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Achieving precise localization in industrial settings presents significant challenges due to dynamic movements, complex layouts, and harsh environmental conditions that cause signal interference and reflections. This requires developing advanced indoor positioning systems that can handle these challenges and perform reliably even in the presence of dynamic movement. In this paper, a novel LoRa-based indoor positioning system designed for dynamic motion in industrial environments is presented. The proposed system integrates LoRa technology with a fingerprinting approach that involves fingerprint collection using the constant motion method and leverages a two-layer Deep Gaussian Process Regression (DGPR) model to overcome the non-linearity characteristics of signal propagation. Through testing on static and motion datasets, it was observed that collecting data in motion yields superior results for tracking dynamic objects. Furthermore, temporal-based enhancements like Temporal Weighted RSSI Averaging and Kalman filtering were introduced. These techniques effectively mitigate RSSI temporal variations and improve the reliability of position estimates. The experimental results, conducted in a real industrial environment, demonstrate that the proposed system achieves a mean positioning error of 1.94 meters and a 90th percentile error of 3.28 meters. These findings highlight the potential of combining LoRa technology with advanced machine learning algorithms and filtering techniques to achieve precise and reliable indoor tracking.

In industrial processes, Electrical Capacitance Tomography (ECT) is a vital non-invasive imaging technique for monitoring multiphase flows. As the speed of flow increases, as for high-speed chemical reactors, it becomes crucial to enhance the acquisition speed to ensure the scan period for one frame is significantly shorter than the time it takes for the fluid to traverse the sensor length. Traditional ECT systems employ a uniform charge integration time across all electrode pairs. To address this challenge without compromising image accuracy and sensitivity, we propose an adaptive integration technique for inter-electrode capacitance measurement. This approach adjusts the charge integration time based on the distance between electrodes, with longer integration times for farther electrode pairs. By doing so, we aim to enhance measurement accuracy while maintaining efficient data acquisition speed. The proposed method is expected to significantly improve the fidelity of ECT images and the overall performance of ECT systems in real-time industrial applications, contributing to better monitoring and control of multiphase flows. Simulations and experimental validation in a high-speed fluid flow demonstrate that our method improves image visibility while maintaining suitable acquisition speeds. The adaptive technique is computationally efficient and scalable to various ECT configurations, promising broader applications in industrial process control and efficiency optimization.

Optimizing machine learning (ML) model performance relies heavily on appropriate data preprocessing techniques. Despite the widespread use of standardization and normalization, empirical comparisons across different models, dataset sizes, and domains remain sparse. This study bridges this gap by evaluating five machine learning algorithms- Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost)- on datasets of varying sizes from the business, health, and agriculture domains. This study assessed the models without scaling, with standardized data, and with normalized data. The comparative analysis reveals that while standardization consistently improves the performance of linear models like SVM and LR for large and medium datasets, normalization enhances the performance of linear models for small datasets. Moreover, this study employs SHapley Additive exPlanations (SHAP) summary plots to understand how each feature contributes to the model’s performance interpretability with unscaled and scaled datasets. This study provides practical guidelines for selecting appropriate scaling techniques based on the characteristics of datasets and compatibility with various algorithms. Finally, this investigation laid the foundation for data preprocessing and feature engineering across diverse models and domains which offers actionable insights for practitioners.

Insulations at high voltage (HV), whether in HVAC or HVDC systems, often encounter an unwanted partial discharge (PD) phenomenon. PD poses a potential danger to HV insulation and can eventually lead to equipment failures. Out of all electromagnetic (EM) sensing techniques for PD diagnosis, the ultra-high frequency (UHF) method has gained popularity as it allows non-invasive detection at varying distances from PD defects. The EM behavior of PD is significantly affected by the distance between a PD defect and a UHF sensor. This effect varies for different PD defects under different HV conditions. So, considering variable distances, there is a need to analyze EM characteristics of different PD phenomena at different HV conditions. To the best of our knowledge, for the first time, electromagnetic characteristics of different PD defects are experimentally investigated in this work by capturing PD signals from variable distances in both HVAC and HVDC conditions and individually validating their characteristics with EM radiation theories. For wirelessly capturing PD signals, a new UHF sensor is designed with a modified elliptical-shaped antenna on an FR-4 material. The fabricated sensor provides an average realized gain of about 3.66 dBi while covering more than 97% of the total UHF range for PD detection. Applying HVAC, HVDC+, and HVDC–, PDs from three defects (surface, void, free wire) are captured from 1–4 m distances. Interpreted results show that EM radiation in the UHF range from a PD defect is heavily impacted by its detection distance and defect formation despite applied voltage and other conditions being unchanged for the defect. This investigation is particularly beneficial to the variable distance-based PD diagnosis, such as PD localization and handheld PD detection at HVAC/HVDC open substations.

