International Journal of Electrical, Electronics and Computer Systems
International Journal of Electrical, Electronics and Computer Systems E-ISSN: 2347-2820 Frequency: Biannual Publisher Website: https://mriindia.com/ Editor in Chief: Dr. Parikshit Mahalle Article Processing Charges: 50 USD Indexing In: Google Scholar, SCILIT, BASE, Semantic Scholar, Scribd, ResearchGate
Women Safety Mobile Application Using ESP32 Camera and Real-Time Crime Data Analysis
The rising cases of harassment and assault against women have become a significant global concern, emphasizing the urgent need for effective, technology- driven safety solutions. This research presents a novel Women Safety Mobile Application integrated with real- time location tracking, crime data analytics, emergency communication, and an IoT-based ESP32 camera module for visual evidence collection. The system combines Android-based application design, Google Maps API, Firebase cloud storage, and ESP32-CAM hardware to provide a comprehensive safety ecosystem. It enables users to send automated SOS alerts containing live GPS coordinates and captures time-stamped images during emergencies. Furthermore, the system analyzes area-wise crime intensity using historical and real-time data, classifying zones into low, medium, or high-risk categories. The integration of IoT and mobile computing provides a cost-effective, efficient, and scalable model that enhances women’s personal security and situational awareness. Experimental results demonstrate high system reliability, rapid SOS response (under six seconds), and accurate crime-zone classification with 93% precision. This research contributes to the field of smart safety technologies by combining IoT, cloud computing, and data analytics to create an intelligent, real-time personal protection framework.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nd/4.0
Deep Learning and Optimization Approaches in Environmental Weather Monitoring and Prediction System Using IoT and Multi-Model Progressive Dense Self-Attention: A Review
Environmental weather monitoring and prediction have become critical components in addressing global climate change, disaster management, agriculture optimization, and smart city planning. Traditional numerical weather prediction (NWP) models, while effective, often struggle with high computational complexity and limited capability in modelling nonlinear spatiotemporal dependencies. The emergence of Internet of Things (IoT) technologies combined with deep learning (DL) and optimization techniques has revolutionized the accuracy, scalability, and responsiveness of weather prediction systems. This paper presents a comprehensive review of advanced deep learning architectures such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Transformer-based models, and attention-driven architectures, integrated with IoT-based environmental monitoring systems. Recent advancements highlight the effectiveness of hybrid models such as CNN-LSTM and attention-based recurrent networks in capturing both spatial and temporal dependencies in weather datasets. Additionally, optimization algorithms including metaheuristic techniques (e.g., Golden Jackal Optimization) and hyperparameter tuning strategies significantly enhance model performance and convergence. The review also explores the role of multi-model progressive dense self-attention mechanisms, which enable efficient feature extraction from heterogeneous IoT sensor data, improving prediction accuracy and robustness. Furthermore, challenges such as data heterogeneity, scalability, energy efficiency, and real-time processing are discussed alongside emerging solutions like federated learning and edge computing. The study concludes by identifying future research directions, emphasizing the need for explainable AI, energy-efficient models, and integrated IoT–AI frameworks for sustainable environmental monitoring systems.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nd/4.0
Deep Learning and Optimization Approaches in Smart Healthcare Patient Monitoring System for IoT-Based Healthcare System Using Enhanced Residual Multi-Scale Diverged Self-Attention Network: A Review
Smart healthcare systems have rapidly evolved with the integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, enabling real-time patient monitoring, early disease detection, and personalized treatment. IoT-based healthcare systems collect continuous physiological data such as heart rate, oxygen saturation, ECG signals, and body temperature using wearable and remote sensing devices. However, the complexity and high dimensionality of such data require advanced analytical models for accurate interpretation. This paper presents a comprehensive review of deep learning and optimization approaches in IoT-based patient monitoring systems, focusing on advanced architectures such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Transformer models, and self-attention-based frameworks. Special emphasis is placed on enhanced residual multi-scale diverged self-attention networks, which improve feature extraction, representation, and prediction accuracy. Recent studies demonstrate that hybrid deep learning models such as CNN-LSTM and attention-based architectures significantly improve the detection of abnormalities in physiological signals. IoT-enabled systems allow real-time monitoring and remote diagnosis, reducing hospital visits and enabling proactive healthcare management. Despite these advancements, challenges such as data heterogeneity, privacy concerns, computational complexity, and energy efficiency persist. Emerging techniques such as edge computing, federated learning, and explainable AI provide promising solutions. This review highlights recent trends (2020–2023), comparative insights, and challenges in AI-driven smart healthcare monitoring systems.
