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Speech Emotion recognition: An Empirical Analysis of machine learning Algorithms Across Diverse data Sets 3rd

Speech Emotion Recognition: An Empirical Analysis of Machine...

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3rd international conference on Applied Intelligence and Informatics (AII)

作者： Ahammed, Mostafiz Sheikh, Rubel Hossain, Farah Liza, Shahrima Mustak Rahman, Muhammad Arifur Mahmud, Mufti Brown, David J. Jahangirnagar Univ Dept CSE Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital Univ Dept Educ Technol Kaliakair Bangladesh Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Nottingham Trent Univ CIRC Nottingham NG11 8NS England Nottingham Trent Univ MTIF Nottingham NG11 8NS England

ISBN: (纸本)9783031686382;9783031686399

Communication is the way of expressing one's feelings, ideas, and thoughts. Speech is a primary medium for communication. While people communicate with each other in several human interactive applications, such as a call center, entertainment, E-learning between teachers and students, medicine, and communication between clinicians and patients (especially important in the field of psychiatry), it is crucial to identify people's emotions to better understand what they are feeling and how they might react in a range of situations. Automated systems are constructed to recognise emotions from analysis of speech or human voice using Artificial Intelligence (AI) or machine learning (ML) approaches, and these approaches are gaining momentum in recent research. This research aims to recognise a range of emotional states such as happy, sad, calm, angry, fear, disgust, surprise, or neutral from input speech signals with greater accuracy than currently seen in contemporary research. In order to achieve this aim, we have used the Support Vector machine (SVM) classification algorithm and formed a feature vector by exploring speech features such as Mel Frequency Cepstral Coefficient (MFCC), Chroma, Mel-spectrogram, Spectral Centroid, Spectral Bandwidth, Spectral Roll-off, Root Mean Squared Energy (RMSE), and Zero Crossing Rate (ZCR) from speech signals. O. The system is tested on the Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS), the Toronto Emotional Speech Set (TESS), and the Surrey Audio-Visual Expressed Emotion database (SAVEE) datasets. Our proposed approach has achieved an overall accuracy of 99.59% on the RAVDESS dataset, 99.82% on the TESS dataset, and 98.95% on the SAVEE dataset for the SVM classifier. A mixed dataset is created from the three speech emotion datasets, which achieved significantly high classification accuracy compared with state-of-the-art methods. This model performs well on a large dataset, is ready to be tested with even bigger d

关键词： Emotion recognition AI ML Feature Extraction SVM MFCC RMSE ZCR RAVDESS TESS SAVEE

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SFPGRN: Spectral detection method of plant diseases based on deep learning 3

SFPGRN: Spectral detection method of plant diseases based on...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Zhou, Yi Pan, Zeng Liu, Zuhao He, Tingwei Yan, Jingwen Department of Information Science and Technology Southwest Jiaotong University China Department of Biomedical Engineering and Instrument Science Zhejiang University China Department of Electronic Information Engineering Shantou University China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

At present, most of the spectral detection methods with plant diseases are based on machine learning, we present a novel approach to spectral analysis using a spectral feature pseudo-graph-based residual network, referred to as SFPGRN, for data in the near-infrared band. The proposed method involves constructing a residual network model using a characteristic surface obtained via natural neighborhood interpolation based on preprocessed near-infrared spectral reflection signal and first-order differential spectral index. The input layer is a pseudo spectral map that is generated through surface projection of the characteristic surface. The generated spectral pseudo image contains a diverse set of spectral features that augment the representation capability of the pseudo-image while retaining the spectral band reflectance information. We evaluated the proposed method on the 2015 spectral dataset of apple leaf diseases and insect pests in China and found that it outperforms traditional machine learning algorithms, achieving a classification accuracy of 93.21%. © 2023 SPIE.

关键词： Image recognition

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IoT-based Car Driver Assistance System using Computer Vision and machine learning 3

IoT-based Car Driver Assistance System using Computer Vision...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shubham, Kumar Satyam, Kumar Kapisway, Pranav Memani, Raghav Anand, Aniket Sathiya Kumar, C. VIT Vellore632014 India

ISBN: (纸本)9798350372120

This paper presents an innovative approach for improving driver convenience and safety by combining Artificial Intelligence (AI) and Internet of Things (IoT) with computer vision techniques like OpenCV. By using intelligent automation, the suggested system, termed the IoT-based Car Driver Assistance System using Computer Vision and machine learning, seeks to both enhance and reduce potential driving-related risks. A camera and sensor suite are installed on the dashboard of the vehicle as part of the system design to enable real-time data collection and processing. By utilizing face recognition algorithms, the system begins by confirming the driver's identification prior to vehicle activation, which improves security. The system always keeps a watch on the driver's condition, looking for indications of drowsiness while operating and sending out alerts in a timely manner to help avoid collisions. The technology improves situational awareness by detecting and alerting the driver to approaching obstacles on the road, helping the driver avoid potentially dangerous situations. The technology also facilitates hands-free driving by utilizing hand gesture detection to provide seamless control over in-car audio operations such as track selection and volume adjustment. Call management is integrated with gesture recognition, enabling drivers to use hand gestures to accept or reject incoming calls. The system aims to improve the driving experience by utilizing facial recognition and mood detection to identify the driver's emotional state and provide tailored Spotify music choices. By recognising traffic signals and lane changes, the technology helps drivers maintain lane discipline, which improves road safety and encourages adherence to traffic laws. In order to lessen the difficulties of parking in crowded areas, the system also offers intelligent parking assistance. With a full feature set that gives drivers more control, awareness, and efficiency while driving, the proposed I