In recent years, the explosive growth of social media platforms has led to the rapid spread of vast amounts of false news and rumors on the internet. This disrupts various news sources such as online news outlets, radio and television stations, and newspapers, especially in Arab countries. Therefore, the fake news detection problem has been raised worldwide. Arabic research in this field is very little compared to English research. Previous researchers had used machine learning and deep learning techniques on a large scale, but recently they used pre-trained models in their studies. Our proposed model works by using the Arabic pre-trained Bidirectional Encoder Representations from Transformers (Arabic BERT) to extract features from the text, then uses a Convolutional Neural Network (1D-CNN or 2D-CNN) to reduce the size of the features and extract the important ones, then passes it to an artificial neural network to perform the classification process. In our experiment we introduce a novel hybrid system consists of two main parts. In the first part we try three Arabic pre-trained Bidirectional Encoder Representations from Transformers model (APBTM) which are AraBERT, GigaBERT or MARBERT, while in the second part, we use 1D-CNN or 2D-CNN. this leads to six architectures from this system. we make our experiment by train and evaluating every architecture using three datasets which are (Arabic News Stance (ANS), AraNews, and Covid19Fakes). A comparison is made between the proposed model and other modern models which used the same dataset. We made three sets of experiments depending on the used datasets. Each set includes a group of experiments, and then we present the results in tables. Our proposed model which is the hybrid model between AraBERT and 2D-CNN has achieved the best F1-scores of 0.6188,0.7837 and 0.8009 when using the ANS dataset, the Ara-News dataset, and the Covid19Fakes dataset respectively. Furthermore, the model reduces the training time by achieving better results with less number of epochs. The results indicate that the proposed model offers the best performance, with 71% accuracy in the Arabic News Stance (ANS) dataset outperforming the model made by Sorour and Abdelkader (2022) and the model made by Shishah (2022) that achieved accuracy of 67% and 66% respectively.

Natural disasters have the potential to inflict damage to building structures, which prompting the structural engineers striving to build buildings that are both safer and durable. This research endeavor aims to enhance the stability of a structure by using control strategy to reduce the hazards of earthquake-induced damage. As a consequence, a hybrid control device incorporates control strategies to improve structural integrity has been investigated. This study proposes control system comprising integrated nonsingular terminal sliding mode control (TSMC), nonsingular TSMC, and TSMC. Integrated nonsingular TSMC merges deep learning methods, specifically long short-term memory (LSTM). LSTM has the capability to forecast subsequently values by analyzing previous sequential data. This character enhances precision in systems, leading to better decision-making. This attribute enhances the precision of any system, contributing to better decision-making. The integration controller empowers the system to adjust its operation to achieve the most optimal mode of operation, hence improving control performance regardless of significant uncertainty. The integrated controller is invented using both the control law and sliding surface. A two-degree-of-freedom (DOF) system structure is constructed in Matlab and validated by experimental work associated with LMS Test.Lab software. In addition, the integrated controller is employed in a 10-DOF system represents a high-level structure. The performance of integrated nonsingular TSMC is compared to nonsingular TSMC and TSMC in terms of displacement response, sliding surface, and damage probability. The outcomes indicated that integrated nonsingular TSMC could mitigate vibration by up to 46% and had a 15% possible risk of inflicting complete damage to the controlled structure of a low-rise building. Its installation in high-rise structures resulted in the suppression of 26%. Consequently, these findings are crucial in enhancing the safety of building structures and residents, and minimizing disaster damage costs.

Air pollution is a critical global concern, demanding precise air quality forecasting to mitigate its severe consequences. Our study introduces Future Air Quality Prediction using Long Short-Term Memory based on Hyper Heuristic Multi-Chain Model (H2MCM) to project future air quality, considering various meteorological factors (MFs) and pollution-related variables like atmospheric pressure, temperature, humidity, and wind patterns. Leveraging 12 units of Long Short-Term Memory neural networks (LSTMs), H2MCM accurately predicts forthcoming air pollutants (APs) concentrations such as particulate matter with diameter $2.5 \; \mu $ m (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2). Additionally, it accounts for spatiotemporal correlations between these APs and MFs, which significantly influence the air quality prediction for the next immediate time interval. H2MCM utilizes a multi-chain mechanism, employing 1-hour prediction models to forecast air quality hourly, enabling approximations for the next 12 hours. Also, for an efficient model selection, Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC), Hannan-Quinn Information Criterion (HQIC), and corrected AIC (AICc) tools are used based on their ability to balance model fit and complexity. Furthermore, it demonstrates the ability to enhance the performance of any predictor. Experimental results substantiate H2MCM’s superiority over various models, including the Support Vector Regressor (SVR), Multi-Layer Perceptron (MLP), Recurrent Air Quality Predictor (RAQP), and Valchogianni models. H2MCM achieves impressive up to 75% better accuracy and consistency compared to SVR, 60% better than MLP, 38% better than RAQP, and 70% better than Valchogianni models.

In the paradigm of Device-to-Device (D2D) and System-on-Chip (SoC) communications, the optimization of high-speed data transfer while minimizing hardware resource utilization imposing paramount importance. Traditional methodologies have often resulted in undesirable outcomes, such as inflated area, latency, and power consumption. Consequently, this study adopts a focused approach by introducing an innovative Route-Controlling Network-on-Chip (RC-NoC) architecture, integrating critical components like FIFO-Buffer, crossbar switching, route control, and arbiter modules. The operational sequence begins with the FIFO-Buffer logic, which strategically manages data storage from diverse devices, allocating and organizing data flow based on distinct IP addresses. Subsequently, the route controller module orchestrates the operation of heterogeneous routers within the crossbar switching fabric, implementing a prioritized scheduling scheme. The arbiter plays a crucial role in overseeing and directing data flow from source to destination, leveraging request-level prioritization for efficient data transmission. Empirical evidence from simulations unequivocally demonstrates the superiority of the proposed RC-NoC architecture. Comparative analysis against essential state-of-the-art NoC architectures reveals significant enhancements in key metrics, notably area efficiency, latency reduction, and power optimization, with over 45% improvement observed across these metrics compared to GALS-NoC, F-NoC, and CE-NoC in the Orion framework. This robust validation substantiates the efficacy and technical prowess of the RC-NoC design paradigm in addressing the challenges of modern D2D and SoC communication paradigms. Furthermore, the optimization process notably decreased routing costs for different packets, with Router2 exhibiting a 40% reduction in Packet1’s routing cost and Router3 displaying a 44% decrease in Packet2’s cost. Moreover, the attained router capacities demonstrated an average increase of 17%, significantly enhancing network efficiency.

