Expert Systems最新文献

{"title":"A Survey on Internet of Medical Things (IoMT): Enabling Technologies, Security and Explainability Issues, Challenges, and Future Directions","authors":"Mert Melih Ozcelik,&nbsp;Ibrahim Kok,&nbsp;Suat Ozdemir","doi":"10.1111/exsy.70010","DOIUrl":"https://doi.org/10.1111/exsy.70010","url":null,"abstract":"<p>Internet of Medical Things (IoMT) paradigm refers to the process of collection, transmission and analysis of healthcare data using communication and information systems over the internet. IoMT consist of medical devices that can link to the internet or other networks, including wearables, sensors, monitoring tools and other medical appliances. IoMT data can be utilised to lower costs, increase the effectiveness of healthcare delivery and improve the patient health status. In addition to the potential benefits IoMT may provide, the impact of COVID19 pandemic has also strengthened the desire to collect patient data remotely and pushed a lot of medical professionals to utilise IoMT applications such as telemedicine, telehealth, remote patient monitoring, remote patient diagnostics and distant consultations etc. The expectation is that IoMT market size and the usage will increase dramatically and IoMT will change the conventional healthcare systems significantly in the upcoming years. Motivated with that growth expectation, this study aims to analyse the IoMT, its components, enabling technologies and applications by emphasising the fundamental pillars (sensing, communication, data analytics, and security) essential for developing a reliable, dependable, and secure IoMT ecosystem. Furthermore, this study conducts a detailed analysis of recent major cyberattacks targeting the healthcare industry, evaluating their impact and discussing the key lessons derived from these incidents by employing DOTMLPFI approach. Additionally, this survey offers a concise overview of the emerging technologies that complement IoMT in the development of smart healthcare systems and explores potential future directions within this evolving field.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689936","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":"Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours","authors":"Wei Song,&nbsp;Qihao Zhang,&nbsp;Simon Fong,&nbsp;Tengyue Li","doi":"10.1111/exsy.70034","DOIUrl":"https://doi.org/10.1111/exsy.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>Learning path recommendation is crucial for guiding learners through a series of courses in a logical sequence based on their previous learning experiences. This is particularly important for improving learning outcomes in massive open online courses (MOOCs) for diverse learners. Because both the historical learning courses and recommended learning paths can be represented as sequential patterns (SPs); it is reasonable to approach this problem through SP mining (SPM). In addition to support, we incorporate three factors, that is, course learning days, grades and engagement, to model frequent high-utility SPs (FHUSPs). When recommending a learning path, FHUSPs that align with the target user's learning history and are common among successful learners, while rare among less successful ones, are prioritised. If there are insufficient matching FHUSPs, we address this by recommending additional courses based on the joint competency and complementarity of learners similar to the target learner. Experimental results on a real-world dataset demonstrate that our method provides highly accurate and relevant recommendations.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689937","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":"Arabic Short-Text Dataset for Sentiment Analysis of Tourism and Leisure Events","authors":"Seham Basabain,&nbsp;Ahmed Al-Dubai,&nbsp;Erik Cambria,&nbsp;Khalid Alomar,&nbsp;Amir Hussain","doi":"10.1111/exsy.70030","DOIUrl":"https://doi.org/10.1111/exsy.70030","url":null,"abstract":"<p>The focus of this study is to present the detailed process of collecting a dataset of Arabic short-text in the tourism context and annotating this dataset for the task of sentiment analysis using an automatic zero-shot labelling technique utilising transformer-based models. This is benchmarked against a baseline manual annotation approach utilising native Arab human annotators. This study also introduces an approach exploiting both manual/handcrafted and automatically generated annotations of the dataset tweets for the task of sentiment analysis as part of a cross-domain approach using a model trained on sarcasm labels and vice versa. The total collected corpus size is 2293 tweets; after annotation, these tweets were labelled in a three-way classification approach as either positive, negative or neutral. We run different experiments to provide benchmark results of Arabic sentiment classification. Comparative results on our dataset show that the highest performing baseline model when utilising manual labels was MARBERT, with an accuracy of up to 87%, which was pre-trained for Arabic on a massive amount of data. It should be noted that this model enhanced its performance additionally after pre-training on a dialectical Arabic and modern standard Arabic corpus. On the other hand, zero-shot automatically generated labels achieved an 84% accuracy rate in predicting sarcasm classes from sentiment labels.