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Digital Communications and Networks 2022年第3期8卷 235-236页

作者： Ruidong Li Shiwen Mao Periklis Chatzimisios Constandinos X.Mavromoustakis Intelligent computation and network laboratory(ICNL)at Kanazawa University Japan the Wireless Engineering Research and Education Center(WEREC) Department of Information and Electronic Engineering at the International Hellenic University(Greece) Constandinos X.Mavromoustakis is currently a Professor at the Department of Computer Science at the University of Nicosia Cyprus

Guest editorial The emerging applications,suchas Augmented and Virtual Realities(AR/VR),InternetofThings(IoT),4K/8Kstreaming,raisestrongrequirementsto movecomputationfrom thecloudtotheedgestobecloser *** are tremendous possibilities for the network edge,which may includeavariety ofentities,such as small datacenters,end devices,and resource-abundant network *** together provide the network computation and intelligence to users.

关键词： network IoT Communications

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Thermomechanical Characterization of Hot Surface Ignition Device Using Phenomenological Heat Flux Model

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JOURNAL OF PROPULSION AND POWER 2022年第4期38卷 656-670页

作者： Kang, Sang-Guk Ryu, Je Ir Motily, Austen H. Numkiatsakul, Prapassorn Lee, Tonghun Kriven, Waltraud M. Kim, Kenneth S. Kweon, Chol-Bum M. U.S. Army Research Laboratory Aberdeen Proving Ground MD 21005 Aerospace Engineer Weapons and Materials Research Directorate Postdoctoral Fellow Computational and Information Sciences Directorate University of Illinois at Urbana-Champaign Urbana IL 61801 Graduate Student Department of Mechanical Science and Engineering. Student Member AIAA Graduate Student Department of Materials Science and Engineering Professor Department of Mechanical Science and Engineering. Associate Fellow AIAA Professor Department of Materials Science and Engineering Mechanical Engineer Computational and Information Sciences Directorate. Member AIAA Program Manager Computational and Information Sciences Directorate. Member AIAA.

Modern aircraft compression ignition engines that seek to achieve fuel-flexible operation require ignition-assisting devices to perform reliably. Many military propulsion systems, and unmanned aircraft in particular, can use hot surface energy addition to improve engine performance. An analysis of the thermomechanical stress behavior of hot surface ignition devices is therefore critical to ensure that they can satisfy new operational requirements. In this paper, a phenomenological heat-flux model is proposed to simulate heat generation inside a heating element. Two finite element analysis models are established using the proposed heat-flux model in order to characterize the transient thermomechanical behavior of an ignition assistant: a two-dimensional (2-D) axisymmetric model with an equivalent cylindrical heating element and a three-dimensional (3-D) solid model with realistic heating element geometry. The transient behavior of the ignition assistant is investigated at standard conditions by characterizing the temperature and thermal stress inside the device for various input voltages. The effect of the nonaxisymmetric heating element geometry on the temperature and thermal stress distribution is assessed. Furthermore, the 3-D ignition assistant model is used to analyze the thermomechanical behavior of an ignition device subjected to a combustion event inside a rapid compression machine. Multiple circumferential orientations of the heating element plane relative to the spray axis are investigated. Results demonstrate that the observed maximum principal stress depends significantly on heating element orientation.

关键词： Heat Flux Stress Distribution Heat Generation Unmanned Aerial Vehicle Propulsion System Surface Energy Adaptive Mesh Refinement Fuel Injection CFD Simulation Fracture Toughness

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An Insight into Real Time Vehicle Detection and Classification Methods using ML/DL based Approach

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Procedia computer science 2024年 235卷 598-605页

作者： Riddhi Mehta Dr. Ankit Shah Research Scholar Faculty of Engineering and Technology Sankalchand Patel University Visnagar Gujarat India Associate Professor Computer Engineering Department Gandhinagar Institute of Technology Gandhinagar University Gujarat India

Vehicle detection and classification is one of the major challenges for automated traffic surveillance as well as for military defence systems to identify enemy vehicles. For efficient real-time surveillance, accurate detection and classification of many vehicle kinds, including cars, trucks, buses, and so forth are important. This paper focuses on different methods of machine learning used toidentify thevehicle and then then categorize each vehicle according to the classes. It also explores the critical insight of existing vehicle detection and classification approaches using various machine learning methods like YOLO, CNN, R-CNN, and AdaBoost. The majority of the research projects now in existence solely consider increasing image-based accuracy, which has poor real-time performance and uses more computational resources.

