计算机科学最新文献_第2页

pytopicgram: A library for data extraction and topic modeling from Telegram channels

IF 2.4 4区计算机科学

SoftwareX Pub Date : 2025-04-02 DOI: 10.1016/j.softx.2025.102141

Juan Gómez-Romero, Javier Cantón Correa, Rubén Pérez Mercado, Francisco Prados Abad, Miguel Molina-Solana, Waldo Fajardo

引用次数: 0

A multi-objective evolutionary algorithm for feature selection incorporating dominance-based initialization and duplication analysis

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-02 DOI: 10.1016/j.swevo.2025.101914

Chuili Chen , Xiangjuan Yao , Dunwei Gong , Huijie Tu

{"title":"A multi-objective evolutionary algorithm for feature selection incorporating dominance-based initialization and duplication analysis","authors":"Chuili Chen , Xiangjuan Yao , Dunwei Gong , Huijie Tu","doi":"10.1016/j.swevo.2025.101914","DOIUrl":"10.1016/j.swevo.2025.101914","url":null,"abstract":"<div><div>The primary objective of feature selection is to reduce the number of features while improving classification performance. Therefore, this problem is typically modeled as a multi-objective optimization problem and can be solved using multi-objective evolutionary algorithms (MOEAs). However, feature selection based on weights derived from preferences may lead to the exclusion of specific features, thereby impacting classification performance. Furthermore, if duplicate individuals are not adequately addressed during the evolutionary process, it may adversely affect the convergence and diversity of the population. In this paper, we propose a multi-objective evolutionary algorithm for feature selection incorporating dominance-based initialization and duplication analysis. To filter features impartially, we transform the correlation issues among features, as well as those between features and labels, into a multi-objective optimization problem by assigning corresponding weights based on their dominance relationships. In addressing the duplication problem within the evolutionary process, the disparity between duplicate individuals as well as between duplicate individuals and elite solutions is analyzed to systematically eliminate redundancy. In the experiments, the proposed method was compared with two classical algorithms and three feature selection algorithms across thirteen datasets. The experimental results indicate that the proposed method exhibits superior classification and optimization performance across the majority of datasets.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101914"},"PeriodicalIF":8.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Neurally Integrated Finite Elements for Differentiable Elasticity on Evolving Domains

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-02 DOI: 10.1145/3727874

Gilles Daviet, Tianchang Shen, Nicholas Sharp, David I.W. Levin

引用次数: 0

Exploring low overhead fingerprint biometric watermark for loop pipelined hardware IPs during behavioral synthesis

IF 3.8 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-04-02 DOI: 10.1016/j.jisa.2025.104041

Anirban Sengupta, Aditya Anshul

{"title":"Exploring low overhead fingerprint biometric watermark for loop pipelined hardware IPs during behavioral synthesis","authors":"Anirban Sengupta, Aditya Anshul","doi":"10.1016/j.jisa.2025.104041","DOIUrl":"10.1016/j.jisa.2025.104041","url":null,"abstract":"<div><div>Loop based applications form an integral component in several consumer electronics systems as hardware intellectual property (IP) cores. Some powerful examples include finite impulse response filter cores, convolution filters etc. For enhanced performance and increased security of hardware IPs, handling loops efficiently while embedding low-cost security information (watermark) as digital evidence is the key. Robust security watermark embedded as digital evidence in the IP cores of CE systems, ensures sturdy detective countermeasure against piracy and counterfeiting, assuring the safety of end consumer. This paper presents a novel behavioral synthesis/high-level synthesis (HLS) based low-cost fingerprint biometric-watermark embedded security methodology for loop pipelined hardware IPs of CE systems. More explicitly, the paper presents the following novel contributions: a) exploration of low overhead fingerprint biometric-watermark embedded security watermark during HLS; b) embedding low-cost fingerprint based security constraints in loop pipelined IP designs used in CE systems; c) enhanced security against IP piracy (pirated designs) from an SoC integrator's and CE systems designers' perspective in terms of digital evidence (resulting into greater tamper tolerance ability, probability of coincidence and entropy) than prior similar approaches, at nominal design overhead.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"90 ","pages":"Article 104041"},"PeriodicalIF":3.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Convolutional Neural Network based model for daily motor activities recognition: A contribution for Parkinson's Disease

