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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66489 条记录，以下是281-290 订阅

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Next-Generation Traffic Control: Adaptive Timer and Emergency Vehicle Priority in Intelligent Traffic Management

Next-Generation Traffic Control: Adaptive Timer and Emergenc...

引用

2024 International Conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Devika, S.G. Govind, A. Lekshmi, D. Sct College of Engineering Dept. of Computer Science Engineering Trivandrum India

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394399

Traffic has always been a troubling issue in our society. The increased number of automobiles on roads has sub-stantially increased traffic congestion polluting the environment and causing stress and anxiety to passengers in the traffic clog. The existing system of fixed traffic control system even though widely used, lacks adaptability and operates on predefined signal timers. The lack of adaptability can lead to inefficient traffic flow, especially during high traffic congestion. To address this issue, we propose a more adaptive and intelligent system that leverages adaptive timer control and prioritizes emergency vehicles like, ambulance. The adaptive timer control system considers the count of vehicles at the previous junction as well as the count of vehicles currently arriving at the intersection to update the signal timers. Additionally, to prevent delays in emergency response times and to provide a better impact on public safety and health outcomes we have provided an added feature to prioritize emergency vehicles and release the traffic upon its arrival at the respective signal. The detection of the emergency vehicle is to be carried out using the YOLO v8(You Look Only Once) algorithm along with siren recognition using a classifier model. The proposed system offers a significant improvement over the existing traffic control infrastructures and has the potential for aiding resilient and smooth transportation. © 2024 IEEE.

关键词： Machine learning

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Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models 1

Forecasting Stock Price by LSTM-CNN Hybrid Model and Compare...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Arora, Aditi Upadhyay, Yashaswi Shukla, Satvik Saket Abes Engineering College Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350377002

The The accurate forecasting of stock prices holds significant importance in devising effective trading strategies and making informed decisions about stock transactions. The inherent volatility in stock prices, driven by factors like geopolitical tensions, corporate earnings, and commodity costs, underscores the need for precise predictions. Domestic factors such as central bank policies, governmental decisions, inflation, and global market uncertainties also trigger stock price fluctuations. Estimating this volatility is a complex challenge for traders. A novel approach is presented in this research, proposing a hybrid model that integrates two distinct algorithms - Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). This model harnesses diverse data representations from stock time series to enhance price prediction accuracy. Comparative analyses are performed with alternative models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks) and Naïve Bayes to ascertain the most effective prediction paradigm with minimal error. The study demonstrates that combining temporal and image features extracted from the same dataset diminishes prediction errors compared to using these features independently. The hybrid model outperforms singular prediction models, highlighting a high enhancement. Among the individual models, the LSTM model exhibits superior performance in terms of prediction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Ethereum Powered E-Tendering System

Ethereum Powered E-Tendering System

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Ananthajothi, K. Jaisuriya, P.K. Haribala, S. Rajalakshmi Engineering College Dept. Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350352689

The use of information technology to trade information, deliver services, and other things is becoming more and more commonplace in the era of technological advancement. Tendering is one such area where the public and private sectors collaborate. E-tendering is a procurement technique that uses the internet and digital technologies to automate and simplify the whole bidding or tendering process. This makes it easier for the bidders to browse through all of the open tenders and select one to proceed with. But it frequently results in a number of challenges that both bids and bidders must deal with. The system is inefficient because of scale and transparency problems brought on by its centralized architecture. Additionally, the system frequently shares information about tenders with other outside organizations, which compromises the confidentiality and integrity of the tenders. To manage these issues, a decentralized method is used in the deployment of the Blockchain e-tendering system. Consequently, the procurement procedures will have the opportunity to leverage the potential of blockchain technology to enhance security and transparency. This research aims to provide an electronic tendering system that is transparent, liquidate, and safe. It will manage tender allocation and use smart contracts to automate the underlying processes with the least amount of human interaction possible. © 2024 IEEE.