The paper investigates the terahertz performance of a mutually injection-locked multi-element high electron mobility avalanche transit time (HEM-ATT) source based on AlGaN/GaN two-dimensional electron gas (2-DEG). Utilizing a nanostrip patch type planar coupling circuit, mutual injection locking between adjacent elements is achieved. The paper provides a comprehensive analysis of the integrated power combining technique in the mutually injection-locked multi-element HEM-ATT oscillator. A ten-element mutually injection-locked integrated power combined source is designed for operation at 1.0 THz, and simulation studies are conducted to examine its DC, large-signal, and avalanche noise characteristics. The capability of generating a narrow-band terahertz wave is verified by introducing various levels of structural mismatches between the elements. Results indicate that the ten-element HEM-ATT oscillator can deliver 2.27 W peak power with a 17% DC to THz conversion efficiency at 1.0 THz. The average noise measure of the oscillator is found to be 12.54 dB. Additionally, the terahertz performance of the mutually injection-locked ten-element HEM-ATT oscillator is compared with other state-of-the-art THz sources to evaluate its potentiality as an excellent integrated THz radiator.

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

Securing user electronics devices has become a significant concern in the digital period, and a forward-thinking solution covers the fusion of blockchain (BC) technology and deep learning (DL) methods. Blockchain improves device safety by transforming access management, storing credentials on a tamper-resistant ledger, mitigating the risk of unauthorized access and giving a robust defence against malevolent actors. Integrating DL into this framework also raises safety measures, as it permits devices to inspect and regulate to develop attacks distinctly. DL models accurately recognize intricate designs and anomalies, allowing the technique to distinguish and threaten possible attacks in real time. The fusion of BC and DL not only improves the reliability of user electronics but also establishes a dynamic and adaptive safety system, enhancing consumer confidence in the safety of their devices. Therefore, this study presents a BC-Based Access Management with DL Threat Modeling (BCAM-DLTM) technique for securing consumer electronics devices in the IoT ecosystems. The BCAM-DLTM technique mainly follows a two-phase procedure: access management and threat detection. Moreover, BC technology can be applied to the access management of consumer electronics devices. Besides, the BCAM-DLTM technique applies a deep belief networks (DBNs) model for proficiently identifying threats. To enhance the recognition results of the DBN model, the hyperparameter tuning procedure uses the reptile search algorithm (RSA). The experimental outcome study of the BCAM-DLTM approach employs the NSLKDD dataset. The comprehensive results of the BCAM-DLTM approach portrayed a superior accuracy outcome of 99.63% over existing models in terms of distinct metrics.

Smart grids are transforming the generation, distribution, and consumption of power, marking a revolutionary step forward for contemporary energy systems. Communication in smart grid environments is majorly performed through Wireless Sensor Networks (WSNs). The WSNs enable real-time monitoring and management inside smart grids. However, the integration of digital technologies and automation in smart grids introduces cybersecurity challenges, including unauthorized access, data breaches, and denial of service attacks. To address these difficulties and maintain the reliability of smart grid infrastructure, this study proposes a comprehensive architecture for strengthening cybersecurity within WSNs operating in smart grid environments. By integrating traffic analysis, node categorization, and machine learning algorithms, the framework intends to effectively detect and prevent cyber threats. Extensive evaluation reveals that traffic analysis using the Random Forest model successfully predicts traffic load within WSNs, achieving a mean squared error (MSE) of 2.772350, a root mean squared error (RMSE) of 1.665038, a mean absolute error (MAE) of 1.099080, and a coefficient of determination (R2) of 0.717982. In intrusion detection, the Random Forest model outperforms Decision Trees and Logistic Regression, with higher precision (0.99), recall (0.99), and F1 scores (0.98) across various attack types. Specifically, Random Forest achieves perfect recall (1.00) in identifying Flooding attacks, underscoring its capability to detect all instances of such intrusions. Leveraging the insights gathered from the WSNBFSF dataset, this study gives significant findings into proactive cybersecurity tactics, stressing the necessity of securing key infrastructure for the reliable and secure distribution of power to consumers.

This research provides a detailed quantitative analysis of the challenges encountered in deploying 6G Unmanned Aerial Vehicle Base Stations UAV-BS in arid environments, specifically focusing on Saudi Arabia. The study meticulously quantifies the impact of dust and sand storms on these advanced communication systems, highlighting the substantial increase in signal attenuation and path loss under such conditions. Key findings include a marked dependency of transmitted power requirements on visibility at millimeter range high frequencies, (at 100 GHz). The results demonstrate that at this frequency, a decrease in ground visibility from 15 to 5 meters necessitates a significant increase in transmitted power by approximately 10 dB. Moreover, a striking 50 dB increase in path loss is observed when transitioning from a 3 GHz carrier frequency to 100 GHz under a ground visibility of 5 meters. This increase is attributed to a 30 dB rise in free space loss FSL due to the change in carrier frequency and an additional 20 dB attributed to increased dust attenuation at 100 GHz compared to 3 GHz. Our approach integrates PSO for dynamic UAV-BS positioning, effectively mitigating the adverse effects of dust and sand on signal quality. The research underscores the importance of considering UAV altitude, signal frequency, and ground visibility for optimal signal propagation. Additionally, it highlights the necessity of dynamic power management strategies within the network architecture to adapt to the unique challenges of arid environments. This study not only illuminates the practical implications for the deployment of UAV-BS in future 6G networks but also emphasizes the need for continuous adaptation of UAV-BS positioning in response to changes in visibility and that due to continuous change in the pattern of active users (asking for network services). The insights gained are crucial for ensuring reliable communication during environmental challenges, paving the way for more resilient and efficient wireless communication systems in demanding conditions.

