveyed 105 Piper chaba farmers from the Chinai union of Rajarhat upazila in the Kurigram district of Bangladesh to assess the PCAFS's present potentials in providing ecosystem services and environmental benefits as well as the potential conservation initiatives that could be adopted to sustain this agroforestry system. Data were collected using mixed-method techniques including direct observation, surveying through semi-structured questionnaires, key informant interviews, and focus group discussions before data triangulation. The study identified twenty common PCAFS combinations, having betel nut-Piper chaba-banana (67.62%), betel nut-Piper chaba-papaya (49.52%), and mango-Piper chaba-paddy (48.57%) as the most prominent. As indicated by the Ecosystem Improvement Index (EII) derived from farmer perceptions, PCAFS significantly enhanced various types of ecosystem services (ES). PCAFS played a vital role in provisioning services by supplying diversified products. It significantly improved air and water quality as well as reduced soil erosion, ranking second and third among the ecosystem services. EII identified the most significant overall improvement as the enhancement of soil fertility, a crucial supporting service delivered by PCAFS. The study revealed that, according to the opinion of the respondents, PCAFS positively influenced various aspects critical to environmental functioning through its contribution to ecosystem services. This agroforestry system excelled in key areas like cost-efficiency, product diversity, and livelihood enhancement. However, it faced significant challenges, notably disease outbreaks and a fragile market system. To address these weaknesses, eight inclusive strategies were pinpointed, with the most critical involving the establishment of a robust market system and the formation of farmers' collectives. The study recommends enhancing PCAFS utilization by investing in more research, development, and oversight to maximize its ecosystem benefits.

Data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective compression algorithm for sensor data. However, implementing the algorithm at IoT edge nodes is hindered due to memory limitations; HCA requires a large amount of memory to construct a Huffman tree to compress data. To address this issue, this paper proposes a new lossless Huffman Deep Compression (HDC) algorithm that incorporates the sliding window technique to fit in memory, reduces the complexity of the Huffman tree using deep learning pruning and pooling techniques, and uses pattern matching with pattern weights instead of using symbol matching and symbol frequencies in HCA. This paper introduces a sliding window approach to minimize memory usage, leveraging pattern matching and weights for higher compression and employing deep learning techniques to reduce the Huffman tree size through pruning and pooling. Experiments were performed using the Esp8266 MCU IoT node on eight numerical attributes from sensors of six of Malaysia’s air pollution station datasets. The findings demonstrate that the HDC algorithm has substantially reduced data size (p-value

Twitter is a useful tool for effectively tracking and managing crisis-related incidents. However, due to many irrelevant features in textual data, the problem of high dimensionality arises, which eventually increases the computational cost and decreases classification performance. Thus, to handle such a problem, this work presents a Spark-based hybrid binary Bat (BBA) and binary Salp swarm algorithm (BSSA) named SBBASSA for feature selection and classification of crisis-related tweets. In the proposed technique, the hybridization of standard BBA and BSSA algorithms is performed to enhance their exploration capabilities, then the combined algorithm is implemented in parallel using Apache Spark framework to reduce the overall execution time during the feature selection process. A support vector machine (SVM) classifier is applied during the wrapper-based feature subset selection and classification. The performance of the proposed SBBASSA was analyzed on six benchmark crisis tweet datasets, namely Hurricane Sandy, Boston Bombings, Oklahoma Tornado, West Texas Explosion, Alberta Floods, and Queensland Floods, and then compared with standard BSSA, BBA, and binary particle swarm optimization (BPSO). Results showed that SBBASSA performed competently in the feature selection and classification, outperformed other algorithms in crisis tweet classification, and achieved the highest accuracy with the lowest feature set in a reduced execution time.

The Internet of Things (IoT) represents a dynamic infrastructure, leveraging sensing and network communication technology to establish ubiquitous connectivity among people, machines, and objects. Due to its end devices’ limited computing resources and storage space, it is not feasible to merely transpose traditional internet security technologies directly to IoT endpoints. Maintaining security while concurrently ensuring performance is a particularly challenging endeavor. This paper provides a review of key agreements and authentication protocols pivotal to the security of IoT. First, this survey discusses the applications that need authentication and key agreement to strengthen their security and current research on these application fields. Subsequently, this paper engages in an in-depth exploration of the phase involved in the scheme of authentication and key agreement, including an examination of the cryptographic techniques employed within these processes. This survey also thoroughly studies the scheme’s security services, potential attacks, formal analysis and informal analysis to ensure resilience against such threats. This study aims to provide a profound understanding of the recent research on authentication and key agreement in IoT applications. It strives to contribute towards strengthening security systems for IoT applications, ensuring their sustainability in the face of evolving threats.