关键词： Internet of things

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Analyzing the Multifaceted Interactions of Modern Digital Dreamscapes and Deep data Insights 3

Analyzing the Multifaceted Interactions of Modern Digital Dr...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mothilal, T. Balaji, V. Natrayan, L. Yamsani, Nagendar Kcg College of Technology Department of Automobile Engineering Karapakkam TN Chennai India Loyola Institute of Technology Department of Mechanical Engineering TN Chennai India Saveetha School of Engineering Simats Department of Mechanical Engineering TN Chennai India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

Through a methodologically exhaustive approach, this study investigates the complex relationships between contemporary digital dreamscapes and deep data insights. Natural Language Processing (NLP), Social Network Analysis (SNA), machine learning Algorithms (MLA), data mining (DM), Topic Modeling (TM), and Sentiment Analysis (SA) are among the six original methods compared to the proposed method, which combines ethnographic research with cutting-edge data analysis techniques. To emphasize the relative performance and ethical implications of the methodologies, four different visualizations are offered, including a bar chart, scatter plot, pie chart, and box plot. According to the results, the suggested technique is superior to the conventional ones in many respects, including accuracy, precision, recall, F1-Score, processing time, privacy, data security, ethics, cultural sensitivity, scalability, and adaptability. Considering these findings, an interdisciplinary approach is necessary for evaluating digital dreamscapes, one that takes into consideration cultural, ethical, and data-driven factors. © 2023 IEEE.

关键词： Scalability

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Evaluation Model and Algorithm of Physical Education Teaching 3rd

Evaluation Model and Algorithm of Physical Education Teachin...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Li, Lei Suiying, Liu Nanchang University College of Science and Technology Jiangxi Nanchang330001 China Foreign Languages Schoolichuan University Jinjiang Collegeeishan Sichuan620800 China

ISBN: (纸本)9783031631320

This article proposes an evaluation model and algorithm based on data mining and machine learning for physical education teaching evaluation, based on the needs of educational evaluation. In this study, we quantified the key factors and elements in the physical education teaching process by developing relevant indicators and standards, and transformed them into a form that can be used for data analysis and processing for evaluation and analysis. The system consists of four steps. The first step is to plan and evaluate. In this step, we should plan our activities based on the goals of each lesson or unit. We can also use some simple tools, such as scales, checklists, etc., to facilitate the preparation of the evaluation. The evaluation teaching model of physical education teaching refers to the process in which teachers evaluate and provide feedback on students’ performance in physical education teaching. This process helps teachers improve their skills and physical education knowledge. The feedback provided by the teacher should be positive so that students can learn from it. This also helps to improve their performance in sports. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： data mining

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Fabrication of a Tool for Prediction of Crops Using machine learning 3rd

Fabrication of a Tool for Prediction of Crops Using Machine ...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Khan, Nausad Firdosh, Nasima Department of Mechanical Engineering Galgotias College of Engineering and Technology Knowledge Park—II Uttar Pradesh Greater Noida201013 India Department of Applied Sciences GGDSD PC College Haryana Palwal121102 India

ISBN: (纸本)9789819727155

Climate change is a major problem all over the world in the current scenario that affects many things but its great impact on agricultural crops in India past 20–25 years has been consistent. The crop yield prediction can be more beneficial and helpful with policymakers and all communities of farmers for making informed decisions regarding the marketing purpose and storage area. This paper aims to develop a K-nearest neighbors (KNN) model for an interactive crop prediction system specifically for the purpose of prediction of crops for providing a better environment by using a machine learning algorithm. This constructive model system will give accurate crop yield predictions to the farmers before the cultivation of crops and also enables them to provide the appropriate decisions for different types of crops for cultivation purposes. This implementation of this KNN model includes a user-friendly web-based interface for easy access the weather conditions. After analyzing all such weather conditions as temperature, humidity, draught, rainfall, and moisture, the KNN model algorithm predicts the effectiveness of crop yield. This data mining approach summarizes important information by analyzing data from various perspectives and conditions. KNN model is a popular machine learning tool algorithm that is adequate for performing reversion and their grouping by making decision trees with training programmed and creating the outcome-based data on the majority of order or mean value for the purpose used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Prediction of Efficient Driving Mode for Vehicle using machine learning 3

Prediction of Efficient Driving Mode for Vehicle using Machi...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Hegde, Ramakrishna Kripa Das, Sujan D Sriraksha, M.S. Siri, R. Siri Gowri, H. Soumyasri, S.M. Vidyavardhaka College of Engg. Computer Science Engineering Mysuru India PES University Computer Science Engineering Bengaluru India The National Institute of Engineering Master of Computer Applications Mysuru India

