检索结果-内蒙古大学图书馆

Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

引用

Annals of Data Science 2024年第6期11卷 2019-2034页

作者： Loomba, Sankalp Dave, Madhavi Arolkar, Harshal Sharma, Sachin School of Cyber Security and Digital Forensics National Forensic Sciences University Gujarat Gandhinagar India Adani Institute of Digital Technology Management Gujarat Ahmedabad India Faculty of Computer Application and Information Technology GLS University Gujarat Ahmedabad India Indrashil University Gujarat Mehsana India

Due to the ever-increasing computing power and easy availability, social-networking platforms like Facebook, Twitter, etc. have become a popular medium to express one’s views instantly, be it about political situations, commercial products, or social occurrences. Twitter is a powerful source of information, whose data can be utilized to investigate the opinions of users through a process called Opinion Mining or Sentiment Analysis. Using the principles of Natural Language Processing and data science, this paper presents a comparative evaluation of multiple lexicon-based sentiment analysis algorithms to extract public opinion from tweets. The study explores the nuances of sentiment analysis using data science methodology, assessing how various lexicon-based algorithms may successfully identify and classify sentiments expressed in tweets from the Indian community about cryptocurrency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Rule-Based Sentiment Analysis for Twitter(X) Data

Rule-Based Sentiment Analysis for Twitter(X) Data

引用

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Mittal, Mudit Prasanna Kumar, H.R. Vinay, S.K. Jahan, Afsar Department Of Computer Science And Engg. Dehradun India Faculty Of Management Studies Bengaluru India Visvesvaraya Technological University Pesitm Dept. Of Information Science & Engg. Shivamogga India Pesitm Dept. Of Information Science & Engg. Shivamogga India Department Of Computer Application Dehradun India

ISBN: (纸本)9798350371567

Sentiment analysis on social media data, particularly Twitter, plays a crucial role in understanding public opinion and user sentiments towards various topics. This paper presents a rule-based approach to sentiment analysis tailored for Twitter data, denoted as Twitter(X). The system incorporates a set of positive and negative words relevant to the context of Twitter conversations. A Python implementation using the Natural Language Toolkit (NLTK) showcases the application of this rule-based sentiment analysis on a sample dataset of Twitter(X) posts. The sentiment analysis algorithm considers the frequency of positive and negative words in each tweet, providing a classification of sentiment as positive, negative, or neutral. While the rule-based approach offers simplicity and interpretability, it acknowledges the challenges associated with linguistic nuances, negation, and context-dependent sentiments in natural language. The proposed method utilizes predefined rules and a basic tokenization strategy to assess the sentiment of tweets. In this paper, four rule-based sentiment analyses are used those are Flair, Textblob, Vader, and the transfer, learning model. Transfer learning gives the highest accuracy in terms of sentiment scores for the Twitter data. The paper concludes with discussions on the limitations of the rule-based approach and suggests potential avenues for enhancing sentiment analysis accuracy on diverse and dynamic social media datasets. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Automation of data flow class testing using hybrid evolutionary algorithms

Recent Advances in Computer Science and Communications

引用

Recent Advances in computer Science and Communications 2021年第1期14卷 317-330页

作者： Jain, Neetu Porwal, Rabins Department of Computer Application Faculty of System Sciences Mewar University Chittorgarh India Department of Information Technology Lal Bahadur Shastri Institute of Management New Delhi India

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data. Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms. Methods: The proposed algorithm performs data flow testing of classes applying all defuses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences. Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations. © 2021 Bentham Science Publishers.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks Predictive models Data models Robustness Tuning

来源：评论

学校读者我要写书评

暂无评论

Optimized Siamese Humboldt Squid Attention Network Architecture for Cyber-Attack Detection in IoT 4.0 4

Optimized Siamese Humboldt Squid Attention Network Architect...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohamed, M. Varusai Nainwal, Ankita Ayshwarya, B. Kumar, H.S. Rabadiya, Dhaval Taqui, Syed Noeman Department of Computer Application Tamil Nadu Chennai600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kristu Jayanti College Department of Computer science Karnataka Bengaluru560043 India Department of Mechanical Engineering Karnataka Nitte India Parul University Faculty of Management studies P.O. Limda Ta. Waghodia Gujarat Vadodara391760 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

