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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson’s Disease Detection

Investigating the Efficacy of Diverse Machine Learning Class...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Sourabarna Roy Tina Debbarma Tannistha Pal Prakash Duraisamy Dept. of Computer Science and Engineering National Institute of Technology Agartala Agartala India Dept. of Computer Science and Engineering University of Wisconsin- Green Bay WI United States

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

To detect Parkinson’s disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson’s disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson’s disease identification, offering insights that can aid in early diagnosis and disease management.

关键词： Support vector machines Radio frequency Accuracy Sensitivity Machine learning algorithms Nearest neighbor methods Sensitivity and specificity Vectors Random forests Diseases

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Enabling Cooperative Autonomous Driving through mmWave and Reconfigurable Intelligent Surfaces

Enabling Cooperative Autonomous Driving through mmWave and R...

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Annual Conference on Wireless On Demand Network Systems and Services (WONS)

作者： Michele Segata Marios Lestas Paolo Casari Taqwa Saeed Dimitrios Tyrovolas George Karagiannidis Christos Liaskos Department of Information Engineering and Computer Science University of Trento Italy Dept. of Electrical Engineering Frederick University Cyprus School of Information Technology Halmstad University Sweden Dept. of Electrical Engineering Aristotle University of Thessaloniki Greece Dept. of Computer Science and Engineering University of Ioannina Greece

Future cooperative autonomous vehicles will be able to organize into flexible platoons to improve both the efficiency and the safety of driving. However, platooning requires dependable coordination through the periodic wireless exchange of control messages. Therefore, challenging propagation scenarios as found, e.g., in dense urban areas, may hinder coordination and lead to undesirable vehicle behavior. While reconfigurable intelligent surfaces (RISs) have been advocated as a solution to improper coverage issues, no system-level simulation exists that accounts for realistic road mobility and communication aspects. To this end, we present one such simulator built on top of the OMNeT++-based P LEXE and Veins frameworks. Specifically, our contribution is a simulator that takes into account vehicle mobility, physical layer propagation, RIS coding, and networking protocols. To test our simulator, we implement an RIS-assisted autonomous platoon merging maneuver taking place at an intersection where the absence of any RIS would limit successful communications to an area dangerously close to the intersection itself. Our results validate the simulator as a feasible tool for system-level RIS-assisted cooperative autonomous vehicle maneuvering, and ultimately show the benefit of RIS as roadside infrastructure for wireless coverage extension.

关键词： Wireless communication Protocols Veins Roads Urban areas Physical layer Safety

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Time-Dependent Multi-Objective Trip Planning by Ant Colony Optimization with Route API

Time-Dependent Multi-Objective Trip Planning by Ant Colony O...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Etsushi Saeki Siya Bao Toshinori Takayama Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University ZENRIN DataCom

In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time stamps and computes time-dependent travel time by utilizing route API. Moreover, we reduced the response time from the route API calls. Compared with two conventional methods, our proposed method provided routes with high time-dependent values. Meanwhile, the number of API calls is reduced by 98.8% on average by introducing the API call reduction.

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Advancing Multi-Agent Systems Integrating Federated Learning with Deep Reinforcement Learning: A Survey

Advancing Multi-Agent Systems Integrating Federated Learning...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Jaemin Kim Gahyun Kim Seonghun Hong Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

Recent advancements in the field of Deep Reinforcement Learning (DRL), such as the development of algorithms like Deep Q-Network, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO), have been significant. However, these algorithms encounter data privacy issues in multi-agent environments. This paper investigates the latest research trends in applying federated learning to address data privacy issues in multi-agent systems within deep reinforcement learning. It delves into various approaches for implementing federated learning in these systems, examines the challenges faced, and explores potential solutions to enhance privacy while maintaining or improving the performance of DRL algorithms in multi-agent setting.

关键词： Surveys Data privacy Privacy Federated learning Deep reinforcement learning Market research Optimization

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THz-Frequency Free-Standing Bi-Layer wire grid Polarizers Fabricated by Stereolithography

THz-Frequency Free-Standing Bi-Layer wire grid Polarizers Fa...

