Sustainable Machine Intelligence Journal最新文献

VisionCam: A Comprehensive XAI Toolkit for Interpreting Image-Based Deep Learning Models VisionCam：解读基于图像的深度学习模型的综合性 XAI 工具包

Sustainable Machine Intelligence Journal Pub Date : 2024-06-11 DOI: 10.61356/smij.2024.8290

Walid Abdullah, Ahmed Tolba, Ahmed Elmasry, Nihal N. Mostafa

{"title":"VisionCam: A Comprehensive XAI Toolkit for Interpreting Image-Based Deep Learning Models","authors":"Walid Abdullah, Ahmed Tolba, Ahmed Elmasry, Nihal N. Mostafa","doi":"10.61356/smij.2024.8290","DOIUrl":"https://doi.org/10.61356/smij.2024.8290","url":null,"abstract":"Artificial intelligence (AI), a rapidly developing technology, has revolutionized various aspects of our lives. However many AI models' complex inner workings are still unknown, frequently compared to a \"black box.\" Particularly in crucial fields, this lack of explainability (XAI) reduces responsible AI research and reduces public confidence, and is accompanied by a growing demand for transparency and interpretability in AI decision-making. In response, this paper introduces a Python Extensible Toolkit for Explainable AI (XAI), This toolkit comprises nine state-of-the-art techniques for explaining AI models (especially deep learning models) decisions in image processing: GradCAM, GradCAM++, GradCAMElementWise, HriesCAM, RespondCAM, ScoreCAM, SmoothGradCAM++, XgradCAM, and AblationCAM. Each tool offers unique insights into model decision-making processes of deep learning models that work with image data, addressing various aspects of interpretability. Through case studies, we demonstrate the toolkit's impact on improving transparency and interpretability in AI systems that analyze visual information. The source code for the VisionCam toolkit is accessible at https://github.com/VisionCAM.","PeriodicalId":471548,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"74 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359534","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

GH-Twin: Graph Learning Empowered Hierarchical Digital Twin for Optimizing Self-Healing Networks GH-Twin：优化自愈网络的图学习赋能分层数字孪生体

Sustainable Machine Intelligence Journal Pub Date : 2024-06-10 DOI: 10.61356/smij.2024.8289

Nour Moustafa

引用次数: 0

A Data-driven Deep Learning Approach for Remaining Useful Life in the ion mill etching Process 离子磨蚀刻工艺中剩余使用寿命的数据驱动深度学习方法

Sustainable Machine Intelligence Journal Pub Date : 2024-06-09 DOI: 10.61356/smij.2024.8288

Ahmed Darwish

{"title":"A Data-driven Deep Learning Approach for Remaining Useful Life in the ion mill etching Process","authors":"Ahmed Darwish","doi":"10.61356/smij.2024.8288","DOIUrl":"https://doi.org/10.61356/smij.2024.8288","url":null,"abstract":"Prognostics and Health Management (PHM) is regarded as an essential element in the scope of intelligent manufacturing. Precise forecasting of the remaining useful life (RUL) of an ion mill is crucial in order to enhance the overall efficiency of the ion mill etching (IME) procedure. This paper proposed a Data-driven Deep Learning (DL) framework that integrates a Temporal Convolution Network (TCN), Long Short-Term Memory (LSTM), and self-attention mechanism to improve the accuracy of RUL prediction in the ion mill etching Process. Initially, sensor input data is divided into two parallel paths - one with TCN blocks for capturing long-range dependencies, and the other with LSTM layers for extracting temporal patterns. The outputs from both paths are then merged and input into an LSTM layer for enhanced learning, followed by a self-attention mechanism to highlight important features then fully connected layer for predicting RUL. The efficacy of this suggested model was assessed through the utilization of the 2018 PHM Data Challenge Dataset and juxtaposed against various Deep Learning models to demonstrate its efficacy. The results from the experiments indicate that ATCN-LSTM serves as a robust option for estimating the RUL in the ion mill etching Process as it outperformed all other models that were compared. The source code is publicly accessible at https://github.com/ion-mill-etching-Process.","PeriodicalId":471548,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367633","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

Responsible Artificial Intelligence for Climate Action: A Theoretical Framework for Sustainable Development 负责任的人工智能促进气候行动：促进可持续发展的理论框架

Sustainable Machine Intelligence Journal Pub Date : 2024-06-03 DOI: 10.61356/smij.2024.88101

Byeong-Gwon Kang, Yunyoung Nam

{"title":"Responsible Artificial Intelligence for Climate Action: A Theoretical Framework for Sustainable Development","authors":"Byeong-Gwon Kang, Yunyoung Nam","doi":"10.61356/smij.2024.88101","DOIUrl":"https://doi.org/10.61356/smij.2024.88101","url":null,"abstract":"Climate change poses an urgent and significant challenge, with far-reaching impacts already affecting our planet, and projections indicating worsening conditions in the future. The concept of sustainable development aims to meet present needs while safeguarding the ability of future generations to meet their own requirements. However, climate change's effects on sustainable development are of paramount concern, as they amplify issues like poverty, food insecurity, and environmental degradation, affecting economic growth, social progress, and environmental protection. Taking immediate action to mitigate climate change and implement sustainable practices is crucial to ensuring a habitable planet for future generations. In this context, Responsible Artificial Intelligence (RAI) emerges as a promising direction, striving for ethical and responsible technology use in diverse sustainable development tasks. RAI proves to be a robust candidate for empowering climate change mitigation and adaptation efforts. This study introduces a theoretical RAI framework designed to support climate action by responsibly enabling more accurate predictions and analysis of climate data, enhancing energy efficiency, and reducing greenhouse gas emissions. The framework emphasizes the need for interdisciplinary collaboration among policymakers, scientists, and technicians to develop RAI solutions that advance sustainable development and alleviate the adverse impacts of climate change. Unlike previous works, this research presents a novel perspective on the principles of RAI that explicitly consider climate-related aspects. By laying the foundations of AI research to bolster our fight against climate change, this article establishes essential pillars that encourage further advancements in this critical endeavor. ","PeriodicalId":471548,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"102 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141272250","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

