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共 3282 条记录，以下是821-830 订阅

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Sentiment Analysis on Telugu Text Translated from English Using NLP and ML

Sentiment Analysis on Telugu Text Translated from English Us...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： A.H.N Karthik Ch. Aneesh Gembali Saumik K.V.V Varun Kavitha C.R. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study mainly uses Natural Language Processing (NLP) and machine learning. The general approach of this tool is to apply sentiment analysis in Telugu Text translated from English. The Technique contains many preprocessing steps, for example, tokenization of texts, normalization and noise removal for translating English text into Telugu which then will use machine translation services. It is followed by sentiment analysis of the statement using NLP models and machine learning approaches and classified them as; positive, negative, or neutral. The main objective of this study is to enhance the accuracy of sentiment identification with non-English language. Environments and also to develop rigorous sentiment analysis tools optimized for Telugu, which are designed as to the language and context-related difficulties to be taken into account. Additionally, the research examines analyses the impact of bilingual datasets on increasing translation accuracy and sentiment classification, while also tackling the obstacles given by idiomatic idioms and context-specific interpretations in Telugu Language.

关键词： Sentiment analysis Translation Accuracy Noise machine learning Tokenization Object recognition machine translation Internet of Things data communication

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Clustering in Game Analysis on FIFA22 Official Players data 3

Clustering in Game Analysis on FIFA22 Official Players Data

引用

3rd international conference on Artificial Intelligence and data Sciences, AiDAS 2022

作者： Akif Md Ali, Muhammad Faidi Muhalith, Muhammad Amir Fahmy Sahran, Muhammad Syazwan Fikri Zubli, Zubli Quzaini Kamarudin, Siti Nur Kamaliah Rahman, Shuzlina Abdul Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Shah Alam Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

This study investigates the similarities of skill sets based on football players' position and performance of the clustering algorithm of two unsupervised learning methods: the k-Means and the Hierarchical Clustering. The study aims to discover the number of clusters and performance of machine learning algorithms, k-Means, and Hierarchical Clustering to group players based on their position. The FIF A22 dataset was obtained from Kaggle, which had undergone a dimensionality reduction method, the Principal Component Analysis (PCA). The comparison of the model for both methods with and without PCA showed a better silhouette score using the PCA data reduction. The hyperparameter tuning was applied to the models with k=3, k=4, k=8 and k=14, where k is the number of clusters, and the value of k is selected based on the player's positioning in the football match. The best model with different hyperparameters was evaluated using the Sum of Squared Error (SSE) and the Silhouette Analysis. The findings indicate k-Means and Hierarchical Clustering model can achieve a silhouette score greater than 0.5. In addition, the k-Means model scores 16 % higher than Hierarchical Clustering on k= 14. Results found that the k- Means algorithm with k=3 is the best performing model. Based on the observation, each cluster is grouped with similar skills values of different players' attributes. © 2022 IEEE.

关键词： data mining

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Use the Direction of the Combined Vector of Accelerometers to Classify Daily Activities 3

Use the Direction of the Combined Vector of Accelerometers t...

引用

3rd international conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Wang, Zhouyang Mo, Lingfei School of Instrument Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665492812

Human Activity recognition (HAR) is important for human physical fitness. Wearable sensor-based HAR uses accelerometers (A), gyroscopes (G), and other sensors to collect human motion data. To obtain a longer battery life for the wearable device and conduct long-term monitoring of human daily activities, it is necessary to adopt a method that can extract features from signals of sensors efficiently. In this paper, we study the extraction method of accelerometer data and combine the three axes of accelerometers into a direction ax. Then encode the extracted directions into numbers 1-62. In different activities, the distribution of directions is significantly different. Using Artificial Neural Network (ANN), K-nearest neighbor (KNN), Random Forest (RF), and Support Vector machine (SVM) as classifiers, and the direction distributions as features input to classifiers, the classification accuracy up to 99.3%, in the best case. © 2022 IEEE.

关键词： Feature extraction Human Activity recognition (HAR) machine learning

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EEG-Driven machine learning Framework for Student Cognitive Load Classification

EEG-Driven Machine Learning Framework for Student Cognitive ...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Varunkumar MS Siddharth Gupta Pramod TC Department of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Karnataka India Department of Computer Science & Engineering Siddaganga Institute of Technology Tumkur Karnataka India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Understanding differences in learning abilities is essential for improving education. This paper presents a framework to differentiate between learners using EEG data. The framework identifies critical factors that influence learning experiences by applying data-driven approaches and statistical methods. It includes analyses such as band power, entropy calculation, and coherence measurement across EEG channels. The extracted features are used to train a classifier and to identify cognitive behavior patterns. The framework's accuracy is verified using cross-validation, classification reports, and confusion matrices. The experimental analysis shows major improvements in cognitive pattern understanding and identification. This framework provides educators with actionable insights to design personalized learning strategies. By enhancing the understanding of individual learning dynamics, the framework has the potential to optimize educational outcomes across diverse learner profiles.

关键词： Support vector machines Accuracy Education machine learning Coherence Feature extraction Brain modeling Cognitive load Electroencephalography Entropy

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Application of Bayesian learning Algorithm in the Design of Precise Funding recognition Management System

Application of Bayesian Learning Algorithm in the Design of ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Yue Su Department of Education and Health Beijing Information Technology College Beijing China

With the expansion of vocational education enrollment, the number of students has been increasing year by year, and the management of students' life and learning in school is particularly important. Especially in the distribution of annual financial aid, the previous approach was based on student applications and jurisdictional certificates, which made it difficult to understand the true situation of students, and effectively identifying impoverished students has become a hot research topic. This article designs and implements a student affairs management system based on data mining, which integrates data mining ideas into the impoverished student management module, achieving accuracy in identifying impoverished students and completing the identification and management of impoverished students. By collecting students' family background data, extracting key indicators, and then using Bayesian algorithm to predict the impoverished students, the true impoverished students can be identified. The results demonstrate the effectiveness of the proposed method in this paper.

