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IEEE International Conference on Electro-Information technology

作者： Diego Rodriguez Garcia Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The utilization of data analytics to gain insights into the game of basketball has seen a remarkable surge in the past decade. Leagues such as the National Basketball Association are continuously exploring innovative methods to analyze game data, an approach that has significantly influenced the dynamics of the game. But to perform these analyses, a growing amount of data is needed, which is traditionally annotated by humans. This work proposes a 3-stage system able to automatically acquire relevant basketball game data from a broadcast video. The first stage is an object detector combined with a tracking algorithm to extract the main elements present in a basketball game video. Then, the players' visual information is analyzed to identify the players based on pixel color analysis and number recognition. Finally, a statistics generation algorithm assigns the game events to the corresponding player and team, so that the system can be used as an aid for box score annotation in major leagues, low-cost annotation in amateur games, or in-depth game video analysis.

关键词： Visualization Annotations Image color analysis Data acquisition Optical character recognition Games Detectors

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Advanced Robotic Surveillance for Urban Air Quality Safety

Advanced Robotic Surveillance for Urban Air Quality Safety

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IEEE International Conference on Electro-Information technology

作者： Guillaume Hansen James R. Benson Muhammad Rafay Danish Khoa Truong Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents a multifunctional quadruped robot, specifically engineered for comprehensive air quality monitoring and emergency assistance. Designed to navigate through urban environments, the robot autonomously collects and transmits real-time air quality data to a centralized database. This interdisciplinary paper integrates robotics, environmental science, and emergency response protocols to propose novel solutions for smart city infrastructure, public health, and disaster management. Specifically, this research highlights the critical role that autonomous systems play in monitoring environmental conditions and advancing safety protocols. This research leverages a comprehensive array of sensors including CO2 (MH-Z19), particle (SDS 011), GPS (ADA 746), DHT 22, MQ 9, and ADS 1115 converter, each meticulously implemented to ensure precise environmental data collection and analysis. Furthermore, the integration of cutting-edge technologies such as Robot Operating System (ROS), .NET MAUI, Python, and MariaDB establishes a robust framework for seamless operation, data processing, and secure storage within the quadruped robot system.

关键词： Temperature sensors Protocols Robot sensing systems Dynamic scheduling Sensors Quadrupedal robots Servers

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Leveraging Machine Learning for Classification of Rheumatoid Arthritis: A Comparative Analysis of Classifier Performance

Leveraging Machine Learning for Classification of Rheumatoid...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Naresh Kumar Trivedi Raj Gaurang Tiwari Himani Maheshwari Vinay Gautam Ambuj Kumar Agarwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Naida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

A systemic inflammatory illness affecting the musculoskeletal system, rheumatoid arthritis (RA) is characterized by chronic, systemic symptoms. Injuries to joints and muscles from wear and tear are expected outcomes of rheumatoid arthritis (RA)., a degenerative illness that worsens with time. Arthritis rheumatoid generally degenerates muscles and joints while damaging bone and cartilage. To categorize medical conditions according to RA, this study employs machine learning approaches. Real-time RA data from the Sakthi Rheumatology Center, which keeps track of one thousand patient profiles has been used in this study. Multiple numerical features of the dataset are used for the classification task RA. The machine learning approaches used in this experiment were support vector machine (SVM), logistic regression, SDG, kNN, and ada-boosting. Ada-boost outperforms other classifiers regarding prediction accuracy rate and generating 98.7% classification accuracy. To classify the data, 20-fold cross-validation is used, and its efficacy is evaluated with metrics like precision, recall, sensitivity, and accuracy. The values of these measures were compared to a range of classifiers and baseline techniques.

