Sustainable Machine Intelligence Journal最新文献_第2页

An Improved Light Spectrum Optimizer for Parameter Identification of Triple-Diode PV Model 一种用于三二极管PV模型参数辨识的改进光谱优化器

Sustainable Machine Intelligence Journal Pub Date : 2023-09-29 DOI: 10.61185/smij.2023.44105

Safaa Saber, Sara Salem

引用次数: 0

Neutrosophic Multi-Criteria Decision-Making Framework for Sustainable Evaluation of Power Production Systems in Renewable Energy Sources 可再生能源发电系统可持续评价的中性多准则决策框架

Sustainable Machine Intelligence Journal Pub Date : 2023-09-20 DOI: 10.61185/smij.2023.44103

Ahmed M.Ali, Myvizhi Muthuswamy

{"title":"Neutrosophic Multi-Criteria Decision-Making Framework for Sustainable Evaluation of Power Production Systems in Renewable Energy Sources","authors":"Ahmed M.Ali, Myvizhi Muthuswamy","doi":"10.61185/smij.2023.44103","DOIUrl":"https://doi.org/10.61185/smij.2023.44103","url":null,"abstract":"To make the change to a cleaner, more sustainable energy future, it is essential that power production systems be evaluated sustainably. To estimate the overall sustainability performance of various power production systems, this evaluation takes into account a wide range of parameters. Impact on the environment, availability of renewable resources, resource efficiency, social and economic implications, economic feasibility, grid integration and dependability, technical maturity, and scalability are all taken into account. Stakeholders may make better judgments and give higher priority to power production systems that take into account environmental impacts, resource efficiency, social impacts, and economic viability by evaluating these factors. So, we used the multi-criteria decision-making (MCDM) approach VIKOR to assess the power production systems in the sustainability criteria. The VIKOR method is used to rank the several alternatives. We integrated the neutrosophic set with the VIKOR method to deal with inconsistent information. We used seven criteria and ten alternatives in this study. The development of a sustainable and resilient energy sector is aided by the sustainable evaluation of power production systems, which in turn helps to reduce the effects of climate change and safeguard the environment.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136380271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories

Sustainable Machine Intelligence Journal Pub Date : 2023-09-18 DOI: 10.61185/smij.2023.44102

Mona Mohamed

{"title":"Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories","authors":"Mona Mohamed","doi":"10.61185/smij.2023.44102","DOIUrl":"https://doi.org/10.61185/smij.2023.44102","url":null,"abstract":"Agriculture stands as the essential foundation of human sustenance, confronting the dual challenge of providing for a burgeoning global populace while safeguarding the integrity of the natural environment. This comprehensive review paper undertakes an exhaustive exploration of the continually evolving sphere of agricultural sustainability, traversing the multifaceted terrain of present-day trends, technological innovations, and the promising trajectories that lie ahead. From the vantage point of precision agriculture and climate-smart methodologies to the strategic integration of deep learning technologies, it offers a comprehensive examination of pioneering approaches that are redefining the agricultural domain. Within, it elucidates the intrinsic relationship between agriculture and sustainability, exemplifying how judicious resource management, the preservation of biodiversity, and the implementation of circular agricultural practices herald an epoch of conscientious agrarian practices. Moreover, this study casts an illuminative gaze toward the future of agriculture, wherein quantum intelligence, meta-learning, deep reinforcement learning, curriculum learning, intelligent nanothings, blockchain technology, and CRISPR gene editing converge to furnish innovative solutions. These solutions aspire to optimize crop yields, mitigate ecological footprint, and fortify global food security. As this academic voyage commences, it is incumbent to reiterate the pivotal assertion that sustainability in agriculture is not merely a desideratum; it is a compelling mandate, and the seeds of transformative innovation have been sown to recalibrate the world's approach to food production and environmental stewardship.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135257077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly Detection in Smart Agriculture Systems on Network Edge Using Deep Learning Technique 基于深度学习技术的网络边缘智能农业系统异常检测

Sustainable Machine Intelligence Journal Pub Date : 2023-06-30 DOI: 10.61185/smij.2023.33104

