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2nd International Conference on Business Data Analytics, ICBDA 2023

作者： Manisha Kumar, Avadhesh Yadav, Dileep Kumar School of Computer Science Engineering Galgotias University Greater Noida India SCSET Bennett University Greater Noida India

ISBN: (纸本)9783031807770

At present, Artificial intelligence (AI) and machine learning (ML) algorithm contribute to transforming the technology environment. Specifically, the development of chatbots has been on a rapid-fire overhead in recent years. The image captioning with ChatGPT is most growing field for text generating for the future. In recent years, the innovation of virtual assistants has boomed with ChatGPT becoming an increasingly popular language model of artificial intelligence. This work evaluates the principles, goals, and problems of ChatGPT in the present study. This study explores the technology's background, growth, and development, along with its applications. In addition, we will look at the benefits of connecting information by ChatGPT in image processing and content writing that the image exposes. However, we believe that ChatGPT will contribute to improving human-AI interaction and bridging scientific gaps by focusing on various research and development opportunities, such as energy conservation, privacy, and security, expanding its scope to other technologies like robotics and computer vision, deep learning, and ML. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ChatGPT model Computer vision NLP

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Dynamic Threshold Spatial-Temporal Filter on FPGAs for Event-based vision Sensors*

Dynamic Threshold Spatial-Temporal Filter on FPGAs for Event...

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IEEE/SICE International Symposium on System Integration

作者： Ryuta Toyoda Kanta Yoshioka Hakaru Tamukoh Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Wakamatsu Kitakyushu Japan Japan Society for the Promotion of Science Chiyoda-ku Tokyo Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology Wakamatsu Kitakyushu Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Event-based vision sensors are high-speed, wide dynamic range image sensors with potential applications in domains such as robotics and visual navigation. However, these sensors are sensitive to noise, particularly under low-light conditions, degrading the data quality. Therefore, developing a filter capable of detecting and removing noise from different sources with high accuracy is crucial. Moreover, removing near-edge noise in high-density areas is particularly challenging using conventional methods because of their high spatial-temporal correlation with actual events. We propose a dynamic threshold spatial-temporal filter that detects high- or low-density areas and removes noise. Detection was achieved by counting the number of events occurring within a certain period in the area surrounding each event. Applying an appropriate threshold for each density significantly enhanced noise processing accuracy, as reflected by the mean square error and peak signal-to-noise ratio metrics. Moreover, we synthesize digital circuits in a field-programmable gate array and demonstrated a notable reduction in processing time compared to that of the central processing unit-based approach, achieving up to 74-fold faster in processing speed. These findings suggest that the proposed filter can significantly enhance real-time event-based vision systems, particularly in environments with varying noise conditions.

关键词： Accuracy Correlation Event detection machine vision Noise Noise reduction vision sensors Real-time systems High dynamic range Field programmable gate arrays

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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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Exploring optimizer efficiency for facial expression recognition with convolutional neural networks

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JOURNAL OF ENGINEERING-JOE 2025年第1期2025卷

作者： Madni, Syed Hamid Hussain Pathmanatan, Lokessh A. L. Faheem, Muhammad Shahzad, Hafiz Muhammad Faisal Shah, Sajid Univ Southampton Malaysia Sch Elect & Comp Sci Johor Baharu Malaysia Univ Teknol Malaysia Fac Comp Skudai Johor Malaysia Univ Vaasa Sch Technol & Innovat Vaasa Finland VTT Tech Res Ctr Finland Ltd Espoo Finland Univ Sargodha Dept Comp Sci Sargodha Pakistan

It's widely accepted that human expressions, considering for roughly sixty percent of all daily interactions, are among the most authentic forms of communication. Numerous studies are being conducted to explore the importance of facial expressions and the development of machine-assisted recognition techniques. Significant progress is being made in facial and expression recognition, largely due to the rapid growth of machine learning and computer vision. A variety of algorithmic approaches and methods exist for detecting and recognizing facial expressions and features. This study investigates various optimization algorithms used with convolutional neural networks for facial expression recognition. The primary focus is on Adam, RMSProp, stochastic gradient descent and AdaMax optimizers. A comprehensive comparison is being made, examining the key aspects of each optimizer, including its advantages and disadvantages. Furthermore, the study also incorporates findings from recent studies that used these optimizers in various applications, highlighting their performance in terms of training time and precision. The aim is to illuminate the process of selecting a suitable optimizer for specific applications, analysing the trade-offs between training speed and higher accuracy levels. Moreover, this study provides a deeper analysis of the role optimizers play in machine learning-based facial expression recognition models. The discussion of the technical challenges posed by these optimizers and future improvements for achieving much more optimal results concludes the study.

