International Journal on Advanced Computer Engineering and Communication Technology
Lightweight Cryptographic Framework with Graph Intelligence for Energy-Efficient MANET Protection
Mobile Ad Hoc Networks (MANETs) have emerged as highly dynamic and decentralized wireless communication systems capable of supporting military operations, disaster recovery, emergency response environments, intelligent transportation systems, healthcare monitoring, and Internet of Things (IoT) applications without relying on fixed communication infrastructure. However, MANETs are highly vulnerable to security threats due to their open wireless communication medium, distributed architecture, limited computational resources, dynamic topology, and absence of centralized management. Traditional cryptographic security mechanisms often introduce high computational overhead, excessive energy consumption, communication latency, and routing complexity, making them unsuitable for resource-constrained MANET environments. Furthermore, conventional intrusion detection systems frequently experience scalability limitations and insufficient adaptability to dynamic routing attacks such as black hole attacks, packet interception, and malicious routing manipulation. To address these challenges, this research proposes a Lightweight Cryptographic Framework with Graph Intelligence for Energy-Efficient MANET Protection that integrates lightweight cryptographic algorithms, graph-based security intelligence, adaptive trust evaluation, energy-aware secure routing, and intelligent anomaly detection into a unified MANET security architecture. The proposed framework utilizes lightweight symmetric encryption, elliptic curve cryptography, hash-based authentication, and graph neural intelligence to provide secure communication, confidentiality, integrity, and adaptive malicious node detection while minimizing computational overhead and energy consumption.
Energy-Aware Traffic Transmission Control in Smart Transportation Networks Using Metaheuristic Optimization
Smart transportation networks have become critical components of intelligent urban infrastructure due to the rapid growth of connected vehicles, autonomous transportation systems, Internet of Things (IoT)-enabled traffic environments, and next-generation wireless communication technologies. Modern transportation systems continuously generate massive traffic data streams requiring efficient communication management, adaptive traffic transmission control, low-latency routing, and energy-efficient network coordination. However, increasing vehicle density, dynamic mobility, communication congestion, limited energy resources, packet transmission delay, and unstable routing conditions create major challenges for traffic transmission reliability and intelligent transportation management. Conventional traffic control mechanisms and traditional routing algorithms often experience high computational complexity, excessive communication overhead, increased latency, inefficient energy utilization, and limited adaptability to highly dynamic smart transportation environments. To address these limitations, this research proposes an Energy-Aware Traffic Transmission Control Framework Using Metaheuristic Optimization for Smart Transportation Networks that integrates adaptive traffic scheduling, energy-efficient routing, metaheuristic optimization, intelligent congestion control, and real-time communication management into a unified transportation communication architecture. The proposed framework utilizes hybrid metaheuristic optimization techniques, intelligent traffic prioritization, dynamic route adaptation, and energy-aware communication coordination to optimize packet transmission efficiency, traffic flow stability, communication reliability, and network scalability while minimizing communication delay, packet loss, routing overhead, and energy consumption. The framework continuously analyzes traffic density, vehicle mobility, communication load, transmission latency, and network congestion to dynamically optimize traffic routing and transmission scheduling decisions. Experimental evaluation demonstrates that the proposed energy-aware metaheuristic framework significantly improves throughput, packet delivery ratio, traffic transmission efficiency, congestion mitigation capability, routing stability, and energy utilization while reducing communication latency, packet loss, and network overhead compared with conventional smart transportation communication systems. The proposed architecture establishes a scalable, adaptive, intelligent, and energy-efficient communication control framework suitable for next-generation smart transportation ecosystems and intelligent vehicular networking infrastructures.
Wavelet-Enhanced Neural Framework for Accurate Detection of Cardiac Arrhythmia
Cardiac arrhythmia is a life-threatening cardiovascular disorder characterized by irregular heart rhythms, requiring accurate and early detection for effective diagnosis and treatment. Traditional electrocardiogram (ECG) analysis methods often struggle with noise interference, feature extraction limitations, and reduced classification accuracy in real-world clinical environments. To address these challenges, this study proposes a Wavelet-Enhanced Neural Framework for accurate detection of cardiac arrhythmia by integrating discrete wavelet transform (DWT) for signal denoising and feature enhancement with deep neural network-based classification. The wavelet transform decomposes ECG signals into multi-resolution frequency components, enabling effective extraction of clinically significant features, while the neural network performs automated pattern recognition and arrhythmia classification. The proposed framework improves robustness against noise, enhances feature representation, and increases detection accuracy compared to conventional machine learning and signal processing approaches. Experimental evaluation demonstrates that the hybrid wavelet-neural model achieves superior performance in terms of accuracy, sensitivity, specificity, and F1-score. The results confirm that the proposed method is highly effective for real-time, reliable, and automated cardiac arrhythmia detection in healthcare monitoring systems.