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2024 IEEE Research and Applications of Photonics in Defense Conference, RAPID 2024

作者： Louisos, Dustin Mclamb, Micheal Boreman, Glenn D. Zhang, Jincheng Willis, Andrew Hofmann, Tino Touma, Jimmy E. University of North Carolina at Charlotte Dept. of Physics and Optical Science CharlotteNC28025 United States University of North Carolina at Charlotte Electrical and Computer Engineering Dept CharlotteNC28025 United States Air Force Research Laboratory Eglin AFBFL32542 United States

Finite element simulations of free-standing, bi-layer wire grid polarizers, with dimensions typical for stereo-lithography, show two orders of magnitude improvement over single-layer wire grid polarizers. © 2024 ... 详细信息

ISBN: (纸本)9798350373097

关键词： Stereolithography

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Protection of Inverter Interfaced Microgrids: Challenges and Solutions

Protection of Inverter Interfaced Microgrids: Challenges and...

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2022 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2022

作者： Dehghanpour, Ehsan Normandeau, Michel Joos, Geza McGill University Dept. Electrical and Computer Engineering Montreal Canada Hydro-Quebec Research Institute Montreal Canada

ISBN: (纸本)9781665443296

Recently, the protection of Inverter-Based Microgrids (IBMs) has attracted a lot of attention. It is partly because the penetration level of Inverter-Based Recourses (IBRs) is increasing continually and partly because of different and sometimes unusual behavior of these resources under fault conditions. The distinctive fault characteristic of IBRs makes the protection of the inverter-based microgrids rather uniquely challenging. This paper tries to shed light on the current challenges in the protection of the IBMs by providing time-domain simulations. Two possible solutions to address some of the main challenges are also discussed by providing simulation-based results using MATLAB Simulink software. © 2022 IEEE.

关键词： Fault detection

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Design and Implementation of an E-Textile SSPP-Based Low-Pass Filter for Advanced Wearable Applications

Design and Implementation of an E-Textile SSPP-Based Low-Pas...

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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Jayshri Kulkarni Jack H Engdahl Gabrielle J Quintiliani Jose Alcala-Medel Douglas E Dow Yang Li Dept. of Electrical & Computer Engineering Wentworth Institute of Technology Boston MA USA Dept. of Mechanical Engineering Wentworth Institute of Technology Boston MA USA Dept. of Electrical & Computer Engineering Baylor University Waco TX USA

ISBN: (数字)9798350389920

ISBN: (纸本)9798350389937

This paper presents the design and implementation of an electronic textile (e-textile) low-pass filter (LPF) based on Spoof Surface Plasmon Polaritons (SSPPs), achieving a cutoff frequency of 6.47 GHz for advanced wearable applications. The proposed filter comprises m-shaped unit cells designed to be patterned on the top side of a denim substrate, complemented by a complete ground plane on the back side. The dimensions of the filter are $2.15 \lambda_{0} \times 0.539 \lambda_{0} \times 0.034 \lambda_{0}$, where $\lambda_{0}$ is the wavelength corresponding to the cutoff frequency. Simulation results reveal a passband frequency range extending from 0 GHz to 6.47 GHz, with a -10 dB impedance bandwidth of 6.47 GHz and a stopband attenuation of -45 dB at 6.90 GHz. The filter demonstrates low insertion loss within the passband, with a maximum value of just 0.8 dB. Finally, the Specific Absorption Rate (SAR) values of $0.0268 \mathrm{~W} / \mathrm{kg}$ and 0.0125 $\mathrm{W} / \mathrm{kg}$ for both the 1 g and 10 g standards, respectively indicate compliance with safety regulations, affirming its suitability for wearable applications on the human body. The e-textile design and SAR analysis ensure that the filter remains both flexible and comfortable, making it well-suited for integration into smart clothing and other wearable devices.

