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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Nobanul Hasan Md Jahid Alam Riad Stabak Das Prosenjit Roy Mahfuzur Rahman Shuvo Mafizur Rahman Department of Management Information System International American University Los Angeles California Master Of Science In Information Technology (MSIT) Washington University of Science and Technology Vienna VA United States Department of Electrical and Computer Engineering Prairie View A & M University Texas USA Department of Computer Science Prairie View A & M University Texas USA MBA MIS International American University Los Angeles California

The expeditious advancement of deep learning in the field of biomedical imaging has facilitated the emergence of innovative diagnostic instruments, hence revolutionising the domain of ophthalmology. This study focuses on exploring the capabilities of the U-Net architecture, a convolutional neural network that is widely recognized for its proficiency in performing image segmentation tasks. By employing retinal pictures, we have successfully trained the U-Net model to accurately outline complex structures, resulting in a notable F1 score of roughly 94.33% and a Jaccard Score of 89.56%. The highest level of precision was noted at approximately epoch 250, serving as evidence of the model’s efficient convergence and capacity. Nevertheless, succeeding periods revealed variations in the loss of validation, highlighting the difficulties in generalizing the model. The results of our study shed light on the diagnostic capabilities of U-Net, highlighting its ability to accurately identify subtle features in retinal pictures. This has the potential to significantly transform the field of ophthalmology by enabling early identification and intervention in many diseases. With the increasing adoption of artificial intelligence (AI) in the healthcare industry, these models hold significant potential as fundamental components for the development of sophisticated, effective, and dependable diagnostic systems. The study not only highlights the promise of deep learning in the field of medical imaging but also lays the foundation for future efforts focused on improving and using artificial intelligence (AI) capabilities in actual clinical environments.

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Light Fidelity and Internet of Things for Medical Applications

Light Fidelity and Internet of Things for Medical Applicatio...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Puput Dani Prasetyo Adi Ros Sariningrum Rizky Rahmatullah Hessy Novita Tiar Lina Situngkir Priyo Hartono Adji Angela Mariana Lusiastuti Antonia Rahayu Rosaria Wibowo Yudi Prastiyono Tjahjo Adiprabowo Muhammad Yassir Fairuz Iqbal Maulana Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia Veterinary Research Center National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Management Faculty of Economics Singaperbangsa University Karawang Indonesia Research Center for Manuscripts Literature and Oral Traditions National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Electrical Engineering Faculty of Engineering Langlangbuana University Bandung Indonesia Department of System and Information System Institut Teknologi dan Bisnis Nobel Makassar Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This study focuses on transmitting data utilizing medical sensors that are vital for patients in hospitals and other settings. In this situation, Light Fidelity refers to transmitting medical sensor data or other data comprising the Transmitter (Tx) and the Receiver (Rx). If the Light Fidelity Principle is used, the Transmitter transmits LEDs or light to the Receiver. At the same time, the Receiver contains a receiver or light detection component known as the Light Dependent Resistor (LDR); using programming and this basic working principle, data can be sent from the LED Transmitter to the LDR Receiver using C++ Programming and its Library to send Medical sensor data. Sensors can include pulse sensors, like in prior research, and additional sensors such as SPO2 or blood pressure sensors. For medical data, a modest bit rate is sufficient, but LiFi applications can be created in such a way that they can be hybridized with WiFi and other RF devices such as LoRa and the Internet of Things.

关键词： Resistors Radio frequency Transmitters Receivers Programming Light fidelity Light emitting diodes Sensors Internet of Things Wireless fidelity

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Efficient Resource Allocation in Computing Power Networks Considering Similar Task Merging: A Lyapunov Optimization-Based DRL Approach

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IEEE Internet of Things Journal 2025年

作者： Jia, Zhonghai Xue, Junxiao Shi, Lei Li, Jie He, Mengyang Zhejiang Lab Research Center for Space Computing System Hangzhou310000 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China SongShan Laboratory Zhengzhou450040 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 Shanghai China

The cloud-edge-terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This paper proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN's intricate features. Lyapunov optimization, integrated into a multi-threaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the trade-off between resource utilization efficiency and system stability follows the relationship O(1/V), O(V). Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies. © 2014 IEEE.

关键词： Computing power

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Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking

arXiv

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arXiv 2024年

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Hu, Ye Liu, Yuchen Yin, Changchuan The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Development of Object Detection system based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

A Development of Object Detection System based on Deep Learn...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Sonsilphong, Suphachoke Sonsilphong, Amornthep Hormdee, Daranee Khampitak, Kovit Khon Kaen University Data Science Health Informatics Program Academic Affairs Faculty of Medicine Khon Kaen Thailand Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Embedded System RD Group Department of Computer Engineering Faculty of Engineering Khon Kean Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two main phases, the training phase, and the real-time operating phase. In the training phase, a modified YOLOv4 algorithm is proposed to be adopted within the Darknet framework for training the weights of detection models. The best detection model is then selected to be assigned to the object detector in the real-time operating phase. The validation result of the detection model across the experiment datasets is 90% accuracy on average during the training phase. To validate the viability of real-time object detection, the proposed system was implemented in two test-cases of gynecologic surgery with the soft-body cadavers. © 2022 IEEE.

