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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Speech Emotion recognition on RAVDESS dataset using Deep learning

Speech Emotion Recognition on RAVDESS Dataset using Deep Lea...

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Emerging Technology (INCET), international conference for

作者： Yash Ammanavar Sachin Patil Anupama P Bidargaddi Prajwal Deshanur Rajendra Rathod Meena S M School of CSE KLE Technological University Hubli India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In the realm of machine learning, integrating state-of-the-art technologies is crucial for enhancing the efficacy of applications, particularly in Speech Emotion recognition (SER), where cutting-edge technologies can substantially enhance performance. this paper investigates the utilization of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) architectures in SER, aiming to surpass traditional method-ologles. MLP, renowned for its capability to capture intricate patterns, and CNN, adapted for analyzing spectrogram-based representations of audio data, are juxtaposed. the study involves a comparative analysis of MLP and CNN, emphasizing feature extraction, data processing, and model architecture. Results indicate that the CNN model surpasses MLP in capturing spatial hierarchies within audio features, thereby improving emotion recognition from speech signals. the CNN MLP achieves an accuracy of 86.4%, showcasing superior capabilities in discerning spatial hierarchies within audio features, while the CNN model, with an accuracy of 81.52%, lags behind in comparison to the CNN's performance.

关键词： Deep learning Emotion recognition Adaptation models Accuracy Speech recognition Speech enhancement Multilayer perceptrons

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Sensing of Surface Texture using Smart Glove for Post Stroke Rehabilitation

Sensing of Surface Texture using Smart Glove for Post Stroke...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： S Priya P Sherin Heera Rj Hemalatha Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Post-stroke patients need to be rehabilitated in order to gain partly or fully some of the motor facilities that seem to have ceased to function effectively. According to the research, post-stroke deficits in the human ability to palpate and comprehend surface features is the major difficulty a stroke survivor may experience; often they cause harm or accidentally destroy objects due to an incorrect manner of holding them. this research offers a new strategy with the aid of wearable haptic interface in the form of a smart glove that employs an ESP32 microcontroller and force sensors to identify surface texture. Using pattern recognition, we can effectively distinguish between a rough and a smooth surface to adapt to the pressure required to hold any object. this innovation apart from improving safety also gives the patient feedback in the process of rehabilitation. the use of machine learning in particular proves to be more preferable when it comes to texture recognition preventing shortcomings observed in other senor-based approaches.

关键词： Wireless sensor networks machine learning Stroke (medical condition) Motors Surface roughness Surface texture Rough surfaces Haptic interfaces Force sensors Surface treatment

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Genetic-Algorithm-Based Hierarchical Echo State Network for Temperature Prediction

Genetic-Algorithm-Based Hierarchical Echo State Network for ...

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machine learning and Computer Application (ICMLCA), international conference on

作者： Sheng Yang Shuaiheng Li Jinghang Research Institute of Computing and Communication Beijing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

data-driven based temperature prediction methods are now highly competitive against numerical weather prediction (NWP) method. However, most deep learning-based prediction models focus on global weather forecasting, which requires substantial computational and data resources. Additionally, these models often exhibit lower accuracy in predicting temperatures in the upper atmosphere, which is crucial for studying the near space. To solve this problem, in this paper, the genetic-algorithm-based hierarchical echo state network (GA-HESN) is proposed. Firstly, the hierarchical architecture can fully extract information from the input sequence. Secondly, a parameter optimization strategy based on the genetic algorithm is proposed to further enhance the network's precision. Finally, experiments on the upper atmosphere temperature dataset for the Beijing region demonstrate the effectiveness of the model.

关键词： Accuracy Atmospheric modeling Computational modeling Weather forecasting Computer architecture Predictive models Prediction algorithms Numerical models data mining Genetic algorithms

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Cybersecurity Scientometric Analysis: Mapping of Scientific Articles using Scopus API for data mining and Webscrapping 5

Cybersecurity Scientometric Analysis: Mapping of Scientific ...

