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Sentiment Analysis Based on Machine-Learning Classifiers for Datasets 8th

Sentiment Analysis Based on Machine-Learning Classifiers for...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Gawali, Piyush P. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Ghorpade, Pradip P. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766772

The study of sentiment analysis in natural language processing (NLP) holds tremendous promise for extracting meaningful emotional cues from textual data as a part of its core capabilities. This paper conducts an extensive exploration of sentiment analysis techniques powered by machine-learning classifiers, tailored to accommodate a variety of datasets. As a critical tool for assessing public opinion and sentiment, this study guides you through the crucial steps of data acquisition, preprocessing, feature extraction, and classifier selection, which are integral to the assessment of public sentiment. The research examines machine-learning approaches to sentiment analysis using a wide range of datasets, including social media content, product reviews, news articles, and much more, to determine if they are adaptable and scalable. In this study, "a number of classification models are examined, including Naive Bayes, Support Vector Machines, Random Forests, and deep learning models, and how their performance varies depending on the type of data". As a result of this analysis, the paper emphasizes the importance of utilizing machine-learning classifiers to distill nuanced sentiments, enabling applications in a wide range of fields, from brand reputation management to political sentiment tracking. With impressive accuracy, precision, F-measure, and correctly classified instances, Naive Bayes demonstrates rapid learning capabilities, while One-R displays promise with 91% accuracy, 97% precision, and 92% F-measure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Review on Classifications of Tracking Systems in Augmented Reality 1

A Review on Classifications of Tracking Systems in Augmented...

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1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Bhakar, Suman Bhatt, Devershi Pallavi Dhaka, Vijaypal Singh Sarma, Yogender Kumar Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Application Manipal University Jaipur India

Augmented Reality (AR) is now becoming an exceptional technology that offers a new world. Users to enable their senses to feel, listen and see the surroundings in different and better ways witness a unique experience. AR technology is used to superimpose the real view of the user with the virtual scenes. This research work illustrates the core details of AR, its definition, history, and development process. In addition, discusses ideas having various approaches during utilization of AR frameworks along with glyph tracking system. Current applications of AR technology completely depend on the proper utilization of AR frameworks. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Augmented reality

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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AI-Enabled Learning for Imbalanced Class Data using Spark Streaming Concepts 21

AI-Enabled Learning for Imbalanced Class Data using Spark St...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Tiwari, Sadhana Zohair, Mohammad Chandra, Ritesh Agarwal, Sonali Agarwal, Parikshit Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Jamia Millia Islamia Department of Computer Engineering New Delhi India SIET Department of Computer Application Prayagraj India

ISBN: (纸本)9798350358230

Chronic Diseases are a very significant problem around the world. Manhandling medical cases often leads to significant repercussions in the treatment process and needs to be detected and dealt with early on. Unusual clinic actions are often related to medical errors, enabling detection and alerting. Huge leaps in machine learning, data science, and big data analytics techniques have led to innovative ways to deal with these issues. As a result, mortality rates can be greatly reduced in all sorts of medical cases. In this work, a new ensemble method is proposed for multiclass classification applied on the US-Chronic Disease Indicator (US-CDI) dataset and validated using UCI-Diabetes data by applying K-means clustering to it to detect chronic disease. The proposed hybrid model, KD-Boost Ensemble, is based on the concept of ensemble voting mechanisms, which combine the predictions of the K-neighbors classifier, Decision Tree classifier, and Gradient Boosting classifier to obtain improved accuracy over the dataset when compared to the individual conventional classifiers. This chronic disease classification achieved 96% accuracy using the proposed model. The devised model is validated through the diabetes dataset in a real-time environment and attains 98% accuracy. © 2023 IEEE.

关键词： Decision trees

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Enhancing Dynamic Abstract Generation for Code Summarization using CODE-OAN Models and DBWTFF Frames 2

Enhancing Dynamic Abstract Generation for Code Summarization...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Bhatt, Chandradeep Sharma, Manish Sharma, Sanjay Gupta, Shefali Singh, Teekam Kumar, Mukesh Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Sgrr Univeristy Computer Application Department Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Punjab India

ISBN: (纸本)9798350306415

This research presents a robustness code abstraction generation method based on an attention mechanism. The method involves extracting high-quality code and its corresponding description language material from the programming community, specifically focusing on the text of query specifications and code responses. Redundant code and its description language material are filtered out, and the query specifications are transformed into declarative sentences for code conversion. Finally, a series model based on the attention mechanism is utilized to generate code summaries. The method effectively eliminates redundancy and noise in the code, leading to improved accuracy rates in automatic judgment, artificial evaluation, and testing. The evaluation results demonstrate superior performance compared to existing baseline methods. © 2024 IEEE.

