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3rd International Conference on neural Computing for Advanced applications, NCAA 2022

ISBN: (纸本)9789811961342

The proceedings contain 77 papers. The special focus in this conference is on neural Computing for Advanced applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis;a Perception Method Based on Point Cloud processing in Autonomous Driving;many-Objective artificial Bee Colony Algorithm Based on Decomposition and Dimension Learning;LCSW: A Novel Indoor Localization System Based on CNN-SVM Model with WKNN in Wi-Fi Environments;large Parallax image Stitching via Structure Preservation and Multi-matching;traffic Congestion Event Mining Based on Trajectory Data;many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition;a New Unified Control Approach for Finite-/Fixed-Time Synchronisation of Multi-weighted Dynamical networks;aperiodic Sampling Based Event-Triggering for Synchronization of Nonlinear Systems;two-Stream 3D MobileNetV3 for Pedestrians Intent Prediction Based on Monocular Camera;Research on Non-intrusive Household Load Identification Method Applying LightGBM;design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm;an Ensemble Deep Learning Model Based on Transformers for Long Sequence Time-Series Forecasting;multi-layer Integrated Extreme Learning Machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker;wind Power Forecast Based on Multiple Echo States;UAV-Assisted Blind Area Pedestrian Detection via Terminal-Edge-Cloud Cooperation in VANETs;a Survey of Optimal Design of Antenna (Array) by Evolutionary Computing Methods;a Span-Based Joint Model for Measurable Quantitative Information Extraction;bayesian Optimization Based Seq2Seq Network Models for Real Estate Price Prediction in Hong Kong;ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects;Cage Mass Center Capture for Whirl Analysis Using an Improved MultiResUNet from the Multimodal Biomedical image Segmentation;master Multiple Real-Time Strategy Game

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Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional neural networks

arXiv

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arXiv 2024年

作者： Wu, Fuzhi Wu, Jiasong Kong, Youyong Yang, Chunfeng Yang, Guanyu Shu, Huazhong Carrault, Guy Senhadji, Lotfi Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Bangladesh Laboratoire Traitement du Signal et de l'Image Univ Rennes France Centre de Recherche en Information Biomédicale Sino-français CRIBs France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Bangladesh

Deep learning and Convolutional neural networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on imageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://***/AlphaWuSeu/MLFM. © 2024, CC BY-NC-ND.

关键词： Computer vision

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Effective Brain Tumor Detection using Convolutional neural networks

Effective Brain Tumor Detection using Convolutional Neural N...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Aman Narang Aman Singh Rajpoot Ramkumar Jayaraman Department of Computing Technologies SRM Institute of Science and Technology Chennai India

In the recent scenario, the move from machine learning to deep learning has played a significant role in various applications such as, healthcare, automation, robotics, etc. Considerign the healthcare, diagnosing diseases, like brain tumor, brest cancer, etc. using Magnetic Resonance Imaging (MRI) images, is a complex process because the images contain various cell/image pixel divisions to identify the condition of the disease. Based on the complexity of the disease, identifying and diagnosing the tumour becomes a challenging task as it may lead to an increase in the medical survey on tumour disease based on the number of patients. In this paper, a Deep Learning (DL) - Convolutional neural networks (CNN) based approach is proposed with the integration of data augmentation and image processing. The proposed approach provides classification of MRI images to diagnose the tumour location on the dense areas based on small tumour cells. The effectiveness of the proposed approach is analyzed based on the data accuracy as it achieves 96% with low complexity rate compared to the existing models such as, CNN and artificial neural networks (ANN) models.

关键词： Training Deep learning Magnetic resonance imaging Computational modeling Medical services Brain modeling Data models

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International Conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional neural networks (CNNs), Deep neural networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Enhanced Hybrid Approach for Real-Time Emotion Recognition Using Canny Edge Detection and Deep Learning on Embedded Systems

Enhanced Hybrid Approach for Real-Time Emotion Recognition U...

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Embedded Systems and artificial Intelligence (ESAI), International Conference on

作者： Ismail Mchichou Rabab Ouchker Mohamed Amine Tahiri Hicham Amakdouf Mohammed Ouazzani Jamil Hassan Qjidaa Laboratory of Electronic Signals and Systems of Information (LESSI) Dhar El Mahrez Faculty of Science Sidi Mohamed Ben Abdellah-Fez University Fez Morocco Engineering Systems and Applications Laboratory National School of Applied Sciences Sidi Mohamed Ben Abdellah-Fez University Fez Morocco Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

This paper presents an innovative approach to real-time emotion recognition, integrating Canny’s edge detection and deep learning on an embedded system, namely the Raspberry Pi 3 board. Our method combines the edge detection capabilities of the Canny algorithm with the robust feature extraction and analytical strengths of advanced deep learning techniques. The pre-processing phase involves applying Canny’s edge detection to highlight facial contours and important features, which are subsequently processed by a convolutional neural network (CNN) to determine emotional states. This hybrid approach improves the accuracy of emotion recognition, enabling real-time application on resource-constrained devices. Extensive experiments on reference datasets demonstrate the effectiveness of our method in real time. This work opens up new perspectives for developing portable and efficient emotion recognition systems for applications in healthcare, and surveillance.