Machine Learning for Intrusion Detection: A Reproducible Baseline is All You Need 入侵检测的机器学习：可重现的基线就是你所需要的一切

Sustainable Machine Intelligence Journal Pub Date : 2024-05-20 DOI: 10.61356/smij.2024.77103

Salma A. Walli, Karam M. Sallam

{"title":"Machine Learning for Intrusion Detection: A Reproducible Baseline is All You Need","authors":"Salma A. Walli, Karam M. Sallam","doi":"10.61356/smij.2024.77103","DOIUrl":"https://doi.org/10.61356/smij.2024.77103","url":null,"abstract":"Ensuring Responsible AI practices is paramount in the advancement of systems founded upon machine learning (ML) principles, particularly in sensitive domains like intrusion detection within cybersecurity. A fundamental aspect of Responsible AI is reproducibility, which guarantees the reliability and transparency of research outcomes. In this paper, we address the critical challenge of establishing reproducible for intrusion detection utilizing ML techniques. Leveraging the NSL-KDD dataset and the Edge-IIoTset, we carry out extensive experiments to evaluate the efficacy of our approach. Our study prioritizes meticulous experiment design and careful implementation setups, aligning with the principles of Responsible AI. Through rigorous experimentation and insightful discussions, we underscore the importance of reproducibility as a cornerstone in ensuring the resilience and reliability of intrusion detection systems. Our findings offer valuable insights for researchers and practitioners striving to develop Responsible AI solutions in cybersecurity and beyond. The source code is publicly accessible at https://github.com/Salma-00/Machine-Learning-for-Intrusion-Detection. ","PeriodicalId":471548,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"43 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122082","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

Diagnosing Brain Tumors from MRI images through a Multi-Fused CNN with Auxiliary Layers 通过带辅助层的多融合 CNN 从核磁共振成像诊断脑肿瘤

Sustainable Machine Intelligence Journal Pub Date : 2024-03-02 DOI: 10.61356/smij.2024.66102

A. Alkhatib, Mohamad Alharoun, Areej Alzoubi, Esraa Muqdadi, Aseel Abu Aqoulah, Almo’men Bellah Alawnah, Razan Abedulhammeed Youn's

{"title":"Diagnosing Brain Tumors from MRI images through a Multi-Fused CNN with Auxiliary Layers","authors":"A. Alkhatib, Mohamad Alharoun, Areej Alzoubi, Esraa Muqdadi, Aseel Abu Aqoulah, Almo’men Bellah Alawnah, Razan Abedulhammeed Youn's","doi":"10.61356/smij.2024.66102","DOIUrl":"https://doi.org/10.61356/smij.2024.66102","url":null,"abstract":"In this study, we proposed a novel Multi-Fused Residual Convolutional Neural Network (MFR-CNN) with Auxiliary Fusing Layers (AuxFL) to diagnose various types of brain tumor MRI images. The MFR-CNN was designed to handle four specific cases, namely glioma, meningioma, pituitary, and healthy brain images, obtained from reliable Kaggle databases. Our proposed model integrated three state-of-the-art models into a single feature extraction pipeline, incorporating partially frozen and truncated layers. This strategic fusion enabled the propagation of robust features and improved diagnostic performance without incurring significant computing costs, unlike most existing state-of-the-art models. Moreover, the MFR-CNN effectively mitigated overfitting and performance saturation issues, providing a notable advantage over models lacking these components. Upon evaluation, our proposed model achieved an outstanding accuracy of 94%, surpassing the efficiency and accuracy of conventionally trained DCNNs. Notably, the MFR-CNN demonstrated potential in enhancing brain tumor diagnosis more cost-efficiently than ensembles and outperforming conventional pre-trained and fine-tuned DCNNs. In conclusion, the proposed MFR-CNN with AuxFL and FuRB exhibits promising capabilities to improve the diagnosis of brain tumors, offering better cost-efficiency and accuracy compared to existing methods.","PeriodicalId":471548,"journal":{"name":"Sustainable Machine Intelligence Journal","volume":"46 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082300","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

Detection of Depression from Arabic Tweets Using Machine Learning 利用机器学习从阿拉伯语推文中检测抑郁症

Sustainable Machine Intelligence Journal Pub Date : 2024-03-02 DOI: 10.61356/smij.2024.11103

Areej Alzoubi, Ahmad Alaiad, Khaled Alkhattib, A. Alkhatib, Aseel Abu Aqoulah, Almo’men Bellah Alawnah, Ola Hayajnah

引用次数: 0

PAM: Cultivate a Novel LSTM Predictive analysis Model for The Behavior of Cryptocurrencies PAM：为加密货币行为建立新颖的 LSTM 预测分析模型

Sustainable Machine Intelligence Journal Pub Date : 2024-02-10 DOI: 10.61356/smij.2024.66101

Mona Mohamed, Mona Gharib

引用次数: 0