In contemporary society, the proliferation of online hateful messages has emerged as a pressing concern, inflicting deleterious consequences on both societal fabric and individual well-being. The automatic detection of such malevolent content online using models designed to recognize it, holds promise in mitigating its harmful impact. However, the advent of “Hateful Memes” poses fresh challenges to the detection paradigm, particularly within the realm of deep learning models. These memes, constituting of a textual element associated with an image are individually innocuous but their combination causes a detrimental effect. Consequently, entities responsible for disseminating information via web browsers are compelled to institute mechanisms that regulate and automatically filter out such injurious content. Effectively identifying hateful memes demands algorithms and models endowed with robust vision and language fusion capabilities, capable of reasoning across diverse modalities. This research introduces a novel approach by leveraging the multimodal Contrastive Language-Image Pre-Training (CLIP) model, fine-tuned through the incorporation of prompt engineering. This innovative methodology achieves a commendable accuracy of 87.42%. Comprehensive metrics such as loss, AUROC, and f1 score are also meticulously computed, corroborating the efficacy of the proposed strategy. Our findings suggest that this approach presents an efficient means to regulate the dissemination of hate speech in the form of viral meme content across social networking platforms, thereby contributing to a safer online environment.

The Internet of Things (IoT) is transforming how we live and work, and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail. Considering its wide range of applications, addressing the security challenges arising from IoT devices’ massive collection and transmission of user data is vital. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research needs a comprehensive summary of deep learning-based IDS in IoT. This paper introduces intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security.

Swarm intelligence (SI) is the collective behavior of several intelligent agents (IAs), and unmanned aerial vehicles (UAV) or drones are widely used as IA in SI, of which UAV-based decentralized networks are one. Since the UAV can move in different directions without any surface in the 3D environment, the problem of UAV-to-UAV (UAV2UAV) based interaction is observed in UAV-based SI processes. In a UAV-based network, each UAV needs to follow the 3D flying pattern, and if there is any problem in determining the 3D position pattern, the network loses its arrangement, which may cause chaos in the entire network system. 3D movement of biological animal is observed based on these six relative directions: right, left, up, down, back, and front. The aim of this study is to establish an IA-enabled interaction based on the mentioned 6 relative directions in the UAV-based SI process to overcome the raised issues. First, the article presents various relative directional 3D positions of a UAV and then proposes a novel UAV-to-UAV Relative Directional Interaction (UAV2UAV-RDI) model. Here, two different algorithms are performed on two different UAVs to execute an optimization-based evolutionary computation for detecting and tracking the relative directional 3D position of a UAV. Consequently, according to the findings of this research, the interaction between two UAVs occurs and one UAV can track the relative directional 3D position of the other UAV. The performance of the proposed model is compared with the three models where the proposed model performed more directionally. Additionally, we also point out the limitations of the proposed model for future improvements.

Energy-outlook from past to future specific years has become essential in energy-economy. Malaysia is a member of ASEAN (Association of South-east Asian Nations), and ASEAN is increasing the use of renewable energy by 2050 to reduce carbon dioxide (CO2) emissions. Coal is Malaysia's primary fossil fuel for energy generation, producing large amounts of CO2 and exacerbating greenhouse problems, including air pollution. Moreover, renewable energy capacity has increased significantly worldwide since 2020 during the COVID-19 pandemic. Therefore, this study provides an energy-outlook study from 1990 to 2050 as a rationale for Malaysia's emphasis on sustainable energy in the post-COVID-19 years, which has been felt to be absent in previous studies. Here, past data as well as projections of future coal trade, hydropower growth, electricity consumption, renewable energy consumption, and CO2 emissions are shown. An energy-outlook procedure is explored by reviewing the literature to derive data visualizations and projections. Business-as-usual (BAU) and Alternative-policy Scenarios (APS) methods have been used for projections. The article prioritizes coal since coal is the main fossil fuel for electricity generation in Malaysia. The study observed a significant increase in CO2 emissions with increasing energy demand in Malaysia, which is a concern, and the article emphasized the importance of renewable energy in mitigating CO2 emissions. Finally, Long-short-term memory (LSTM) has been used as an initial experiment on how Artificial Intelligence (AI) can be used in the energy outlook study, which indicates the research scope of AI.

