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arXiv 2024年

作者： Hong, Yige Xie, Qiaomin Chen, Yudong Wang, Weina Computer Science Department Carnegie Mellon University United States Department of Industrial and Systems Engineering University of Wisconsin-Madison United States Department of Computer Sciences University of Wisconsin-Madison United States

We consider the infinite-horizon average-reward restless bandit problem. We propose a novel two-set policy that maintains two dynamic subsets of arms: one subset of arms has a nearly optimal state distribution and takes actions according to an Optimal Local Control routine;the other subset of arms is driven towards the optimal state distribution and gradually merged into the first subset. We show that our two-set policy is asymptotically optimal with an O(exp(−CN)) optimality gap for an N-armed problem, under the mild assumptions of aperiodic-unichain, non-degeneracy, and local stability. Our policy is the first to achieve exponential asymptotic optimality under the above set of easy-to-verify assumptions, whereas prior work either requires a strong global attractor assumption or only achieves an O(1/√N) optimality gap. We further discuss obstacles in weakening the assumptions by demonstrating examples where exponential asymptotic optimality is not achievable when any of the three assumptions is violated. Notably, we prove a lower bound for a large class of locally unstable restless bandits, showing that local stability is particularly fundamental for exponential asymptotic optimality. Finally, we use simulations to demonstrate that the two-set policy outperforms previous policies on certain RB problems and performs competitively *** Codes 90C40 © 2024, CC BY.

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Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： Sv.Shri Bharathi Paidipati Lakshman Sai Paidipati Charan Ram Ayyappa Kakuru Jaya Praveen Reddy Gangireddy Sai Preetham Reddy Katuri Madhava Rajesh Department of Data Science and Business Systems SRMIST- KTR Chennai Tamil Nadu India Department of Computer Science and Engineering VIT Chennai Tamil Nadu India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices.

关键词： Support vector machines Machine learning algorithms Water quality Machine learning Prediction algorithms Real-time systems Classification algorithms

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Track Damage And Obstacle Detection Using Multi Sensor Fusion Framework

Track Damage And Obstacle Detection Using Multi Sensor Fusio...

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International Conference on Computing and Networking Technology (ICCNT)

作者： S.Padma Priya P S Abarna C.Manjula Devi S Shahul Hammed M.P. Karthikeyan K.Muthu Saravanan Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The integration of Internet of Things (IoT) technology into railway track monitoring systems marks a pivotal advancement in ensuring the safety and efficiency of railway operations. Through strategic deployment of sensors along the tracks, this innovative system offers real-time detection of obstacles, furnishing vital data for timely intervention to avert potential accidents. This capability not only elevates passenger and crew safety but also bolsters the reliability of railway transportation systems as a whole. The system constantly scans the track ahead of the train for any anomalies using sensors positioned toward the front of the vehicle. The system immediately notifies the application faults it detects, enabling prompt action. Additionally, it updates the status of an IoT platform and displays it for personnel on board via an LCD panel. An audio alarm is generated in the event of critical sensor readings to ensure prompt attention. By lowering potential risks and responding quickly to track problems, This IoT-based solution holds the promise of revolutionizing railway operations by furnishing augmented safety, efficiency, and reliability, thereby addressing the burgeoning demands of contemporary transportation infrastructure.

关键词： Fault diagnosis Machine learning algorithms Transportation Railway safety Sensor systems Real-time systems Sensors Safety Internet of Things Monitoring

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Detection of Various Dental Conditions on Dental Panoramic Radiography Using Faster R-CNN

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IEEE Access 2023年 11卷 127388-127401页

作者： Chen, Shih-Lun Chen, Tsung-Yi Mao, Yi-Cheng Lin, Szu-Yin Huang, Ya-Yun Chen, Chiung-An Lin, Yuan-Jin Chuang, Mian-Heng Abu, Patricia Angela R. Chung Yuan Christian University Department of Electronic Engineering Taoyuan320314 Taiwan Feng Chia University Department of Electronic Engineering Taichung407102 Taiwan Chang Gung Memorial Hospital Department of General Dentistry Taoyuan61363 Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan26047 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing Department of Program on Semiconductor Manufacturing Technology National Cheng Kung University Tainan717005 Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei243303 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon1108 Philippines

The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists' efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model's accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions. © 2013 IEEE.

关键词： Image segmentation

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Improving Breast Cancer Diagnosis through Multi-Class Segmentation using Attention UNet Model

Improving Breast Cancer Diagnosis through Multi-Class Segmen...

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International Conference on Advances in Information Technology (ICAIT)

作者： G S Pradeep Ghantasala Bhuvan Unhelkar Prasun Chakrabarti Pellakuri Vidyullatha Meghana Pyla Department Of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida USA Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

One of the prominent causes of death for women universally is breast cancer. The number of early deaths is reduced with early detection. Using an ultrasound scan, the data examine the ultrasound images of breast cancer. Three categories, including normal, benign, and malignant images, comprise the Breast Ultrasound Dataset. Incorporating machine learning with breast ultrasound images might yield significant outcomes for the identification, classification, and segmentation of breast cancer. Through a mediocre image size of 500 500 pixels, the collection comprises 750 images. Attention U-Net/U- Net is the best-known model for Multi-Class Segmentation to create an attention U-Net Model. The Model will consist of a Decoder Block Encoder Block and an Attention Gate. The attention gate layers have neurons with sizes of 256, 128, and 64, which typically correspond to the number of channels in the feature maps. The output layer in the U-Net model characteristically consists of a single convolutional layer through a kernel size of 1 1. The activation function used in the output layer was sigmoid; whichever is collective for binary classification tasks like image segmentation. The sigmoid activation function outputs values between 0 and 1, representing the possibility of each pixel fitting to the target class. The loss function stated is “binary_crossentropy,” which is appropriate in binary classification tasks. The optimizer specified has Adam, which is well-liked for neural network training because of its adaptable learning rate characteristics. In contrast to conventional gradient descent techniques, the Adam optimizer adjusts the learning rate during training, which might result in faster convergence and improved results with 98% accuracy at epoch 17 with a validation loss of 0.05.