引用

5th international conference on data Science and Information Technology, DSIT 2022

作者： Godoy, Carlo H. Diego, Nicole Jehru R. Tagumasi, Russel E. Lerit, Jordan C. Costales, Jefferson A. Bayan Academy Quezon City Philippines Eulogio Amang Rodriguez Institute of Science and Technology Manila Philippines

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

In terms of computers, cybersecurity has experienced massive technological and operational transformations in recent years, with data science at the forefront. Cybersecurity in the Philippines is still believed to be in its infancy. though it is commonplace in other areas of the world, just a few scholars in the Philippines have dared to concentrate in this field. As a result, a need for assistance in promoting cybersecurity in the Philippines must be identified. Because it is difficult to persuade scholars in the Philippines to work on a certain field of computing without figures, Scientometric Analysis is required to persuade scholars in the Philippines to handle cybersecurity. the findings of this study revealed that there is no link between the frequency with which authors publish cybersecurity and their chances of receiving a high number of citations, either by year or overall. the study also found that having a lot of publications does not ensure a higher Google Scholar ranking. According to the study, the most interesting themes in the field of cybersecurity are to connect it with another discipline, thus an author should choose an attractive topic in this field to acquire a higher rank in Googlescholar and especially in Scopus. machine learning and IoT are the two fields that most people want to see integrated into cybersecurity. Hence, this topics can be used to encourage cybersecurity experts in the Philippines to used this topic on their research or speaking engagements. © 2022 IEEE.

关键词： Mapping

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Research on Face Attribute Privacy Protection Method Based on Multi task learning

Research on Face Attribute Privacy Protection Method Based o...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Fenfei Gu Eric Blancaflor Mideth Abisado College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China School of Information Technology Mapua University Manila Philippines

With the widespread application of facial image recognition in fields such as finance, social media, and transportation, the privacy data of facial images is also frequently leaked. In order to effectively protect facial images, the protection of facial attributes is particularly important. therefore, this article combines the method of multi task learning to divide deep learning models into low-level models and top-level models. the underlying model serves as a data feature extractor; the top-level model serves as a classifier, specifically responsible for three tasks in multi task learning: business classification, privacy protection, and image restoration. the goal of the business classification task is to identify user facial attributes; the goal of privacy protection tasks is to minimize the inclusion of privacy related feature information in data features as much as possible; the goal of image restoration task is to make it difficult to recover the original image containing privacy attributes from the extracted data features. In order to measure the privacy of the data feature extractor, a loss function is set for each task, and finally, the loss functions of these three tasks are fused to obtain a data feature extractor that can protect privacy. Finally, through experimental data analysis, this method ensures both the accuracy of facial image recognition and the leakage of facial attribute privacy data.

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Use of machine learning Techniques to Predict Diabetes at an Early Stage 5

Use of Machine Learning Techniques to Predict Diabetes at an...

引用

5th IEEE international conference on Networking and Advanced Systems, ICNAS 2021

作者： Samet, Sarra Laouar, Mohamed Ridda Bendib, Issam Tebessa Algeria

ISBN: (纸本)9781665428217

Because of its ability to improve disease prediction, machine learning has taken a prominent place in healthcare services (HCS). Artificial intelligence and machine learning techniques have already been used in this area. In order to anticipate disease at an early stage, data mining techniques are commonly used. Diabetes has recently become a well-known public chronic condition all across the world. It is rapidly increasing as a result of improper lifestyles, increased consumption of junk food, and a lack of health awareness. Predictive analytics in healthcare is a difficult task, but it can ultimately assist practitioners in making timely decisions regarding a patient's health and treatment based on huge data. For the purpose of predicting diabetes, seven of the most important machine learning classification techniques have been examined. As a result of a comparison of the multiple machine learning approaches utilized in this study, it has been determined which algorithm is best for prediction of diabetes. With an F1 score of 0,94, XGBoost outperformed other classifiers. To help doctors and practitioners anticipate diabetes earlier using machine learning approaches with more accuracy, this study was written. Models were shown to be more effective than existing work. © 2021 IEEE.

关键词： data mining

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Model Selection and Evaluation for learning Analytics via Interpretable machine learning

Model Selection and Evaluation for Learning Analytics via In...

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Computer Science and Technologies in Education (CSTE), international conference on

作者： Zhuoran Li Zehui Zhan Department of Economics Department of Statistics University of Washington Seattle USA School of Information Technology in Education South China Normal University Guangzhou China

learning analytics is one of the most crucial tasks in understanding the relationship between learners' learning behaviors and academic performance in MOOCs. machine learning methods that enable us to learn the pattern of complex data make it more effective to conduct learning analytics. However, among a variety of models, only some of them can provide us with sufficient interpretability to understand the modeling result and propose pedagogically instructive suggestions for improving learners' learning strategies. By demonstrating the performance and interpretability of 17 machine learning models, this study would select the CART, which could balance the model performance and interpretability well, as the optimal model for learning analytics in MOOCs. through the further study of the CART, we concluded that the social interaction behaviors and prompt completion of learning tasks could most significantly help with improving learners' academic performance.