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-02 DOI: 10.1016/j.engappai.2025.110706

Helena R. Gonçalves , Luís Martins , Nuno Ferrete Ribeiro , Luís Abreu , Ana Margarida Rodrigues , Cristina P. Santos

{"title":"A Convolutional Neural Network based model for daily motor activities recognition: A contribution for Parkinson's Disease","authors":"Helena R. Gonçalves , Luís Martins , Nuno Ferrete Ribeiro , Luís Abreu , Ana Margarida Rodrigues , Cristina P. Santos","doi":"10.1016/j.engappai.2025.110706","DOIUrl":"10.1016/j.engappai.2025.110706","url":null,"abstract":"<div><div>Daily motor activities are affected by motor disabilities caused by Parkinson's disease (PD). Monitoring motor disabilities frequently observed in PD is difficult for physicians, as they are limited to the information observed or self-reported during routine consultations, resulting in subjectivity and limited assessment. Thus, it is necessary to evaluate more frequently and objectively, ideally in a continuous manner including daily life tasks. While wearable sensory devices, such as inertial sensors, and their applications are steadily growing, the advances in artificial intelligence studies have revolutionized their ability to extract deeply hidden information for accurate detection and interpretation of motor activities. However, further studies are required, mainly focused on PD. This study aimed to implement a deep learning (DL) based model for recognizing daily motor activities based on inertial data to contribute to PD. The model relied on a convolutional neural network (CNN) architecture, trained and tested on a created custom dataset. We further benchmarked our model against other popular DL frameworks. The dataset included inertial data captured from 18 patients while performing trivial quotidian tasks, such as walking, turning, sitting, and lying. We hypothesized that a DL model based on a CNN architecture could be an appropriate solution for modeling daily motor non-steady and steady-state tasks from a single inertial sensor. We measured an F1 score of 0.906 and an accuracy of 91.1 % on final testing with our optimized CNN model, being the tasks of standing and walking the most accurately recognized by the model. Future challenges should cover exploring attention-based models and increasing the dataset.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"151 ","pages":"Article 110706"},"PeriodicalIF":7.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747896","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}

引用次数: 0

From ensemble to knowledge distillation: Improving large-scale food recognition

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-02 DOI: 10.1016/j.engappai.2025.110727

Liming Nong , Guohao Peng , Tianyang Xu , Jinlin Zhu

{"title":"From ensemble to knowledge distillation: Improving large-scale food recognition","authors":"Liming Nong , Guohao Peng , Tianyang Xu , Jinlin Zhu","doi":"10.1016/j.engappai.2025.110727","DOIUrl":"10.1016/j.engappai.2025.110727","url":null,"abstract":"<div><div>Food recognition on a large scale presents significant challenges due to high intra-category similarity and inter-category variability. Addressing these challenges is crucial for developing robust and accurate food recognition systems, which have applications in health monitoring, dietary assessment, and automated food logging. This study aims to tackle these issues by employing ensemble learning and knowledge distillation. We use ensemble learning to effectively combine the local perception capability of convolutional neural networks (CNNs) and the global modeling capability of Vision Transformers. The synergistic ensemble enhances the model's ability to discern subtle differences within categories and capture a spectrum of diverse patterns across various categories. To reduce the number of base models in an ensemble, we employed a method combining knowledge distillation and re-ensembling. Specifically, we used the collective knowledge of four base models to guide the re-learning process of student models. Subsequently, we re-ensembled these distilled models, significantly enhancing the recognition performance of the ensemble while maintaining the same computational efficiency. Finally, we fine-tuned the optimal ensemble weights to further boost the recognition performance of the ensemble model. We conducted extensive experiments on the large-scale food datasets Food2k and CNFood241, achieving state-of-the-art performance. Specifically, on the Food2k dataset, our method achieved a top-1 accuracy of 86.22 % with 131.56M parameters, outperforming the state-of-the-art algorithms by 2.1 %, demonstrating its effectiveness.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"151 ","pages":"Article 110727"},"PeriodicalIF":7.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Multimodal feature adaptive fusion for anchor-free 3D object detection

