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Music Emotion Recognition Based on Deep learning: A Review

IEEE ACCESS 2024年 12卷 157716-157745页

作者： Jiang, Xingguo Zhang, Yuchao Lin, Guojun Yu, Ling Sichuan Univ Sci & Engn Sch Automat & Informat Engn Zigong 643000 Peoples R China Artificial Intelligence Key Lab Sichuan Prov Yibin 644005 Peoples R China

In recent years, with the development of the digital era, music emotion recognition technology has been widely used in the fields of music recommendation system, music classification, psychotherapy, music visualization, background music generation, smart home, and other applications of music emotion recognition, and has received attention from all walks of life. Especially the rapid development of artificial intelligence and deep learning, the music emotion recognition model using efficient deep neural network composition has become the mainstream model. This paper provides a more detailed overview of music emotion recognition, first introducing the background of music and emotion, and briefly summarizing the content of related works as well as the content framework. In the process, we also compare the similarities and differences in the content of other researchers' reviews of related research areas. And in the middle section, we provide a detailed account of datasets, emotion models, feature extraction, and emotion recognition algorithms. Finally, we discuss the current challenges in music emotion recognition and explore future research priorities.

关键词： Emotion recognition Reviews Music Feature extraction Deep learning Brain modeling Speech recognition machine learning algorithms Visualization Support vector machines Artificial intelligence Music emotion recognition deep learning artificial intelligence music emotion datasets

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machine learning Surrogate Model Optimized by Improved Sparrow Search Algorithm for Multi-Objective Optimization of Permanent Magnet Synchronous Motor Direct-Drive Pump

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 12773-12786页

作者： Zhang, Yiming Fei, Liangyu Chui, Chee-Kong Chng, Chin-Boon Zhao, Shengdun Li, Jingxiang Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore Xian Key Lab Intelligent Equipment & Control Xian 710049 Peoples R China

This study introduces an Improved Sparrow Search Algorithm (ISSA) to address the challenges of machine learning Hyperparameter Optimization (HPO) and efficient modeling of Electric Coolant Pumps (ECP) in electric vehicles. By integrating L & eacute;vy flight and Bernoulli mapping, ISSA enhances global search capabilities and ability to escapes from local optima. Experimental validations across 12 benchmark functions demonstrate ISSA outperforming the standard Sparrow Search Algorithm (SSA) and other advanced optimization algorithms in terms of exploratory and exploitative effectiveness. Specifically, ISSA proves exceptionally effective in autonomously handling the HPO for three typical machine learning (ML) algorithms, demonstrating superior performance over SSA and random search methods. A novel ISSA-ML surrogate model for ECP, incorporating structural and operational parameters, showcases significant improvements in predictive accuracy and robustness over traditional polynomial regression and ML models under conventional HPO methods. Furthermore, the application of this surrogate model in multi-objective optimization design for ECPs significantly reduces development time and computational costs, offering a streamlined and cost-effective solution for optimizing ECP performance. This study highlights potential future research directions, including the integration of other ML enhancements and the inclusion of more comprehensive feature parameters, to further improve the model's universality and applicability.

关键词： Optimization Computational modeling machine learning algorithms Motors machine learning Pumps Integrated circuit modeling Electric coolant pump machine learning multi-objective optimization sparrow search algorithm surrogate model

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Applications, Challenges, and Future Directions of Human-in-the-Loop learning

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IEEE ACCESS 2024年 12卷 75735-75760页

作者： Kumar, Sushant Datta, Sumit Singh, Vishakha Datta, Deepanwita Kumar Singh, Sanjay Sharma, Ritesh Indian Inst Technol BHU Varanasi Dept Comp Sci & Engn Varanasi 221005 India Digital Univ Kerala Formerly IIITM Kerala Sch Elect Syst & Automat Thiruvananthapuram 695317 India Indian Inst Management IIM Sambalpur Dept Informat Syst Management Sambalpur 768025 India Manipal Inst Technol Manipal Acad Higher Educ Dept Informat & Commun Technol Manipal 576104 Karnataka India

machine learning (ML) has become a popular technique for various automation tasks in the era of Industry 4.0, such as the analysis and synthesis of visual data such as images and videos, natural language and speech, financial data, and biomedical applications. However, ML-based automation techniques are facing difficulties like decision-making, thus incorporating user expertise into the system might be advantageous. The goal of adding human domain expertise with ML-based automation is to provide more accurate prediction models. Human-in-the-loop (HITL) systems that integrate human expertise with ML algorithms are becoming more and more common in various industries. However, there are a number of methodological, technical, and ethical difficulties with the development and application of HITL systems. This paper aims to explore the methodologies, challenges, and opportunities associated with HITL systems implementations. We also discuss a number of issues that must be resolved for HITL systems to be effective, including data quality, bias, and user engagement. Besides, we also explored several approaches that can be utilized to enhance the performance of HITL systems, such as active learning (AL), iterative ML, and reinforcement learning, as well as the current state of the art in HITL systems. We also selectively highlighted the advantages of HITL systems, such as their potential to increase decision-making process accountability and transparency by utilizing human experience to improve ML decision-making capability. The paper will be very useful for researchers, practitioners, and policymakers.