Hybrid Convolutional Attention Models for Intelligent Cardiac Risk Assessment
Cardiovascular diseases remain the leading cause of global mortality, necessitating advanced intelligent systems for early and accurate cardiac risk assessment. Traditional risk prediction methods often rely on static clinical parameters and fail to capture complex nonlinear interactions present in physiological signals. To address these limitations, this study proposes a Hybrid Convolutional Attention Model (HCAM) for intelligent cardiac risk assessment using electrocardiographic and clinical data. The proposed framework integrates Convolutional Neural Networks (CNNs) for local feature extraction with attention mechanisms to enhance feature weighting and interpretability. The CNN module captures spatial dependencies in cardiac signals, while the attention layer dynamically focuses on the most relevant physiological patterns contributing to cardiac risk. This hybrid architecture improves both predictive accuracy and model interpretability, making it suitable for clinical decision support systems. The model is evaluated using standard cardiovascular datasets, and performance is measured in terms of accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that the proposed HCAM significantly outperforms conventional machine learning models and standard deep learning architectures. The findings highlight the potential of attention-based deep learning models in improving early cardiac risk detection and supporting preventive healthcare systems.
Intelligent Neurological Assessment Using Sparse Graph Learning on EEG Signals
Neurological disorders such as epilepsy, Parkinson’s disease, and cognitive impairment require accurate and early diagnosis for effective treatment and management. Electroencephalography (EEG) provides a non-invasive method to capture brain electrical activity; however, EEG signals are inherently noisy, high-dimensional, and exhibit complex spatial-temporal dependencies, making automated neurological assessment challenging. This study proposes an Intelligent Neurological Assessment framework using Sparse Graph Learning (SGL) on EEG signals. The proposed model represents EEG channels as graph nodes and learns sparse connectivity patterns that reflect functional brain interactions. A graph learning mechanism is employed to construct adaptive adjacency matrices, while deep graph neural networks extract meaningful spatial-temporal representations. The sparsity constraint improves computational efficiency and reduces redundant connections, enhancing interpretability and robustness. The proposed framework is evaluated on standard EEG datasets, and performance is measured using accuracy, sensitivity, specificity, F1-score, and ROC-AUC. Experimental results demonstrate that the sparse graph learning approach significantly outperforms conventional machine learning and deep learning models. The framework is well-suited for real-time neurological diagnosis and clinical decision-support systems.
Optimization-Driven Intelligent Diagnosis of Neuropsychiatric Conditions Using EEG Analytics
Neuropsychiatric disorders such as depression, schizophrenia, anxiety disorders, bipolar disorder, and attention-deficit/hyperactivity disorder represent a major global healthcare burden due to their increasing prevalence and complex neurological manifestations. Early and accurate diagnosis remains challenging because traditional clinical assessments often rely on subjective evaluations and behavioral observations. Electroencephalography (EEG) has emerged as an effective non-invasive neuroimaging technique capable of capturing neural activity associated with cognitive and psychiatric abnormalities. However, EEG signals are characterized by high dimensionality, nonlinearity, noise, and temporal variability, making automated diagnosis a difficult task. To address these challenges, this study proposes an Optimization-Driven Intelligent Diagnosis Framework for Neuropsychiatric Conditions Using EEG Analytics (OIDF-NC) that integrates EEG signal processing, optimization-based feature selection, deep representation learning, and intelligent classification mechanisms.
Dual-Channel Neural Intelligence for Healthcare Localization in Assisted Living Environments
Healthcare localization has become a critical component of modern assisted living environments, enabling continuous monitoring, emergency response, patient tracking, and intelligent healthcare service delivery. With the growing elderly population and increasing demand for independent living solutions, accurate indoor localization technologies are essential for ensuring patient safety, improving quality of care, and supporting healthcare professionals in real-time decision-making. Conventional localization systems based on Wi-Fi, Bluetooth, RFID, and sensor networks often suffer from signal fluctuations, environmental interference, multipath effects, and reduced positioning accuracy. Recent advances in artificial intelligence have demonstrated significant potential for enhancing healthcare localization through intelligent feature learning and adaptive decision-making mechanisms. This research proposes a Dual-Channel Neural Intelligence Framework for Healthcare Localization in Assisted Living Environments (DCNI-HL) that integrates dual-channel sensor analytics, neural representation learning, and intelligent localization strategies for accurate patient positioning and activity monitoring.
