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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsurumi, Kazuki Marutsuka, Ryo Koutaki, Gou Kumamoto University Dept. of Computer Science and Electrical Engineering Kumamoto860-8555 Japan

ISBN: (纸本)9798350355079

Research has primarily focused on fully automatic instrument-playing robots. However, it is difficult to support the experience of playing musical instruments for people with physical disabilities or those who are inexperienced in playing musical instruments. To address this issue, we studied a "semi-automatic"instrument-playing robot based on human-robot collaboration. Our research specifically focused on the guitar, which are stringed instruments with complex pressed strings. The developed robot enables individuals with physical disabilities and those without musical experience to easily play musical instruments. Additionally, the instruments can be easily removed without causing damage. © 2024 IEEE.

关键词： User centered design

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Comprehensive Music Genre Classification: Exploring Advanced Machine Learning Techniques with the GTZAN Dataset 5

Comprehensive Music Genre Classification: Exploring Advanced...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Meghraj, G.K. Nishanth, Y. Kuttan, Akshat Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350376685

Music is a universal language that comes in a wide variety of genres to suit different interests and moods. In order to achieve this organization, music genre classification - the process of automatically classifying a piece of music according to its auditory content - is essential. A multi-class music genre identification model has been constructed in the proposed study, and ten distinct classifiers, including K-NN, Random Forest, Cat Boost, XG Boost, Decision Tree, etc., have been tested. A popular benchmark dataset for classifying musical genres, the GTZAN dataset offers a wealth of information for developing and assessing classification algorithms. In contrast, CatBoost reports the best outcome, with an F1-score of 0.89. © 2024 IEEE.

关键词： GTZAN Dataset Multi-class Identification Music Genres Performance Measures

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ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platform for Privacy and yet Secured Access 6

ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platfor...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Quach, Vinh Dantu, Ram Talapuru, Sirisha Zaman, Shakila Pokharel, Apurba University of North Texas Dept. of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350386745

As concerns over privacy and trust escalate among individuals and corporations, traditional memory-based trust systems are increasingly seen as inadequate and undesirable. The rise of microservice architecture complicates this by making data paths harder to track. Given these concerns, the critical question arises: How can we establish trust or verify an entity's credibility without collecting private information or relying on comparisons with pre-existing data, while knowing the exact data path of each request? To address this challenge, we introduce a lightweight, scalable, ZCube platform that employs nested Zero Knowledge Proofs (ZKPs), eliminating reliance on memory or evaluations based on past actions. This platform verifies and maintains trust in real-time without centralizing trust in any specific part of the system. It anticipates intentions and rigorously monitors them, ensuring operational integrity while preserving privacy. Distinctive for its ZKP trustless setup incorporating blockchain, each request begins anew and is rigorously monitored bidirectionally during execution. Our tests on the realistic example of a microservice-based distributed system, OpenTelemetry Demo, confirm that our novel approach - combining proof segmentation with plan adherence - exhibits fast response times of less than 200ms on average for most test cases. This ZCube platform surpasses traditional methods in efficiency and security. Our analysis shows comprehensive security and enhanced performance while effectively maintaining bidirectional trustlessness. © 2024 IEEE.

关键词： Differential privacy

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks 1

Reinforcement Learning for Adaptive Traffic Signal Control U...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Bohra, Asha Rani Selvi, Thamarai Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Jignesh, Bhavsar Nitya New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520540

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices. © 2025 IEEE.

关键词： Traffic congestion

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COVID-19 Improved Diagnoses Based on the Open-morphology Filter and Deep-learning

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Recent Patents on engineering 2022年第3期16卷 122-130页

作者： Younus, Majid D. Zedan, Mohammad J. M. Malallah, Fahad L. Saeed, Mustafa G. Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer Science Dept. College of Science Cihan-Slemani University Slimani Iraq

Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus, there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying COVID-19-infected patients by computed tomography CT images. The contribution of this paper is that it proposes a pre-processing technique that increases the recognition rate compared to the techniques existing in the literature. Methods: The proposed pre-processing technique, which consists of both contrast enhancement and open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning for the training and testing operations. The dataset used in this research has been publically published, in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed of 2482 CT scans images, which include 1252 CT scans of SARS-CoV-2 infected patients and 1230 CT scans of non-infected SARS-CoV-2 patients. Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported accuracy of the dataset by 97.3%. Conclusion: The performance in terms of accuracy has been improved up to 0.5% by the proposed methodology over the published dataset and its method. © 2022 Bentham science Publishers.

关键词： COVID-19

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Sea Erosion Prediction and Monitoring Using IOT and ML 2

Sea Erosion Prediction and Monitoring Using IOT and ML

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Usha Bala, V. Harshavardhan Sai, Y. Slipa Shramila, V. Asritha, M. Bharath, M. Chandu, A. Anil Neerukonda Institute of Technology & Sciences Dept. of Computer Science & Engineering Visakhapatnam India

ISBN: (纸本)9798350366570

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis. © 2024 IEEE.

