Security in public networks has always been a challenge. With an ever-expanding landscape of cyberattacks, it is imperative to explore new/alternate mechanisms of implementing intrusion detection. Machine learning and deep learning techniques have emerged as promising for intrusion detection in the recent past, with increased efficiency. This study explores machine learning algorithms, namely Random Forest, Naive Bayes, and Decision Tree, for the detection of web-based attacks in public networks, using a combination of Principal Component Analysis and Haar Wavelet Transform for feature extraction. The results of these models are compared, and related issues and approaches to alleviate them are explored.
The research paper introduces a quantitative comparison of two reactive power compensation methods of improving the power factor (PF) performance in institutional buildings: automated capacitor banks and synchronous condensers at Universiti Teknikal Malaysia Melaka (UTeM). Three academic buildings, namely the Faculty of Electrical Technology and Engineering (FTKE), the Faculty of Electronic and Computer Technology and Engineering (FTKEK), and the Faculty of Information and Communication Technology (FTMK), were measured with a three-phase power quality analyzer that met the IEC 61000-4-30 Class A standards at 400 V with a frequency of 50 Hz. The measurement results of active power, reactive power, and PF profiles were set into a MATLAB/Simulink simulation model to assess reactive power compensation under realistic operating conditions. The power factor improvement indicator (ΔPF) was used to measure technical performance with the help of the reactive power sizing sensitivity analysis. The regulated requirement of a PF correction target of 0.85 was applied to prevent the penalties on utility surcharges according to Tenaga Nasional Berhad (TNB) regulations. The results of the simulations suggest that both compensation methods are effective in enhancing the PF compared to the utility benchmark, with the system with a low baseline PF having a greater correction sensitivity. Automated capacitor banks offered a cost-efficient response to steady-state compensation, whereas synchronous condensers offered better dynamic reactive response power support and better voltage stability under varying load conditions. The results indicate that effective Power Factor Correction (PFC) strategies can significantly reduce reactive current flow, minimize distribution losses, and improve the utilization of real power, thereby providing a technically and economically viable approach for sustainable energy management in institutional electrical distribution networks.
Smart grid technologies have evolved rapidly to enable intelligent monitoring, forecasting, and control of modern energy systems. However, the increasing integration of renewable energy sources, variable demand, and dynamic grid conditions poses significant challenges for effective energy management. Traditional methods are often not well-suited to handling the nonlinear and time-varying nature of smart grid data, which can lead to reduced prediction accuracy and scheduling efficiency. To address these challenges, this paper proposes a hybrid Sine Cosine Optimization (SCO)–Bidirectional Long Short-Term Memory (BiLSTM)–Deep Reinforcement Learning (DRL) framework for smart grid energy management. The BiLSTM model is employed to accurately forecast energy demand, whereas the SCO algorithm optimizes model parameters to enhance prediction performance. Experimental results show that the proposed model achieves lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values and higher prediction accuracy than conventional models, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and BiLSTM. The proposed framework improves energy utilization efficiency, reduces operating costs, and enhances electrical grid stability. Overall, the proposed approach provides a scalable and efficient solution for next-generation smart grid energy management.
Artificial Intelligence (AI) has shown significant potential in healthcare, particularly for the early detection of glaucoma. This study presents a comprehensive evaluation of glaucoma classification using the ACRIMA dataset. Handcrafted descriptors, such as Edge Histogram (EH), Fuzzy Color and Texture Histogram (FCTH), and Pyramid Histogram of Gradients (PHOG), are combined with deep features extracted from ConvNeXt and Vision Transformer (ViT) models, and their classification performance is evaluated using three different classifiers, namely Random Forest (RF), Gradient Boosting (GB), and LightGBM (LGBM). Four ensembling frameworks, namely Feature-level (FL), Hybrid Feature-level (HF), Voting-based (VB), and Stacking ensembles, are investigated to integrate handcrafted and deep features effectively. Among the proposed approaches, the stacking ensemble using Logistic Regression (LR) as a meta-classifier outperformed other approaches by providing an accuracy of 95.44404% with an F1-score of 96.0638% and an AUC of 0.9887.
This paper proposes a current control strategy for a single-phase grid-tied inverter under conditions of parameter uncertainty and disturbance. Inductance and filter resistance deviations degrade the performance of traditional model-based control methods. To overcome this, a Radial Basis Function (RBF) neural network is used as an online observer to approximate and compensate for aggregate errors due to uncertainty and unmodeled dynamics. Simultaneously, Super-Twisting Sliding Mode Control (STSMC) is applied to reduce the chattering phenomenon and ensure finite-time convergence with a continuous control signal. The RBF–STSMC combination reduces conservatism in parameter selection and enhances system robustness. Stability is demonstrated using Lyapunov, and MATLAB/Simulink simulation results show superior performance compared to traditional SMC and PID controllers.