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689466","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":"SRAD: Autonomous Decision-Making Method for UAV Based on Safety Reinforcement Learning","authors":"Wenwen Xiao,&nbsp;Xiangfeng Luo,&nbsp;Shaorong Xie","doi":"10.1111/exsy.70004","DOIUrl":"https://doi.org/10.1111/exsy.70004","url":null,"abstract":"<div>\u0000 \u0000 <p>Unmanned aerial vehicles (UAVs) are increasingly vital across numerous sectors, from logistics and rescue operations to military endeavours and beyond. However, ensuring safety in the decision-making processes surrounding UAV operations in real-world settings has become an urgent and complex challenge. At present, the main methods to minimise the risk of drone decision-making include utilising pre-established control rules, expert prior knowledge and regularisation constraints. However, these methodologies require UAVs to meet demanding prerequisites, including the acquisition of extensive decision-making experience and the establishment of comprehensive rules. Regrettably, these strict requirements often lead to frequent UAV crashes in uncertain environments and subsequent mission failures. In order to tackle these issues, we propose a self-decision-making method for quadcopter UAVs based on safe reinforcement learning. Our method utilises a multilevel cascading feature semantic space for reinforcement learning, integrating depth images, greyscale images, semantic segmentation images and object detection results as inputs. This approach aims to facilitate safe autonomous learning. Moreover, we integrate real offline labelled data to enhance the safety policy. Depending on the varying levels of risk encountered during the UAV's decision-making process, we dynamically select different safety policies. Through this iterative process, the UAV progressively eliminates extreme actions and reverts to the UAV learning policy module. Experimental results indicate that our method not only ensures safe decision-making for UAVs in uncertain environments but also exhibits superior safety decision-making efficacy compared to certain baseline methods.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689467","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":"GRIMP: A Genetic Algorithm for Compression-Based Descriptive Pattern Mining","authors":"M. Zohaib Nawaz,&nbsp;M. Saqib Nawaz,&nbsp;Philippe Fournier-Viger,&nbsp;Nazha Selmaoui-Folcher","doi":"10.1111/exsy.70033","DOIUrl":"https://doi.org/10.1111/exsy.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional frequent pattern mining algorithms often report an overwhelming number of patterns in large datasets, many of which are redundant. To address this issue, Minimum Description Length (MDL)-based methods have been employed, which use data compression to capture a smaller yet significant set of patterns. However, finding a good set of patterns according to MDL involves a very large search space, and current MDL-based techniques often suffer from long runtimes and find suboptimal solutions. To discover better sets of patterns in less time, this paper introduces GRIMP (a Genetic algoRIthm for coMpression-based descriptive Pattern mining), a novel framework that combines a genetic algorithm with MDL-based pattern selection. Multiple genetic algorithm variants are explored within the GRIMP framework, and their effectiveness is compared using a large number of datasets. Experimental results demonstrate that GRIMP consistently outperforms previous methods by achieving higher compression ratios, generating more representative itemsets, and requiring less time. Additionally, the extracted patterns improve downstream classification tasks, highlighting the ability of GRIMP to find more representative patterns within the data.