关键词： Soil erosion

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Advancements in Distributed Deep Learning: Federated Learning, AutoML Integration, and Beyond

Advancements in Distributed Deep Learning: Federated Learnin...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Kavitha, T. Manikandan, S.P. Patil, Bhimaraya Patil, Anita Dept. of Computer Science and Engineering New Horizon College of Engineering Bangalore India CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg. Bangalore India Dept. of Information Science and Engg. City Engineering College Bengaluru India

ISBN: (纸本)9798331505134

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication overhead. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research. © 2024 IEEE.

关键词： Federated learning

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Epileptic Seizure Detection Using Quantum Support Vector Classifier

Epileptic Seizure Detection Using Quantum Support Vector Cla...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Soni, Barkha Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding the dynamics of epileptic brain activity. It offers a non-invasive means for monitoring and detecting epileptic seizures. This paper explores the use of quantum machine learning algorithms to improve the precision and effectiveness of utilizing EEG data for epileptic seizure detection. Quantum machine learning, leveraging principles from quantum mechanics, holds promise for addressing these challenges by harnessing quantum computational power and exploiting quantum entanglement to process information more efficiently. Quantum features and quantum kernels are employed to capture intricate patterns within the EEG signals, allowing for improved discrimination between normal brain activity and seizure events. Results indicate that the Quantum Support Vector Classifier exhibit a satisfactory classification accuracy compared to traditional techniques despite having constraints with resources. The findings suggest that quantum computing holds great promise in revolutionizing the field of epilepsy diagnosis by providing more robust and accurate tools for distinguishing between epileptic and non-epileptic EEG patterns. © 2024 IEEE.

关键词： Quantum entanglement

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Enhancing Cervical Cancer Diagnosis: Deep Learning and Explainable AI in Multiclass Image Classification 1

Enhancing Cervical Cancer Diagnosis: Deep Learning and Expla...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Shreya, Kuthati Divakarla, Lasya Priya Kumar, Kanderi Johith Singh, Tripty Afnaan, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Cervical Cancer is a major health concern amongst many women around the world. Cervical Cancer is usually caused by the Human Papilloma Virus (HPV). This causes unusual growth in the cervix region. All though this is such a serious health concern, proper treatment and diagnosis are not that prominent and available to the patients. Regular checkups and tests are quite necessary which can help in early detection and diagnosis. It has been observed if these cancerous growths are detected at an early stage then the patient has a high recovery rate. If not detected early then there are chances that these cancer cells spread to the other organs which can be a fatal condition for the patient. A technique has been introduced involving the use of deep learning models to categorize cancer cells into four distinct classes, aiding in accurate identification and effective diagnosis. Explainable AI techniques like Local Interpretable Model Agnostic Explanations (LIME) have been used with these models in order to identify the regions in the medical images that contribute towards the image classification of the cancer cells which can further provide a better understanding for performing diagnosis. © 2024 IEEE.

关键词： Lung cancer

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A Multi-Routing Paths' Exploration Algorithm for Multi-paired Communicating Vehicles' Data Transmission Offloading using the Multi-access Edge Computing Architecture 4

A Multi-Routing Paths' Exploration Algorithm for Multi-paire...

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4th European Conference on Communication Systems, ECCS 2024

作者： Huang, Chung-Ming Tsai, Cheng-Han National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350377927

This work proposed a novel method for multiple communicating paired vehicles' data transmission offloading using the Multi-access Edge Computing (MEC) technique. When two paired communicating vehicles are communicating with each other through cellular network's Base Stations (BSs), the data transmission can be offloaded to the Cellular Vehicle-to-Vehicle (C-V2V) path that exists between these two vehicles using 5G NR's sidelink communication;these two paired communicating vehicles can switch back to the Cellular Vehicle-to-Infrastructure (C-V2I) communication to communicate with each other when the corresponding C-V2V path is broken. Using the proposed method, the MEC architecture serves as a centralized computing mechanism to determine the availability of k-hop C-V2V paths for paired communicating vehicles' data transmission offloading. Each vehicle periodically reports its context, such as position, speed, driving direction, and C-V2V sidelinks' Channel Busy Ratios (CBRs), to the MEC server to derive feasible k-hop C-V2V data transmission offloading paths. The simulation result demonstrates that the proposed method has (i) better throughput compared to the method of relying solely on C-V2I communication through BSs and (ii) better throughput, higher offloading fraction and lower average data loss rate comparing with the other k-hop-limited CV2V data transmission offloading method. © 2024 IEEE.

关键词： Edge computing

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