关键词： Deep learning Emotion recognition Embedded systems Accuracy image edge detection Surveillance Feature extraction Real-time systems Convolutional neural networks Security

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Application of artificial intelligence in aquaculture - Recent developments and prospects

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AQUACULTURAL ENGINEERING 2025年 111卷

作者： Roy, Subha M. Beg, Mirza Masum Bhagat, Suraj Kumar Charan, Durga Pareek, C. M. Moulick, Sanjib Kim, Taeho Chonnam Natl Univ Smart Aquaculture Res Ctr Yeosu 59626 South Korea Ramnagar Coll Dept Fisheries Sci Purba Medinipur 721453 West Bengal India Marwadi Univ Res Ctr Fac Engn & Technol Dept Civil Engn Rajkot 360003 Gujarat India IIT Kharagpur Dept Aerosp Engn Kharagpur 721302 West Bengal India IIT Kharagpur Agr & Food Engn Dept Kharagpur 721302 West Bengal India Kalinga Inst Ind Technol KIIT Univ Sch Civil Engn Bhubaneswar 751024 Odisha India Chonnam Natl Univ Dept Marine Prod Management Yeosu 59626 South Korea

artificial intelligence (AI) offers innovative and efficient solutions to contemporary challenges in sustainable aquaculture. Machine learning (ML) and deep learning (DL) are integral components of smart aquaculture, driving significant advancements in the field. The integration of AI with ML, and DL technologies is transforming traditional aquaculture practices by enhancing operational efficiency, optimizing fish health management, improving environmental conditions, monitoring water quality and supporting advanced decision-making processes. This review highlights the latest applications of AI, including ML, and DL in aquaculture, emphasizing their roles in real-time water quality monitoring, disease detection, and automated estimation of fish biomass etc. Key techniques, including predictive modeling, image and video processing, and sensor data integration, are enabling these breakthroughs. Moreover, DL algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have emerged as powerful tools for processing complex data and predicting critical events within aquaculture systems. Despite the notable progress, challenges such as the need for large, labeled datasets, high computational costs, and issues related to model interpretability continue to limit broader adoption. The current review aims to offer researchers and practitioners with a comprehensive overview of AI and its subfields such as ML and DL applications in smart aquaculture, discussing both the opportunities and challenges while suggesting future research directions to overcome existing limitations and expand AI-driven innovations in the industry.

关键词： artificial intelligence Smart aquaculture Deep learning

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Dense Pixel-to-Pixel Harmonization via Continuous image Representation

arXiv

引用

arXiv 2023年

作者： Chen, Jianqi Zhang, Yilan Zou, Zhengxia Chen, Keyan Shi, Zhenwei Image Processing Center School of Astronautics Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any handcrafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code is available at https://***/WindVChen/INR-Harmonization. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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High performance artificial visual system with plasmon-enhanced 2D material neural network

arXiv

引用

arXiv 2023年

作者： Zhang, Tian Guo, Xin Wang, Pan Li, Linjun Tong, Limin

artificial visual systems (AVS) have gained tremendous momentum because of its huge potential in areas such as autonomous vehicles and robotics as part of artificial intelligence (AI) in recent years. However, current machine visual systems composed of complex circuits based on complementary metal oxide semiconductor (CMOS) platform usually contains photosensor array, format conversion, memory and processing module. The large amount of redundant data shuttling between each unit, resulting in large latency and high power consumption, which greatly limits the performance of the AVS. Here, we demonstrate an AVS based on a new design concept, which consists of hardware devices connected in an artificial neural network (ANN) that can simultaneously sense, pre-process and recognize optical images without latency. The Ag nanograting and the two-dimensional (2D) heterostructure integrated plasmonic phototransistor array (PPTA) constitute the hardware ANN, and its synaptic weight is determined by the adjustable regularized photoresponsivity matrix. The eye-inspired pre-processing function of the device under photoelectric synergy ensures the considerable improvement of the efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and ultralow energy consumption per spike (2.4 ×10-17 J). © 2023, CC BY.

关键词： neural networks

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DEFECT INSPECTION USING GRAVITATION LOSS AND SOFT LABELS

DEFECT INSPECTION USING GRAVITATION LOSS AND SOFT LABELS

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IEEE International Conference on image processing (ICIP)

作者： Guan, Zhihao Guo, Zidong Lyu, Jie Yuan, Zejian Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China CognVis Technol Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665441155

Defect inspection is a widely studied computer vision task with a vast range of applications. However, due to the great variety of defects and high difficulty of collecting ample abnormal images of rare occasions, such a task full of confusing noisy samples faces tremendous challenges of generalization. In this paper, we propose a practical framework for defect inspection to better discover and utilize the connections among these samples. Specifically, Gravitation Loss is proposed to enhance the discriminative power of embedding vectors learned by neural networks. Besides, Soft Loss is designed by introducing soft labels which provide more supervision for noisy samples to improve generalization. With joint supervision of Gravitation Loss, Soft Loss, and a regular classification loss, experimental results show that our method outperforms other state-of-the-art approaches on a newly-built resin plug-hole dataset.

关键词： Defect inspection discriminative embedding features gravitation loss soft labels

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AI based Analysis of Hyperspectral images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Samarth Yogesh Jadhav Rutuja Rajatam More Bhagyashree Ashok Tingare G. Prasanna Lakshmi Shivam Kolse N.K. Darwante Department of Information Technology Sanjivani College of Engineering Kopargaon MH India Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune School of Computer Science and Engineering Sandip University Nashik Maharashtra Department of Electronics and Computer Engineering Sanjivani College of Engineering Kopargaon MH India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control.

关键词： Accuracy image analysis Visual analytics Surveillance Quality control Real-time systems Environmental monitoring Object recognition artificial intelligence Hyperspectral imaging

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