关键词： image classification image processing image recognition optimisation convolution neural network machine leanring

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization machine learning algorithms image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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A Cnn Model for Computationally Efficient Haze Removal in Railway Monitoring images

A Cnn Model for Computationally Efficient Haze Removal in Ra...

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International Conference on Knowledge and Smart Technology (KST)

作者： Pitchayapatchaya Srikram Atid Aumchoo Anurak Khamchaimongkhon Panrawee Rungskunroch Dept. Computer engineering RMUTT Pathum thani Thailand Dept. Industrial Engineering RMUTT Pathum thani Thailand

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Haze degrades the accuracy of computer vision algorithms for railway monitoring applications. This study proposed a dehazing algorithm emphasizing image quality, performed on GPUs and CPUs rather than on the IoT or low power devices, as a CNN-based haze removal model for an edge device. Our optimized CNN model utilized the ADD operation to relieve concatenation between two convolutional layers. The model was tested on a TFlite file, with results showing acceptable SSIM and PSNR image quality assessment values. The average computational time was $\mathbf{2 1 0}$ milliseconds on an Intel Xeon processor while a Raspberry Pi 4 Model B, operating as an offline edge inference device, achieved a processing time of $\mathbf{2 8 0}$ milliseconds per image.

关键词： image quality Performance evaluation Computational modeling image edge detection Tiny machine learning Rail transportation Computational efficiency Convolutional neural networks Monitoring Testing

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MoColl: Agent-Based Specific and General Model Collaboration for image Captioning

arXiv

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arXiv 2025年

作者： Yang, Pu Dong, Bin The School of Mathematical Sciences Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Peking University China

image captioning is a critical task at the intersection of computer vision and natural language processing, with wide-ranging applications across various domains. For complex tasks such as diagnostic report generation, deep learning models require not only domain-specific image-caption datasets but also the incorporation of relevant general knowledge to provide contextual accuracy. Existing approaches exhibit inherent limitations: specialized models excel in capturing domain-specific details but lack generalization, while vision-language models (VLMs) built on large language models (LLMs) leverage general knowledge but struggle with domain-specific adaptation. To address these limitations, this paper proposes a novel agent-enhanced model collaboration framework, which we called MoColl, designed to effectively integrate domain-specific and general knowledge. Specifically, our approach is to decompose complex image captioning tasks into a series of interconnected question-answer subtasks. A trainable visual question answering (VQA) model is employed as a specialized tool to focus on domain-specific visual analysis, answering task-specific questions based on image content. Concurrently, an LLM-based agent with general knowledge formulates these questions and synthesizes the resulting question-answer pairs into coherent captions. Beyond its role in leveraging the VQA model, the agent further guides its training to enhance its domain-specific capabilities. Experimental results on radiology report generation validate the effectiveness of the proposed framework, demonstrating significant improvements in the quality of generated reports. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Context-Aware Automated System for image Caption to ASL Translation for Improved Accessibility in Media applications

Context-Aware Automated System for Image Caption to ASL Tran...

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IEEE Southeastcon

作者： Oluwafolahanmi Aluko Vineetha Menon Dept. of Computer Science The University of Alabama in Huntsville Huntsville Alabama USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

With the emergence of Artificial Intelligence (AI) and the rapid growth of Generative AI, significant progress has been made in image captioning, delivering remarkably precise captions from static images and, more recently, videos [1]. Alt-Text has significantly progressed due to initiatives aimed at improving the context and sentiment recorded during automated captioning. This paper seeks to elaborate on a portion concerning Natural Language processing (NLP) and its significance in enhancing accessibility and context-awareness within the framework of the American Sign Language (ASL) application. This work presents a case study on significance of providing complete context regarding a caption or image to the end user, encompassing emotions and conscious attributes present within the phrase. This paper extensively elaborates on the accessibility functions presently involved in image-captioning. Is user-dependent (requiring users to supply a caption for the image) or standard machine-created alt-text sufficient for complete accessibility? Might incorporating a sign language, such as American Sign Language (ASL), be essential in providing users with more information than what the text of a caption communicates? The key question is: how can we enhance this image-caption translation to be more context-sensitive to convey the subtleties that could be crucial for user comprehension? The aim is to create a world where all voices resonate and every person feels accepted, and this paper represents an initial move towards an AI revolution that might be crucial in realizing that vision.

