Expert Systems最新文献_第9页

Intelligent Computing for Crop Monitoring in CIoT: Leveraging AI and Big Data Technologies

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-18 DOI: 10.1111/exsy.13786

Imran Ahmed, Misbah Ahmad, Haythem Ghazouani, Walid Barhoumi, Gwanggil Jeon

{"title":"Intelligent Computing for Crop Monitoring in CIoT: Leveraging AI and Big Data Technologies","authors":"Imran Ahmed, Misbah Ahmad, Haythem Ghazouani, Walid Barhoumi, Gwanggil Jeon","doi":"10.1111/exsy.13786","DOIUrl":"https://doi.org/10.1111/exsy.13786","url":null,"abstract":"<div>\u0000 \u0000 <p>Consumer Internet of Things (CIoT) has revolutionised agriculture by integrating intelligent computing, artificial intelligence and big data technologies in crop monitoring. This paper explores the application of intelligent computing and deep learning methodologies in crop monitoring within the CIoT framework. In CIoT-based crop monitoring, a vision sensor collects real-time data from crop leaf images. The image dataset is processed using state-of-the-art deep learning models and intelligent computing algorithms. This integration enables the early detection of crop diseases by leveraging computer vision and deep learning. Intelligent computing systems provide accurate disease classification, real-time alerts, and actionable recommendations for optimised crop management practises. This advanced system empowers farmers to make data-driven decisions, such as irrigation optimization, targeted pesticide application and nutrient supplementation, to maximise crop productivity and minimise losses. A benchmark dataset of leaf images is used, and a deep learning based model is presented for classifying healthy and diseased leaves. Experimental results demonstrate an accuracy rate of 0.98, with detailed validation, including dataset size and model parameters. Key benefits of intelligent computing in CIoT-based crop monitoring include enhanced resource efficiency, reduced environmental impact, and improved sustainability. The paper also addresses the challenges of implementing AI and big data technologies, such as data privacy, security, interoperability and resource management in agricultural settings.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116473","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}

引用次数: 0

RETRACTION: An Information Extraction Method Based on Improved Mixed Text Density Web Pages

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-18 DOI: 10.1111/exsy.13796

引用次数: 0

LightAuth: A Lightweight Sensor Nodes Authentication Framework for Smart Health System

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-13 DOI: 10.1111/exsy.13756

Zain Ul Islam Adil, Majid Iqbal Khan, Kahkishan Sanam, Saif U. R. Malik, Syed Atif Moqurrab, Gautam Srivastava

{"title":"LightAuth: A Lightweight Sensor Nodes Authentication Framework for Smart Health System","authors":"Zain Ul Islam Adil, Majid Iqbal Khan, Kahkishan Sanam, Saif U. R. Malik, Syed Atif Moqurrab, Gautam Srivastava","doi":"10.1111/exsy.13756","DOIUrl":"https://doi.org/10.1111/exsy.13756","url":null,"abstract":"<p>Counterfeit medical devices pose a threat to patient safety, necessitating a secure device authentication system for medical applications. Resource-constrained sensory nodes are vulnerable to hacking, prompting the need for robust security measures. Token-based authentication schemes, such as one-time passwords (OTPs), smart cards, key fobs, and mobile authentication apps, along with certificate-based authentication methods, such as client and code-signing, employ cryptographic frameworks like elliptical curve cryptography (ECC) and physical unclonable functions (PUF). However, these methods face challenges, including block sequence issues and susceptibility to side-channel attacks. To address these issues, we propose a framework for mutual authentication using private Ethereum. This framework integrates private Ethereum and cryptographic techniques for encrypting and decrypting data using mathematical algorithms to overcome block sequence issues and side-channel attacks. Similarly, fog nodes are utilised to enhance local computing, storage, and networking capabilities for sensors. The framework is evaluated using metrics such as communication costs, execution costs, and computation costs based on Ethereum gas consumption. The performance of the LightAuth framework is compared with that of the Smart Contracts Against Counterfeit IoMT (SCACIoMT) framework, designed for Internet of Medical Things (IoMT) devices. The effectiveness of LightAuth is verified through formal security analysis using BAN logic.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114629","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}

引用次数: 0

Crude Oil Markets Volatility Forecasting: A Novel Deep Learning Hybrid Model

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-13 DOI: 10.1111/exsy.13772

Zixiao Lin, Bin Tan, Yu Lin, Qin Lu

引用次数: 0

DSL-Driven Approaches and Metamodels for Chatbot Development: A Systematic Literature Review

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-13 DOI: 10.1111/exsy.13787

Charaf Ouaddi, Lamya Benaddi, El Mahi Bouziane, Abdeslam Jakimi, Abdellah Chehri, Rachid Saadane

