This research presents a systematic analysis framework for wind farm integration in scaled IEEE 33-bus agricultural distribution systems (×26.38, representing 98 MW peak agricultural load), focusing on comprehensive power quality, reliability, and economic assessment. The scaled system exhibits baseline voltage violations (24 buses) characteristic of extended radial distribution topologies serving concentrated agricultural loads, consistent with real utility system characteristics in Indonesian agricultural regions. Four wind penetration scenarios (0%, 15%, 30%, 45%) are evaluated using an integrated backward-forward sweep methodology coupling fundamental power flow with empirical harmonic calculations validated against full harmonic load flow analysis (±0.3% accuracy). The analysis employs realistic harmonic injection models for VFD-dominated agricultural loads and full-converter wind turbines, time-domain operational profiles capturing diurnal variations, and revised reliability modeling incorporating protection coordination constraints and battery energy storage system limitations. Results demonstrate that 30% penetration achieves optimal multi-objective performance validated through systematic sensitivity analysis across 10-45% penetration levels: active power loss reduction of 35.5% (210.9 kW to 136.1 kW), harmonic distortion mitigation up to 46.1% THD_V reduction at the wind connection point (1.52% to 0.82%), and realistic reliability improvements of 5.0% SAIDI reduction (8.52 to 8.09 hours/year) accounting for islanding effectiveness constraints (19% successful islanding events with proper battery energy storage and protection infrastructure). The 45% penetration scenario introduces voltage regulation challenges (V_max = 1.052 p.u.) that offset incremental benefits. Economic analysis reveals that system-wide benefits (loss reduction: $670,000/year) substantially exceed agricultural productivity gains ($4,800/year with realistic nonlinear yield models), positioning wind integration as primarily a power quality and efficiency enhancement with supplementary agricultural reliability benefits. Practical implementation requires comprehensive voltage regulation equipment ($2.1-2.5M investment), harmonic filters for VFD-dominated buses ($240,000), and advanced protection schemes for islanding operation ($450,000-$700,000). The integrated analysis framework provides utilities and agricultural operators with quantitative guidance for optimal distributed generation deployment, balancing technical performance, economic viability, and operational constraints.

Virtual power plants (VPPs) are smart energy systems that aggregate geographically distributed energy resources (DERs), including renewable and non-renewable energy sources, energy storage devices, and controllable loads, into a single virtual plant. These plants are integrated into distribution networks to enhance grid stability and reliability. VPPs require a two-way communication framework to monitor and control all generation sources and loads, ensuring a balance between supply and demand while providing services to distribution or transmission network operators. In VPPs, various applications are used, each with specific communication quality requirements, such as reliability, latency, and bandwidth. Therefore, selecting communication technologies and protocols that meet these requirements and deliver optimal performance for each application is essential. This research paper presents a comprehensive review of the literature on VPPs, exploring their applications, communication requirements, and structural frameworks. It also examines the protocols and standards necessary to ensure reliability, security, and communication quality. Finally, the paper summarizes the global development and implementation of  VPPs over the past two decades.

The design of an invisible cloak has attracted attention owing to its potential use in espionage and military applications. Advances in computer vision and image processing have enabled the creation of invisible cloaks. This study presents the design and detection of an invisible cloak using a cost-effective monocular camera. The proposed algorithm uses the OpenCV library in Python to create and detect the cloak by analyzing individual pixels in video frames to identify areas with minimal or no change in pixel values. The approach relies on pixel-level analysis using Gaussian curves for detection. Experimental validation of self-created and publicly available datasets demonstrates the effectiveness of the method. Although the algorithm performs well under static environmental conditions, challenges remain in dynamic settings, which will be addressed in future work to improve robustness. This study contributes to the development of practical and affordable invisibility cloak technology and reliable detection methods to mitigate potential misuse.

