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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. This investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. The study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). The experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. The Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. The key findings are as follows. (1) The participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) The consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) The K-factor mean across participants tended toward K > 0. (5) The average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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An Efficient Reconfigurable Parameterized Convolutional Neural Network Accelerator on FPGA Platform

An Efficient Reconfigurable Parameterized Convolutional Neur...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Yeong-Kang Lai Chien-Hung Lin Department of the Electrical Engineering Master Program in Semiconductor and Green Technology National Chung Hsing University Taichung Taiwan R.O.C

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper presents an efficient reconfigurable parameterized convolutional neural network (CNN) accelerator designed for FPGA platforms. The core of the accelerator allows flexible adjustments of input channel parallelism (Pc) and convolution filter parallelism (Pn) to meet the needs of various CNN models, optimizing both resource utilization and computational efficiency. The architecture is applied specifically to infrared (IR) and visible light (VI) image fusion, enabling real-time processing on an FPGA with significantly reduced latency. Experimental results demonstrate that the accelerator reduces latency to 192 ms while maintaining high image quality, achieving efficient image fusion on the FPGA platform.

关键词： Image quality Convolution Computer architecture Parallel processing Real-time systems Convolutional neural networks Resource management Image fusion Field programmable gate arrays Hardware acceleration

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An Environmentally Friendly Fishing Android-Based Mobile Device App for Penghu Islands (Pescadores), Taiwan

An Environmentally Friendly Fishing Android-Based Mobile Dev...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Hung-Tu Lin Ke-Qing Yan Ying-Zun Fang Xiang-Rui Huang Liang-Bi Chen Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Penghu Islands (Pescadores), Taiwan's outlying islands surrounded by the sea, boast rich fishing resources that attract many anglers. However, successful fishing requires more than just gear: it involves understanding the varying fish species available in different locations, times, and climates, as well as adhering to relevant laws and regulations. To address these needs, this paper proposes an environmentally friendly fishing Android-based mobile device App specifically for the Penghu Islands. The proposed Android-based mobile device App aims to gather essential information, including local fishing regulations, aquatic safety tips, and important precautions. Its goal is to equip fishermen with the resources necessary to fish successfully and with peace of mind.

关键词： Sea measurements Length measurement Fish Regulation Mobile applications Tides Protection Smart phones Meteorology Lenses

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A Reconfigurable Compute-In-Memory Design with Adaptive Bit-Precision and Power Adjustment

A Reconfigurable Compute-In-Memory Design with Adaptive Bit-...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： I-Chieh Hsu Chun-Fu Chen Dao-Long Huang Yun-Kai Yeh Wei-Chieh Lee Qi-Hua Lin Jinjia Zhou I-Chyn Wey Electrical Engineering Department College of Engineering Chang-Gung University Taiwan School of Electrical Engineering National Taiwan University of Science and Technology Taiwan Master Program in Nano-Electronic Engineering and Design Chang Gung University Taiwan Department of Applied Informatics Faculty of Science and Engineering Hosei University Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

With the successful development of artificial intelligence (AI), Convolutional Neural Networks (CNNs) occupy a large amount of computing time and power consumption in the entire AI computing process. However, in traditional von Neumann architectures, the separation of the computing element and memory leads to the memory-wall problem of memory data transmission bandwidth from memory to processing element when executing multiply-accumulate-based CNN operations. The data transmission time and power consumption all are much higher than the CNN computing part. Therefore, this paper proposes a memory-in-computation design that can flexibly adjust energy usage, achieve high energy efficiency, and support multiple operation frequencies. By controlling the switches of each memory row, unnecessary energy consumption is avoided, leading to a reduction in total power consumption by 15% to 60%. Additionally, by adjusting the pulse width, the charging power consumption of capacitors is reduced by 65% per charge cycle.

关键词： Energy consumption Power demand Capacitors Bandwidth In-memory computing Control systems Energy efficiency Convolutional neural networks Data communication Artificial intelligence

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys Computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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An Improved Vision Transformer Architecture-Based Computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiang-Rui Huang Huai-Jhan Ke Szu-Yu Kuo Liang-Bi Chen Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species.

关键词： Computer vision Electric potential Accuracy Computational modeling Computer architecture Computer applications Flowering plants Network architecture Transformers Consumer electronics

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Leveraging ROTI map derived from Indonesian GNSS receiver network for advancing study of Equatorial Plasma Bubble in Southeast/East Asia

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Earth and Planetary Physics 2025年第1期9卷 101-116页

作者： Prayitno Abadi Ihsan N.Muafiry Teguh N.Pratama Angga Y.Putra Suraina Gatot H.Pramono Sidik T.Wibowo Febrylian F.Chabibi Umar A.Ahmad Wildan P.Tresna Asnawi Research Center for Climate and Atmosphere Indonesian National Research and Innovation Agency(BRIN)BandungIndonesia School of Electrical Engineering Telkom UniversityBandungIndonesia Directorate of Laboratory Management Facilities and Science and Technology Park Indonesian National Research and Innovation Agency(BRIN)PontianakIndonesia Geophysics Laboratory Master Program in PhysicsUniversitas Gadjah Mada(UGM)YogyakartaIndonesia Research Center for Space Indonesian National Research and Innovation Agency(BRIN)BandungIndonesia Indonesian Geospatial Information Agency(BIG) JakartaIndonesia Research Center for Photonics Indonesian National Research and Innovation Agency(BRIN)Tangerang SelatanIndonesia Department of Earth and Planetary Sciences Hokkaido UniversitySapporoJapan

This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning *** by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude ***-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast *** provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this *** generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific *** preliminary findings from the ROTI maps of Ina-CORS are ***,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther ***,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East *** have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,*** observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities *** sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionosphe

关键词： Equatorial Plasma Bubble(EPB) GNSS receivers’network Indonesia Continuously Operating Reference Station(Ina-CORS) ionospheric map Rate of TEC change index(ROTI)map

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Speech Abusive Language Detection System Using MFCC Speech Feature Extraction and Convolutional Neural Network

Speech Abusive Language Detection System Using MFCC Speech F...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yi-Chang Zhu Ying-Hsiu Hung Yen-Ching Chang Jhen-Kai Tang Wen-Kai Tsai Shin-Chi Lai The Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Department of Electrical Engineering Master's Program National Formosa University Huwei Taiwan Dept. Electrical Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

As bullying rapidly spreads in schools, many parents are becoming increasingly concerned about their children being subjected to bullying by peers, which could negatively impact their physical and mental health. This paper proposes an abusive language detection system that combines Mel-frequency Cepstrum Coefficients (MFCC) speech features with a Convolutional Neural Network (CNN). Audio files which contain daily abusive phrases in the Minnan dialect are initially preprocesses to eliminate non-speech interference. Subsequently, the MFCC algorithm is applied to extract speech features that closely capture auditory characteristics. The CNN classification model is trained to learn the MFCC features and classify the phrases into their respective categories. Experimental results indicate that the CNN model achieves an average recognition accuracy of 91.9%. Finally, the CNN model was deployed on an embedded platform, where it achieves an average recognition time of 0.54 seconds per phrase and an average recognition accuracy of 86.25%.

关键词： Pediatrics Accuracy Semantics Speech recognition Mental health Speech enhancement Feature extraction Convolutional neural networks Mel frequency cepstral coefficient Optimization

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