Global Mainstream Journal of Innovation, Engineering & Emerging Technology

INNOVATIVE APPROACHES TO SUSTAINABLE SUPPLY CHAIN MANAGEMENT IN THE MANUFACTURING INDUSTRY: A SYSTEMATIC LITERATURE REVIEW

Hasib Ahmed, Mahboob Al Bashar, Md Abu Taher & Md Ashiqur Rahman — 2024-03-04

This systematic literature review examines innovative approaches to Sustainable Supply Chain Management (SSCM) within the manufacturing sector, an area increasingly recognised as critical due to ethical imperatives and economic incentives. We evaluated studies sourced from Scopus, Web of Science, and PubMed, adhering to strict eligibility criteria emphasising SSCM's applicability to manufacturing. Our search and screening process identified 381 studies, from which 46 were selected for an in-depth review of technological innovations and collaborative strategies. Preliminary analysis indicates that SSCM integration correlates positively with operational efficiency, cost-effectiveness, and environmental sustainability. Despite potential limitations such as methodological diversity among studies and publication bias, the findings highlight the importance of technology and collaboration in advancing SSCM. These results indicate the need for further investigation into the long-term effects and adaptability of SSCM in various manufacturing environments.

Exploring Convolutional Neural Networks for Facial Expression Recognition: A Comprehensive Survey

AKM Monzurul Islam — 2024-03-20

Facial emotion recognition is a very sought-after field in recent times as it has many important applications such as effective calculations, video game testing and motion capture in video games, human-computer interaction through machine vision, computer research etc. Facial expression is considered a nonverbal form of communication, as it reveals an individual's internal sentiments and emotional states through changes in multiple facial landmark points. Facial identification provides a more comprehensive insight into the person's thoughts and these expressions are analyzed using deep learning methods, such as CNN. The accuracy rates achieved are compared to other methods. In this paper, a concise exploration of diverse applications within Facial Expression Recognition (FER) fields and the publicly accessible data sets employed in FER studies is outlined. FER using multiple different CNN algorithms is also presented. Finally, through comparing multiple different studies of various CNN algorithms, a table and a chart are provided for a better understanding of the rate of accuracy achieved throughout the use of different datasets.

Enhancing Energy Efficiency in Wireless Sensor Networks Using Virtual MIMO Technology

Md Farad Ahmmed, Abdur Rahman, Md Mukter Hossain Emon & Md Mahfuzur Rahman Enam — 2024-03-27

This study investigates utilizing Virtual Multiple Input Multiple Outputs (MIMO) methodologies to boost energy efficiency in wireless sensor network communication. It proposes an innovative approach to routing that capitalizes on virtual MIMO technology, scrutinizing its effectiveness across consistent and adaptable transmission rates. By employing a cluster-based virtual MIMO framework, this study aims to adjust operational variables to enhance communication energy efficiency significantly. The research prioritizes optimizing routing paths, specifically tailoring them to minimize the virtual MIMO communication cost. This strategic focus aims to extend the operational lifespan of the network's initial node, delaying its energy depletion. Through comprehensive simulation analyses, findings reveal that the virtual MIMO-based routing strategy significantly surpasses the energy efficiency of traditional Single Input Single Output (SISO) approaches and various other MIMO configurations, especially notable when managing communications over extended distances. This advancement holds promising implications for developing more sustainable and efficient wireless sensor networks.

Keywords Wireless Network, Virtual MIMO, Space-time block code, Diversity Data rate

STRATEGIC INTEGRATION OF ENTERPRISE RISK MANAGEMENT FOR COMPETITIVE ADVANTAGE

Md Rasel Ul Alam — 2024-03-28

This research paper investigates the strategic integration of Enterprise Risk Management (ERM) and its potential to propel organizations toward competitive advantage amidst the complexities of the contemporary business landscape. A comparative case study approach analyzes two organizations recognized for their exemplary ERM practices. The study investigates how these organizations cultivate resilience, enhance the quality of strategic decision-making, and unlock innovation capabilities by integrating ERM. Critical success factors such as unwavering executive sponsorship and developing a risk-aware culture are examined. The article also explores the ongoing challenge of quantifying certain strategic risks and offers strategies for addressing them. Finally, recommendations are provided to guide organizations seeking to strategically integrate ERM, focusing on seamlessly incorporating risk assessments into strategic planning, fostering risk awareness throughout all organizational levels, and utilizing specialized technology solutions to streamline risk management processes.