关键词： Training Image segmentation Ultrasonic imaging Protocols Neurons Imaging Standardization Logic gates Breast cancer Planning

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Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning

arXiv

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arXiv 2024年

作者： Hassan, Sheikh Salman Park, Yu Min Tun, Yan Kyaw Saad, Walid Han, Zhu Hong, Choong Seon The Department of Computer Science and Engineering Kyung Hee University Yongin446-701 Korea Republic of The Department of Electronic Systems Aalborg University København SV2450 Denmark The Department of Electrical and Computer Engineering Virginia Tech VA24061 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association in NTNs. Traditional reinforcement learning (RL) methods for wireless network optimization often rely on manually designed reward functions, which can require extensive parameter tuning. To overcome these limitations, we employ inverse RL (IRL), specifically leveraging the GAIL framework, to automatically learn reward functions without manual design. We augment this framework with an asynchronous federated learning approach, enabling decentralized multi-satellite systems to collaboratively derive optimal policies. The proposed method aims to maximize spectrum efficiency (SE) while meeting minimum information rate requirements for RUEs. To address the non-convex, NP-hard nature of this problem, we combine the many-to-one matching theory with a multi-agent asynchronous federated IRL (MA-AFIRL) framework. This allows agents to learn through asynchronous environmental interactions, improving training efficiency and scalability. The expert policy is generated using the Whale optimization algorithm (WOA), providing data to train the automatic reward function within GAIL. Simulation results show that the proposed MA-AFIRL method outperforms traditional RL approaches, achieving a 14.6% improvement in convergence and reward value. The novel GAIL-driven policy learning establishes a novel benchmark for 6G NTN optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite communication systems

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PResCon: Physiological Response Augmented Continuous Emotion Annotation Tool

PResCon: Physiological Response Augmented Continuous Emotion...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Swarnali Banik Akhilesh Adithya Bivas Mitra Sougata Sen Snehanshu Saha Surjya Ghosh Department of Computer Science and Information Systems BITS Pilani K K Birla Goa India Amazon India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India APPCAIR BITS Pilani K K Birla Goa India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

We present an annotation tool designed to collect fine-grain continuous emotion ground truth labels and physiological responses as users watch different videos. In the existing literature, continuous emotion annotations are typically collected as self-reports based on the 2D Circumplex model through auxiliary devices (a joystick or similar tools) during video consumption. However, the users face challenges as they need to use an auxiliary device and rely on the Circumplex model’s valence and arousal axis without clear indication of distinct emotion labels. Therefore, to overcome these challenges, we introduce PResCon, a tool that allows to collect emotion annotations using keyboard arrow keys only (without using any auxiliary device) and guiding users towards the discrete emotions (e.g., amused, stressed, bored, relaxed) by embedding them on the Circumplex plane. The tool encompasses two major components - (a) a web interface that displays various stimuli videos and provision of collecting emotion labels (valence and arousal) by moving the keyboard arrow keys and (b) a sensor setup (based on Arduino) that allows to collect the physiological responses (heart rate and galvanic skin response) passively during video consumption. In summary, the PResCon tool facilitates research in the domain of affective computing by allowing to collect fine-grain continuous emotion annotations and the physiological responses in real-time during video consumption.

关键词： Heart rate Affective computing Annotations Communication systems Keyboards Physiology Skin Real-time systems Faces Videos

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Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits

arXiv

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arXiv 2024年

We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward the optimal distribution. We show that our policies are asymptotically optimal with an O(1/√N) optimality gap for an N-armed problem, assuming only a unichain and aperiodicity assumption. Our approach departs from most existing work that focuses on index or priority policies, which rely on the Global Attractor Property (GAP) to guarantee convergence to the optimum, or a recently developed simulation-based policy, which requires a Synchronization Assumption (SA).MSC Codes 90C40 © 2024, CC BY.

关键词： Optimal systems

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Multi-Task Deep Network for Semantic Segmentation of Building in Very High Resolution Imagery

Multi-Task Deep Network for Semantic Segmentation of Buildin...

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2021 International Conference of Technology, science and Administration, ICTSA 2021

作者： Moghalles, Khaled Li, Heng-Chao Al-Huda, Zaid Hezzam, Essa Abdullah Southwest Jiaotong University School of Computer Science and Technology Chengdu China Taibah University College of Computer Science and Engineering Department of Information Systems Medina Saudi Arabia

ISBN: (纸本)9781665428620

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. The automatic generation of buildings from satellite images presents a considerable challenge due to the complexity of building shapes. Compared with the traditional building extraction approaches, deep learning networks have shown outstanding performance in this task by using both high-level and low-level feature maps. Recently, many deep networks derived from U-Net has been extensively used in various buildings segmentation tasks. However, in most of the cases, U-net produce coarse and non-smooth segmentations with lots of discontinuities. To improve and refine the performance of U-Net network, we propose a deep end-to-end network, which use a single encoder and two parallel decoders along with performing the mask predictions also perform distance map estimation. The distance map aid in ensuring smoothness in the segmentation predictions. We also propose a new joint loss function for the proposed architecture. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only (RGB) images demonstrated that the proposed framework can significantly improve the quality of building segmentation. © 2021 IEEE.

关键词： Feature extraction

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