Wireless Sensor-Driven Smart Agriculture Using Scalable Quantum Convolutional Models
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
Blockchain-Integrated Deep Intelligence for Distributed Wireless Network Protection
Distributed wireless networks are increasingly exposed to security threats such as data tampering, spoofing attacks, node compromise, and unauthorized access. Traditional security mechanisms struggle to provide scalable, real-time, and tamper-resistant protection in dynamic network environments. To address these challenges, this study proposes a Blockchain-Integrated Deep Intelligence framework for Distributed Wireless Network Protection (BDI-DWNP). The proposed model integrates blockchain technology for decentralized security, immutability, and trust management with deep learning-based intrusion detection for intelligent threat classification. The blockchain layer ensures secure transaction validation and node authentication, while the deep learning module analyzes network traffic patterns to detect anomalies and malicious behavior. The system is evaluated using network intrusion datasets, and performance is measured using accuracy, precision, recall, F1-score, and detection latency. Experimental results demonstrate that the proposed framework significantly improves detection accuracy and enhances network security compared to conventional intrusion detection systems. The framework is suitable for IoT networks, wireless sensor networks, and large-scale distributed communication systems.
Secure Cloud-IoT Communication Using Siamese Heterogeneous Neural Architectures
The rapid growth of Internet of Things (IoT) technologies and cloud computing infrastructures has transformed modern digital ecosystems by enabling intelligent connectivity, large-scale data analytics, real-time monitoring, and automated decision-making across diverse applicAbstractation domains. Despite these advancements, secure communication between cloud platforms and IoT devices remains a significant challenge due to increasing cyber threats, unauthorized access attempts, data breaches, spoofing attacks, and communication vulnerabilities. Traditional security mechanisms often struggle to provide adaptive protection against evolving attack patterns and heterogeneous communication environments. Recent developments in artificial intelligence and deep learning have demonstrated substantial potential for enhancing cybersecurity through intelligent feature learning and adaptive threat analysis. This research proposes a Secure Cloud-IoT Communication Framework Using Siamese Heterogeneous Neural Architectures (SCIC-SHNA) that integrates cloud communication analytics, Siamese neural learning, heterogeneous feature extraction, adaptive security intelligence, and intelligent threat detection mechanisms. The proposed framework utilizes parallel neural branches to learn similarities and dissimilarities among communication patterns while effectively capturing complex relationships between cloud and IoT network traffic.
Elliptic Curve Enhanced Neural Encryption Models for Privacy Preservation in Smart Healthcare
The rapid digitization of healthcare systems has led to the widespread adoption of smart healthcare technologies, including wearable devices, remote patient monitoring systems, and IoT-enabled medical infrastructures. However, the sensitive nature of medical data raises critical concerns regarding privacy, security, and unauthorized access. Traditional encryption methods often struggle to balance computational efficiency and strong cryptographic security in resource-constrained healthcare environments. This study proposes an Elliptic Curve Enhanced Neural Encryption Model (ECENEM) for privacy preservation in smart healthcare systems. The proposed framework integrates Elliptic Curve Cryptography (ECC) for lightweight and secure key generation with deep neural networks for adaptive encryption pattern learning and anomaly-resistant secure communication. The model enhances privacy protection by combining mathematical cryptographic strength with AI-driven dynamic encryption behavior. Performance is evaluated using encryption strength, computational overhead, latency, and security resistance against common attacks. Experimental results demonstrate that the proposed model achieves higher security efficiency with lower computational cost compared to conventional encryption techniques.
Intelligent Thermo–Electro–Mechanical Modeling of MEMS Devices Using Deep Fusion Learning
Micro-Electro-Mechanical Systems (MEMS) devices operate under complex multiphysics interactions involving thermal, electrical, and mechanical domains. Accurate modeling of these coupled phenomena is essential for improving device reliability, performance, and design optimization. Traditional finite element methods (FEM) and analytical models often struggle with high computational cost and limited adaptability for nonlinear MEMS behavior. This study proposes an Intelligent Thermo–Electro–Mechanical Modeling framework for MEMS devices using Deep Fusion Learning (DFL-TEMM). The proposed approach integrates deep neural networks to fuse thermal, electrical, and mechanical feature representations for accurate predictive modeling of MEMS behavior under varying operating conditions. The model leverages multi-branch neural architectures to independently learn domain-specific features and a fusion layer to combine them into a unified representation. Performance is evaluated using prediction accuracy, RMSE, computational efficiency, and generalization ability. Experimental results demonstrate that the proposed model significantly improves accuracy and reduces simulation time compared to traditional FEM-based approaches.