Abstract Network security protocols are implemented to address network security challenges. Computer networks and applications have advanced and developed significantly in recent years, but consumers ‘excitement for network technology and high‐tech devices has been dampened by continual exposure to data security vulnerabilities. As of now, some individuals refuse to use smart devices due to concerns about the authenticity, confidentiality and integrity of data security leaks. This not only prompts Internet service providers to follow market protection mechanisms but also requires software developers to apply appropriate security protocols to protect computer network security. These applications’ dependability and integrity are dependent not just on the effectiveness of cryptographic algorithms, but also on key management protocols. Understanding network security protocols and implementing high‐quality standards to govern the transmission of data in the network are critical components of guaranteeing network security. The article explores data security, primarily at the application layer, various attack methods for different network security protocols and highlights the potential security implications. The study also looks at the corresponding, practical security measures and future research prospects for certain kinds of attacks. Finally, some technical challenges that remain unsolved at the time of writing are summarised, and future trends in cybersecurity are discussed.

A cloud computing-based power optimization system (CC-POS) is an important enabler for hybrid renewable-based power systems with higher output, optimal solutions to extend battery storage life, and remotely flexible power distribution control. Recent advancements in cloud computing have begun to deliver critical insights, resulting in adaptive-based control of storage systems with improved performance. This study aims to review the recently published literature on the topic of power management systems and battery charging control. The role of intelligent based cloud computing is to improve the battery life and manage the battery state of charge (SoC). To achieve this purpose, publishers’ databases and search engines were used to obtain the studies reviewed in this paper. We identify and review the purpose, achievements, tools/algorithm, and recommendations for each survey work, and thus outline a number of key findings and future directions. Furthermore, the review includes a listing of novels and recently used algorithms. Additionally, a critical review of 174 research articles were analyzed as per Web of Science (WoS) and Scopus database. The key findings of this study are discussed in two key conceptual frameworks that contain a power optimization system and an optimal battery management system. The power transmission, distribution, and charge and discharge processes are controlled and stored on cloud computing using the power mix between hybrid renewable energy and other power sources.

Today, social media sites like Twitter provide effective platforms to share opinions and thoughts in public with millions of other users. These opinions shared on such sites influence a large number of people who may easily retweet them and accelerate their spread. Unfortunately, some of these opinions were expressed by extremists who promoted hateful content. Since Arabic is one of the most spoken languages, it is crucial to automate the process of monitoring Arabic content published on social sites. Therefore, this study aims to propose a hybrid technique to detect extremism in Arabic social media texts and articles to monitor the situation of published extremist content. The proposed technique combines the lexicon-based approach with the rough set theory approach. The rough set theory is employed with two approximation strategies: lower approximation and accuracy approximation. The hybrid technique used the rough set theory as a classifier and the lexicon-based as a vector. Furthermore, this study built three types of corpuses (V1, V2, and V3) collected from Twitter. The experimental findings show that among the proposed hybrid methods, the accuracy approximation was superior to the lower approximation with seed vector. It was also revealed that hybrid methods outperformed machine learning techniques in terms of efficiency. Moreover, the study recommends using an accuracy approximation method with seed vector to identify the polarity of the text.

Smart grid system is evident with control technologies and digital communications systems. The cyber–physical system is a critical infrastructure connected with complex drives and devices. The modern smart grid’s prominent communication standard adopts high scalability, supports numerous communication devices’ input/outputs, and has multi-vendor interoperability. Cyber-attack leads to an unstable power grid system and enormous economic loss. Throughout different cyber-attack, distributed denial of service attack is mostly destructive to smart grid infrastructure. This paper investigates smart grid cyber security systems and the communication vulnerabilities of other communication protocols. A comprehensive study on distributed denial of service attack techniques and detection approaches is present. Finally, examine a new hybrid machine learning-based distributed denial of service attack detection technique for a sustainable smart grid system.

