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Bibliometric Analysis of Image Segmentation with Deep Learning: An Analytical Study 1

引用

International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Windarto, Agus Perdana Yuhandri, Y. Bukhori, Saiful Information Technology Study Program Universitas Putra Indonesia YPTK Padang Indonesia STIKOM Tunas Bangsa Pematangsiantar Indonesia Universitas Putra Indonesia YPTK Padang Indonesia Computer Science Department University of Jember Jember Indonesia

ISBN: (数字)9789819984985

ISBN: (纸本)9789819984978

This study presents a comprehensive bibliometric analysis of research pertaining to the utilization of deep learning techniques for image segmentation using CNN algorithms. A dataset comprising 1078 publications from journals and conference proceedings between 2018 and 2022 was examined through keyword search, co-occurrence network analysis, and keyphrase analysis. The study offers valuable insights into the present research landscape and identifies prominent areas of investigation, encompassing deep learning's application in medical imaging, disease detection, and object detection. Our analysis reveals China as a leading contributor to deep learning research for image segmentation, with 279 publications and 4,380 citations in Scopus. Advances in Biomedical Optics and Imaging—Proceedings of SPIE are pinpointed as the most productive source for deep learning research in image segmentation, with a specific emphasis on biomedical optics and imaging. The co-occurrence network analysis highlights the red cluster primarily focusing on methods and algorithms associated with deep learning in image segmentation. In contrast, the blue cluster demonstrates the application of these methods and algorithms to other objects or methods, with the "human" node standing out in terms of frequency and centrality. Our keyphrase analysis reveals the growing trend of Cellular Artificial Neural Networks over the past five years, indicating a shift in research focus toward this domain. Overall, this study's findings demonstrate the potential of deep learning to deliver precise and efficient segmentation of medical images, thereby enhancing clinical outcomes and patient care. Furthermore, our study contributes to an enhanced understanding of the current research landscape and identifies avenues for future exploration in deep learning techniques for image segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging 26

ProMIL: Probabilistic Multiple Instance Learning for Medical...

引用

26th European Conference on Artificial Intelligence, ECAI 2023

作者： Struski, Lukasz Rymarczyk, Dawid Lewicki, Arkadiusz Sabiniewicz, Robert Tabor, Jacek Zieliński, Bartosz Faculty of Mathematics and Computer Science Jagiellonian University Poland Faculty of Applied Computer Science University of Information Technology and Management Rzeszow Poland Department of Pediatric Cardiology and Congenital Heart Diseases Medical University of Gdansk Poland Ues Ltd. Doctoral School of Exact and Natural Sciences Jagiellonian University Poland

ISBN: (纸本)9781643684369

Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available. © 2023 The Authors.

关键词： Medical applications

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Mobile Application for Haircut Art Community 2

Mobile Application for Haircut Art Community

引用

2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Fahmi, Hilal Nugroho, Muhammad Ivan Setiawan, Kristian Daniel Gary, Vittorio Moedjiono, Sardjoeni Warnars, Harco Leslie Hendric Spits School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Budi Luhur University Faculty of Information Technology Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

The haircut art community is a community that is interested in the art of hair or hair-related issues and information. This haircut art community has several members, but they need the application of technology to support their hobby as a community. In this mobile application, a system where users can make transactions connected to the haircut community has been proposed. Users can make online appointments with barbers' haircuts, make online payments, engage with forums, and get information about the barbershop community. In addition, this mobile app will allow people in the community to sell and buy anything related to haircuts. In addition, it is hoped that it can be used as an income opportunity for people who have expertise in the art of barbering. This application is implemented using visual studio and MySQL database for the database. This application is modeled using use case diagrams to describe its business processes and class diagrams to describe the database design. © 2022 IEEE.

关键词： Database systems

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

引用

21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

引用

2024 International Conference on Engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach

Initial Study of Oil Palm Crop Condition Detection Through D...