关键词： Task analysis Data models Artificial intelligence Annotations Human in the loop Training Natural language processing machine learning Fourth Industrial Revolution Human-in-the-loop (HITL) machine learning algorithms accountability transparency

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DDoS Attack Detection on Cloud Computing Services using algorithms of machine learning: Survey

DDoS Attack Detection on Cloud Computing Services using Algo...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Chilla Sathvika Vuyyuru Satwika Yarrapothu Sruthi Maddali Geethika Suneetha Bulla Swathi K Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Nowadays cloud computing services have become the most popular internet-based computing and many organizations use their services. Due to this, many cyber-attacks are happening in the cloud. One of those attacks is the Distributed-Denial-Of-Service (DDoS) attack. It floods unreal traffic, hence troubles the availability of the resources. This article is about DDoS attacks and detection of DDoS attacks using machine learning. There are many famous machine learning algorithms such as naïve bayes, random forest, support vector machines etc. These machine learning algorithms can be used to detect the DDoS attacks on doud. There are several datasets available for the researchers to test their proposed models which include NSL-KDD, ICDX, CIDDS-001, CICIDS 2017 etc. This paper presents a detailed study on different machine learning based techniques proposed by various authors to detect the DDoS attack in the cloud environment. A brief explanation has been provided on the available datasets and further discussed about the general methodology.

关键词： Support vector machines Cloud computing machine learning algorithms Heuristic algorithms Organizations Denial-of-service attack Feature extraction

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Implementation Of Grover's and Shor's algorithms In Quantum machine learning

Implementation Of Grover's and Shor's Algorithms In Quantum ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： D Kiran Kumar Elaprolu Hari Venkata Krishna Rangu Ushasri Vasiraju Jahnavi Kolla Bhanu Prakash Sagar Imambi Department Of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

algorithms for quantum machine learning are responsible for identifying essential information and predicting outcomes of the raw data samples. Unlike other mathematical methods that rely on a predetermined model to make predictions, these algorithms use existing data to continuously improve their ability to predict outcomes. Quantum computing algorithms have the capability to solve complex problems by leveraging the power of parallel computing, which allows them to perform many calculations simultaneously. The effectiveness of quantum computing algorithms in machine learning allows quantum technology to reach an advanced level of improvement. This study examines application-based algorithms, namely Grover's algorithm and Shor's algorithm, which are essential and most widely used quantum machine learning algorithms.

关键词： machine learning algorithms Quantum algorithm Quantum entanglement Qubit machine learning Predictive models Parallel processing

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Enhancing Urban Parking Efficiency Through machine learning Model Integration

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IEEE ACCESS 2024年 12卷 81338-81347页

作者： Sneha Channamallu, Sai Kermanshachi, Sharareh Michael Rosenberger, Jay Pamidimukkala, Apurva Univ Texas Arlington Dept Civil Engn Arlington TX 76019 USA Univ Texas Arlington Dept Ind Engn Arlington TX 76019 USA

An increase in vehicular traffic and a scarcity of parking spaces are creating significant challenges for urban parking management. This study aims to tackle these issues that escalate congestion and pollution and decrease urban productivity, by utilizing machine learning models to accurately predict parking space availability and categorize occupancy levels. It employs a dataset from a college campus garage collected from January 2022 to June 2023 and analyzes the performance of random forest, decision tree, linear regression, and support vector models by comparing them, using multiple evaluation metrics. The results revealed that the random forest model was the most reliable, as it demonstrated strong performance in both the regression and classification analyses and was adept at estimating the exact number of available parking spaces. A concurrent classification analysis that categorized parking occupancy into different levels proved valuable for enhancing the quality of communication and decision-making. An analysis of the importance of various features clearly highlighted the influence of the day of the week on parking demand and patterns;the impact of seasonality on the volume of parking usage;and the time of day, which plays a crucial role in determining parking behavior. The research will benefit urban planners, facility managers, and policymakers by providing them with insights and tools that will enhance the urban parking experience and address the complex challenges of modern urban environments.

关键词： Predictive models machine learning Random forests Decision trees Urban areas Adaptation models machine learning algorithms Support vector machines Linear regression Parking management predictive modeling random forest model decision tree support vector machine linear regression

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A Bayesian optimized machine learning approach for accurate state of charge estimation of lithium ion batteries used for electric vehicle application

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JOURNAL OF ENERGY STORAGE 2024年 86卷

作者： Selvaraj, Vedhanayaki Vairavasundaram, Indragandhi VIT Univ Sch Elect Engn Vellore India