关键词： accuracy rate attention mechanism code abstraction code summarization declarative sentences description language high-quality code redundancy filtering robustness

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Fog Computing in IoT: Architecture and applications 9

Fog Computing in IoT: Architecture and Applications

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Agrawal, Anurag Vijay Shashi Gowridurga, A. Suresh Balakrishnan, T. Shaikh, Anwar Ahamed Kalaipriya, V. J. P. Institute of Engineering And Technology Department of Electronics And Communication Engineering Uttar Pradesh Meerut250001 India Ccs University Department of Computer Application Meerut250004 India Velammal Institute of Technology Department of Computer Science And Engineering Tamil Nadu 601204 India Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Thandalam602105 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India

ISBN: (纸本)9798350365092

This exploration paper explores the paradigm of Fog Computing as a transformative armature in the Internet of effects(IoT) geography. Fog Computing represents a decentralized approach, positioning computing coffers closer to the edge of the network, thereby addressing quiescence, bandwidth, and data sequestration challenges current in traditional polycentric IoT infrastructures. The paper provides an in-depth analysis of Fog Computing's armature, expounding its crucial factors similar to fog bumps, middleware, and edge bias. likewise, it investigates the different operations of Fog Computing in IoT, ranging from realtime data analytics, effective resource operation, to enhanced security and sequestration measures. By bridging the gap between edge bias and centralised pall waiters, Fog Computing emerges as a vital result, fostering the elaboration of scalable, effective, and responsive IoT ecosystems. This exploration contributes precious perceptivity to the architectural nuances and practical operations, paving the way for the integration of Fog Computing as a foundation in the ever-expanding realm of IoT technologies. © 2024 IEEE.

关键词： Fog computing

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The Open Source Threat Intelligence Relational Dataset and Its Optimal Implementation

The Open Source Threat Intelligence Relational Dataset and I...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Yang, Yaru Li, Junyu Zhang, Ronghua Sah, Abhishek Pratap Yadav, Amit Khan, Asif Chengdu Neusoft University Department of Computer and Software Chengdu China University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China Charles Darwin University College of Engineering IT and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350361537

Threat intelligence provides a platform for cybersecurity engineers for attack traceability, which provides substantial knowledge database logs to defend against future security threats. Threat intelligence relationship extraction based on deep learning solves the challenge of threat knowledge construction to a certain extent but still faces problems such as lack of open-source datasets and the inability of the model to accurately correlate threat entities with potential relationships. Therefore, for cybersecurity research work, this paper designs a threat ontology, constructs the threat relationship dataset TreatRE by remote supervision, and opens this dataset. The dataset contains 12000 utterances and 12 threat relations from 500 CTIs, and it performs well in multiple relation models trained on deep learning methods. Meanwhile, we propose a multisensory attention-based threat intelligence relationship extraction method MAtt, which combines location perception, self-attention perception, and neuronal memory perception to further improve the threat relationship extraction effect. Experimental results show that the trained model based on TreatRE can more accurately extract the knowledge objects and their relationships described in threat intelligence. An accuracy score of 95.4% can be obtained using the MAtt method, which is 3.48% more than the best baseline compared with the same type of relationship extraction model. © 2024 IEEE.

关键词： Cybersecurity

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Kernel Based Fuzzy Simulation Model for Early Detection of Cancer to Avoid Metastasis 3

Kernel Based Fuzzy Simulation Model for Early Detection of C...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Santra, Soumen Majumdar, Dipankar Mandal, Surajit Deyasi, Arpan Techno International New Town Department of Computer Application Kolkata India RCC Institute of information Technology Department of Computer Science and Engineering Kolkata India B. P. Poddar Institute of Management & Technology Department of Electronics and Communication Engineering Kolkata India RCC Institute of Information Technology Department of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

In this paper, novel cancer detection method using Kernel Based Fuzzy Simulation Model (KBFSM) represented with enhanced accuracy of 12%, considering MRI based breast cancer images. Developed framework, as suggested in this paper, Effectiveness and superiority of the suggested approach are proven by thorough testing on a variety of datasets and thorough assessment metrics, highlighting its capacity to produce accurate and nuanced segmentation results. Fuzzification and defuzzification of images using Fuzzy C means algorithm, speaks in favor of the proposed analysis, as tested over several dataset, owing to minute pixel intensity modification when associated with malignant tissues. Result is supported by histogram analysis, which paves the way of future research in the field of lung cancer using the same technique. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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An Assessment of Analysis Techniques

An Assessment of Analysis Techniques

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Salaria, Amita Balu, Vincent Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Hod Department of Mechanical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

The Supervised Learning in Organizations (PPDM) method, data analysis, mining and data, and subjects related to data mining have all been covered in this page. Due to the enormous quantity of readily viewable info and the urgent need to transform it into useful skills and data, data analysis has grown in popularity both in the data business and the general public in past years. To convert collected data into usable research and wisdom, innovative technology and methods are needed to keep up with the ever-growing data amounts. Some many data analysis techniques are required in the offering of information facilities, including cloud computing and Wifi services, in in order to better comprehend user behaviour, ease congestion, and increase cash flow. To meet this need, information mining strategies will be contrasted from the perspective of a file scientist. The report classifies and compares these data mining methods. © 2023 IEEE.

关键词： Data warehouses

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