引用

International Conference on Informatics, Multimedia, Cyber and information System (ICIMCIS)

作者： Winata Nugraha Ermatita Deris Stiawan Faculty of Computer Science Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Information System Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2]

关键词： YOLO Productivity Technological innovation Oils Multimedia systems Plantations Feature extraction Informatics Monitoring Drones

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Spam Text Detection using Machine Learning Model

Spam Text Detection using Machine Learning Model

引用

2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Ketcham, Mahasak Ganokratanaa, Thittaporn Pramkeaw, Patiyuth Chumuang, Narumol King Mongkut's University of Technology North Bangkok Dept. of Information Technology Management Bangkok Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand Dept. of Digital Media Technology Thailand

ISBN: (纸本)9798350326413

This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97 © 2023 IEEE.

关键词： Machine learning

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Enhancing Warehouse Inventory Management through IoT Tools for Monitoring Stock Items

Enhancing Warehouse Inventory Management through IoT Tools f...

引用

technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Sunaryo Winardi Ng Poi Wong Arifin Apriyanto Halim Sunario Megawan Undergraduate Program of Computer Science Universitas Mikroskil Medan Indonesia Undergraduate Program of Information Technology Universitas Mikroskil Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

The increasing demand for personalized products and a global presence complicates inventory management, requiring accurate and timely stock information. To address this, businesses must leverage Internet of Things (IoT) solutions, which connect various electronic devices to the internet via advancements in cloud technology, sensor networks, and wireless communication. This research proposes a tailored IoT architecture for warehouse inventory checking processes, focusing on real-time tracking and visibility, cost reduction, enhanced security, improved inventory management, and increased supply chain speed. Implementing an IoT-based Warehouse Inventory Management system, designed with four layers (Physical, Gateway, Middleware, and Application), significantly improves operational efficiency and inventory tracking. While human involvement remains necessary, IoT solutions optimize inventory management and enhance overall efficiency.

关键词： Wireless communication Technological innovation Supply chains Inventory management Logic gates Real-time systems Internet of Things Security Middleware Monitoring

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Automatic computer Shutdown with Image Processing via Webcam to Save Energy 2

Automatic Computer Shutdown with Image Processing via Webcam...

引用

2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Chumuang, Narumol Ketcham, Mahasak Ganokratanaa, Thittaporn Pramkeaw, Patiyuth Yimyam, Worawut Chuamoo Dep. of Digital Media Technology Thailand King Mongkut's University of Technology North Bangkok Dep. of Information Technology Management Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand Phetchaburi Rajabhat University Dep. of Business Information Management Thailand

ISBN: (纸本)9798350336573

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. The researcher has developed an automatic computer shutdown system. with image processing via a webcam By using image processing techniques (Image Processing) applied to a webcam. By using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the Face Detection technique using the algorithm of Haar-like Features to detect human faces and ROI. Define a specific area of interest The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing This research can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well. © 2023 IEEE.

关键词： Face recognition

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ArabicQuest: Enhancing Arabic Visual Question Answering with LLM Fine-Tuning

ArabicQuest: Enhancing Arabic Visual Question Answering with...

引用

Intelligent Methods, Systems, and Applications (IMSA)

作者： Asmaa ElMaghraby Samaa Maged Mohamed Essawey Rawan ElFaramawy Esraa Negm Ghada Khoriba Artificial Intelligence Program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

In an attempt to bridge the semantic gap between language understanding and visuals, Visual Question Answering (VQA) offers a challenging intersection of computer vision and natural language processing. Large Language Models (LLMs) have shown remarkable ability in natural language understanding; however, their use in VQA, particularly for Arabic, is still largely unexplored. This study aims to bridge this gap by examining how well LLMs can improve VQA models. We use state-of-the-art AI algorithms on datasets from multiple fields, including electric devices, Visual Genome, RSVQA, and ChartsQA. We introduce ArabicQuest, a Text Question Answering (TQA) tool that combines Arabic inquiries with visual data. We assess the performance of LLMs across various question types and image settings and find that fine-tuning me thods su ch as LLaMA-2, BLIP-2, and Idefics-9B-Instruct models provide encouraging results, although challenges still arise in counting and comparison tasks. Our findings demonstrate the importance of advancing VQA further—especially for Arabic—to enhance accessibility and user satisfaction in a variety of applications.

关键词： Bridges Performance evaluation Visualization Large language models Semantics Refining Linguistics

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