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Driver Drowsiness Detection using machine learning Algorithms 2

Driver Drowsiness Detection using Machine Learning Algorithm...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Cheerla, Sreevardhan Reddy, Devarapalli Priyatham Raghavesh, Kodi Sai Andhra Pradesh 522502 India

ISBN: (纸本)9781665442909

Driver Drowsiness Detection model is a model in which the drowsiness of a driver is detected. Accidents are the very cause that claims lakhs of lives every year and majority of them occur due to human error. These human errors maybe intentional like drinking and driving, rash driving, jumping signal and anything that is done of where the driver is conscious and fully aware of the decision taken or non-intentional like driver being drowsy and not being aware of it. The first kind of error that we spoke about can be totally avoided by just abiding by the traffic rules whereas the later kind is something that we need to find a solution to, as the driver themselves are not totally aware of the fact that they are causing accidents. These kinds of errors can be reduced by making use of the technology present today. We have many kinds of technologies for driver drowsiness detection which work on different methodologies and different algorithms. Out of all those the most popular model is driver drowsiness detection using facial features. We have developed a driver drowsiness detecting model based on the facial features like eyes by using algorithms and methodologies which improve the model accuracy as much as possible. © 2022 IEEE.

关键词： Accidents

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Distributed Multi-kernel learning Based on Gaussian Mixture Model with Missing Data 2

Distributed Multi-kernel Learning Based on Gaussian Mixture ...

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2nd international conference on signal processing, Computer Networks and Communications, SPCNC 2023

作者： Chen, Sicong Liu, Ying College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798400716430

Distributed classification, as an important distributed learning task, has received much attention for more than two decades. However, it is still a challenge to achieve acceptable classification performance on some real datasets that follow complex distributions. Moreover, in various practical scenarios, some data features are unavoidably missing due to environmental interference or human factors, which affect the performance of classification. Considering this, in this paper, we propose a distributed Gaussian Mixture Model-based Multi-Kernel learning (dGMM-MKL) algorithm for classification of missing data distributed over a network. In the proposed algorithm, a distributed imputation method based on Gaussian Mixture Model (GMM) is used to make imputation for the missing components. Then, a distributed multi-kernel support vector machine (SVM) is developed to achieve classification based on the imputed data. Finally, we present experiment results on some datasets. Compared with state-of-the-art algorithms, the proposed dGMM-MKL algorithm shows better classification performance and higher robustness under different missing probabilities. © 2023 ACM.

关键词： Support vector machines

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Sign Language Identification Using Deep learning Methods 2nd

Sign Language Identification Using Deep Learning Methods

引用

2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： Saxena, Ishaan C. Anand, Rohan Subashini, M. Monica School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819979530

Hearing impaired persons communicate via hand gestures, so regular people have difficulty understanding their language. Due to this, technologies that recognize various gestures and communicate the information to ordinary people are required. In this Identification technique, we use the Convolution Neural Network (CNN). These are strong Image processing and AI technologies that employ Deep learning to carry out expressive and rewarding duties. It uses a system that is like the Multilayer Perceptron which has the function of reducing processing requirements. The CNN is mainly comprised of an Input layer, an Output layer, and a Hidden layer (which comprises a large no of convolution layers), Pooling layers, Fully-Connected layers, etc. The person will give their hand sign as an image in our project. The letters and words can then be converted using images and inputs. The dataset used in our project has been created by us solely. The hand signal will now be given as input, and the neural network will anticipate what it means and provide the results. It will be given a dataset and, based on the analysis, will attempt to provide the desired outcome. It is anticipated that a 98% accuracy rate is achieved at the end. It can be scaled up much further by adding inputs and enhancing our Neural Net’s efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Comparing signal processing and Neural Networks for DFT Applications 2

Comparing Signal Processing and Neural Networks for DFT Appl...

引用

2nd IEEE international conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Akasha, T. Kulkarni, Keerti BNM Institute of Technology Dept. of ECE Bengaluru India

ISBN: (纸本)9781665493963

machine learning Algorithms have found their application is all the domain of engineering. In this work, we try to compare the performance of traditional signal processing algorithms and neural networks for the calculation of Discrete Fourier Transform. The Discrete Fourier Transform (DFT) is of greater importance in all areas of digital signal processing. Calculating the frequency spectrum of a signal is the fundamental goal of DFT. The phase, frequency, and magnitude of a signal's component sinusoids are all contained in its frequency spectrum. Given the importance of DFT, it is necessary to evaluate which algorithm or method is efficient and takes very less time in computing the results. In this paper, two different methods, namely Fast Fourier Transform (FFT), which is a tradition signal processing algorithm and Deep Neural Networks (DNN) are used to compute DFT on a random discrete signal and are compared based on the time taken to execute the algorithms. © 2022 IEEE.