关键词： Laparoscopy

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Automated Robot (Car) using Artificial Intelligence 12

Automated Robot (Car) using Artificial Intelligence

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12th IEEE Integrated STEM Education Conference, ISEC 2022

作者： Mishra, Satyam Minh, Cao Sy Chuc, Hoang Thi Long, Tran Viet Nguyen, Thanh Tung Vietnam National University International School Informatics and Computer Engineering Hanoi Viet Nam Vietnam National University International School Management and Information System Hanoi Viet Nam Vietnam National University International School Department of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9781665484299

This paper discusses about researching an automated robot car using artificial intelligence;training its neural network using AlexNet model, using YOLO (you only look once algorithm) for object detection phase and for practical deduction and judging component we have used Open Neural Network Exchange (ONNX) format. Our Robot Car model is agile and cost efficient. It detects objects efficiently in front of it and movement of it is smooth. It moves through sensors in motors which makes it different than other models in the world. © 2022 IEEE.

关键词： Stochastic systems

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Fairness-aware Multiobjective Evolutionary Learning

arXiv

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arXiv 2024年

作者： Zhang, Qingquan Liu, Jialin Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Data Science Lingnan University Hong Kong

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisation objectives of MOEL throughout model training. However, the determination of a representative measure set relies on dataset, prior knowledge and requires substantial computational costs. What's more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this paper proposes to dynamically and adaptively determine a representative measure set online during model training. The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time. Extensive experimental results on 12 well-known benchmark datasets demonstrate that our proposed framework achieves outstanding performance compared to state-of-the-art approaches for mitigating unfairness in terms of accuracy as well as 25 fairness measures although only a few of them were dynamically selected and used as optimisation objectives. The results indicate the importance of setting optimisation objectives dynamically during training. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Development of Automatic Voltage Stabilization system for Substation Using Deep Learning 1

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5th International Conference on Innovative Computing, IC 2022

作者： Moon, Jiyong Son, Minyeong Oh, Byeongchan Jin, Jeongpil Kim, Kwangil Shin, Younsoon Department of Business Administration Dongguk University Seoul Korea Republic of Department of Medical Biotechnology Dongguk University Seoul Korea Republic of Department of Statistics Dongguk University Seoul Korea Republic of Department of Industrial System Engineering Dongguk University Seoul Korea Republic of Dongguk Institute of Convergence Education Dongguk University Seoul Korea Republic of Department of Computer Science and Engineering Dongguk University Seoul Korea Republic of

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

The voltage adjustment process is currently done manually by resident staff. As such, voltage regulation based on human judgement not only entails great uncertainty about voltage stabilization but also makes efficient operation in consideration of the economic feasibility of power facilities impossible. Therefore, this paper proposes an automatic voltage stabilization system that can automatically perform voltage adjustment. The proposed system predicts the required input capacity, and then predicts the optimal adjustment method considering the efficiency of power facility operation by adding an optimization process. In addition, through the development of UI, it is possible to visualize the operation of the algorithm and effectively communicate the prediction of the model to the user. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stabilization

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A Blockchain Approach to IoT Security and Reliability Analysis 3

A Blockchain Approach to IoT Security and Reliability Analys...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Jain, Nilesh Wahid, Nazia Al-Farhani, Liwa H. Manideep, A. Sai Bhardwaj, Vikash Sangeeth Kumar, M. Mandsaur University Department of Computer Applications Madhya Pradesh Mandsaur India Vishwakarma University Faculty of Science & Technology Department of Mathematics & Statistics Maharashtra Pune India RDUN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Vignan's Foundation for Science Technology and Research Department of Management Studies Andhra Pradesh Guntur India Dewan VS Institute of Engineering & Technology Uttar Pradesh Meerut India Sona College of Technology Mechanical Engineering Department Tamilnadu Salem India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

An investigation area of interest including the use of blockchain technology to address Internet of Things security concerns (IoT). Despite the fact that numerous relevant ideas have been offered, there are few published works that provide hypothetical and information backing. The examination of model building and execution assessment is the focus of this research. First, an IoT security paradigm is outlined, using blockchain and the Inter-Planetary-File-system (IPFS) in a view. Many of the security risks associated with traditional IoT designs can be avoided with this paradigm, and framework execution is entirely focused on distributed large limit stockpiling, simultaneity, and query. In addition, the suggested model's display is evaluated using normal idleness and throughput, which are important for further investigation and enhancement of this course. The blockchain-based security model's feasibility has been demonstrated via analysis and experiments. © 2022 IEEE.

关键词： Blockchain

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