关键词： AdaBoost Machine Learning Vehicle Detection Vehicle Tracking YOLO

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A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

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Journal of Infrastructure Preservation and Resilience 2022年第1期3卷 1-14页

作者： Dong, Shaoyang Jiang, Yusheng Yu, Xiong Graduate Research Assistant Department of Civil and Environmental Engineering Case Western Reserve University ClevelandOH United States Opal J. and Richard A. Vanderhoof Professor and Chair Department of Civil and Environmental Engineering Professor of Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH United States

This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions. © The Author(s) 2022.

关键词： Pavements

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Intelligent Methods for Early Prediction of Heart DiseaseBehaviors

Intelligent Methods for Early Prediction of Heart DiseaseBeh...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Hamdi A. Al-Jamimi Hamdi A. Al-Jamimi is an associate professor specializing in Computer Science & Engineering at the Research Institute KFUPM

The early diagnosis of heart disease relies on accurately assessing symptoms that manifest at different stages of the disease. The health care sector holds extensive patient records containing invaluable data that necessitate in-depth investigation for uncovering hidden patterns crucial to early disease diagnosis. Timely detection of cardiovascular disease has the potential to save lives. Consequently, the application of intelligent methods in biomedical research has garnered significant interest, leading to the exploration of various artificial intelligence techniques at different levels within the medical domain. These techniques aim to utilize the existing data to improve early detection and mitigate the risks of numerous diseases, including heart disease. This study highlights the importance of leveraging machine learning (ML) methods for accurate heart disease prediction. By utilizing a diverse range of ML algorithms and a carefully collected dataset, we have successfully developed and evaluated multiple classifiers for heart disease diagnosis. The performance of these classifiers is evaluated using a newly published dataset that was collected from the Medical Help Center, a private hospital and heart center in Erbil, Iraq. This dataset comprises 333 cases and 21 attributes, which are utilized for training and validating the developed ML models. Experimental results demonstrate that the employed techniques achieve an accuracy level of over 92%. Notably, the gradient-boosting classifier exhibits the highest performance, achieving an accuracy level of 98.51 % during the testing phase. The promising results obtained demonstrated the potential of ML techniques in enhancing early detection and improving patient outcomes.

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative adversarial networks

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Decoding Sentiments: Harnessing the Power of NLP for Comparative Analysis of ML Algorithms

Decoding Sentiments: Harnessing the Power of NLP for Compara...

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International Conference on Computing and Machine Learning, CML 2024

作者： Varma, Nadimpallli Madana Kailash Harshini, Marisetti Madhan Mohan, R. Arora, Gagandeep Singal, Swati Student Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Department of Computer Science and Engineering Faculty of Engineering Annamalai University Chidambaram India Head of the Department Professor Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819765874

Machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Sentiment decoding libraries. Researcher’s comments from published papers were analyzed using the various approaches, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Support Vector Machine consistently outperforms other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection. This paper presents a study focused on sentiment analysis, aimed at identifying the most effective machine learning algorithm among various contenders. Traditional conclusions regarding the superiority of specific algorithms are often based on their performance on specific datasets or tasks, which may not provide a comprehensive comparison due to the diverse nature of algorithms. To address this, a standard dataset encompassing the advantages and disadvantages of all algorithms is utilized. Three approaches employing VADER, TextBlob, and transformers are employed for sentiment analysis. Results consistently highlight SVM as the top-performing algorithm, followed by Associative analysis. This standardized conclusion contributes to the broader understanding of sentiment analysis within the realm of machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Review on Big Data Analytics in Internet of Things (IoT) and Its Roles, Applications and Challenges 2nd

A Review on Big Data Analytics in Internet of Things (IoT) a...