The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents’ environment and well-being. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM model enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

Deaf and Hard of Hearing (DHH) individuals often encounter significant challenges in communication and accessing digital content, with Sign Language as their primary mode of communication. Traditional Sign Language interpretation services and existing accessibility measures may not always be readily available or effective. This study explores the acceptance of signing avatars, specifically the culturally adapted BuHamad avatar, among DHH individuals in Qatar. Semi-structured interviews with 40 DHH participants, representing 10% of the deaf population in Qatar, were conducted using the Technology Acceptance Model (TAM). Factors such as perceived usefulness, ease of use, attitude, and behavioral intention to use the BuHamad avatar were examined through descriptive statistics and thematic analysis of qualitative data. Findings reveal that while participants generally hold positive attitudes towards the signing avatar and its potential to enhance accessibility, they do not view it as a substitute for human interpreters. Key usability issues, including avatar movement and appearance, were identified. This study introduces the Technology Acceptance Model of Signing Avatars (TAMSA) and provides insights into factors influencing the acceptance of signing avatars in Qatar. These insights can guide developers and designers in creating more effective and user-friendly avatars for the DHH community in Qatar and the broader Gulf region.

Aspect extraction is the most important factor influencing the quality of Aspect-Based Sentiment Analysis (ABSA). Aspect extractions are divided into three approaches: supervised, unsupervised, and hybrid methods. Most previous aspect extraction algorithms focus on the explicit aspect, which lacks meaningful ABSA. Thus, considering the implicit aspect has become significant as most customer reviews consist of these by about 30%. Hybrid approaches have attempted to solve both aspects by integrating the frequency-based approaches (FB) with syntax, hybrid Conditional Random Field (CRF) with syntax, and hybrid FB with CRF, but the performance is still low. Extracting the implicit and compound noun aspects is challenging due to their hidden nature and special syntax needs. The implicit aspect needs FB, syntax dependency, and specialized tagging with feature embedding using natural language processing (NLP) solution, while the compound noun aspect needs FB, syntax dependency-based NLP solving. Therefore, this paper proposes a noble hybrid method combining frequency-based, syntax-based dependency, and CRF algorithms using tagging and labeling to extract both implicit and explicit aspects. It also provides a mechanism for extracting and calculating implicit and explicit aspects and a method for accurately identifying compound noun aspects. Experiments with the benchmark SemEval and Amazon review dataset validate the suggested hybrid model’s effectiveness. The proposed method produced remarkable improvement in extracting explicit and implicit aspects and improved overall results with precision-recall and accuracy between 5-15 percent. Moreover, this study has shown the number and the list of explicit and implicit aspects. The proposed method has obtained (2269, 368), (523, 60), (322, 55) number of explicit versus implicit aspects for SemEval 16, Amazon (Canon), and Amazon (Nokia) datasets, respectively. Hybrid with frequency-based, CRF’s superiority tagging and syntax help solve implicit and explicit aspects, including compound nouns.

The effective usage of energy becomes crucial for the successful deployment and operation of unmanned aerial vehicles (UAVs) in different applications, such as surveillance, transportation, and communication networks. The increasing demand for UAVs in different industries such as agriculture, logistics, and emergency response has led to the development of more sophisticated and advanced UAVs. However, the limited onboard energy resource of UAVs poses a major problem for their long-term operation and endurance. In addition, artificial intelligence (AI) and machine learning (ML) could allow UAVs to make more informed and intelligent decisions regarding their operations, resulting in sustainable and more energy-efficient UAV deployment. This article designs a Hybrid Snake Optimizer-based Route Selection Approach for Unmanned Aerial Vehicles Communication (HSO-RSAUAVC) technique. The goal of the HSO-RSAUAVC technique is to explore and select optimal routes for UAV communication. In the presented HSO-RSAUAVC technique, the SO algorithm is integrated with Bernoulli Chaotic Mapping and Levy flight (LF) for enhanced performance. In addition, the HSO-RSAUAVC method derives a fitness function including residual energy (RE), distance, and UAV degree. By incorporating the HSO-RSAUAVC technique, we can dynamically adapt UAV paths to overcome obstacles, decrease communication interference, and optimize energy utilization. To validate the performance of the proposed model, a series of simulations were performed. The comparative result analysis illustrates the better performance of the HSO-RSAUAVC technique in improving the performance and reliability of UAV communication.

This work introduces a wideband metamaterial (MM) absorber designed to operate effectively across a wide reception angle and be polarization-insensitive within ISM band (2.4 GHz) applications. The proposed absorber unit cell comprises four copper sectors loaded with lumped resistors and a full copper ground plane hosted onto two FR4 substrates. Furthermore, an air layer suspended between the ground plane and a FR4 substrate is applied to achieve wideband absorption. In addition, the simulation results show that particular design factors, such as lumped resistors and unit cell geometry, can be optimized to improve the efficiency of the absorber. The simulations demonstrate that the proposed absorber achieves a wideband absorption, exceeding 90%, over a broad frequency range from 1.94 GHz to 2.98 GHz. The designed absorber was fabricated and tested, and the simulation and measurement results were agreed well.

