Sustainable Machine Intelligence Journal最新文献

The Convergence of Ikigai and Design Thinking: Crafting a Purposeful Framework 生财术与设计思维的融合：创建有目的的框架

Sustainable Machine Intelligence Journal Pub Date : 2024-04-09 DOI: 10.61356/smij.2024.77101

V. Christianto, F. Smarandache

{"title":"The Convergence of Ikigai and Design Thinking: Crafting a Purposeful Framework","authors":"V. Christianto, F. Smarandache","doi":"10.61356/smij.2024.77101","DOIUrl":"https://doi.org/10.61356/smij.2024.77101","url":null,"abstract":"In an era where innovation is not just about solving problems but also about enhancing human experiences and fostering personal fulfillment, the convergence of Ikigai principles with Design Thinking methodology offers a promising avenue for holistic problem-solving and innovation. This paper explores the intersection of Ikigai—a Japanese concept representing one's reason for being—and Design Thinking—a human-centered approach to innovation. We propose a conceptual framework, termed Ikigai-Driven Design (IDD), which integrates the principles of Ikigai with the stages of Design Thinking. IDD comprises five main stages: Empathize, Define, Ideate, Prototype, and Test, each combining elements of Ikigai and Design Thinking to foster purposeful innovation. The Empathize stage emphasizes understanding what users love and what the world needs, drawing insights from human-centered research methods. In the Define stage, practitioners frame problems through the lens of Ikigai, aligning identified needs with their own passions and strengths. The Ideate stage encourages divergent thinking, leveraging practitioners' Ikigai to generate creative solutions. Prototypes created in the Prototype stage embody practitioners' purpose and values, tested and refined based on user feedback in the Test stage. By integrating Ikigai principles with Design Thinking methodology, IDD offers a structured yet flexible approach to innovation that resonates deeply with users and contributes to practitioners' sense of fulfillment and meaning.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723121","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

Integrating Machine Intelligence to Estimate PM2.5 Concentration for Sustainable Urban Air Quality Management 整合机器智能估算 PM2.5 浓度，实现可持续的城市空气质量管理

Sustainable Machine Intelligence Journal Pub Date : 2023-12-31 DOI: 10.61185/smij.2023.44106

Ahmed Metwaly, A. Sleem, Ibrahim Elhenawy

{"title":"Integrating Machine Intelligence to Estimate PM2.5 Concentration for Sustainable Urban Air Quality Management","authors":"Ahmed Metwaly, A. Sleem, Ibrahim Elhenawy","doi":"10.61185/smij.2023.44106","DOIUrl":"https://doi.org/10.61185/smij.2023.44106","url":null,"abstract":"Air quality degradation, particularly the proliferation of fine particulate matter (PM2.5), poses a critical threat to environmental sustainability and public health. This paper introduces a comprehensive machine learning (ML) framework designed to predict PM2.5 concentrations, addressing the complexities inherent in heterogeneous urban environments. Drawing from a review of existing literature encompassing diverse ML methodologies applied to PM2.5 prediction, this study proposes an innovative approach amalgamating various data sources, including meteorological, geographical, and anthropogenic factors. Leveraging ensemble learning techniques and novel algorithmic models, our framework aims to surpass limitations encountered in current predictive models, enabling accurate and localized PM2.5 predictions. The significance of this research lies in its potential to offer a robust tool for environmental policymakers and urban planners, facilitating informed decisions towards mitigating PM2.5 pollution and fostering sustainable environments. Through evaluation of multiple ML algorithms, this paper contributes a novel predictive model crucial for enhancing air quality management.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"114 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139133457","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

Towards Sustainable Equine Welfare: Comparative Analysis of Machine Learning Techniques in Predicting Horse Survival 实现可持续的马匹福利：预测马匹存活率的机器学习技术比较分析

Sustainable Machine Intelligence Journal Pub Date : 2023-11-29 DOI: 10.61185/smij.2023.55105

