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machine learning music emotion recognition based on audio features 6

Machine learning music emotion recognition based on audio fe...

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2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Wang, Keju Qian, Cheng Zhang, Lijun Humanities School Xi'an Jiaotong University Xi'an Conservatory of Music Orchestra Department Xi'an China Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xi'an China

ISBN: (纸本)9798350313444

Music is a kind of language expressing emotion, different structures and content contain different emotions. For the music emotion classification problem, a novel music emotion recognition strategy is proposed using audio features and machine learning. the developed scheme includes four stages: preprocessing, feature extraction, feature selection, and classification. As a first step, the signals in the data set are stably continuous by framing and windowing in processing. Secondly, a 1x140 size feature is derived from signal to profile the detailed music emotion. In the third step, the extracted features are selected and pre-weighted based on the feature weighting algorithms (ReliefF), max-relevance and min-redundancy (mRMR). In the final step, the data are led to the convolutional neural network and the long short-term memory (CNN-LSTM) combination model to recognize music emotions. the experimental results approve that the proposed CNN-LSTM based on ReliefF & mRMR (RM-CNN-LSTM) feature processing method could obtain higher identification accuracy, and all indicators of the proposed model improve significantly. © 2023 IEEE.

关键词： feature weighting algorithms fusion model max-relevance and min-redundancy music emotion recognition

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the Football Matches Outcome Prediction for English Premier League (EPL): A Comparative Analysis of Multi-class Models 6th

The Football Matches Outcome Prediction for English Premier ...

引用

6th international conference on Soft Computing and data mining (SCDM)

作者： Adnan, Nur Amirah Asri, Luqman Al Hakim Mohd Mustapha, Aida Razali, Muhammad Nazim Univ Tun Hussein Onn Malaysia Fac Appl Sci & Technol KM 1Jalan Panchor Panchor 84600 Johor Malaysia Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia

ISBN: (纸本)9783031669644;9783031669651

Football match outcome prediction has evolved into a dynamic field of research, driven by the integration of machine learning models to achieve precise forecasts. this paper embarks on a comprehensive exploration, presenting a comparative analysis of four prominent multiclass machine learning (ML) models-Multiclass Logistic Regression, Multiclass Neural Network, Multiclass Decision Forest, and Multiclass Decision Jungle. Furthermore, it explores the application of Azure machine learning for predicting English Premier League (EPL) match results in terms of win, draw, and lose, underscoring the league's significance in the global football landscape through multiclassification ML models. the paper aims to contribute valuable insights to the field, emphasizing the importance of ML model selection for enhanced predictive performance. As the result, Multiclass Logistic Regression emerges as the most accurate model with a 57.08% success rate, followed closely by the multiclass decision forest at 56.71%. the multiclass neural network exhibits a respectable accuracy of 55.59%, while the multiclass decision jungle performs similarly with an identical accuracy of 55.59%. these findings show critical role of a choosing suitable and well-performing ML model in achieving accurate football match outcome predictions.

关键词： Football Matches Prediction Multi-class Classification machine learning Logistic Regression Neural Network Decision Forest Decision Jungle

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A Comprehensive Review of machine learning and Deep learning Techniques for Rice Leaf Disease Classification 6th

A Comprehensive Review of Machine Learning and Deep Learning...

引用

6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Tabassum, Tasnim Rafiq, Shifat Ara Zulfiker, Md. Sabab Ritu, Nadia Afrin Biswas, Al Amin Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE Sheikh Hasina University Netrokona Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh Department of CSE Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9789819780891

Rice is one of the most significant primary crops and provides a substantial portion of the world’s food for billions of people worldwide. However, several leaf-related diseases pose significant risks to the rice crop’s yield and quality. the traditional approaches to disease identification and classification are frequently arduous and require specialized expertise. the domain of machine learning (ML), deep learning (DL), and the convergence of both of these techniques has emerged as a potential approach that can address this issue and enable immediate and accurate diagnosis of rice leaf disease. Additionally, this study offers a thorough analysis and contrast different approaches, focusing on addressing the initial research questions and identifying potential future possibilities. this study functions as a comprehensive resource for research groups engaged in the development of ML and DL applications specifically targeted at the categorization of rice leaf diseases. In addition to aiding various studies in creating feasible applications based on ML and DL techniques, this review effort will instruct the researchers on how these methods might facilitate the identification of rice leaf disease at an early stage. the insights derived from the study offer useful direction for the advancement of research related to rice leaf diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Approach to Recognize Human Activities based on ConvLSTM and LRCN 6

An Approach to Recognize Human Activities based on ConvLSTM ...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Bhatia, Shradha Chauhan, Tushar Gupta, Sumita Gambhir, Sapna Panchal, Jitesh H. Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India J. C Bose University of Engineering and Technology Department of Computer Engineering Faridabad India Purdue University Department of Mechanical Engineering United States

ISBN: (纸本)9798350346961

In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches' heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.. © 2023 IEEE.