Deep Learning and Optimization Approaches in Task Scheduling and Computing Resource Allocation for VR Video Services in Advanced 6G Networks: A Review
The rapid advancement of sixth-generation (6G) wireless networks is expected to enable immersive applications such as Virtual Reality (VR) video services, which require ultra-low latency, high bandwidth, and efficient resource utilization. Traditional cloud-centric architectures struggle to meet these stringent requirements due to communication delays and limited scalability. As a result, integrating deep learning with optimization techniques in edge computing environments has emerged as a promising solution for efficient task scheduling and computing resource allocation. Deep learning models, particularly Deep Reinforcement Learning (DRL), have demonstrated strong capabilities in handling dynamic and complex decision-making problems. These models enable intelligent task offloading and adaptive resource allocation by learning optimal policies in uncertain environments. Recent studies show that DRL-based scheduling frameworks outperform traditional heuristic and greedy algorithms, significantly reducing task completion time and improving system efficiency. Optimization techniques such as Lyapunov optimization and convex optimization further enhance system performance by providing theoretical guarantees for stability and resource efficiency. These approaches enable balancing multiple objectives, including latency, energy consumption, and throughput. Moreover, hybrid models combining deep learning and optimization techniques have been shown to improve scalability and adaptability in edge-cloud environments. VR video services introduce unique challenges such as real-time rendering, high data transmission rates, and strict Quality of Experience (QoE) requirements. Efficient task scheduling and resource allocation are therefore critical for ensuring seamless service delivery. This review systematically analyses recent advancements in deep learning and optimization-based approaches, compares their effectiveness, and identifies key research challenges.
Deep Learning and Optimization Approaches in Graph Neural Networks with Optimized Attention Long-Range CNN for Traffic Prediction and Resource Allocation in 6G Wireless Systems: A Review
The emergence of 6G wireless systems has introduced unprecedented challenges in managing network traffic and resource allocation due to ultra-dense connectivity, massive data generation, and stringent latency requirements. Accurate traffic prediction and efficient resource allocation are critical to ensuring quality of service (QoS) and energy efficiency in next-generation networks. Recently, deep learning approaches, particularly Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) with long-range attention mechanisms, have shown significant potential in modelling complex spatio-temporal dependencies in network traffic. This review explores advanced deep learning and optimization techniques for traffic prediction and resource allocation in 6G systems. It focuses on Graph Neural Networks integrated with optimized attention-based long-range CNN architectures to capture both spatial and temporal correlations in network data. Additionally, reinforcement learning and optimization algorithms are analysed for dynamic resource allocation. The study highlights recent advancements, including spatio-temporal GNN models, attention-based architectures, and hybrid optimization frameworks. Results from existing literature indicate that combining GNNs with attention-based CNNs significantly improves prediction accuracy and resource utilization efficiency. Finally, the paper discusses open challenges such as scalability, computational complexity, and real-time deployment, providing insights for future research in intelligent 6G wireless systems.
Deep Learning and Optimization Approaches in Semantic Segmentation and Classification for Ovarian Cancer Detection Using EfficientNetB0 with FPN and Causal Dilated Convolutional Neural Networks: A Review
Ovarian cancer remains one of the most lethal gynecological malignancies due to its asymptomatic nature in early stages and late diagnosis. Advances in medical imaging and artificial intelligence have enabled automated systems for early detection and classification, improving clinical outcomes. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown strong potential in enhancing diagnostic accuracy through semantic segmentation and classification of histopathological and radiological images. This review highlights deep learning and optimization approaches for ovarian cancer detection, focusing on EfficientNetB0, Feature Pyramid Networks (FPN), and causal dilated convolutional neural networks (CDCNNs). EfficientNetB0 provides efficient feature extraction with reduced computational complexity, making it well-suited for medical imaging tasks, while FPN improves segmentation by integrating multi-scale feature representations for precise tumor localization. CDCNNs enhance contextual understanding by expanding receptive fields without increasing computational cost, enabling better capture of spatial dependencies. The integration of these architectures supports hybrid frameworks capable of accurate segmentation and classification. Despite improved performance, challenges such as limited annotated data, model interpretability, and computational demands persist, encouraging further research in explainable AI, multimodal integration, and lightweight models for real-time clinical deployment.
