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检索条件"机构=M.Tech Computer Science and Engineering"

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Detecting Driver Fatigue with Python and OpenCV

Detecting Driver Fatigue with Python and OpenCV

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International Conference on Communications and Signal Processing

作者： C Pandi T P Anish S. Selvanayaki P. Jagadeesan m. Vedaraj R Sanjay Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Computer Science and Engineering R.M.K College of Engineering and Technology Tamil nadu India Artificial Intelligence and Data Science Saveetha Engineering College Thandalam Tamil nadu India Computer Science and Engineering R.M.D Engineering College Tamil nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Utilizing eye aspect ratio analysis and facial landmarks, the sleep detection tool is intended to detect indicators of sleepiness or drowsiness in people. By leveraging the face recognition library, the program detects and extracts facial landmarks, with a particular focus on locating the positions of the eyes. The software analyzes the eye aspect ratio (EAR), which takes into account the relative distances between particular eye landmarks, to ascertain if an individual has fully or partially closed their *** video frame capture is performed from a chosen video source, typically a webcam, allowing real-time monitoring of the user. Each frame is processed to detect and analyze the eye regions, computing the EAR values for both the left and right eyes. If the EAR falls below a predefined threshold for a consecutive number of frames, it indicates that the person’s eyes are closed for an extended duration, suggesting potential drowsiness or *** enhance user safety, an alarm sound is played when the program detects drowsiness, providing an immediate alert to the individual. The alarm sound serves as an auditory cue, helping the person regain alertness and avoid potential accidents or undesired consequences. Additionally, the program displays the computed EAR on the video feed, allowing for visual feedback and monitoring of the eye aspect ratio values for debugging purposes and fine-tuning the system’s *** employing this sleep detection program, various applications can benefit from its capabilities. For instance, it can be utilized in the context of drowsy driving detection systems, alerting drivers when they exhibit signs of fatigue and helping prevent accidents due to impaired driving. moreover, the program holds potential for use in other scenarios where monitoring individuals’ alertness and detecting sleep-related patterns is essential, such as workplace safety or medical *** summary, the sleep detection program employs faci

关键词： Visualization Sleep Webcams Ear Streaming media Real-time systems Safety

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Developments In Hybrid Electric Vehicles: Investigating Plans, management techniques, and Power Resources for Sustainable Travel

Developments In Hybrid Electric Vehicles: Investigating Plan...

引用

International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Aditee mattoo Bhuvana J Vijay Kumar Pandey Department of IT and M.Tech Integrated Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of Mechanical Engineering Vivekananda Global University Jaipur India

technological developments in energy production and use are crucial to the shift to a future with fewer emissions and better health. Vehicle technology is impacted by a number of variables, including pollution-induced climate change, stricter environmental rules for autos, depletion of petrol and diesel supplies, and price volatility for transit systems. One of the most notable possibilities for clean automobile technology is the hybrid electric vehicle (HEV). This research explores several HEV designs and approaches, clarifying development guidelines and ways to enhance control algorithms. We also look at the various battery-powered drives that HEVs employ. Performance is the main area of study since control methods are essential to improving the fuel economy and lowering pollutants of hybrid engine systems.

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Big Data Analytics for Smart meter Data in Power Systems 9

Big Data Analytics for Smart Meter Data in Power Systems

引用

9th International Conference on science, technology, engineering and mathematics, ICONSTEm 2024

作者： Banu, E. Afreen Lavanya, R. Sandhiya, R. Vijayakumar, Bondu Kavitha, K. Vibhakar, Chirag Department of Computer Science And Engineering Chennai Tamil Nadu Thandalam602105 India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Informtion Technology Chennai Tamil Nadu Avadi600062 India R M K College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India Madanapalle Institute of Technology & Science Department of Electrical & Electronics Engineering Andhra Pradesh Madanapalle517325 India Annamalai University Department of Electrical & Electronics Engineering Tamil Nadu 608002 India Gujarat Power Engineering & Research Institute Gujarat Technological University Department of Electrical Engineering Gujarat Ahmedabad382710 India