关键词： Support vector machines Logistic regression Accuracy Sensitivity Machine learning Rheumatology Muscles Arthritis Real-time systems Vectors

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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Advanced Research in computing (ICARC), International Conference on

作者： N W Chanaka Lasantha M W P Maduranga Ruvan Abeysekara Valmik Tilwari Nilotpal Chakraborty Debashree Sharma Faculty of Graduate Studies IIC University of Technology Phnom Penh Cambodia Department of Electrical and Electronic Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Faculty of Computing BCAS Campus Colombo Sri Lanka Department. of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Engineering Indian Institute of Information Technology Guwahati India Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands.

关键词： Adaptation models Accuracy Cloud computing security Scalability NIST Transformers Data models Threat assessment Robustness Anomaly detection

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Reinforcement Learning for Power Management in Low-margin Optical Networks

Reinforcement Learning for Power Management in Low-margin Op...

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International Conference on Transparent Optical Networks

作者： Sze Ka Tse Xiaoyang Zhao Anita Chan Di Tang Aanchan Mohan Carlos Natalino Michal Aibin Khoury College of Computer Sciences Northeastern University Vancouver BC Canada Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Computing British Columbia Institute of Technology Vancouver BC Canada

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

We explore the Q-learning and Proximal Policy Optimization (PPO) algorithms for solving the Routing, Wavelength, and Power Allocation (RWPA) problem. The results indicate that reinforcement learning can significantly optimize launch power in low-margin optical networks, with Q-learning achieving better results than PPO, delivering improvement in GSNR of over $175 \%$ and over $48 \%$ in small and large networks, respectively.

关键词： Q-learning Network topology Power system management Power distribution Optical fiber networks Routing Topology

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Correction to: Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network (Multimedia Tools and Applications, (2024), 10.1007/s11042-024-19254-0)

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Multimedia Tools and Applications 2024年 1-2页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

The original publication of this article contains an error in the affiliation of authors Fadwa Alrowais and Hanen Karamti. Incorrect: department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia Correct: department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia The original article has been corrected. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

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Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN)

Plant Disease Detection and Classification using Emperor Pen...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Manjula Shanbhog Neha Bhatia A Swathi Singh M Pavithra Department of Computer Science School of Sciences Christ (Deemed to be University) Delhi NCR India Department of Information Technology Panipat Institute of Engineering and Technology Panipat Haryana India Department of Information and Technology School of Computing MIT-ADT Pune Maharashtra India Department of Computer Science and Business Systems Panimalar Engineering College Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Agriculture stands as India’s most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%.

关键词： Deep learning Plant diseases Technological innovation Quantum computing Refining Feature extraction Market research

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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A Two-Stage Emission Mismatch Penalty Game to Facilitate Carbon and Electricity Market Interaction 6

A Two-Stage Emission Mismatch Penalty Game to Facilitate Car...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Xu, Gaoyuan Shi, Jian Jiang, Wenqian Wu, Chenye Wang, Dan Han, Zhu The Chinese University of HongKong School of Data Science Shenzhen China Technology University of Houston Department of Engineering United States The Chinese University of HongKong School of Science and Engineering Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Houston Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350347159

The electricity sector is undergoing a low carbon transition to combat the looming threats caused by climate change. Given the urgency of meeting the climate goals set by the Intergovernmental Panel on Climate Change (IPCC), the cap-and-trade carbon emission market has emerged as an instrumental tool for policy-makers to impose emission restrictions on generation companies (Gencos). In this paper, we seek to model the sophisticated interactions among the CMO-carbon market operator, ESO-electricity system operator, and Gencos in a coupled electricity and carbon market. We propose a two-stage structure in which CMO conducts a uniform price auction to allocate the gross emission allowance to each Genco in the form of hourly allowance allowances in the first stage. Then, in the next stage, ESO conducts an emission-aware economic dispatch whereas the Gencos may further adjust the hourly schedules based on the dispatch outcomes. The coupling of the carbon market and the electricity market is characterized by an emission mismatch penalty mechanism, and we develop an iterative solving algorithm to reach the equilibrium. Through numerical studies, we highlight that the proposed mechanism is effective in emission reduction and market management. © 2022 IEEE.

关键词： Power markets

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