关键词： Context-aware services Sign language Sentiment analysis Translation Generative AI machine learning Media Standards Videos

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ElectroCom61: A multiclass dataset for detection of electronic components

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DATA IN BRIEF 2025年 59卷 111331页

作者： Sayeedi, Md. Faiyaz Abdullah Osmani, Anas Mohammad Ishfaqul Muktadir Rahman, Taimur Deepti, Jannatul Ferdous Rahman, Raiyan Islam, Salekul United Int Univ Dept Comp Sci & Engn Dhaka Bangladesh North South Univ Dept Elect & Comp Engn Dhaka Bangladesh

In contemporary industrial, robotics, and technical education settings, the efficient detection and sorting of electronic components play a pivotal role in advancing automation and increasing efficiency in these sectors. To address this need, we present "ElectroCom61," a comprehensive multi- class object detection dataset encompassing 61 commonly used electronic components. Our dataset, sourced from the electronic components collection at United International University (UIU) in Dhaka, Bangladesh, comprises 2121 meticulously annotated images. We ensured that these images reflect real-world conditions, incorporating varied lighting, backgrounds, distances, and camera angles to bolster the potential machine learning model's robustness. We also divided the dataset into training, validation, and test sets to facilitate deep learning model development. Additionally, we conducted minimal pre-processing to optimise model training and performance. "ElectroCom61" stands as a valuable asset for developing cutting-edge electronic component detection systems, with far-reaching applications in both education and industry. Its potential applications span from interactive educational tools to e-waste management systems and streamlined inventory management processes in electronic manufacturing and automation. The code for technical validation of this dataset is available on GitHub: https: //***/faiyazabdullah/ElectroCom61 (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)

关键词： Electronic component Deep learning image processing Object detection Computer vision E-waste management Industrial manufacturing Industrial automation

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GAIA: A Global, Multi-modal, Multi-scale vision-Language Dataset for Remote Sensing image Analysis

arXiv

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arXiv 2025年

作者： Zavras, Angelos Michail, Dimitrios Zhu, Xiao Xiang Demir, Begüm Papoutsis, Ioannis Orion Lab School of Rural Surveying and Geoinformatics Engineering National Technical University of Athens Athens15772 Greece Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens11810 Greece Department of Informatics and Telematics Harokopio University of Athens Athens17676 Greece Chair of Data Science in Earth Observation Technical University of Munich Munich80333 Germany Munich Center for Machine Learning Munich80333 Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany

The continuous operation of Earth-orbiting satellites generates vast and ever-growing archives of Remote Sensing (RS) images. Natural language presents an intuitive interface for accessing, querying, and interpreting the data from such archives. However, existing vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency results in poor performance on RS-specific tasks, as commonly used datasets often lack detailed, scientifically accurate textual descriptions and instead emphasize solely on attributes like date and location. To bridge this critical gap, we introduce GAIA, a novel dataset designed for multi-scale, multi-sensor, and multi-modal RS image analysis. GAIA comprises of 205,150 meticulously curated RS image-text pairs, representing a diverse range of RS modalities associated to different spatial resolutions. Unlike existing vision-language datasets in RS, GAIA specifically focuses on capturing a diverse range of RS applications, providing unique information about environmental changes, natural disasters, and various other dynamic phenomena. The dataset provides a spatially and temporally balanced distribution, spanning across the globe, covering the last 25 years with a balanced temporal distribution of observations. GAIA’s construction involved a two-stage process: (1) targeted web-scraping of images and accompanying text from reputable RS-related sources, and (2) generation of five high-quality, scientifically grounded synthetic captions for each image using carefully crafted prompts that leverage the advanced vision-language capabilities of GPT-4o. We also release an automated processing framework developed for this purpose, enabling the broader research community to generate captions for RS images using the web-crawled image-text data. Our extensive experiments, including fine-tuning of CLIP and BLIP2 models, demonstrate that GAIA significan

关键词： Visual languages

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