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检索条件"机构=Dept. of Scientific Computing and Computer Application"

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Examining driver sleepiness based on his visual actions with ML Techniques

Examining driver sleepiness based on his visual actions with...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Rawat, Vandana Patwa, Kaushal Negi, Harendra Singh Sharma Ghai, Anupriya Graphic Era Deemed to be University Dept. Of Computer Scienceand Engineering Dehradun India Graphic Era Deemed to be University Dept. Of Computer Application Dehradun India Graphic Era Hill University Dept. Of School of Computing Dehradun India

ISBN: (纸本)9798350378092

Driver drowsiness is a significant risk factor for road accidents. It is essential for road safety to develop efficient sleepiness detection systems. This work concentrate around analyzing driver sluggishness in view of visual activities, expecting to recognize solid marks of To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are drowsiness utilizing PC vision methods. To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are extracted from real-time video footage captured by an onboard camera using a combination of image and video processing algorithms. To prepare and assess the sluggishness location model, a different dataset of drivers in different conditions of languor is gathered, including both reproduced and genuine situations. The dataset is clarified with ground truth marks showing various degrees of languor, got through physiological estimations, driver self-reports, and master assessments. © 2024 IEEE.

关键词： Eye movements

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A Comprehensive Analysis of Advanced Change Detection Technique for Planning and Monitoring Sustainable Mango Crop Areas using Landsat-8 Images 10

A Comprehensive Analysis of Advanced Change Detection Techni...

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17th INDIACom;2023 10th International Conference on computing for Sustainable Global Development, INDIACom 2023

作者： Verma, Harish Chandra Ahmed, Tasneem Icar - Central Institute for Subtropical Horticulture Crop Improvement and Biotech Division Lucknow India Integral University Dept. of Computer Application Lucknow India Integral University Advanced Computing Research Lab. Dept. of Computer Application Lucknow India

ISBN: (纸本)9789380544472

An efficient and accurate mango (Mangifera Indica L.) crop planning is a very important factor for sustainable mango crop production. Mango is a perennial fruit crop and alternate bearer, which is a major challenge for production sustainability. The main objective of this study is to detect the land cover changes using satellite images for sustainable land cover planning in three main mango-producing districts, namely Lucknow, Bulandshahr, and Meerut of Uttar Pradesh State, India. In this study, Landsat-8 OLI images from 2013-14 to 2020-21 have been used to detect the changes by implementing the log ratio (LR) and principal component analysis (PCA) fusion-based change detection method. In order to extract meaningful information from Landsat-8 images, Red and NIR band images were used to create soil-adjusted vegetation index (SAVI) images. Thereafter, the fusion-based change detection method using LR-PCA has been applied to retrieve the enhanced image. To obtain the final change images with the detected temporal changes in the area under the mango crop, image thresholding has been used. After conducting a detailed analysis, it is observed that the change detection accuracy of the LR-PCA fusion-based change detection technique is retrieved as 92%, which is quite high. In the future, proper mango monitoring techniques may play a very important role in developing sustainable mango orchards and maintaining environmental health. Future applications of this study will include change detection in mango crop areas in all major mango-producing districts in Uttar Pradesh. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Principal component analysis

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Next Generation HR: Transforming Talent Management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-dept. analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

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VGG-16 Technique to Reduce the Global Food Crises and Enhance the Crop Yields: Deep Learning Approaches 3

VGG-16 Technique to Reduce the Global Food Crises and Enhanc...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Depuru, Sivakumar Amala, K. Supriya, Pera Basi Reddy, A. Gireesh, Ravala.Sai Mohan Babu University Dept of Cse School of Computing Tirupati India Dept.of. Ece A.P Tirupati India Mohan Babu University Department of Computer Application Andhra Pradesh Tirupati India Mohan Babu University Dept. Of. Computer Application A.P Tirupati India

ISBN: (纸本)9798350375190

In the digital era, enhancing agriculture plays a critical role in addressing food crises by improving crop yield and ensuring food security. Early detection and management of crop diseases are paramount for agricultural sustainability, significantly affecting food security and farmer livelihoods. Farmers often struggle to identify crop diseases at early stages, leading to reduced yields and potential food shortages. This research aims to assist farmers in early disease identification, thereby increasing crop yield and preventing food crises. This study introduces a machine learning approach using Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to accurately identify crop diseases. VGG-16's architecture is particularly effective in recognizing patterns in images, making it a superior choice for diagnosing plant diseases compared to existing models. By leveraging VGG-16, the research demonstrates improved accuracy in detecting crop diseases at early stages, providing a valuable tool for farmers to manage crop health proactively. The findings of this research highlight the potential of advanced machine learning techniques to enhance agricultural practices and contribute to global food security. © 2024 IEEE.