This study examines the relationship betweenatmospheric factors [temperature and relative humidity] and network performance [call drop and Radio Resource Control] in Lokoja Metropolis. Monthly data collected from Globacom[GLO] office in Lokoja and Nigerian Meteorological Agency [NIMET] office in Lokoja over a one-year period reveals significant correlations between atmospheric variables [Temperature and relative humidity] and network performance [Call drop and Radio Resource Control]. We observed that call drop is directly proportional to temperature and relative humidity, while Radio Resource Control [RRC] has an inverse proportionality with temperature and relative humidity, assuming other meteorological variables are kept constant. Statistically, call drop showed a positive correlation of 0.35493 with temperature and 0.63769 with relative humidity, while RRC showed a positive correlation of 0.37289 with temperature and 0.5756 with relative humidity. Taken together, these findings indicated that increased temperature and humidity increased call drops and lower Radio Resource Control [RRC] success rates. This research has provided insights useful to the telecom operators and regulatory bodies to ensure network reliability, better resource allocation, environmental consideration and quality of service in tropical regions similar to Lokoja. Additionally, this research can also be useful in identifying key performance indicators, developing mitigation strategies, improving network maintenance and enhancing customer experience. Correlations were measured using Pearson correlation coefficients at a 5% significance level. These findings imply that network operators must account for atmospheric variability in optimizing reliability, resource allocation, and planning for mobile services in tropical climates. Despite being weak, this correlation is statistically significant and meaningful in real-world scenarios where multiple environmental and operational factors collectively influence call drops.

The Internet of Things (IoT) has ushered in significant advancements in networked technologies, yet it simultaneously raises concerns about the security and privacy of connected devices. Traditional authentication methods based on cryptographic protocols are increasingly vulnerable to attacks as the number of devices grows and attackers develop more sophisticated strategies. In this paper, we propose a novel quantum-based authentication method using Quantum Physical Unclonable Functions (QPUFs) to address the security challenges in IoT devices. Quantum PUFs exploit the inherent quantum properties of devices to generate unique, unclonable responses to challenges, providing a high level of security and resilience against attacks such as cloning, spoofing, and interception. We present the architecture of the proposed quantum authentication system, discuss the challenge-response protocol, and evaluate the system’s performance. Experimental results show that this approach offers strong security guarantees with minimal computational overhead. We Evaluate the Security, Scalability of our approach in simulated adverse IoT environment.

This research focuses on object detection using Convolutional Neural Networks (CNN) applied to underwater image datasets. Underwater images often suffer from issues such as low clarity and quality, which pose challenges for accurate object identification. To address this, the research employs image enhancement techniques, including image illumination methods, to improve image quality and facilitate object detection algorithms. Subsequently, the study developed algorithms capable of detecting objects and accurately predicting their categories. The primary objective is to achieve optimal accuracy and efficiency in underwater recognition. This research utilizes Machine Learning techniques through Tensor Flow and Image Processing to accomplish underwater object detection. Deep learning techniques, particularly feature learning, object classification, and detection, have gained significant attention and momentum. In this research we implemented different image enhancement techniques on dataset and evaluated their performance. While one metric, IQI (Image Quality Index), slightly favoured histogram equalization (HE), the other three metrics strongly favoured the enhanced version of HE known as Contrast Limited Adaptive Histogram Equalization (CLAHE).

Handwritten character recognition is a crucial challenge in artificial intelligence and computer vision, particularly for complex scripts like Devanagari. Devanagari is widely used in Hindi, Marathi, and Nepali and consists of intricate multilevel compound characters, ligatures, and highly variable handwriting styles. Despite advances in optical character recognition (OCR) technology, accurately recognizing handwritten Devanagari characters remains difficult. This study compares various deep learning models, including Convolutional Neural Networks (CNN), CNN-SVM, Long Short-Term Memory (LSTM), EfficientNet, and a newly proposed attention-based CNN model. Extensive experiments were conducted on a diverse dataset containing simple and compound handwritten Devanagari characters. The proposed attention-based CNN model outperforms traditional methods, achieving a recognition accuracy of 96.5%, significantly higher than CNN (88.0%), CNN-SVM (88.5%), LSTM (92.0%), and EfficientNet (93.0%). The study employs advanced data augmentation techniques to enhance model robustness, making it adaptable to various handwriting styles. The attention mechanism in the proposed CNN model allows for improved feature extraction, leading to higher recognition accuracy and efficiency. This research contributes to developing robust OCR systems for the Devanagari script, enabling improved digitisation and preservation of Indian languages. The proposed approach can be extended to other complex scripts like Bengali and Tamil, further advancing multilingual OCR technologies, is now exploratory and has not been experimentally verified in this study. Future work can address the need for thorough cross-script evaluation and transfer learning studies to verify the adaptability of the attention-based CNN architecture, despite its inherent script-agnostic nature. The findings of this study hold significant implications for text digitization, historical document preservation, and automated language processing applications.

