Indonesian Journal of Electrical Engineering and Computer Science
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Enhanced vegetation encroachment detection along power transmission corridors using random forest algorithm
Get PDFVegetation encroachment along power transmission corridors poses significant risks to infrastructure safety and reliability, necessitating effective monitoring and management strategies. This study introduces an innovative methodology for detecting vegetation encroachment using a combination of manual and automatic processes integrated with the random forest algorithm. The issue of vegetation encroachment is critical as it can lead to power interruptions and safety hazards if not addressed promptly. The objective of this research is to develop a scalable and cost-effective solution for vegetation management in power infrastructure maintenance. The methodology involves manual patch extraction and labeling to ensure the accuracy of the training dataset, combined with automatic feature extraction techniques to capture relevant information from satellite imagery. Leveraging the random forest algorithm, the model constructs an ensemble of decision trees based on the extracted features, achieving robust classification accuracy. Findings from this study demonstrate that the proposed approach enables consistent and timely identification of vegetation encroachment in new satellite imagery. Stored model parameters facilitate efficient testing, enhancing the system's ability to provide proactive interventions. This scalable solution significantly reduces reliance on manual labor and offers a cost-effective method for continuous monitoring, ultimately contributing to the resilience and safety of power transmission infrastructure
Banking security and performance of islamic banks in middle east: the role of regulatory quality
Get PDFTransaction security is critical for the reputation and trust of banks. Few studies examined how transaction security can impact the financial performance of Islamic banks in developing countries with mixed results emerging in the literature. The research examines how transaction security affects bank financial performance. Three indicators are used to measure the financial performance and includes return on assets (ROA), return on equity (ROE), and Tobin’s Q. Regulatory quality are proposed as a moderating variable. Data was collected from 59 banks in MENA between 2015 and 2022. The results showed that transaction security affected positively ROE and Tobin’s Q. However, there is no significant effect on ROA. Regulatory quality moderated only the effect of transaction security on ROE and Tobin’s Q. Enhance the transaction security and improving the regulatory quality will enhance the financial performance of banks in MENA
Analytical study of a single slope solar still: experimental evaluation
Get PDFEven though water covers the surface of the Earth in three quarters, many nations face shortages of drinkable water due to rapid global population and industrial growth. Solar power emerges as an efficient solution, particularly in hot climates with water and energy scarcity. This research focuses on a practical solar solution known as a solar still, a basic apparatus designed to convert available salty water into potable water. In this study, a single-slope solar still using acrylic material is experimentally analysed, predicting daily distillate production under varying climatic conditions. Using heat and solar radiation, solar distillation offers a simple, affordable, and small-scale approach to clean water production. The solar still, utilizing acrylic sheets as a basin material, minimizes heat losses and enhances water evaporation rates, making it a promising technology for addressing water scarcity issues. The experimental analysis results revealed a distillate output of 420 ml per 0.49 m² per day
Boosting carbon removal efficiency in wastewater treatment systems using a fuzzy model predictive control stategy
Get PDFThe efficient removal of carbon pollution has always presented a growing challenge facing wastewater treatment plants (WWTPs) operating with activated sludge process (ASP) technology. Enhancing pollution removal efficiency to meet standard wastewater quality limits remains a problematic in water pollution management. Recent progress in modeling and automatic control techniques can significantly improve the hydric pollution removal. In this paper, an effective carbon elimination strategy combining TakagiSugeno (TS) fuzzy modeling and model predictive control (MPC) is proposed to achieve high purification performance in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD5) and total suspended solids (TSS) indicators. A fuzzy TS model is established based on the concepts of quasi-linear parameter-varying (LPV) forms and convex polytopic transformations of the system nonlinearities. The concentrations of heterotrophic biomass, biodegradable substrate and dissolved oxygen as well as the effluent volume are controlled and maintained around their desired references with the aim of increasing pollution removal. Comparisons with the previously most used state-of-the-art parallel distributed compensation (PDC) are performed. High and competitive pollution removal percentages of 91% for COD and BOD5 indicators, and 92% for TSS metric, are achieved with the proposed MPC-based design, thus complying with the normative limits defined in WWTPs
Classification of voice pathologies using one dimensional feature vector and two dimensional scalogram
Get PDFMost research work focus only on binary classification of voice pathologies such as normal and pathological classification. However, the current work gives importance to multiclass classification too. The paper compares onedimensional (1D) feature vectors based machine learning (ML) techniques and two-dimensional (2D) scalogram image based deep learning (DL) model for binary and multiclass classification of voice pathology. The multiclass classification classifies the voice signal into four categories which are healthy, hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis. The current work demonstrates the evaluation of 1D feature vectors extracted from speech signal such as MFCC (mel-frequency cepstral coefficient) and pitch with various ML techniques like K-nearest neighbor (KNN), Naïve Bayes, and discriminant analysis (DA). Another technique that uses time-frequency scalograms derived using three different wavelets, i.e., analytical Morlet (amor), Bump, and Morse, are used for training a pretrained GoogleNet architecture, which is a very popular DL model. Experimental results show that 2D scalogram image based DL model for binary (96.05%) and multiclass (89.8%) classification of voice pathology gives better performance while comparing with 1D feature vectors based ML techniques
