Expert Systems最新文献

{"title":"Advancing Disability Healthcare Solutions Through Privacy-Preserving Federated Learning With Theme Framework","authors":"Madallah Alruwaili,&nbsp;Muhammad Hameed Siddiqi,&nbsp;Muhammad Idris,&nbsp;Salman Alruwaili,&nbsp;Abdullah Saleh Alanazi,&nbsp;Faheem Khan","doi":"10.1111/exsy.13807","DOIUrl":"https://doi.org/10.1111/exsy.13807","url":null,"abstract":"<div>\u0000 \u0000 <p>The application of machine learning, particularly federated learning, in collaborative model training, has demonstrated significant potential for enhancing diversity and efficiency in outcomes. In the healthcare domain, particularly healthcare with disabilities, the sensitive nature of data presents a significant challenge as sharing even the computation on these data can risk exposing personal health information. This research addresses the problem of enabling shared model training for healthcare data—particularly with disabilities decreasing the risk of leaking or compromising sensitive information. Technologies such as federated learning provide solution for decentralised model training but fall short in addressing concerns related to trust building, accountability and control over participation and data. We propose a framework that integrates federated learning with advanced identity management as well as privacy and trust management technologies. Our framework called <i>Theme</i> (Trusted Healthcare Machine Learning Environment) leverages digital identities (e.g., W3C decentralised identifiers and verified credentials) and policy enforcements to regulate participation. This is to ensure that only authorised and trusted entities can contribute to the model training. Additionally, we introduce the mechanisms to track contributions per participant and offer the flexibility for participants to opt out of model training at any point. Participants can choose to be either contributors (providers) or consumers (model users) or both, and they can also choose to participate in subset of activities. This is particularly important in healthcare settings, where individuals and healthcare institutions have the flexibility to control how their data are used without compromising the benefits. In summary, this research work contributes to privacy preserving shared model training leveraging federated learning without exposing sensitive data; trust and accountability mechanisms; contribution tracking per participant for accountability and back-tracking; and fine-grained control and autonomy per participant. By addressing the specific needs of healthcare data for people with disabilities or such institutions, the Theme framework offers a robust solution to balance the benefits of shared machine learning with critical need to protecting sensitive data.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets","authors":"Mohammed Salem Atoum,&nbsp;Ala Abdulsalam Alarood,&nbsp;Eesa Abdullah Alsolmi,&nbsp;Areej Obeidat,&nbsp;Moutaz Alazab","doi":"10.1111/exsy.13808","DOIUrl":"https://doi.org/10.1111/exsy.13808","url":null,"abstract":"<div>\u0000 \u0000 <p>Worldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism-related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF–IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTION: Optimization Using Internet of Agent Based Stacked Sparse Autoencoder Model for Heart Disease Prediction","authors":"","doi":"10.1111/exsy.13751","DOIUrl":"https://doi.org/10.1111/exsy.13751","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>V. Baviskar</span>, <span>M. Verma</span>, <span>P. Chatterjee</span>, <span>G. Singal</span> and <span>T. R. Gadekallu</span>, “ <span>Optimization Using Internet of Agent Based Stacked Sparse Autoencoder Model for Heart Disease Prediction</span>,” <i>Expert Systems</i> (Early View): e13359, https://doi.org/10.1111/exsy.13359.\u0000 </p><p>The above article, published online on 10 June 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that parts of the methods in the article lack sufficient detail such that the research cannot be reproduced. A relevant discussion and discrimination for different cardiovascular diseases is missing. The editors have therefore decided to retract this article. The authors disagree with the retraction.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTION: A Predictive Typological Content Retrieval Method for Real-time Applications Using Multilingual Natural Language Processing","authors":"","doi":"10.1111/exsy.13752","DOIUrl":"https://doi.org/10.1111/exsy.13752","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>S. Baskar</span>, <span>S. Dhote</span>, <span>T. Dhote</span>, <span>G. Jayanandini</span>, <span>D. Akila</span> and <span>S. Doss</span>, “ <span>A Predictive Typological Content Retrieval Method for Real-time Applications Using Multilingual Natural Language Processing</span>,” <i>Expert Systems</i> <span>41</span>, no. <span>6</span> (<span>2024</span>): e13172, https://doi.org/10.1111/exsy.13172.\u0000 </p><p>The above article, published online on 28 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that this article was not reviewed in line with the journal's peer review standards. The editors have therefore decided to retract this article. The authors disagree with the retraction.