Smart grid networks face several cyber-attacks, where distributed denial-of-service (DDoS) attacks distract the grid network. The synchrophasor technique protects the wide-area measurement system (WAMS) from the complex problem and addresses different issues in a grid. The DDoS attack detection strategy is complicated due to attack complexities, vendor specifications, and communication standard protocols. Attacker target phasor measurement unit (PMU) data on the phasor data concentrator (PDC) database in WAMS. However, during the cyber-attack, the framework ensures the end application uses the normal PDC datastream. The proposed attack detection technique efficiently verifies PMU-generated data in WAMS. However, numerous machine learning algorithms are used to detect DDoS attacks, but the best detection model is still given open choices. The motivation of this study: (a) which machine learning algorithm will be suitable for DDoS attack detection and (b) what would be the accuracy of training algorithms. This study presents a machine learning-based hybrid technique that achieves 83.23% accuracy. Python compiler is used to execute the proposed model, and the result shows that the proposed detection approach efficiently improves the DDoS attack detection accuracy.

Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation.

Logistics Performance (LP) is one of the fundamental catalysts that serve as a podium for the integration of the world economy. This study is conducted to observe the combined effects of Liner Shipping Connectivity (LSC) and Global Competitiveness Index (GCI) on LP in mediating the Quality of Port and Infrastructure (PORT). We selected 28 Asian economies and 1 special administrative region (Hong Kong) counting the year of 2007–2018. Partial Least Square – Structural Equation Model (PLS–SEM) with an extension of the Importance–Performance Map Analysis (IPMA) was applied. Empirical evidence derived from the path diagram has revealed that LSC and global competitiveness in mediating the PORT have significant effects of accelerating LP, leading to higher competitiveness in terms of strategic development and yielding better connectivity. Due to limited resources, Asian decision-makers need a guidance to focus on how to improve LP. This work also helps to highlight the dimensions of LSC and global competitiveness factors to be concentrated on and the policies to be implemented in this regard.

Abstract In this letter, an optimal arrangement of the Light‐Emitting Diodes (LED) for an indoor Visible Light Communication (VLC) setup is presented by using the simulation model. The idea is to get the maximum angle of covering the area of the LED as an access point during user mobility in keeping the connectivity between the user and the access point. As a result, an analysis of data rate measurement is discussed using the maximum angle rotation of the LED as an access point. The authors present validation simulation results for a Line‐of‐Sight configuration. The authors demonstrated that the angle change movement can be carried out efficiently. Results suggest that the directivity at the transmitter can enlarge the area region of the transmitter and user mobility.

Intrusion detection system (IDS) is a combination of software application and hardware devices that monitors the network and filters activities for malicious or unauthorized access attempts. IDS are deployed for generating large volumes of stream data, which can be challenging to identify relevant features and reduce false positives. Feature selection is a candidate solution for preserving only relevant features and filtering out non-relevant features in IDS. The feature selection is performed using multi-objective optimization based meta-heuristic searching algorithms (MOO-MHS), minimizing two objective functions: error rate and memory usage. Traditional FS has limited suitability due to the stream nature of IDS data and the occurrence of concept drift. To solve this challenge, the MOO-MHS is promising to be updated to support dynamic feature selection (DFS) for serving IDS. Therefore, this paper proposes a novel framework for DFS in IDS using MOO-MHS. The proposed framework is an Enhanced Dynamic Filter-Based Feature Selection (E-DFBFS) that enables periodic call of non-dominated sorting genetic algorithm 2 (NSGA-II) that provides a novel solution selection algorithm using two selection types, namely, mean and median. The performance of the proposed E-DFBFS framework is compared with existing state-of-the-art benchmarking algorithms considering both synthetic and real-world data, providing superiority over DFBFS in terms of accuracy and F-measure and most classification metrics.’

