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Modular Deep Reinforcement Learning: Enhancing Flexibility through Agent Splitting

Modular Deep Reinforcement Learning: Enhancing Flexibility t...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Tian, Chong Rawat, Danda B. Howard University Dept. of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

Deep reinforcement learning (DRL) has shown remarkable success in tackling complex tasks by learning representations directly from raw data. However, as DRL agents become increasingly sophisticated, they often face challenges in adapting to new tasks or environments due to their monolithic structure. In this paper, we prove that by decomposing the agent into modular components, we enable more efficient adaptation as well as more efficient than that of the singular DRL agent on tackling complex tasks. Overall, our work contributes to advancing the field of DRL by introducing a flexible and scalable framework for agent decomposition. By enabling the creation of modular agents, we empower DRL systems to efficiently adapt to changing environments, learn new tasks with minimal intervention, and achieve higher levels of performance and versatility. © 2024 IEEE.

关键词： Deep reinforcement learning

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Flex Sensor Data Analysis for Hand Rehabilitation using Wearable Glove 3

Flex Sensor Data Analysis for Hand Rehabilitation using Wear...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Thangam, S. Reddy, Anusha C. Varun, Ginnaram Hemasri, M. Virupaxappa Kumari, J. Jesy Janet Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

Every day we encounter many people with unexpected situations where they suffer from many neuropathological diseases that cause a severe impact on the daily lives of individuals, especially about hand movements, thereby directly affecting the quality of their lives. Traditional methods often lack insights into personalized progress and optimal medication outcomes, which take longer to recover. We have designed an approach based on a novel concept using wearable technology to monitor and assess finger movements. The proposed solution of wearable device integrated flex sensor technology to enhance the rehabilitation process by providing real-time insights by the input of the flex sensors captured by the finger movements of the patients. By sensor data analysis, people get a chance to know their progress and the professionals gain accurate tracking of the progress so that they enhance identifying the areas of improvement and customize the treatment. This innovative approach to designing a wearable device serves as a helping aid to improve patient outcomes by categorizing the stages of improvement into Normal, Mild, and Severe Impairment for a better understanding and helps improve the rehabilitation experience. © 2024 IEEE.

关键词： Personalized medicine

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Reverse Video Search Engine Using Audio Fingerprint & Convolutional Neural Networks 22

Reverse Video Search Engine Using Audio Fingerprint & Convol...

引用

22nd OITS International Conference on Information Technology, OCIT 2024

作者： Traore, Adama Seydou Dept. of Computer science & engineering SRM University AP Andhra Pradesh Amaravati India

ISBN: (纸本)9798331510404

Nowadays, at the time of social networks such as Facebook, Instagram, TikTok, Twitter (X), and so on, people are uploading hundreds of thousands of data, including images and videos, every single day. This volume creates challenges in identifying the original sources of these videos. For example, users might see a scene on the networks but struggle to find its original video (movie, series, news, etc.). A proposed solution is to develop a tool that can trace the original video details based on a given scene. This system would first receive the original video via a database, then extract audio and frames from the video file, using the frames to train a convolutional neural network (CNN) to predict the original video. Simultaneously, audio processing would generate a fingerprint linked to the original video. By analyzing a sample video, the tool could find a match and retrieve the original video with its details (title, authors, description,..) from the database. © 2024 IEEE.

关键词： Content based retrieval

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Secure Fuzzy Keyword Search in Cloud Computing using Levenshtein Distance and Cryptographic Indexing 5

Secure Fuzzy Keyword Search in Cloud Computing using Levensh...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Jahnavi, M. Haritha, K. Gowtham, R.S.V. Rajgopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