This study discusses the widespread problem of chattering phenomena and high frequency oscillations in sliding mode controllers (SMCs) used for maximum power point (MPP) tracking (MPPT) in photovoltaic (PV) systems. Moreover, this study introduces an adaptive sliding mode controller (ASMC) made just for PV panels with MPPT capability to address these problems. The main goal of the control method is to make PV systems more reliable and effective by precisely tracking the MPP, even when the weather quickly changes. This strategy demonstrates superior static and dynamic responsiveness compared with traditional methods. The particle swarm optimization (PSO) algorithm is employed to determine the best gains for the SMC method, specifically for controlling the variable step of the standard perturb and observe (P&O) algorithm. This algorithm used in the study with a Soltech 1STH-250-WH module is linked to a boost DC-DC converter. The aforementioned converter provided power to a resistive load, which was then controlled by a voltage regulator. Subsequently, the research highlights the utilization of a new voltage source inverter: a 9-level diode clamp inverter (DCI) employed to mitigate harmonics, hence enhancing power quality through the reduction of its components and generating a sinusoidal AC voltage that has multiple uses.

In modern railway traffic systems, direct current (DC) electrification is a prevalent choice, with numerous traction networks adopting a variety of voltage levels to accommodate varying load current dynamics. These dynamics are influenced by passenger density, aggregate demand for electrical power, headways, and frequency of locomotive operations. Load currents are prone to surges during periods of dense traffic and transient phases such as acceleration, deceleration, and the start–stop sequences of trains. Such surges hold the potential to precipitate fault currents within the traction system, which are similar to those engendered by external anomalies. Conventional protection systems, such as the Détection Défaut Ligne’—French for ’Line Fault Detection), may not always effectively identify remote faults or prolonged overcurrent situations. These scenarios necessitate an advancement beyond the traditional fault detection methodologies, which primarily rely on fixed thresholds and may not account for the dynamic nature of the railway system’s electrical load. This paper addresses the limitations inherent in the existing DDL protection mechanisms by focusing on the feeder attributes specific to the DC Traction System. In pursuit of this objective, we introduce an innovative adaptive current DDL algorithm to refine the rigid threshold paradigm inherent in the conventional approach. To facilitate a pragmatic assessment, the Rapid Rail network of Malaysia serves as a reference for emulating the railway’s electrical system. This comprehensive analysis yields insights that are potentially useful for safety protocols in DC electrified railroad traffic systems.

For an extended period, a technological architecture known as cloud IoT links IoT devices to servers located in cloud data centers. Real-time data analytic are made possible by this, enabling better, data-driven decision making, optimization, and risk reduction. Since cloud systems are often located at a considerable distance from IoT devices, the rise of time-sensitive IoT applications has driven the requirement to extend cloud architecture for timely delivery of critical services. Balancing the allocation of IoT services to appropriate edge nodes while guaranteeing low latency and efficient resource utilization remains a challenging task. Since edge nodes have lower resource capabilities than the cloud. The primary drawback of current methods in this situation is that they only tackle the scheduling issue from one side. Task scheduling plays a pivotal role in various domains, including cloud computing, operating systems, and parallel processing, enabling effective management of computational resources. In this research, we provide a multiple-factor autonomous IoT-Edge scheduling method based on game theory to solve this issue. Our strategy involves two distinct scenarios. In the first scenario, we introduced an algorithm containing choices for the IoT and edge nodes, allowing them to evaluate each other using factors such as delay and resource usage. The second scenario involves both a centralized and a distributed scheduling approach, leveraging the matching concept and considering each other. In addition, we also introduced a preference-based stable mechanism (PBSM) algorithm for resource allocation. In terms of the execution time for IoT services and the effectiveness of resource consolidation for edge nodes, the technique we use achieves better results compared with the two commonly used Min-Min and Max-Min scheduling algorithms.

The significance of automatic text summarization (ATS) lies in its task of distilling textual information into a condensed yet meaningful structure that preserves the core message of the original content. This summary generated by ATS plays a crucial role in simplifying the processing of textual information, as it captures the primary ideas of the source text while eliminating lengthy and irrelevant textual components. At present, the landscape of ATS is enriched with a multitude of innovative approaches, with a notable focus on optimization-based methods. These optimization-driven ATS techniques have introduced new perspectives, illuminating the field with their heightened accuracy in terms of metrics like ROUGE scores. Notably, their performance closely rivals other cutting-edge approaches, including various methodologies within the realm of machine learning and deep learning. The review presented in this paper delves into recent advancements in extractive ATS, centering mainly on the optimization-based approach. Through this exploration, the paper underscores the gains and trade-offs associated with adopting optimization-based ATS compared to other strategies, specifically with the application of real-time ATS. This review serves as a compass, pointing towards potential future directions that the optimization-based ATS approaches should consider traversing to enhance the field further.

Recently, deep learning models have become more prominent due to their tremendous performance for real-time tasks like face recognition, object detection, natural language processing (NLP), instance segmentation, image classification, gesture recognition, and video classification. Image captioning is one of the critical tasks in NLP and computer vision (CV). It completes conversion from image to text; specifically, the model produces description text automatically based on the input images. In this aspect, this article develops a Lighting Search Algorithm (LSA) with a Hybrid Convolutional Neural Network Image Captioning System (LSAHCNN-ICS) for NLP. This introduced LSAHCNN-ICS system develops an end-to-end model which employs convolutional neural network (CNN) based ShuffleNet as an encoder and HCNN as a decoder. At the encoding part, the ShuffleNet model derives feature descriptors of the image. Besides, in the decoding part, the description of text can be generated using the proposed hybrid convolutional neural network (HCNN) model. To achieve improved captioning results, the LSA is applied as a hyperparameter tuning strategy, representing the innovation of the study. The simulation analysis of the presented LSAHCNN-ICS technique is performed on a benchmark database, and the obtained results demonstrated the enhanced outcomes of the LSAHCNN-ICS algorithm over other recent methods with maximum Consensus-based Image Description Evaluation (CIDEr Code) of 43.60, 59.54, and 135.14 on Flickr8k, Flickr30k, and MSCOCO datasets correspondingly.