Ensuring the security of critical Industrial Internet of Things (IIoT) systems is of utmost importance, with a primary focus on identifying cyber-attacks using Intrusion Detection Systems (IDS). Deep learning (DL) techniques are frequently utilized in the anomaly detection components of IDSs. However, these models often generate high false-positive rates, and their decision-making rationale remains opaque, even to experts. Gaining insights into the reasons behind an IDS’s decision to block a specific packet can aid cybersecurity professionals in assessing the system’s effectiveness and creating more cyber-resilient solutions. In this paper, we offer an explainable ensemble DL-based IDS to improve the transparency and robustness of DL-based IDSs in IIoT networks. The framework incorporates Shapley additive explanations (SHAP) and Local comprehensible-independent Clarifications (LIME) methods to elucidate the decisions made by DL-based IDSs, providing valuable insights to experts responsible for maintaining IIoT network security and developing more cyber-resilient systems. The ToN_IoT dataset was used to evaluate the efficacy of the suggested framework. As a baseline intrusion detection system, the extreme learning machines (ELM) model was implemented and compared with other models. Experiments show the effectiveness of ensemble learning to improve the results.

Parkinson’s Disease (PD) in the set of neuro-degenerative disorders stimulates due to the loss of dopaminergic neurons from the substantia nigra. Electroencephalogram (EEG) signals are being extensively utilized for diagnosing PD. The existing approaches extract the features using various frequency transformations that lose valuable signal information. An optimized Deep Convolutional Neural Network (CNN) inspired by the encoder part of U-Net architecture is proposed for classifying PD incorporating the resting electroencephalogram (EEG) signal dataset. The proposed model follows the U-Net architecture for extracting the features from the signals. The EEG recordings are taken from two datasets: the University of Mexico (UNM) EEGs and the University of California San Diego (UCSD) resting state dataset. The EEGs are pre-processed with a basic pre-processing pipeline, then separated into single channels, plotting each channel as a simple graph. These graphs are then fed to the proposed 23-layered convolutional neural network (CNN) for classifying PD from the normal control. Consequently, the model achieved maximum values of 93.10%, 93.18%, 93.09%, and 0.9313 of accuracy, precision, recall, and F1-score respectively for the UNM dataset, whereas, 97.90%, 98%, 97.87% and 0.9794 of accuracy, precision, recall, and F1-score respectively for UCSD dataset. The results show improved scores compared to the individual Machine Learning and CNN models applied on the same datasets.

One of the key goals of Informetrics is to identify citation-based popular articles among so many other aspects, such as determining popular research topics, identifying influential scholars, and predicting hot trends in science. These can be achieved by applying network science approaches to scientific networks and formulating the problem as a popular (most-cited) node ranking task. To rank the papers based on their future citation gain. In this work a deep learning based framework is proposed. Which helps in automatic node level feature extraction and can make node level prediction in dynamic graphs such as citation networks. To achieve this we have learned global ranking preserve d dimensional node embedding. We have only considered temporal features, which makes it suitable for generalisation to other networks. Although our model can consider node level explicit features also. Further we have given novel cost function which can be easily solve ranking problem for dynamic graphs using probabilistic regression method. Which can be easily optimised. Another novelty of our work is that our model can be trained using different snapshots of the graph and different time. Further trained model can be used to make future prediction. The proposed model has been tested on an arXiv paper citation network using six standard information retrieval-based metrics. The results show that our proposed model outperforms, on average, other state-of-the-art static models as well as dynamic node ranking models. The outcome of this research study leads to informed data-driven decision-making in science, such as the allocation and distribution of research funds and investment in strategic research centers. When considering past time window size as 10 months and making prediction after 10 months our proposed model’s performance on various ranking based evaluation metrics are as follows: AUC-0.974, Kendal’s rank correlation tau-0.455, Precision- 0.643, Novelty-0.0456, Temporal novelty-0.375 and on NDCG-0.949. Our model is able to make long term trend prediction with just training on short time window.

The linguistics related research and particularly, sentiment analysis using data-driven approaches, has been growing in recent years. However, the large number of users and excessive amount of information available on social media, make it difficult to detect extremism text on these platforms. The literature revealed a plethora of research studies focusing the sentiment analysis primarily, for English texts, however, very limited studies are available concerning the Arabic language which is the 4th mostly spoken language in the world. We first time in this study, propose a text detection mechanism for extremism orientations distinction in Arabic language, to improve the comprehension of subjective phrases. The study introduces a novel method based on Rough Set theory to enhance the accuracy of selected models and recognize text orientation reliably. Experimental outcomes indicate that the proposed method outperforms existing algorithms by contributing towards feature discriminations. Our method achieved 90.853%, 81.707% and 71.951% accuracies for unigram, bigram, and trigram representations, respectively. This study significantly contributes to the limited research in the field of machine learning and linguistics in Arabic language.

Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area.

One of the big challenges of our modern life is to find the right items or contents on the Internet and particularly in social media. One way of addressing the information overload problem in social media is to predict the future trends and popularity of online items. The popularity of an item can be measured by its attractiveness, i.e., the number of times it is being used. This popularity prediction can be translated to a link prediction and ranking problem, which aims to predict the link gain of the items in a user-item interaction network. User-item interactions in an online environment can be modelled as a bipartite network, where a link represents an event, reflecting a user buys or collects an item. Popularity prediction problem in temporal bipartite networks is of great interest to researchers. In this study, we propose a heuristic based model which only consider nodes collective link gain in a recent past time window of time as well as total link gain. To evaluate our model’s efficiency, we tested them on co-evolving social media items. We also evaluated the models’ performance on five information retrieval metrics (i.e., Area Under the Receiver Operating Characteristic, Kendall’s rank correlation tau, Precision, Novelty, and temporal novelty). The proposed model does not need hyper-parameter learning, which makes it the best choice for highly temporal and data streaming scenarios.

Electronic Health monitoring system has performed an essential role in managing healthcare monitoring. E-health can provide effective and valuable facilities for the patients to monitor. Though, there are protection disputes in the current E-Health system. The current e-health system, on the other hand, has security issues. Malevolent doctors may work together with cloud Storage Service Providers (CSPs) to interfere with patients' electronic health records (EHRs) or promptly leak EHR matter to other enemies for income. (EHRs). The malevolent doctors may conspire with the Patient Healthcare Monitoring Service Provider (PHMSP) to manipulate with the patients'. For profit, EHRs or directly divulge the EHR content of EHRs to other opponents. Block-chain has recently appeared as one of the most powerful methods in the protection and secrecy fields. It is assumed to be the promised security approach that will eventually replace the security challenges in existing e-health monitoring systems. Encryption in blockchain refers to technical methods that make accessing encrypted data difficult for unauthorized resources. This research proposed a blockchain-based encryption framework to provide security-based solutions using a computational intelligence methodology. The proposed approach provides better results in terms of 0.93 in the training phase and 0.91 in the validation accuracy.

Robots are most widely used to replace human contribution with machine generated response. When humans interact with robots, its mandatory for both to forecast actions based on current conditions. Huge efforts have been channelized towards attaining this perfect coordination. To decipher complex environments, the inference of robotic mobility and alteration of random unstructured scenarios is a complicated task in the field of visual processing and imaging. To address this issue, a new Vision-Based Interaction Model based on deep neural networks has been suggested. The proposed model solves the error amplification issue by the application of past inputs through features as reposed by a Deep Belief Network (DBN). In addition, a novel Vision-Based Robotics Learning model is also proposed for scene understanding and recognition using deep neural network understanding. Moreover, a vision theory-based smart learning algorithm is also suggested to decide positive possible outcomes.Therefore, the model is capable of using object motions to extract relevant information used for Turning, Griping and object mobility.To validate the suggested model, a number of experiments have been performed on benchmark datasets and it showed a higher performance as evaluated against some of the niche methods.

In this paper, an active equalization circuit based on resonant converter is presented. This equalization circuit has been proposed to equalize the direct cell-to-cell voltage in a string. All electrochemical energy storage devices are connected in series. Using this equalization circuit energy transfer from higher energy and charge capacitive cell to lower energy and charge cell in the string. All MOSFET switches are operated by complementary Pulse-Width Modulation (PWM) signal. The working principle of this equalization circuit like that of a switches-capacitor equalization circuit. In this circuit, a single Inductor (L) capacitor (C) energy carrier and bidirectional low voltage MOSFET switches are used so that it can recover maximum energy, reduce conduction loss, and improve the switching loss drawback, reduce the equalization time duration between two cells and achieved zero voltage gap. This equalization circuit is bidirectional and operates under the three modes, namely, charging, discharging, and relaxation mode. The proposed circuit details, the working principle, and the mathematical analyzes are presented. The simulation results are validated by the demonstration of an experimental prototype hardware testbed. The experimental results based on lithium-ion, lead–acid, and super-capacitor are presented. This equalization circuit is low cost and occupies little space with efficiencies of 96%, 94.2%, and 84.43% for Li-ion, lead–acid and super-capacitor respectively.