{"title":"DSL-Driven Approaches and Metamodels for Chatbot Development: A Systematic Literature Review","authors":"Charaf Ouaddi, Lamya Benaddi, El Mahi Bouziane, Abdeslam Jakimi, Abdellah Chehri, Rachid Saadane","doi":"10.1111/exsy.13787","DOIUrl":"https://doi.org/10.1111/exsy.13787","url":null,"abstract":"<p>Chatbots have emerged as ubiquitous tools for enhancing user interaction across various platforms, from customer service to personal assistance. They are computer programs that simulate and process human conversation, either written, spoken or both. However, developing efficient chatbots remains a challenge, primarily due to the intricate nature of critical components of chatbots like natural language understanding (NLU) requiring a subscription from intent recognition providers like Dialogflow and Amazon Lex. This makes chatbots closely linked to NLP services and can be locked in. Recently, various research studies have provided solutions to reduce the workload of developers and designers. These approaches have proposed model-driven development via domain-specific languages (DSLs), which make the chatbot development process more accessible and more automated. This advancement aims to enhance effectiveness in chatbot development by leveraging DSLs. This study aims to provide a comprehensive overview of DSLs for developing chatbots, with the first contribution comprising various research topics, tools, approaches, and technologies employed to implement DSLs. Second, this work aims to assess and contrast the primary DSLs currently available for chatbot development, focusing on presenting the key elements used in constructing these DSLs. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114634","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}

引用次数: 0

Dragon Boat Optimization: A Meta-Heuristic for Intelligent Systems

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-13 DOI: 10.1111/exsy.13785

Xiang Li, Long Lan, Husam Lahza, Shaowu Yang, Shuihua Wang, Wenjing Yang, Hengzhu Liu, Yudong Zhang

{"title":"Dragon Boat Optimization: A Meta-Heuristic for Intelligent Systems","authors":"Xiang Li, Long Lan, Husam Lahza, Shaowu Yang, Shuihua Wang, Wenjing Yang, Hengzhu Liu, Yudong Zhang","doi":"10.1111/exsy.13785","DOIUrl":"https://doi.org/10.1111/exsy.13785","url":null,"abstract":"<div>\u0000 \u0000 <p>Dragon boat racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. It models the unique behaviours of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in various situations. During each iteration, DBO implements different state updating strategies. By accurately modelling the crew's behaviour and employing adaptive state update strategies, DBO consistently achieves high optimization performance, as validated by comprehensive testing on 29 benchmark functions and 2 structural design problems. Experimental results indicate that DBO outperforms 7 and 16 state-of-the-art meta-heuristic algorithms across these test functions and problems, respectively.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114635","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}

引用次数: 0

Enhancing Psychologists' Understanding Through Explainable Deep Learning Framework for ADHD Diagnosis

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-13 DOI: 10.1111/exsy.13788

Abdul Rehman, Jerry Chun-Wei Lin, Ilona Heldal

{"title":"Enhancing Psychologists' Understanding Through Explainable Deep Learning Framework for ADHD Diagnosis","authors":"Abdul Rehman, Jerry Chun-Wei Lin, Ilona Heldal","doi":"10.1111/exsy.13788","DOIUrl":"https://doi.org/10.1111/exsy.13788","url":null,"abstract":"<div>\u0000 \u0000 <p>Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is challenging to diagnose and requires advanced approaches for reliable and transparent identification and classification. It is characterised by a pattern of inattention, hyperactivity and impulsivity that is more severe and more frequent than in individuals with a comparable level of development. In this paper, an explainable framework based on a fine-tuned hybrid Deep Neural Network (DNN) and Recurrent Neural Network (RNN) called <i>HyExDNN-RNN</i> model is proposed for ADHD detection, multi-class categorization and decision interpretation. This framework not only detects ADHD but also provides interpretable insights into the diagnostic process so that psychologists can better understand and trust the results of the diagnosis. We use the Pearson correlation coefficient for optimal feature selection and machine and deep learning models for experimental analysis and comparison. We use a standardised technique for feature reduction, model selection and interpretation to accurately determine the diagnosis rate and ensure the interpretability of the proposed framework. Our framework provided excellent results on binary classification, with <i>HyExDNN-RNN</i> achieving an F1-score of 99% and 94.2% on multi-class categorization. XAI approaches, in particular SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI), provided important insights into the importance of features and the decision logic of models. By combining AI with human expertise, we aim to bridge the gap between advanced computational techniques and practical psychological applications. These results demonstrate the potential of our framework to assist in ADHD diagnosis and interpretation.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114636","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}

