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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Kaluza, Daniel Seiler, Marco Kashef, Rasha Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Canada

ISBN: (纸本)9798350323153

There has been a sharp rise in the number of fatalities and injuries caused by traffic accidents in urban areas. Cities often have video and image resources that can be analyzed manually using operators to address this problem. This paper introduces an automated collision detection system that utilizes publicly available images captured by Toronto's traffic camera system. The system is based on a Deep Learning model, specifically a DenseNet-161, employed to classify accidents and non-accidents. The results of this classification are then displayed on a graphical user interface. The primary aim of this study is to reduce medical response time and ultimately save lives by issuing automatic alerts. The proposed system has the potential to minimize the severity of accidents and decrease the number of fatalities by notifying emergency services once an accident is detected. © 2023 IEEE.

关键词： CCTV Collision detection Deep learning Internet of Things (IoT) machine learning Smart Cities traffic obfuscation

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models 2

Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Mo...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ravikumar, S. Raj, Arockia Babu, R. Vijay, K. Ramani, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai602117 India Department of Computer Science and Engineering P.S.R. Engineering College Tamilnadu Sivakasi626140 India

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Network architecture

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Exploring nonintrusive measurements of spatio-temporal portrait of microservices

Exploring nonintrusive measurements of spatio-temporal portr...

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作者： Zeng, Tao Xu, Zichen Wu, Dan Li, Xiaoling Liu, Biyong Hu, Haichuan Tan, Shuang Tan, Yusong Xu, Chenren Stewart, Christopher Zhou, Qihe Cao, Ye School of Mathematics and Computer Sciences Nanchang University Nanchang China College of Computer National University of Defense Technology Hunan China School of Computer Science Peking University Beijing China Computer Science and Engineering The Ohio State University ColumbusOH United States Faculty of Data Science City University of Macau China

As cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non-intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice-based applications. We model microservice applications using a queue analysis-based approach and filter the dominant frequency components of average response time signals by employing k-means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two-tier microservices-based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions. © 2023 John Wiley & Sons, Ltd.

关键词： Fast Fourier transforms

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Physician Scheduling using Constraint Satisfaction Programming 11

Physician Scheduling using Constraint Satisfaction Programmi...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

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5G Wireless Communication and IoT: Vision, Applications, and Challenges 2

5G Wireless Communication and IoT: Vision, Applications, and...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhivgade, Anushka Puri, Chetan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

This article looks at the opportunities, application and challenges of 5G Wireless Communication Technologies and Internet of Things (IoT). A brief history from 1G to 5G is presented and the limitations of 4G and the introduction of 5G as a solution for problems with latency and speed. The paper also describes the 5G IoT concept, the architecture and advanced technologies with focus on different IoT applications such as smart agriculture and smart city. When speaking of aspects such as energy consumption, privacy and security concerns, as well as trade-offs strategies, it underlines how important collaboration is to achieve the benefits that the 5G IoT will bring. The paper itself provides a roadmap for next studies with a purpose of advancing development of the integrated, safe, and efficient wireless. As 5G Wireless Communication and IoT progresses the expectation is that it will enhance and revolutionalize several domains that will offer a scalable connectivity that is faster and more reliable. This join venture is believed to open new value adding opportunities for application in smart city, automobile, medical, industrial, robotic and so on several other fields. The term 5G IoT has also been used with the intention of establishing connections of several devices, very low latency and high degree of data rate for real time analysis and decision making. © 2024 IEEE.

关键词： Smart agriculture

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and engineering.