The use of multimedia communication has grown significantly in recent years, which has raised demand for image data compression. One popular technique for representing an image in an efficient format is image compression. It results in low rates of transmission by precisely lowering the number of bits required to store the images. Since medical image data is growing so quickly, there is a lot of research being done on how to upload and store large amounts of medical images in real time while having a limited amount of storage space and network bandwidth. But still, at this time, medical image compression technology is unable to optimize both rate and distortion. The goal of the proposed hybrid compression technique is to increase compression performance without losing the standard of the image. Even though they need a lot of storage, 3D medical images provide detailed information about disease. Optimal Multi-linear Singular Value Decomposition (OMLSVD) and deep auto-encoders are used in the current work to compress 3D healthcare images. The Federated Learning technique addresses the issues of data privacy and leakage by having each user train a model on its dataset before sending the model's local weights to a global federated server. So, use a federated server to get a global dataset weight without leaking or publishing datasets, protecting privacy. The quality of 3D compression images can be improved by using the proposed Hybrid approach. Experimental evaluation demonstrates SSIM values near 1 and high PSNR, indicating excellent reconstruction quality. Additionally, it compares the image with the compressed JPEG2000 and the proposed Hybrid approach. Since the images have different storage sizes, they all appear to be identical.

The study applies machine learning algorithms to the diagnosis of heart disease. Data were collected from multiple sources in hospital and clinic records, along with time-based comparison other studies. The second part of the study, Clinical Decision Support, simulated the daily work of a physician and helped them make patient-centered medical decisions. The results revealed significant potential for machine learning to improve heart disease detection efficiency and accuracy, which could benefit future effective disease management and reduce patient burden. The study findings will enable future healthcare providers to harness new technology to achieve better prevention and superior care outcomes for heart disease screening. The study recommendations include optimal diagnostic skills and intervention-oriented preventive measures.

Plant diseases have a significant impact on global food security, especially in staple crops like maize (Zea mays). Traditional disease detection systems depend on professional visual inspection, which is labor-intensive, time-consuming, and not scalable for large agricultural areas. Convolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically. A curated dataset of approximately 7,000 high-resolution maize leaf photos was created, representing four classes: healthy, Common Rust (Puccinia sorghi), Northern Leaf Blight (Exserohilum turcicum), and Gray Leaf Spot (Cercospora zeae-maydis). Data were sourced from the Plant Village dataset, real-world field collections from Indian farms, and supplemented synthetically to simulate varied climatic circumstances. Advanced methods including as adaptive learning rate scheduling, gradient clipping, and significant data augmentation were used to train a bespoke CNN model that was improved by transfer learning with ResNet50 and VGG16 backbones. The model attained a test accuracy of 98.2%, beating classic machine learning algorithms like SVM (88.5%) and Random Forest (84.3%). Visualization approaches such as feature maps, Grad-CAM, and LIME improved interpretability and showed the model's capacity to locate disease-relevant features. Web-based user engagement is made possible by deployment-ready implementation, which enables farmers to upload leaf photos for immediate diagnosis. With the potential to cut maize crop losses by 20–30%, this research offers a scalable and affordable alternative to early disease detection in precision agriculture. Future research will investigate autonomous farm management with drone-based real-time surveillance and IoT system integration.

Lung cancer is responsible for many deaths from cancer around the globe, primarily because it is difficult to find malignant lung nodules early enough to be treatable. We developed a hybrid deep learning approach to the automated classification of lung nodules from chest computed tomography (CT) images. Our model uses convolutional neural networks (CNNs) for hierarchical feature extraction, an attention mechanism for feature refinement in targeted regions of interest, and a support vector machine (SVM) classifier for robust margin-based decision making. Furthermore, we use a patch-based learning strategy within the model to improve sensitivity to small and ambiguous lung nodules. The model is tested on the publicly available LIDC-IDRI dataset and achieves 94.2% accuracy, 95.1% recall, and an area under the receiver operating characteristic curve (AUC-ROC) score of 0.971, which outperforms multiple baseline deep learning methods. The proposed method provides a synergistic integration of attention-weighted feature enhancements and traditional machine learning classifications as compared to traditional end-to-end architectures, resulting in improved model generalization and interpretability. Grad-CAM visualizations are also used to provide qualitative insights into the model decision-making process. The proposed hybrid approach provides a novel and interpretable solution for the classification of lung nodules from CT images that may assist in the development of computerized systems to assist physicians in making diagnoses using medical images.