关键词： data handling

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Research on Action pattern recognition Based on DeepConvLSTM 6

Research on Action Pattern Recognition Based on DeepConvLSTM

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2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Guo, Pengkai Li, Zhuo Sun, Yiyan Hohai University School of Artificial Intelligence and Automation Nanjing China Hohai University College of Energy and Electrical Engineering Nanjing China

ISBN: (纸本)9798350313444

pattern recognition is an important way to improve the quality of life and promote scientific research. through the big data captured by the three-axis sensor, this paper focuses on the establishment of relevant models for the accurate classification of 19 motion modes , and analyses of the generalization ability and overfitting. this paper establishes DeepConvLSTM model by combining the original deep learning convolutional neural network (CNN) and long- term short-term memory recursion (LSTM). the model code about DeepConvLSTM was written by Python and relevant data was imported. In terms of methods, this paper evaluates the generalization ability of the model by using setaside method and K-fold cross-validation method respectively from the algorithm level. In terms of indicators, this paper selects accuracy, accuracy rate, recall rate and F1 value. Finally, the conclusion is drawn as follows: when epoch increases to 40, Train loss and Val loss basically fall together, indicating that the accuracy and stability of the model are *** paper use logistic regression model to solve the classification problem of human behavior. It is found that the classification effect of DeepConvLSTM model was better than that of LR model with higher accuracy and no overfitting phenomenon. in addition, Recall is used to analyze the sensitivity of the model. After several tests, it is found that the model shows good sensitivity and stability when the training times reaches 60. On this basis, considering the cost and efficiency of training comprehensively, the number of training is set as 60 in this paper, and the sensitivity and robustness of the model are both good. In conclusion, this paper establishes a scientific action pattern recognition model according to the conditions of the subject and the data given. the model is simple and easy to popularize. After verification and analysis, the model in this paper has strong accuracy, robustness and sensitivity, and has certain pra

关键词： action recognition deep learning DeepConvLSTM pattern recognition python

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BAFL: Federated learning with Base Ablation for Cost Effective Communication 26

BAFL: Federated Learning with Base Ablation for Cost Effecti...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Kundalwal, Mayank Kumar Saraswat, Anurag Mishra, Ishan Mishra, Deepak IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning is a distributed machine learning setting in which clients train a global model on their local data and share their knowledge with the server in form of the trained model while maintaining privacy of the data. the server aggregates clients' knowledge to create a generalized global model. Two major challenges faced in this process are data heterogeneity and high communication cost. We target the latter and propose a simple approach, BAFL (Federated learning for Base Ablation) for cost effective communication in federated learning. In contrast to the common practice of employing model compression techniques to reduce the total communication cost, we propose a fine-tuning approach to leverage the feature extraction ability of layers at different depths of deep neural networks. We use a model pretrained on general-purpose large scale data as a global model. this helps in better weight initialization and reduces the total communication cost required for obtaining the generalized model. We achieve further cost reduction by focusing only on the layers responsible for semantic features (data specific information). the clients fine tune only top layers on their local data. Base layers are ablated while transferring the model and clients communicate parameters corresponding to the remaining layers. this results in reduction of communication cost per round without compromising the accuracy. We evaluate the proposed approach using VGG-16 and ResNet-50 models on datasets including WBC, FOOD-101, and CIFAR-10 and obtain up to two orders of reduction in total communication cost as compared to the conventional federated learning. We perform experiments in both IID and Non-IID settings and observe consistent improvements.

关键词： Costs Federated learning Semantics Neural networks Distributed databases Focusing Feature extraction

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Applications of machine learning in Rheumatoid Arthritis - An Insight 6

Applications of Machine Learning in Rheumatoid Arthritis - A...

