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2025 IEEE International students' Conference on Electrical, Electronics and computer science, sCEECs 2025

作者： Joseph Rajan, D. Antony sivanesan, T.M. Mohankumar, N. srinivasan, M. sivaraman, Audithan velmurugan, s. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Pune India P. S. v College of Engineering and Technology Department of Information Technology Tamilnadu Krishnagiri India Department of Information Technology Jafferpur New Delhi110073 India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331529833

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers. © 2025 IEEE.

关键词： Convolutional neural networks

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CNN based Optimized Autoencoder for satellite Image Compression

CNN based Optimized Autoencoder for Satellite Image Compress...

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Interdisciplinary Approaches in Technology and Management for social Innovation (IATMsI), IEEE Conference on

作者： P. sowmya sri s. Karthi M. Kalaiyarasi s. saravanan Computer Science and Engineering (Data Science) B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering V.S.B.Engineering College India Institute of AI & ML Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of EEE B V Raju Institute of Technology Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper introduces an innovative Convolutional Neural Network (CNN) based Optimized Autoencoder for the compression of satellite images, with a focus on high resolution sentinel-2 and Landsat imagery. In this method, the satellite images are resized and normalized before an optimal autoencoder architecture is implemented. The encoder compresses the image, while the decoder reconstructs it with minimum quality loss. Convolutional and down sampling layers in the encoder and up sampling layers in the decoder preserve image features. MsE, PsNR, ssIM, and CR were used to assess compression performance. The simulation outcomes show that the CNN-based Autoencoder can retain image quality across images with a high compression ratio of 512, an average MsE of 0.0064, an average PsNR of 25.214 db, and an ssIM of 0.62. This satellite image compression approach is efficient enough for storage and transmission in satellite data applications.

关键词： Image quality Technological innovation Image coding satellites Autoencoders Memory Real-time systems satellite images Decoding Convolutional neural networks

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Comparative Analysis on Prediction Model of Hate speech Detection based on social Media Data Using Artificial Intelligence

Comparative Analysis on Prediction Model of Hate Speech Dete...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Pravin D. Kaware Anjali b. Raut Department of Computer Science & Engineering H.V.P.M’s College of Engineering & Technology Amravati India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The exponential expansion of social media networking platforms has magnified the dissemination of hate speech, thereby requiring strong recognition systems to minimize its influence. The present work offers a comparative examination of many prediction models for the recognition of hate speech using social media data, employing Artificial Intelligence (AI) methodologies. The paper intends to assess the efficiency of several Machine Learning (ML) and Deep Learning (DL) algorithms for accurately detecting hate speech. The various strategies are evaluated using known datasets and measures including accuracy, recall, precision, and F1-score. In addition, useful insights on the most effective methods for identifying hate speech are presented. The findings suggest that deep learning approaches are exceptionally effective at catching the contextual nuances of hate speech. AI-based hate speech detection apps improve accuracy and efficiency. such systems are critical for creating a safer and more inclusive online.

关键词： Deep learning Analytical models Accuracy social networking (online) Computational modeling Hate speech speech recognition Predictive models Data models Context modeling

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Detection of Emergency vehicles in Traffic and Assign Traffic Free Path Using Deep Learning 4

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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4th International Conference on sentiment Analysis and Deep Learning, ICsADL 2025

作者： vikruthi, sriharsha singasani, Tejesh Reddy Ram Pavan Kumar, v.T.M. Nagendrudu, P.v.v.s.D. Raghavendra, Ch sahith, R. B v Raju Institute of Technology Dept of Cse Telangana Medak Narsapur502313 India University of Cumberlands LouisvilleKY United States Kakaraparti Bhavanarayana College Dept of Computer Science Andhrapradesh Vijayawada India Aditya Univeristy Department of Artificial Intelligence and Machine Learning Suramplaem India Andhra Pradesh Guntur India Cvr College of Engineering Dept of Computer Science and Engineering Telangana Hyderabad501510 India

ISBN: (纸本)9798331523923

Too much of Traffic jams are in a highly populated country. sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. so, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision. © 2025 IEEE.

关键词： Recurrent neural networks

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Multivariate Time series Forecasting for Cloud Machine Utilization

Multivariate Time Series Forecasting for Cloud Machine Utili...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Archana Naik Kavitha sooda Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LsTM, GRU, Prophet, and XGboost across the diverse datasets. The performance is evaluated using RMsE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMsE value for Informer is 0.13, Prophet is 0.097, XGboost is 0.096, LsTM is 0.1725, and GRU is 0.1547.

