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Federated Learning-based Techniques for Healthcare IoV: Challenges and Future Directions -A Knowledge Review 9

Federated Learning-based Techniques for Healthcare IoV: Chal...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Narsimhulu, Pallati Sahay, Rashmi Dept. of Cse Hyderabad India Chaitanya Bharathi Institute of Technology Department of Computer Engineering And Technology Hyderabad500075 India

ISBN: (纸本)9798350394474

Federated Learning (FL) is a cutting-edge method for creating Machine Learning (ML) models using networked data sets. It supports advanced Artificial Intelligence (AI), keeps information neighborhoods, protects personal information, and allows for ownership. FL's features make it a good candidate for IoT networks due to its inherent distributed infrastructure. On the other hand, FL provides several challenges, the most significant of which is training on IoT devices with mostly heterogeneous data sources. The diversity of devices and algorithms in complex Internet of Vehicles (IoV) networks significantly impacts the FL training process and renders traditional FL unsuitable for direct deployment. While numerous recent publications claim to lessen the negative impacts of variety in FL networks, the efficacy of these proposed ways has never been tested and assessed. In this study, we examine the impact of heterogeneity in FL in dept. and provide an overview of the actual challenges imposed by the system and statistical heterogeneity. We researched on cutting-edge algorithms, emphasizing their practical application in IoT networks. We also compared the popular FL algorithms on a dynamic IoV network. Our study shows that existing methods fail to alleviate the issue appropriately, emphasizing the significance of considering system and statistical diversity while developing FL systems. To properly investigate the consequences of the network heterogeneity, we tested 10%, 30%, 50%, 70%, and 90% stragglers. These conditions serve as a training ground, allowing us to see how various degrees of network heterogeneity impact a technique's efficiency. The test-bed results show that the Scafold technique outperforms existing techniques in terms of training time reduced to 18% and accuracy increased to 21% © 2024 IEEE.

关键词： Learning systems

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RenalNet: An End-to-End Hybrid Deep Learning and Ensemble Model for Accurate Kidney Disorder Classification 3

RenalNet: An End-to-End Hybrid Deep Learning and Ensemble Mo...

引用

3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Haque, Fariha Siddique, Md Abu Ismail Shojol, Md. Shojeb Hossain Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

Kidney disorders are a major public health issue affecting millions worldwide. Renal calculi and cancer are prevalent, causing chronic kidney disease(CKD). Artificial intelligence can assist in the precise diagnosis of kidney *** developed an end-to-end system using a custom CNN model to extract informative features from kidney images. Multiple ML classifiers provide complementary perspectives. A High-Performance Filter(HPF) selects the top classifiers for an ensemble model. The interactive web interface enables real-time prediction. Our approach achieves excellent performance metrics with an accuracy of 99.98%. Overall, We demonstrate an automated system combining deep learning and ensembling for the accurate characterization of diverse kidney abnormalities, which can aid in the early detection and treatment of CKD. © 2024 IEEE.

关键词： Classification (of information)

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Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification 38

Unified Mechanism-Specific Amplification by Subsampling and ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Schuchardt, Jan Stoian, Mihail Kosmala, Arthur Günnemann, Stephan Dept. of Computer Science Munich Data Science Institute Technical University of Munich Germany Dept. of Engineering University of Technology Nuremberg Germany

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results. © 2024 Neural information processing systems foundation. All rights reserved.

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Sentiment Analysis on the Elevated Expressway in Bangladesh: A Machine Learning and Deep Learning Approach 2

Sentiment Analysis on the Elevated Expressway in Bangladesh:...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Jeba, Samiha Maisha Khan, Rajib Ahmed, Md Shoaib Aurpa, Tanjim Taharat Dash, Satya Ranjan International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Boise State University Department of Computer Science Idaho Boise United States Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Educational Technology Bangladesh School of Computer Applications Kiit University Odisha India

ISBN: (纸本)9798331517984

User comments on social media or any video streaming platforms show how people feel about something, including different opinions, feelings, and problems of people interacting with it. It can assist people and organizations in making wellinformed choices by offering insightful information on how people feel about a topic or issue. The Elevated Expressway is a crucial infrastructure in Bangladesh and a controversial project that has sparked public debate and criticism. The 46.73 km road project connecting Hazrat Shahjalal International Airport to Kutubkhali on the Dhaka-Chittagong Highway has eased the commute time from the Airport to Farmgate from over an hour to 10 minutes. Regarding this project, People's comments on YouTube as a digital platform for voicing their opinions and contributing to a shared narrative that goes beyond traditional media. In this work, we examine users' sentiments about Elevated Expressway from YouTube comments, employing various Machine Learning (ML) algorithms and Deep Learning (ML) architecture. Based on these analyses, the BERT architecture arises as the most satisfying model, with an accuracy of 84.34% and a macro f1 score of 80.14% for classifying public sentiment as positive, negative, or neutral. © 2024 IEEE.

关键词： Video streaming

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6G on the Horizon:Technologies, Requirements, Trends, and Potential Techniques 4

6G on the Horizon:Technologies, Requirements, Trends, and Po...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yunus, Nur Arzilawati Md Hanapi, Zurina Mohd Kamarudin, Shafinah Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Communication Technology and Network Selangor 43400 Malaysia

ISBN: (纸本)9798350395914

The advance of sixth generation (6G) wireless communication technology represents a paradigm shift in the field of telecommunications, promising unprecedented levels of connectivity, speed, and reliability. This review delves into the fundamental requirements driving the development of 6G, highlighting the need for ultra-reliable and low latency. By examining cutting-edge technologies such as terahertz communication, massive Multiple-Input Multiple-Output (MIMO), and Artificial Intelligence (AI) driven network optimization, this paper identifies key trends shaping the future of wireless communication networks. Moreover, it explores potential innovations including holographic communication, self-organizing networks, and quantum communication that have the potential to redefine the capabilities of 6G networks and unlock new opportunities for connectivity and collaboration. © 2024 IEEE.