Deep Learning and Optimization Approaches in Segmentation and Classification of Renal Tumors Using EfficientNet-Based U-Net and Epistemic Neural Networks: A Review
Renal tumor detection and segmentation are critical tasks in medical imaging, significantly influencing early diagnosis, treatment planning, and patient survival. Traditional manual segmentation methods are time-consuming and prone to inter-observer variability, necessitating automated solutions. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have demonstrated remarkable performance in medical image segmentation. Among these, U-Net and its variants have become the dominant architectures due to their ability to learn from limited datasets and produce precise segmentation outputs. Furthermore, the integration of EfficientNet as an encoder backbone enhances feature extraction efficiency and model scalability, leading to improved segmentation accuracy. In addition, epistemic neural networks introduce uncertainty quantification, addressing the reliability issues of deep learning models in clinical settings. These models help identify uncertain predictions, thereby improving trustworthiness in automated diagnosis. This review provides a comprehensive analysis of deep learning techniques for renal tumor segmentation and classification, focusing on EfficientNet-based U-Net architectures and epistemic learning approaches. A comparative study of recent literature (2020–2023) is presented, highlighting performance metrics, datasets, and limitations. The study concludes by identifying research gaps and future directions toward developing robust, explainable, and clinically reliable AI systems for renal tumor analysis.
Deep Learning and Optimization Approaches in Segmentation and Classification of White Blood Cancer Cells in Bone Marrow Microscopic Images Using Deep Kronecker Neural Networks: A Review
White blood cancer, particularly leukemia, is a life-threatening hematological disorder that requires early and accurate diagnosis for effective treatment. Bone marrow microscopic image analysis plays a critical role in detecting abnormal white blood cells; however, manual examination is time-consuming, subjective, and prone to human error. In recent years, deep learning techniques have significantly improved the automation of segmentation and classification tasks in medical imaging. This review focuses on deep learning and optimization approaches for segmenting and classifying white blood cancer cells, with particular emphasis on Deep Kronecker Neural Networks (DKNNs). These networks leverage Kronecker product-based factorization to reduce model complexity while preserving feature representation, making them efficient for high-dimensional medical data. Advanced architectures such as U-Net, ResNet, DenseNet, and hybrid CNN-based models have demonstrated high accuracy in leukemia detection tasks. Additionally, optimization techniques including metaheuristic algorithms, transfer learning, and attention mechanisms further enhance model performance and generalization. The review analyzes studies from 2020 to 2024, highlighting recent advancements, comparative performance, and challenges. Results indicate that hybrid and optimized deep learning models achieve classification accuracy above 98%. However, challenges such as data imbalance, computational complexity, and interpretability remain. Future research directions include lightweight architectures, explainable AI, and integration of multi-modal data for improved clinical decision support.
Deep Learning and Optimization Approaches in Secure AI for 6G Mobile Devices: Deep Kronecker Neural Network Optimized with Hybrid Cat Hunting Optimization to Combat Side-Channel Attacks: A Review
The advancement of sixth-generation (6G) communication networks is expected to enable intelligent, ultra-low latency, and high-speed mobile systems powered by Artificial Intelligence (AI). However, the integration of AI into resource-constrained mobile devices introduces critical security challenges, particularly vulnerability to side-channel attacks (SCAs), which exploit physical leakages such as power consumption and electromagnetic emissions to extract sensitive information. Traditional cryptographic defenses are insufficient to mitigate these threats, necessitating advanced AI-driven security mechanisms. This paper presents a comprehensive review of deep learning and optimization approaches for secure AI in 6G mobile devices, focusing on Deep Kronecker Neural Networks (DKNN) optimized using Hybrid Cat Hunting Optimization (HCHO). DKNN architectures leverage Kronecker product representations to reduce model complexity while maintaining high-dimensional learning capability. The integration of HCHO enhances parameter optimization, improving detection accuracy and convergence speed. The study reviews recent developments from 2020–2023, highlighting the effectiveness of hybrid deep learning and metaheuristic optimization in combating SCAs. Results indicate that DKNN-HCHO models outperform conventional approaches in terms of accuracy, computational efficiency, and robustness. Future directions include lightweight model design, federated learning integration, and explainable AI for secure and scalable 6G systems.