关键词： Smart textiles Surface plasmon polaritons Low-pass filters Radar antennas Specific absorption rate Safety Passband Microwave filters Wearable devices Substrates

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Exploring the EmoBone Dataset with Bi-Directional LSTM and Attention for Emotion Recognition via Bone Conducted Speech

Exploring the EmoBone Dataset with Bi-Directional LSTM and A...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Sarwar Hosain Yosuke Sugiura Mousumi Haque M. Shahidur Rahman Tetsuya Shimamura Dept. of Information and Computer Sciences Saitama University Saitama Japan Dept. of Information and Communication Engineering University of Rajshahi Rajshahi Bangladesh Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research investigation explores the use of deep learning for emotion recognition using bone-conducted (BC) speech. It focuses on the EmoBone dataset, which is the first dataset specially created for this purpose. Bone-conducted speech offers several advantages, such as improved speech quality in noisy environments, reduced interference from ambient sounds, and enhanced user privacy, as the sound is directly transmitted to the auditory system through bone conduction. Although deep learning is commonly employed for voice emotion recognition, these methods frequently encounter challenges, including the degradation problem and information loss in the deeper stages of deep neural networks. To resolve these issues, we propose implementing a bi-directional long short-term memory (BiLSTM) network that utilizes BC speech and incorporates attention mechanisms. This network will effectively utilize information and mitigate the adverse impacts caused by degradation. However, the attention mechanism significantly improved the performance of the model, resulting in an accuracy gain of approximately 91.45%. The confusion matrices demonstrate that the attention mechanism enhances recognition accuracy for all emotional categories. This study exhibits the capability of enhancing emotion recognition in BC speech applications by integrating BiLSTM with attention.

关键词： Degradation Deep learning Emotion recognition Attention mechanisms Accuracy Transfer learning Bidirectional long short term memory Speech recognition Speech enhancement Bones

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Modeling of hydrodynamics and mechanical aspects of a Wave Energy Converter (WEC) to understand the main principles

Modeling of hydrodynamics and mechanical aspects of a Wave E...

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2022 International Symposium on Power Electronics, electrical Drives, Automation and Motion, SPEEDAM 2022

作者： Delmiglio, Francesco Frosini, Lucia Anglani, Norma University of Pavia Dept. of Civil Engineering and Architecture Pavia Italy University of Pavia Department of Electrical Computer Biomedical Engineering Pavia Italy

ISBN: (数字)9781665484596

ISBN: (纸本)9781665484596

This work aims at illustrating a first-approach modeling of an all-purpose wave energy converter (WEC), and reproducing its allegedly behavior, once there is a need to forecast its production from a location, where all the monitoring data are available, to another location. A few commercial software provide such a feature but, to proficiently use them, several practical parameters are needed and this makes the learning quite hard. In the proposed model, some parameters are offered and can be tuned according with the device to reproduce, and its known data (e.g. power matrix). The Simulink/Matlab model needs the couple (H,T) (wave height and period) as input, and it provides mechanic, and then, electric power as output: using a time series of (Hi,Ti), it is able to produce a suitable power profile, useful to be used-for instance-in feasibility studies. © 2022 IEEE.

关键词： Simulink

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Efficient mmWave Beam Selection using ViTs and GVEC: GPS-based Virtual Environment Capture 14

Efficient mmWave Beam Selection using ViTs and GVEC: GPS-bas...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Srajan Saxena, Navrati Roy, Abhishek Ravi, Akshay San Jose State University Dept. of Computer Science San Jose United States Mediatek Inc. Engineering San Jose United States

ISBN: (纸本)9798350335095

Millimeter waves (mmWaves) providing higher bandwidth is used by 5G network technology to achieve higher network capacity and faster data transfer. However, the process of beam sweeping across multiple antenna arrays can be time-consuming and inefficient. Machine learning (ML) models can address this issue by predicting the optimal beam pair based on data from on-vehicle light detection and ranging (LIDAR) and global positioning system (GPS) sensors. This paper proposes a new vision transformer (ViT) ML model for beam selection using GPS and LIDAR data. GPS-based virtual environment capture (GVEC) has been introduced to overcome to overcome noise in the LIDAR data caused by adverse weather conditions. The proposed solution demonstrates improved performance compared to previous approaches when tested on noisy LIDAR data, achieving a 92% accuracy in searching among the top 10 beams. © 2023 IEEE.

关键词： Millimeter waves

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