关键词： Quantum communication

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DFT-based Channel Estimation with Multiple Windows for ATSC 3.0 Receiver 13

DFT-based Channel Estimation with Multiple Windows for ATSC ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Liu, Yu-Sun Li, Yin-Shan You, Shingchern D. National Taipei University of Technology Dept. of Electronic Engineering Taipei Taiwan National Taipei University of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350355079

This study introduces a novel method for Discrete Fourie Transform (DFT)-based interpolation for channel estimation through the application of multiple windows in the channel impulse response. Utilizing a genetic algorithm, we determine the optimal positions and lengths of these windows. Our simulation results demonstrate that this approach has lower channel's Mean Squared Error (MSE) than other high-performance estimation approaches. © 2024 IEEE.

关键词： Mean square error

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Enhancing Solar Energy Efficiency: A Deep Learning Approach for Dust Detection

Enhancing Solar Energy Efficiency: A Deep Learning Approach ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Vigna Harish, R. Ramya, A. Siva, S. Santhi, S. Kalaiselvi, S. Anitha, V. National Engineering College Dept. of Information Technology Tuticorin India National Engineering College Dept. of Computer Science and Engineering Tuticorin India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In recent decades, power generation from PV systems has become increasingly popular. However, several environmental variables, such as dust deposition on PV panels, have significantly reduced PV energy production. Several approaches are under investigation to identify dust and decrease power loss. These include thermal imaging, image processing, sensors, and IoT devices, machine learning, and deep learning. The orientation of a solar panel and the angular inclination relative to the horizontal plane affect one of the environmental factors that impact its performance: since they have an impact on how much solar energy that enters into the photovoltaic panel's surface. Dust, for one, is another example of environmental factors: the quantity of energy output can be influenced by coming into contact with dust. Dust particles lying on the panel's surface have the potential to reduce light from reaching the solar modules, which would drastically reduces the energy output. Cleaning or reducing modules is critical, but these procedures must be continuously monitored and assessed for their effects. Image processing algorithms can be used to detect dust to optimize solar panels. In this paper, a novel dataset from Kaggle comprising images of dusty and clean solar panels is presented, aiming to facilitate the research on automated dust detection algorithms. The dataset is utilized to evaluate the performance of existing state-of-the-art (SOTA) image classification methods. Subsequently, a new Convolutional Neural Network (CNN) architecture, ResNet-50 with an additional attention mechanism, is proposed, specifically designed for solar panel dust detection. The experimental results shows that the proposed method outperforms other SOTA algorithms, achieving an accuracy level of 96.2%. © 2024 IEEE.

关键词： Thermography (imaging)

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Leveraging Information and Communication technology (ICT) To Combat Corruption: A Systematic Review

Leveraging Information and Communication Technology (ICT) To...

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2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oluwambo Tolulope, Olowe Ariyo Adebiyi, Ayodele Akindeji, Kayode Landmark University Dept. of Computer Science Omuaran Nigeria Durban University of Technology Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350358155

Corruption, a global phenomenon disproportionately affecting developing nations, undermines governance and economic development. This systematic literature review examines the role of Information and Communications technology (ICT) in mitigating corruption. Despite conflicting evidence, ICT interventions, including transactional and transparency reforms, offer promising avenues for combating corrupt practices. The review highlights the diverse methodologies employed, with a significant focus on AI-related approaches. Collaboration among researchers, policymakers, and stakeholders is essential for developing comprehensive strategies to address corruption effectively, particularly leveraging ICT advancements. © 2024 IEEE.

关键词： Artificial intelligence

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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Mawarnie, Hana Patricia Widodo Nugraheni, Murien Universitas Negeri Jakarta Dept. of Informatics and Computer Jakarta Indonesia Universitas Negeri Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798331511166

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%. © 2024 IEEE.

关键词： Tweets

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Smart Shopping Cart Using Radio Frequency Identification

Smart Shopping Cart Using Radio Frequency Identification

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

作者： Kesava, Gudipati Rishi Reddy, Vanukuri Karthik Trivikram, Sadineni Santhi Sri, K. Vignan's Foundation for Science Technology & Research Deemed to Be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350348460

The "Smart Shopping Cart Using Radio Frequency Identification"project revolutionizes the shopping experience through RFID technology, offering real-time cost updates to shoppers. This innovative system incorporates RFID readers, a microcontroller for instant cost computations, and an LCD screen for displaying the total expense. By tackling common issues associated with traditional shopping carts, like prolonged checkout procedures and the absence of live cost monitoring, our project significantly enhances the efficiency and convenience of shopping. Shoppers can now enjoy a seamless experience as the RFID technology effortlessly scans tagged products, allowing the microcontroller to promptly calculate the overall cost, which is then displayed on the LCD screen. This breakthrough not only streamlines the shopping process but also eliminates the need for manual price entries, reducing errors and enhancing overall customer satisfaction. The successful integration of these technologies marks a promising step towards the future of retail, where smart shopping carts pave the way for quicker, more accurate, and enjoyable shopping ventures. © 2024 IEEE.

关键词： Efficiency and Convenience Radio Frequency Identification (RFID) Real-time Cost Updates RFID technology Integration Seamless Shopping Experience Smart Shopping Cart

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