Sustainable Machine Intelligence Journal最新文献_第3页

Internet of Things (IoT) in Supply Chain Management: Challenges, Opportunities, and Best Practices 供应链管理中的物联网:挑战、机遇和最佳实践

Sustainable Machine Intelligence Journal Pub Date : 2023-03-27 DOI: 10.61185/smij.2023.22103

Mona Mohamed, Karam Sallam, Ali Mohamed

{"title":"Internet of Things (IoT) in Supply Chain Management: Challenges, Opportunities, and Best Practices","authors":"Mona Mohamed, Karam Sallam, Ali Mohamed","doi":"10.61185/smij.2023.22103","DOIUrl":"https://doi.org/10.61185/smij.2023.22103","url":null,"abstract":"The advent of the Internet of Things (IoT) has ushered in a transformative era in supply chain management, revolutionizing the way organizations monitor, analyze, and optimize their operations. This comprehensive survey paper explores the multifaceted landscape of IoT applications in supply chain management, shedding light on the challenges, opportunities, and best practices that define this technological paradigm shift. The paper delves into the fundamental principles of IoT, elucidating how sensor-laden devices, real-time data streams, and advanced analytics empower organizations with unprecedented visibility and control across their supply chains. It systematically examines IoT applications in key supply chain domains, including inventory management, asset tracking, cold chain monitoring, predictive maintenance, route optimization, and waste reduction. Each application is scrutinized for its role in enhancing efficiency, reducing costs, ensuring product quality, and advancing sustainability. Furthermore, this paper addresses the challenges inherent in implementing IoT within supply chains, such as data security, interoperability, scalability, and regulatory compliance. It underscores the importance of change management and workforce development in harnessing the full potential of IoT and presents a roadmap for best practices to overcome these obstacles. The paper culminates in a forward-looking exploration of future trends and innovations in the IoT-driven supply chain landscape. By offering a comprehensive overview of IoT's role in supply chain management, this paper equips practitioners, researchers, and decision-makers with a holistic understanding of the transformative power of IoT, empowering them to navigate the complexities, seize opportunities, and implement best practices that will define the future of supply chain management.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135950533","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

SFMR-SH: Secure Framework for Mitigating Ransomware Attacks in Smart Healthcare Using Blockchain Technology SFMR-SH:使用区块链技术减轻智能医疗中勒索软件攻击的安全框架

Sustainable Machine Intelligence Journal Pub Date : 2023-03-27 DOI: 10.61185/smij.2023.22104

Jamal Alenizi, Ibrahim Alrashdi

{"title":"SFMR-SH: Secure Framework for Mitigating Ransomware Attacks in Smart Healthcare Using Blockchain Technology","authors":"Jamal Alenizi, Ibrahim Alrashdi","doi":"10.61185/smij.2023.22104","DOIUrl":"https://doi.org/10.61185/smij.2023.22104","url":null,"abstract":"As the healthcare industry increasingly relies on digital technology and the Internet of Things (IoT) to improve patient care and streamline operations, the vulnerability to ransomware attacks has become a significant concern. In response to this pressing issue, we present SFMR-SH (Secure Framework for Mitigating Ransomware Attacks in Smart Healthcare), a groundbreaking approach that integrates IoT devices with blockchain technology to fortify healthcare data security. SFMR-SH leverages blockchain's inherent properties, including immutability and transparency, to create an impervious fortress for sensitive patient data. Through comprehensive simulations employing machine learning algorithms (KNN, SVM, Random Forest, Gradient Boosting, and XGB), we assess the framework's ability to detect and mitigate ransomware attacks. Results underscore the framework's effectiveness, achieving an impressive detection accuracy of 99.33%. This research represents a significant stride in fortifying smart healthcare systems, providing a secure environment amid the escalating threat landscape, and ensuring the uninterrupted delivery of vital healthcare services. Our findings highlight the exceptional promise of SFMR-SH in revolutionizing healthcare data security, safeguarding patient privacy, and fortifying the future of smart healthcare systems in an increasingly digitalized healthcare landscape.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135950532","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

Protecting IoT Devices from BotNet Threats: A Federated Machine Learning Solution 保护物联网设备免受僵尸网络威胁:联邦机器学习解决方案

