Expert Systems最新文献

{"title":"Class integration of ChatGPT and learning analytics for higher education","authors":"Miguel Civit,&nbsp;María José Escalona,&nbsp;Francisco Cuadrado,&nbsp;Salvador Reyes-de-Cozar","doi":"10.1111/exsy.13703","DOIUrl":"10.1111/exsy.13703","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Active Learning with AI-tutoring in Higher Education tackles dropout rates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>To investigate teaching-learning methodologies preferred by students. AHP is used to evaluate a ChatGPT-based studented learning methodology which is compared to another active learning methodology and a traditional methodology. Study with Learning Analytics to evaluate alternatives, and help students elect the best strategies according to their preferences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Comparative study of three learning methodologies in a counterbalanced Single-Group with 33 university students. It follows a pre-test/post-test approach using AHP and SAM. HRV and GSR used for the estimation of emotional states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Criteria related to in-class experiences valued higher than test-related criteria. Chat-GPT integration was well regarded compared to well-established methodologies. Student emotion self-assessment correlated with physiological measures, validating used Learning Analytics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Proposed model AI-Tutoring classroom integration functions effectively at increasing engagement and avoiding false information. AHP with the physiological measuring allows students to determine preferred learning methodologies, avoiding biases, and acknowledging minority groups.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206868","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":"Selection preference and effectiveness quantification of provincial energy security policies in China","authors":"Liangpeng Wu, Yujing Tang, Qingyuan Zhu","doi":"10.1111/exsy.13711","DOIUrl":"https://doi.org/10.1111/exsy.13711","url":null,"abstract":"Energy security constitutes a pivotal determinant in safeguarding the seamless functioning of economies. This research endeavours to shed light on the underlying predilections and potential scopes for enhancement within China's provincial energy security policies. By delving into an array of policy documents procured from the esteemed Legal Information Network of Peking University, it offers a meticulous exploration. Employing sophisticated text analysis methodologies, the study constructs a two‐tier analytical framework, meticulously encapsulating both the policy instruments employed and the intricate processes of their execution. Leveraging the power of Nvivo 12 Plus software, pertinent policy contents are systematically coded, with those aligning with the defined analytical dimensions aggregated for frequency computations. Furthermore, a Policy Measurement and Categorization (PMC) index model is devised, harnessing word frequency statistical data to assign a quantitative assessment to the policies under scrutiny. The empirical results demonstrate a noteworthy disparity in the adoption of policy tools among various provinces, with command‐and‐control mechanisms, economic incentive structures, and societal engagement strategies emerging as the most recurrent policy types. Among the energy security policies scrutinized, approximately 84.85% were categorized as effective, while a smaller yet significant portion, 6.06%, was classified as outstanding. Despite the overall robustness of China's provincial energy security policies, the investigation identifies several avenues for further refinement. The study suggests that the government could bolster these measures through intensified focus on transformative adjustments to energy structures, augmentation of green loan guarantee systems, and fostering enhanced inter‐sectoral collaboration. These strategic enhancements may serve as key levers to propel China's provincial energy security policies towards even greater effectiveness and resilience.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"14 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206867","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":"Artificial electric field algorithm with repulsion mechanism","authors":"Gengfei Zhang,&nbsp;Jiatang Cheng","doi":"10.1111/exsy.13715","DOIUrl":"https://doi.org/10.1111/exsy.13715","url":null,"abstract":"<p>Due to its outstanding performance in addressing optimization problems, artificial electric field (AEF) algorithm has garnered increasing notice in recent years. Nevertheless, numerous studies indicate that AEF is susceptible to premature convergence when the region influenced by the global optimum constitutes a small fraction of the entire solution space. By conducting micro-level research on the particles during the evolution process of AEF, it is revealed that the primary factors influencing optimization performance are the Coulomb's electrostatic force mechanism and the fixed attenuation factor. Inspired by this observation, we propose an improved version named artificial electric field algorithm with repulsion mechanism (RMAEF). Specifically, in RMAEF, a repulsion mechanism is incorporated to make particles escape from local optima. Furthermore, an adaptive attenuation factor is employed to update dynamically Coulomb's constant. RMAEF is compared with AEF and its state-of-art variants under 44 test functions from CEC 2005 and CEC 2014 test suites. From the experiment results, it is obvious that among 14 benchmark functions from CEC 2005 on 30D and 50D optimization, the RMAEF algorithm exhibits superior performance on 8 and 9 functions compared with advanced variants of AEF. For CEC 2014 on 30D and 50D optimization, the RMAEF algorithm produces the best results on 11 and 12 functions, respectively. In addition, three real-world problems are also used to verify the versatility and robustness. The results demonstrate that RMAEF outperforms its competitors in terms of overall performance.