引用次数: 0

A Social Group Chatbot System by Multiple Topics Tracking and Atkinson-Shiffrin Memory Model Using AI Agents Collaboration

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-13 DOI: 10.1111/exsy.13766

Guoshuai Zhang, Jiaji Wu, Gwanggil Jeon, Penghui Wang

{"title":"A Social Group Chatbot System by Multiple Topics Tracking and Atkinson-Shiffrin Memory Model Using AI Agents Collaboration","authors":"Guoshuai Zhang, Jiaji Wu, Gwanggil Jeon, Penghui Wang","doi":"10.1111/exsy.13766","DOIUrl":"https://doi.org/10.1111/exsy.13766","url":null,"abstract":"<div>\u0000 \u0000 <p>The widespread use of Internet has accelerated the explosive growth of data, which in turn leads to information overload and information confusion. This makes it difficult for us to communicate effectively in social groups, thereby intensifying the demands for emotional companionship. Therefore, we propose a novel social group chatting framework based on Large Language Model (LLM) powered multiple autonomous agents collaboration in this article. Specifically, BERTopic is used to extract topics from history chatting content for each social group everyday, and then multiple topics tracking is realised through multi-level association by adaptive time sliding-window mechanism and optimal matching. Furthermore, we use topic tracking architecture and prompts to design and implement an AI Chatbot system with different characters that can conduct natural language conversations with users in online social group. LLM, as the controller and coordinator of the whole AI Chatbot for sub-tasks, allows different AI Agents to autonomously decide whether to participate in current topic, how to generate response, and whether to propose a new topic. Each AI Agent has their own multi-store memory system based on the Atkinson-Shiffrin model. Finally, we construct a verification environment based on online game that is consistent with real society. Subjective and objective evaluation methods were deployed to perform qualitative and quantitative analyses to demonstrate the performance of our AI Chatbot system.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114630","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}

引用次数: 0

MeDi-TODER: Medical Domain-Incremental Task-Oriented Dialogue Generator Using Experience Replay

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-08 DOI: 10.1111/exsy.13773

Minji Kim, Joon Yoo, OkRan Jeong

{"title":"MeDi-TODER: Medical Domain-Incremental Task-Oriented Dialogue Generator Using Experience Replay","authors":"Minji Kim, Joon Yoo, OkRan Jeong","doi":"10.1111/exsy.13773","DOIUrl":"https://doi.org/10.1111/exsy.13773","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) technology has brought groundbreaking changes to the healthcare domain. Specifically, the integration of a medical dialogue system (MDS) has facilitated interactions with patients, identifying meaningful information such as symptoms and medications from their dialogue history to generate appropriate responses. However, shortcomings arise when MDS lacks access to the patient's cumulative history or prior domain knowledge, resulting in the generation of inaccurate responses. To address this challenge, we propose a medical domain-incremental task-oriented dialogue generator using experience replay (MeDi-TODER) that applies the continual learning technique to the medical task-oriented dialogue generator. By strategically sampling and storing exemplars from previous domains and rehearsing it as it learns, the model effectively retains knowledge and can respond to the novel domains. Extensive experiments demonstrated that MeDi-TODER significantly outperforms other models that lack continual learning in both natural language generation and natural language understanding. Notably, our proposed method achieves the highest scores, surpassing the upper-class benchmarks.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113633","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}

引用次数: 0

Interval Time Series Forecasting: An Innovative Approach Transforming Interval to Single Time Series

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-11-07 DOI: 10.1111/exsy.13783

George Varelas, Giannis Tzimas

{"title":"Interval Time Series Forecasting: An Innovative Approach Transforming Interval to Single Time Series","authors":"George Varelas, Giannis Tzimas","doi":"10.1111/exsy.13783","DOIUrl":"https://doi.org/10.1111/exsy.13783","url":null,"abstract":"<p>Interval Time Series are present in everyday life. An example is the opening and closing value of some stock in a market for certain time intervals. The forecasting plays an essential role in many financial organisations. The development of new mathematical tools or improving the existing ones will lead to more accurate forecasting techniques. Interval Arithmetic is a mathematical field that uses intervals by nature and algorithms that use it are involved in the solution of Interval Time Series. Another classical algorithm is VAR models. In this paper, a method that comes from the insurance sector is used to forecast Brent Oil monthly values. The innovation of what we propose is that it converts the Interval Time Series system into a single time series and can propagate the results back to each time series of the system. This way the researcher works with only one time series instead of two (or more). The forecasting algorithm is a choice of the researcher, expediting the development of forecasting (even ARIMA can be applied). We demonstrate our methodology in forecasting the Brent Oil monthly prices by applying the ANFIS algorithm.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112871","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}

引用次数: 0