ISBN: (纸本)9798350365092

The integration of big data analytics in power systems has surfaced as a promising approach to influence smart cadence data for perfecting functional effectiveness, enhancing grid trustability, and enabling demand- side operation. This paper explores the operation of big data analytics ways to reuse, dissect, and excerpt precious perceptivity from smart cadence data in power systems. By employing the vast quantum of data generated by smart measures, serviceability can gain a deeper understanding of client energy consumption patterns, identify anomalies, and optimize grid operations in real- time. The paper discusses colorful big data analytics methodologies, including machine literacy algorithms, data mining ways, and prophetic analytics models, applied to smart cadence data for cargo soothsaying, fault discovery, and energy optimization. Through a comprehensive review of being literature and case studies, this exploration elucidates the implicit benefits, challenges, and unborn directions of big data analytics for smart cadence data in power systems, offering precious perceptivity for experimenters, interpreters, and policymakers in the energy sector. © 2024 IEEE.

关键词： Big data

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Cyberattack Aware Load Frequency Control in Power Systems 58

Cyberattack Aware Load Frequency Control in Power Systems

引用

58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Fahim, Shahriar Rahman Atat, Rachad Kececi, Cihat Takiddin, Abdulrahman Ismail, muhammad Davis, Katherine R. Serpedin, Erchin Texas A&m University Electrical & Computer Engineering Department College StationTX77843 United States Lebanese American University Department of Computer Science & Mathematics Beirut Lebanon Florida State University Department of Electrical & Computer Engineering TallahasseeFL32310 United States Research and Outreach Center Tennessee Tech University Department of Cybersecurity Education CookevilleTN38505 United States

ISBN: (纸本)9798350354058

The load frequency control (LFC) is crucial for stabilizing the frequency of the power grid in the intermittency of renewable energy sources. modern LFC systems utilize open communication and automation networks that expose power systems to potential cyberattacks, which could lead to frequency instability throughout the network. Current research often treats frequency control and cybersecurity independently, which could lead to ineffective solutions and increased system instability. This paper introduces a cyber-resilient LFC strategy that improves both control performance and security. We employ reinforcement learning for adaptive frequency control and integrate it with a graph convolutional neural network to enhance control responses during a cyberattack event. Our strategy also includes a graph auto encoder-based attack detector that is trained in various scenarios and has shown more than 92% in detection rate while tested on the Iberian power system topology, which includes 486 buses. This highlights the robustness and reliability of our approach in ensuring resilience against cyberattacks within the realm of frequency control in power systems. © 2024 IEEE.

关键词： Graph neural networks

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A Detailed Observation on Association Rule mining 2

A Detailed Observation on Association Rule Mining

引用

2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

作者： Sujatha, Pothula Elavarasan, G. Nithya, P. Kadhiravan, N. Gosh, Arindam Adhikary, Asmita Pondicherry University M.Tech-CSE Department of Computer Science and Engineering Kalapet Puducherry India

ISBN: (纸本)9781728153742

Association rule mining is utilized to take care of various types of area issues. These days' affiliation rule mining has been stretched out to huge information. As a result of the gigantic measure of information, a solitary way the sequential procedure isn't adequate subsequently. The most basic piece of ARm is discovering a potential relationship between things in an enormous exchange based dataset. The well-known algorithms in particular apriori and FP Growth has been identified implemented those algorithms using the WEKA (version 3.7.10) tool. © 2020 IEEE.

关键词： Association rules

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Cardiovascular Abnormalities Identification by machine Learning Classifiers and its Comparision

Cardiovascular Abnormalities Identification by Machine Learn...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Surendran, S. Reddy, B.malakonda Krishnammal, P.muthu Venkataramanaiah, B. murugesan, S. Joany, R.m. Tagore Engineering College Department of Computer Science and Engineering Chennai India Narayana Engineering College Andhra Pradesh Gudur India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350394580

Heart diseases related to the heart increasing everywhere. Human beings are suffering from most dangerous cardiovascular disorders (CVD). Features are main parameters to identify diseases. The feature extraction is done from the signals and make ready for the analysis. Where machine learning (mL) is a technique that tells the potential emergence of cardiac disease. Information science uses machine learning to address a variety of difficulties. The basic use of machine learning is the forecasting of a result based on data that is already available. The machine applies the designs from the current dataset to a hidden dataset in order to predict the disorders. There are several arranging calculations to be completed with sufficient accuracy. We have used various machine learning classifiers for identifying CVDs. Random forest (RF) classifier producing highest accuracy compared to other classifiers. © 2023 IEEE.