A Comprehensive Review of Distributed Barrier Functions for Preventing Routing Manipulation: Security Models, Optimization Techniques, and Emerging Computing Applications
Distributed routing systems form the backbone of modern communication networks, enabling data exchange across heterogeneous and geographically dispersed infrastructures. However, protocols such as the Border Gateway Protocol (BGP) suffer from inherent security vulnerabilities due to weak trust assumptions, making them susceptible to routing manipulation attacks including prefix hijacking, route leakage, and path forgery. These vulnerabilities highlight the need for advanced security mechanisms such as distributed barrier functions, which act as protective constraints to prevent malicious routing behaviors. This paper presents a comprehensive review of distributed barrier functions applied to routing security, focusing on verification mechanisms, optimization strategies, and scalable computing perspectives. A total of 30 studies published between 2018 and 2023 are systematically analyzed. The review explores how distributed security models, including blockchain-based routing, secure multi-party verification, and trust-aware frameworks, enhance routing integrity and prevent manipulation. Recent research indicates that traditional centralized security mechanisms, such as Public Key Infrastructure (PKI) and Resource PKI (RPKI), suffer from limitations including single points of failure and deployment complexity. In contrast, distributed approaches leverage decentralization, cryptographic validation, and consensus mechanisms to ensure secure and verifiable routing decisions. The paper also examines optimization techniques such as lightweight cryptographic schemes, adaptive routing verification, and AI-driven anomaly detection, which improve efficiency while maintaining strong security guarantees. Furthermore, scalable computing paradigms including cloud, edge, and blockchain architectures are analyzed for their role in supporting large-scale distributed routing environments. The findings reveal that while distributed barrier functions significantly enhance routing security, challenges such as computational overhead, scalability, and interoperability remain open research problems.
A Comprehensive Review of Graph Neural Networks for Malware Classification Pipelines: Architectures, Robustness, and Intelligent Security Applications
The rapid evolution of cyber threats, particularly malware, has driven the need for advanced detection and classification techniques capable of handling complex and dynamic attack patterns. Traditional signature-based and heuristic approaches are increasingly ineffective against polymorphic and zero-day malware, creating a demand for more adaptive solutions. In this context, Graph Neural Networks (GNNs) have emerged as a powerful paradigm for modeling structured relationships in malware data, including function call graphs, network traffic flows, and system interactions. Unlike conventional machine learning models, GNNs capture non-Euclidean relationships, enabling superior representation learning and improved classification accuracy. This review analyzes GNN-based malware classification pipelines, focusing on architectural designs, robustness strategies, and security applications. It highlights the integration of graph construction methods, feature extraction, and classification models, while also examining issues such as adversarial robustness, scalability, and explainability. Findings suggest that models like Graph Convolutional Networks, Graph Attention Networks, and hybrid approaches outperform traditional deep learning techniques by leveraging relational dependencies. Despite their promise, challenges such as computational overhead, dataset limitations, and adversarial vulnerabilities remain, indicating the need for lightweight, interpretable, and privacy-preserving GNN frameworks.
A Systematic Review of Combinatorial Testing Strategies for Cross-Border E-Commerce Platforms: Methods, Architectures, and Future Research Directions
Cross-border e-commerce platforms have significantly transformed global trade by enabling seamless transactions between buyers and sellers across diverse geographical regions. These platforms operate in complex environments characterized by varied user preferences, multiple payment gateways, diverse regulatory requirements, and multilingual interfaces, making reliability, scalability, and correctness critical challenges. Combinatorial testing has emerged as an effective approach to systematically evaluate interactions among multiple input parameters, reducing testing effort while maintaining high fault detection capability. This review examines combinatorial testing strategies applied to cross-border e-commerce systems, focusing on testing methods, architectural considerations, and emerging research trends. Techniques such as pairwise testing, t-way testing, constraint-based testing, and adaptive combinatorial testing are analyzed alongside modern architectures including microservices, cloud-based infrastructures, and distributed systems. The study highlights trends such as the integration of artificial intelligence, automated test generation, and optimization methods for large-scale applications. Despite its advantages, challenges related to handling complex constraints, scalability, and real-time testing persist. The findings underscore the need for hybrid testing strategies, enhanced tool support, and the incorporation of machine learning techniques to improve testing efficiency, with future directions emphasizing intelligent and adaptive testing frameworks.