The rapid expansion of IoT within Industry 4.0 has heightened the risk of cyber-attacks, underscoring the need for advanced detection mechanisms. Existing cyber-attack detection systems in this context often struggle with reduced accuracy, high computational time, complexity, and elevated error rates. This study addresses these challenges by proposing an Optimized Siamese Humboldt Squid Attention Network (OSH-SqAtNet) architecture for cyber-attack detection in IoT Industry 4.0 environments. The input data, sourced from datasets like CSE-CIC-IDS2018, undergoes preprocessing via a Mixture of Conventional Fast Bilateral Filter (MC2FB) to enhance data quality. A Gold Rush Optimizer (GRO) is then employed for feature selection, isolating the most significant features to optimize classification performance. The preprocessed and optimized data are classified using the OSH-SqAtN et architecture for accurate cyber-attack detection. For secure data transmission, Rabin Fuzzy Cryptosystem assisted Elliptic Curve Cryptography (RFC-ECC) is utilized. Experimental simulations conducted on the Python platform reveal that the proposed method outperforms existing techniques, achieving higher accuracy (98%) and recall (0.993%). These results demonstrate the approach's superior efficacy and potential for advancing cybersecurity in IoT Industry 4.0. © 2024 IEEE.

关键词： Shellfish

来源：评论

学校读者我要写书评

暂无评论

Cryptocurrency and Data Privacy in Human Resource management 3

Cryptocurrency and Data Privacy in Human Resource Management

引用

3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Chandrasekeran, Indhumathi Dharmaraj, A. Juyal, Ashima Shravan, M. Deb Barman, Rajesh Lourens, Melanie Department of Commerce with Computer Application PSG College of Arts & Science CoimbatoreTamil Nadu India Department of Management Karpagam Academy of Higher Education CoimbatoreTamil Nadu India Uttaranchal Institute of Technology Uttaranchal University India School of Business SR University WarangalTelangana India Department of Commerce Bodoland University Kokrajhar BTR Assam783370 India Faculty of Management Sciences Durban University of Technology South Africa

ISBN: (纸本)9798350399264

Cryptocurrency and data privacy are two important considerations in human resource management. Cryptocurrency, such as Bitcoin, can be used to pay employees, provide incentives, and manage payroll, but it also poses risks such as price volatility and regulatory issues. Data privacy, on the other hand, is the protection of personal information from unauthorized access and use. The use of blockchain technology, which is the underlying technology behind many cryptocurrencies, can enhance data privacy in human resource management by providing a secure and tamper-proof record of all transactions. However, it's important to comply with all applicable laws and regulations related to the use of cryptocurrency and data privacy in human resource management. © 2023 IEEE.

关键词： Human resource management

来源：评论

学校读者我要写书评

暂无评论

Emerging Trends of Language Models and Their Significance in Digital Era

Emerging Trends of Language Models and Their Significance in...

引用

International Conference on Advances in Information Technology (ICAIT)

作者： Rishi Kumar Praveen Gujjar J Guru Prasad M S Raghavendra M Devadas Lubna Ambreen Rahul Kumar Pal Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Gitam School of Technology Gitam (Deemed-to-be University) Bengaluru India Department of Computer Application Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This research investigates the rapidly evolving landscape of language models and their emerging trends. Language models, powered by innovative techniques in machine learning and natural language processing, have witnessed significant advancements in recent years. Language models have seen remarkable advancements in recent years, with emerging trends shaping the landscape of natural language processing (NLP). This research explores the latest developments in language models, such as transformer architectures and deep learning techniques, and assesses their significance in various applications. This research tries to understand the impact of these trends on tasks like text generation, sentiment analysis, and machine translation. This research also explores these advancements' potential societal and ethical implications, highlighting the need for responsible AI development. This study underscores the importance of staying updated on emerging trends in language models to harness their full potential while ensuring responsible and ethical usage in the ever-evolving field of NLP.

关键词： Training Ethics Data privacy Sentiment analysis Medical services Market research Transformers Natural language processing Data models Machine translation

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：