关键词： Deep neural networks

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A Deep learning Approach for YouTube Music Recommendation Based on Facial Emotion 2

A Deep Learning Approach for YouTube Music Recommendation Ba...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Renugadevi, R. Selvamuthukumar, M. Manikanta, K. Jeyaprakash, K. Kalaiarasi, G. Arul Edwin Raj, A. Vignan 's Foundation for Science Technology and Research Computer Science and Engineering Andhrapradesh Guntur India Vignan 's Foundation for Science Technology and Research Mechanical Engineering Andhrapradesh Guntur India Kalasalingam Academy of Research and Education Electronics and Communication Engineering Tamilnadu Virudhunagar India Vignan 's Foundation for Science Technology and Research Advanced Computer Science and Engineering Andhrapradesh Guntur India Universiti Tun Hussein Onn Malaysia Persiaran Tun Dr. Ismail Johor86400 Malaysia

ISBN: (纸本)9798350366570

In the contemporary digital era, the intersection of emotion recognition technology and music recommendation systems has paved the way for a transformative user experience. This research introduces an innovative 'Emotion-Based Music Recommendation System,' designed to enhance the personalization of music consumption. Leveraging real-time facial expression analysis and a curated music dataset, the system dynamically adapts to users' emotional states, providing a sealess and engaging music discovery process. The integration of Facial Recognition (FR) technology enables the system to capture and interpret users' moods instantaneously, eliminating the need for manual classification of songs. This novel approach not only streaDlines the user experience but also holds significant potential for mitigating revenue loss associated with prolonged music search times. The research utilizes Python and PyCharm, employing Haar cascades for face detection and collaborative filtering and content-based filtering methods for music recommendation. The datasets utilized include FER, containing grayscale images of faces classified into seven emotions, and YouTube Music data, incorporating diverse attributes for mood-based classification. Through this synthesis of facial recognition and music datasets, the proposed system stands poised to redefine the landscape of music recommendation, emphasizing real-time adaptability and user-centricity. © 2024 IEEE.

关键词： Collaborative filtering

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Research on Computational Fluid Dynamics Literature Mining Methods Using Natural Language processing 4

Research on Computational Fluid Dynamics Literature Mining M...

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4th international conference on signal processing and Computer Science, SPCS 2023

作者： Li, Qing Duan, Yuting Zheng, Tao Li, Li China Aerodynamics Research and Development Center Sichuan Mianyang621000 China

ISBN: (纸本)9781510672505

In our paper, we propose a methodology that uses natural language processing technology, including the Latent Dirichlet Allocation model and Word2Vec word embedding model, along with various machine learning and deep learning algorithms. This approach allows us to analyze Computational Fluid Dynamics literature accurately and efficiently. By applying these techniques to extract topic and word relationships from the literature data, we can discover hotspots, research directions, and emerging trends in the field. Our results validate the effectiveness of this approach, providing valuable insights. This research also offers a framework for researchers to analyze scientific literature, helping them understand development trends and explore knowledge associations in their respective fields. © 2023 SPIE.

关键词： Computational fluid dynamics

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Heart Sound Classification using a Hybrid of CNN and GRU Deep learning Models 2

Heart Sound Classification using a Hybrid of CNN and GRU Dee...

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2nd international conference on machine learning and Data Engineering, ICMLDE 2023

作者： Choudhary, Ravi Raj Singh, Mamata Rani Jain, Puneet Kumar Computer Science Central University of Rajasthan Rajasthan India Computer Science and Engineering NIT Rourkela Odisha India

Auscultation is a process where a stethoscope is used to listen to the heart sound signal to analyse the heart's functionality. Due to the stethoscope's non-invasiveness, convenience, and cost-effectiveness, it is the most common primary screening tool medical fraternities use. However, the scarcity of medical experts and the subjectivity in the analysis hinders the reliability of diagnosis using auscultation. Therefore, computer-aided analysis of heart sound signals will be helpful in this scenario. This paper presents a hybrid deep learning-based method to classify the heart sound signal into five classes. The method begins with the signal pre-processing followed by decomposition using Discrete Wavelet Transform (DWT) up to five levels. The obtained DWT coefficients are used to train the hybrid model, composed of two Convolution neural network (CNN) layers following one Gated Recurrent Unit (GRU) network layer. CNN models are suitable for extracting meaningful features, while the GRU exploits the time-dependent features. This combination helps classify the heart sound signal since they exhibit complex quasi-cyclic features. An overall accuracy of 99.3% is obtained for a publicly available dataset. It shows the proposed method's efficacy for classifying heart sound signal and superiority over the existing methods. Such a method will be beneficial in reducing the burden of heart valve diseases by early detection of diseases and initiating the proper medication. © 2024 Elsevier B.V.. All rights reserved.

关键词： Discrete wavelet transforms

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A Review of Physiological signal processing via machine learning (ML) for Personal Stress Detection 2

A Review of Physiological Signal Processing via Machine Lear...

