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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 IEEE.

关键词： Deep learning

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Numerical study on sloshing in coaxial shells 69

Numerical study on sloshing in coaxial shells

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69th International Conference on Vibroengineering

作者： Choudhary, Neelam Rana, Saurabh Degtyarev, Kirill Kriutchenko, Denys Strelnikova, Elena Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida India A. Podhorny Institute of Mechanical Engineering Problems Kharkiv Ukraine

Sloshing in coaxial shells partially filled with liquid is investigated using reduced boundary element method. Conical shells are considered as storage tanks. An ideal and incompressible fluid is assumed in the shells. The spectral boundary problem for the liquid vibrations in rigid shells is solved. The results demonstrate the high accuracy of the presented approach. © 2024 Neelam Choudhary, et al.

关键词： Boundary element method

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CFNet: Cross-Domain Bearing Fault Diagnosis Under Different Operating Conditions 5

CFNet: Cross-Domain Bearing Fault Diagnosis Under Different ...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Liu, Zhengyu Sun, Tong Xu, Juan Wu, Tong Wang, Yewei Xu, Rui School of Mechanical engineering Hefei University of Technology Anhui Hefei China School of Computer Science and Information engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798331529192

The methods based on unsupervised domain adaptation are now widely used in the diagnosis of bearing faults. However, the global dependence of features in the feature extraction process is often overlooked. This global dependence is crucial for accurately diagnosing bearing faults, as it can reveal the distribution and variation patterns of fault signals in the overall structure. Therefore, we propose a Centralized Features Network (CFNet) for bearing fault diagnosis. The core of CFNet lies in its Transformer-based feature extractor, which not only captures the local details of fault signals but also preserves the global dependence, thereby achieving comprehensive analysis of fault signals. Furthermore, an Explicit Visual Center Module is proposed to further improve the fusion of long-distance features and local angular regions. Finally, a subdomain adaptation module is also proposed to transform the features of each domain to achieve subclass alignment of the feature space distribution. We have performed experiments on the CWRU and a self-built dataset using the mentioned model. The results demonstrate the effectiveness of our model. © 2024 IEEE.

关键词： Fault detection

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TOWARDS PHYSICALLY TALENTED AERIAL ROBOTS WITH INTELLIGENT SWARM BEHAVIOR THEREOF: AN EFFICIENT CO-DESIGN APPROACH

TOWARDS PHYSICALLY TALENTED AERIAL ROBOTS WITH INTELLIGENT S...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： KrisshnaKumar, Prajit Paul, Steve Manjunatha, Hemanth Corra, Mary Esfahani, Ehsan Chowdhury, Souma Department of Mechanical and Aerospace Engineering University at Buffalo BuffaloNY United States Department of Computer Science Engineering University at Buffalo BuffaloNY United States

ISBN: (纸本)9780791888360

The collective performance or capacity of collaborative autonomous systems such as a swarm of robots is jointly influenced by the morphology and the behavior of individual systems in that collective. In that context, this paper explores how morphology impacts the learned tactical behavior of unmanned aerial/ground robots performing reconnaissance and search & rescue. This is achieved by presenting a computationally efficient framework to solve this otherwise challenging problem of jointly optimizing the morphology and tactical behavior of swarm robots. Key novel developments to this end include the use of physical talent metrics and modification of graph reinforcement learning architectures to allow joint learning of the swarm tactical policy and the talent metrics (search speed, flight range, and cruising speed) that constrain mobility and object/victim search capabilities of the aerial robots executing these tactics. Implementation of this co-design approach is supported by advancements to an open-source Pybullet-based swarm simulator that allows the use of variable aerial asset capabilities. The results of the co-design are observed to outperform those of tactics learning with a fixed Pareto design, when compared in terms of mission performance metrics. Significant differences in morphology and learned behavior are also observed by comparing the baseline design and the co-design outcomes. Copyright © 2024 by ASME.

关键词： Reinforcement learning

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Design Simulation for Chassis of Electric Solar Vehicle

Design Simulation for Chassis of Electric Solar Vehicle

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International Conference on Production and Industrial engineering, CPIE 2023

作者： Verma, Neetu Kumar, Krishan Mishra, O.P. Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonipat Murthal India Department of Mechanical Engineering J. C. Bose University of Science and Technology YMCA Faridabad India

ISBN: (纸本)9789819983421

Solar energy is the demanding field in present era and keeping in view the limited resources like petroleum etc., a solar electric vehicle is proposed in this work. CAE analysis of chassis for the solar vehicle has been performed using Hypermesh V-13 under different boundary conditions. The frame of the vehicle under consideration is designed & analyzed with a chain sprocket system along with motor. As per previous works, the finite element analysis of automobile structural frames has been carried out and chassis is proven to be the most important part of the vehicle, which is not just the structural frame or a skeleton of a vehicle but also the passenger compartment which is responsible to protect the driver from acts like collisions. The current work is an attempt to analyze the loading, meshing and considering all boundary conditions to provide realistic environment to the roll cage under consideration. The roll cage is designed to accommodate two people at a time. It has been attempted to keep the design optimum one and to support various equipments which the vehicle will finally house in it. Flexible solar panels, chromyl pipes, aluminum knuckles, light weight and much efficient lithium ion battery etc. have been accommodated during the actual fabrication of vehicle in order to reduce the weight without hampering the strength. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chassis