关键词： Cloud computing Accuracy scalability Time series analysis Predictive models benchmark testing Data models Internet of Things Forecasting Long short term memory

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A Comparative study of Optimized-LsTM Models Using Tree-structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan santos Gracia villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport systems(ITs)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long short-term Memory(LsTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LsTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean square Propagation(RMsProp),and stochastic Gradient Descend with Momentum(sGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LsTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LsTM hyperparameter tuning hybrid prediction model

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solar-Powered smart Wheelchair with voice Controller for Physically Challenged People

Solar-Powered Smart Wheelchair with Voice Controller for Phy...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： M. Karthik G. Hemalatha L. sreevidya R. Deepalakshmi K Premalatha Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

It is challenging for those with strong mental faculties but physical disabilities to get around in a conventional hand-powered wheelchair. This project will enable any existing wheelchair to have an inexpensive assembly that enables a smart motion automation system that can be controlled by speech recognition on a smartphone. Continuous operation is guaranteed by the dual-charging system, which also offers longer mobility without the need for frequent recharging and independence from external power outlets. This enables users to adjust to varying conditions by depending on solar power during the day and switching to battery charging when solar energy is scarce. The main concept is a smartphone with built-in bluetooth connectivity and the Android operating *** the distribution of the application we have developed, the goal of our project may be extended to other mobile devices that run Android software. The second main component of our system design is the microprocessor, which drives the wheelchair's dc motor in different directions. The wheelchair may now travel in a variety of directions thanks to this. The suggested model operates autonomously and on its own.

关键词： Automation Gears Navigation Wheelchairs Operating systems Wheels solar energy DC motors Mobile handsets Telecommunication computing

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Detecting Image-based Fake News with Neural sleuths 5th

Detecting Image-Based Fake News with Neural Sleuths

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5th International Conference on Artificial Intelligence and speech Technology, AIsT 2023

作者： Rathod, Nachiket Ramteke, Prabhakar Department of Computer Science and Engineering H.V.P. Mandal’s College of Engineering and Technology Maharashtra Amaravati India

ISBN: (纸本)9783031751660

In the period of extensive social media use, unconventional formats for news sharing often lack reliability, with misleading information disseminated through graphics emerging as a unique tactic. While the verification of the authenticity of news is a broad research domain, scholars have mainly focused on textual data. The misleading information detection model described in this paper uses neural sleuth-based networks to capture image data from the freely accessible Fakeddit dataset. The results are scrutinized using a confusion matrix, and a comprehensive evaluation of the model's performance metrics is conducted across six categories: "true," "satire," "false connection," "imposter content," "manipulated content," and "misleading content." This work contributes an inclusive analysis of the model's effectiveness as a problem posed by misleading information on social media. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Convolution Deep Network Image Fake News Neural Network

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Decision support system for Critical Cyber security Infrastructure: AI and ML Approach 12th

Decision Support System for Critical Cyber Security Infrastr...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： shambharkar, shubhangi Kumar, Anil Dari, sukhvinder singh Joshi, Ira bhoite, sonali Prashant shirkande, Aparna s. Indurkar, Ira Computer Technology Department Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Artificial Intelligence and Data Science Poornima Institute of Engineering and Technology Rajasthan Jaipur India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra Pune India S. B. Patil College of Engineering Indapur Maharashtra Pune India Department of Neuroscience DMCOP Datta Meghe College of Physiotherapy Maharashtra Nagpur India

ISBN: (纸本)9789819601462

The overall increase in cyber-attacks which takes place has had a major impact on the working of an organization and its related forensics department. With increased amount of breaches, the technical aspects of a developer are at stake. This had therefore led to the evolution of innovation and automation in the security domain. However, the risks which are associated with the same remains stagnant;since the digital transformation have been on a constant improvization factor with respect to the strategies thus involved. In such a scenario, the usage and implementation and artificial intelligence (AI) becomes a mandatory task. Cyber-attacks and breaches which are based on AI concepts have had a major impact and have revolutionized the overall working of the networking sector. An increase in the cyber-attack surface has been observed vividly. With this increase in the resilience of the networking sector, the author of the proposed research paper tends to highlight the need for AI- and ML-based decisive strategies which must be amalgamated in cyber security. The study also aims to focus on the role of various cyber forensic departments that have been successfully adopted by multiple companies. An understanding and working of a cyber-criminal is also needed in such a scenario. Hence, the knowledge of cyber security along with cyber forensic appears to be fruitful. The research paper thereby presents a systematic review of AI amalgamated with cyber forensics and provides various problem statements and methodologies associated with the same. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： AI Cyber forensics Methodologies ML Resilience

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Design a High-Frequency Dual Active bridge Converter for Discrete Time Frequency

Design a High-Frequency Dual Active Bridge Converter for Dis...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： M. Karthik G. Hemalatha M. Mathankumar L. sreevidya R. Deepalakshmi v. Kandasamy Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The dual active bridge (DAb) has become a crucial interface element in today's sophisticated systems. Renewable energy sources produce DC power, which is then stored in batteries, ultra-capacitors, and other energy storage devices. DAb is a crucial part of power exchange between two different buses because of its galvanic separation and bidirectional power transfer capabilities. The operation of the DAb is compatible with a broad range of input voltages and output loads. The creation of DAb is covered in most works. A well-known semiconductor integrated circuit with programmable control parameters is used in this straightforward, inexpensive method.

关键词： Time-frequency analysis Renewable energy sources Costs bridge circuits switched reluctance motors stability analysis batteries Telecommunication computing Time-domain analysis stress

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