Nowadays, network technologies are developing very rapidly. The growing volume of transmitted information (video, data, VoIP, etc.), the physical growth of networks, and inter-network traffic are forcing manufacturers to produce more powerful and “smart” devices that use new methods of transferring and sorting data. Such connected smart devices (IoT) are used in intelligently controlled traffic for self-driving vehicles in Vehicle Adhoc Networks (VANET), in electricity and water in smart cities, and in-home automation in smart homes. These types of connected Internet of Things (IoT) devices are used to leverage different types of network structures. Such IoT sensor devices can be deployed as a wireless sensor network (WSN) in a mesh topology. Both WSNs and Wireless Mesh Networks (WMNs) are easy to organize as well as to deploy. In this case, there are many reasons for combining these different types of networks. In particular, the detailed sensory capabilities of sensor networks may be improved by increasing bandwidth, reliability and power consumption in the mesh topology. However, there are currently only a handful of studies devoting to integrate these two different types of networks. In addition, there is no systematic review of existing interconnection methods. That is why in this article we explore the existing methods of these two networks and provide an analytical basis for their relationship. We introduce the definition of WSN and WMN and then look at some case studies. Afterward, we present several challenges and opportunities in the area of combined Wireless Mesh Sensor Network (WMSN) followed by a discussion on this interconnection through literature review and hope that this document will attract the attention of the community and inspire further research in this direction.

In this research, prediction of crude oil cuts from the first stage of refining process field is laid out using rough set theory (RST) based adaptive neuro-fuzzy inference system (ANFIS) soft sensor model to enhance the performance of oil refinery process. The RST was used to reduce the fuzzy rule sets of ANFIS model, and its features in the decision table. Also, discretisation methods were used to optimise the continuous data’s discretisation. This helps to predict the two critical variables of light naphtha product: Reid Vapor Pressure (RVP) and American Petroleum Institute gravity (API gravity), which detect the cut’s quality. Hence, a real-time process of Al Doura oil refinery is examined and the process data of refining crude oil from these two sources improve the knowledge provided by the data. The response variables represent the feedback measured value of cascade controller in the top of the splitter in crude distillation unit (CDU) in the rectifying section, which controls the reflux liquid’s flow towards the splitter’s head. The proposed adaptive soft sensor model succeeded to fit the results from laboratory tests, and a steady-state control system was achieved through an embedded virtual sensor. The predictive control system has been employed using cascade ANFIS controller in parallel with the soft sensor model to keep the purity of the distillate product in the stated range of the quality control of oil refinery. The results obtained from the proposed ANFIS based cascade control have no over/undershoots, and the rise time and settling time are improved by 26.65% and 84.63%, respectively than the conventional proportional-integral-derivative (PID) based cascade control. Furthermore, the results of prediction and control model are compared with those of other machine learning techniques.

In providing an accurate approximate analytical solution to the non-linear system of fractional-order susceptible-infected-recovered epidemic model (FOSIREM) of childhood disease has been a challenge, because no norm to guarantees the convergence of the infinite series solution. We compute an accurate approximate analytical solution using the optimal homotopy asymptotic method (OHAM). The fractional differential equations operator (FDEO) is given as conformable derivative operator (CDO). We show the basic idea of the proposed method, the CDO sense, equilibrium points, local asymptotic stability, reproduction number, and the convergence analysis of the proposed method. Numerical results and comparisons with other approximate analytical methods are given to validate the efficiency of the method. The proposed method speedily converges to the exact solution as the fractional-order derivative approaches 1, proved as an excellent tool for solving, and predicting the model.

Solar Photovoltaic (PV) system is one of the most significant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efficient Newton Raphson (HAOAENR) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efficiently designed to roughly predict the initial root values of the PV equation. Secondly, the Lévy flight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the fluctuation behaviours of the PV models. The experimental findings revealed that the proposed HAOAENR outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.

In the high dimensional space, the problem of feature selection (FS) can be regarded as combinatorial optimization problem with high complexity due to the huge number of candidate features. In this article, a novel type of meta-heuristic searching based on variable length of solution space is proposed in order to solve the high dimensionality issue of the FS and to obtain more optimal results. The proposed algorithm uses the original black hole optimization as baseline for development. Blackhole optimization assumes in fixed solution space which decreases the efficiency when the number of features is high. Furthermore, the algorithm is subject to stagnation due to the single exemplar or black hole selection. Hence, or novel variable length black hole modifies the original black hole algorithm with various aspects, namely, it enables decomposing the solution space into subset of dimensions and searching within each dimension separately with selecting an exemplar for each dimension which represents the black hole of the corresponding dimension. In addition, it enables length changing of the solutions based on stagnation criterion. Furthermore, it proposes new concept of energy to the black hole which indicates to the decrease in the effectiveness of black hole with time in an exponential way and use it to replace the black hole when it is not effective anymore. The proposed algorithm which is designated as variable length black hole optimization VLBHO is compared with the variable length particle swarm optimization. The approach has increased the accuracy from 50% to 67% for forest cover dataset and from 38% to 80% for wine dataset.