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Big Data Security through Privacy - Preserving Data Mining (PPDM): A Decentralization Approach 2

Big Data Security through Privacy - Preserving Data Mining (...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Josphineleela, R. Kaliapp, S. Natrayan, L. Garg, Ashish Panimalar Engineering College Department of Computer Science Engineering Chennai600123 India Velammal Institute of Technology Department of Mechanical Engineering Chennai601204 India Saveetha School of Engineering Department of Mechanical Engineering Chennai602107 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350346640

The increasing familiarity and improvements in data mining technologies bring serious security threats to the data of individuals. An emerging technology that provides better security and data security is PPDM: Privacy-Preserving Data Mining. The fundamental tenet of PPDM is to alter the data in such a manner that data mining techniques may be carried out successfully without affecting the privacy of sensitive data included in the data. The research article is focused on reducing the security and privacy risk that is created by data mining operations. During the process of data collection, Information processing, and publication of data there may be much security breached in the processing of the data in the data mining process. This paper focuses on the wide problems and is concerned with data mining, and finding a solution to ensure security and access through the data by diving the centralized data into various segments to ensure security by privacy-preserving methods. Through the process of finding various user interfaces related to data processing and issues related to the security of private information, this article will also show the vital role of PPDM in data mining and security. © 2023 IEEE.

关键词： Data mining

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Liquid induced vibrations of partially liquid-filled elastic cylindrical-conical shells 68

Liquid induced vibrations of partially liquid-filled elastic...

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68th International Conference on Vibroengineering

作者： Choudhary, Neelam Degtyarev, Kirill Strelnikova, Elena Rana, Saurabh School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India A. Podhorny Institute of Mechanical Engineering Problems Kharkiv Ukraine

The main objective of this study to develop efficient numerical techniques based on coupled finite and boundary element methods to estimate natural vibration frequencies in compound reservoirs with liquid. Elastic structures composed of cylindrical and conical shells connected by rings have been examined. The space between the shells is filled with an ideal, incompressible fluid. The advantage of the proposed approach lies in the ability to investigate both free and forced vibrations of empty and liquid-filled shells structures within the framework of the single computer technology. The benchmark test calculations demonstrated the high accuracy and efficiency of the proposed approach. The novelty and practical value of the obtained results lie in the ability to investigate fuel tanks of complex shapes under various conditions. © 2024 Neelam Choudhary, et al.

关键词： Finite element method

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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Investigating EEG Connectivity Differences Between Right and Left-Handers 3

Investigating EEG Connectivity Differences Between Right and...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Almabruk, Tahani Adalhamid, Samia Almaghairbe, Rafig Hadoth, Laila Yousef, Aisha Tan, Tele Omar Al-Mukhtar University Faculty of Science Department of Computer Science Elbeida Libya University of Derna Faculty of Science Department of Computer Science Derna Libya Curtin University Faculty of Science and Engineering Department of Mechanical Engineering Perth Australia

ISBN: (纸本)9798350319897

An extensive knowledge of cognitive development from early infancy to late adolescence is required to investigate brain neural pathways. The success in recognizing diverse stimuli is one of the remarkable frameworks that used to examine the essential variations in cognitive processes. Recognizing the intended stimuli has been proven to be influenced by several factors such as participants 'gender and handedness. In this study, we investigated whether these cognitive variations regarding handedness take place at age nine-year. 33-EEG channels were recorded while seven right-handed and seven left-handed participants completed the Eriksen Flanker task-experimental paradigm with a variety of stimuli for evaluating attention and cognition. In each handedness group, the imaginary coherence was computed to evaluate the functional connectivity of each child. It was observed that left and right-handers activate distinct cortical regions, particularly in the alpha and beta bands. Additionally, the findings showed that brain connectivity at the incongruent Flanker stimuli can be utilized to differentiate the two targeted group. © 2023 IEEE.

关键词： coherence Flanker conflict handedness

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Intelligent Virtual Laboratory Development and Implementation using the RASA Framework 7

Intelligent Virtual Laboratory Development and Implementatio...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Josphineleela, R. Kaliappan, S. Natrayan, L. Bhatt, Upendra Mohan Department of Computer Science Engineering Panimalar Engineering College Poonamallee600123 India Department of Mechanical Engineering Velammal Institute of Technology Chennai601204 India Department of Mechanical Engineering Saveetha School of Engineering SIMATS 602107 India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun248002 India

ISBN: (纸本)9781665464086

Virtual environments for learning with intelligent support are in high demand right now. This is due to the fact that smart e-learning environments must be built internally in order to increase students' hands-on abilities. Through inquiry and demonstration in a smart e-lab environment, these talents are linked to several disciplines of science and engineering. Students and instructors may make the transition from working in virtual environments to doing experiments using actual materials. Lab assistants often spend the bulk of their time making the same mistakes. As a result, this research looked at the technology, proposed approach, and implementation of an intelligent virtual C programming laboratory. This ensures that virtual assistants employ chatbots to fix student faults. The environment is based on his NLP (natural language processing) and machine learning models, with web application development using his MERN stack and virtual assistant production using the RASA, Artificial Intelligence (AI) framework. © 2023 IEEE.

关键词： Students

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