检索结果-内蒙古大学图书馆

Twitter sentiment analysis using conditional generative adversarial network

International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

引用

Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

引用

Procedia Computer Science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

来源：评论

学校读者我要写书评

暂无评论

Satellite Image Classification using CNN with Particle Swarm Optimization Classifier

引用

Procedia Computer Science 2024年 233卷 979-987页

作者： Vidhya S Balaji M Kamaraj V Asst. Professor/Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Asso. Professsor/Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Professor /Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches.

关键词： Deep Learning Convolutional Neural Network Particle Swarm Optimization Satellite Image Classifier Satellite Images

来源：评论

学校读者我要写书评

暂无评论

A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision

引用

Procedia Computer Science 2024年 233卷 343-352页

作者： Senthil G. A R. Prabha Arun M A. Sahaya Anselin Nisha S. Prabu Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Assistant Professor Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Professor Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Assistant Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Land subsidence is a natural disaster caused by the extraction of the earth's material strength on the surface that causes the earth's surface to settle and sink. Land subsidence can also result in significant financial losses since it causes structural damage and expensive maintenance costs for roads, trains, barriers, pipelines, ground water, oil, natural gas, mines, and buildings. The Geographic Information System (GIS) and Google Earth Platform (GEP) can be used to map this land subsidence. This provides a comprehensive view of land pattern subsidence and its interactions with other geographical elements. It applies to both progressive subsidence caused by fluid extraction (groundwater, oil, or natural gas) and rapid surface subsidence caused by the collapse of an underground mine. Risk mitigation is the most effective way to deal with the experimental model. This research study focuses on the use of Predictive Data Analysis (PDA), a powerful environment in data analytics that provides a set of tools that make it easier to evaluate optimal models. The proposed model makes use of a dataset that includes specific land subsidence circumstances as input. The prediction result is achieved using a hybrid machine learning technique that combines Stochastic Gradient Descent (SGD) with Long Short-Term Memory (LSTM) and is compared to K-Nearest Neighbor (KNN), Generative Adversarial Network (GAN), and Artificial Neural Network (ANN). The proposed system employs LSTM and SGD as optimizers, resulting in a hybrid model that improves accuracy. Among the four algorithms investigated the LSTM-SGD network has the highest accuracy (97%), followed by KNN (96%), GAN (90%), and ANN (89%). Due to their ability to capture complicated nonlinear correlations in data and manage temporal dependencies, optimized hybrid ML models LSTM-SGD and KNN models appear to be particularly well-suited for land subsidence prediction.

关键词： Land Subsidence Predictive Data Analysis (PDA) Geographic Information System Machine Learning Computer Cision Susceptibility Mapping Stochastic Gradient Descent (SGD) Generative Adversarial Network (GAN) Long Short-Term Memory (LSTM) K-Nearest Neighbor (KNN) Artificial Neural Network (ANN) Google Earth Platform

来源：评论

学校读者我要写书评

暂无评论

Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

引用

Procedia Computer Science 2025年 258卷 1677-1687页

作者： Chandralekha E S. Vinodhini Kandasamy V Rama P Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India-600062 Assistant Professor Department of Information Technology Velammal Engineering College Chennai Tamilnadu India-600062 Assistant Professor Panimalar Engineering College Chennai Tamilnadu India – 600123 Assistant Professor Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India-603203

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision – 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement.

关键词： Anomaly Detection Heart Rate Data Isolation Forest Local Outlier Factor One-Class SVM Elliptic Envelope

来源：评论

学校读者我要写书评

暂无评论

Gene Therapy Bio-factory: Mathematical Modeling of the Human Eye Pharmacokinetics 1

引用

1st Southern Georgia Mathematics Conference, SGMC 2021

作者： Carichino, Lucia Guidoboni, Giovanna Kansara, Viral Ciulla, Thomas Harris, Alon School of Mathematics and Statistics Rochester Institute of Technology RochesterNY United States College of Engineering and Computing Professor Electrical and Computer Engineering The University of Maine OronoME United States Clearside Biomedical Inc. AlpharettaGA United States Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (数字)9783031697104