In recent times, battery technology used in electric vehicles has drawn numerous researchers' attention. Monitoring the battery condition, particularly the State of charge, is required to ensure the battery's safe and reliable performance. Despite the fact that many SOC estimation methods have been proposed, further research is necessary to identify an approach that adapts versatile lithium-ion battery chemistries. In the recent study it has been demonstrated that machine learning approaches have better prediction accuracy compared to conventional methods. To maximize the performance of machine learning models, it is imperative to select the optimal hyperparameters and employ appropriate input parameters. At present, researchers employ, established heuristics methods to select hyperparameters, which may involve manual tuning or exhaustive search techniques such as random search and grid search. These techniques make the models less accurate and inefficient. In this paper, a systematic, automated process for selecting hyperparameters with a Bayesian optimization algorithm is proposed. In addition, along with the battery parameters (voltage, current and temperature), vehicle velocity, road condition, motor characteristics and environmental conditions are used as the input parameters for accurate SOC prediction. The highly correlated input features are selected through the MRMR algorithm. The performance of six ML algorithms, namely SVM, ANN, GPR, Ensembler, linear regression and Decision Tree, is tested and validated with and without hyperparameter tuning for different data sets. The experimental results demonstrate that the hyperparameter-tuned model outperforms the standard model in estimating the State of charge (SOC). In addition, the model's estimation error is <1 % across a range of vehicle velocity, road condition, motor torque, battery voltage, current, and temperature.

关键词： Electric vehicle State of charge estimation machine learning algorithms Feature selection Hyperparameter tuning Bayesian optimisation

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Importance of machine learning and Deep learning algorithms in Earthquake Prediction: A Review

Importance of Machine Learning and Deep Learning Algorithms ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Engineering and Natural Sciences Maltepe University İstanbul Turkey Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Chattanooga Chattanooga TN US

Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep learning will provide more successful results in future studies due to its ability to work with larger data sets compared to machine learning and its ability to improve itself from errors.

关键词： Deep learning machine learning algorithms Earthquakes Radon Neural networks Estimation Prediction methods

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Mask-Enhanced Contrastive learning for Hyperspectral Image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Cao, Xianghai Yu, Jiayu Xu, Ruijie Wei, Jiaxuan Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710126 Peoples R China Agr Bank China Tianjin R&D Dept Tianjin 300000 Peoples R China

Recently, self-supervised learning (SSL) has gained great prominence in hyperspectral image classification (HSIC) due to its powerful capability to alleviate data-hunger problem. The generative-based method and the contrastive-based method have become two main streams in the field of SSL. To fully integrate the merits of both of them, we propose an efficient hybrid SSL method, that is, mask-enhanced contrastive learning (MECL). Essentially, MECL remains a prototypical contrastive-learning (CL) method, but incorporates the masking-and-predicting idea of the generative-based method. When the sample gradually matches the prototypes, feature reconstruction is implicitly performed in MECL step by step. Furthermore, we design a spatial-spectral multimasking mechanism for hyperspectral data and also propose two complementary strategies to prevent MECL from collapsing. To demonstrate the performance of MECL, a wide range of experiments are carried out in our work. On the one hand, the results of ablation experiments prove that HSIC does benefit from the fusion of SSL methods. On the other hand, the classification results on three hyperspectral datasets confirm that our MECL is an effective SSL method with high stability and strong reliability.

关键词： Hyperspectral imaging Feature extraction Generative adversarial networks Contrastive learning Image classification Classification algorithms machine learning algorithms Image reconstruction Attention mechanisms Annotations Contrastive learning (CL) hyperspectral image classification (HSIC) masking self-supervised learning (SSL)

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Research on Arthroscopic Images Bleeding Detection Algorithm Based on ViT-ResNet50 Integrated Model and Transfer learning

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IEEE ACCESS 2024年 12卷 181436-181453页

作者： Liu, Zewen Zhou, Shaoyi Chu, Jianqiao Chai, Zhiyuan Chang, Dongdong Yuan, Yi Qin, Jinling Wang, Xiancheng Ningbo 2 Hosp Ningbo Clin Res Ctr Orthoped & Exercise Rehabil Ningbo 315010 Peoples R China Ningbo Univ Coll Sci & Technol Ningbo 315300 Peoples R China

Arthroscopic surgery is a major technique for the treatment of joint-related diseases, however, intraoperative bleeding often produces a blood mist that severely affects the surgeon's field of vision and requires prompt high-flow drainage to remove the mist. Therefore, accurate bleeding detection is a prerequisite for effective blood mist removal. This paper proposes an arthroscopic image bleeding detection method based on the ViT-ResNet50 integrated model and transfer learning to solve the problem of relying on naked eye to identify bleeding in existing arthroscopic surgery. Firstly, Vision Transformer model and ResNet50 model are used to learn features by transfer learning on ImageNet dataset respectively. Then, a difference-enhanced proportional sampling method is proposed to enhance the unbalanced data. Finally, the two sub-network models are integrated through weighted soft voting method to realize bleeding detection in arthroscopic images. In order to evaluate the performance of the model proposed in this paper, experimental results on real data show that the integrated model is superior to a single deep learning model in various performance indicators and has good effects in detecting bleeding in arthroscopic images.

关键词： Hemorrhaging Surgery Feature extraction Data models Videos Deep learning Residual neural networks machine learning algorithms Convolutional neural networks Transfer learning Arthroscopy surgery bleeding detection transfer learning integrated model Vision Transformer ResNet50

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