International Journal of Intelligent Systems最新文献

{"title":"Continual Learning Inspired by Brain Functionality: A Comprehensive Survey","authors":"Muhammad Azeem Aslam,&nbsp;Muhammad Hamza,&nbsp;Zhu Shuangtong,&nbsp;Hu Hongfei,&nbsp;Xu Wei,&nbsp;Muhammad Irfan,&nbsp;Zheng Jiangbin,&nbsp;Saba Aslam","doi":"10.1155/int/3145236","DOIUrl":"https://doi.org/10.1155/int/3145236","url":null,"abstract":"<div>\u0000 <p>Neural network–based models have shown tremendous achievements in various fields. However, standard AI-based systems suffer from catastrophic forgetting when undertaking sequential learning of multiple tasks in dynamic environments. Continual learning has emerged as a promising approach to address catastrophic forgetting. It enables AI systems to learn, transfer, augment, fine-tune, and reuse knowledge for future tasks. The techniques used to achieve continual learning are inspired by the learning processes of the human brain. In this study, we present a comprehensive review of research and recent developments in continual learning, highlighting key contributions and challenges. We discuss essential functions of the biological brain that are pivotal for achieving continual learning and map these functions to the recent machine-learning methods to aid understanding. Additionally, we offer a critical review of five recent types of continual learning methods inspired by the biological brain. We also provide empirical results, analysis, challenges, and future directions. We hope that this study will benefit both general readers and the research community by offering a complete picture of the latest developments in this field.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3145236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Automatic Identification and Counting of South African Animal Species in Camera Traps Using Deep Learning","authors":"Siyabonga Mamapule,&nbsp;Michael Esiefarienrhe,&nbsp;Ibidun Christiana Obagbuwa","doi":"10.1155/int/1561380","DOIUrl":"https://doi.org/10.1155/int/1561380","url":null,"abstract":"<div>\u0000 <p>In the area of ecology, counting animals to estimate population size and types of species is important for the wildlife conservation. This includes analysing massive volumes of image, video or audio/acoustic data and traditional counting techniques. Automating the process of identifying, classifying and counting animals would be helpful to researchers as it will phase out the tedious human–labour tasks of manual counting and labelling. The intention of this work is to address manual identification and counting methods of images by implementing an automated solution using computer vision and deep learning. This study applies a classification model to classify species and trains an object detection model using deep convolutional neural networks to automatically identify and determine the count of four mammal species in 3304 images extracted from camera traps. The image classification model reports a classification accuracy of 98%, and the YOLOv8 object detection model automatically detects buffalo, elephant, rhino and zebra school mean average precision of 50 of 89% and mean average precision of 50–95 of 72.2% and provides an accurate count over all animal classes. Furthermore, it performs well across various image scenarios such as blurriness, day, night and images displaying multiple species compared to the RT-DETR model. The results of the study display that the application of computer vision and deep learning methods on data-scarce and data-enriched scenarios, respectively, can conserve biologists and ecologists an enormous amount of time used on time-consuming human tasks methods of analysis and counting. The high-performing deep learning models developed capable of accurately classifying and localising multiple species can be integrated into the existing conservation workflows to process large volumes of camera trap images in real time. This integration can significantly reduce the manual labour required for labelling and counting, improve the consistency and speed of wildlife surveys and enable timely decision-making in habitat protection, population assessment and antipoaching initiatives. Additionally, these automated identification techniques can contribute towards enhancing wildlife conservation and future studies.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/1561380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An Innovative Coverage Path Planning Approach for UAVs to Boost Precision Agriculture and Rescue Operations","authors":"Nur Mohammad Fahad,&nbsp;Selvarajah Thuseethan,&nbsp;Sheikh Izzal Azid,&nbsp;Sami Azam","doi":"10.1155/int/4700518","DOIUrl":"https://doi.org/10.1155/int/4700518","url":null,"abstract":"<div>\u0000 <p>Unmanned aerial vehicles (UAVs) have been employed for a variety of inspection and monitoring tasks, including agricultural applications and search and rescue (SAR) in remote areas. However, traditional monitoring methods tend to focus on optimizing one aspect. This study aims to propose a complete framework by integrating advanced methods to provide a robust and accurate path coverage solution. The combination of edge detection and area decomposition with a pathfinding algorithm can improve the overall performance. An effective edge detection model is developed that simultaneously detects the boundary and segments the area of interest (AOI) from the aerial land images and provides precise area mapping of the area. An intuitive grid decomposition with grid-to-graph mapping improves the flexibility of the area decomposition and ensures maximal coverage and safe operation routes for the UAVs. Finally, a robust modified simulated annealing (MSA) algorithm is introduced to determine the shortest path coverage route. The performance of the proposed methodology is tested on aerial imagery. Area decomposition ensures that there are no gaps in the AOI during the coverage planning. The MSA algorithm obtains the minimum length cost, charge consumption cost, and minimum number of turns to cover the area. It is shown that the integration of these techniques enhances the performance of the coverage path planning (CPP). A comparison of the proposed approach with benchmark algorithms further demonstrates its effectiveness. This study contributes to creating a complete CPP application for UAVs, which may assist with precision agriculture as well as safe and secure rescue operations.