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2nd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Lourens, Melanie Beram, Shehab Mohamed Borah, Bidyut Bikash Dube, Anand Prakash Deka, Aniruddha Tripathi, Vikas Management Durban University of Technology Department of Human Resources South Africa Department of Computing and Information Systems Kuala Lumpur Malaysia Electronics and Communication Tezpur University Assam Tezpur India Shuats Department of Computer Science & Information Technology U.P. Prayagraj India The Assam Royal Global University Opposite Tirupati Balaji Temple Computer Science and Engineering Assam Betkuchi Guwahati India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Personal stress is maintained and measured by machine learning. The device which is wearable has been used for the monitoring of personal self stress and data collection. In this research, it has been talked about the factors by which the physiological signal of the stress has been assessed. On the other hand, different type of technology has been used for the detection of the personal stress such as Electrocardiography (ECG) and many other devices. The observation and difficulties has been seen in this research by using this device and the technology. Stress disorder or ailment is one of the most common ailments in all individuals around the world. Stress and anxiety can greatly influence the life, emotion, behavioural pattern and thinking attributes of individuals. It is important to address this issue sooner or later. Psychological signal processing through machine learning effectively assists to detect the stress disorder at an early stage. The general system often considers some variables to detect stress. They are electrocardiogram, galvanic response, heart rate, respiration and many other elements. The ML tend to use algorithms to compare and contrast data to fetch effective e results. The paper has also carried out a statistical analysis based on three variables to fetch a proper result that provided the study group to comprehend a better understanding of the scenario. The researchers have taken the 'percentage of stress rate' cases' are considered independent variables whereas 'usage of a machine learning system' is considered a dependant variable. The study group has fetched and collected numerous data related to these three variables to get a better understanding. © 2022 IEEE.

关键词： machine learning

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A Smart Model to Manage Traffic Using Infrared Sensors and Bell Detection System: A Computer Controlled Traffic System 2

A Smart Model to Manage Traffic Using Infrared Sensors and B...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Saxena, Ashendra Kumar Adlin, Jebakumari S. College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India University Karnataka Bangalore India

ISBN: (纸本)9798350369960

The escalating challenge of traffic congestion in urban areas necessitates innovative solutions at the intersection of computer science and artificial intelligence. The main problem is in the outdated nature of conventional traffic signal systems, which function based on predetermined time intervals and fail to appropriately adjust to the ever-changing patterns of urban traffic. This study presents a sophisticated traffic management system that utilizes machine learning and data mining concepts to optimize real-time traffic signal regulation. The suggested system utilizes infrared sensors to constantly monitor the level of traffic congestion at crossings. The data is transmitted to an advanced microcontroller, which utilizes machine learning techniques to forecast traffic patterns and subsequently modify the timing of traffic signals. This approach enables a more seamless and adaptable control of traffic flows, resulting in a substantial decrease in congestion and enhancement of overall traffic efficiency compared to traditional systems. By incorporating computer vision techniques, the system's ability to detect and evaluate several traffic parameters, such as vehicle count, speed, and categorization, is improved. The data is essential for the machine learning models to make well-informed recommendations regarding the durations of traffic lights. Furthermore, the system integrates robotics components to physically manipulate and operate traffic light hardware, guaranteeing smooth functioning and immediate response to the computational decisions made by the microcontroller. This research not only addresses the immediate practical concerns of urban traffic management but also contributes to the broader field of AI and robotics in realworld applications. The synergy of data mining, machine learning, and computer vision within a robust traffic management system exemplifies the transformative potential of AI technologies in urban infrastructure management. © 2023 IEEE.

关键词： Audio signal Detection machine learning Algorithms Real-Time Data processing Traffic Optimization Techniques

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A Smart Technique of Gearbox Fault Diagnosis Based on Advanced signal processing and machine learning 2nd

A Smart Technique of Gearbox Fault Diagnosis Based on Advanc...

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2nd international and 14th National conference on Industrial Problems on machines and Mechanisms, IPRoMM 2022

作者： Mukherjee, Subrata Kumar, Vikash Chopra, Hetarth Rohit, Jino Hooda, Nishtha Rana, Prashant Singh Sarangi, Somnath Thapar Institute of Engineering & Technology Punjab Patiala147004 India Indian Institute of Technology Patna Bihar Patna801106 India Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Indian Institute of Information Technology Una Himachal Pradesh Una174303 India

ISBN: (纸本)9789819942695

This paper develops and compares a methodology for gearbox fault diagnosis based on calculus-enhanced energy operator (CEEO) and machine learning. This paper used three directional, i.e. X, Y, and Z, vibration signals of a bevel gearbox to classify the gearbox faults. Therefore, three directional, i.e. X, Y, and Z, vibration signals of a bevel gearbox were extracted based on three different speeds and loading conditions. Then, the raw vibration signal from all directions was pre-processed using CEEO. Next, the CEEO vibration signal was used to compute twelve time features for all directional vibration responses. Afterwards, a random tree (RT) and J48 were used to select the significant time features. Lastly, five different types of neural networks were used to classify the gearbox fault based on specified time features of RT and J48. A comparative assessment of the classification accuracy for gearbox fault diagnosis is also presented. The results showed that the RT performed significantly better than the J48 for gearbox fault classifications based on CEEO vibration signals. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Failure analysis

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