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A Phishing Detection Method Based on data mining 3

A Phishing Detection Method Based on Data Mining

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3rd international conference on Applied machine learning, ICAML 2021

作者： Li, Chunzhi College of Cyberspace Security and Computers HeBei University HeBei China

ISBN: (纸本)9781665421256

data mmmg technology is a very important technology in the current era of data explosion. With the informationization of society and the transparency and openness of information, network security issues have become the focus of concern of people all over the world. This paper wants to compare the accuracy of multiple machine learning methods and two deep learning frameworks when using lexical features to detect and classify malicious URLs. As a result, this paper shows that the Random Forest, which is an ensemble learning method for classification, is superior to 8 other machine learning methods in this paper. Furthermore, the Random Forest is even superior to some popular deep neural network models produced by famous frameworks such as TensorFlow and PyTorch when using lexical features to detect and classify malicious URLs. © 2021 IEEE

关键词： Network security

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Cultural-Aware AI Model for Emotion recognition

Cultural-Aware AI Model for Emotion Recognition

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Iranian machine Vision and Image Processing (MVIP)

作者： Mehrdad Baradaran Payam Zohari Abtin Mahyar Hossein Motamednia Dara Rahmati Saeid Gorgin Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran Department of Computer Engineering Khaje Nasir University Of Technology Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Chosun University Gwangju South Korea

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multimodal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

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Fault pattern recognition Method of Rolling Bearing Based on MTF-2DWDCNN 3

Fault Pattern Recognition Method of Rolling Bearing Based on...

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3rd international conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xun, Zhiwen Miao, Xiaodong Yu, Hu Gu, Yinji Nanjing Tech University School of Mechanical and Power Engineering Nanjing China Nanjing Tech University School of computer science and technology Nanjing China

ISBN: (纸本)9781665492812

2-D fault feature images have advantages as neural network input for fault pattern recognition, but the most deep image recognition algorithms are non-sensitive to the time-series features of images. This paper proposes a new algorithm named MTF-2DWDCNN based on Markov transfer field(MTF) jointly improved deep convolutional neural networks with wide first-layer Kernel(WDCNN). Firstly, the original 2-D sequence signal is converted into a 2-D time series image by MTF, used as the input of the neural network;secondly, the convolution mode of the original WDCNN is improved to adapt to 2-D time series;finally, the accuracy and error experiments are carried out to verify its effectiveness. Experimental results show that the method proposed in this paper has certain advantages over traditional algorithms regarding recognition accuracy. © 2022 IEEE.

关键词： Bearing deep learning fault diagnosis MTF

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Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor data Using Multi-Task Transformers with Diverse Attention Mechanism

Robust Fault Diagnostics of Industrial Motors: The Signal-to...

引用

international conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Sudipta Mukhopadhyay Indrajit Kar Zonunfeli Ralte Zensar Technologies Bangalore India

Vibration analysis serves as a valuable method to identify defects within mechanical systems. This process involves monitoring the auditory signals generated by the system and discerning patterns that signify potential issues. Nevertheless, due to the intricate nature of motors, relying solely on one modality (such as acoustic signals) might prove inadequate for comprehensively detecting all faults. Our research introduces a novel approach to enhance fault detection in mechanical systems, specifically focusing on motors. A more comprehensive system health assessment is achieved by utilizing multi-modal vibration analysis, combining acoustic signals and heat signatures. Effective information fusion is facilitated by integrating attention-based transformers, capable of capturing cross-modal correlations. The proposed neural network employs classification and regression loss functions for fault identification and severity assessment respectively. Evaluation on a motor fault dataset demonstrates significant classification accuracy improvements compared to conventional deep and machine learning methods. Incorporating a signal-to-image transformation further aids in fault pattern recognition. This method offers a promising avenue for accurate and comprehensive fault diagnosis in mechanical systems, showcasing the potential of multi-modal analysis and attention mechanisms for industrial equipment maintenance.

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Optimizing GIS Spatial Analysis Method by Using Big data mining Technology

Optimizing GIS Spatial Analysis Method by Using Big Data Min...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yao Chen Wuhan Donghu University Wuhan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the advent of the era of big data, geographic information system (GIS) is facing the challenge of massive, multi-source and heterogeneous data when processing and analyzing spatial data. This paper discusses how to use big data mining technology to optimize GIS spatial analysis methods to improve the efficiency and accuracy of spatial data processing. Firstly, the research emphasizes the importance of data preprocessing and cleaning, and ensures the quality of data through data integration, transformation and specification. Then, the paper introduces the process of spatial data mining in detail, including data import, feature extraction, and the selection and application of mining algorithms, such as cluster analysis, association rule mining and classification prediction. Through practical case study, this paper shows how to apply big data mining technology to identify the spatial pattern and temporal variation law of urban traffic flow, which provides a scientific basis for urban planning and management. Finally, this paper points out that although big data mining technology has made remarkable progress in GIS spatial analysis, it still needs to face challenges such as spatial heterogeneity, spatial scale effect, data integration and spatial data visualization. Future research needs to comprehensively use spatial statistics, machine learning, data mining and other methods, combined with domain knowledge for in-depth research and analysis. Through these methods, big data mining technology can be used more effectively to optimize GIS spatial analysis methods and provide more accurate spatial data analysis support for decision makers.

关键词： Accuracy data analysis Urban planning data integration Big data Prediction algorithms Spatial databases data mining Traffic congestion Geographic information systems

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