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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

共 8828 条记录，以下是221-230 订阅

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Comparative Analysis on Classification of Unstructured Text data and Specific Summarization 2

Comparative Analysis on Classification of Unstructured Text ...

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2nd ieee International conference on Advances in information technology, ICAIT 2024

作者： Harisha, S. Bhat, Subrhamanya Srinivas University Karnataka Mangaluru India Institute of Computer and Information Science Srinivas Unversity Karnataka Mangaluru India

ISBN: (纸本)9798350383867

Unstructured text data, prevalent in emails, social media posts, articles, and more, poses unique challenges and opportunities for classification. This abstract delves into methods and applications of classifying unstructured text data. Classification techniques for unstructured text data leverage natural language processing (NLP), machine learning, and deep learning. NLP techniques, including tokenization, stemming, and part-of-speech tagging, preprocess raw text to extract meaningful features. Traditional machine learning algorithms such as Naive Bayes, Support Vector Machines, and Random Forests, alongside deep learning architectures like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), are employed for classification tasks. Topic modeling techniques categorize text data into topics or themes, facilitating content organization and recommendation systems. Despite the advancements, challenges persist in unstructured text data classification. Ambiguity, context dependency, and linguistic nuances pose hurdles in accurately categorizing text data. Domain-specific jargon and slang further complicate classification tasks, necessitating domain adaptation and continuous model refinement. In conclusion, classification of unstructured text data is a multidimensional endeavor, requiring interdisciplinary approaches and continual innovation to effectively derive insights and value from vast textual information sources. © 2024 ieee.

关键词： Adversarial machine learning

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Threat Reduction Framework for Intelligent Systems 2

Threat Reduction Framework for Intelligent Systems

引用

2nd International conference on Intelligent Control and Computing, IC-C 2024

作者： Boonkrong, Sirapat Institute of Digital Arts and Science Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (纸本)9798350351873

Intelligent systems have been developed and evolved since the advent of artificial intelligence together with the growth of the Internet. They have provided convenience in many areas including solving complex problems. Intelligent systems have also been integrated into many applications in business, education as well as healthcare. Unfortunately, the growing trend of intelligent systems comes with information security problems which comprise data theft, data modification, machine learning alteration, and data poisoning. This research, therefore, proposes a framework that can be used to reduce risks for intelligent systems. The framework applies several information security mechanisms, namely authentication, encryption, and integrity detection. These mechanisms can be applied at all stages of an intelligent systems, including data collection, data transmission, data storage, computation, and output. The threat reduction framework is proposed in such a way that it is generic enough to be applied as a foundation of any intelligent systems. © 2024 ieee.

关键词： data integrity

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Personalized Intelligent Recommendation of English Vocabulary Learning Based on Weight Kernel Learning-Support Vector Regression 2

Personalized Intelligent Recommendation of English Vocabular...

引用

2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Li, You Chengdu College of University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350372892

The essential of vocabulary knowledge as one of the most difficult aspects of learning a foreign language is widely recognized. However, there has been a lack to analysis on English vocabulary that concentrated on authority of content and quality of Wikipedia data, such as content authority, which affect classification accuracy. This paper, proposed Weight Multiple Kernel Learning -Support Vector Regression (WMK-SVR) technique is performed efficiently in classification for enhance the accuracy. The WMK-SVR are used to transform input data into a higher dimensional space where linear regression and capture different aspects of the data, enhancing the model learning capability. wiki dataset and pre-processing involved Square Root Cubature Kalman Filter (SRCKF) is enable to cleaning the data from the document words in the English text. Feature extraction using Local Maximum Synchro Squeezing Transform (LMST) is extract the feature and offers better frequency resolution compared to conventional wavelet transforms. The proposed WMK-SVR method is evaluated using the Wikipedia data achieving a higher accuracy of 91.25% respectively. The existing techniques such as Neural Machine Translation (NMT)and Natural Language Processing (NLP) are evaluated the proposed method. © 2024 ieee.

关键词： Support vector regression

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Enhancing Predictive Analytics Through Hybrid Machine Learning Models: A Comparative Analysis Of SVM-NB And Lasso-Ridge Techniques 2

Enhancing Predictive Analytics Through Hybrid Machine Learni...