Maize is a cornerstone of food security and economic stability in Nigeria, yet its production is severely hampered by crop diseases that cause significant yield losses and threaten the livelihoods of millions of smallholder farmers. Despite advances in machine learning (ML) and deep learning (DL) for plant disease detection, existing solutions often lack generalizability, scalability, and accessibility for resource-limited settings. This research used a robust, predictive system that leverages convolutional neural networks, specifically ResNet50 and EfficientNet trained on diverse, annotated datasets of maize leaf images. By integrating computer vision, transfer learning, and user-centric mobile application design, the system aimed to provide real-time, accurate diagnosis and actionable recommendations for disease management. This study compared the performance of the ResNet50 and the EfficientNet. At the end of the research, ResNet50 achieved marginally higher accuracy than EfficientNet under the same experimental conditions, although the performance difference is small and not statistically tested. The ResNet50 model was thereafter deployed into a scalable mobile application tool that can empower farmers and extension workers with early disease detection capabilities, potentially reducing crop losses, improving productivity, and enhancing food security across sub-Saharan Africa.

This study presents the development and validation of a mobile-to-cloud road surface condition monitoring system using citizen-sourced smartphone sensor data in the Philippines. The proposed framework integrates mobile data acquisition, local preprocessing, secure cloud transmission, supervised classification, and geospatial visualization within a unified architecture. Tri-axial accelerometer and gyroscope signals were collected at 50 Hz, segmented into overlapping windows, and transformed into statistical feature vectors prior to cloud-based inference. Field deployment was conducted across 48 urban and peri-urban road segments, generating 12,485 processed feature windows. A labeled subset of 4,200 windows was used for supervised evaluation. The classification model achieved an overall accuracy of 88.4%, with balanced precision and recall across smooth, moderate, and severe surface condition categories. Confusion matrix analysis showed that misclassifications were primarily concentrated between adjacent condition levels rather than between extreme classes. System-level evaluation demonstrated near-real-time responsiveness, with an average end-to-end latency of 1.8 seconds under stable network conditions. Offline buffering achieved 100% synchronization success following connectivity restoration, ensuring data integrity in environments with intermittent network coverage. The results indicate that smartphone-based vibration sensing, when integrated with cloud analytics and geospatial visualization, provides a scalable and cost-efficient approach for preliminary road surface monitoring. The proposed framework offers a practical complement to conventional inspection methods and supports data-driven infrastructure prioritization in developing urban contexts.

The number of heart disease patients has significantly increased in recent years, and heart illness is linked to a high death rate. Furthermore, as technology advanced, several sophisticated devices were created to assist patients in measuring their health at home and estimating their risk of developing heart disease. Using six machine learning models, the study seeks to determine how accurate self-measured physical health indicators are at predicting heart disease when compared to all indicators assessed by medical professionals. Logistics Regression, K Nearest Neighbors, Support Vector Model, Decision Tree, Random Forest, and Gradient Boosting Classifier were among the six models employed to forecast heart disease. Twelve different test findings and 1189 patients' heart disease risks are included in the database utilized for the study. While the metrics contains six outcomes that could be tested, the all metrics contained all twelve test results. The accuracy score and false negative rate were calculated for each of the five models that were built for the all metrics.The findings demonstrated that in all five models, all metrics had greater accuracy scores than existing metrics. For five machine learning models, all metrics had false negative rates that were either lower or equivalent to that of existing metrics. The results showed that all physical indicators were more accurate in predicting patients' risk of heart disease than metrics measured physical health indicators. Therefore, all physical health indicators are preferable for assessing the risk for cardiac illnesses in the absence of future development of indicators. 

Multiple Log-Periodic Array (MLPA) antennas have attracted significant research interest due to their broadband capability and high directivity. Their performance, however, depends critically on design parameters, often referred to as design factors, which directly influence the radiation characteristics. This paper presents an analytical evaluation of the influence of design factors on MLPA antenna performance, focusing on directivity, side lobe level (SLL), and beamwidth. The study employs the magnetic vector potential method to derive the array factor and investigates how variations in divergence spacing, scaling factor, and element length distribution affect the radiation properties. Analytical derivations are validated with numerical simulations to reveal the trade-offs between bandwidth enhancement and radiation stability. Results show that optimal tuning of design factors enhances radiation efficiency while minimizing undesirable sidelobe effects. The findings contribute to the optimization of MLPA antennas for applications in wireless communications, radar, and electronic warfare systems.