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19th International Joint Symposium on Artificial Intelligence and Natural Language processing

作者： Khueankham, Pamorn Tantrairatn, Suradet Suranaree Univ Technol Sch Mechatron Engn Inst Engn Nakhon Ratchasima Thailand

ISBN: (纸本)9798331509927;9798331509910

This research presents an innovative approach to dormitory surveillance at Surawiwat School by employing an unmanned aerial vehicle (UAV) for autonomous monitoring. The UAV is used for aerial reconnaissance, allowing for efficient surveillance of the dormitory's perimeter to enhance security. By capturing high-resolution aerial images, the system aims to identify and track potential intruders, specifically those attempting to climb the dormitory fence. The images captured by the UAV are processed using advanced machine learning techniques, with a focus on object detection through deep learning. The system is built around a Convolutional Neural Network (CNN) and leverages the YOLOv8 (You Only Look Once) algorithm. YOLOv8 is recognized for its high accuracy and real-time processing capabilities, making it an ideal choice for real-time surveillance and detection tasks. The CNN-based model is trained to accurately detect human figures and identify unusual activities within the captured images. When the system detects an intruder, it sends an immediate alert, along with the captured aerial image, through the Line application to designated personnel. This instant notification system enhances response times, allowing school security to address potential threats proactively. Overall, this research demonstrates a sophisticated, AI-driven surveillance solution that combines UAV capabilities with state-of-the-art object detection, contributing to enhanced safety and security for school dormitories.

关键词： Machine learning deep learning image processing Object detection

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A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids

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SENSORS 2025年第10期25卷 3082-3082页

作者： Sunwoo, Hasik Lee, Seungwoo Paik, Woojin Konkuk Univ Dept Comp Engn Glocal Campus268 Chungwon Daero Chungju Si 27478 Chungcheongbuk South Korea

Accurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diverse clinical settings. This study introduces a software-defined sensing approach that leverages semantic segmentation using the pyramid scene parsing network (PSPNet) to estimate the remaining IV fluid volumes directly from images captured by standard smartphones. The system identifies the IV container (vessel) and its fluid content (liquid) using pixel-level segmentation and estimates the remaining fluid volume without requiring physical sensors. Trained on a custom IV-specific image dataset, the proposed model achieved high accuracy with mean intersection over union (mIoU) scores of 0.94 for the vessel and 0.92 for the fluid regions. Comparative analysis with the segment anything model (SAM) demonstrated that the PSPNet-based system significantly outperformed the SAM, particularly in segmenting transparent fluids without requiring manual threshold tuning. This approach provides a scalable, cost-effective alternative to hardware-dependent monitoring systems and opens the door to AI-powered fluid sensing in smart healthcare environments. Preliminary benchmarking demonstrated that the system achieves near-real-time inference on mobile devices such as the iPhone 12, confirming its suitability for bedside and point-of-care use.

关键词： smart healthcare deep learning semantic segmentation PSPNet medical image processing IV fluid monitoring software-defined sensors real-time computer vision

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THE CONSTRUCTION OF MATHEMATICAL MODEL OF SWIMMERS’ TECHNICAL MOVEMENTS USING MULTIMODAL deep learning FRAMEWORK

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Scalable Computing 2025年第3期26卷 1440-1447页

作者： Wang, Mengmeng He, Yangwen School of Physical Education Jiangsu University of Technology Jiangsu Changzhou213001 China Department of Orthopedics Changzhou West the Taihu Lake Hospital Jiangsu Changzhou213149 China

This paper proposes a mathematical model construction method based on a multi-modal deep learning framework aiming at the accuracy and real-time requirements of swimmers’ technical movement analysis. The model can extract the image features and timing information of athletes’ movements from video sequences by integrating spatiotemporal modules. This paper introduces the translation partial channel strategy to overcome the limitation of spatiotemporal information separation in traditional methods, which can seamlessly integrate spatiotemporal features and enhance the recognition ability of complex action patterns. In addition, NetVLAD is used as the feature aggregation layer. This layer can capture and encode the global and local features of the athlete’s movements, thereby improving the classifier’s performance. In the experimental part, the model is strictly verified, and the results show that compared with the prior art, the model in this paper shows higher accuracy and faster processing speed in the swimmer’s action classification task. This provides the possibility of immediate feedback for coaches and athletes and lays a solid foundation for further research in the field of sports science. © 2025 SCPE.

