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Real-Time CNN-Based Driver Distraction&Drowsiness Detection System

Intelligent Automation & Soft Computing 2023年第8期37卷 2153-2174页

作者： Abdulwahab Ali Almazroi Mohammed A.Alqarni Nida Aslam Rizwan Ali Shah University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia University of Jeddah College of Computer Science and EngineeringDepartment of Software EngineeringJeddahSaudi Arabia Department of Computer Science National College of Business Administration&EconomicsBahawalpur Campus63100Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjab64200Pakistan

Nowadays days,the chief grounds of automobile accidents are driver fatigue and *** the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing *** paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor *** the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is *** of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement *** suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.

关键词： Deep learning convolutional neural network Tensorflow drowsiness and yawn detection seat belt detection object detection

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An EEG-Driven Framework for Emotion Recognition During Gameplay 5

An EEG-Driven Framework for Emotion Recognition During Gamep...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Shahzad, Khurram Ali, Zohaib Rauf, Usman Rehman, Anees Ur Khan, Sheharyar Noorani, Sadam Hussain Comsat University Department of Elect & Comp Engineering Islamabad Pakistan Bahria University Department of Computer Science Islamabad Pakistan University of Engineering & Technology Department of Computer Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

Gaming transcends mere entertainment, serving as a unique realm to explore human emotions. This research utilizes Brain-computer Interface (BCI) technology, specifically Electroencephalogram (EEG) signals, to unravel player emotions. Pioneering an innovative approach, our study decodes and interprets emotions exhibited during gaming experiences. Through meticulous analysis, we demonstrate the efficacy of EEG signals in revealing emotional states. Findings illuminate the dynamic interplay between the brain and gaming environments. Leveraging datasets like GAMEEMO, our study employs various data and feature extraction processes to detect emotions (boring, calm, horror, funny) with a accuracy of 98.21%, achieved by the Random Forest model. © 2024 IEEE.

关键词： Electroencephalography

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An Application of Graph Labeling—Cryptographic Technique with Magic Labeling 4th

An Application of Graph Labeling—Cryptographic Technique wi...

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4th international conference on Energy Systems, Drives and Automations, ESDA 2021

作者： Prasanna, Nissankara Lakshmi Sudhakar, Nagalla Department of Computer Science and Engineering Vasireddy Venkatadri Institute of Technology Guntur India Department of Computer Science and Engineering Bapatla Engineering College Bapatla India

ISBN: (纸本)9789819936908

Graph theory is an important branch of mathematics which is used to solve variety of problems in computer science by structural models. This structural modeling makes the computer discipline problems to find solution in easier way. Graph labeling is assigning additional information to the graph elements. A lot of research has been surveyed on graph labeling. Magic labeling is a sort of graph labeling. The three types of magic labeling are vertex, edge, and total magic labeling. A lot of research has been surveyed on labeling and its applications in broad. Designing applications over specific labeling algorithm is an interesting and raising domain. Very few authors addressed such applications which can be solved with specific type of labeling. One such application over magic labeling of graphs is addressed here in detail. This article presents a cryptographic technique for data encryption and decryption by using magic labeling of graphs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of computer science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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Face Recognition-Based Door Locking System with OTP Verification 6th

Face Recognition-Based Door Locking System with OTP Verifica...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Rajavel, M. Santhosh Kumaar, R. Suvedha, D. Dharani, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819619801

In the world of the digital era, AI has had a major influence. With the advancement of technology, people can experience extraordinary innovations that not only make life more comfortable, but also make it simpler. As a part of AI, we have created a door locking mechanism that uses face recognition with OTP verification using OpenCV. At the stage of identification of faces, Histogram of oriented Gradients algorithm [HOG] is used for finding the face at the backend. This project is developed using both hardware and software. With the help of smart doorbells, homeowners can get notifications when a visitor is at the door and identify the visitor. They significantly raise people’s quality of life and advance the development of smart homes. The purpose of this project is to make the home or office area secure and can also be used to minimize criminality. Only one form of authentication is offered by the existing systems, which makes them less secure. Thus, for secure access, we have proposed a safe door locking system with two factor authentication that can authenticate and validate the user as well as open the door in real time. We have come up with an idea which should recognize the guest, relatives or a stranger and alert the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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computer Systems science & engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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A Comprehensive Multi-modal Approach for Enhanced Product Recommendations Based on Customer Habits