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707818","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":"Application of visual attribute transfer technology in analysing changes in emotional expression in picture books","authors":"Yue Wang, Yin Wang, Yansu Qi, Sheng Miao, Weijun Gao","doi":"10.1111/exsy.13677","DOIUrl":"https://doi.org/10.1111/exsy.13677","url":null,"abstract":"In picture books, readers can obtain different emotional perceptions according to different image style attributes. Artists often use different combinations of colours, textures, materials, and other style elements in images to convey different emotions in their creations. Especially in picture books for children, there is a strong correlation between the perceived effect of the work and the accuracy and degree of emotional expression. In the process of creating picture books, various factors will affect the efficiency of artists trying to transfer styles to meet their creative needs. With the development of image style transfer technology based on a deep convolutional neural network, artists can use this technology to create works with different styles of emotional changes efficiently. In this paper, we select illustrations of picture books and use deep convolutional neural networks to transfer image styles from three aspects: colour style transfer, texture style, and material style transfer. Through sampling survey experiments, we discuss the changes in image attributes, emotional expression, and emotional perception in picture books for children. The survey results found that the most direct and evident influence on the emotional changes of picture book images is the transfer of colour style attributes, material style attributes, and texture style attributes. The results of this study can provide a valuable reference for improving the accuracy of emotional expression, the depth of meaning extension, and the height of artistic value in picture books for children during the process of an artist's creation. This research stands out by systematically analysing the distinct impact of each style attribute transfer, offering a comprehensive framework that can be utilized by artists and technologists alike to enhance the emotional and artistic quality of children's picture books.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"3 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206607","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":"CATcAFSMs: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems","authors":"Alishba Nawaz, Waseem Iqbal, Ayesha Altaf, Abrar Almjally, Hatoon AlSagri, Bayan Alabdullah","doi":"10.1111/exsy.13687","DOIUrl":"https://doi.org/10.1111/exsy.13687","url":null,"abstract":"Fog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity <jats:italic>O</jats:italic>(<jats:italic>n</jats:italic>).","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"2 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227718","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":"Prospect of large language models and natural language processing for lung cancer diagnosis: A systematic review","authors":"Arushi Garg,&nbsp;Smridhi Gupta,&nbsp;Soumya Vats,&nbsp;Palak Handa,&nbsp;Nidhi Goel","doi":"10.1111/exsy.13697","DOIUrl":"10.1111/exsy.13697","url":null,"abstract":"<p>Lung cancer, a leading cause of global mortality, demands a combat for its effective prevention, early diagnosis, and advanced treatment methods. Traditional diagnostic methods face limitations in accuracy and efficiency, necessitating innovative solutions. Large Language Models (LLMs) and Natural Language Processing (NLP) offer promising avenues for overcoming these challenges by providing comprehensive insights into medical data and personalizing treatment plans. This systematic review explores the transformative potential of LLMs and NLP in automating lung cancer diagnosis. It evaluates their applications, particularly in medical imaging and the interpretation of complex medical data, and assesses achievements and associated challenges. Emphasizing the critical role of Artificial Intelligence (AI) in medical imaging, the review highlights advancements in lung cancer screening and deep learning approaches. Furthermore, it underscores the importance of on-going advancements in diagnostic methods and encourages further exploration in this field.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206853","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":"Comparative analysis of paraphrasing performance of ChatGPT, GPT-3, and T5 language models using a new ChatGPT generated dataset: ParaGPT","authors":"Meltem Kurt Pehlivanoğlu,&nbsp;Robera Tadesse Gobosho,&nbsp;Muhammad Abdan Syakura,&nbsp;Vimal Shanmuganathan,&nbsp;Luis de-la-Fuente-Valentín","doi":"10.1111/exsy.13699","DOIUrl":"10.1111/exsy.13699","url":null,"abstract":"<p>Paraphrase generation is a fundamental natural language processing (NLP) task that refers to the process of generating a well-formed and coherent output sentence that exhibits both syntactic and/or lexical diversity from the input sentence, while simultaneously ensuring that the semantic similarity between the two sentences is preserved. However, the availability of high-quality paraphrase datasets has been limited, particularly for machine-generated sentences. In this paper, we present ParaGPT, a new paraphrase dataset of 81,000 machine-generated sentence pairs, including 27,000 reference sentences (ChatGPT-generated sentences), and 81,000 paraphrases obtained by using three different large language models (LLMs): ChatGPT, GPT-3, and T5. We used ChatGPT to generate 27,000 sentences that cover a diverse array of topics and sentence structures, thus providing diverse inputs for the models. In addition, we evaluated the quality of the generated paraphrases using various automatic evaluation metrics. Furthermore, we provide insights into the strengths and drawbacks of each LLM in generating paraphrases by conducting a comparative analysis of the paraphrasing performance of the three LLMs. According to our findings, ChatGPT's performance, as per the evaluation metrics provided, was deemed impressive and commendable, owing to its higher-than-average scores for semantic similarity, which implies a higher degree of similarity between the generated paraphrase and the reference sentence, and its relatively lower scores for syntactic diversity, indicating a greater diversity of syntactic structures in the generated paraphrase. ParaGPT is a valuable resource for researchers working on NLP tasks like paraphrasing, text simplification, and text generation. We make the ParaGPT dataset publicly accessible to researchers, and as far as we are aware, this is the first paraphrase dataset produced based on ChatGPT.