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4700518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144688244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Basic Probability Assignment Generation Method Based on Normal Cloud Similarity and Its Application in Evidence Combination","authors":"Nuo Cheng,&nbsp;Xin Wang","doi":"10.1155/int/8839165","DOIUrl":"https://doi.org/10.1155/int/8839165","url":null,"abstract":"<div>\u0000 <p>The effective utilization of Dempster–Shafer (D-S) evidence theory depends on the accurate establishment of the basic probability assignment (BPA). How to generate more effective BPA for different situations is always an open and hot topic. In this study, we present an approach for obtaining BPA based on the normal cloud model called combined fuzzy similarity measure (CFSM). The method first constructs the normal cloud model of each class of sample in each attribute by an interval number and uses the mean standard deviation to obtain the interval number for the test sample, thereby obtaining the normal cloud model. Then, the similarity between the test samples and the training samples is quantified based on the area relationship, thereby obtaining the BPA of the test samples. Finally, the evidence combination method based on the intuitionistic fuzzy earth mover’s distance (IFEMD) is used for experimental analysis. The experimental results verify the effectiveness of the method and its applicability in the case of small sample data.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/8839165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Variable Dimensional Multiobjective Lifetime Constrained Quantum PSO With Reinforcement Learning for High-Dimensional Patient Data Clustering","authors":"Chen Guo,&nbsp;Heng Tang,&nbsp;Huifen Zhong,&nbsp;Hua Xiao,&nbsp;Ben Niu","doi":"10.1155/int/5521043","DOIUrl":"https://doi.org/10.1155/int/5521043","url":null,"abstract":"<div>\u0000 <p>Ming potential patterns from patient data are usually treated as a high-dimensional data clustering problem. Evolutionary multiobjective clustering algorithms with feature selection (FS) are widely used to handle this problem. Among the existing algorithms, FS can be performed either before or during the clustering process. However, research on performing FS at both stages (hybrid FS), which can yield robust and credible clustering results, is still in its infancy. This paper introduces an improved high-dimensional patient data clustering algorithm with hybrid FS called variable dimensional multiobjective lifetime constrained quantum PSO with reinforcement learning (VLQPSOR). VLQPSOR consists of two main independent stages. In the first stage, a dimensionality reduction ensemble strategy is developed before clustering to reduce the patient dataset’s dimensionality, resulting in subdatasets of varying dimensions. In the second stage, an improved multiobjective QPSO clustering algorithm is proposed to simultaneously conduct dimensionality reduction and clustering. To accomplish this, several strategies are employed. Firstly, the variable dimensional lifetime constrained particle learning strategy, the continuous-to-binary encoding transformation strategy, and multiple external archives elite learning strategy are introduced to further reduce the dimensionality of the subdatasets and mitigate the risk of QPSO getting trapped in local optima. Secondly, an improved reinforcement learning–based clustering method selection strategy is proposed to adaptively select the optimal classical clustering algorithm. Experimental results demonstrate that VLQPSOR outperforms five representative comparative algorithms across four validity indexes and clustering partitions for most patient datasets. Ablation experiments confirm the effectiveness of the proposed strategies in enhancing the performance of QPSO.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/5521043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Blind Recognition Algorithm of Convolutional Code via Convolutional Neural Network","authors":"Pan Deng,&nbsp;Tianqi Zhang,&nbsp;Lianghua Wen,&nbsp;Baoze Ma,&nbsp;Ying Wei,&nbsp;Linhao Cui","doi":"10.1155/int/3183819","DOIUrl":"https://doi.org/10.1155/int/3183819","url":null,"abstract":"<div>\u0000 <p>Pointing at the vexed question of blind recognition in the convolutional code class, this paper proposes a convolutional code blind identification method via convolutional neural networks (CNNs). First, this algorithm uses the traditional method to generate different convolutional codes, and the feature extraction algorithm adopts the theorem of Euclid’s algorithm. Then, the input signal is loaded to the CNN; next, the feature is extracted by convolutional kernel. Finally, the Softmax activation function is applied to full-connection layer network. After the input signals pass through the above layers, the system classifies the signals. The research results indicate that the presented algorithm has improved the recognition performance of code length and rate. For different convolutional codes with parameters of (5, 7), (15, 17), (23, 35), (53, 75), and (133, 171) and similar convolutional codes with parameters of (3, 1, 6), (3, 1, 7), (2, 1, 7), (2, 1, 6), and (2, 1, 5), the recognition rate of parameter classification can reach 100% at signal-to-noise ratio (SNR) of 3 dB.