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1537-1545页

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Yenduri, Lakshmana Kumar Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India Department of IT Vignan’s Foundation for Science Technology and Research Vadlamudi522213 India San Francisco Bay Area United States

In response to the evolving demands and challenges in recommendation systems, a comprehensive and innovative approach is proposed for delivering personalized and accurate product recommendations while mitigating existing limitations. This approach combines cutting-edge methodologies from collaborative filtering, content-based recommendation systems, and state-of-the-art deep learning architectures to enhance the standard of recommendations and improve user contentment. The hybrid approach encompasses various modules, including data preprocessing, feature engineering, deep learning model development, recommendation generation processing, and post-processing. Utilizing the H &M Fashion dataset, a rich collection of product images and metadata, for experimentation, the dataset undergoes preprocessing to ensure data quality, and features are extracted from both text and images using transfer learning approaches. These features are then integrated into a unified representation using fusion techniques. Subsequently, a deep learning model is developed to generate personalized recommendations based on the integrated features. Empirical findings on benchmark H &M dataset demonstrate the superiority of the proposed hybrid approach over traditional content-based and collaborative-based models, with improvements in accuracy, recall, and F1 score. The hybrid model effectively combines different recommendation techniques, offering robustness, adaptability, and enhanced recommendation quality. This research contributes to the advancement of recommendation systems, offering insights and solutions for delivering tailored recommendations in the fashion retail industry. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

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Influence of Opinion Formation on Polarization in Social Networks 14

Influence of Opinion Formation on Polarization in Social Net...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Das, Susmita Dutta, Sangita Chakraborty, Susanta Biswas, Samit Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Shibpur711103 India

ISBN: (纸本)9798350335095

Online social networks have introduced diverse discussions regarding different topics. At the same time, these platforms have also garnered the idea of shaping opinions. The increase in user engagement in case of arguments has led to the issue of polarization in online social media platforms. In this paper, we have studied the influence of opinion change on homophily in social networks and the variation in polarization. Our own new method has been proposed for analysing the modification of user opinions dynamically in different timestamps. The probability of information sharing varies depending on the opinion similarity and homophily in social networks alters accordingly. Our approach portrays the opinion modification dynamics for different network features using complex networks concepts. We have observed that on social interaction networks like Twitter, opinion, information sharing and homophily perform a significant role in the determination of online polarization. Our approach has been compared with an established approach over external datasets and the results obtained are promising. © 2023 IEEE.

关键词： Polarization

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Equity-Enhancing AI: Integrating Counseling Marks for Fair Student Selection 10

Equity-Enhancing AI: Integrating Counseling Marks for Fair S...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Bhuvaneswaran, B. Vijay, Priya Varshini, J. Yokeswaran, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350353068

Students and parents face major problem in deciding college based on their interest and academic acheiments. There is always a trade off between counselling marks and college selection. The proposed system, which would drastically change the current scenario, students and their families would receive personalized college recommendations based on their academic interests, career aspirations, and 12th grade cutoff scores. The system examines historical cutoff scores, admission trends, and other pertinent data to provide you with accurate, data-driven insights into your chances of being admitted to various colleges. Comprehensive information about the participating universities is provided, along with streamlined techniques for data retrieval and analysis and easily navigable interfaces. This system's main goals are to reduce the amount of lessthan-ideal college choices made by participants in the TNEA counseling program, improve admissions process transparency, and promote data-driven decision-making. To assist students and their families in making informed decisions that support their academic and career goals, the system makes use of sophisticated algorithms and a vast amount of data. The system aims to enable students to make wellinformed decisions about their future educational pathways through the integration of sophisticated analytics and user-friendly interfaces. The system's goal is to improve the college selection process and outcomes for students enrolled in the TNEA counseling program by providing information on admission probabilities and helping students identify colleges that best suit their preferences and aspirations. In summary, the suggested system leverages data-driven insights to assist students in making informed decisions, which is a significant advancement in the higher education admissions process. © 2024 IEEE.

关键词： Students

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