Factor analysis of environmental effects in circular closed-loop supply chain network design and modelling under uncertainty in the manufacturing industry Sunil Kumar Karunakaran, Narayanan Ramasamy, Manoharan Dev Anand, Nagarajan Santhi Environmental Quality Management, 2024 Sustainable development and competitive advantage are impacted by strategic choices that maximize resource value and reduce waste. Numerous instances of thriving OEM remanufacturing can be observed, predominantly in the business‐to‐business domain. The significance of Closed‐Loop Supply Chain (CLSC) in generating value and managing the recovery process is widely acknowledged within the supply chain industry. Manufacturing companies now have to come up with green supply chain and process design strategies in response to recent changes in environmental regulations. This study designates the specific features of circular closed‐loop supply‐chain design considering end‐of‐life products. Uncertainty in various aspects, such as acquisition, processing, and market stages, is impeding the progress of circular economies and also sustainable development in closed‐loop supply chains (CLSCs). This has led to increased complexity in remanufacturing processes and decreased efficiency. To address this issue, the study proposes a comprehensive, integrative approach for establishing a sustainable CLSC network that adapts to fluctuating demand through a questionnaire analysis. Moreover, the study introduces a multi‐objective optimization model for a dual‐channel supply chain network, aiming to enhance the flow. This model considers both economic and environmental objectives to achieve a sustainable and efficient supply chain system. To determine the ideal circular closed‐loop supply chain (CLSC) network design, the research proposes linear programming model with mixed integer, where recovery can take place in one of three ways: through material recovery, component recovery, or product recovery. Numerous findings from a thorough analysis by data backup that inform CLSC managers of ways to improve product returns in terms of quantity and quality. To address the uncertainty problem, the research also developed the fuzzy credibility constraint technique with a Simulated Annealing algorithm. Using cutting‐edge methods, it is explored and compared how sensitivity analysis results, the impact of altering the problem parameters, and the performance of the suggested model respectively.
Unlocking the Future of Stroke Diagnosis-Bayesian CNN and MRI Fusion Beevi Fathima M, N. Santhi, N. Ramasamy International Conference on E Mobility Power Control and Smart Systems Futuristic Technologies for Sustainable Solutions Icemps 2024, 2024 Stroke, as a life-threatening medical condition, necessitates rapid and accurate diagnosis to ensure timely treatment and optimal patient outcomes. In addressing the complex task of identifying strokes within Magnetic Resonance Imaging (MRI) images, a Bayesian Convolutional Neural Network is introduced. This paper addressing the urgent demand for enhanced stroke detection in medical imaging. By harnessing the Bayesian framework, Convolutional Neural Network (CNN) achieves a notable accuracy rate of 92.98%, underscoring its effectiveness in delivering precise diagnoses. The approach distinguishes itself by its ability to measure prediction uncertainty, a critical element frequently disregarded in conventional models. This capability equips healthcare professionals with valuable information regarding the model's confidence in its stroke predictions, thereby enhancing clinical decision-making and ultimately elevating the standard of patient care in the context of stroke management.
PV system simulation using various incremental algorithms applied in maximum power point tracking: A Comparative Study International Journal of Recent Technology and Engineering, 2019
A heuristic fuzzy clustering approach for defect detection on titanium coated metal surface Journal of Advanced Research in Dynamical and Control Systems, 2018
