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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhang, Liyan Qi, Chao Liu, Zengli Zhao, Xuanzhi Rong, Shaowei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Kunming University of Science and Technology Computing Center Kunming650500 China

ISBN: (纸本)9781510689251

Aiming at the problems of low recognition rate of Chinese dialects and poor robustness in noisy environment, this paper proposes an improved feature extraction algorithm, which combines the Sparrow Search Algorithm (SSA) and Variational Mode Decomposition (VMD) with the improved Gamma-tone Frequency Cepstral Coefficients (GFCC) and Teager Energy Operator Cepstral Coefficient (TEOCC). Firstly, the VMD parameters are optimized by SSA, the dialect signal is decomposed into different modal components, and the correlation coefficients with the original signal are calculated. Then, the component of the dominant signal is screened according to the correlation, which is used to calculate the GFCC characteristics. Subsequently, the improved GFCC features are fused with TEOCC, and Principal Component Analysis (PCA) is used to reduce the dimension of the fusion features. Finally, the Gaussian mixture model-universal background model (GMM-UBM) is used for dialect recognition. The experimental results show that the proposed method can improve the recognition rate compared with the traditional GFCC and single feature in both noise-free and noise-free environments, and verify the effectiveness of the improved algorithm in dialect recognition. © 2025 SPIE.

关键词： Variational mode decomposition

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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SV-data2vec: Guiding Video Representation Learning with Latent Skeleton Targets

SV-data2vec: Guiding Video Representation Learning with Late...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Zorana Doždor Tomislav Hrkać Zoran Kalafatić Faculty of Electrical Engineering and Computing University of Zagreb Croatia

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advancements in action recognition leverage both skeleton and video modalities to achieve state-of-the-art performance. However, due to the challenges of early fusion, which tends to underutilize the strengths of each modality, existing methods often resort to late fusion, consequently leading to more complex designs. Additionally, self-supervised learning approaches utilizing both modalities remain underexplored. In this paper, we introduce SV-data2vec, a novel self-supervised framework for learning from skeleton and video data. Our approach employs a student-teacher architecture, where the teacher network generates contextualized targets based on skeleton data. The student network per-forms a masked prediction task using both skeleton and visual data. Remarkably, after pretraining with both modalities, our method allows for fine-tuning with RGB data alone, achieving results on par with multimodal approaches by effectively learning video representations through skeleton data guidance. Extensive experiments on benchmark datasets NTU RGB+D 60, NTU RGB+D 120, and Toyota Smarthome confirm that our method outperforms existing RGB based state-of-the-art techniques. The code is available at github. com/zoranadozdor/SVdata2vec.

关键词： Representation learning Visualization Computer vision Codes Self-supervised learning Computer architecture Benchmark testing Skeleton Software development management

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How Much Do Activity Patterns Change With Age? 6th

How Much Do Activity Patterns Change With Age?

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Jurčić, Krunoslav Gómez, Andrea Juliana Ruiz Zequera-Diaz, Martha Magjarević, Ratko Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia School of Engineering BASPI/FootLab Pontificia Universidad Javeriana Cra. 7 #40–62 Bogotá Colombia

ISBN: (纸本)9783031863226

This paper presents a comparison of features derived from various biomedical signals from both younger as well as elderly subjects. The dataset consists of accelerometer, gyroscope and altimeter signals acquired from wearable sensors, describing three different activities of daily living: walking, running and sitting on a chair. The features of all three signal data were processed, and later analysed to determine whether significant differences between signals recorded by younger subjects and elder subjects are present. Results of this comparison could prove valuable in the field of signal processing for various applications (activity recognition, fall detection, elderly care etc.) in terms of rehabilitation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aneroid altimeters

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Enhanced Layer Extraction for Efficient Plant Disease Classification using Efficientnet B1

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SN Computer Science 2025年第4期6卷 1-15页

作者： Thakur, Arastu Thakur, Abhilasha Vivek, V. Mahesh, T.R. Krishna, K. Murali Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India Department of Agriculture Marwadi University Gujarat Rajkot India Alliance School of Advanced Computing Alliance University Chandapura - Anekal Main Road Bengaluru Anekal 562106 India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore 562112 India