关键词： Wavelet transforms

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Gabor Wavelet Based Fused Texture Features for Identification of Mungbean Leaf Diseases 1st

Gabor Wavelet Based Fused Texture Features for Identificati...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Majumder, Sarna Mazumder, Badhan Islam, S. M. Taohidul Faculty of Computer Science and Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Electrical and Electronics Engineering Patuakhali Science and Technology University Patuakhali Bangladesh

ISBN: (纸本)9783031346187

Early diagnosis of crop plant disease is crucial since many of these diseases pose a considerable threat not only to global food security but also towards agricultural productivity. Aiming that, for detection of mungbean leaf diseases at the beginning stage, we introduce a novel approach based on Gabor Wavelet Transform (GWT) and Cubic SVM in this paper. We perform GWT to decompose the given images into eighteen directional sub-bands and then extract fusion of Gabor Wavelet (GW) based texture features from each detailed GWT coefficient sub-band. Finally, Cubic SVM was deployed to classify three different disease classes by using these GW based features, conducting cross validation at 10 fold. Outcomes of our experimental evaluation using our self-prepared dataset of mungbean leaf diseases exhibit that our proposed method yields overall a sensitivity of 91.11%, a specificity of 95.56%, a precision of 91.39% and an accuracy of 91.11%. Moreover, outcomes obtained from our comparative analysis confirm the supremacy of our proposed framework over 3 currently existing approaches. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Crops

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Industry 4.0 – Applications, challenges and opportunities in industries and academia: A review

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Materials Today: Proceedings 2023年 79卷 389-394页

作者： Rana, Bharti Rathore, Sanjay S. Department of Computer Science University of Delhi Delhi India Skill Faculty of Engineering and Technology Shri Vishwakarma Skill University Dudhola Palwal Haryana India

Industrial revolutions have always influenced the other sectors since the first industrial revolution. "Industry 4.0 (I4.0)", the fourth industrial revolution, in particular, has impacted the ecosystem of the industries where the focus is on developing cyber-physical systems (CPS). The ultimate goal of I4.0 is building "smart" machines that can predict and take intelligent/smart decision(s) using enablers of I4.0 like data analysis, communication, and internet-of-things. I4.0 has applications in many sectors like healthcare, agriculture, wood, food, and education. It has boosted not only these sectors but also new concepts like Smart Material, Agriculture 4.0, Health 4.0, Smart Operator 4.0, and many more have evolved in the literature. Adopting I4.0 also highlighted creating a new educational paradigm that focuses on developing the Smart Workforce, Smart Operators, Smart Material and Smart Project Managers and boosted the concept of Education 4.0. The objective of the present work is to review the research works of I4.0, its applications, and challenges in the above-listed prominent sectors. The focus of the work is to report (i) the current state of various technologies of I4.0 in the industries, (ii) the present status of I4.0 in the academia ecosystem, and (iii) key identified challenges in the adoption of I4.0. It was observed that technologies, mainly Artificial Intelligence, IoT, machine learning, CPS, and robotics, are popularly used. However, the popularity of technologies varies according to the sector or application area. Also, an attempt was made to report the various opportunities for the researchers and how Academia can support the demands of I4.0. © 2023

关键词： Industry 4.0

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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A Novel Adaptive Non-uniform Conformal Meshing Method for Efficient FDTD Simulation

A Novel Adaptive Non-uniform Conformal Meshing Method for Ef...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Chen, Wei-Xin Wang, Jian Shi, Wen Liu, Qi-Feng Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Chongqing University School of Electrical Engineering Chongqing400044 China

ISBN: (纸本)9798350355581

In the process of generating conventional FDTD meshes, the model voxelization often takes up too many computational resources. To address this issue, this paper is devoted to presenting an effective mesh voxelization technique with adaptive partitioning and non-uniform grid generation. In this scheme, the entire computational domain is divided into subintervals based on the model's demarcation points, and the mesh generation parameters can be adaptively adjusted according to the information of each sub-interval in order to generate a reasonable non-uniform mesh. Besides, the scheme corrects mesh generation conditions for voxels across the model interface and records their conformal information, resolving the staircase approximation issue caused by traditional voxelization. The proposed mesh generation technique can minimize the number of grids while ensuring the geometrical accuracy of the model. The mesh quality is further validated by applied to meshing some typical objects in computational electromagnetics problems. © 2024 IEEE.

关键词： Mesh generation

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