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Drowsiness Detection System Using the Movement of the Eye of A Human Through machine learning Algorithms in Order to Avoid Accidents

Drowsiness Detection System Using the Movement of the Eye of...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Jenifer Jothi Mary A Subhashish Goswami B G Rahul T. Sakthika Dewank Mahajan Bhasker Pant Department Computer Science St. Joseph's College (Autonomous) Affiliated to Bharathidasan University Tiruchirappalli Department of Computer Science Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Department of Civil Engineering Koneru Lakshmaiah Educational Foundation Zoology A.P.C. Mahalaxmi College for Women Thoothukudi India Data Analyst University of Michigan Department of Computer Science & Engineering Deemed to be University Dehradun Uttarakhand India

This function is meant to keep individuals safe in the event of an accident. In this study, the development of ADAS is shown, with a focus on driver fatigue identification as the main goal of decreasing traffic fatalities. In this situation, we use artificial intelligence to anticipate the condition and sentiments of the operator, improving traffic safety. For this research, we'll use CNN, which can accurately detect driver fatigue from driving images. In this study, we can employ the Electrocardiographic to measure weariness psychologically. Artificial intelligence refers to a system's ability to learn and grow without having to be programmed. We also employ OpenCv, Keras, and Tensorflow, among other machine learning algorithms [1].Road accidents are rising quickly each year as a result of vehicle technology and mechanical developments that allow for fast driving. Road accidents claim the lives of 1.35 million people worldwide every year, with 151 thousand fatalities reported in India alone in 2016. From this, drivers are at fault for almost 78 percent of traffic incidents. Drunk driving, over speeding, and sleepiness are the main causes of these incidents, with drowsiness accounting for roughly 40% of all *** are aware of the dangers of driving after drinking, but they are not aware of the dangers of tiredness since there are no tools available to gauge driver *** a motorist is distracted while driving, it slows down their response time and affects how they steer. In order to address this issue, we'll employ machine learning to determine whether or not the driver is sleepy [2]. By setting a threshold number, we can determine if the driver is tired or not. Typically, when someone feels sleepy, the rate at which his or her eyes blink slows *** application offers a method for scanning facial features and, after recognising the face, utilising those crucial cues for eye tracking. This guarantees that the driver has complete control over his

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Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Swati Arya Syed Anas Ansar Shruti Aggarwal Department of Computer Application Babu Banarsi Das University Lucknow India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing.

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AC Operation Hardware Neural Circuit and the Design of Deep learning Model

AC Operation Hardware Neural Circuit and the Design of Deep ...

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2018 3rd international conference on computer Science and Information Engineering(ICCSIE 2018)

作者： Masashi Kawaguchi Naohiro Ishii Masayoshi Umeno Department of Electrical & Electronic Engineering National Institute of TechnologySuzuka College Department of Information Science Aichi Institute of Technology Department of Electronic Engineering Chubu University

In the machine learning field,many application models such as pattern recognition or event prediction have been *** Network is typically basic methods of machine *** analog neural network models were composed of the additional circuit and solid *** circuit was realized by operational *** weights means the solid resistance of *** the reason of the resistance is fixed,changing resistance value and connecting weight is quite ***,in the case of using variable resistance,we have to adjust the resistance value by our *** this study,we used analog electronic circuits using alternative current to realize the neural network learning *** circuits are composed by a rectifier circuit,Voltage-Frequency converter,amplifier,subtract circuit,additional circuit and *** connecting weights describe the frequency,converted to direct current from alternating current by rectifier *** connection weights are able to changed easily compare with another hardware neural *** model's architecture is on the analog *** learning time and working time are very short because this system is not depending on clock ***,we suggest the realization of the deep learning model about proposed analog hardware neural circuit.

关键词： Analog electronic circuit neural network AC operation deep learning

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Smart Use of machine learning in Heart Disease Identification

Smart Use of Machine Learning in Heart Disease Identificatio...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ramakrishnan Raman Vaijanath Shintre Kapil Joshi G.S.L.B.V. Prasanthi Afshan Kaleem Kapil Rajput Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Electronics Engg Sanjay Ghodawat University Kolhapur Maharashtra Uttaranchal Institute of Technology Uttaranchal University Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand

Heart patients are increasing in number on a daily basis, and each year in every region of the globe, a significant number of people lose their lives unexpectedly as a result of sudden heart *** a result of this, it is essential to diagnose cardiovascular illness at an early stage in order to avert the death of It is necessary for the area of medicine to make use of some kind of technology-based application in order to identify cardiac patients with greater precision and in much shorter time. It is possible to effectively identify heart illness by making use of data mining methods, and there are a considerable number of people suffering from heart disease who are now being treated in hospitals all over the globe. Data mining is the practice of sifting through enormous amounts of raw data in search of knowledge or information that can be put to good use. During the prediction analysis, many machine learning algorithms are put to use in order to locate useful patterns and make accurate projections on forthcoming events or trends. Through the use of a reworked machine learning algorithm, this research study will attempt to forecast the risk that individuals would suffer from coronary heart disease. Before being classified, the incoming data go through a number of processes, including preprocessing, grouping, and the selection of useful features. Four different algorithms, including random forest (RF), K Nearest Neighbor (KNN), Particle swarm optimization (PSO) and logistic regression are combined in order to find out what kind of cardiac disease the patient *** enhance performance and shorten the amount of time needed for training, this method removes features from the cardiac dataset that are not relevant to the problem at hand. The random forest approach is the one that finishes off this procedure. In order to group all of the data points that are considered to be outliers, KNN are optimized using PSO algorithm. In the end, a method known as logistic regres

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computer Mathematical Statistics and UGC-Based Survey of on the Current Status of Medical Treatment by machine learning Algorithm 2021

Computer Mathematical Statistics and UGC-Based Survey of on ...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Rui Gao Shanghai University China

ISBN: (纸本)9781450385046

This article uses user-generated content(UGC) data crawled from social media(Zhihu) to obtain users' true opinions on the current status of “difficult and expensive medical treatment” in China,hoping to help the government prescribe the right medicine and effectively solve people's livelihood *** article adopts a"machine-human hybrid method",using natural language processing(NLP) technology and K-Means clustering algorithm to improve efficiency by reducing sentence redundancy,and finally manual review to obtain users’ opinions.A total of two different experiment processes are designed, and the methods for extracting key sentences are *** comparing the results of the two experiment processes,the general experiment process most suiTable for such problems is ***,through the analysis of the seven types of keywords and corresponding sentences extracted by the machine learning algorithm,the reasons why Chinese citizens think"difficult and expensive medical treatment"are *** main influencing factors that form the Chinese citizens' perception of"difficult and expensive medical treatment in Chinese society"are described as a whole,which is divided into four categories and seven sub-categories.

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Garbage Recognition and Classification System Based on Convolutional Neural Network VGG16

Garbage Recognition and Classification System Based on Convo...

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Advanced Electronic Materials, computers and Software Engineering (AEMCSE), international conference on

作者： Hao Wang Wuhan University of Technology Wuhan China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

To study the application of deep learning in the field of environmental protection, the convolutional neural network VGG16 model is used to solve the problem of identification and classification of domestic garbage. This solution first used the OpenCV computer vision library to locate and select the identified objects and preprocessed the images into 224×224 pixel RGB images accepted by the VGG16 network. Then after data enhancement, a VGG16 convolutional neural network based on the TensorFlow framework is built, by using the RELU activation function and adding BN layer to accelerate the model's convergence speed, while ensuring recognition accuracy. This project finally classifies domestic garbage into recyclable garbage, hazardous garbage, kitchen waste and other garbage. After actual tests, the correct classification rate of the garbage classification system based on VGG16 network proposed in this paper is 81.1%, the result meets the needs of daily use.

关键词： component Deep learning VGG16 Network Garbage Recognition Taxonomy Wastes Garbage Neural network images machine learning Recognition (Psychology) problem identification

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Research and application of Intelligent Detection Technology Based on Deep learning Algorithm 2021

Research and Application of Intelligent Detection Technology...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Xinwei Zhou Yong Zhu Gongqing College Nanchang University China

ISBN: (纸本)9781450385046

With the development of computer and information technology, the advancement of sensor technology, the level of intelligent detection technology continues to improve. Manual detection is gradually eliminated, and deep learning algorithms are gradually combined with intelligent detection technology, which has injected new development momentum into the development of detection technology. This article aims to explore the research and application of intelligent detection technology based on deep learning algorithms. This article takes intelligent detection technology as the research object, combined with deep learning algorithms, first analyzes the current existing intelligent detection technology, and then designs an intelligent detection system based on deep learning algorithms, and introduces the various functional modules of the system. This article has verified the effectiveness of the system through system testing. Experimental data shows that the detection accuracy of the system in the training sample is 90.36%, and the detection accuracy of the system in the test sample is 94.4%. It can be seen that the intelligent detection system based on the deep learning algorithm has high detection accuracy.

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Shared Word Embedding Space Modeling Method Based on Orthogonal Projection 2019

Shared Word Embedding Space Modeling Method Based on Orthogo...