Two-dimensional object detection techniques can detect multiscale objects in images. However, they lack depth information. Three-dimensional object detection provides the location of the object in the image along with depth information. To provide depth information, 3D object detection involves the application of depth-perceiving sensors such as LiDAR, stereo cameras, RGB-D, RADAR, etc. The existing review articles on 3D object detection techniques are found to be focusing on either a singular modality (e.g., only LiDAR point cloud-based) or a singular application field (e.g., autonomous vehicle navigation). However, to the best of our knowledge, there is no review paper that discusses the applicability of 3D object detection techniques in other fields such as agriculture, robot vision or human activity detection. This study analyzes both singular and multimodal techniques of 3D object detection techniques applied in different fields. A critical analysis comprising strengths and weaknesses of the 3D object detection techniques is presented. The aim of this study is to facilitate future researchers and practitioners to provide a holistic view of 3D object detection techniques. The critical analysis of the singular and multimodal techniques is expected to help the practitioners find the appropriate techniques based on their requirement.

Abstract Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. Therefore, researchers have deliberated the privacy and security solution of 5G users. The 5G wireless network offers a futuristic concept that helps to solve challenges affecting previous communications generations. The key concern to many scholars in the field of mobile networking is user privacy, which is long‐term subscription identifier as International Mobiles Subscribers Identifiers (IMSIs) and short‐term subscription identifier as Temporary Mobiles Subscribers Identifiers and Cell‐Radio Networks Temporary Identifiers (TMSIs and C‐RNTIs), which are used for permanent identifying, paging, and location update. This article investigates the existing literature survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies that handle user identifications in authentication, paging, and location update. This article discusses the various privacy issues in the 5G network that use IMSI in clear text or temporary identities such as TMSI & C‐RNTI with IMSI to disclose user identity privacy. This article also investigates the existing literature on user identity and location privacy and highlights the key parameters, issues, challenges, and future recommendations with potential solutions.

Abstract The Internet of Vehicles (IoV) and Vehicle‐to‐Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud‐radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high‐speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi‐layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

Abstract The recent advancements of Internet of Things (IoT) embedded systems, wireless networks, and biosensors those have assisted in the rapid development of implanting wearable sensors are reviewed here. The applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services are also reviewed here. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain‐based approaches that can help in improving the confidentiality of IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Despite multiple security threats, there are novel cryptographic techniques, such as access control, identity authentication, and data encryption that can help in improving the security and reliability of IoMT devices.

Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel log ratio change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: “positive change”, “no change”, and “negative change” using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92% in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.

A significant aspect of today’s digital information is attributed networks, which combine multiple node attributes with the basic network topology to extract knowledge. Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in several high-impact areas. Most current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. However, they suffer from the problem of ignoring the latent codes’ embedding distribution, which results in poor representation in many instances. In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, like vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed. As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy. They are trained jointly as an end-to-end framework. DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters. The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization. Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach. The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.

In the 21st century, any pandemic especially, SARS-COV-2 is a global burden due to high incidence, mortality and mutation rate. Although several techniques have already been identified to control the pandemic or treat patients and adverse impact on mental health, relatively only fewer peoples have little concern about finding effective mitigation strategies to improve mental health. Therefore, this study aimed to find some common and unique approaches to manage mental health during a pandemic. Some strategies for the better management of mental health induced by SARS-COV-2 are required for all classes of peoples. Early management is vital, and those must be associated with frontlines and people who are staying at home, particularly in isolation centers and already identified as active cases. Experts point out the need to pay specific attention to proper daily life. To manage abnormal mental conditions during the pandemic, exercise, meditation and psychological motivation with adequate diet and sleep have significant roles in regulating some biological mechanism, incredibly immune, hormonal, and neural process. Social media can also treat mental health and improve satisfaction level by sharing reliable or authentic news. Management of mental health is mandatory for all at the time of the SARS-COV-2 pandemic. We can consider all of the above mentioned strategies to treat mental health during and after the COVID-19 pandemic condition. [ J Adv Biotechnol Exp Ther 2021; 4(3.000): 276-289]

Abstract Wireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.

