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共 636 条记录，以下是81-90 订阅

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Rohit Dahatonde, Shubham Acharjya, Srimanta Kumar Verma, Sourabh Singh Dhaka, Vijaypal Singh Rawat, Ramesh Singh School Of Computing & Intelligent Systems Manipal University Jaipur India C. V Raman Global University Department of Computer Science and engineering Bhubaneswar India Graphic Era deemed to be University Dept. of computer Science and Engg Uttrakhand Deharadun India

ISBN: (纸本)9798350379716

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency. © 2024 IEEE.

关键词： Satellite imagery

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Non-zero Coefficients Removing Method to Improve the Ising Machine Solving Performance

Non-zero Coefficients Removing Method to Improve the Ising M...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Kinya Iwata Masashi Tawada Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University Green Computing Systems Research Organization Waseda University

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The performance of an Ising machine in solving combinatorial optimization problems is significantly influenced by the energy landscape of an input quadratic unconstrained binary optimization (QUBO). Previous studies have shown that the number of non-zero terms in QUBO can control the energy landscape. In this paper, we propose a QUBO conversion method, which efficiently removes non-zero coefficient terms in QUBO to improve the solving performance of Ising machines. Our pro-posed method uses the removal ratio parameter, which defines the amount of removed non-zero terms, and efficiently removes the non-zero coefficient terms in QUBO. We confirm the effectiveness of the proposed method compared to the conventional method using the capacitated vehicle routing problem (CVRP) on an Ising machine. Experimental results demonstrate that our proposed method improves the path length in CVRP up to 17% compared to the conventional method.

关键词： Quantum computing Vehicle routing Optimization

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Harnessing the Power of Artificial Intelligence in Materials Science: An Overview

Harnessing the Power of Artificial Intelligence in Materials...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adedotun Adetunla Esther Akinlabi Tien Chien Jen Samuel-Soma Ajibade Dept. of Mechanical Engineering Science University of Johannesburg South Africa Dept. of Mechanical & Construction Engineering Northumbria University Newcastle United Kingdom Dept. of Computing & Information Systems School of Engineering & Technology Sunway University Malaysia

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The integration of artificial intelligence (AI) into the realm of material science has ushered in a new era, by changing the process of material discovery and design. Leveraging advanced computational methods, machine learning algorithms, and predictive modeling, AI accelerates the identification of novel materials with tailored properties. From quantum simulations to high-throughput experimentation, AI-driven techniques enable rapid screening and prediction of material behaviors, significantly reducing the time and resources traditionally required for innovation. This synergy has paved the way for the creation of smart and adaptive materials, responsive to external stimuli and tailored for specific applications across industries. The marriage of AI and material science extends beyond discovery, encompassing efficient process optimization, manufacturing improvements, and the management of vast datasets through materials informatics. Challenges, including ethical considerations, data privacy, and responsible AI practices, must be navigated for sustainable integration. Looking forward, the collaborative potential of AI and material science promises continuous advancements. Ongoing research in machine learning, deep learning, and materials informatics anticipates breakthroughs in material applications, pushing the boundaries of innovation and sustainability. The union of AI and material science not only reshapes the landscape of scientific discovery but also holds the key to unlocking unprecedented opportunities for intelligent materials that will define the technological future.

关键词： Materials science and technology Technological innovation Ethics Collaboration Medical services Predictive models Artificial intelligence

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Analysing the Roles of Robotics in Manufacturing Organizations in the Era of Industry 4.0

Analysing the Roles of Robotics in Manufacturing Organizatio...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The era of Industry 4.0 has ushered in a new approach in manufacturing, leveraging advanced technologies to transform traditional production methods. Robotics is the science of using robots to improve manufacturing processes with automation, flexibility, and efficiency. This article aims to comprehensively examine the role of robotics in manufacturing within the context of Industry 4.0. It seeks to explore the fundamental principles underlying robotics, their integration with Industry 4.0 technologies, applications across diverse manufacturing sectors, challenges faced in implementation, and the future directions of robotics in shaping the manufacturing landscape.

关键词： Industries Ethics Technological innovation Service robots Collaboration Transforms Market research

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Saliency-based Explainable Artificial Intelligence (XAI) for Interpreting U-Net Semantic Segmentation of Synthetic Liquid Spray Images

Saliency-based Explainable Artificial Intelligence (XAI) for...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chin, Teck Wai Lau, Sian Lun Wong, Richard T.K. Bravo, Luis Koneru, Rahul Babu Khare, Prashant Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Malaysia Dept. of Engineering School of Engineering and Technology Sunway University Bandar Sunway Malaysia DEVCOM Army Research Laboratory Army Research Directorate Aberdeen Proving GroundMD21005 United States Department of Aerospace Engineering University of Cincinnati CincinnatiOH45221-0070 United States Department of Aerospace Engineering University of Maryland College ParkMD20742 United States