关键词： deep learning

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Hybrid deep learning Approach Based on LSTM and CNN for Malware Detection

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期136卷 1879-1901页

作者： Thakur, Preeti Kansal, Vineet Rishiwal, Vinay APJ Abdul Kalam Tech Univ Lucknow Uttar Pradesh India APJ Abdul Kalam Tech Univ IET Lucknow Uttar Pradesh India MJP Rohilkhand Univ Dept Comp Sci & Informat Technol Bareilly Uttar Pradesh India

Malware analysis is essential for detecting and mitigating the effects of malicious software. This study introduces a novel hybrid approach using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) to enhance malware analysis. The proposed work uses a malware classification method combining image processing and machine learning. Malware binaries are converted into grayscale images and analyzed with CNN-LSTM networks. Dynamic features are extracted, ranked, and reduced via Principal Component Analysis (PCA). Various classifiers are used, with final classification by a voting scheme, providing a robust solution for accurate malware family classification. Our approach processes binary code inputs, with the LSTM capturing temporal dependencies and the CNN performing parallel feature extraction. PCA is employed for prominent feature selection, reducing computational time. The proposed approach was evaluated on a public malware dataset and captured through network traffic, demonstrating state-of-the-art performance in identifying various malware families. It significantly reduces the resources required for manual analysis and improves system security. Our approach achieved high precision, recall, accuracy, and F1 score, outperforming existing methods. Future research directions include improving feature extraction techniques and developing real-time detection models that offer a powerful malware detection and analysis tool with promising results and potential for further advancements.

关键词： Malware CNN LSTM Hybrid model image analysis Machine learning

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Intelligent weed management using aerial image processing and precision herbicide spraying: An overview

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CROP PROTECTION 2025年 194卷

作者： Ehrampoosh, Armin Hettiarachchi, Pushpika Koirala, Anand Hassan, Jahan Islam, Nahina Ray, Biplob Nabi, Md Nurun Tolba, Mohamed Mazid, Abdul Md Xu, Cheng-Yuan Ashwath, Nanjappa Dzitac, Pavel Moore, Steven Cent Queensland Univ Sch Engn & Technol Norman Gardens Australia Cent Queensland Univ Sch Hlth Med & Appl Sci Norman Gardens Australia Cent Queensland Univ Ctr Machine Learning Networking & Educ Technol CML Norman Gardens Australia Cent Queensland Univ Ctr Reg Econ & Supply Chains Norman Gardens Australia Charles Darwin Univ Res Inst Environm & Livelihoods Casuarina Australia Res Inst Northern Agr Casuarina Australia Leica Biosyst Mt Waverley Australia

Modern agriculture is increasingly adopting intelligent technologies to enhance productivity while minimizing production costs and reducing adverse environmental impacts. A prime example of this synergy is the use of image processing to identify weeds, enabling targeted herbicide spraying with autonomous devices such as robots and drones. This approach not only reduces production costs but also ensures sustainable farming while minimizing negative environmental impacts. Designing an intelligent weed management system requires a multidisciplinary approach, combining agriculture, big data processing, machine learning, computer science, robotics, and plant science. Currently, independent studies have focused on some of these aspects, but few have taken a holistic approach to address the issue. This paper highlights the approach taken in developing innovative and ecologically sustainable weed management systems for agriculture. It also presents a comprehensive overview of a weed management system that integrates coordinated weed detection and spraying, detailing its unique components. The paper reviews and contrasts various image analysis techniques used in weed detection, particularly those employing artificial intelligence and imagery captured by unmanned aerial vehicles (UAVs). Furthermore, the paper highlights recent advancements in image processing platforms, such as the shift towards local and edge computing, and the growing need for near-real-time processing in agricultural applications. It also explores the development of commercial weed-spraying drones and discusses various aspects of an autonomous weed control system, including design, navigation, and spraying mechanisms for targeted application. Finally, the paper identifies key research needs for developing an AI-based, targeted herbicide spraying system that could significantly contribute to sustainable, economically viable, and efficient agricultural practices.

关键词： Aerial image processing Artificial intelligence Blanket spraying Cloud and edge computing deep learning Machine learning Precision herbicide spraying Smart farming Sprayer-drones UAVs-based spot spraying Weed detection Weed management

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AgriDet: Plant Leaf Disease severity classification using agriculture detection framework

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期119卷

作者： Pal, Arunangshu Kumar, Vinay Natl Inst Technol Jamshedpur Dept Comp Sci & Engn Adityapur 831014 Jharkhand India

In the field of modern agriculture, plant disease detection plays a vital role in improving crop productivity. To increase the yield on a large scale, it is necessary to predict the onset of the disease and give advice to farmers. Previous methods for detecting plant diseases rely on manual feature extraction, which is more expensive. Therefore, image-based techniques are gaining interest in the research area of plant disease detection. However, existing methods have several problems due to the improper nature of the captured image, including improper background conditions that lead to occlusion, illumination, orientation, and size. Also, cost complexity, misclassifications, and overfitting problems occur in several real-time applications. To solve these issues, we proposed an Agriculture Detection (AgriDet) framework that incorporates conventional Inception-Visual Geometry Group Network (INC-VGGN) and Kohonen-based deep learning networks to detect plant diseases and classify the severity level of diseased plants. In this framework, image pre-processing is done to remove all the constraints in the captured image. Then, the occlusion problem is tackled by the proposed multi-variate grabcut algorithm for effective segmentation. Furthermore, the framework performs accurate disease detection and classification by utilizing an improved base network, namely a pre-trained conventionally based INC-VGGN model. Here, the pre-trained INC-VGGN model is a deep convolutional neural network for prediction of plant diseases that was previously trained for the distinctive dataset. The pre-trained weights and the features learned in this base network are transferred into the newly developed neural network to perform the specific task of plant disease detection for our dataset. In order to overcome the overfitting problem, a dropout layer is introduced, and the deep learning of features is performed using the Kohonen learning layer. After percentage computation, the improved base netw