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-02 DOI: 10.1007/s10489-025-06454-w

Yanli Wu, Junyin Wang, Hui Li, Xiaoxue Ai, Xiao Li

{"title":"Multimodal feature adaptive fusion for anchor-free 3D object detection","authors":"Yanli Wu, Junyin Wang, Hui Li, Xiaoxue Ai, Xiao Li","doi":"10.1007/s10489-025-06454-w","DOIUrl":"10.1007/s10489-025-06454-w","url":null,"abstract":"<div><p>LiDAR and camera are two key sensors that provide mutually complementary information for 3D detection in autonomous driving. Existing multimodal detection methods often decorate the original point cloud data with camera features to complete the detection, ignoring the mutual fusion between camera features and point cloud features. In addition, ground points scanned by LiDAR in natural scenes usually interfere significantly with the detection results, and existing methods fail to address this problem effectively. We present a simple yet efficient anchor-free 3D object detection, which can better adapt to complex scenes through the adaptive fusion of multimodal features. First, we propose a fully convolutional bird’s-eye view reconstruction module to sense ground map geometry changes, for improving the interference of ground points on detection results. Second, a multimodal feature adaptive fusion module with local awareness is designed to improve the mutual fusion of camera and point cloud features. Finally, we introduce a scale-aware mini feature pyramid networks (Mini-FPN) that can directly regress 3D bounding boxes from the augmented dense feature maps, boosting the network’s ability to detect scale-varying objects, and we additionally construct a scene-adaptive single-stage 3D detector in an anchor-free manner. Extensive experiments on the KITTI and nuScenes datasets validate our method’s competitive performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

The optimization-based fuzzy logic controllers for autonomous ground vehicle path tracking

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-02 DOI: 10.1016/j.engappai.2025.110642

Ibrahim Aliskan

{"title":"The optimization-based fuzzy logic controllers for autonomous ground vehicle path tracking","authors":"Ibrahim Aliskan","doi":"10.1016/j.engappai.2025.110642","DOIUrl":"10.1016/j.engappai.2025.110642","url":null,"abstract":"<div><div>Nowadays, it is possible to see Autonomous Ground Vehicles (AGV) in traffic, factories and even restaurants. One of the key elements of these vehicles is the path tracking controller. Path tracking performance of the controller has a direct effect on the ride experience of the vehicle. This work proposes Fuzzy Logic Controllers (FLC) for steering of an autonomous vehicle. A fuzzy controller does not need for a certain model to steer the vehicle. However, the rules and membership functions should be determined with the aid of an expert. Here, with the purpose of getting one step close to the automatically tunable fuzzy control systems, the Artificial Bee Colony (ABC) algorithm is utilized on tuning the output membership functions of the FLC. Three different fuzzy controllers are developed for an autonomous vehicle. The first one is provided from the literature, two others are developed with the help of ABC algorithm. The base widths of the output membership functions of two other fuzzy controllers are determined by using cost functions of the Integral of Absolute Error (IAE) and the Integral of Time Absolute Error (ITAE). The performances of the developed controllers are evaluated through the results of various simulation studies, keeping in view performance indicators such as overshoot, settling time and Mean Squared Error (MSE). The results demonstrate superiority of the controllers proposed in this work to the reference one (the first fuzzy controller). Finally, this work reveals that the parameters of the fuzzy controller devised to steer an AGV can be tuned by using metaheuristic optimization algorithms.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"151 ","pages":"Article 110642"},"PeriodicalIF":7.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Interpretable spatio-temporal prediction using Deep Neural Network - Local Interpretable Model-agnostic Explanations: A case study on leptospirosis outbreaks in Malaysia

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-04-02 DOI: 10.1016/j.engappai.2025.110665

Fariq Rahmat , Zed Zulkafli , Asnor Juraiza Ishak , Ribhan Zafira Abdul Rahman , Wardah Tahir , Jamalludin Ab Rahman , Veianthan Jayaramu , Simon De Stercke , Salwa Ibrahim , Muhamad Ismail