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206857","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":"DemocracyGuard: Blockchain‐based secure voting framework for digital democracy","authors":"Mritunjay Shall Peelam, Gaurav Kumar, Kunjan Shah, Vinay Chamola","doi":"10.1111/exsy.13694","DOIUrl":"https://doi.org/10.1111/exsy.13694","url":null,"abstract":"Online voting is gaining traction in contemporary society to reduce costs and boost voter turnout, allowing individuals to cast their ballots from anywhere with an internet connection. This innovation is cautiously met due to the inherent security risks, where a single vulnerability can lead to widespread vote manipulation. Blockchain technology has emerged as a promising solution to address these concerns and create a trustworthy electoral process. Blockchain offers a decentralized network of nodes that enhances transparency, security, and verifiability. Its distributed ledger and non‐repudiation features make it a compelling alternative to traditional electronic voting systems, ensuring the integrity of elections. To further bolster the security of online voting, we propose <jats:italic>DemocracyGuard</jats:italic> platform on the Ethereum blockchain, which incorporates facial recognition technology to authenticate voters. By leveraging these advancements, <jats:italic>DemocracyGuard</jats:italic> aims to provide a secure and resilient platform for online voting, paving the way for its broader adoption and revolutionizing the electoral landscape.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"50 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206856","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":"Semantic community query in a large-scale attributed graph based on an attribute cohesiveness optimization strategy","authors":"Jinhuan Ge,&nbsp;Heli Sun,&nbsp;Yezhi Lin,&nbsp;Liang He","doi":"10.1111/exsy.13704","DOIUrl":"10.1111/exsy.13704","url":null,"abstract":"<p>The task of a semantic community query is to obtain a subgraph <span></span><math>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 </mrow></math> based on a given query vertex <span></span><math>\u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow></math> (or vertex set) and other query parameters in an attributed graph <span></span><math>\u0000 <mrow>\u0000 <mi>G</mi>\u0000 </mrow></math> such that <span></span><math>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 </mrow></math> belongs to <span></span><math>\u0000 <mrow>\u0000 <mi>G</mi>\u0000 </mrow></math>, contains <span></span><math>\u0000 <mrow>\u0000 <mi>q</mi>\u0000 </mrow></math> and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named <span></span><math>\u0000 <mrow>\u0000 <mi>SCQ</mi>\u0000 </mrow></math> in a large-scale attributed graph. First, the <i>k</i>-core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large-scale attributed graphs, <span></span><math>\u0000 <mrow>\u0000 <mi>SCQ</mi>\u0000 </mrow></math> applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large-scale attributed graphs.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206855","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":"Efficient malware detection using hybrid approach of transfer learning and generative adversarial examples with image representation","authors":"Yue Zhao, Farhan Ullah, Chien‐Ming Chen, Mohammed Amoon, Saru Kumari","doi":"10.1111/exsy.13693","DOIUrl":"https://doi.org/10.1111/exsy.13693","url":null,"abstract":"Identifying malicious intent within a program, also known as malware, is a critical security task. Many detection systems remain ineffective due to the persistent emergence of zero‐day variants, despite the pervasive use of antivirus tools for malware detection. The application of generative AI in the realm of malware visualization, particularly when binaries are depicted as colour visuals, represents a significant advancement over traditional machine‐learning approaches. Generative AI generates various samples, minimizing the need for specialized knowledge and time‐consuming analysis, hence boosting zero‐day attack detection and mitigation. This paper introduces the Deep Convolutional Generative Adversarial Network for Zero‐Shot Learning (DCGAN‐ZSL), leveraging transfer learning and generative adversarial examples for efficient malware classification. First, a normalization method is proposed, resizing malicious images to 128 × 128 or 300 × 300 for standardized input, enhancing feature transformation for improved malware pattern recognition. Second, greyscale representations are converted into colour images to augment feature extraction, providing a richer input for enhanced model performance in malware classification. Third, a novel DCGAN with progressive training improves model stability, mode collapse, and image quality, thus advancing generative model training. We apply the Attention ResNet‐based transfer learning method to extract texture features from generated samples, which increases security evaluation performance. Finally, the ZSL for zero‐day malware presents a novel method for identifying previously unknown threats, indicating a significant advancement in cybersecurity. The proposed approach is evaluated using two standard datasets, namely dumpware and malimg, achieving malware classification accuracies of 96.21% and 98.91%, respectively.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"255 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206858","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}