A Systematic Review of Graph-Partition-Based Attack Mitigation in Dense Mesh Networks: Methods, Architectures, and Future Research Directions
Dense mesh networks have emerged as a critical backbone for modern distributed systems, including IoT ecosystems, edge computing infrastructures, and decentralized communication platforms. However, their highly interconnected topology introduces significant vulnerabilities, particularly to coordinated attacks such as routing manipulation, flooding, and partition-based adversarial disruptions. This paper presents a systematic review of graph-partition-based attack mitigation techniques in dense mesh networks, emphasizing algorithmic strategies, architectural frameworks, and integration within secure software engineering pipelines. The study synthesizes findings from recent literature to analyze how graph partitioning, spectral clustering, and AI-driven segmentation approaches can enhance resilience against adversarial behaviors. Furthermore, the review explores the intersection of cryptographic mechanisms, chaotic systems, and generative artificial intelligence in strengthening network security. Key contributions include a structured taxonomy of mitigation techniques, identification of research gaps in scalability and real-time adaptability, and recommendations for future research directions. The findings demonstrate that hybrid approaches combining graph theory, cryptography, and AI offer promising solutions for robust attack mitigation in increasingly complex network environments.
A Systematic Review of Graph-Theoretic Approaches to Post-Quantum Cryptographic Protocols: Methods, Architectures, and Future Research Directions
The rapid advancement of quantum computing poses a significant threat to classical cryptographic systems, necessitating the development of robust post-quantum cryptographic (PQC) protocols. Among emerging approaches, graph-theoretic techniques have gained prominence due to their computational hardness, structural flexibility, and applicability in designing secure cryptographic primitives. This paper presents a systematic review of graph-theoretic approaches to post-quantum cryptographic protocols, focusing on methods, architectures, and future research directions. The study analyzes recent developments from 2018 to 2025, examining how graph-based constructs such as expander graphs, isogeny graphs, lattice graphs, and combinatorial structures contribute to secure key exchange, encryption, and authentication mechanisms. Additionally, the integration of chaotic systems and generative artificial intelligence is explored to enhance entropy generation and adaptive security mechanisms. The review identifies key trends, including hybrid graph-chaotic models, optimization of graph traversal algorithms for cryptographic efficiency, and AI-assisted cryptanalysis resistance. Contributions of this work include a structured synthesis of 30 studies, identification of research gaps in scalability and standardization, and a comprehensive evaluation of graph-theoretic PQC within secure software engineering frameworks. The findings emphasize the potential of graph-based cryptography as a resilient paradigm in the quantum era while highlighting the need for further interdisciplinary research.
A Systematic Review of Algebraic Curve Constructions for Lightweight Key Establishment: Methods, Architectures, and Future Research Directions
Lightweight key establishment has emerged as a fundamental requirement in resource-constrained environments such as Internet of Things ecosystems, embedded systems, and edge computing infrastructures. Algebraic curve constructions, particularly those derived from elliptic and hyperelliptic curves, have gained prominence due to their efficiency, compact key sizes, and strong security guarantees rooted in hard mathematical problems. This paper presents a systematic review of algebraic curve-based approaches for lightweight key establishment, focusing on methods, architectures, and emerging research directions. The study analyzes recent advancements between 2018 and 2025, emphasizing curve optimization techniques, implementation strategies, and integration with modern software engineering paradigms. It also explores the intersection of algebraic cryptography with generative artificial intelligence for automated parameter tuning and security validation. The findings reveal a shift toward hybrid constructions, AI-assisted cryptographic design, and post-quantum considerations. The paper contributes a structured synthesis of existing research, identifies key limitations such as side-channel vulnerabilities and scalability constraints, and outlines future research opportunities in adaptive cryptographic systems and secure DevSecOps pipelines.
