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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Single-Phase Unidirectional Multilevel Rectifiers Based on Cascaded Cells

Single-Phase Unidirectional Multilevel Rectifiers Based on C...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Amanda Pereira Monteiro Cursino Brandão Jacobina Filipe Antônio Da Costa Bahia Reuben Palmer Rezende de Sousa Jean Torelli Cardoso Alan Robson Andrade Pinto Mechanical Engineering Dept. Federal University of Ceará Fortaleza Brazil Electrical Engineering Dept. Federal University of Campina Grande Campina Grande Brazil Electrical and Computer Engineering Dept. Federal University of Bahia Salvador Brazil Electronics and Systems Dept. Federal University of Pernambuco Recife Brazil

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes and investigates two new single-phase unidirectional multilevel rectifiers based on cascaded cells. Compared to conventional topology, as the number of cascaded cells increases, proposed ones employ, proportionally, a reduced controlled switch count and driver circuits, which may reduce control complexity and increase reliability. The dc-link voltage ratios are calculated for asymmetrical operation to provide the maximum number of voltage levels generated in the ac side of the rectifiers, ensuring a high power quality. Besides, the proposed configurations can synthesize a higher number of voltage levels by employing the same controlled switch count as the conventional one so that it is possible to provide a higher levels-per-switch ratio. Simulation and experimental results are presented to validate the performance and feasibility of the proposed topologies.

关键词： Reactive power Power quality Rectifiers Switches Control systems Reliability engineering Topology Power system reliability Voltage control Switching circuits

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Jiayuan Jin Xiantao Jiang Tian Song Fei. Richard Yu WeiLi Jin Liu Dept. Information Engineering Shanghai Maritime University Shanghai China dept. Electrical and Electronics Engineering Tokushima University Tokushima Japan dept. Systems and Computer Engineering Carleton University Ottawa Canada

A new Quadtree with nested multi-type tree(QTMT) partitioning method is adopted in the latest video coding standard Versatile Video Coding(VVC) to achieve effective encoding. Compared with Quadtree(QT) in HEVC, QTMT can provide enhanced encoding performance. However, this improved coding performance is obtained at the cost of additional computational load from recursive and nested searches for the best CU structure. A context similarity-enabled CU partitioning algorithm is proposed to achieve a balance between coding efficiency and compression quality in VVC. According to the difference between frames, the method of frame difference algorithm is used to calculate the possibility that the current block can follow the same subblock division mode of the previous frame, so as to skip the unnecessary division selection process. The experimental results show that the proposed method can save the encoding time up to 18.22% with about 0.91% BD-rate degradation on average compared with VVC software reference VTM-9.3

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SCC: Efficient Error Correction Codes for MLC PCM

SCC: Efficient Error Correction Codes for MLC PCM

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International SoC Design Conference, ISOCC

作者： Yujin Lim Dongwhee Kim Jungrae Kim Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor and Display Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon Korea

Phase Change Memory (PCM) has presented as a potential replacement of DRAM due to its better scalability. However, MLC PCM encounters reliability problems such as resistance drift error and cell wear-out. To improve reliability, implementing strong Error Correction Codes (ECC) is required but this incurs substantial overhead to memory system. Existing ECC schemes have trade-offs between error correction capability and overhead. This paper proposes Single Cell Correction (SCC) codes, an efficient ECC scheme which can correct single cell errors in MLC PCM with small overhead.

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Understanding the Impact of AI-driven Clinical Decision Support systems 15

Understanding the Impact of AI-driven Clinical Decision Supp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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Evaluation of Incremental Conductance MPPT Algorithm Under Varying Conditions

Evaluation of Incremental Conductance MPPT Algorithm Under V...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ahmad M. NEZAM Musaria K. MAHMOOD Sulaiman M. KARIM Abdirahim ADDAWE dept. of Energy Systems Engineering Ankara Yildirim Beyazit University Ankara Türkiye dept. of Medical Instr. Tech. Eng. Jannat Iraq College Ramadi Iraq dept. of Computer Technology Engineering Al-Hadba'a University College Mosul Iraq dept. of Computer Engineering Karabük Üniversitesi Karabük Turkey