Sustainable Machine Intelligence Journal Pub Date : 2023-03-20 DOI: 10.61185/smij.2023.22105

Ahmed Metwaly, Ibrahim Ibrahim

{"title":"Protecting IoT Devices from BotNet Threats: A Federated Machine Learning Solution","authors":"Ahmed Metwaly, Ibrahim Ibrahim","doi":"10.61185/smij.2023.22105","DOIUrl":"https://doi.org/10.61185/smij.2023.22105","url":null,"abstract":"The proliferation of Internet of Things (IoT) devices has brought unprecedented convenience to our lives, but it has also opened the door to new security challenges. One of the most pressing threats in the IoT landscape is the proliferation of BotNets, which can compromise and control a multitude of devices for malicious purposes. In this paper, we propose a novel approach to address this issue: a Federated Machine Learning Solution for BotNet detection in IoT environments. Our method leverages the collective intelligence of distributed IoT devices while respecting privacy constraints, ensuring that sensitive data never leaves the device. We present a detailed methodology for federated model construction, including data collection, local model training, and secure aggregation. The resulting federated model offers improved accuracy and robustness in BotNet detection, as demonstrated through rigorous evaluation on the N-BaIoT dataset. Our findings underscore the effectiveness of this approach in enhancing IoT device security by detecting and mitigating BotNet threats while safeguarding data privacy. This paper contributes to the advancement of IoT security strategies and provides a framework for protecting IoT devices against evolving threats in a federated and privacy-preserving manner.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136000","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

Assessment and Contrast the Sustainable Growth of Various Road Transport Systems using Intelligent Neutrosophic Multi-Criteria Decision-Making Model 基于智能中性多准则决策模型的道路交通系统可持续增长评估与对比

Sustainable Machine Intelligence Journal Pub Date : 2023-03-20 DOI: 10.61185/smij.2023.22102

Nada Nabeeh

{"title":"Assessment and Contrast the Sustainable Growth of Various Road Transport Systems using Intelligent Neutrosophic Multi-Criteria Decision-Making Model","authors":"Nada Nabeeh","doi":"10.61185/smij.2023.22102","DOIUrl":"https://doi.org/10.61185/smij.2023.22102","url":null,"abstract":"This study analyses the elements and approaches to creating sustainable transport systems, with a focus on road travel. The study examines the environmental and economic aspects of sustainable road transport and stresses the need to curb carbon emissions, boost energy efficiency, clean the air, ensure everyone has easy access to transport, and think about societal goals as a whole. Important considerations including environmental effect, energy efficiency, legislative frameworks, and economic impact are highlighted in the study. The MCDM model is used as a complexity instrument to strike a balance between competing objectives and criteria. This research may help stakeholders use the MCDM method to better comprehend the existing condition of transport networks and to better plan for future sustainability actions. The primary goal of this article is to analyze and contrast how various present road transport systems have progressed toward a more sustainable future. Sustainability in road transport systems is discussed, and a framework procedure is presented based on the integrated single-valued neutrosophic set and DEMATEL approach. The factor relationship was built using the DEMATEL technique. There were 14 secondary criteria employed in addition to the four primary ones.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136001","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

Building a Sustainable Social Feedback Loop: A Machine Intelligence Approach for Twitter Opinion Mining 建立一个可持续的社会反馈循环:Twitter意见挖掘的机器智能方法

Sustainable Machine Intelligence Journal Pub Date : 2022-05-20 DOI: 10.61185/smij.2022.2315

A. Abdelhafeez, Alber Aziz, Nariman Khalil

{"title":"Building a Sustainable Social Feedback Loop: A Machine Intelligence Approach for Twitter Opinion Mining","authors":"A. Abdelhafeez, Alber Aziz, Nariman Khalil","doi":"10.61185/smij.2022.2315","DOIUrl":"https://doi.org/10.61185/smij.2022.2315","url":null,"abstract":"This paper presents a sustainable machine intelligence approach for Twitter opinion mining, focusing on building a socially responsible feedback loop. We propose a methodology that combines advanced machine learning algorithms with eco-conscious practices to extract sentiment-related insights from Twitter data while minimizing environmental impact. The preprocessing steps involve removing special characters, tokenization, stop word removal, handling user handles and URLs, and lemmatization or stemming. Sentiment classification is performed using the Extra Tree Classifier, an ensemble learning algorithm that incorporates random feature selection and bagging techniques. Experimental results demonstrate the effectiveness of our approach in accurately classifying tweets into positive, negative, and neutral sentiment categories. The visualizations of class distribution, number of tokens per tweet, and word clouds provide further insights into the sentiment landscape on Twitter. Our research contributes to the development of sustainable and inclusive approaches for Twitter opinion mining, ensuring minimal environmental impact while capturing valuable sentimental information.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131019064","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