Bandwidth-Constrained Intelligent Control Framework for MEMS Motion Stabilization
Micro-Electro-Mechanical Systems (MEMS) are widely employed in aerospace, automotive, biomedical, industrial automation, navigation, and communication applications due to their compact size, high sensitivity, low power consumption, and rapid response characteristics. However, maintaining stable MEMS motion under bandwidth-constrained communication and control environments remains a significant challenge. Limited communication bandwidth, transmission delays, signal losses, sensor noise, and dynamic environmental disturbances can adversely affect motion stability, control accuracy, and system reliability. Conventional control approaches often require continuous high-rate feedback transmission, resulting in excessive communication overhead and reduced efficiency in resource-constrained environments. Therefore, intelligent control mechanisms capable of achieving robust motion stabilization while operating under limited bandwidth conditions have become increasingly important. This research proposes a Bandwidth-Constrained Intelligent Control Framework for MEMS Motion Stabilization (BCICF-MMS). The framework integrates intelligent feedback scheduling, adaptive bandwidth allocation, deep learning-based state estimation, predictive control mechanisms, and dynamic stabilization strategies to ensure accurate MEMS motion control under communication constraints. The proposed architecture utilizes neural state prediction models to compensate for delayed or missing feedback information while optimizing control decisions using adaptive bandwidth-aware policies. Multi-domain motion features, including displacement, velocity, acceleration, and vibration characteristics, are fused to improve stabilization performance and disturbance rejection capability.
Intelligent Energy Resource Coordination Using Connected Smart Management Devices
The rapid growth of smart grids, renewable energy systems, distributed energy resources, and Internet of Things (IoT)-enabled energy infrastructures has transformed the modern energy management landscape. Efficient coordination of energy resources has become a critical challenge due to the increasing complexity of energy generation, storage, distribution, and consumption processes. Traditional energy management systems often suffer from limited adaptability, delayed decision-making, inefficient resource allocation, and inadequate integration of renewable energy sources. These limitations lead to energy wastage, operational inefficiencies, and reduced grid reliability. Recent advancements in connected smart management devices, intelligent sensing technologies, artificial intelligence, and real-time communication networks provide new opportunities for adaptive energy coordination and optimization. This research proposes an Intelligent Energy Resource Coordination Using Connected Smart Management Devices (IERC-CSMD) framework that integrates smart sensors, connected management devices, intelligent monitoring systems, machine learning-based optimization, dynamic resource allocation, and real-time energy control mechanisms. The proposed framework continuously monitors energy demand, generation capacity, storage availability, and network conditions to coordinate energy resources efficiently.
IoT-Enabled Demand Response Framework for Cost-Efficient Smart Energy Systems
The rapid growth of smart grids, Internet of Things (IoT) technologies, and distributed energy resources has transformed traditional power systems into intelligent and interconnected energy ecosystems. Demand Response (DR) programs have emerged as an effective solution for balancing electricity demand and supply while reducing operational costs and improving grid reliability. However, conventional demand response mechanisms often face challenges related to limited real-time monitoring, inefficient load management, delayed decision-making, and insufficient consumer participation. To address these limitations, this research proposes an IoT-Enabled Demand Response Framework for Cost-Efficient Smart Energy Systems (IDRF-CSES). The framework integrates IoT-based energy monitoring, real-time demand forecasting, intelligent load scheduling, adaptive consumer engagement, and cost optimization mechanisms to improve energy efficiency and grid performance. The proposed framework utilizes smart meters, IoT sensors, cloud-based analytics, and machine learning algorithms to continuously monitor energy consumption patterns and dynamically adjust electricity demand according to pricing signals and grid conditions. Intelligent scheduling and automated control mechanisms optimize energy utilization while minimizing electricity costs and peak demand.
Scalable Intelligent Energy Allocation for Renewable Vehicle Charging Networks
The rapid adoption of electric vehicles (EVs) combined with increasing integration of renewable energy sources has created significant challenges in managing large-scale vehicle charging networks. The intermittent nature of renewable energy, coupled with dynamic charging demand, requires intelligent, scalable, and adaptive energy allocation mechanisms. This study proposes a Scalable Intelligent Energy Allocation Framework for Renewable Vehicle Charging Networks (SIEA-RVCN). The framework leverages artificial intelligence-based prediction models and optimization techniques to dynamically allocate renewable energy across distributed EV charging stations. The system aims to maximize energy utilization, minimize grid overload, and ensure fair distribution among charging nodes. The proposed model is evaluated using simulated EV charging datasets with renewable energy variability. Performance is measured in terms of energy efficiency, load balancing accuracy, charging delay, and system scalability. Experimental results demonstrate that the proposed approach significantly improves energy distribution efficiency compared to traditional scheduling and heuristic-based methods.