关键词： Smart contract

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Five Shades of BERT: Assessing Performance in Question Answering through Comparative Analysis 4

Five Shades of BERT: Assessing Performance in Question Answe...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Raikwar, Kanishka Rajusth, Priyansh Prabhu, Sania Shrivastava, Navya Kj Somaiya College of Engineering Dept. Computer Engineering Mumbai India

ISBN: (纸本)9798350354218

The Bidirectional Encoder Representations from Transformers (BERT) model has transformed natural language processing, achieving great results across a wide range of applications. In this research paper, we conduct a comprehensive comparative analysis of the five BERT models and their performance for question answering tasks. For the purpose of our comparison, the models we have chosen are BERT, RoBERTa, ELECTRA, ALBERT and DistilBERT. Our methodology involves fine-tuning these models using the SQuAD dataset for extractive question answering tasks and evaluating their performance based on their training and validation losses. Our research provides insights into the similarities, differences and results of fine-tuning on the different BERT models. Our findings provide guidance for selecting an appropriate BERT model to utilise its full potential in real-world applications such as question answering tasks as NLP continues to evolve. © 2024 IEEE.

关键词： Natural language processing systems

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An Approach for XAI Visualizations for Explainability of Alzheimer's Detection 2

An Approach for XAI Visualizations for Explainability of Alz...

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2nd International Conference on Networking, Embedded and Wireless systems, ICNEWS 2024

作者： Khanapur, Shraddha Bharadwaj, Chirag B. Bhardwaj, Raghav Nayak, Jyothi S. BMS College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367461

In the field of medical image analysis, the integration of machine learning (ML) models has significantly enhanced the process of identifying and diagnosing Alzheimer's disease. However, the opacity of these models, often termed as "black boxes,"raises concerns regarding their interpretability and reliability in clinical settings. Explainable artificial intelligence (XAI) techniques have emerged as crucial tools to address these challenges by clarifying the reasoning behind model outputs. This paper offers an extensive review of literature concerning the employment of Explainable Artificial Intelligence (XAI) methodologies, such as SHAP, LIME, GradCAM, DeepLIFT, Saliency Maps, and LRP to augment the comprehensibility of ML models applied in the detection of Alzheimer's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Streamline Pro Next-Gen Recruitment Solution

Streamline Pro Next-Gen Recruitment Solution

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Kumar, Vinod Sangeetha, G. Gopal, K. Kumar, P. Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

An innovative full-stack project called 'StreamlinePro Next-Gen Recruitment Solution' aimsto transform the hiring process. With its modern technologies, this fully inclusive platform enhances the hiring process to satisfy the demands of administrators, HR professionals, and job seekers. The Job Board Application is developed around a powerful system for posting jobs and accepting applications. Experts in human resources can readily post job openings, andneed to offer corporate papers to validate their skills. This ensures that reliable sources are used to publish job openings. Conversely, candidates can use the website to look for jobs that fit their qualifications and apply immediately. Posting resumes is required, and an applicant tracking system (ATS) reviews them after are uploaded. After reviewing the entries, the ATS rates the resumes according to theirquality and applicability. One of the primary aspects of the StreamlinePro Next-Gen Recruitment Solution is its exclusive referral mechanism. Employees in a company may indicate that are willing to suggest others. Job searchers can contact these references, and if accepted, both parties can communicate through an integrated chat system. This recommendation system encourages networking and enhances job-seeking opportunities by leveraging existing staff networks. The application also offers the capability of real-time job status tracking. Applicants can monitor the progress of their applications and receive alerts regarding their receipt, review, and acceptance by employers. This tool helps clarity and informs job seekers during the hiring process. © 2024 IEEE.