引用

6th international conference on Computing Methodologies and Communication, ICCMC 2022

作者： Ganeshbabu, K. Ilayaraja, M. Kalasalingam Academy of Research and Education Krishnan Kovil Virudhunagar India

ISBN: (纸本)9781665410281

this paper deals with the review of different data mining and machine learning approaches engaged for the study of Rheumatoid Arthritis (RA). RA is a constant illness that affects the joints located in the fingers, knees, and feet. It is a kind of chronic inflammatory disease that causes severe sickness. In the era of machine learning applications, they lead to some technological as well as clinical advancement for the arthritis diagnosis, prediction, and prognosis of auto immune rheumatoid arthritis. this study provides some vital analysis of the machine learning techniques that are presently practiced in the analysis of medical data for RA and how it is used in the drug development, target identification drug repurposing and delivery. © 2022 IEEE.

关键词： Diseases

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Generation of data for Training Retinal Image Segmentation Models 10th

Generation of Data for Training Retinal Image Segmentation M...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Bhuiya, Srinjoy Chakraborty, Suchandra Sadhukhan, Subhopriyo Mandal, Deba Prasad Bhandari, Dinabandhu Heritage Inst Technol Kolkata 700107 West Bengal India Indian Stat Inst Kolkata 700108 West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Biomedical image segmentation requires pixel-wise labelling which is extremely time-consuming and the availability of additional training data is highly beneficial for training Deep learning models. In addition to using Classical Image augmentation, generative adversarial networks have been used to augment the training data. this work is an investigation of the usefulness of generated medical images for Deep learning segmentation models. An attempt has been made to create a computer-generated retinal image segmentation dataset using various state-of-the-art image generative deep learning models and use that dataset to train a supervised image segmentation model. Our experiments demonstrate that the generated data can be used successfully for medical segmentation tasks and improves the model's performance over using classical augmentations.

关键词： Retinal Image Segmentation Image Generation Image Augmentation Image Translation

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Multimodal machine learning for Sign Language Prediction 6th

Multimodal Machine Learning for Sign Language Prediction

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6th international conference on Nanotechnologies and Biomedical Engineering, ICNBME 2023

作者： Khalafaoui, Yassèr Grozavu, Nistor Matei, Basarab Rogovschi, Nicoleta ETIS ENSEA UMR 8051 CY Cergy Paris University 2 Avenue Adolphe Chauvin Cergy Pontoise Cedex95302 France LIPN UMR CNRS 7030 Sorbonne Paris Nord University 99 Avenue J-B Clément Villetaneuse93430 France LIPADE Paris-Decartes University 45 rue des Saintes Pères Paris75005 France

ISBN: (纸本)9783031427817

Numerous applications, including translation tools, interpreting services, video remote interpreting, human-computer interaction, online hand tracking of human communication in desktop settings, real-time multi-person recognition systems, games, virtual reality settings, robot controls, and natural language communications, benefit from sign language recognition advantages. Multimodal data contains information from different sources such as video, sensors, electrocardiograms (ECGs), while emotions refer to the non-verbal cues that accompany language use, such as facial expressions and body posture. Integrating these additional sources of information helps to better understand the user’s intent, which improves the performance of the sign language recognition model. To build such a model, a set of multimodal data and emotions must be collected. this data set should be differentiated and cover different individual/isolated signs, emotions and body gestures. the model is designed to integrate multimodal data and emotions, which would involve combining different machine and deep learning algorithms adapted to different types of data. In addition, the model will need to be trained to recognize the different emotions that accompany sign language. Once the model is trained, it can be tested on the test dataset to assess its performance and also plan for a test on real data (with signing people). In this paper we propose a study to use the multi-modal machine learning for sign recognition language. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Transfer learning: Kernel-Based Domain Adaptation with Distance-Based Penalization 10th

Transfer Learning: Kernel-Based Domain Adaptation with Dista...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Prakash, Jainendra Ghorai, Mrinmoy Sanodiya, Rakesh Indian Inst Informat Technol Sri City India

ISBN: (纸本)9783031451690;9783031451706

this paper introduces a novel approach to address the challenges of transfer learning, which aims to efficiently train a classifier for a new domain using supervised information from similar domains. Traditional transfer learning methods may fail to maintain the discriminative features of the target domain due to the scarcity of labelled data and the use of irrelevant source domain data distribution subspace, resulting in poor metrics. To overcome these challenges, the proposed approach, called KDADP, transforms the data distribution of both the source and target domains into a lower-dimensional subspace while preserving their discriminatory information. the KDADP model maximizes between-class variance and minimizes within-class variance with L1 penalization, enabling the recovery of the most useful characteristics and reducing the model's complexity. Experimental results on three real-world domain adaptation datasets demonstrate that the proposed KDADP model significantly improves classification performance and outperforms state-of-the-art primitive, shallow, and deeper domain adaptation methods.

关键词： classification domain adaptation features space transfer learning supervised learning semi-supervised learning

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