Hybrid Intelligent Controller Design for Advanced EV Onboard Chargers
The rapid advancement of electric vehicles (EVs) has increased the demand for highly efficient, compact, and intelligent onboard charging systems. Traditional onboard chargers rely on conventional control strategies such as PID or fixed-parameter controllers, which often struggle to handle nonlinear dynamics, varying load conditions, and bidirectional power flow requirements. This study proposes a Hybrid Intelligent Controller (HIC) for Advanced EV Onboard Chargers, integrating artificial intelligence-based adaptive control with classical control theory and power electronic optimization techniques. The hybrid controller combines neural network-based prediction, fuzzy logic decision-making, and robust feedback control to improve charging efficiency, stability, and dynamic response. The proposed system is evaluated under varying load conditions, grid disturbances, and battery state-of-charge variations. Performance metrics include efficiency, voltage stability, harmonic distortion, and response time. Experimental results demonstrate superior performance compared to conventional control strategies in EV onboard charging systems.
Equivariant Neural Learning Models for Financial Trend Forecasting
Financial markets exhibit complex, nonlinear, and highly dynamic behaviors, making accurate trend forecasting a challenging task. Traditional statistical models often fail to capture structural invariances and spatial-temporal dependencies present in financial time-series data. This study proposes an Equivariant Neural Learning Model (ENLM) for financial trend forecasting. The framework leverages equivariant neural networks, which preserve transformation consistency under input shifts and structural variations, improving robustness and generalization in volatile financial environments. The model integrates deep learning-based sequence modeling with symmetry-aware feature extraction to enhance predictive accuracy. The proposed system is evaluated on historical financial datasets, and performance is measured using accuracy, RMSE, directional accuracy, and prediction stability. Experimental results demonstrate that equivariant neural models outperform conventional deep learning and statistical forecasting techniques in capturing market trends.
Deformable Graph Intelligence for Financial Market Prediction Using NLP
Financial markets are highly complex systems influenced by structured relationships between assets and unstructured textual information from news, social media, and financial reports. Traditional forecasting models often fail to effectively integrate relational graph structures with natural language signals. This study proposes a Deformable Graph Intelligence Framework (DGI-NLP) for financial market prediction. The model integrates graph neural networks (GNNs) with natural language processing (NLP) and introduces deformable graph attention mechanisms to dynamically adjust inter-asset relationships based on evolving market sentiment and structural changes. The proposed framework is evaluated on multimodal datasets combining historical price data and financial news sentiment. Performance is measured using prediction accuracy, RMSE, F1-score for trend classification, and robustness under market volatility. Experimental results demonstrate that deformable graph intelligence significantly improves forecasting performance compared to conventional graph-based and NLP-based financial models.
Transformer-Driven Deep Learning Framework for Brain Tumour Detection
Brain tumours are among the most life-threatening neurological disorders, requiring accurate and timely diagnosis for effective treatment planning and improved patient outcomes. Magnetic Resonance Imaging (MRI) has become the primary imaging modality for detecting and characterizing brain tumours due to its superior soft-tissue contrast and non-invasive nature. However, manual analysis of MRI scans is time-consuming, subjective, and highly dependent on radiological expertise. Conventional machine learning and convolutional neural network (CNN)-based approaches have demonstrated promising performance in automated brain tumour detection, yet they often struggle to capture long-range spatial dependencies and complex tumour characteristics. Recent advancements in Transformer architectures have introduced powerful self-attention mechanisms capable of learning global contextual information from medical images. This research proposes a Transformer-Driven Deep Learning Framework for Brain Tumour Detection (TDDLF-BTD) that integrates MRI preprocessing, intelligent feature extraction, transformer-based attention learning, tumour classification, and decision optimization
Intelligent Financial Risk Analytics Using Auto-Associative Convolutional Neural Networks
Financial risk analytics plays a crucial role in modern financial systems by supporting investment management, portfolio optimization, credit assessment, fraud detection, and market stability analysis. However, accurately identifying and predicting financial risks remains a challenging task due to market volatility, high-dimensional financial datasets, nonlinear relationships among financial variables, and rapidly changing economic conditions. Traditional statistical risk assessment models often struggle to capture hidden patterns and complex dependencies present in financial data, leading to reduced prediction accuracy and ineffective risk management strategies. Recent advancements in deep learning and neural architectures have provided new opportunities for developing intelligent financial risk analytics systems capable of improving prediction reliability and decision-making performance. This research proposes an Intelligent Financial Risk Analytics Framework using Auto-Associative Convolutional Neural Networks (IFRA-AACNN) to enhance financial risk prediction, anomaly detection, portfolio risk assessment, and investment decision support. The framework integrates financial data preprocessing, deep feature extraction, auto-associative learning mechanisms, convolutional neural architectures, risk estimation models, and intelligent analytics into a unified framework. The auto-associative convolutional neural network learns hidden representations of financial patterns and reconstructs complex financial behaviors to identify potential risks and anomalies.
