Resilience and Adaptive Learning in Hybrid Education for Students with Disabilities: A Quantitative Analysis International Journal of Special Education, 2026
Sustainable Development in Education Using Quantum-Classical Synergistic Fibroblast Dense Nested Convolutional Attention Network K. Sankar Ganesh, M. Shunmugasundaram, V. Mohana Sundari, S. Gangadharan, G. Kannan International Journal of Computational Intelligence and Applications, 2025 Sustainable development in education is crucial for advancing global educational standards and addressing disparities. Existing methods for predicting sustainable development in education often struggle with data quality and complexity, which can limit their accuracy and applicability. This study aims to overcome these limitations. This study presents a novel approach to predicting Sustainable Development in Education using the quantum-classical synergistic fibroblast dense nested convolutional attention network (Qua-CSFib-DNCANet). In this, input educational data is sourced from the Europe and Central Asia dataset. Then, these educational data are pre-processed using the preprocessing methods within new adjusted min–max with decimal scaling and statistical column normalization (NAMDSSCN) include normalization of statistical column, normalization of decimal scaling, normalization of adjusted decimal scaling, min–max normalization, normalization of adjusted 1-min–max, normalization of adjusted 2-min–max, normalization of adjusted 3-min–max, and normalization of adjusted 4-Min–Max. Multiple discrete orthonormal S-transforms extract features by providing an efficient method to represent complex educational metrics. The Qua-CSFib-DNCANet mechanism is applied to accurately predict indicators of sustainable development in education. The performance of the suggested system is assessed using academic information from the Europe and Central Asia databases, and it runs on a Python platform. With an astounding 99.9% correctness and 99.8% recall, the Qua-CSFib-DNCANet model outperforms current techniques in terms of efficiency and shows promise for future development in the sector. This approach aims to provide deeper insights and improved predictions, addressing the challenges associated with educational data analysis and supporting strategic initiatives for educational sustainability.
Fraud detection in the banking sector using Gated Green Anaconda Progressive Generative Axial Adversarial Attention Network K Prakash, M Franklin, M Shunmugasundaram, K Sankar Ganesh, S Gangadharan Intelligent Decision Technologies, 2025 In the realm of digital banking, financial fraud has become an escalating concern due to the rapid adoption of online transaction systems. In the banking sector, fraud detection is critical, when deceptive practices outcome in large financial losses and can undermine trust in banking institutions. Traditional detection methods often struggle with false positives, high computational costs, and adaptability to evolving fraud patterns. This work is recommended to solve this problem. This work deals with effective identification of the various types of fraud that exist in the banking industry using a complex network known as the Gated Green Anaconda Progressive Generative Axial Adversarial Attention Network (2GAP-G3A-Net). The datasets used in this work include Financial-Fraud-Detection, and synthetic data that are derived from the financial payment system dataset that needs to be preprocessed for cleansing and normalization uses NLP based methods. Feature selection is carried out using the sea horse optimization algorithm to reduce model complexity and improve effectiveness of the model in predicting fraud cases while selecting the most important variables. The 2GAP-G3A-Net is then used to develop another state of the art and highly accurate fraud detection framework that integrates the PgAN and GAO with an additional GAAN to identify intricate transactional patterns. These techniques for using the 2GAP-G3A-Net model demonstrate a nearly perfect mean per-voxel Dice coefficient of 0.999 and show the efficiency of the model and higher potentiality compared with the current techniques. The approach enhances the accuracy of fraud prediction and minimizes false positives, can operate in a constantly changing environment and work with large data sets, which make it an effective tool in the banking industry.
A Federated Learning and Blockchain Framework for IoMT-Driven Healthcare 5.0 Denis R, N. Venkateswaran, S. Gangadharan, M. Shunmugasundaram, Guduri Chitanya, et al. International Journal of Basic and Applied Sciences, 2025 This paper presents an innovative framework integrating federated learning, blockchain, and the Internet of Medical Things (IoMT) to revolutionize healthcare systems in the context of Healthcare 5.0. By harnessing advanced sensors and leveraging 5G technology, the framework enables continuous, real-time data collection and intelligent analysis, facilitating highly personalized and timely medical interventions. Federated learning enables decentralized model training across edge devices, preserving data privacy and enhancing security. Simultaneously, blockchain ensures the integrity and transparency of healthcare records through a decentralized and tamper-proof ledger. The synergy of these technologies fosters secure and efficient communication across a network of interconnected medical devices. This framework significantly enhances healthcare delivery by promoting proactive, patient-focused, and adaptive care models. Additionally, IoMT expands the capabilities of medical equipment by enabling remote monitoring, automated data transmission, and comprehensive patient oversight. As the vision of Healthcare 5.0 progresses, embracing such cutting-edge technological solutions is vital for improving patient outcomes, streamlining operations, and accelerating medical innovation. Through the combined power of federated learning, blockchain, and IoMT, the healthcare sector stands on the brink of a transformative shift toward secure, intelligent, and personalized care.
AN EFFECTIVE METHOD FOR MANAGING WASTE IN SMART CITIES BASED ON DEEP RESIDUAL NEURAL NETWORK APPROACH Journal of Environmental Protection and Ecology, 2024
A study on police personnel’s perception about causes for stress and frequency of stress occurrence International Journal of Advanced Science and Technology, 2020
A study on sources of occupational stress among police constables International Journal of Applied Engineering Research, 2015
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