Mechanical and Water-Absorption Properties of Cotton-Bamboo/Epoxy Composites
This study examines the mechanical and water-absorption characteristics of epoxy composites reinforced with cotton and bamboo fibers. By evaluating tensile strength, durability, and moisture uptake, the research highlights how natural fibers influence the composite’s performance. Bamboo improves structural stiffness, while cotton enhances flexibility, creating a balanced material. Water-absorption analysis reveals the composite’s suitability for real-world […]
Multiple Pattern Synthesis in 4D Antenna Arrays
This study focuses on generating multiple radiation patterns using 4D antenna arrays, which operate across spatial, temporal, frequency, and polarization domains. The approach enhances beamforming flexibility and enables dynamic adjustment of antenna patterns for improved communication performance. By analyzing different synthesis techniques, the research demonstrates how 4D arrays can adapt to varying scenarios, reduce interference, […]
Advancing Spacecraft Rendezvous Using Reinforcement Learning
This research explores the use of reinforcement learning to enhance autonomous spacecraft rendezvous operations. By training AI models to make real-time navigation and control decisions, the system optimizes orbital paths and reduces maneuvering errors. The study demonstrates improved precision, fuel efficiency, and adaptability compared to traditional guidance methods. These advancements support safer and more reliable […]
Scheduling & Controlling Production in IoT for Industry 4.0
This study explores how IoT technologies enhance production scheduling and control within Industry 4.0 environments. By connecting machines, sensors, and automated systems, IoT enables real-time decision-making, efficient workflow coordination, and predictive management of resources. The research highlights improvements in productivity, reduced operational delays, and optimized manufacturing processes. Through intelligent communication between devices, factories can achieve […]
Correction to LTE-NBP with UWB-WBAN for Biomedical Applications
This work provides corrections to the LTE-NBP model integrated with UWB-based Wireless Body Area Networks for biomedical applications. It refines communication pathways between body sensors and monitoring systems to improve data accuracy and reduce interference. The study highlights enhanced transmission reliability, better signal stability, and improved energy efficiency. These corrections strengthen the performance of biomedical […]
Stub Signature-Based Efficient Public Data Auditing in Cloud Computing
This study explores a stub signature–based framework designed to improve the efficiency and security of public data auditing in cloud environments. The approach minimizes computational overhead by using lightweight cryptographic signatures while ensuring strong data integrity verification. It enables third-party auditors to validate cloud-stored information without accessing the actual data, preserving privacy. The method enhances […]
Improving Offline Gurmukhi Character Recognition: A Comparative Study of Feature Selection Techniques
This study investigates how different feature selection techniques enhance offline Gurmukhi character recognition. By analyzing various feature extraction methods and comparing their performance across machine learning models, the research identifies approaches that improve accuracy while reducing computational load. The findings support the development of more efficient and reliable OCR systems for Gurmukhi script, enabling better […]
Performance Analysis for Predicting Primary School Dropouts Using Machine Learning
This study evaluates the performance of various machine learning models in predicting primary school dropout risks by analyzing student-related data such as attendance, academic performance, and socio-economic factors. By comparing multiple algorithms, the research identifies the most effective approaches for early detection of at-risk students. The model’s predictive insights help educators implement targeted interventions, improve […]
Quantum Behaved Binary Gravitational Search Algorithm with Random Forest for Twitter Spammer Detection
This hybrid model detects Twitter spammers using quantum-inspired optimization and random forest classification. The gravitational search algorithm improves feature selection and search efficiency. Random Forest enhances decision accuracy for identifying malicious accounts. Together, they deliver a robust system for social media spam detection. For more information, click on the link below. Results in Engineering (SCI)2025https://www.sciencedirect.com/science/article/pii/S2590123025000817
Quantum-Inspired Firefly Algorithm with Ant Miner Plus for Fake News Detection
This study integrates firefly swarm intelligence with ant-based rule learning for detecting fake news. The hybrid algorithm improves classification accuracy and reduces misinformation spread. Quantum-inspired optimization enhances search efficiency within large datasets. Designed to support reliable and automated news verification systems. For more information, click on the link below. International Journal of Modern Physics C […]