LG Fusion++: Adaptive Nonlinear Frequency-Aware and Task-Driven Multimodal Image Fusion
Multimodal image fusion aims to combine the complementary information from infrared, visible and medical imaging modalities to generate a single enhanced image. Existing methods based on wavelet transform and convolutional neural network are often not able to simultaneously preserve the global structural information and the local texture details well. In this paper, we propose LG Fusion++, an adaptive nonlinear frequency-aware fusion framework that can effectively separate low-frequency structural information and high-frequency texture details. The framework incorporates lightweight attention mechanisms and Mamba based context modeling for efficient global feature learning. A contrast-aware perceptual loss and multi-task optimization strategy are adopted to achieve better edge preservation, texture enhancement and downstream task performance. The experimental results indicate that the proposed framework outperforms existing approaches in terms of entropy, edge preservation, and structural similarity, making it suitable for surveillance, medical imaging, and autonomous systems.
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Deep Learning-based Reliability Control for Robust Image Watermarking in Encrypted and Compressed Domains
In order to meet the increasing need for cloud storage and multimedia transmission, ownership authentication of digital images must be securely achieved. Traditional water-marking techniques may lose robustness after image encryption and lossy compression. In this paper, a deep learning-based reliability control framework of image watermarking in the encrypted and compressed domains, using a Recurrent Neural Network (RNN) structure, is presented. In this approach, the RNN structure can effectively model the sequential dependencies among image blocks to adaptively adjust the watermark strength according to the contextual features, thereby achieving a balance between robustness and imperceptibility. The proposed method can be securely applied in the encrypted domain and could resist lossy compression attacks, including JPEG and HEVC intra-coding attacks. In the experimental results, improvements in terms of PSNR, SSIM, and BER are achieved compared to traditional methods. The RNN-based reliability control framework of image watermarking is efficient and secure for real-time image and video applications.
Early Detection of Paddy Crop Diseases Using Drone Images and Machine Learning–Deep Learning Techniques
Global food security is seriously threatened by paddy diseases, which have the ability to reduce yearly harvests by 20–40%. An automated approach for targeted pesticide distribution and real-time paddy disease detection is presented in this research. A high-resolution camera-equipped drone (Drone 1) takes live field images and sends them to a cloud platform over Wi-Fi. A hybrid deep learning model that combines EfficientNet-B7 and an Improved Swin Transformer is used to extract disease features from images after they have been denoised using a Modified Non-Local Means Filter (M-NLMF). The leaf state is then divided into four groups by an Extreme Learning Machine (ELM) 4 classes. Once the disease is confirmed, a second drone (Drone 2) finds the GPS-tagged diseased area on its own and exclusively sprays the afflicted plants with pesticide. The technology drastically lowers chemical waste and environmental impact while achieving 99.30% classification accuracy.
An Attention-Driven Multi-Scale Ensemble Framework for Underwater Image Enhancement
Underwater image enhancement plays a critical role in improving image quality for applications such as marine exploration, underwater robotics, environmental monitoring, surveillance, and computer vision. However, underwater images often suffer from severe degradation caused by light absorption, scattering, low contrast, color distortion, and reduced visibility. To address these challenges, this study proposes an advanced underwater image enhancement framework that combines hybrid preprocessing techniques with deep learning-based enhancement strategies. Initially, Underwater White Balance (UWB) and Variational Contrast and Saturation Enhancement (VCSE) are applied to restore color balance and improve image contrast. The enhanced images are further processed using ensemble attention-driven convolutional networks, multi-scale feature fusion, and adaptive contrast-saturation optimization. Experimental evaluation using standard image quality metrics demonstrates significant improvement in color restoration, structural detail preservation, visibility, and overall enhancement performance across diverse underwater environments.
Clock-Gated Dual Edge-Triggered Flip-Flop Architectures for Low-Power VLSI Systems
As digital systems move toward ultra-low power operation and higher performance, reducing clockrelated power has become a major challenge in modern VLSI design, from the clock network alone can consume a large portion of total chip power. This work presents a Dual Edge Triggered flip-flop clock-gated architecture aimed at minimizing dynamic power dissipation while maintaining high-speed performance. Both rising and falling clock edges are captured by Dual Edge Triggered flip-flops, allowing the frequency of clock to be reduced for the same throughput, thereby lowering switching losses; however, conventional DET designs still suffer from unnecessary clock transitions during idle conditions. To overcome this limitation, a fine-grain clock gating mechanism is introduced as the primary contribution of this work, where gating logic intelligently disables clock activity when data transitions are absent, significantly reducing switching power in both the flip-flop and the clock distribution path. Six DET flip-flop architectures—SDET, TSPC-SDET, FS-TSPC-DET, STC-DET, LPLD-DET, and LPHD-DET—are designed and simulated using predictive 45nm CMOS technology, and comprehensive SPICE simulations are performed to evaluate propagation delay, average power, and area across 0.5 V to 1.0 V of voltage supply at 50 MHz. Simulated results show that integrating clock gating achieves substantial power savings compared to non-gated DET designs with minimal delay overhead. Among the evaluated designs, the proposed clock-gated LPHD-DET and LPLD-DET flip-flops provide the best performance balance between speed, area, and power optimization, making the architecture highly applicable for low-power VLSI systems such as IoT devices, wearable electronics, and batteryoperated embedded systems.