Growing usage of the cloud for computing, the necessity for safe and security storage and retrieval of information procedures has become important. This study provides a novel way to tackle the difficulty of searching across encrypted information in the cloud with supporting an inherent fuzzy in queries made by users. The purpose is to allow users to execute searches for keywords on their secure data saved in the cloud while maintaining the secrecy of the data. The approach we propose uses cryptographic methods, notably to secure the information before transferring it to the service. To support fuzzy searches for keywords, we apply approaches such as secure indexing building and similarity rating. This enables users to receive useful data regardless of whether their search request includes errors in spelling, typos, or small changes in the terms. The system structure makes sure a cloud server stays clueless to the details of the encrypted information and the queries used for searching. In addition, we incorporate safety measures to defend against illegal access and to preserve integrity of data throughout the searching process. Thorough tests and experiments, we show the speed as well as effectiveness of our strategy in the areas of search performance, processing overhead, and speed of response, not just enhancing the security of data provided by users but additionally provides an efficient method for safe and flexible retrieval of data in cloud-based systems of storage. © 2024 IEEE.

关键词： Cloud security

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rakshitha, Sai Nithyashree, H.V. Rakshitha, S.L. Shravan, Motamarri Sai Uttej, K Sai Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content. © 2024 IEEE.

关键词： Convolutional neural networks

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SafeSync: A Comprehensive Safety and Monitoring System for Two-Wheeler Users Using Machine Learning, ESP32, GPS, and GSM 6

SafeSync: A Comprehensive Safety and Monitoring System for T...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sunil, K.S. Adithyan, P. Krishnan, Anjana Hashim Sharaf, C.P. Paul, Jerin John, Salvi Hanna Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350381689

Our work, 'SafeSync' proposes a complete safety and monitoring system aimed to address the considerable hazards encountered by two-wheeler users, such as accidents and rash driving scenarios. Unlike other systems, SafeSync combines powerful technology such as ESP32, GPS, and GSM with an innovative application of machine learning methods. This system specializes in detecting and alerting to accidents, tracking vehicles in real time, and identifying risky driving behaviour. SafeSync goes beyond traditional safety measures by not only identifying ongoing accidents but also forecasting potential events. This predictive feature enables the system to proactively inform users or authorities in advance, assisting in the prevention of accidents. The Flutter-built dedicated mobile app serves as a user-friendly interface, offering real-time updates on the vehicle's status and location. The program also allows users to customise monitoring settings and add parental restrictions. By actively recognizing and warning users to dangerous practises, SafeSync strives to improve rider safety and promote responsible driving behaviour. Finally, the initiative helps overall road safety by providing a solid solution for two-wheeler users to monitor and enhance their road safety. © 2024 IEEE.

关键词： Accident Alert ESP32 GPRS GPS GSM MPU6050 Rash Driving

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Reference-based Burst Super-resolution 24

Reference-based Burst Super-resolution

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Ko, Seonggwan Koh, Yeong Jun Cho, Donghyeon Dept. of Electronics Engineering Chungnam National University Daejeon Korea Republic of Dept. of Computer Science & Engineering Chungnam National University Daejeon Korea Republic of Dept. of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Burst super-resolution (BurstSR) utilizes signal information from multiple adjacent frames successively taken to restore rich textures. However, due to hand tremors and other image degradation factors, even recent BurstSR methods struggle to reconstruct finely textured images. On the other hand, reference-based super-resolution (RefSR) leverages the high-fidelity reference (Ref) image to recover detailed contents. Nevertheless, if there is no correspondence between the Ref and the low-resolution (LR) images, the degraded output is derived. To overcome the limitations of existing BurstSR and RefSR methods, we newly introduce a reference-based burst super-resolution (RefBSR) that utilizes burst frames and a high-resolution (HR) external Ref image. The RefBSR can restore the HR image by properly fusing the benefits of burst frames and a Ref image. To this end, we propose the first RefBSR framework that consists of Ref-burst feature matching and burst feature-aware Ref texture transfer (BRTT) modules. In addition, our method adaptively integrates features with better quality between Ref and burst features using Ref-burst adaptive feature fusion (RBAF). To train and evaluate our method, we provide a new dataset of Ref-burst pairs collected by commercial smartphones. The proposed method achieves state-of-the-art performance compared to both existing RefSR and BurstSR methods, and we demonstrate its effectiveness through comprehensive experiments. The source codes and the dataset are available at https://***/SeonggwanKo/RefBSR. © 2024 ACM.