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689383","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":"CancerFusionPrompt: A Novel Framework for Multimodal Cancer Subtype Classification Using Vision-Language Model","authors":"Ruonan Liu,&nbsp;Muhammad Ayoub,&nbsp;Junaid Abdul Wahid","doi":"10.1111/exsy.70039","DOIUrl":"https://doi.org/10.1111/exsy.70039","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Cancer subtype classification plays a pivotal role in personalised medicine, requiring the integration of diverse data types. Traditional prompting methods in vision-language models fail to fully leverage multimodal data, particularly when working with minimal labelled data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address these limitations, we propose a novel framework that introduces the CancerFusionPrompt, a specialised prompting method for integrating imaging and multi-omics data. Our proposed approach extends the few-shot learning paradigm by incorporating in-context learning for cancer subtype classification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed method significantly outperforms state-of-the-art techniques in cancer subtype classification, achieving notable improvements in both accuracy and generalisation. These results demonstrate the superior capability of CancerFusionPrompt in handling complex multimodal inputs compared to existing prompting methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The CancerFusionPrompt framework offers a powerful solution for integrating multimodal data in cancer subtype classification tasks. By overcoming the limitations of current prompting methods, CancerFusionPrompt approach enables more accurate and robust predictions with minimal labelled data.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689537","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":"ST-IDS: Spatio-Temporal Feature-Based Multi-Tier Intrusion Detection System for Artificial Intelligence-Powered Connected Autonomous Vehicles","authors":"Amol Ghanshyam Bhatkar,&nbsp;Shashank Gupta,&nbsp;Priyansh Patel","doi":"10.1111/exsy.70026","DOIUrl":"https://doi.org/10.1111/exsy.70026","url":null,"abstract":"<div>\u0000 \u0000 <p>Advancements in 3GPP specifications and the extensive deployment of 5G networks have driven significant growth in the Internet of Vehicles (IoVs). This development has led to an increase in Connected and Autonomous Vehicles (CAVs), which provide capabilities such as automated navigation, ADAS, cruise control, and environmentally sustainable transportation in real-time. Additionally, the widespread adoption of CAVs has also escalated vulnerabilities within the IoV ecosystem, exposing it to potential cyberattacks. The integration of various functional interfaces has enlarged its attack surface, thereby increasing the risk of vehicle infiltration. Researchers have proposed various Intrusion Detection Systems (IDS) to address the ongoing risk of vehicle attacks, without applying encryption and related authentication methods for intra-and inter-vehicular communications. However, a significant limitation of many IDSs is their dependency on characteristics specific to a particular category of vehicles, which limits their adaptability. Additionally, current IDSs frequently rely on one-dimensional features such as traffic, time, etc., which limits their capability of detecting attacks in adverse scenarios. Moreover, incorporating machine learning algorithms into IDSs deployed in automated automobiles causes an increase in computational demands. We propose to develop a collaborative IDS specifically designed for cloud-based vehicle environments. We aim to improve our capabilities of identifying intrusion detection and differentiate which are malicious by using multidimensional features. A customised Convolutional Neural Network (CNN), optimised through hyperparameter tuning, is also developed for detecting the malicious vehicles and enhancing the overall IDS. To address the challenge of data diversity, we integrate various vehicular datasets into a unified feature space. This integration allows a single model to efficiently perform multi-classification tasks without frequent adjustments. Our feature space integrates dimensions such as traffic, time and so forth, levels, thereby expanding the spectrum of detectable attack scenarios. By identifying abnormal data points within this comprehensive feature framework, our system effectively identifies intrusions across a diverse range of vehicle types. As a result, our methodology supports robust intrusion detection through comprehensive multiclass vehicle classification.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689319","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":"G-WVDTW: A Generalised Weighted Variance Dynamic Time Warping Algorithm for Subsequence Matching in Multivariate Time Series","authors":"Danyang Cao,&nbsp;ZiFeng Lin,&nbsp;Di Liu,&nbsp;Xiaoyuan Chai","doi":"10.1111/exsy.70036","DOIUrl":"https://doi.org/10.1111/exsy.70036","url":null,"abstract":"<div>\u0000 \u0000 <p>Dynamic time warping (DTW) is an algorithm used to measure the similarity between sequences, with widespread applications in domains such as speech recognition, image processing and video synchronisation. However, when matching a shorter multivariate time subsequence to a longer time series containing a similar