Digital Twin-Driven Engineering Management Systems an Integrated Approach for Predictive Decision-Making and Lifecycle Optimization in Smart Manufacturing
With the rapid development of Industry 4.0 technologies, traditional manufacturing paradigms have been profoundly reshaped. The field of intelligent manufacturing urgently requires a data-driven engineering management framework. This paper originally proposes the digital twin-driven DT-EMS system, which builds a two-way data pipeline connecting physical assets and their virtual replicas. It supports predictive decision-making and full-lifecycle optimization, and is equipped with functions including reducing production and operation costs while boosting efficiency, as well as full-link resource allocation and quality control. Verified via a simulation case, the system delivers significant improvements over traditional management methods across three core metrics: response speed, decision accuracy, and manufacturing flexibility. The strategic embedding of digital twin technology can serve as a core engine for the future ecological transformation of intelligent manufacturing.
Design Of Wireless Power Transfer System Including Dynamic Charging for Electric Vehicle Applications
An electric automobile should have a Wireless Power Transfer (WPT) system visible when it has the appropriate ground clearance. This thesis uses the Archimedes optimization algorithm (AOA) to describe a wireless power transfer system with dynamic charging for electric car applications. In order to accomplish wireless charging of electric vehicles, the next stage in the regularization of modern cars and electric vehicle chargers is to do away with the need for wires connections sandwiched between the vehicle and charger. Because this state-of-the-art technology does not require user intervention, it may be utilized with any electric vehicle (EV). One of the biggest obstacles to wireless charging is ground clearance, which lowers power transfer efficiency. The concept of WPT for variable ground clearance is explained and the related cordless charging device is analyzed. It was demonstrated that the cordless charger device could deliver about 45 volts of energy and had a maximum ground clearance of 20 cm. For charging an electric car, a 3.8KWh super capacitor (SC) and a 4.5KWh battery are adequate.
HALE-Net: An Attention-Gated Heterogeneous Ensemble of Convolutional and Transformer Networks for High-Precision Biomedical Image Classification
Automated interpretation of biomedical images is limited by high inter-class similarity, severe class imbalance, and the divergent inductive biases of individual deep networks, which cause single-architecture classifiers to generalise inconsistently across imaging modalities. This work introduces HALE-Net, a heterogeneous attention-gated learner ensemble for high-precision biomedical image diagnosis. The objectives are threefold: to exploit the complementary representational strengths of convolutional, transformer, and hybrid backbones; to fuse them through a confidence-aware mechanism that suppresses unreliable predictions; and to preserve deployability on resource-limited clinical hardware. Three base learners—ConvNeXt-T, Swin-T, and a compact convolution–attention hybrid (ConvFormer)—are trained under a class-balanced focal objective and combined by an attention-gated stacking meta-learner that weights each learner per sample according to its calibrated posterior and predictive entropy. Computational cost is contained through depthwise-separable convolutions, a confidence-triggered early-exit path, and knowledge distillation of the ensemble into a 6.2 M-parameter student (HALE-S). On two public benchmarks—HAM10000 (10,015 dermoscopic images, seven classes) and BUSI (780 breast-ultrasound images, three classes)—HALE-Net attains 94.8% accuracy, 0.912 macro-F1 and 0.991 AUC on HAM10000, and 97.9% accuracy, 0.972 macro-F1 and 0.995 AUC on BUSI, improving over the strongest single backbone by 2.0–2.7 percentage points in macro-F1. The distilled student retains 93.9% accuracy at one-eleventh of the parameters and a 7.3 ms latency, while early-exit lowers the mean cost of the full model by approximately 45%. The findings indicate that calibrated heterogeneous fusion, rather than deeper individual networks, is an effective route to dependable diagnosis, with direct relevance to point-of-care dermatological screening, breast-cancer triage, and other real-time computer-aided diagnosis settings.