Mahmoud Ismail

引用次数: 0

Enhancing Sustainability through Automated Waste Classification: A Machine Intelligence Framework 通过自动废物分类提高可持续性：机器智能框架

Sustainable Machine Intelligence Journal Pub Date : 2023-11-21 DOI: 10.61185/smij.2023.55106

A. Sleem

引用次数: 0

Predictive Intelligence Technique for Short-Term Load Forecasting in Sustainable Energy Grids 用于可持续能源网短期负荷预测的预测智能技术

Sustainable Machine Intelligence Journal Pub Date : 2023-11-15 DOI: 10.61185/smij.2023.55104

Ahmed Metwaly, Ibrahim Elhenawy

{"title":"Predictive Intelligence Technique for Short-Term Load Forecasting in Sustainable Energy Grids","authors":"Ahmed Metwaly, Ibrahim Elhenawy","doi":"10.61185/smij.2023.55104","DOIUrl":"https://doi.org/10.61185/smij.2023.55104","url":null,"abstract":"Short-term load forecasting remains pivotal in managing sustainable energy grids, with accuracy directly influencing operational decisions. Conventional forecasting methodologies often falter in adapting to the dynamic complexities inherent in modern energy systems. This paper introduces a predictive intelligence technique rooted in machine learning aimed at enhancing short-term load forecasting accuracy within sustainable energy grids. Leveraging historical data, weather patterns, grid operations, and consumer behavior insights, our study develops a robust predictive model. The model's adaptability to evolving patterns and real-time data integration offers a promising solution to the limitations of existing forecasting methods. Through a comparative analysis and validation against established benchmarks, the proposed technique showcases superior performance, demonstrating its potential for more efficient resource allocation and improved grid management. This research contributes to advancing sustainable energy practices by offering a reliable and adaptive solution for short-term load forecasting, fostering more resilient and responsive energy grid operations.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271863","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

High-Performance Technique for Estimating the Unknown Parameters of Photovoltaic Cells and Modules Based on Improved Spider Wasp Optimizer 基于改进蜘蛛蜂优化器的光伏电池和组件未知参数高性能估计技术

Sustainable Machine Intelligence Journal Pub Date : 2023-10-29 DOI: 10.61185/smij.2023.55102

Safa Saber, Sara Salem

引用次数: 0

A Neutrosophic Multi-Criteria Model for Evaluating Sustainable Soil Enhancement Methods and their Cost Implications in Construction 评价可持续土壤改良方法及其施工成本影响的中性多准则模型

Sustainable Machine Intelligence Journal Pub Date : 2023-10-25 DOI: 10.61185/smij.2023.55101

Ahmed El-Douh, SongFeng Lu, Ahmed Abdelhafeez, Alber Aziz

{"title":"A Neutrosophic Multi-Criteria Model for Evaluating Sustainable Soil Enhancement Methods and their Cost Implications in Construction","authors":"Ahmed El-Douh, SongFeng Lu, Ahmed Abdelhafeez, Alber Aziz","doi":"10.61185/smij.2023.55101","DOIUrl":"https://doi.org/10.61185/smij.2023.55101","url":null,"abstract":"This paper provides a comprehensive overview of the application of life cycle assessment (LCA) to promote sustainable soil practices in construction foundation projects. The aim is to evaluate the environmental impact and long-term sustainability of soil-related activities in construction through the lens of LCA. This assessment encompasses a wide array of factors, including soil quality, erosion control, contaminant remediation, soil stability, conservation, drainage management, and compliance with regulatory standards. To address this multifaceted evaluation, we employ a multi-criteria decision-making (MCDM) model, specifically introducing the Multi-Attributive Border Approximation Area Comparison (MABAC) method. This MCDM technique is utilized to appraise the sustainability of soil practices and is integrated with a neutrosophic set to handle imprecise information. Our study incorporates nine criteria and eight alternative methods. Through the application of LCA, construction professionals can uncover strategies to minimize the carbon footprint of their projects, optimize soil utilization, and enhance the long-term resilience of their structures. Achieving a comprehensive LCA tailored to the specific requirements of each project and local regulations necessitates close collaboration between soil engineers, environmental experts, and construction practitioners.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"98 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135216570","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 Comparative Analysis of Machine Learning Models for Prediction of Chronic Kidney Disease 慢性肾脏疾病预测机器学习模型的比较分析