A Systematic Review of Multiscale Mathematical Modelling of Cellular Mechan transduction Signalling: Methods, Architectures, and Future Research Directions
Cellular mechanotransduction—the process by which cells convert mechanical stimuli into biochemical signals—plays a fundamental role in regulating cellular behavior, tissue development, and disease progression. Understanding this phenomenon requires integrative modeling frameworks capable of capturing interactions across multiple spatial and temporal scales, from molecular signaling networks to tissue-level mechanical responses. This systematic review presents a comprehensive analysis of multiscale mathematical models for mechanotransduction signaling. Advances in computational biology and applied mathematics have enabled frameworks that integrate mechanical deformation, intracellular signaling pathways, and extracellular matrix interactions. These models commonly combine continuum mechanics, reaction–diffusion systems, agent-based modeling, and stochastic simulations to describe the bidirectional coupling between mechanical forces and biochemical processes. Key signaling pathways such as Rho GTPase and YAP/TAZ are modeled using coupled reaction–diffusion and elasticity equations, illustrating how cell shape and substrate stiffness influence signaling dynamics. Multiscale approaches include hierarchical, concurrent, and hybrid frameworks, each balancing computational efficiency and biological realism. Emerging models also incorporate chemical–mechanical coupling to simulate tissue growth and morphogenesis. Despite progress, challenges remain in data integration, experimental validation, and computational complexity, though machine learning is improving predictive capabilities and simulation efficiency.
A Systematic Review of Hyperbolic PDE models for dynamo-type magnetic field behaviour: Methods, Architectures, and Future Research Directions
Hyperbolic partial differential equation (PDE) models have emerged as a powerful mathematical framework for describing dynamo-type magnetic field behavior in complex physical systems, including astrophysical plasmas, geophysical flows, and engineered electromagnetic environments. Unlike parabolic formulations that emphasize diffusion-dominated processes, hyperbolic PDEs capture wave propagation, finite signal speeds, and transient dynamics that are essential for understanding magnetic field generation and evolution. This paper presents a comprehensive systematic review of hyperbolic PDE-based models for dynamo mechanisms, focusing on their mathematical formulations, computational architectures, and integration with modern computational paradigms such as machine learning and generative artificial intelligence. The study examines recent advances between 2018 and 2025, highlighting numerical schemes, stability considerations, and hybrid modeling approaches. Key findings indicate a growing shift toward high-resolution shock-capturing methods, physics-informed neural networks, and multi-scale coupling strategies that enhance predictive accuracy while maintaining computational efficiency. The review also identifies critical challenges, including stiffness handling, scalability, and uncertainty quantification. The primary contribution of this work lies in synthesizing interdisciplinary advancements, establishing connections between classical dynamo theory and emerging AI-driven methodologies, and outlining future research directions that emphasize robustness, real-time simulation, and integration into software engineering ecosystems.
A Systematic Review of Hybrid dynamical system models for human–robot interaction: Methods, Architectures, and Future Research Directions
Hybrid dynamical systems have emerged as a powerful mathematical and computational framework for modeling complex interactions between continuous and discrete processes in human–robot interaction (HRI). These systems enable the integration of physical dynamics, decision-making logic, and adaptive control, which are essential for safe and efficient collaboration between humans and robots in dynamic environments. This paper presents a comprehensive systematic review of hybrid dynamical system models for HRI, focusing on methodologies, architectures, and future research directions. The review synthesizes recent advances between 2018 and 2025, examining key modeling paradigms such as switched systems, hybrid automata, and learning-based hybrid frameworks. The findings highlight the growing convergence of control theory, machine learning, and cognitive modeling in HRI systems, emphasizing improvements in safety, adaptability, and real-time responsiveness. The paper contributes a structured analysis of 30 representative studies, identifies research gaps in scalability, interpretability, and robustness, and outlines future directions including AI-integrated hybrid models and secure human-aware robotic systems.