CATR-Net: A Cross-Attention-Driven Transformer–CNN Framework with Bidirectional Feature Fusion for High-Accuracy Image Recognition
Introduction: Convolutional neural networks (CNNs) encode strong local inductive biases but capture global context poorly, whereas vision transformers (ViTs) model long-range dependencies yet are data-hungry and weak at fine spatial detail. Hybrid CNN–transformer models attempt to reconcile these properties, but the majority couple the two streams through sequential stacking or one-directional attention, so the interaction between local and global evidence remains shallow and the fusion weights are fixed rather than input-adaptive. Objectives: This study designs a dual-branch architecture in which convolutional and transformer representations are exchanged symmetrically and combined through a data-dependent gate, with the aim of raising recognition accuracy without a disproportionate increase in cost. Methodology and proposed model: We propose CATR-Net, a framework whose parallel ConvNeXt-style local branch and Swin-style global branch are linked by a Bidirectional Cross-Attention Fusion (BCAF) block. In BCAF, convolutional queries attend to transformer keys and values and, simultaneously, transformer queries attend to convolutional keys and values; the mutually enhanced descriptors are then merged by an Adaptive Gated Fusion module that predicts per-channel mixing coefficients. The network is trained end-to-end with label-smoothed cross-entropy. Results: On CIFAR-100 and CUB-200-2011, CATR-Net attains 86.8% and 91.2% top-1 accuracy, improving over the strongest recent baseline (MaxViT) by 1.7 and 1.1 percentage points, respectively, and over CoAtNet by 1.9 and 1.6 points, while retaining a comparable parameter budget (28.6 M) and 4.7 GFLOPs. Macro-averaged F1 reaches 0.865 and 0.909, and an ablation confirms that bidirectionality and gated fusion each contribute measurable gains. Conclusion: Symmetric cross-branch attention with adaptive gating is an effective mechanism for high-accuracy recognition. Real-time applications—The framework is applicable to fine-grained visual inspection, biomedical image screening, intelligent surveillance, and on-device retrieval where both discriminative detail and global scene understanding are required.
Automated Driver Drowsiness Detection System Using Artificial Intelligence
This paper presents an AI-based driver drowsiness detection system that monitors driver fatigue in real time using a low-cost webcam. The system detects facial features with OpenCV and classifies eye state using a Convolutional Neural Network (CNN). It also analyses yawning and ocular redness to estimate a weighted drowsiness score. An adaptive calibration process personalises detection for each driver, while graded audible alerts provide timely warnings. To improve energy efficiency, the system supports Manual, Continuous, and Auto operating modes. The proposed approach is low-cost, non-intrusive, and suitable for various vehicles, helping reduce fatigue-related accidents and improve road safety.
Production-Oriented Extensions for a User-Space Runtime Function Interposition Framework
The runtime function interposition allows for changing of running applications without a complete service restart. The patching mechanism is basic; while it allows function redirection to be done in a way that is safe for the instructions, it is also important for practical deployment to have monitoring, authentication, validation, rollback and administrative control. It is an extension of a user space runtime interposition framework for x86-64 Linux userspace and production mechanisms for patch deployment that provide more safety and observability. The proposed method will take runtime measurement metrics, administrative interface, patch registration and backup management, authentication and signature verification, canary validation, and automatic rollback and place them in the current patching pipeline. Process control is implemented via ptrace, instruction-boundary via Capstone, and function redirection via inline trampolines. The extended workflow first resolves the target symbol, checks the patch, suspends and audits the application threads and saves the original instructions, adds the trampoline and restores the application's execution. The patch can then be kept in place in the post deployment environment or automatically reverted to the original implementation if a canary failure occurs. These extensions provide better observability, recovery and operational reliability of live patching while maintaining the light weight user space architecture.
Face Recognition Based Attendance System
Attendance management is a fundamental administrative activity in educational institutions, corporate organizations, and government offices. Conventional attendance recording methods, including manual registers, RFID cards, and biometric systems, often suffer from limitations such as time consumption, human errors, proxy attendance, and increased administrative workload. To address these challenges, this research presents a Face Recognition Based Attendance System that automates attendance recording through advanced computer vision and facial recognition technologies. The proposed system is developed using Python, OpenCV, the Face Recognition library, and SQLite database management. It captures facial images through a webcam, performs face detection and preprocessing, extracts unique facial features, and compares them with stored facial encodings for identification. Once a valid match is identified, the system automatically records attendance along with the corresponding date and time, eliminating the need for manual intervention. The integration of image processing techniques enhances recognition accuracy under varying lighting conditions, facial expressions, and viewing angles. The system provides a contactless, secure, and efficient attendance management mechanism that minimizes attendance fraud and improves operational transparency. Furthermore, the centralized database enables effective storage, retrieval, and reporting of attendance records. Experimental evaluation demonstrates high recognition accuracy, real-time processing capability, and reliable performance in practical environments. The proposed solution significantly reduces administrative effort while enhancing attendance monitoring efficiency. Due to its scalability, cost-effectiveness, and ease of deployment, the system is well suited for schools, colleges, universities, offices, industries, and other organizations requiring automated and intelligent attendance management solutions.