Sustainable Machine Intelligence Journal Pub Date : 2023-10-25 DOI: 10.61185/smij.2023.55103

Nariman Khalil, Mohamed Elkholy, Mohamed Eassa

{"title":"A Comparative Analysis of Machine Learning Models for Prediction of Chronic Kidney Disease","authors":"Nariman Khalil, Mohamed Elkholy, Mohamed Eassa","doi":"10.61185/smij.2023.55103","DOIUrl":"https://doi.org/10.61185/smij.2023.55103","url":null,"abstract":"Prediction of chronic kidney disease (CKD) has emerged as a useful technique for early detection of at-risk persons and the introduction of appropriate management strategies. Machine learning and data-driven methods have been used in predictive modeling to examine massive databases of patient demographics, medical histories, test findings, and genetic information. These cutting-edge methods allow for the profiling of high-risk patients and the tailoring of healthcare administration approaches. Patient outcomes, complication rates, and healthcare system efficiency may all benefit greatly from CKD screening and prediction. Responsible use of CKD prediction algorithms, however, requires resolving issues with data availability, integration, and ethics. The area of medicine has benefited greatly from the use of Machine Learning (ML) methods, which have played an increasingly central role in illness prediction. In this study, we use a strategy that makes use of ML methods to construct effective tools for predicting the development of CKD. Multiple ML models are trained, and their results are compared using a variety of criteria. We applied five ML methods such as logistic regression (LR), Decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The LR and KNN have the highest accuracy with 99%.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"12 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135218203","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 Intrusion Detection in Vehicular Controller Area Networks using Machine Intelligence Paradigm 基于机器智能范式的车辆控制器区域网络可持续入侵检测

Sustainable Machine Intelligence Journal Pub Date : 2023-09-30 DOI: 10.61185/smij.2023.44104

Ahmed Metwaly, Ibrahim Elhenawy

{"title":"Sustainable Intrusion Detection in Vehicular Controller Area Networks using Machine Intelligence Paradigm","authors":"Ahmed Metwaly, Ibrahim Elhenawy","doi":"10.61185/smij.2023.44104","DOIUrl":"https://doi.org/10.61185/smij.2023.44104","url":null,"abstract":"The advent of smart mobility and the proliferation of connected vehicles have introduced new challenges in securing Vehicular Controller Area Networks (CANs) against cyber threats. This paper proposes an innovative machine intelligence paradigm for sustainable intrusion detection within vehicular networks. We present a Deep Neural Network (DNN) model that effectively classifies CAN traffic into categories, including Normal, Denial of Service (DoS), Gear Attack (Spoofing), RPM Attack (Spoofing), and Fuzzy Attack. The DNN's architecture is designed to learn and adapt to the dynamic nature of vehicular communications, enhancing its ability to detect network intrusions. The study encompasses an inclusive exploration of the CAN bus architecture, message data format, and related security vulnerabilities to provide a solid foundation for intrusion detection. Our methodology employs mathematical representations of the DNN model, offering insight into its training process. Visualizations of results, such as confusion matrices, ROC-AUC curves, T-SNE plots, and SHAP explanations, provide a holistic view of the model's performance and offer valuable insights for system refinement. By bridging the gap between machine intelligence and vehicular security, this research contributes to the ongoing efforts to fortify critical infrastructure, ensuring the reliability and sustainability of vehicular networks in the era of connected and autonomous vehicles.","PeriodicalId":148129,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135083737","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

An Improved Binary Quadratic Interpolation Optimization for 0-1 Knapsack Problems 0-1背包问题的改进二元二次插值优化

Sustainable Machine Intelligence Journal Pub Date : 2023-09-29 DOI: 10.61185/smij.2023.44101

Sara Salem

引用次数: 0