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Remote Sensing Technologies and Applications in Urban Environments IX 2024

作者： Halder, Dyutisree Harshit, Harshit Garg, Rahul Dev Geomatics Engineering Group Department Indian Institute of Technology Roorkee India Dept. of Computer Science IT University Copenhagen Denmark

ISBN: (纸本)9781510681040

Urban environments are increasingly experiencing heat-related challenges due to climate change and rapid urbanization. To address these challenges, it is essential to improve our understanding of urban thermal dynamics. Digital twin technologies provide an innovative way to integrate multiple data sources and generate high-fidelity, real-time models of urban landscapes, allowing for deeper insights into urban heat distribution. In this study, we showcase three distinct workflows for generating digital twin environments for urban settings with varying degrees of complexity and visualization fidelity, focusing on thermal radiance mapping and geospatial analysis. The first workflow presents a low-level integration utilizing OpenStreetMap (OSM) building footprints to create a fundamental digital twin. Here, OSM data is leveraged to map urban geometries, providing the basic framework for thermal radiance analysis by associating building shapes and layouts with thermal data. This workflow is ideal for lightweight, accessible applications that focus on simple 2D urban heat mapping with readily available open-source tools. In the second workflow, we integrate Cesium Ion into the QGIS environment to create an enhanced 3D urban digital twin. Cesium Ion's 3D tiling capabilities are used to visualize urban geometries in three dimensions, enabling more detailed geospatial analysis. Combined with QGIS's robust spatial data processing, this workflow facilitates advanced urban thermal analysis, including the impact of building heights and materials on heat distribution. Finally, the third workflow demonstrates a cutting-edge approach utilizing NVIDIA Omniverse's implementation of Open Universal Scene Description (OpenUSD) to create highly detailed and realistic 3D urban environments. This state-of-the-art framework allows for the development of photorealistic urban digital twins, capable of supporting complex simulations of thermal dynamics and interactions. With high-definition re

关键词： Thermal modeling

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A Holistic Analysis and Experimental Testing of a Passive Heat Path for Electric Motors Slots

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8128-8138页

作者： Hebala, Ahmed Connor, Peter H. Nuzzo, Stefano Gerada, Chris Galea, Michael Arab Academy for Science Technology and Maritime Transport Electrical and Control Engineering department Alexandria5528341 Egypt Research Group Faculty of Engineering NottinghamNG7 2RD United Kingdom University of Modena and Reggio Emilia Department of Engineering Enzo Ferrari Modena41121 Italy University of Malta Department of Electrical Engineering MsidaMSD 2080 Malta

This article presents a thorough and broad analysis of the passive slot heat path (HP) concept. Six HP shapes are proposed and compared in terms of their combined thermal and electrical performance. Through an extensive search pool that covers a wide range of motor and slot designs and operating conditions, this article assesses the heat removal capabilities of the HP from the slot. The analysis is performed through a lumped parameter thermal network (LPTN) approach. Additionally, statistical analyses using correlations and linear regression models are carried out to improve understanding of this concept. Furthermore, a neural-network model is created to improve the regression model accuracy in predicting the slot temperature and temperature reduction. Finally, experimental testing and validation of the modeling are carried out on one of the six HP shapes. © 2015 IEEE.

关键词： Lumped parameter networks

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Evaluating the Potential of Mobile Applications for Mental Health Prediction: A Review 2nd

Evaluating the Potential of Mobile Applications for Mental H...

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2nd Congress on control, Robotics, and Mechatronics, CRM 2024

作者： Gupta, Sahil Vashist, Nimish Pal, Prashant Kumar Sharma, Swati Department of Computer Science and Engineering KIET Group of Institution Delhi-NCR Meerut Rd Uttar Pradesh Ghaziabad201206 India

ISBN: (纸本)9789819768097

The human mind is a remarkable cognitive powerhouse, influencing perception, decision-making, and behavior. Its effectiveness lies in its ability to process information, solve problems, adapt to new situations, and experience emotions. A healthy mind is essential for well-being, productivity, and personal growth. However, mental health issues have become a global concern, with increasing attention to accessible and cost-effective interventions. This meta-analysis endeavors to fill this critical gap by systematically investigating the efficacy of standalone psychological interventions for mental health via smartphone application. By synthesizing insights from previous research and analyzing trends in mobile health interventions, this study aims to delineate effective behavior change techniques, ascertain participant perceptions regarding the feasibility and functionality of mental health applications, and evaluate the precision of many popular machine learning techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power UniversityChina Department of Mathematics&Computer Science Fayetteville State UniversityFayettevilleNCUSA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA Department of Computer Science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Exploring the Potential of Convolutional Neural Networks in Automatic Diagnosis of Dragon Fruit Diseases from Plant Photographs 7

Exploring the Potential of Convolutional Neural Networks in ...

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7th International Conference on Intelligent Computing and control Systems, ICICCS 2023

作者： Mehta, Shiva Kukreja, Vinay Bansal, Ankit Kaur, Kushwant Singh, Amandeep Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department of Computer Science and Engineering Mohali India Gulzar Group of Institutions Department of Computer Science and Engineering Ludhiana India

ISBN: (纸本)9798350397253

Numerous instances of dragon fruit diseases over the years have caused an estimated 14% annual crop loss globally, affecting untold millions of people. The study of dragon fruit illnesses, or pathology, aims to increase plants' chances of surviving in the presence of pathogenic parasite microbes and poor climatic conditions. Environment-related elements that influence the development of dragon fruit diseases include temperature, pH, humidity, and moisture. Misdiagnosis can result in chemical overuse that costs money, environmental imbalance and pollution, and the establishment of pathogen strains that are resistant to treatment. The process of diagnosing diseases nowadays is time-consuming, expensive, and dependent on human scouting. Compared to the current method, automatic disease segmentation and diagnosis from dragon fruit photos can be somewhat useful. This study uses Convolutional Neural Network (CNN) models, to accurately identify four kinds of apple plant diseases from photographs of apple plant leaves. 'Healthy,' 'scab,' 'rust,' and 'many illnesses' are among the classifications. © 2023 IEEE.

关键词： Fruits

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Heart Disease Prediction Model using various Supervised Learning Algorithm 12

Heart Disease Prediction Model using various Supervised Lear...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Upadhyay, Sachin Dwivedi, Anjali Verma, Ashutosh Tiwari, Vatsya Gla University Department of Computer Engineering & Application Mathura India Government Girls Polytechnic Computer Science and Engineering Amethi India G.L. Bajaj Group of Institutions Computer Science & Engineering Mathura India Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India

ISBN: (纸本)9781665462617

A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (ML). Machine learning's core concept is to replicate how the brain functions. ML approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine Learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), Logistic Regression (LR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all ML algorithms in the trial (0.87). © 2023 IEEE.

关键词： Support vector machines

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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