关键词： Image reconstruction

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Using Blockchain for Incentive-Driven FHIR-based Health Record Exchange 24

Using Blockchain for Incentive-Driven FHIR-based Health Reco...

引用

39th Annual ACM Symposium on Applied Computing, SAC 2024

作者： Rosar, Moritz Chuang, Kun-Ta Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400702433

Medical data is highly sensitive data, and patients are particularly concerned about the exposure of their identities [1]. A study found that 64% of participants were agreeable to sharing their health data when anonymized, highlighting the importance of privacy in medical data sharing [5]. © 2024 Copyright held by the owner/author(s).

关键词： Sensitive data

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Deep Learning based approach for detection of Resource Depletion Attacks in 5G Networks 5

Deep Learning based approach for detection of Resource Deple...

引用

5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Sharma, M V Sri Shiva Shourya Tilak Venkata Sai, M. Sankaran, Sriram Amrita Vishwa Vidyapeetham Electronics and Computer Engineering Dept.of Electronics and Communications Kollam India Amrita Vishwa Vidyapeetham Center for Cybersecurity Systems and Networks Kollam India

ISBN: (纸本)9798350376753

Resource Depletion Attacks pose significant challenges to the stability and efficiency of 5G networks, with potential repercussions on service availability, network performance, and user experience. This problem is motivated by the need to understand the complexities of RDAs and their specific impact on the 5G Core networks, to develop effective countermeasures. First, we investigate the vulnerabilities of 5G networks, which make them susceptible to Resource Depletion Attacks. By identifying and analyzing these weaknesses, we aim to gain a comprehensive understanding of potential attack vectors. The problem of modelling and detecting Resource Depletion Attacks (RDA) on 5G Core, aiming to enhance their security and resilience against cyber threats is discussed in this paper. Our proposed attack model will incorporate essential 5G network elements, including base stations, user equipment, and core network components while integrating realistic traffic patterns and usage behaviours. Finally, a Deep learning-based mechanism will be evaluated to counter RDA on 5G Core. We incorporated two deep learning algorithms, specifically LSTM and GRU, which exhibited robust performance by achieving accuracies of 97.62 % and 99.07 %, respectively. The outcomes of our work will contribute to the development of resilient 5 G networks capable of withstanding sophisticated cyber threats, ensuring uninterrupted and secure communication and connectivity services by addressing the challenges posed by Resource Depletion Attacks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Multi-Trajectory Drone Surveillance: Amplifying Monitoring Capabilities in Disaster Management

Multi-Trajectory Drone Surveillance: Amplifying Monitoring C...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Mourya, Gojja Sree Jami, Abhinav Ajith, Abhinav Seikh, Ayan Habib Kumar, J. Sathish Motilal Nehru National Institute of Technology Dept. of Computer Science and Engineering Allahabad India

ISBN: (纸本)9798350348460

In the disaster-affected area, whether it's a search and rescue operation, mock drill, managing food distribution, issuing warnings or anything else, the key to successfully carrying out these activities is preparation. To ensure that plans are accurate, one needs to know what actually happened in the disaster-affected area and for this purpose thoroughly and quickly the entire area need to be studied. To address this issue, we use UAV or drone navigation autonomously in required trajectories. By using UAVs you can cover surveillance across the vast acres of land quickly and know what happened and plan rescue operations accordingly. In this paper, we presented our work on moving the drones in multiple trajectories and swarm, thus amplifying surveillance capabilities. Each trajectory is maneuvered autonomously and a corresponding trajectory-algorithm running at the back end is presented along with the simulation results. © 2024 IEEE.

关键词： Disaster management Drones Multi-Agent Systems Surveillance Swarm Trajectories Unmanned Aerial Vehicles (UAV)

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