DeepHAR-Net: A Novel Machine Intelligence Approach for Human Activity Recognition from Inertial Sensors 深度网络:一种新的惯性传感器人体活动识别的机器智能方法

Sustainable Machine Intelligence Journal Pub Date : 2022-05-20 DOI: 10.61185/smij.2022.8463

Ahmed Abdelhafeez, Ahmed M.Ali

{"title":"DeepHAR-Net: A Novel Machine Intelligence Approach for Human Activity Recognition from Inertial Sensors","authors":"Ahmed Abdelhafeez, Ahmed M.Ali","doi":"10.61185/smij.2022.8463","DOIUrl":"https://doi.org/10.61185/smij.2022.8463","url":null,"abstract":"Human activity recognition (HAR) from inertial sensor data plays a pivotal role in various domains, such as healthcare, sports, and smart environments. In this paper, we present a groundbreaking approach, DeepHAR-Net, for enhancing the accuracy and robustness of human activity recognition using inertial sensor data. Traditional methods in this field often rely on handcrafted features and shallow models, which may struggle to capture the intricate patterns and nuances within complex activities. DeepHAR-Net overcomes these limitations by leveraging the power of deep learning to automatically learn hierarchical representations from raw sensor data. The proposed DeepHAR-Net architecture employs a novel combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This fusion enables the model to effectively capture both spatial and temporal dependencies present in multi-dimensional sensor sequences. Additionally, we introduce a data augmentation strategy tailored to inertial sensor data, further enhancing the model's ability to generalize across variations in sensor placement and orientation. We rigorously evaluate DeepHAR-Net on benchmark datasets, comparing its performance against state-of-the-art methods. The experimental results demonstrate significant improvements in accuracy, outperforming existing techniques in various activity recognition scenarios. Notably, DeepHAR-Net showcases remarkable adaptability to different sensor configurations, showcasing its potential for real-world deployment in diverse applications.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075587","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: 2

Empowering Smart Farming with Machine Intelligence: An Approach for Plant Leaf Disease Recognition 用机器智能增强智能农业:一种植物叶片病害识别方法

Sustainable Machine Intelligence Journal Pub Date : 2022-05-20 DOI: 10.61185/smij.2022.1094

A. Sleem

{"title":"Empowering Smart Farming with Machine Intelligence: An Approach for Plant Leaf Disease Recognition","authors":"A. Sleem","doi":"10.61185/smij.2022.1094","DOIUrl":"https://doi.org/10.61185/smij.2022.1094","url":null,"abstract":"The growing global demand for sustainable agriculture has led to increased interest in leveraging machine intelligence to address critical challenges in modern farming practices. This paper introduces an innovative approach for plant leaf disease recognition in smart agriculture using the Vision Transformer (ViT) model. The proposed framework combines the power of self-attention mechanisms and transformer-based architectures to capture intricate relationships between image patches, enabling accurate and efficient disease identification. Leveraging the widely recognized PlantVillage dataset as a case study, our experiments demonstrate the efficacy of the ViT model in achieving superior disease recognition performance. The results highlight the model's ability to generalize across diverse crops and diseases, making it a promising tool for empowering farmers with timely disease detection and management. Additionally, the paper emphasizes inclusivity, ensuring the accessibility and effectiveness of the approach for farmers across diverse regions, backgrounds, and resources. Through this work, we contribute to the advancement of smart farming practices and pave the way for sustainable agriculture in the era of machine intelligence","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122566472","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

A Machine Learning Solution for Securing the Internet of Things Infrastructures 保护物联网基础设施的机器学习解决方案

Sustainable Machine Intelligence Journal Pub Date : 2022-05-20 DOI: 10.61185/smij.hpao9103

A. Abdel-Monem, M. Abouhawwash

引用次数: 1