Mobile learning is posited to be an effective learning tool for various learners. However, limited studies have been conducted to explore formal part-time learners’ behavior towards mobile learning. Therefore, this study investigated the level of acceptance of mobile learning by formal part-time learners in Malaysia. A theoretical model was developed based on the unified theory of acceptance and use of technology (UTAUT) and formal part-time learners’ attributes such as self-directed learning, prior mobile learning experience, learning readiness, and orientation to learning. The developed model and hypotheses were examined using a questionnaire, and measurement and structural models were analysed using SmartPLS (v 3.2.7). The participants of the study comprised 394 formal part-time learners enrolled in five public universities in Malaysia. Finding confirmed the significant influence of self-directed learning, learning readiness, effort expectancy, performance expectancy, social influence, and facilitating conditions in predicting the mobile learning behavioral intention and usage behavior. These findings can be considered by practitioners to enhance behavioral intention towards m-learning usage. The attributes of formal part-time learners toward the usage of m-learning in public universities were properly underlined in this study. This study’s main contribution would be to aid in the exploration of formal part-time learners’ characteristics, which would aid m-learning practitioners in developing the necessary applications and strategies.

Fifth-generation mobile technologies introduce the concept of network slicing, which allows the creation of logical networks consisting of network services and the associated physical and virtual network functions. The early form of network slicing allowed for fixed resource allocation and static network function deployment. However, this approach can lead to inefficiency and service degradation. This study aims to optimize the deployment of virtual network functions within a hybrid cloud infrastructure from the perspective of mission-critical communications. The first task involves designing a deep reinforcement learning-based scheme to determine a significant deployment policy that minimizes the overall delays and costs of logical networks. The scheme performance is evaluated by using a simulated traffic dataset that followed Poisson distributions for a wide range of configurations. In dynamic environments with stationary traffic patterns, simulation results show that the scheme outperforms the one-step look-ahead and fixed-location algorithms by 35.80% and 52.16%, respectively, on average. A value iteration-based scheme is used as a benchmark and only surpasses the proposed scheme by 3.5% on average. Simulation results using a real-world traffic dataset show that the scheme can support nonstationary traffic patterns and cater to large-scale scenarios with many suitable deployment locations by leveraging a function that indicates the relative importance of selecting one location over the others.

The nature of decision making in healthcare is complex and crucial. It is essential to have a tool that helps with accurate and correct decisions based on real-time data. Moreover, the healthcare process itself is complex, comprising various stages from primary to palliative, closely related to each other, and the process is different depending on the type of disease. Each stage has a crucial decision to be made relying on other stages’ decisions. Thus, an intelligent decision support system (IDSS) model based on a data mining approach becomes a prominent solution. However, the existing IDSS and Group Decision Support System (GDSS) applied a single-stage approach and primarily focused on development at a certain stage for specific outcomes. In contrast, the nature of healthcare decision-making in each stage is related to the previous stages, which change dynamically. Therefore, this paper proposes an interrelated decision-making model (IDM) for IDSS in healthcare that aims to have an effective decision by utilizing knowledge from previous and following treatment stages known as IDM-IDSS-healthcare. The experiment was conducted using simulated diabetes treatments data that were validated by the medical expert. Eight data sets with distinct sizes were constructed and classified into two types of decision-making categories. Each data sets consists of primary and secondary care stages with a range of 25 to 58 attributes and 300-11,000 instances. The experiment results show algorithms J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet and AdaBoostM1 obtained the best accuracy in sequence from 46% to 99%. The result also shows the improvement of decision-making efficiency with the prediction model accuracy has increased up to 56%. In addition, all respondents agreed in a focus-group discussion with medical and information technology (IT) experts that the proposed IDM-IDSS-healthcare is practical as a healthcare solution. Moreover, the solution for the development of IDM-IDSS-healthcare should use the multi-agent approach.

Device-to-device (D2D) communication is one of the most promising technologies in wireless cellular networks that can be employed to improve spectral and energy efficiency, increase data rates, and reduce links latency. This paper investigates fifth generation and beyond (5GB) networks, the basics of D2D communication, applications, and classification. Herein, D2D in in-band (IBD) and out-band (OBD) modes are discussed. This paper also presents the integration of D2D communication with other prominent technologies and demonstrates the importance of integration with possible solutions in improving network performance. We further investigate the challenges of D2D communication, opportunities, and future research directions of D2D in 5GB networks. In addition, D2D communication in 6G network challenges and open research areas are introduced.

Modern power utilization has been forced to operate close to their stability limits, which may lead to voltage instability or collapse owing to the stressed operating conditions of networks. This raises the need to develop a new technique to predict the level of voltage security under different operating conditions to detect critical lines or buses that operate close to its stability limit. This study proposes a new line voltage stability index (BVSI) to identify weak lines and buses for various loading conditions and network configurations. Comparative studies between the existing voltage stability indices (VSIs) and the proposed index have been performed to comprehensively highlight the indices’ foundation, performance, and overall behavior for several different IEEE benchmark test systems; 14-bus, 30-bus, 118-bus test systems and 33-bus radial distribution system. The sensitivity towards different loading conditions due to variables such as active power, reactive power, angular difference between sending and receiving bus voltage, line resistance, and shunt admittance, which have been neglected in the formulation of VSIs, are briefly discussed. The findings of this work are aimed to provide a general guideline for other researchers in selecting the appropriate VSIs for various applications, particularly in solving optimization problems such as distributed generation (DG) and reactive power compensation (RPC) placement, optimal power flow, optimal reactive power dispatch, optimal network reconfiguration for various loading occasions, and different networks. Lastly, the application of the proposed index has been adopted as an analytical approach for solving optimal location and sizing of DG(s) problems. The results prove the effectiveness of the proposed index in accessing the voltage stability state and stress condition of lines. Besides, the proposed approach also capable of determining the potential candidate bus for single DG location and sizing effectively.

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.