Agriculture is crucial for the global economy, providing sustenance and resources for various industries. However, plant diseases threaten crop quality and yield, risking severe economic impacts. Traditional plant disease diagnosis methods, like manual inspection or basic machine learning, are slow, require expertise, and struggle with scalability and adapting to new diseases. This study introduces a deep learning model using convolutional neural networks (cnns) to autonomously detect and classify plant diseases from images, enhancing speed, scalability, and accuracy. We utilize the efficientnetb1 architecture, pretrained on imagenet and fine-tuned on a dataset of bell pepper, potato, and tomato diseases, recognizing 14 diseases plus healthy plants. The model achieves 98.80% accuracy on the test dataset, with high precision, recall, and F1-scores, demonstrating its effectiveness in plant disease diagnosis. This advancement marks a significant step in automating plant disease diagnosis, offering a robust tool for effective disease management in agriculture. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agriculture Automated diagnosis Deep learning Disease management Efficientnetb1 Image classification Plant disease detection Transfer learning

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Designing Lookahead Relocation Rules for the Container Relocation Problem with Genetic Programming 28th

Designing Lookahead Relocation Rules for the Container Rel...

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28th European Conference on Genetic Programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Đurasević, Marko Đumić, Mateja Gil-Gala, Francisco Javier Jakobović, Domagoj Faculty of Electrical Engineering and Computing University of Zagreb Zagreb10000 Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek31000 Croatia Department of Computing University of Oviedo Gijón Spain

ISBN: (纸本)9783031899904

The container relocation problem is an important combinatorial optimisation problem commonly found in warehouses and container ports. The goal of this problem is to retrieve all of the containers from the yard with the fewest container relocations between the stacks. Since the problem is NP-hard, various heuristics have been proposed to solve it, among which are relocation rules (RRs), simple constructive heuristics that incrementally construct the solution. However, it is quite difficult to design such RRs manually, so genetic programming has been applied to design new RRs automatically. A significant problem with RRs, whether manually or automatically designed, is that they usually have a limited view of the problem. This means that they will often make decisions that can negatively influence the future, meaning that the current decision would cause additional relocations. Therefore, this study investigates different relocation schemes that can be used within RRs to obtain rules with lookahead ability. These rules will enable containers to be relocated based on future information and, consequently, arranged better in the yard. For that purpose, three novel relocation schemes for automatically designed RRs are defined and evaluated on an existing problem set. The results demonstrate that integrating additional elements to evolve lookahead RRs can significantly improve the results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

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Computers, Materials & Continua 2025年第2期82卷 3005-3018页

作者： Yahia Said Yahya Alassaf Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Civil Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafha91911Saudi Arabia College of Science Northern Border UniversityArar91431Saudi Arabia

Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.

关键词： Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation

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Evaluation of Wood Ash and Sodium Chloride Mixtures as Alternatives Grounding Enhancement Material for Grounding Systems 5

Evaluation of Wood Ash and Sodium Chloride Mixtures as Alter...

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5th International Conference on Emerging Electrical Energy, Electronics and computing Technologies, ICE4CT 2024

作者： Abubakar, Banjo O. Wooi, Chin-Leong Khor, Shing-Fhan Tang, Jia-Wen Afrouzi, Hadi Nabipour Abdullah, Ahmad Zaidi Anuar, Aminudin HVTrans CERE Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Pauh Putra Campus Perlis Arau02600 Malaysia College of Engineering Faculty of Computing Engineering and the Built Environment Birmingham City University West Midlands BirminghamB4 7XG United Kingdom

A grounding system is crucial for safeguarding electrical systems, ensuring they function effectively under both normal and fault conditions. This research investigates the effectiveness of enhancement materials, specifically wood ash and sodium chloride (NaCl), compared to conventional bentonite using fall-of-potrnial method. The study evaluates the suitability, advantages, and limitations of these materials. Experiments involved filling holes with 10 kg of bentonite, wood ash, and various mixtures of wood ash and NaCl (7 kg wood ash + 3 kg NaCl, 8 kg wood ash + 2 kg NaCl, 9 kg wood ash + 1 kg NaCl), using a reference hole without enhancement material. Copper earth rods was employed over 12 weeks. Results showed that the 7 kg wood ash + 3 kg NaCl mixture performed similarly to 10 kg of bentonite, achieving a 62% and 62.7% reduction in ground resistance, respectively. This indicates that a carefully mixed combination of NaCl and wood ash can be a viable alternative to bentonite. The data indicates that resistance generally decreases with higher rainfall and is lower for systems using NaCl mixtures, particularly the one with the highest NaCl content. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Electric grounding

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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