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2nd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2020

作者： Mothe, Rajesh Tharun Reddy, S. Vijay Kumar, B. Rajeshwar Rao, A. Chythanya, Kanegonda Ravi Research Scholar School of Computer Science and Artificial Intelligence S R University Warangal India Department of CSE SR Engineering College Warangal India Warangal India Associate Professor School of Computer Science and Artificial Intelligence S R University Warangal India

ISBN: (纸本)9789811636899

Big Data Analytics is a popular term all around utilized for a huge arrangement of data and it's analytics, it manages a large volume of heterogeneous information as organized, semi-organized and unstructured. We are having some knowledge on how information is pulled back from various sources and get converged for information preparing. In this paper, our major concentration on basic importance of the Big Data Analytics and IoT (Internet of things) and the multi-faced motivation behind Big data Analytics in IoT like its roles, applications, and challenges. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Traffic sign detection and recognition using deep learning-based approach with haze removal for autonomous vehicle navigation

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 7卷

作者： Rani, A. Radha Anusha, Y. Cherishama, S.K. Laxmi, S. Vijaya Associate Professor Department of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC Autonomous) Maisammaguda Hyderabad India

Autonomous vehicle navigation technology is increasing rapidly. However, automatic sign recognition in complex illumination environments like low-light, hazy regions is a significant challenge in in-vehicle navigation. So, haze removal and robust traffic sign detection and recognition (TSDR) are critical for ensuring the vehicle's and its passengers' safety. However, the conventional methods failed to perform both haze removal and TSDR operations simultaneously. Further, the conventional haze removal methods eliminate the wanted pixels, still the presence of haze, which results in reduced traffic sign detection performance. Moreover, the conventional sign recognition methods classify a few types of traffic signs. So, this work aims to develop a unified model for multi-class sign recognition in complex environmental conditions. Therefore, this work introduced the deep learning model for haze removal based on TSDR (DLHR-TSDR). Initially, the CURE-TSD dataset is considered. The haze removal U-network (HRU-Net) module inputs a hazy image and outputs a haze-free image trained to learn the mapping between hazy and haze-free images. Then, the TSDR-convolutional neural network (CNN) module takes the haze-free image from the previous module as input and outputs the location traffic signs in the image. The simulation results on the Carleton University Retinal Eye-Traffic Sign Dataset (CURE-TSD) dataset show that the DLHR-TSDR method developed in the study resulted in 99.01 % accuracy, higher than traditional methods. © 2024 The Authors

关键词： Convolutional neural network Deep learning Haze removal Traffic sign detection and recognition

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IoT-Enabled Predictive Healthcare Monitoring Using Machine Learning Models 3rd

IoT-Enabled Predictive Healthcare Monitoring Using Machine L...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Patil, Nitin Jagannath Sharma, Avinash Prasad, K.D.V. Yogi, Kottala Sri Dankan Gowda, V. Department of Instrumentation Engineering D. N. Patel College of Engineering Shahada Maharashtra Untavad India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Pune India Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India

ISBN: (纸本)9789819751457

This study explores the capabilities of integrating the Internet of Things (IoT) with machine learning to monitor health in real time. Using IoT-powered devices like heart rate monitors, glucose meters, and blood pressure devices, health metrics were consistently gathered and sent to a cloud-based system. After refining the data for better quality, machine learning techniques were used to create health prediction models. The findings showed significant precision, especially when using neural networks, in predicting health-related events. This combined system can identify and notify healthcare experts about potential health concerns immediately. However, the promise of ongoing health tracking and early detection comes with concerns regarding data protection, data handling, and model reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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