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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151-160 订阅

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Evaluation of Opinion Mining Approaches: Using Deep Learning for Large-Scale data 2

Evaluation of Opinion Mining Approaches: Using Deep Learning...

引用

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

作者： Singh, Navjot Bathla, Gaurav Chandigarh University Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350372892

This study explores the efficacy of various classification techniques-Naive Bayes, SVM, and Logistic Regression-applied to tagged data from Kaggle and GitHub, focusing on shorter text inputs. IMDb Reviews are utilized for analysis, employing feature extraction and ML/DL techniques to identify optimal features for improved accuracy. This work introduces RNN, CNN, LSTM, and hybrid models prevalent in deep learning (DL) across domains like sentiment analysis, speech recognition, and image classification. Experimentation on Twitter and IMDb datasets, alongside DL models (LSTM, CNN, bi-LSTM, CNN-LSTM hybrid), assesses their behaviour on varied data sizes. Phase 1 analyzes supervised and unsupervised sentiment analysis, favouring SVM for accuracy. Phase 2 extends to DL for speech data analysis. This multi-model approach addresses text and speech data, enriching understanding of ML/DL techniques across different modalities. Furthermore, this work also investigates the impact of parameter tuning and hyper-parameter optimization on model performance for both text and speech based datasets. © 2024 ieee.

关键词： Deep learning

来源：评论

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Centralized Platform for Sensor Integration and AI Models in Precision Agriculture 2

Centralized Platform for Sensor Integration and AI Models in...

引用

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

作者： Chowdary, Ch Srinivasa Akshith Likith, S. Maya, B.S. Lokesh, S.V. Manish, Y. Asha, T. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India Bangalore Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A centralized platform for integrating various sensors and AI models in precision agriculture. The platform connects soil moisture sensors with AI models to generate crop recommendations and irrigation alerts, aiming to enhance resource utilization and crop yield. Key outcomes include real-time monitoring, actionable insights, and data-driven decision support for farmers. The practical implications for farmers and the agricultural industry are profound, as this system enables them to adopt precision agriculture techniques, reducing waste, increasing efficiency, and ultimately improving profitability © 2024 ieee.

关键词： Smart agriculture

来源：评论

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

引用

2nd ieee International conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (ndVI) and normalized difference water index (ndWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 ieee.

关键词： data accuracy

来源：评论

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Multifaceted Understanding of Student Success: A data Mining Approach 2

Multifaceted Understanding of Student Success: A Data Mining...

引用

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

作者： Udit Krishna, S. Barath Lakshman, A.N. Mukund, R. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Ramapuram India

ISBN: (纸本)9798350381665

This research explores the potential of data Mining in Education, particularly in the capability to predict student performance and identify the factors that influence academic outcomes. Employing the UC Irvine Student Performance dataset, the study investigates the efficiency of data mining strategies for predicting student outcomes and the co-relating elements. Amongst the nine ML models evaluated, including an ANN model, the linear SVM emerged as the optimal choice, achieving a significantly higher accuracy of 94.9% and an ROC AUC score of 0.959 with a Logarithmic loss of 1.74 on the test set compared to the other candidates. Beyond prediction, the study examines the influence of various factors on academic performance using the coefficients of the Logistic Regression model. Apart from the two mid-term grades, history of failures, internet access, external school support and the area of residence influence the student's performance in the final examination. By interpreting these findings, the research lays the groundwork for developing data-driven and personalized learning strategies, potentially encouraging a more fair and effective learning environment for all students. © 2024 ieee.

关键词： data mining

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Machine Learning Based Web-App to Predict Lung Disease Using SMOTE Analysis 2

Machine Learning Based Web-App to Predict Lung Disease Using...

引用

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

作者： Prajna, K.B. Shekar, Sai Achuth C. Kudva, Shantanu Balakrishna Prasanna, Dhaatri Nitte Meenakshi Institute Of Technology Department Of Electronics And Communication Engineering Bengaluru India

ISBN: (纸本)9798350381665

Lung disease pose significant health risks world-wide, encompassing a range of conditions affecting respiratory function. Leveraging machine learning, this study explores predictive models for early detection of lung disease. By analyzing the dataset encompassing symptomatology and clinical features, machine learning algorithms facilitate accurate identification of potential cases. Among various techniques, logistic regression emerges as a robust method, demonstrating high accuracy in predicting disease onset. Synthetic Minority Over-sampling Technique (SMOTE) Analysis is also been explored and is proven to be a useful tool to improve the quality of the prediction. A user-friendly interface is developed to empower individuals in monitoring their respiratory health proactively. © 2024 ieee.

关键词： User interfaces

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Revolutionizing Education through Digital Twin technology: Key Techniques and Applications 2

Revolutionizing Education through Digital Twin Technology: K...

引用

2nd ieee International conference on Emerging Research in Computational science, ICERCS 2024

作者： Valarmathie, P. Guruvarshni, G. Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Digital Twin technology, which was first employed in industry, is now providing exciting opportunities for education. Imagine kids learning through interaction with virtual representations of real-world systems experimenting, making mistakes, and comprehending complex processes in a secure environment. However, today's Digital Twin educational applications often fall lacking when it comes of flexibility and differentiation. The study explores the key techniques used in the digital twin technology in education, includes Real-Time data Integration, AI-Driver Feedback Mechanisms, Simulation and Interactive Learning, Personalization, Performance analytics and Learning Insights. The essential aspects of the framework, such as the data collection for gathering raw data, the data processing for preparing, the data transformation for formatting data for the Virtual Learning Environment (VLE), dashboard and data Visualization to provide feedback and insights for educators. Our platform is designed to be adaptable and scalable, making it easy to fit into various educational settings and adapt to different learning goals. This survey aims to make these powerful learning tools accessible to more students, enhancing their education in meaningful ways. © 2024 ieee.