Utilities produce and supply products following local requirements and with the synchronizations which connect subscribers. Harmonics is a power efficiency/quality variable caused by electronic devices that domestic and industrial consumers use. The famous IEEE Standard 519 is maintained to calculate harmonic limits, which ensures power efficiency. In a standard power system, currents and voltages generate pure sine wave signals during regular operations. As harmonics influence the power system, they cause interference in the sine wave signals. So, the best practice method should be used to resolve the harmonics issue. One of the problem-solving techniques of harmonics is the measurement and reduction of harmonics detection, and it uses Fast Fourier Transform (FFT). Therefore, power output should assess in a peer-to-peer Blockchain scheme by measuring and minimizing harmonics detection. This paper uses a Shunt Active Power Filter (SHAPF). It describes the simulation analysis and reduction of harmonics detection in a peer-to-peer interconnected 3-phase power system with the help of an FFT algorithm. This research was carried out to assess the efficiency of the AC signal by collecting, processing, and evaluating power data. Using the shunt active filter, the proposed design outperformed the traditional methods for both six and twelve pulse rectifiers, achieving total harmonic distortion (THD) of only 1.42% and 0.92%, respectively.

This study investigates the composition of and preferences by farmers related to trees and crops planted in agroforestry systems, and their role on the livelihood of tribal farmers in a tropical moist deciduous forest in Tangail, Bangladesh. Data was collected from 150 tribal farmers practicing different types of agroforestry systems in Madhupur Sal forest, using a mixed-method strategy that included a survey, focus group discussion, key informant interviews, and direct observation. According to the results, tribal farmers used a total of 22 trees and 33 crop species in their existing agroforestry systems, indicating a rich composition and high diversity. Acacia auriculiformis was the most common tree species (with 82% of farmers possessing this species), followed by Mangifera indica (75%), Acacia sp. (73%), and Gmelina arborea (54%). Interviews revealed that agroforestry systems have provided numerous benefits and greatly enhanced farmers’ livelihoods through better access to food, timber, fodder, and fuelwood and greater access to livelihood capitals (except social capital). Though agroforestry practices increase species diversity, provide economic returns, and help farmers maintain their livelihoods, tribal farmers face several constraints including bureaucracy and a lack of alternative market facilities. Our study can be of interest for future policy interventions focusing on sustainable reforestation practices, how to solve the problems faced by the farmers, and livelihood improvement in Bangladesh.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

The use of advanced technologies has increased drastically to maintain any sensitive records related to education, health, or finance. It helps to protect the data from unauthorized access by attackers. However, all the existing advanced technologies face some issues because of their uncertainties. These technologies have some lapses to provide privacy, attack-free, transparency, reliability, and flexibility. These characteristics are essential while managing any sensitive data like educational certificates or medical certificates. Hence, we designed an Industry 5.0 based blockchain application to manage medical certificates using Remix Ethereum blockchain in this paper. This application also employs a distributed application (DApp) that uses a test RPC-based Ethereum blockchain and user expert system as a knowledge agent. The main strength of this work is the maintenance of existing certificates over a blockchain with the creation of new certificates that use logistic Map encryption cipher on existing medical certificates while uploading into the blockchain. This application helps to quickly analyze the birth, death, and sick rate as per certain features like location and year.

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.

The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques.

Timing synchronization has a vital role in swarm drones’ network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones’ network, master drones’ (MD) neighbor drone’s timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.

This study presents a novel multilevel inverter structure that can operate in both switched capacitor and asymmetric DC source modes. In the first mode, it can produce seven-level output voltage employing two switched capacitors and one single DC supply. The five-level output voltage is produced while operating the second mode. The voltage ratio between the input and output voltage for the capacitor mode is 1:3 (triple voltage gain). During the first mode, the capacitor of the inverter is self -balanced whereas the inverter can produce higher voltage output in the DC source mode. The proposed inverter reduces the total standing voltage in both modes of operations as it can generate the output voltage without requiring any additional H-bridge circuit. The feasibility and predominate features of the proposed inverter have been established by comparing with existing topologies in terms of power components count. Results obtained from this study are validated using simulation employing sinusoidal pulse width modulation (SPWM). A hardware prototype has also been developed for further validation.