A Review of Temperature-Dependent Encryption Scaling in IoT Chipsets: Intelligent Modeling, Electronics Integration, and Real-World Applications
The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices operating under varying environmental conditions, where temperature fluctuations significantly affect the performance, reliability, and security of IoT chipsets. Encryption mechanisms, essential for ensuring data confidentiality, integrity, and authentication, are highly sensitive to hardware constraints such as power consumption, processing capability, and environmental stress. Temperature-induced variations in semiconductor behavior can influence encryption latency, energy efficiency, and error rates, thereby impacting overall system performance. This review presents a comprehensive analysis of temperature-dependent encryption scaling in IoT chipsets, focusing on intelligent modeling, hardware–electronics integration, and real-world applications. It highlights the limitations of traditional encryption methods in resource-constrained environments and emphasizes recent advancements such as adaptive encryption scaling, temperature-aware cryptographic design, lightweight algorithms, and hybrid approaches. Additionally, hardware-based primitives and emerging frameworks enhance security. However, challenges persist in balancing energy efficiency with robust security and in developing standardized, scalable solutions.
A Systematic Review of Hybrid Symbolic–Neural Models for Protocol Misuse Detection
Protocol misuse detection has become a critical area in cybersecurity due to the growing complexity of network protocols and the rise of sophisticated attacks exploiting protocol-level vulnerabilities. Traditional intrusion detection systems rely on either symbolic rule-based approaches or data-driven neural models, both of which have notable limitations. Symbolic methods offer interpretability and precise rule enforcement but lack adaptability to novel threats, whereas neural models provide strong pattern recognition yet function as black-box systems with limited explainability. To overcome these challenges, hybrid symbolic–neural architectures have emerged as a promising solution, combining deep learning with rule-based reasoning to enhance both accuracy and transparency. This review synthesizes findings from multiple studies, focusing on architectural designs, optimization strategies, security models, and application domains. It categorizes approaches into neuro-symbolic intrusion detection systems, graph-based hybrid models, rule-enhanced deep learning frameworks, and reinforcement-driven adaptive detection methods. The findings highlight a growing emphasis on explainable AI, where symbolic reasoning is integrated with convolutional, recurrent, and graph neural networks to reduce false positives and improve interpretability. Despite these advancements, challenges such as scalability, real-time deployment, dataset imbalance, and lack of standardized benchmarks persist, indicating important directions for future cybersecurity research.
A Comprehensive Review of IoT Edge Gateways: Models, Methods, and Emerging Applications
The rapid proliferation of Internet of Things (IoT) ecosystems has intensified the demand for efficient, scalable, and secure data processing architectures, positioning IoT edge gateways as a critical component in modern distributed systems. These gateways act as intermediaries between edge devices and cloud infrastructures, enabling real-time data processing, protocol translation, and localized decision-making. This paper presents a comprehensive review of IoT edge gateway models, methods, and emerging applications, with a strong emphasis on intelligent processing, security integration, and software engineering perspectives. The study systematically analyzes recent advancements in edge gateway architectures, including virtualization-based models, containerized microservices, AI-enabled gateways, and software-defined edge frameworks. Key findings reveal a shift from traditional rule-based processing toward adaptive, AI-driven edge intelligence, enhancing latency reduction, bandwidth optimization, and security enforcement. The review also identifies critical challenges such as resource constraints, interoperability issues, and security vulnerabilities in distributed edge environments. The primary contribution of this work lies in synthesizing recent research trends, identifying methodological gaps, and proposing future research directions that integrate edge intelligence with secure software engineering practices and DevSecOps pipelines.
A Systematic Review of Mathematical Modelling of Epigenetic Regulatory Mechanisms in Gene Expression: Methods, Architectures, and Future Research Directions
Epigenetic regulation plays a fundamental role in controlling gene expression without altering the underlying DNA sequence. Mechanisms such as DNA methylation, histone modification, and chromatin remodelling dynamically influence transcriptional activity and cellular differentiation. Mathematical modelling, particularly using differential equations and dynamical systems, has emerged as a powerful framework for understanding these complex regulatory processes. This systematic review examines advances between 2018 and 2023 in modelling epigenetic regulatory mechanisms governing gene expression. Recent studies demonstrate that ordinary differential equation (ODE) models are widely used to describe temporal dynamics of gene regulatory networks (GRNs), while stochastic and fractional models capture variability and memory effects in epigenetic processes. Additionally, hybrid and multiscale models integrating gene regulation with epigenetic feedback loops have significantly improved predictive capability. Mathematical models allow the analysis of nonlinear regulatory circuits, feedback loops, and attractor states that determine cellular phenotypes. Emerging approaches include data-driven parameter estimation, machine learning-assisted differential equation models, and integration of single-cell sequencing data. Despite these advancements, challenges remain in parameter estimation, data integration, and model validation due to the complexity of epigenetic systems. This review synthesizes modelling approaches, compares architectures, and identifies future research directions such as multiscale integration, hybrid AI–mechanistic modelling, and personalized epigenetic modelling.