关键词： Decision Tree KNN Logistic Regression mL RF Support Vector machine

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D mRI Images

Brain Tumor Classification Using UNet Deep Neural Networks f...

引用

Electronics, Computing, Communication and Control technology (ICECCC), International Conference on

作者： mohammad Khaja Shaik D Kalpana U Sesadri Saptarshi mukherjee C Dastagiraiah Pundru Chandra Shaker Reddy Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Departmet of CSE-Data Science Malla Reddy Engineering College (Autonomous) India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telanagana India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Computer Science and Engineering Anurag University Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (mRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of mRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(mL) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of mRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top.

关键词： Training Image segmentation Solid modeling Three-dimensional displays magnetic resonance imaging Computational modeling Artificial neural networks

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic minority Over-Sampling technique (SmOTE), and data preparation with SmOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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Performance Investigation on Cross Layered Routing Protocol in Cognitive Radio Networks (CRN) 3

Performance Investigation on Cross Layered Routing Protocol ...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Senthil Kumar, N.K. Prathipa, R. Uma Priyadarsini, S. Subha, T.D. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India Panimalar Institute of Technology Electronics and Communication Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering India R.M.K. Engineering College Electronics and Communication Engineering

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

The Cognitive Radio Network (CRN) has been developed to fulfill the high needs of future wireless communication systems (5G). Rapid use of wireless communication services has made a huge demand for spectrum and bandwidth allocation. Proper spectrum sharing and efficient usage of spectrum are the two ways to meet the necessary demand. This Cognitive Radio technology makes use of these two ways to improvise the bandwidth utilization and achieve better quality of services. To maximize the spectrum utilization, the Cognitive Radio is adapted with various other combining techniques. By properly designed with Dynamic Spectrum Access (DSA), multihop cognitive radio is the best approach to provide better bandwidth. This DSA addresses major challenges like spectrum awareness, quality route establishment, route maintenance and node mobility. Gravitational Search Genetic Algorithm (GSGA) protocol has been developed for the betterment of speed of convergence without compromising the accuracy of the predictor model. Conventional approaches, although improved channel usage, lead to a rise in overhead as the number of nodes grows. To overcome these drawbacks, the proposed GSGA protocol has been designed that efficiently utilizes the channel and predicts the availability of the channel. When it comes to throughput and stability, simulations show that the proposed cross-layered routing protocol is superior. © 2022 IEEE.

关键词： Genetic algorithms

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Analysis and Estimation of Discounts on Product Prices in E-Commerce Sites

Analysis and Estimation of Discounts on Product Prices in E-...

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Computing, Power and Communication technologies (IC2PCT), IEEE International Conference on

作者： Anitha Julian Challa maheswar R Ramyadevi m S murali Dhar S Selvi Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K Engineering College Chennai India

Sales on e-commerce platforms are significantly influenced by discounts. When a discount is offered, 80% of buyers who initially show disinterest end up making a purchase, per a Retail me-not survey. This increases revenue and draws in new clients who might not have otherwise thought about making a purchase. Another survey by Accenture reveals that 75% of consumers are more inclined to choose a brand that provides personalized discounts. To retain customers, e-commerce websites can implement tailored discount strategies based on customers’ past purchases or preferences. The decision-making process for discounts involves various factors. Pricing strategies and discounts, as identified in the study, are pivotal in influencing consumer behavior and encouraging purchases, particularly among new customers. This research delves into the complexities of discounting practices and their impact on the online retail ecosystem. Employing a comprehensive methodology that combines quantitative and qualitative techniques, the study investigates the frequency, trends, and effectiveness of discounts across different e-commerce platforms. Through extensive data collection, including details on discount percentages, durations, and consumer feedback, the research aims to reveal insights into the strategic implementation of discounts. The study’s main objective is to analyze discounts that are given on a variety of products by gathering information from an online store and using the characteristics, reviews, and ratings of the products as factors to calculate the discount value.

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