subsequence, existing DTW variants struggle to accurately determine the matching path. To address this issue, we propose an improved algorithm, generalised weighted variance DTW (G-WVDTW). We extend the DTW algorithm to multivariate time series and introduce a weighted variance-based approach to calculate local distances. This allows the algorithm to better assess the distance between different time points in multivariate time series. Additionally, we modify the algorithm's boundary conditions, enabling it to handle subsequence matching tasks in multivariate time series. We conducted similarity retrieval experiments using public datasets and evaluated the algorithm's performance with the AUC metric, achieving up to a 19% improvement on certain datasets. Furthermore, we performed alignment experiments on industrial data, where we artificially generated aligned sequences and quantitatively assessed the alignment errors, which were lower than those produced by other DTW variants. Finally, we validated the algorithm's superior performance in multivariate time series subsequence matching tasks using a synthetic dataset and showcased its use in motif detection using a wind power generation dataset. The algorithm can be applied in fields such as industrial, meteorological and electrocardiogram (ECG) signal analysis for tasks like time series retrieval, matching and data labelling.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688902","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":"Automatic Speech Recognition: Comparisons Between Convolutional Neural Networks, Hidden Markov Model and Hybrid Architecture","authors":"Lyndainês Santos,&nbsp;Nícolas de Araújo Moreira,&nbsp;Robson Sampaio,&nbsp;Raizielle Lima,&nbsp;Francisco Carlos Mattos Brito Oliveira","doi":"10.1111/exsy.70032","DOIUrl":"https://doi.org/10.1111/exsy.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>Automatic Speech Recognition (ASR) systems have been widely used as a practical method of interaction between humans and devices. They are typically employed to enhance the accessibility of devices and to improve the security of systems, among other purposes. However, the design of speech-based systems imposes many challenges due to their particularities. Currently, the majority of ASR systems is based on the Hidden Markov Model (HMM), and, more recently, on Convolutional Neural Networks (CNN). The present research evaluates the performance of Hidden Markov Model (HMM) and Convolutional Neural Network (CNN) algorithms in speech recognition and proposes a novel hybrid approach that combines both methods. The study assesses various performance metrics, including accuracy, precision, recall, F1-score, response time, and computational cost. The experimental tests show that the integration between HMM and CNN increased the accuracy by 6% and 8% when compared to HMM and CNN isolated, respectively, in accordance with results presented in previous papers. However, the results of the ANOVA test revealed that the difference in question is not statistically significant, and the HMM-only approach still being an interesting option for embedded systems due to its lesser demanded computational effort.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688954","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":"Generalised Entropies for Decision Trees in Classification Under Monotonicity Constraints","authors":"Oumaima Khalaf,&nbsp;Salvador Garcia,&nbsp;Anis Ben Ishak","doi":"10.1111/exsy.70035","DOIUrl":"https://doi.org/10.1111/exsy.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>Several decision-making approaches involve ordinal labelling between feature values and decision outcomes. These issues refer to ordinal classification under monotonicity constraints. Recently, some machine learning approaches have been designed to deal with these kinds of problems. Indeed, numerous experiments have shown that these algorithms are widely used in real-life applications because of their flexibility and efficiency in terms of interpretation and predictions. In this paper, we introduce novel approaches for measuring feature quality and information quantity, called Rényi-Tsallis Monotonic Tree (RTMT), which uses the advantages of Rényi and Tsallis entropies while incorporating monotonicity constraints through an optimisation framework. Moreover, we introduce Mono-CART, a variant of the CART approach adapted for monotonic classification. New decision tree algorithms are designed on the basis of aforementioned entropies while considering the monotonicity constraints within an optimisation system. The experiments conducted using some benchmark datasets demonstrate the superiority of the proposed approaches compared to existing methods.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602517","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}