Vehicular ad hoc Networks (VANETs) are an emerging technology with robust applications in Intelligent Transport System. It consists of smart vehicles and roadside infrastructure which communicate through open access wireless networks. The rapid growth in vehicles results in VANETs becoming large-scale, dynamic, heterogeneous and it is possible for the attacker to harm vehicular communication which leads to life-endangering situations. VANETs must ensure secured vehicular communication using strong privacy-preserving and authentication mechanisms. In addition, efficiency is also a major concern in VANETs. Numerous studies have been discussed in literature for VANETs privacy and security. Nevertheless, no one covered the privacy and security issues as a holistic view. In this paper, we have given a detailed background overview of VANETs. Details of different possible attacks in VANET are also given in this paper. We have classified privacy and authentication schemes into four major groups with their security mechanisms, security requirements, strength, limitations, attacks countermeasures and performance measures. Finally, we have discussed some open issues in the field of VANETs security.

Information networks organized according to Internet of Things (IoT) mesh topology have received great recognition over the past one and a half to two decades of years. The scale of projects grows from millions of users connected to hundreds of thousands of access points. IoT mesh nodes offer more fascinating regulations linking a variety of networks and radio technologies and therefore fully meet the ever-growing requirements of subscribers as mobility, quality of service (QoS) and the security. The possibility of organizing local or metropolitan networks using a mesh network topology, desegregating into wide area networks without any difficulty or effort, is considered a tempting condition for urban and individual users. This article, introduces optimal routing protocol for IoT Wireless Mesh Network (WMN) by optimizing the channel and frequency resources of the network based on mathematical calculations for resource allocation. We propose an approach to the synthesis of mesh networks, which allows determining the required number of connections between network nodes, as well as their channel width for a given frequency range while ensuring the minimum data transmission delay time. Optimization results show that the function value is improved for more than 16% and the proposed algorithm provides the most rational use of the allocated frequency range.

Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superior damping. To address this issue, a Farmland Fertility Algorithm (FFA-PSSs controller) was used to solve an optimization problem for optimal design of PSSs system parameters, and its performance efficiency was compared to GA and PSO-based PSSs controllers. In addition to PSS, flexible current transmission (FACTS) devices are widely used. PSSs controllers and FACTS devices are frequently constructed in tandem to improve the dampening efficiency of the system. In this study, an Interline Power Flow Controller (IPFC) FACTS device will be added to the PSSs controller to improve the power system’s oscillatory stability. PSSs optimal design and supplemental controller of power fluctuations for IPFC were conducted out on WSCC multi-machine test systems using a system linear model. Using time-domain simulations and quantitative analysis, the proposed IPFC model was compared to the FFA-PSSs controller in terms of performance and efficiency. The main disadvantage of this technique is the difficulty in designing a dynamic IPFC model in test systems, as well as the burden of IPFC coordinated PSSs optimization. In both PSSs design using FFA method and FFA-optimized PSS with IPFC cases, rise in the computational and simulation costs was found unavoidable. To compensate for these flaws and obtain the research contribution, this paper proposes a Neuro-Fuzzy Controller (NFC) developed as a damping controller that can take the place of the two controllers (research objectives three). The application of the NFC substitute the computational and simulation cost involved in designing multi-machine PSS and IPFC-FACTS systems simultaneously. With the availability of NFC in SIMULINK, a dynamic model of the WSCC three-machine system was developed under a variety of operating situations. Quantitative analysis results from the WSCC test system simulation show that when comparing the proposed NFC model to the IPFC model for the WSCC test system, the proposed NFC model was found to be 149 percent and 0 percent efficient in terms of the time to settle of rotor angle respond for G2 and G3, respectively, but 394 percent efficient when compared to the uncontrolled model. The decreased settling time values ensured the proposed NFC model’s efficacy in damping down the LFO and achieving superior stability over the two controllers. The proposed NFC model has shown significant performance improvement in both the transient and steady-state areas than when the system was design with the two damping controllers.

Cloud computing provides solutions to a large number of organizations in terms of hosting systems and services. The services provided by cloud computing are broadly used for business and scientific applications. Business applications are task oriented applications and structured into business workflows. Whereas, scientific applications are data oriented and compute intensive applications and structured into scientific workflows. Scientific workflows are managed through scientific workflows management and scheduling systems. Recently, a significant amount of research is carried out on management and scheduling of scientific workflow applications. This study presents a comprehensive review on scientific workflows management and scheduling in cloud computing. It provides an overview of existing surveys on scientific workflows management systems. It presents a taxonomy of scientific workflow applications and characteristics. It shows the working of existing scientific workflows management and scheduling techniques including resource scheduling, fault-tolerant scheduling and energy efficient scheduling. It provides discussion on various performance evaluation parameters along with definition and equation. It also provides discussion on various performance evaluation platforms used for evaluation of scientific workflows management and scheduling strategies. It finds evaluation platforms used for the evaluation of scientific workflows techniques based on various performance evaluation parameters. It also finds various design goals for presenting new scientific workflow management techniques. Finally, it explores the open research issues that require attention and high importance.

Metaheuristic algorithms are widely used to solve NP-complete problems in several domains. Distributed predicates detection is a fundamental distributed systems problem that has many useful applications. The problem of distributed predicates detection, in general, is known to be an NP-complete problem. In this paper, we developed a detection algorithm inspired by the particle swarm optimization algorithm, one of the well-known metaheuristic algorithms applied to solve problems in several domains. The proposed detection algorithm deal with distributed predicates under the possibly modality. We compared the performance of the proposed distributed predicates detection algorithm with several other detection algorithms. The experimental results reveal the effectiveness of the suggested distributed predicates detection algorithm.