Bandar Alanazi, Ibrahim Alrashdi

{"title":"Anomaly Detection in Smart Agriculture Systems on Network Edge Using Deep Learning Technique","authors":"Bandar Alanazi, Ibrahim Alrashdi","doi":"10.61185/smij.2023.33104","DOIUrl":"https://doi.org/10.61185/smij.2023.33104","url":null,"abstract":"With the widespread adoption of Internet of Things (IoT) technologies across various domains, including smart agriculture, urban environments, and homes, the threat of zero-day attacks has surged. This research delves into the application of deep learning techniques to detect anomalies in smart agricultural systems at the network edge, with a specific focus on safeguarding them against Distributed Denial of Service (DDoS) attacks. In this study, we propose an anomaly detection model based on CNN-LSTM to analyze sensor data collected from IoT devices. We rigorously train and test our model using two distinct datasets of sensor readings, simulating potential DDoS attack scenarios. The model's performance is assessed using key metrics such as detection accuracy, recall, and F1-score. Our results demonstrate the effectiveness of our approach, achieving an impressive anomaly detection accuracy of 99.7%. This research contributes significantly to the development of robust and efficient attack and anomaly detection techniques for smart agriculture systems at the network edge, ultimately enhancing the reliability and sustainability of agricultural practices.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136365480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape 机器智能时代的可持续供应链管理:应对挑战，利用机遇，塑造未来景观

Sustainable Machine Intelligence Journal Pub Date : 2023-06-25 DOI: 10.61185/smij.2023.33103

Ahmed Abdel-Monem, Myvizhi Muthuswamy

{"title":"Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape","authors":"Ahmed Abdel-Monem, Myvizhi Muthuswamy","doi":"10.61185/smij.2023.33103","DOIUrl":"https://doi.org/10.61185/smij.2023.33103","url":null,"abstract":"In today's rapidly evolving business landscape, the convergence of sustainable supply chain management (SSCM) and machine intelligence, encompassing artificial intelligence (AI) and machine learning (ML), represents a dynamic and transformative nexus. This comprehensive survey paper navigates the intricate terrain of sustainable supply chain practices, delving into its principles, challenges, and the pressing need for organizations to embrace environmental responsibility, ethical sourcing, and social equity. Simultaneously, it explores the disruptive potential of machine intelligence, offering insights into its underlying principles, vast applications, and its pivotal role in optimizing supply chain operations. Through a systematic analysis, this paper uncovers the complex interplay between SSCM and machine intelligence, starting with the foundational principles of each discipline. It then scrutinizes the challenges encountered in integrating machine intelligence with sustainability, including data complexities, ethical dilemmas, and the need for skilled personnel. Conversely, the paper illuminates the myriad opportunities that arise from this synergy, from enhancing demand forecasting and inventory management to fostering sustainable sourcing practices and reducing waste. In closing, the paper anticipates the future landscape of sustainable supply chains in the age of machine intelligence, highlighting emerging trends, technological innovations, and the ethical considerations that will shape the trajectory of this evolving field. It is our hope that this survey serves as a valuable resource for businesses, policymakers, and researchers alike, inspiring the pursuit of environmentally responsible, economically viable, and ethically sound supply chains in an increasingly interconnected world.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135842530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting Zero-Trust Security for Internet of Things 重新审视物联网的零信任安全性

Sustainable Machine Intelligence Journal Pub Date : 2023-06-24 DOI: 10.61185/smij.2023.33106

Mahmoud Ismail, Amal F.Abd El-Gawad

引用次数: 0

Assessment the Health Sustainability using Neutrosophic MCDM Methodology: Case Study COVID-19 使用中性粒细胞MCDM方法评估健康可持续性:以COVID-19为例

Sustainable Machine Intelligence Journal Pub Date : 2023-06-21 DOI: 10.61185/smij.2023.33101