ISBN: (纸本)9783031697098

Neovascular age-related macular degeneration (nAMD) is one of the leading causes of irreversible blindness in the industrialized world. In nAMD, abnormal blood vessel growth occurs under the macula, leading to exudation and scarring, distorting central vision. Current nAMD standard of care involves repeated intraocular injections of drugs that target the vascular endothelial growth factor (VEGF) signaling pathway (anti-VEGF drugs). These treatments have a high burden on patients, caregivers and physicians. Gene therapy based long-lasting production of anti-VEGF proteins in the eye (bio-factory approach) may address some of the limitations of current therapies. We adapt a previously developed three-compartment (retina, vitreous humor, aqueous humor) pharmacokinetic model in rabbits to simulate the pharmacokinetics of anti-VEGF proteins in the human eye delivered by intravitreal injection or produced via the gene therapy bio-factory approach. The model is used to estimate the protein levels in the vitreous humor and retina, and the retina anti-VEGF production rate starting from clinically measured anti-VEGF protein levels in the aqueous, using gene therapy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Gene therapy

来源：评论

学校读者我要写书评

暂无评论

Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model

引用

Procedia Computer Science 2024年 233卷 537-546页

作者： E Kasthuri S. Subbulakshmi Rajasree Sreedharan Research Scholar Department of Computer Science and Engineering Assistant Professor Department of Computer Science and Applications Amrita School of Computing Amritapuri India Associate Professor Division of Nephrology Department of Pediatrics Medical College of Wisconsin Milwaukee Wisconsin USA

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model.

关键词： Epilepsy Seizure Chatbot Natutal Language Processing LSTM Long short-term memory Recurrent neural network Deep neural network

来源：评论

学校读者我要写书评

暂无评论

Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI

引用

Procedia Computer Science 2024年 233卷 45-55页

Anemia among young people has positive correlation with susceptibility and impede cognitive development. As per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia.

关键词： Explainable Artificial Intelligence Machine learning Anemia Data preprocessing

来源：评论

学校读者我要写书评

暂无评论

A Novel Modified Shuffled Frog Leaping Algorithm (MSFLA) MPPT Controller for Photovoltaic Systems

A Novel Modified Shuffled Frog Leaping Algorithm (MSFLA) MPP...

引用

Trends in Electrical, Electronics, Computer engineering Conference (TEECCON)

作者： P Suneetha K Kumar V Lakshmi Devi V Sekhar A Basi Reddy Assistant Professor Dept. of EEE Sri Venkateswara College of Engineering Tirupati India Professor Dept. of EEE Sri Venkateswara College of Engineering Tirupati India Dept. of EEE Kuppam Engineering College Kuppam India Assistant Professor Dept. of CSE School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

The exhaustion of fossil fuel reserves and increasing environmental issues have intensified the need for renewable energy. The focus on enhancing solar energy efficiency is particularly strong among various renewable sources. In photovoltaic (PV) systems, temperature and solar irradiance are crucial factors that significantly affect performance, making it essential to operate these systems at their maximum power point. Specifically for PV systems operating in partial shade, this study introduces a new method for improving the performance of Maximum Power Point Tracking (MPPT) controllers. The proposed method integrates a Modified Shuffled Frog Leaping Algorithm (MSFLA) with the MPPT controller to maximize power extraction from PV panels. Inspired by the social dynamics of frog populations, the MSFLA has been tailored to address the complexities of partial shading, which often results in multiple local maxima on the power-voltage curve. Comparative analysis reveals that MSFLA achieves the highest tracking speed and output power accuracy among bio-inspired optimization algorithms, achieving an efficiency of 99.24%.

关键词： Maximum power point trackers Photovoltaic systems Solar irradiance Renewable energy sources Temperature Heuristic algorithms Market research Control systems Fossil fuels Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：