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13752","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTION: Natural Language Processing With Deep Learning Enabled Hybrid Content Retrieval Model for Digital Library Management","authors":"","doi":"10.1111/exsy.13753","DOIUrl":"https://doi.org/10.1111/exsy.13753","url":null,"abstract":"<p><b>RETRACTION</b>: <span>M. Ragab</span>, <span>A. Almuhammadi</span>, <span>R. F. Mansour</span> and <span>S. Kadry</span>, “ <span>Natural Language Processing With Deep Learning Enabled Hybrid Content Retrieval Model for Digital Library Management</span>,” <i>Expert Systems</i> <span>41</span>, no. <span>6</span> (<span>2024</span>): e13135, https://doi.org/10.1111/exsy.13135.\u0000 </p><p>The above article, published online on 13 September 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that this article was accepted on the basis of a compromised peer review process. The editors have therefore decided to retract this article. The authors disagree with the retraction.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTION: Hybrid Multi Agent Optimization for Optimal Battery Storage Using Micro Grid","authors":"","doi":"10.1111/exsy.13743","DOIUrl":"https://doi.org/10.1111/exsy.13743","url":null,"abstract":"<p><b>RETRACTION</b>: N. Bacanin, “Hybrid Multi Agent Optimization for Optimal Battery Storage Using Micro Grid,” <i>Expert Systems</i> 40, no. 4 (2023): e12995. https://doi.org/10.1111/exsy.12995.</p><p>The above article, published online on 14 March 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the author, N. Bacanin; the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that this article was accepted on the basis of a compromised peer review process. Furthermore, parts of the methods and figures in the article lack sufficient detail such that the research cannot be reproduced. Therefore the decision to retract this article was taken.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTION: Internet of Agents System for Age and Gender Classification Using Grasshopper Optimization With Deep Convolution Neural Networks","authors":"","doi":"10.1111/exsy.13746","DOIUrl":"https://doi.org/10.1111/exsy.13746","url":null,"abstract":"<p>\u0000 \u0000 <b>RETRACTION</b>: <span>A. K. Dutta</span>, <span>B. Qureshi</span>, <span>Y. Albagory</span>, <span>M. Alsanea</span>, <span>D. Gupta</span>, and <span>A. Khanna</span>, “ <span>Internet of Agents System for Age and Gender Classification Using Grasshopper Optimization With Deep Convolution Neural Networks</span>,” <i>Expert Systems</i> <span>40</span>, no. <span>4</span> (<span>2023</span>): e13115. https://doi.org/10.1111/exsy.13115.\u0000 </p><p>The above article, published online on 02 August 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that parts of the methods in the article lack sufficient detail such that the research cannot be reproduced. Furthermore, images of individuals have been used in figures 1, 3, 4 and 5 without any information provided regarding copyright and consent to use images. The editors have therefore decided to retract this article.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT)","authors":"Qasem Abu Al‐Haija, Ayat Droos","doi":"10.1111/exsy.13726","DOIUrl":"https://doi.org/10.1111/exsy.13726","url":null,"abstract":"The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"15 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MTFDN: An image copy‐move forgery detection method based on multi‐task learning","authors":"Peng Liang, Hang Tu, Amir Hussain, Ziyuan Li","doi":"10.1111/exsy.13729","DOIUrl":"https://doi.org/10.1111/exsy.13729","url":null,"abstract":"Image copy‐move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy‐move forgery detection from the perspective of multi‐task learning and summarize two characteristics of this problem: (1) Homology and (2) Manipulated traces. Consequently, we propose a multi‐task forgery detection network (MTFDN) for image copy‐move forgery localization and source/target distinguishment. The network consists of a hard‐parameter sharing feature extractor, global forged homology detection (GFHD) and local manipulated trace detection (LMTD) modules. The difference of feature distribution between the GFHD module and the LMTD module is significantly reduced by sharing parameters. Experimental results on several benchmark copy‐move forgery datasets demonstrate the effectiveness of our proposed MTFDN.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"4 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STP‐CNN: Selection of transfer parameters in convolutional neural networks","authors":"Otmane Mallouk, Nour‐Eddine Joudar, Mohamed Ettaouil","doi":"10.1111/exsy.13728","DOIUrl":"https://doi.org/10.1111/exsy.13728","url":null,"abstract":"Nowadays, transfer learning has shown promising results in many applications. However, most deep transfer learning methods such as <jats:italic>parameter sharing</jats:italic> and <jats:italic>fine‐tuning</jats:italic> are still suffering from the lack of parameters transmission strategy. In this paper, we propose a new optimization model for parameter‐based transfer learning in convolutional neural networks named STP‐CNN. Indeed, we propose a Lasso transfer model supported by a regularization term that controls transferability. Moreover, we opt for the proximal gradient descent method to solve the proposed model. The suggested technique allows, under certain conditions, to control exactly which parameters, in each convolutional layer of the source network, which will be used directly or adjusted in the target network. Several experiments prove the performance of our model in locating the transferable parameters as well as improving the data classification.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"14 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}