{"title":"Interpretable spatio-temporal prediction using Deep Neural Network - Local Interpretable Model-agnostic Explanations: A case study on leptospirosis outbreaks in Malaysia","authors":"Fariq Rahmat , Zed Zulkafli , Asnor Juraiza Ishak , Ribhan Zafira Abdul Rahman , Wardah Tahir , Jamalludin Ab Rahman , Veianthan Jayaramu , Simon De Stercke , Salwa Ibrahim , Muhamad Ismail","doi":"10.1016/j.engappai.2025.110665","DOIUrl":"10.1016/j.engappai.2025.110665","url":null,"abstract":"<div><div>Leptospirosis is a widespread zoonotic disease with complex spatio-temporal dynamics. This study investigates the use of Deep Neural Network (DNN) in combination with Local Interpretable Model-Agnostic Explanations (LIME) for weekly spatio-temporal predictions of leptospirosis occurrence. The predictive model integrates hydroclimatic and environmental data to assess its effectiveness in predicting leptospirosis cases and quantifying key input variables in Negeri Sembilan, Malaysia.</div><div>Using a DNN architecture with hyperparameter tuning via grid search, we developed a globally trained model that achieved an overall prediction accuracy of 70.5% across 214 pixels. We identified acidic soil and a higher presence of rubber plantations as strong predictors of leptospirosis occurrence. Additionally, mean temperature and minimum rainfall emerged as important hydroclimatic contributors.</div><div>These insights enable public health authorities to proactively identify and prioritize high-risk areas for targeted interventions, improving disease mitigation strategies. Furthermore, the methodology is adaptable to other regions with similar environmental and socio-economic conditions, strengthening early warning systems and enhancing preparedness against future leptospirosis outbreaks.</div><div>While demonstrated on leptospirosis prediction, the proposed DNN-LIME framework is adaptable to spatio-temporal challenges in diverse domains such as supply chain optimization, urban planning, and industrial risk management. The integration of interpretability via LIME ensures actionable insights for stakeholders beyond public health, bridging the gap between complex models and real-world decision-making.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"151 ","pages":"Article 110665"},"PeriodicalIF":7.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Heterogeneous approximation-assisted search for expensive multi-objective optimization

IF 8.2 1区计算机科学

Swarm and Evolutionary Computation Pub Date : 2025-04-02 DOI: 10.1016/j.swevo.2025.101926

Shufen Qin, Chaoli Sun

{"title":"Heterogeneous approximation-assisted search for expensive multi-objective optimization","authors":"Shufen Qin, Chaoli Sun","doi":"10.1016/j.swevo.2025.101926","DOIUrl":"10.1016/j.swevo.2025.101926","url":null,"abstract":"<div><div>The cheap surrogate model is commonly used to guide the multi-objective optimization algorithm in the search for the optimum of the expensive optimization problem. However, modeling diversity and its quality are the keys that affect the performance of approximating the original problem. Using multiple heterogeneous models can provide more diverse approximations for complicated optimization problems. Meanwhile, the location relationship between individuals and training samples is a potential benefit for selecting infill individuals to update the model. Therefore, this paper proposes to train two heterogeneous models for each expensive objection function, with the update of the models using the promising individuals based on the approximated domination relationship and the crowding distance between individuals and evaluated samples. Differently, the function estimation of each individual is the sum of two predicted values in a probability-weighted way together with its uncertainty. In addition, the promising individuals are selected by the dominant numbers or the distance to the decision domain center and the crowding distance to the neighbors, otherwise adopting the difference in convergence and crowding distance between all candidates and the training samples to select the individual for expensive function evaluations if the training set dominates all offspring individuals. Experimental studies analyze the effectiveness of the heterogeneous approximation-based guiding search and examine the superiority of the proposed algorithm compared to five recent epidemic optimization algorithms for DTLZ, WFG benchmark problems, and a practical application.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"95 ","pages":"Article 101926"},"PeriodicalIF":8.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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