关键词： Apprentices

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Genetic Data for Early Detection and Treatment of Cystic Fibrosis through Machine Learning 8

Genetic Data for Early Detection and Treatment of Cystic Fib...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Deepthi, P. Bhuvaneshwari, M. Chithra, K. Dhanasree, L.P. Jananee, V. Layola, J.A. Adlin Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Cystic Fibrosis (CF) is a genetic disorder that significantly impacts the respiratory and digestive systems, requiring early detection and precise management to improve patient outcomes. This research study presents a machine learning model designed to predict the presence, severity, and potential treatments for Cystic Fibrosis using genetic data. By leveraging collaborative filtering techniques, our model not only identifies individuals at risk but also enables personalized treatment plans based on genetic predispositions. The proposed model aims to revolutionize Cystic Fibrosis diagnosis and management by automating the screening process on a mass scale. This automation facilitates the identification of demographic areas and genetic populations with a higher susceptibility to Cystic Fibrosis, enabling targeted interventions. Through large-scale genetic data analysis, our approach ensures that at-risk populations receive timely and appropriate care, ultimately improving the quality of life for Cystic Fibrosis patients. The findings suggest that the implementation of this predictive model in clinical settings can lead to earlier and accurate diagnoses, personalized treatment plans, and significant advancements in Cystic Fibrosis care. The implications of this research extend beyond Cystic Fibrosis, offering a framework for applying machine learning in the diagnosis and treatment of other genetic disorders. © 2024 IEEE.

关键词： Personalized medicine

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A Survey on smoking and Alcohol Tracking System: State of the Art, Analysis, and Performance Evaluation 2

A Survey on smoking and Alcohol Tracking System: State of th...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Velavendan, R. Shubram, S. Shyam Sundar, V. Padmavathi, B. Kavitha, D. Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Addiction to alcohol and tobacco is still a major worldwide health issue that requires innovative approaches to early detection and monitoring. This review of the literature provides a thorough summary of recent research projects in the area of tracking systems for alcohol and tobacco use that are both intrusive and non-intrusive for correct classification. The main goal of this research is to investigate how different IOT-based models might improve the precision and effectiveness of alcohol and smoking detection. The measurement of several IoT models used in tracking systems for alcohol and smoking makes up a sizeable section of this work. The study assesses the benefits and drawbacks of each model. Additionally, how effectively it can identify individuals who smoke or drink and how well it can work with various datasets. This research intends to direct future research efforts and encourage the development of more precise and effective tracking systems for smoking and alcohol use, ultimately resulting in improved public health outcomes. © 2024 IEEE.

关键词： Internet of things

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FingerPrintInsight:Unveiling Crime Pattern Through Deep Fingerprint Analysis 4

FingerPrintInsight:Unveiling Crime Pattern Through Deep Fing...

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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Kavitha, T. Revappa Handignur, Sachin B Sachin, Y.M. Purandare, Sumit S Rathish, C.R. New Horizon College of Engineering Dept. of Computer Engineering Bangalore India

ISBN: (纸本)9798350393354

A fingerprint identification system employing deep machine learning and Convolutional Neural Networks could automate analysis. Images from crime scene investigation techniques are entered into a database. Partial latent prints are often difficult to classify. The system operates in three phases: preprocessing, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points. False minutiae removal further refines the data. The preprocessed fingerprints serve as input to train and test the model. As new prints are incorporated along with confirmed matches to improve accuracy, the system facilitates identification. The approach scales without proportionate increase in human effort. Accelerated evidence analysis could potentially solve more crimes by linking cases in a database. © 2024 IEEE.

关键词： Convolutional neural networks

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Recommendation System For Social Platform Using Deep Learning Techniques 8

Recommendation System For Social Platform Using Deep Learnin...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nishchal Narayan, S. SriVidya, M.S. Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and mutual friends, the system aims to provide accurate friend suggestions. This recommendation methodology can be applied across various domains, including search engine recommendations (Google, Explorer, Microsoft Edge), music recommendations (Wynk), video recommendations (YouTube), movie recommendations (Amazon Prime), and product recommendations (Flipkart, Amazon). The study employs similarity coefficient calculations like Jaccard Distance and Cosine Similarity, alongside ranking measures such as PageRank. Furthermore, the system's performance is evaluated using advanced deep learning techniques, including Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Comprehensive experimentation involves measuring the F'l-Score and comparing the accuracy of various machine learning and deep learning algorithms to identify the most effective methods for friend recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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