Research ArticleOpen accessIn the keywordsSpeed Control of Separately Excited DC MotorMoleykutty GeorgeThis paper proposes the speed control of a separately excited dc motor varying armature voltage. The novelty of this paper lies in the application of nonlinear autoregressive-moving average L2 controller for the speed control of SEDM. This paper also discusses speed control of a SEDM using chopper circuit. The performance of the proposed system has been compared with the traditional … …American Journal of Applied Sciences, Vol. 5, Issue 3 · 31 Mar 2008 · 9,683 accessesView PDFCite
Research ArticleOpen accessIn the keywordsArtificial Intelligence Based Three-Phase Unified Power Quality ConditionerMoleykutty GeorgePower quality is an important measure of the performance of an electrical power system. This paper discusses the topology, control strategies using artificial intelligent based controllers and the performance of a unified power quality conditioner for power quality improvement. UPQC is an integration of shunt and series compensation to limit the harmonic contamination within 5 %, the limit imposed by … …Journal of Computer Science, Vol. 3, Issue 7 · 31 Jul 2007 · 6,911 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOn the Pulsewidth Analysis in the Presence of PMD and PDL in Optical Fibers Using Neural Network AlgorithmA. Sivasubramanian, V. C. RavichandranIn long haul networks, the random birefringence induced in the optical fiber leads to a considerable Polarization Mode Dispersion (PMD). Polarization Dependent Loss (PDL) mainly occurs in optical components and depends on the state of polarization of optical signals. The presence of PMD and PDL causes pulsewidth narrowing and the pulsewidth reduction depends on states of polarization at which the … …Journal of Computer Science, Vol. 3, Issue 4 · 30 Apr 2007 · 7,053 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOptimal Thyristor Control Series Capacitor Neuro-Controller for Damping OscillationsM. Magaji, M. W. MustafaThis study applies a neural-network-based optimal TCSC controller for damping oscillations. Optimal neural network controller is related to model-reference adaptive control, the network controller is developed based on the recursive "pseudo-linear regression". Problem statement: The optimal NN controller is designed to damp out the low frequency local and inter-area oscillations of the large power system. Approach: Two multilayer-perceptron neural networks … …Journal of Computer Science, Vol. 5, Issue 12 · 31 Dec 2009 · 7,281 accessesView PDFCite
Research ArticleOpen accessIn the keywordsStudy of Cutoff Frequency of High Collector Current Density in SiGe Single-Heterojunction Bipolar TransistorG. M. Khanduri, B. S. PanwarThe cutoff frequency performance of an NPN Si/SiGe/SiGe Single-heterojunction bipolar transistor (SiGe SHBT) at high collector current densities has been analyzed using a 2-dimensional MEDICI device simulator. A conventional NPN Si/SiGe/Si Double-heterojunction bipolar transistor (SiGe DHBT) having uniform 14%Ge in the base region has been investigated for comparison. The analysis shows the formation of a retarding potential barrier for minority … …American Journal of Applied Sciences, Vol. 1, Issue 3 · 30 Sep 2004 · 6,891 accessesView PDFCite
Research ArticleOpen accessIn the author namesForecasting Corporate Financial Distress Using Explainable Machine Learning: Implications for Risk Management TechnologyKanjana Hinthaw, Warawut NarkbunnumFinancial distress prediction is a critical task in financial risk management, yet it remains challenging due to class imbalance, complex financial structures, and limited model interpretability. This study develops an integrated machine learning framework that combines feature selection, class imbalance handling, threshold optimization, and Explainable Artificial Intelligence (XAI) to improve both predictive performance and decision-making reliability. Using a dataset of … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 1,130 accessesView PDFCite
Research ArticleOpen accessIn the author namesHybrid BiGRU-BiLSTM Model for Real-Time ECG Arrhythmia Detection Using Wearable SensorsPrem Narayan Singh, Rajendra Prasad MahapatraAccurate and real-time detection of cardiac arrhythmias is essential for timely medical intervention. Advances in wearable devices and deep learning have made it feasible to continuously monitor electrocardiogram (ECG) signals, facilitating early identification of abnormal heart rhythms. For arrhythmia detection, this study presents a hybrid deep learning architecture combining Bidirectional Gated Recurrent Units (Bi-GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM). … …Journal of Computer Science, Vol. 22, Issue 5 · 5 Jun 2026 · 1,331 accessesView PDFCite
Research ArticleOpen accessIn the author namesImproving Clustering Robustness through Fuzzy Ensemble of K-Means and Mean ShiftLNC. Prakash K., Palamakula Ramesh Babu, Shaik Thaseentaj et al.Ensemble clustering has emerged as a powerful strategy to improve the robustness and accuracy of unsupervised learning, particularly when individual algorithms struggle with noisy, heterogeneous, or high-dimensional data. This study introduces a fuzzy-based ensemble approach that integrates the complementary strengths of K-Means and Mean Shift clustering, followed by fuzzy membership assignment for data points that remain ambiguous. The inclusion of … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,257 accessesView PDFCite
Research ArticleOpen accessIn the author namesLSTM-Based AI Model for Sinkhole Attack Detection With Legal Basis in an Ecuadorian Public InstitutionEstefanía Alejandra Mora Parra, Rubén Nogales Portero, Moisés Toapanta T. et al.Wireless Sensor Networks (WSN) are an essential component of the Internet of Things (IoT). However, their decentralized nature, data transmission over unencrypted channels, and the physical exposure of nodes make them especially vulnerable to attacks, among which the sinkhole attack stands out. This research aims to develop a machine learning–based model to detect sinkhole‐type attacks in wireless sensor networks, with … …Journal of Computer Science, Vol. 22, Issue 3 · 4 Mar 2026 · 1,648 accessesView PDFCite
Research ArticleOpen accessIn the author namesDeveloping an Effective Churn Prediction Model for Telecommunications: Enhancing Customer Retention through Advanced Machine Learning TechniquesAshu Goyal, Anuj Gupta, Sharad Kumar et al.Customer churn poses a significant challenge for the telecommunications sector, resulting in substantial revenue losses and increased customer acquisition costs. This research creates an efficient churn prediction model that combines state-of-the-art machine learning with ensemble learning to maximize customer retention. With the IBM Telco Customer Churn dataset, several baseline models, including Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and Support … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 3,164 accessesView PDFCite