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2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Abdullah, A Sheik Mishra, Utkarsh Akash, K. Ravi, Athul Mohan Arvind, S.P. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372892

During the moment of data-driven decision-making, effective classification algorithms play a major part in different areas. This study showcases a comparative analysis of Support Vector Machines (SVM) and Naïve Bayes (NB), two standout machine learning systems, through various data sets. We suggest a unique hybrid method that cleverly blends SVM and NB, tackling their separate inadequacies while leveraging their strengths. Likewise, we present a hybrid model that combines lasso and ridge regression methods for improved performance. Our experimental outcomes reveal the superior predictive accuracy of the proposed SVM-NB hybrids over singular algorithms and the Lasso-Ridge Hybrid. This research highlights the potential of hybrid methods in advancing predictive analytics, providing a hopeful path for optimizing classification duties in practical situations. © 2024 ieee.

关键词： Predictive analytics

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Personalized Recommendation Algorithm with data Mining for College Libraries using Long Short Term Memory Technique 2

Personalized Recommendation Algorithm with Data Mining for C...

引用

2nd International conference on data science and information System, ICDSIS 2024

作者： Wang, Fang BaoJi Vocational And Technical College Library BaoJi China

ISBN: (纸本)9798350381665

With the popularization of the internet and information technology, ordinary users in university are also struggling to read books that can really interest them or have some real value in a sea of books by simply using traditional book finding services. Therefore, this research proposed data mining and new book recommendation service by using the semantic classification with deep learning for university libraries. The recommendation algorithm which considers the semantic classification constructs the book feature model, which is title keywords depending model and Reader Preference Model. Based on the classes divided above, combine them by using the book feature model and the reader preference model, then establish the book recommendation list by using collaborative filtering recommendation algorithm and content-based recommendation algorithm. Artificial intelligent system integrated with Long Short Term Memory (LSTM) to design a recommender system for semantic classification of user preferences. The recommendation strategies that can be adopted in the system framework are described. In the context of the experiment featured in this research, the division of the personalized recommendation approach for semantic classification is applied. The proposed method attained the 93.7% accuracy in semantic classification. © 2024 ieee.

关键词： Long short-term memory

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A Machine Learning Approach for Recommending Soil Nutrient Levels 2

A Machine Learning Approach for Recommending Soil Nutrient L...

引用

2nd ieee International conference on Networking and Communications, ICNWC 2024

作者： Anitha, R. Ramathilagam, A. Jeyavelan, M. Pandi, C. Karthi Karthieswaran, R.A. P.S.R Engineering College Department of Computer Science and Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798350365269

India's coastal state of Tamil Nadu is confronted with difficulties in agriculture as a result of climate-related variables and unpredictability. Even though both the people and the area are expanding, there is no way to achieve the desired rise in productivity. The expansion of the information technology industry has resulted in the creation of a system that allows farmers to get suggestions about the production of crops. The recommendation of crops is based on the amount and meteorological conditions of the area, and data analytics makes it possible to extract meaningful information from large agricultural datasets. An analysis of the crop dataset has been performed, and suggestions have been given depending on the season and the level of production. © 2024 ieee.

关键词： Crops

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Evaluating the Performance of Clinical data using Machine learning Approach- An Ensemble Model 2

Evaluating the Performance of Clinical data using Machine le...

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2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Abdullah, A. Sheik Vinod, A Aashish Pranav, A G Vishal Govindarajan, Yeshwanth Selvakumar, Subramanian Vellore Institute of Technology School of Computer Science and Engineering Chennai India Bahir Dar University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350372892

In the rapidly evolving field of healthcare, accurate clinical predictions are paramount for effective disease management and treatment planning. This paper introduces a novel ensemble machine learning model that utilizes the combined strengths of decision trees, neural networks, random forests, and support vector machines, integrated through a sophisticated linear regression meta-learner. This advanced integration strategy enhances predictive accuracy by optimally weighting the contributions of each base model based on their predictive reliability and effectiveness. The ensemble model amalgamates diverse algorithms with a meta-learning approach to harness a broader spectrum of data insights, thereby overcoming the limitations of traditional single-model approaches. Comparative analysis reveals that the ensemble model significantly outperforms existing methods, achieving superior accuracy in clinical predictions. These results underscore the potential of ensemble approaches in revolutionizing predictive analytics in healthcare, offering a promising tool for improving diagnostic precision and patient outcomes. © 2024 ieee.