关键词： Deep learning

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Looking into the Black Box: Monitoring computer Architecture Simulations in Real-Time with AkitaRTM 57

Looking into the Black Box: Monitoring Computer Architecture...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Mosallaei, Ali Isaacs, Katherine E. Sun, Yifan University of Michigan Electrical and Computer Engineering Dept. Ann Arbor United States Scientific Computing and Imaging Institute University of Utah Salt Lake City United States William & Mary Dept. of Computer Science Williamsburg United States

ISBN: (纸本)9798350350579

computer architecture simulators are essential for validating novel chip designs. However, they often provide little transparency during execution. This opaqueness limits the ability of users to identify issues during the simulation, leading to both wasted computational and human time. We address these issues by providing an intuitive user experience during simulation execution. Particularly, we reveal the status of executions and allow users to control the execution of a computer architecture simulator through AkitaRTM, an interactive web-based tool for real-time monitoring of computer architecture simulations. We based its design on the design workflow inefficiencies experienced by computer architects when using simulations. We demonstrate AkitaRTM's utility through two case studies, the second leading to a patch in the simulator. Additionally, we conducted a user study with computer architects, aiming to validate AkitaRTM. We found that, in addition to solving the observed problems, AkitaRTM also provided an educational benefit by making simulators more transparent to users. Based on these findings, we reflect upon the design of AkitaRTM and provide guidance for future human-centered tools in this space. © 2024 IEEE.

关键词： computer monitors

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An Effective Prediction of Rainfall Using Machine Learning Technique 5

An Effective Prediction of Rainfall Using Machine Learning T...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Rana, Nisha Guru Prasad, M. Sharma, Ashutosh Pratap, M.S. Anand Kumar, M. Aditya Pai, H. Dept. of Computer Science and Engineering Rishikesh India Graphic Era Deemed University Dept. Computer Science and Engineering Dehradun India Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Dehradun India Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9781665493604

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting. © 2023 IEEE.

关键词： Machine learning

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A Design of Software Architecture for Validate and Verification of Linear-Optical Quantum computing Platform 4

A Design of Software Architecture for Validate and Verificat...

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4th IEEE International Conference on Quantum computing and Engineering, QCE 2023

作者： Lee, Duwon Kwon, Yong Kim, Kanghyeon Lee, Jaehyeok Choi, Byung-Soo Pukyong National University Dept. of Physics Korea Republic of Pukyong National University Dept. of Computer Engineering Korea Republic of Pukyong National University Dept. of Scientific Computing Korea Republic of

ISBN: (纸本)9798350343236

Compared to other semiconductor quantum computing platform, it is relatively untouched about the verification of linear-optical quantum computing (LOQC) platform. To overcome this problem, we propose a universal and versatile software platform which can support any variation of LOQC layout structure. It also supports the user-friendly interface for smooth experiment. © 2023 IEEE.

关键词： Verification

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Industrial IoT Smart Manufacturing Systems based on Cloud computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Evaluation of Machine-Learned Price Prediction Models for Block-Chain Technology 1

Evaluation of Machine-Learned Price Prediction Models for Bl...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Goel, Vikas Sharma, Ashutosh Ranjan, Raju Sharma, Amit Kumar KIET Group of Institutions Dept. of IT Ghaziabad India IMSUC-Ghaziabad Dept. of Computer Application Ghaziabad India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

ISBN: (纸本)9798350345957

As a new form of technology, blockchain has much to offer. The technology has a wide range of potential uses and advancements, depending on the field of application. However, further study is required to realise the potential of advanced technologies like Blockchain and smart contracts. There is a lot of potential for investigation because the technology is so new. This research looked at the origins of the Blockchain and analysed several different blockchain solutions. Ethereum was discovered to be the most often used platform for constructing distributed apps after being compared to other platforms. Therefore, a distributed smart energy microgrid application will be built on the Ethereum platform. There is a coin, or token, exclusive to each blockchain platform. Bitcoin's relationship to other cryptocurrencies was measured using the Karl Pearson correlation coefficient. The research concludes that ether's price is most sensitive to changes in the price of bitcoin. On a preferred Ethereum network, a distributed application for managing smart microgrids has been built. The smart contract constitutes the heart of this programme. As a result, we also take care of the smart contract's layout, coding, and optimization. In comparison to the original, less GAS is consumed by this smart contract. The entire distributed app has been built and thoroughly tested. Smart Energy Microgrid Distributed application framework results demonstrate that the application functioned as expected. © 2023 IEEE.

关键词： Blockchain

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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