Generalized domain tutoring framework for AI agents with integrated explainable AI techniques
Get PDFThis paper proposes a novel approach to integrate tutoring functionality into AI systems to counteract the potential decline of human intelligence caused by AI-driven over-automation. Existing explainable AI methods primarily emphasize transparency while lacking inherent educational functionality. Consequently, users are essentially left as passive recipients of AI-driven decisions without any structured learning mechanism in place. To address this, this paper introduces the knowledge-sharing-bridge (KSB), a component designed to transform AI into an active tutor. Unlike traditional intelligent tutoring systems (ITS), which operate separately from AI decision-making processes, the KSB is embedded within AI frameworks, ensuring continuous and context-aware learning opportunities. The proposed framework uses structured knowledge representation tools, such as category maps and word-clouds, to improve the user’s understanding of the decisions made by the AI systems. Prototype implementation demonstrates how these elements work together to provide real-time, interactive learning experiences. The results indicate that integrating KSB into AI enhances both explainability and user learning. This approach promotes a more in-depth interaction with AI insights and enables AI systems to become lifelong learning companions, closing the gap between automation and education
A comparative study of solar photovoltaic array configurations to optimize power harvesting in a real-world system under various partial shading conditions
Get PDFPartial shading (PS) significantly reduces power generation and efficiency in solar photovoltaic (PV) systems. This research presents a novel totalcross-tied (TCT) methodology designed to mitigate shading effects by optimizing array layout while preserving electrical connectivity. The TCT method is compared to three established configurations: series-parallel (S-P), bridge-linked (B-L) and honey-comb (H-C). MATLAB simulations on a (9×9) PV array under variousshading conditions demonstrate TCT’s superior performance in achieving the global maximum power point (GMPP). Key findings indicate that TCT surpasses the other configurations, reaching a maximum power output of 16,650W at GMPP, with a mismatch power loss of 2,600W, a power loss of 13.32%, a fill factor (FF) of 38.27, and an execution ratio (ER) of 0.866
Image recognition using deep learning: a review
Get PDFThis paper presents a comprehensive review of recent advancements in image recognition, with a focus on deep learning (DL) techniques. Convolutional neural networks (CNNs), in particular, have significantly transformed this domain, enabling substantial improvements in both accuracy and efficiency across diverse applications. The review explores state-of-the-art methods, highlighting their practical implementations and the progress achieved. It also addresses key challenges such as data scarcity and model interpretability, offering perspectives on emerging opportunities and future directions. By synthesizing current trends with forward-looking insights, the paper aims to serve as a valuable resource for researchers and practitioners seeking to navigate and contribute to the evolving landscape of image recognition. Moreover, the paper examines critical challenges that persist in the field, such as transfer learning, data augmentation, and explainable artificial intelligence (AI) approaches. By synthesizing current trends with emerging innovations, the review not only maps the trajectory of progress but also highlights future directions and research opportunities. This synthesis aims to provide researchers, developers, and industry practitioners with a solid understanding of the dynamic and rapidly evolving environment surrounding image recognition technologies
Evaluation of the impact of machine learning on the prediction of residential energy consumption
Get PDFThe objective of this research was to compare the performance of machine learning models and traditional statistical methods for the prediction of residential energy consumption, using a dataset with relevant variables such as consumption, temperature, time of day, type of housing, and energy usage habits. A quantitative and comparative methodology was applied, involving data preprocessing, variable encoding, and normalization, as well as division into training and testing sets. The random forest, support vector machine (SVM), deep neural network (MLP), and linear regression models were trained and evaluated using standard metrics such as mean absolute error (MAE), root mean squared error (RMSE), and R² on test and cross-validation sets. Results show that SVM and linear regression achieved better accuracy and generalization capability, while random forest and the deep neural network exhibited lower explanatory power, reflected in negative R² values. Using the trained models, a projection of residential energy consumption for the 2026–2030 period was performed, revealing a generally increasing trend across all models, although with differences in the magnitude of the predictions. In conclusion, under the current conditions, traditional models demonstrate greater robustness, highlighting the need to tailor algorithm selection to the data context. These projections provide a valuable tool for future energy planning
Optimizing YOLOv8: OpenVINO standard quantization vs accuracy-controlled for edge deployment
Get PDFObject detection models, such as you only look once (YOLO), are widely utilized for real-time applications; however, their computational complexity often restricts deployment on edge devices. This research investigates the optimization of YOLO models using OpenVINO, both with and without accuracy control, to enable efficient inference while preserving model accuracy. A two-step pipeline is proposed: first, YOLO models are converted into OpenVINO’s intermediate representation (IR) format, followed by the application of post-training quantization (PTQ) to reduce model size and enhance latency. Additionally, an accuracy-aware quantization approach is introduced, which maintains model performance by calibrating with a validation dataset. Experimental results illustrate the tradeoffs between standard and accuracy-controlled quantization, demonstrating improvements in inference speed while ensuring minimal accuracy degradation. This study provides a practical framework for deploying lightweight object detection models on edge devices, particularly in realworld scenarios such as autonomous systems, smart surveillance, and smart queue management systems