A Comprehensive Review of Secure Aggregation of Environmental Data via Error-Bounded Encoding: Security Models, Optimization Techniques, and Emerging Computing Applications
The rapid expansion of environmental monitoring systems, driven by the widespread adoption of Internet of Things devices and wireless sensor networks, has resulted in the generation of large volumes of distributed data. Ensuring secure aggregation of this data is essential for maintaining confidentiality, integrity, and efficiency in applications such as climate monitoring, smart agriculture, disaster management, and smart cities. Due to the resource-constrained nature of sensor devices and communication limitations, lightweight and privacy-preserving aggregation techniques are required. Secure aggregation protocols enable the computation of aggregate functions, such as sum and average, without exposing individual data values. Recent advancements have introduced error-bounded encoding techniques that allow approximate data representation within controlled error limits, reducing communication overhead while preserving analytical accuracy. This review examines secure aggregation methods incorporating such encoding strategies, including cryptographic approaches, coding-theoretic methods, federated learning-based techniques, and hybrid frameworks integrating edge computing and blockchain. While homomorphic encryption and secret sharing provide strong privacy guarantees, they often incur high computational costs, whereas encoding-based methods improve efficiency with minimal accuracy loss. Key challenges include balancing accuracy and efficiency, ensuring robustness against adversarial threats, and enabling real-time processing, highlighting the need for scalable and intelligent aggregation solutions.
A Systematic Review of Topology-Based Models for Protein–Protein Interaction Networks: Methods, Architectures, and Future Research Directions
Protein–protein interaction (PPI) networks play a crucial role in understanding cellular processes, disease mechanisms, and drug discovery. Topology-based models have emerged as powerful tools for analysing the structural and functional organization of these networks by leveraging graph theory, network science, and topological data analysis. This systematic review examines advances from 2018 to 2023 in topology-based modelling of PPI networks, focusing on methodologies, computational architectures, and emerging research directions. The review explores classical graph-theoretic approaches, including centrality measures, clustering, and modularity detection, alongside advanced techniques such as persistent homology, network embedding, and graph neural networks (GNNs). Special attention is given to algorithms for protein complex detection, topological scoring, and multi-scale network analysis. The findings indicate that while traditional topological models provide strong interpretability and biological relevance, modern hybrid approaches integrating machine learning and topological features significantly enhance prediction accuracy and scalability. However, challenges remain in handling noisy datasets, dynamic interactions, and computational complexity. Future research directions include topology-driven deep learning frameworks, multi-layer biological networks, and interpretable AI models for PPI analysis. This review provides a comprehensive foundation for developing next-generation topology-aware computational frameworks in systems biology.
A Comprehensive Review of Resource-Constrained Encryption Design with Hospital Information Systems: Security Models, Optimization Techniques, and Emerging Computing Applications
The rapid digital transformation of hospital information systems (HIS) has significantly improved healthcare delivery by enabling real-time patient monitoring, electronic health record (EHR) management, and telemedicine services. However, it has also introduced critical security challenges, particularly in resource-constrained environments involving IoT-based medical sensors, wearable devices, and embedded systems. These devices require efficient encryption mechanisms that ensure data confidentiality without excessive computational overhead. Traditional cryptographic methods, although secure, often fail to meet these requirements due to high energy consumption and processing demands. This paper provides a comprehensive review of resource-constrained encryption design in HIS, focusing on security models, optimization techniques, and emerging computing applications. It examines recent research on lightweight cryptography, homomorphic encryption, and quantum-resistant frameworks tailored for healthcare systems. The study highlights key trade-offs between security strength, efficiency, and energy usage, and explores integration with technologies such as artificial intelligence, edge computing, and blockchain. While lightweight and hybrid encryption models enhance performance, challenges remain in achieving scalable, secure, and user-friendly solutions, indicating important directions for future research.