关键词： Contrastive Learning

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Vocal Performance Evaluation Based on Bidirectional Gated Recurrent Units and Caps Net 2

Vocal Performance Evaluation Based on Bidirectional Gated Re...

引用

2nd ieee International conference on data science and Network Security, ICDSNS 2024

作者： Wu, Hui School of Literature and Arts Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350373110

The vocal performance evaluation tests hypothesis that automated methods for diagnosing voice disorders from speech recordings benefit from contextual data found in continuous speech. However, evaluating vocal performance is difficult and suffer from vanishing and exploding gradients, affecting accuracy. This paper proposes Bidirectional Gated Recurrent Units (Bi-GRU) and CapsNet model for classification, which performs efficiently and handles large vocal data, achieving high accuracy. Initially, data is obtained from the Saarbruecken Voice database (SVD) and pre-processed using Short-Time Fourier Transform (STFT) to enhance quality of voice recordings. The Mel-Frequency Cepstral Coefficients (MFCC) using feature extraction for determine the spectral information of the voice. The Principal Component Analysis (PCA) using feature selection for lower dimensionality of data and select relevant features. Finally, Bi-GRU and CapsNet is used for classification in vocal performance evaluation, enhancing sequential information to classify efficiently. The proposed Bi-GRU and CapsNet technique is evaluated on vocal data, achieving high accuracy of 97.92%, F1-score of 97.90%, precision of 97.85% and recall of 98.68% on SVD dataset. The existing methods, such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), are comparison of proposed method. © 2024 ieee.

关键词： Convolutional neural networks

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Hybrid Model for Predicting Used Car Prices: Integrating Natural Language Processing with Random Forest Regressor 2

Hybrid Model for Predicting Used Car Prices: Integrating Nat...

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2nd ieee International conference on Advances in information technology, ICAIT 2024

作者： Tyagi, Shiva Sirohi, Shagun Singh, Yash Siddhant Vishwakarma, Akash Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350383867

The automotive sector has a notable upswing in the sales of pre-owned vehicles, hence necessitating the development of precise pricing models to enable knowledgeable decision-making for both purchasers and vendors. In order to predict used automobile prices, we provide a novel hybrid methodology in this work that combines Natural Language Processing (NLP) methods with the Random Forest regressor. Our method leverages structured and unstructured data sources to extract valuable contextual information that is hidden in textual listings for automobiles. Preprocessing a heterogeneous dataset with unstructured textual descriptions and structured attributes (make, model, year, mileage) is part of the methodology. We leverage natural language processing (NLP) approaches to extract informative features from textual representations, improving the model's comprehension of subtleties in the conditions and attributes linked to individual car listings. ©2024 ieee.

关键词： Random forests

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NeuroDetect: Advanced Parkinson's Disease Identification Using Machine Learning Techniques 2

NeuroDetect: Advanced Parkinson's Disease Identification Usi...

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2nd ieee International conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Mansoori, Sana Gupta, Adarsh Rawat, Daksh Bhakuni, Vihan Singh Joshi, Agastya Aeri, Manisha Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India R/S Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9798350372892

Parkinson's disease is a neurological disease that is more severe and impairs quality of life, especially for those who are older than 50. It severely inhibits motor abilities, making it difficult for patients to move and communicate. As a result, scheduling in-person appointments for treatment and monitoring becomes challenging. Even though not all patients can return to normal lives, early detection and treatment are essential since they can greatly improve patient outcomes. The need for Parkinson's disease to be detected as early, accurately, remotely, and predictively as possible is highlighted by the world's ageing population. In order to facilitate early and precise remote disease detection, this study compares user-input data with historical data using machine learning techniques. By gathering and comparing vast volumes of data from people who have already experienced symptoms, the methodology seeks to identify Parkinson's disease in its early stages, potentially improving the quality of life for many affected individuals. © 2024 ieee.

关键词： Patient treatment

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