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3183819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Applying LLMs to Active Learning: Toward Cost-Efficient Cross-Task Text Classification Without Manually Labeled Data","authors":"Yejian Zhang,&nbsp;Shingo Takada","doi":"10.1155/int/6472544","DOIUrl":"https://doi.org/10.1155/int/6472544","url":null,"abstract":"<div>\u0000 <p>Machine learning–based classifiers have been used for text classification, such as sentiment analysis, news classification, and toxic comment classification. However, supervised machine learning models often require large amounts of labeled data for training, and manual annotation is both labor-intensive and requires domain-specific knowledge, leading to relatively high annotation costs. To address this issue, we propose an approach that integrates large language models (LLMs) into an active learning framework, achieving high cross-task text classification performance without the need for any manually labeled data. Furthermore, compared to directly applying GPT for classification tasks, our approach retains over 93% of its classification performance while requiring only approximately 6% of the computational time and monetary cost, effectively balancing performance and resource efficiency. These findings provide new insights into the efficient utilization of LLMs and active learning algorithms in text classification tasks, paving the way for their broader application.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6472544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Ensuring Supply Chain Transparency by Deploying Blockchain-Enabled Technology: An Overview With Demonstration","authors":"Ahm Shamsuzzoha,&nbsp;Khuram Shahzad,&nbsp;Essi Nousiainen,&nbsp;Mikko Ranta,&nbsp;Petri Helo,&nbsp;Kannan Govindan","doi":"10.1155/int/7304193","DOIUrl":"https://doi.org/10.1155/int/7304193","url":null,"abstract":"<div>\u0000 <p>It is nowadays quite challenging to manage and control the supply chain concerning transparency, traceability, and zero-trust security. Digital technology such as blockchain has shown promising features to ease the global supply chain for tracking, tracing, and authenticity. This study critically examines the potential of blockchain technology and smart contracts to manage global supply chain sustainability. It also analyzes the inherent opportunities, benefits, and common barriers to deploying blockchain in the supply chain. Moreover, an overview of blockchain technology and its application in the various industries’ supply chain management is illustrated in this study. Furthermore, an application demo related to blockchain in the supply chain is provided within the scope of this study with the view to demonstrating how various transactions in the supply chain are executed with higher authenticity. The study is concluded with several future research propositions and directions that may provide insight into overcoming current challenges and the adoption of blockchain for the supply chain.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/7304193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Artificial Intelligence for Text Analysis in the Arabic and Related Middle Eastern Languages: Progress, Trends, and Future Recommendations","authors":"Abdullah Y. Muaad,&nbsp;Md Belal Bin Heyat,&nbsp;Faijan Akhtar,&nbsp;Usman Naseem,&nbsp;Wadeea R. Naji,&nbsp;Suresha Mallappa,&nbsp;Hanumanthappa J.","doi":"10.1155/int/6091900","DOIUrl":"https://doi.org/10.1155/int/6091900","url":null,"abstract":"<div>\u0000 <p>In the last 10 years, there has been a rise in the number of Arabic texts, which necessitates a more profound understanding of algorithms to efficiently understand and classify Arabic texts in many applications, like sentiment analysis. This paper presents a comprehensive review of recent developments in Arabic text classification (ATC) and Arabic text representation (ATR). We analyze the effectiveness of various models and techniques. Our review finds that while deep learning models, particularly transformer-based architectures, are increasingly effective for ATC, challenges such as dialectal variations and insufficient labeled datasets remain key obstacles. However, developing suitable representation models and designing classification algorithms is still challenging for researchers, especially in Arabic. A basic introduction to ATC is provided in this survey, including preprocessing, representation, dimensionality reduction (DR), and classification with many evaluation metrics. In addition, the survey includes a qualitative and quantitative study of the ATC’s existing works. Finally, we conclude this work by exploring the limitations of the existing methods. We also mention the open challenges related to ATC, which help researchers identify new directions and challenges for ATC.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6091900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An Anomaly Detection System for High-Dimensional Industry Time Series Data Based on GNN and Apache Flink","authors":"Feng Ye,&nbsp;Kaibo Zhang,&nbsp;Jun Sun,&nbsp;Na Li","doi":"10.1155/int/4370827","DOIUrl":"https://doi.org/10.1155/int/4370827","url":null,"abstract":"<div>\u0000 <p>Intelligent systems have been widely used in various fields. They generate a large number of high-dimensional time series monitoring data in the process of operation, which often hide various potential abnormal conditions, which bring hidden dangers to the stable operation of the system. Existing anomaly detection methods mainly focus on the sequence characteristics of time series data, but often ignore the correlation between different variables of multivariate data, and the detection efficiency is low when facing high-dimensional time series data. To solve the above problems, we propose a deep anomaly detection method based on graph neural network, and combined with the big data computing framework Apache Flink, we construct a real-time anomaly detection system for large-scale high-dimensional time series data. Experimental results on SWaT and WADI show that our proposed method can accurately detect anomalies in multivariate time series data, and can perform low-latency real-time anomaly detection on high-dimensional industrial streaming data.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4370827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}