关键词： Plant disease detection Severity classes deep learning Segmentation Occlusion Classification

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Development of a Model for Detection and Grading of Stem Rust in Wheat Using deep learning

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MULtimeDIA TOOLS AND APPLICATIONS 2023年第16期83卷 47649-47676页

作者： Nigus, Eyerusalem Assefa Taye, Getie Balew Girmaw, Dagne Walle Salau, Ayodeji Olalekan Haramaya Univ Dept Informat Technol Haramaya Ethiopia Univ Gondar Dept Informat Technol Gondar Ethiopia Haramaya Univ Dept Informat Technol Dire Dawa Ethiopia Afe Babalola Univ Dept Elect Elect & Comp Engn Ado Ekiti Nigeria Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai Tamil Nadu India

Stem rust is a highly prevalent and damaging fungal disease that affects wheat crops worldwide. It is caused by the Puccinia graminis f. sp. tritici fungus, and can significantly reduce wheat production and quality, by 10% to 50%. In severe cases, it causes up to a 90% decrease in grain yield if left uncontrolled. Manual diagnosis by pathologists and experts which use visual inspection is usually costly, time-consuming, and prone to errors. Moreover, the scarcity of pathologists in rural areas poses a challenge in detecting and grading wheat stem rust. To address these issues, this study presents a system for automated detection and classification of wheat stem rust based on the modified Cobbs scale (CIMMYT) guidelines. The system employs image pre-processing techniques such as adaptive thresholding for segmentation and the Gabor filter for feature extraction which enhances textural features to identify key disease characteristics. For disease classification, a deep learning approach was utilized which employs a 12-way Softmax to assign specific classes: Resistant (tR), Moderately resistant (MR), and Susceptible (S). The proposed model is trained and tested using an image dataset collected in collaboration with pathologists from Haramaya University. The proposed model achieves a training accuracy of 92.02% and a testing accuracy of 92.01% in grading wheat stem rust, outperforming state-of-the-art models such as CNN. The study's main achievements are achieved in the areas of real-time monitoring, and grading precision. real-time monitoring provides timely updates on disease presence and severity, enabling proactive management and minimizing crop losses. Grading precision offers a quantitative assessment of disease damage, facilitating targeted treatment strategies and resource allocation.

关键词： Stem rust deep learning CNN Gabor filter

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Defect Detection Methods for Industrial Products Using deep learning Techniques: A Review

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ALGORITHMS 2023年第2期16卷 95-95页

作者： Saberironaghi, Alireza Ren, Jing El-Gindy, Moustafa Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada Ontario Tech Univ Dept Automot & Mechatron Engn Oshawa ON L1G 0C5 Canada

Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing.

关键词： defect detection surface defect detection defect detection for X-ray images defect recognition deep learning

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Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第5期16卷 1914-1932页

作者： Sasithradevi, A. Persiya, J. Roomi, S. Mohamed Mansoor Perumal, D. Arumuga Prakash, P. Vijayalakshmi, M. Ebenezer, L. Brighty Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai Tamilnadu India Natl Inst Technol Karnataka Dept Mech Engn Surathkal Mangalore India Anna Univ Dept Elect Engn MIT Campus Chennai India

Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption.

关键词： Fault diagnosis Infrared thermography Electrical equipment Machine learning deep learning Segmentation

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GEEG-YOLOV8: GAUSSIAN ENHANCED EUCLIDEAN NORM GHOST ATTENTION FOR real-time POLYP DETECTION 31

GEEG-YOLOV8: GAUSSIAN ENHANCED EUCLIDEAN NORM GHOST ATTENTIO...

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2024 International Conference on image processing

作者： Phuong Thao Nguyen Watanabe, Hiroshi Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo Japan

ISBN: (纸本)9798350349405;9798350349399

Research on computer-aided polyp detection in gastrointestinal endoscopy has spanned the past few decades. Despite notable progress, the challenge of achieving automatic accurate and real-time polyp detection remains unresolved. This is because of the large differences in polyp characteristics such as shape, texture, size, and color, and the artifacts that are similar to polyp during endoscopy procedure. In this paper, we propose a novel Gaussian Enhanced Euclidean norm Ghost attention (GEEG) module for reliable real-time polyp detection on endoscopic images and videos. The new attention mechanism strengthens the features generated by Ghost convolution's cheap operations by increasing the ability to extract inter-channel and spatial information inside the convolution layer. This module is integrated into the backbone of YOLOv8, creating a new model named GEEG-YOLOv8, to overcome above obstacles in polyp detection. Experiment results on three public datasets show that our proposed method outperforms existing state-of-the-art methods in both accuracy and speed.

关键词： Attention mechanism yolov8 deep learning polyp detection medical image analysis

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