Ahmed A. El-Douh, SongFeng Lu, Ahmed Abdelhafeez, Alber S. Aziz

{"title":"Assessment the Health Sustainability using Neutrosophic MCDM Methodology: Case Study COVID-19","authors":"Ahmed A. El-Douh, SongFeng Lu, Ahmed Abdelhafeez, Alber S. Aziz","doi":"10.61185/smij.2023.33101","DOIUrl":"https://doi.org/10.61185/smij.2023.33101","url":null,"abstract":"As a result of the severe difficulties presented by the COVID-19 pandemic, a holistic response is required, one that takes into account both the urgent needs of patients and the long-term viability of healthcare institutions. This study aims to give a complete knowledge of the tactics and techniques necessary to maintain the continuing well-being of people and communities by examining the idea of health sustainability in the context of the COVID-19 pandemic. This study conducts a literature review to investigate the many facets of health sustainability, such as emergency preparedness, mental health care, health workforce support, health education and communication, research and innovation, international cooperation, and resilience in the face of pandemics. This study's results call attention to the necessity for universal healthcare access, mental health services, the upkeep of critical services, and international coordination as part of the COVID-19 response strategy. Societies may construct robust healthcare systems that can deal with the short- and long-term effects of the pandemic if they use a comprehensive strategy that takes into account social, economic, and environmental aspects. So, we used the concept of multi-criteria decision-making (MCDM) to deal with various criteria of health sustainability. The AHP MCDM method is used to deal with various criteria and give the weights of these criteria. The AHP used a comparison between various criteria, so we used the neutrosophic environment to deal with the vague data in the comparison process. The proposed framework is applied in the application of COVID-19.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135090586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empowering deep learning based organizational decision making: A Survey 授权基于深度学习的组织决策:一项调查

Sustainable Machine Intelligence Journal Pub Date : 2023-06-15 DOI: 10.61185/smij.2023.33105

Mona Mohamed

{"title":"Empowering deep learning based organizational decision making: A Survey","authors":"Mona Mohamed","doi":"10.61185/smij.2023.33105","DOIUrl":"https://doi.org/10.61185/smij.2023.33105","url":null,"abstract":"The advent of deep learning has revolutionized the landscape of organizational decision-making by offering powerful tools for data analysis and prediction. In this comprehensive survey, we explore the intersection of deep learning and organizational decision-making, elucidating the theoretical underpinnings, empirical evidence, and practical implications of this synergy. Theoretical foundations and research hypotheses are rigorously examined, providing a solid framework for understanding the role of deep learning models in enhancing decision-making processes. We delve into the systematic survey, which encompasses a wide spectrum of applications across various industries and domains, showcasing how deep learning empowers decision support systems, augments data-driven decision-making, and refines decision-making frameworks. Drawing inspiration from the Egyptian Vision 2030, we explore the implications of deep learning-based decision-making on national development strategies and policy implementation. Our analysis sheds light on the transformative potential of these technologies, offering insights into how organizations, particularly in Egypt, can harness these advancements to achieve their developmental goals. Finally, we outline future directions in this field, highlighting emerging trends, technological advancements, and potential areas for further research. As the digital age continues to reshape the landscape of decision-making, this survey serves as a valuable resource for researchers, policymakers, and practitioners seeking to leverage deep learning for empowered, data-driven, and informed organizational decisions.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable Supplier Selection using Neutrosophic Multi-Criteria Decision Making Methodology 利用中性多准则决策方法选择可持续供应商

Sustainable Machine Intelligence Journal Pub Date : 2023-06-10 DOI: 10.61185/smij.2023.33102

Zenat Mohamed, Mahmoud M. Ismail, Amal Abd El-Gawad

{"title":"Sustainable Supplier Selection using Neutrosophic Multi-Criteria Decision Making Methodology","authors":"Zenat Mohamed, Mahmoud M. Ismail, Amal Abd El-Gawad","doi":"10.61185/smij.2023.33102","DOIUrl":"https://doi.org/10.61185/smij.2023.33102","url":null,"abstract":"Sustainable supplier selection is an important part of supply chain management since it encourages ethical and eco-friendly procedures. This study examines the most important criteria and factors for judging the sustainability performance of suppliers and presents a thorough overview of sustainable supplier selection. A decision-making framework for sustainable supplier selection is created via a review of relevant research, case studies, and best practices. Key factors for assessing suppliers include environmental performance, social responsibility, and economic viability, all of which are included in the framework. The results stress the need to take into account suppliers' energy efficiency, waste management, and social responsibility initiatives including fair labor practices and community involvement. Other essential considerations for long-term sustainability include economic viability and supply chain resilience. Organizations may improve their environmental impact, reduce supply chain risks, and boost overall performance by using sustainable supplier selection practices. This study used the TOPSIS method to rank sustainable suppliers. The TOPSIS method is employed with the single-valued neutrosophic set to deal with vague information. A case study in a food company is conducted to show the best supplier","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135050780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluation Factors of Solar Power Plants to Reduce Cost Under Neutrosophic Multi-Criteria Decision-Making Model 中性多准则决策模型下太阳能电站降低成本的评价因素

Sustainable Machine Intelligence Journal Pub Date : 2023-03-29 DOI: 10.61185/smij.2023.22101

Mohamed Abouhawwash, Mohammed Jameel

引用次数: 0