ISBN: (纸本)9798350340358

The field of autonomous driving cars is a rapidly growing area of research, machine learning and image recognition technology plays a pivotal role in ensuring their safe navigation on the roads. Different driving modes are offered by modern cars to accommodate various road conditions and driver preferences. However, manually selecting these modes can be cumbersome. Addressing the dynamic nature of mode switching in autonomous driving systems presents a multifaceted challenge. The system must continuously assess a multitude of variables such as traffic patterns, road conditions, weather conditions, and the wear and tear of vehicle components. Traffic conditions can change rapidly, requiring real-time analysis of traffic flow and congestion. Road conditions, including the presence of obstacles, construction zones, or slippery surfaces, demand instant adaptation for safe navigation. Weather conditions further complicate the scenario, as rain, snow, fog, or even intense sunlight can impact visibility and road grip. Addressing these challenges requires sophisticated algorithms capable of processing vast amounts of data from various sensors in real time. machine learning models must be trained to recognize patterns in diverse conditions, enabling the system to make informed decisions swiftly and accurately. Moreover, the system must possess the ability to prioritize these factors based on their immediate impact on driving safety and efficiency. This can be a future enhancement of the paper. By leveraging machine learning algorithms, the mode selection process can be automated. The system constantly monitors driving conditions, learns from real-time camera sensor data and selects the most optimal mode to adapt to current situation. Different models such as Convolutional Neural Network (CNN), CNN with MobileNetV2 as base and decision trees are analyzed. © 2023 IEEE.

关键词： Decision trees

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Remote Sensing Image Generation Model Based on Generative Adversarial Network 3

Remote Sensing Image Generation Model Based on Generative Ad...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Chen, Lei Yao, Jie Space Engineering University No. 1 Bayi Road Huairou District Beijing101400 China Beijing Jiaotong University No. 3 Shangyuancun District Handian Beijing100044 China

ISBN: (纸本)9781510664814

In recent years, object recognition methods of satellite remote sensing images based on deep learning have developed rapidly. The deep learning method, which needs a lot of labeled data to train the network, can achieve higher performance than the traditional method. However, it is extremely time-consuming and costly to obtain a large amount of labeled remote sensing target image data. Therefore, how to get high performance remote sensing target classifier by using only a few labeled target images training is an urgent problem to be solved. Based on WGAN-GP, this paper optimizes the constraint conditions of neural networks, and proposes a depth generation model, namely CCWGAN-GP, and applies it to remote sensing image generation. The experimental results show that the image generated by CCWGAN-GP has a high similarity to the real image, and can significantly improve the performance of the classifier under the training condition with only a few tags. © 2023 SPIE.

关键词： Remote sensing

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High Frequency Forecasting on Stock Market using machine learning 3

High Frequency Forecasting on Stock Market using Machine Lea...

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3rd international conference for Emerging Technology, INCET 2022

作者： Khot, Amruta Bhat, Rashmi Urunkar, Abhijeet Mudegol, Nandinee Walchand College of Engineering Sangli India St. John College of Engineering and Management Palghar India

ISBN: (纸本)9781665494991

Intraday stock trading has become a popular trend in US, Europe, and Indian markets and forecasting these rapid market movements have become an important topic in finance. With the emergence of technology and computing power machine learning and neural network, methodologies are becoming a reality and efficient way to solve problems. The trend of intraday trading has made the researchers and developers explore neural networks approach to forecast the high frequency changing data. We focused on gathering minute to minute changing data of 80 stocks belonging to 8 different sectors from S&P 500 and developed a custom double layer neural network which efficiently captures the dependencies in the stock market and allows us to predict the returns of a specific stock at the end of next 5th minute time interval. © 2022 IEEE.

关键词： data mining

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Skin texture recognition of leg images based on chaotic mapping reorganization features 3

Skin texture recognition of leg images based on chaotic mapp...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Zhang, Tianshu Tang, Chaoying Huang, Qiaoyue Nanjing University of Aeronautics and Astronautics Automation College Nanjing China

ISBN: (纸本)9781510661301

In criminal and victim identification, when information such as fingerprint and facial images cannot be obtained, the development of other new bio-metric recognition has become an important task. It is found from investigations that the lower leg is often exposed in an evidence image. If we can use the information to identify the identity of the suspect, it will be a great break-through for criminal investigation. Unlike faces or fingerprints, the image of a lower leg does not have many discriminative features. Therefore, capturing more information from an image has become the key to the success of recognition. Despite the rapid development of deep learning in recent years and the wide application of convolutional neural networks in biometric recognition, they are very dependent on a large amount of training data. In this paper, we propose a recognition method based on the combination of chaotic mapping and local binary pattern (LBP), which captures the nonlinear features of the image. Experimental results show that the method proposed in this paper has a better recognition rate and an encouraging performance. © 2023 SPIE.

关键词： Local binary pattern

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