关键词： Federated learning

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Predicting Illnesses through Symptomatic Patterns through Ensemble Voting Regressor 2

Predicting Illnesses through Symptomatic Patterns through En...

引用

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

作者： Ghosh, Prantik Shukla, Abhivesh Bibin Christopher, V. Yakub, Fitri SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India Japan International Institute of Technology University Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350383867

This paper utilizes the GitHub Collaborative Disease-Symptom dataset enabling the identification of disease patterns through symptom analysis. Aimed at boosting the accuracy and efficiency of disease prediction systems, this initiative trains models like Logistic Regression, SVM, and KNN on a rich dataset of symptoms and diseases, seeking to provide personalized diagnostic insights for improved patient care. The methodolog capitalizes on this diverse dataset to uncover complex patterns, utilizing metrics such as precision, accuracy, and the F1 score for enhanced predictive performance. Results have shown significant progress in disease prediction, with confusion matrices revealing refined model predictions marked by high precision and accuracy in disease classification from reported symptoms. Ensemble Classifier yielded a record-breaking 98.96% accuracy, demonstrating the power of combining models for superior predictions. This endeavor highlights machine learning's transformative impact on healthcare diagnostics, offering data-driven insights for personalized medicine and targeted treatment strategies, thus emphasizing the critical role of sophisticated data analysis in elevating patient care and healthcare outcomes. ©2024 ieee.

关键词： Personalized medicine

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Tulu Manuscript OCR: Preserving Ancient Wisdom through Character Recognition 2

Tulu Manuscript OCR: Preserving Ancient Wisdom through Chara...

引用

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

作者： Jayashree, K.R. Sinchana Manisha, K. Deekshitha Sudarshan, K. Kannadaguli, Prashanth Srinivas Institute Of Technology Information Science And Engineering Mangalore India Srinivas Institute Of Technology The Department Ise Mangalore India Dhaarini Academy Of Technical Education Bengaluru India

ISBN: (纸本)9798350381665

Tulu, largely spoken in coastal Karnataka, has a distinct alphabet that used to be written on palm leaves. This study addresses the scarcity of efficient OCR solutions. Employing machine learning algorithms that include decision tree, k-nearest neighbors (KNN), and random forest. The system achieves its highest accuracy of 92.35% with the random forest algorithm. The system's versatility in handling diverse font styles and sizes is crucial for Tulu character recognition. The inclusion of a classifier-level fusion strategy enhances recognition accuracy, which is vital given the intricate nature of Tulu characters. This research advances OCR technology for Indian languages, specifically meeting the unique needs of the Tulu script. The effectiveness of the random forest algorithm, achieving high accuracy, underscores its potential for broader applications. The proposed Tulu Character Recognition System represents a pivotal step in addressing the OCR gap for Indian languages, holding promise for future linguistic technology advancements. © 2024 ieee.

关键词： Optical character recognition

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AI-Powered Business Revolution: Elevating Efficiency and Boosting Sales through Cutting-Edge Process Re-engineering with Machine Learning 2

AI-Powered Business Revolution: Elevating Efficiency and Boo...

引用

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

作者： Nair, Sooraj S. Nathiya, R. Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India

ISBN: (纸本)9798350381665

The integration of Artificial Intelligence (AI) with Business Process Re-engineering (BPR) has revolutionized modern businesses. This research delves into the background, methods used, results achieved, and concluding remarks of this trans-formative amalgamation. Background: Traditional BPR principles have evolved with AI, offering unprecedented efficiency and innovation potentials. Methods Used: Automation, data analysis, natural language processing, and machine learning algorithms are employed to enhance processes and customer experiences. Result Finding: The synergy between AI and BPR leads to increased efficiency, innovation, and competitiveness, improving cost-effectiveness, customer experiences, and response times. Concluding Remarks: Meticulous planning and adept management are essential for leveraging AI in BPR effectively. Adopting chat bots, virtual assistants, and intellectual computing enhances customer support and problem-solving capabilities. This research highlights the establishment of intelligent, customer centric businesses capable of adapting to various challenges. © 2024 ieee.

关键词： Efficiency

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Research on Image Processing technology Based on Artificial Intelligence and Visual Algorithms 2

Research on Image Processing Technology Based on Artificial ...

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

作者： Bi, Shihong Chengdu NeuSoft University Chengdu China

ISBN: (纸本)9798350381665

Deep Learning (DL), a branch of Artificial Intelligence (AI), is extensively used in Computer Vision (CV) technology, particularly in medical image analysis. The DL approaches have achieved notable success in the classification of medical images. However, the existing AI algorithms have limitations such as poor detection, misclassification, time complexity, and so on. To overcome the problems, a Spatio-Temporal Graph Convolutional Network (ST-GCN) is proposed to classify medical images correctly by learning from relevant features. The image processing techniques such as preprocessing, segmentation, feature extraction, and classification for medical images are described in this research. The presented ST-GCN model achieved a high accuracy of 96.3%, recall of 96.1%, precision of 96.4%, and F1-score of 96.2% which is high when compared with previous AI and computer vision algorithms such as Convolutional Neural Network (CNN, Deep Neural Networks (DNN), Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Bidirectional -LSTM (BiLSTM), Gated Recurrent Unit (GRU). © 2024 ieee.

关键词： Image segmentation

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