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A Deep learning Approach for Kannada Handwritten Character recognition

A Deep Learning Approach for Kannada Handwritten Character R...

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Emerging Technology (INCET), international conference for

作者： U K Samarth Srushthi Joshi Satish Chikkamath Nirmala S R Suneeta Budihal dept. of electronics and communication KLE Technological University Hubli India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Handwritten character recognition is a crucial process that involves converting handwritten text on different surfaces, such as paper and postcards, into digital formats, making it distinguishable from scanned images. Remarkable advancements have been made in this field, particularly in India, where many languages with complex scripts like Kannada, English, and Marathi are used. Kannada is one of the widely used languages in southern India, and recognizing its handwritten characters is challenging due to its complicated script. the article explains how CNN, a type of dense net, is used to recognize Kannada's handwritten characters. this technology can help protect many historical documents from damage and destruction, which is a significant achievement. the article also highlights that the versatility of this machine learning model extends beyond Kannada and can be used in various applications and technology development. the proposed research has used CNN and transfer learning due to its efficient layers, and the data set from Kaggle consisted of 16 classes of 25 images each for experimentation. the results showed a remarkable 97.50 percent accuracy in 50 epochs, demonstrating the potential of the CNN network in character recognition.

关键词： Deep learning Handwriting recognition Accuracy Navigation Transfer learning data augmentation data models

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Research on Match Competition Prediction Algorithm Based on LightGBM

Research on Match Competition Prediction Algorithm Based on ...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Haiyan Zhang Xu Liu Wanyu Chen Guihua Gan Electronic Countermeasures Academy National University of Defense Technology Hefei China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

With the advent of the era of big data, artificial intelligence and machine learning have developed rapidly. At present, there are many competitive competition data, which can objectively predict the level of players in the future through data analysis. Firstly, based on the data of Wimbledon men's singles in 2023, this paper preprocesses and constructs the index system, and carries out the consistent processing and correlation analysis of the indicators. Among them, player 1 serve depth, player 1 serve speed, player 2 serve speed, player 1 break success rate, player 2 break success rate are the most positively correlated, and finally 5 indicators are determined. then, the LightGBM prediction algorithm is established to train the 185 games data of five matches, and the results of 42 games in the next game are predicted. the prediction accuracy is 83.72%. Compared with the random forest algorithm and ridge regression, the prediction algorithm has the highest accuracy. Finally, the LightGBM prediction competition fluctuation model was applied to other players, and it was found that the prediction accuracy of the model was 86.21 %. the prediction results show that the generalization effect of LightGBM algorithm is better, which provides a scientific basis for the level prediction of players in competitive competitions.

关键词： Accuracy machine learning algorithms Systematics Games Predictive models Prediction algorithms pattern recognition Indexes Random forests Sports

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Stacked Convolutional Autoencoder with Multi-label Extreme learning machine (SCAE-MLELM) for Bangla Regional Language Classification 22

Stacked Convolutional Autoencoder with Multi-label Extreme L...

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5th international conference on Signal Processing and machine learning, SPML 2022

作者： Hossain, Prommy Sultana Chakrabarty, Amitabha Piran, Jalil Suh, Doug Young Department of Computer Science and Engineering Brac University Dhaka Bangladesh Department of Computer Science and Engineering Sejong University Seoul Korea Republic of Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of

ISBN: (纸本)9781450396912

Several studies emphasized the importance of detecting the dialect spoken by the user, to improve customer-agent relationships in call centers operated by telecomunication companies. and to create advanced speech recognizing Artificial Intelligence devices using machine learning (ML) techniques. However, the extensive Bangla speech recognition research performed in the past did not address this issue. therefore, in this paper, we propose a model that uses deep learning (DL) techniques, as a subset of ML, to classify the regional language in the audio signal of the Bangla speech. We build a model based on Stacked Convolutional Autoencoders (SCAE) and Multi-label Extreme learning machines (MLELM). SCAE extracts the spatial and temporal features from the input data and produces a detailed feature vector. In order to provide a soft classification score and hard labels, the information is passed onto a series of MLELM networks. For Bangla speech dataset created during this research, the proposed method produced an accuracy score of 95%. Furthermore, the proposed system performs remarkably well in classifying the age and gender of the speaker in Bangla. © 2022 Association for Computing machinery.

关键词： Speech recognition

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