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Proceedings of the 3rd international conference on Vision, Image and Signal Processing

作者： Gang Liu Kai Wang Wangyang Liu Yang Cao Guang Li Harbin Engineering University College of Computer Science and Technology Heilongjiang Harbin China CETC big data research institute company limited Guiyang China

ISBN: (纸本)9781450376259

With the continuous development of computer technology, machine learning has been applied in more and more fields. However, the application of word embedding technology in bilingual Chinese and English still needs to be developed. In this paper, we propose a model construction process based on orthogonal projection, and analyze the validity of the model from multiple perspectives. We carry out word sense similarity experiments and word analogy experiments for the quality of single language in the model, and cross-language text similarity experiments for different linguistic quality in the model. Through the analysis of the experimental results, it can be proved that the proposed shared word embedding space model achieves good results compared with the traditional word embedding model, and the effect of the model achieves the desired purpose.

关键词： Cross Language Document Similarity Shared Word Embedding Technology Orthogonal Projection machine learning

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Climate Change Analysis Using machine learning

Climate Change Analysis Using Machine Learning

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Delhi Section conference (DELCON), IEEE

作者： Dolly Sharma Pawan Gupta Gourav Rawat Sharad Kumar Suraj Malik Nidhi Bansal School of Computing Department Graphic Era Hill University Haldwani India Faculty of Information Technology BIET Jhansi Dr. A. P. J. Abdul Kalam Technical University Lucknow India Department of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida India Faculty of Computer Science & Engineering BIET Jhansi Dr. A. P. J. Abdul Kalam Technical University Lucknow India Department of Computer Science and Engineering IIMT University Meerut India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

More analysis is required day to day due to the frequent changes in the weather. Every second the subject needs accurate analysis. This research paper is suggesting a useful technique for analysis using machine learning classification. Dynamic change occurs frequently in this scenario as the real-time application needs to get the process within the machine for accurate output. Accuracy is also a constraint for the situation. The dataset designed based on the selected location is divided by the number of zones, only then the analysis is done for processing to show the result. Sometimes an area is also restricted due to heavy disasters. Due to this care, the problem is divided into the sample area and classification is applied. The results generated by this research paper can show the optimal solution for the determined scenario.

关键词： Climate change Accuracy Disasters machine learning Programming Real-time systems Meteorology

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MangoFruitDDS: A Standard Mango Fruit Diseases Dataset Made in Africa 3rd

MangoFruitDDS: A Standard Mango Fruit Diseases Dataset Made ...

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3rd international conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023

作者： Faye, Demba Diop, Idy Mbaye, Nalla Dione, Doudou Diedhiou, Marius Mintu Polytechnic School of Dakar Cheikh Anta DIOP University Dakar Senegal Plant Protection and Phytochemistry Laboratory Department of Plant Biology Cheikh Anta DIOP University Dakar Senegal

ISBN: (纸本)9783031489297

Mango is a lucrative fruit produced in tropical and sub-tropical areas. It is the third most traded tropical fruit after pineapple and avocado in the international market. In Senegal, the average production of mango fruits between the 2015–2016 and 2021–2022 seasons is 126,551 tons. Mango fruit is also leading the fruit exportation of the country. For example, in the 2017–2018 season, the quantity of mangoes exported was estimated at 17.5% of the country’s fruit production, ahead of melon (13.4%) and watermelon (11.6%). There are, therefore several pests and diseases that reduce both the quantity and quality of mango production in the country. Several solutions based on Convolutional Neural Networks (CNNs) are proposed by researchers during the last years to automatically diagnose these pests and diseases. But the main limitation of these solutions is the lack of data since CNNs are data-intensive. Due to climatic variations from one geographical area to another, these solutions can only be adapted to certain areas. We propose in this work a mango fruit diseases dataset of 862 images collected from an orchard located in Senegal. Two combinations of data augmentation techniques, namely "Flip_Contrast_AffineTransformation" and "Flip_Zoom_AffineTransformation" are used to generate respectively two datasets: Dataset1 and Dataset2 of 37,432 images each one. Eight CNNs, including seven well-known ones and a proposed light weight Convolutional Neural Network (LCNN), are applied to both datasets to detect and identify the treated diseases. Results show that on Dataset1, DenseNet121 and ResNet50 give the best accuracy and F1_score both equal to 98.20%, on Dataset2, InceptionV3 and MobileNetV2 achieve both the best accuracy and F1_score of 98.20%. The proposed LCNN also achieved excellent results (accuracy: 95.25% and F1_score: 95.20%) on dataset1. Due to its light weight, it is therefore deployed in an offline Android mobile application to help users detect mango diseases fr

关键词： classification CNN data augmentation Deep learning diseases mango

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