ISBN: (纸本)9798331530303

Deep learning models, particularly U-Net, have been widely used for image segmentation tasks in various domains such as medical, biological, autonomous driving, and industrial applications. However, the black-box nature of these models raises public concerns about the lack of transparency and interpretability of their decision-making processes. Unlike typical static objects, the segmentation of dynamic objects from synthetic fluid datasets are more complicated as the properties like position and shape change over time. This paper investigates these issues by exploring the uses of saliency-based Explainable Artificial Intelligence (XAI) techniques such as Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++ and Segmentation Grad-CAM (Seg-Grad-CAM) to generate heatmap visualizations for providing insights into the prediction of U-net semantic segmentation. Using a dataset of synthetic liquid spray images, we systematically apply these techniques to visualize the model’s attention and highlights the properties and regions of the image that are influential in the model’s predictions for different classes such as lobe, ligaments and droplets. Our results demonstrate that while all techniques provide interpretable visualizations, Seg-Grad-CAM and Grad-CAM++ offer better localization of salient features, providing more precise explanations for U-Net’s segmentation decisions. The comparison of the results using quantitative analysis such as Pointing Game Accuracy and Mean Localization Error also indicated that Seg-Grad-CAM and Grad-CAM++ are more effective for interpreting complex segmentation models in liquid spray analysis. ©2024 IEEE.

关键词： Semantic Segmentation

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Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study

Synthetic Time Series Data Generation for Healthcare Applica...

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Annual Conference on Information Sciences and systems (CISS)

作者： Ainaz Jamshidi Muhammad Arif Sabir Ali Kalhoro Alexander Gelbukh Dept. of Information Systems University of Maryland Baltimore County USA Dept. of Information Systems Colorado State University USA Dept. of Electrical Engineering North Dakota State University USA Dept. of Computing Research Center Instituto Politécnico Nacional Mexico

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories — WaveNet, DoppelGANger, and DiffWave — to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs.

关键词： Heart Measurement Time series analysis Pipelines Medical services Data collection Data models Robustness Forecasting Phonocardiography

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Exploring multi-food detection using deep learning-based algorithms

Exploring multi-food detection using deep learning-based alg...

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Pattern Recognition systems (ICPRS), International Conference on

作者： Roberto Morales Juan Quispe Eduardo Aguilar Dept. of Computing and Systems Engineering Universidad Católica del Norte Antofagasta Chile

People are becoming increasingly concerned about their diet, whether for disease prevention, medical treatment or other purposes. In meals served in restaurants, schools or public canteens, it is not easy to identify the ingredients and/or the nutritional information they contain. Currently, technological solutions based on deep learning models have facilitated the recording and tracking of food consumed based on the recognition of the main dish present in an image. Considering that sometimes there may be multiple foods served on the same plate, food analysis should be treated as a multi-class object detection problem. EfficientDet and YOLOv5 are object detection algorithms that have demonstrated high mAP and real-time performance on general domain data. However, these models have not been evaluated and compared on public food datasets. Unlike general domain objects, foods have more challenging features inherent in their nature that increase the complexity of detection. In this work, we performed a performance evaluation of Efficient-Det and YOLOv5 on three public food datasets: UNIMIB2016, UECFood256 and ChileanFood64. From the results obtained, it can be seen that YOLOv5 provides a significant difference in terms of both mAP and response time compared to EfficientDet in all datasets. Furthermore, YOLOv5 outperforms the state-of-the-art on UECFood256, achieving an improvement of more than 4% in terms of mAP@.50 over the best reported.

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Real-Time Person Re-Identification to Improve Human-Robot Interaction

Real-Time Person Re-Identification to Improve Human-Robot In...

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2024 IEEE International Conference on Real-Time computing and Robotics, RCAR 2024

作者： Carlsen, Per Antoine Taylor, Angelique M. Chan, Darren M. Uddin, Md Zia Riek, Laurel Torresen, Jim Logistics Oda Oslo Norway Cornell University College of Computing and Information Science Cornell Tech New York United States HRL Laboratories LLC MalibuCA United States Sustainable Communication Technologies SINTEF Digital Oslo Norway University of California Dept. of Computer Science and Engineering San Diego United States University of Oslo Dept. of Informatics and RITMO Oslo Norway

ISBN: (纸本)9798350372601

Mobile robots deployed in the homes of disabled people and older adults can play a vital role as companions and aid in activities of daily living, enhancing people's quality of life. For these robots to effectively assist, it can be helpful if they possess social and interactive skills to facilitate natural and personalized interactions. Personalization, including using proper names and remembering preferences, is crucial for building strong social connections and fostering trust in human-robot interaction. However, achieving personalized interactions necessitates the robot's ability to autonomously recognize and re-identify individuals without relying on specific constraints or prior knowledge. This work introduces two lightweight Dual convolutional neural networks, LuNet Light and LuNet Lightest, designed for person re-identification in a robotic context without the limitations of existing systems. We introduce a lightweight architecture, LuNet Lightest which achieves near state-of-the-art performance on the MARS dataset. It is well-suited for real-time applications on low-cost, hardware-constrained robots. This person-identification system enables assistive mobile robots to accurately identify users, fostering trust and facilitating natural, personalized interactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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