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Photovoltaic-based renewable energy production is constantly developing and becoming an increasing part of mod-ern energy markets. The power efficiency of a PV system is one of the most challenging problems, especially in changing climate conditions. Tracking the Maximum Power Point (MPP) to achieve optimal power production is one of the most important requirements of any PV-based system. This article explores the functionality of the Incremental Conductance (INC) algorithm as a highly effective approach for Maximum Power Point Tracking (MPPT). Simulations are conducted to investigate the effectiveness of INC across diverse operational case studies, considering both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). In this investigation, key factors including system power efficiency, settling time, and steady-state power oscillation are taken into account. Simulation reveals rapid tracking and reduced oscillation under STC, meanwhile, it occasionally demonstrates inadequacy in accurately tracking the MPP under PSC in the case of multi-panel systems.

关键词： Maximum power point trackers Solar irradiance Photovoltaic systems Performance evaluation Renewable energy sources Radiation effects Production

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Energy Efficient joint user association and power allocation using Parameterized Deep DQN

Energy Efficient joint user association and power allocation...

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International Conference on computer and Communication engineering, ICCCE

作者： Amna Mughees Mohammad Tahir Muhammad Aman Sheikh Angela Amphawan Kian Meng Yap Mohamed Hadi Habaebi Md. Rafiqul Islam Dept. of Computing and Information Systems Sunway University Petaling Jaya Malaysia Dept. of Computing University of Turku Turku Finland Dept. of Electronics and Computer Systems Engineering Cardiff Metropolitan University Cardiff U.K Dept. of Electrical and Computer Engineering International Islamic University Kuala Lumpur Malaysia

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems.

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AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding

AIX Implementation in Image-Based PM2.5 Estimation: Toward a...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Utomo, Sapdo John, A. Pratap, Ayush Jiang, Zhi-Sheng Karthikeyan, P. Hsiung, Pao-Ann National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India

ISBN: (纸本)9781665477123

In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model's output, LIME has been implemented. According to LIME's explanation, the proposed model's output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model's decision. We hope that with this discovery, we can contribute to the theme of 'AI for social good,' notably in the domains of the environment and human welfare. © 2023 IEEE.

关键词： Air quality

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A Bi-LSTM Based Model for Social Media Extremism Detection in Context of Bangladesh

A Bi-LSTM Based Model for Social Media Extremism Detection i...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sajib Saha Zahidul Islam Tanvirul Islam Md Monzur Morshed Tithee Chakma Mama Subhenur Latif Md Walid Bin Jashim Dept. of Computer Science & Engineering Daffodil Internation University Dhaka Bangladesh Department of Accounting & Information Systems University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s modern world, digital technology has advanced social networking with revolutionary changes. People use some most popular platforms like Facebook, YouTube and some other social media platforms to engage with each other for business, information sharing and so on. Attacking someone on social media platforms through comments, in response to their speech, has become a common occurrence in Bangladesh as well as worldwide. Very little research has been done in this related field for Bengali NLP. In this study we primarily focus on classifying these comments into two categories: extremist and non-extremist. Comments were collected from two of the most popular platforms, YouTube and Facebook, used in Bangladesh to build our "BEC (Bangla Extremist Comments)" dataset. To classify the comments, a CNN + Bi-LSTM hybrid model was employed considering extremist comments detection systems using deep learning-based sentiment analysis techniques. The proposed model achieved an accuracy rate of 85% in identifying the extremist comments. This research opens opportunities for future researchers to take advantage of contributing and collaboration in this field.

关键词： Deep learning Analytical models Sentiment analysis Video on demand Social networking (online) Computational modeling Information sharing Collaboration Web sites Context modeling

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK 12

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mousaei, Arash Naderi, Yahya University of Tabriz Dept. Computer and Electrical Engineering Tabriz Iran Ricardo Aea Sustainable Energy Systems Department Glasgow United Kingdom

ISBN: (纸本)9798331525132

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development. © 2025 IEEE.

关键词： Linear regression

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