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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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PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking

arXiv

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arXiv 2023年

作者： Liu, Xinran Liu, Xiaoqiong Yi, Ziruo Zhou, Xin Le, Thanh Zhang, Libo Huang, Yan Yang, Qing Fan, Heng Institute of Software Chinese Academy of Sciences Dept. of Computer Science & Engineering University of North Texas United States

Planar object tracking is a critical computer vision problem and has drawn increasing interest owing to its key roles in robotics, augmented reality, etc. Despite rapid progress, its further development, especially in the deep learning era, is largely hindered due to the lack of large-scale challenging benchmarks. Addressing this, we introduce PlanarTrack, a large-scale challenging planar tracking benchmark. Specifically, PlanarTrack consists of 1,000 videos with more than 490K images. All these videos are collected in complex unconstrained scenarios from the wild, which makes PlanarTrack, compared with existing benchmarks, more challenging but realistic for real-world applications. To ensure the high-quality annotation, each frame in PlanarTrack is manually labeled using four corners with multiple-round careful inspection and refinement. To our best knowledge, PlanarTrack, to date, is the largest and most challenging dataset dedicated to planar object tracking. In order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and conduct comprehensive comparisons and in-dept. analysis. Our results, not surprisingly, demonstrate that current top-performing planar trackers degenerate significantly on the challenging PlanarTrack and more efforts are needed to improve planar tracking in the future. In addition, we further derive a variant named PlanarTrackBB for generic object tracking from PlanarTrack. Our evaluation of 10 excellent generic trackers on PlanarTrackBB manifests that, surprisingly, PlanarTrackBB is even more challenging than several popular generic tracking benchmarks and more attention should be paid to handle such planar objects, though they are rigid. All benchmarks and evaluations will be released at the project webpage. Copyright © 2023, The Authors. All rights reserved.

关键词： Augmented reality

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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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Data Augmentation Techniques for Chinese Disease Name Normalization

Data Augmentation Techniques for Chinese Disease Name Normal...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqian Cui Xiangling Fu Shaohui Liu Mingjun Gu Xien Liu Ji Wu Irwin King Dept. Computer Science and Enginnering The Chinese University of Hong Kong Hong Kong China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Dept. Electronic Engineering Tsinghua University Beijing China Dept. Electronic Engineering College AI Tsinghua University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data 1 .

关键词： Training Biological system modeling Training data Medical services Data augmentation Data models Natural language processing Bioinformatics Diseases

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Data-Driven Modeling and Verification of Perception-Based Autonomous systems

arXiv

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arXiv 2023年

作者： Waite, Thomas Robey, Alexander Hassani, Hamed Pappas, George J. Ivanov, Radoslav Dept. of Computer Science Rensselaer Polytechnic Institute United States Dept. of Electrical and Systems Engineering University of Pennsylvania United States

This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems. We assume the perception model can be decomposed into a canonical model (obtained from first principles or a simulator) and a noise model that contains the measurement noise introduced by the real environment. We focus on two types of noise, benign and adversarial noise, and develop a data-driven model for each type using generative models and classifiers, respectively. We show that the trained models perform well according to a variety of evaluation metrics based on downstream tasks such as state estimation and control. Finally, we verify the safety of two systems with high-dimensional data-driven models, namely an image-based version of mountain car (a reinforcement learning benchmark) as well as the F1/10 car, which uses LiDAR measurements to navigate a racing track. Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Blockchain Revolution: Empowering the Electric Vehicle Industry through Integration and Case Study Analysis

Blockchain Revolution: Empowering the Electric Vehicle Indus...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ajmery Sultana Md Moniruzzaman Lian Zhao Dept. of Computer Science Algoma University Brampton Canada Dept. of Software Engineering Lakehead University Thunder Bay Canada Dept. of Electrical Computer and Biomedical Eng. Toronto Metropolitan University Toronto Canada

In the rapidly evolving landscape of electric vehicles (EVs), blockchain technology emerges as a transformative force, empowering the industry with enhanced security, transparency, and efficiency in transactions and energy management. However, the successful integration of blockchain technology in the context of EV is still in its infancy. Thus, this work aims to provide a comprehensive study of this young field from a broader perspective. We first discuss the integration process of blockchain technology in the EV domain. Then we explore the potential applications of blockchain technology in enhancing the efficiency, reliability, and sustainability of EV infrastructure and discuss some of the promising research in each category. Finally, we provide a typical application scenario and its specific embodiment in the EV infrastructure and strive to shed light on all-inclusive future research directions, which may facilitate the integration of blockchain technology in the EV ecosystem from theory to practice.

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Kid Activity Recognition: A Comprehensive study of kid activity recognition with monitoring activity level using YOLOv8s Algorithms 3

Kid Activity Recognition: A Comprehensive study of kid activ...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Bisla, Tiya Shukla, Ritik Dhawan, Mansi Islam, Md. Rahatul Koyasu, Takao Teramoto, Keisuke Kataoka, Yui Horio, Keiichi Dronacharya Group of Institution Dept. of Computer Science & Engineering Greater Noida India Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering Fukuoka Japan Kyushu Women's Junior College Fukuoka Japan Aichi University of Education Aichi Japan Fukuoka Kodomo Junior College Fukuoka Japan

ISBN: (纸本)9798350372120

Recognizing human activities and behavior is a cutting-edge field of research because of the complexity and limited availability of data. Our research involves the use of the trending YOLOv8s deep learning algorithm for the classification of active states (sitting, standing, running, laying, and jumping). The research focuses on the detection of a child's activities in the kindergarten playroom. To train the model, we created the KAR (Kids Action Recognition) dataset using our own sources. We labeled over 6,489 images in the dataset using ***. Each image is annotated with activity labels and the location of the student (in a room or on a mat). This assisted in presenting insights on the student's vitality level and physical involvement over a period of approximately an hour. We analyzed the whole activity and compared it with the times. Standing in the room and mat, standing in the room, and jumping in the room and mat are most of the activities of the kid. The model achieved an impressive overall accuracy of 88.7 %. We conducted all the training in a Google collaborative environment with A-100 GPU specifications. The results demonstrate that the model can identify and classify each activity with high precision. © 2024 IEEE.

关键词： Graphics processing unit

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Ensuring Electronic Health Record (EHR) Privacy using Zero Knowledge Proofs (ZKP) and Secure Encryption Schemes on Blockchain

Ensuring Electronic Health Record (EHR) Privacy using Zero K...

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Advancements in Computing (ICAC), International Conference on

作者： Ranaweera T.A.V.Y Hewage H.N.H Hapuhinna H.K.D.W.M.C.B. Preethilal K.L.K.T Amila Senarathne Laneesha Ruggahakotuwa Dept. Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud.

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