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IBDR-Net: A Computerized Framework for Irregular Brain Signal data recognition 13th

IBDR-Net: A Computerized Framework for Irregular Brain Signa...

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13th international conference on Health Information Science, HIS 2024

作者： Alvi, Ashik Mostafa Manami, Nishat Tasnim Siuly, Siuly Sun, Lili Victoria University MelbourneVIC Australia

ISBN: (纸本)9789819655960

Brain signal data are records of the electrical activity of the brain made with electroencephalography (EEG). EEG data mining is one of the key topics which has received lots of attention. State-of-art machine learning (ML) methods are incompetent to dive deep and learn the EEG data pattern. Moreover, additional feature extraction methods are employed for feature extraction when a classical ML is in action which adds up additional computational burden. Lastly, the multi-class EEG data classification performance is not quite satisfactory. To overcome these issues, we propose a computerized framework for efficient and effective irregular brain signal data recognition network (IBDR-Net). Our suggested IBDR-Net is made up of four steps: (1) EEG data acquisition, (2) Pre-processing, (3) recognition of irregular brain signal data using IBDR-Net, and (4) Performance evaluation. IBDR-Net framework has been tested and trained with real human EEG dataset and this study has set a new milestone for the EEG researchers and boosted the multi-class performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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A Comparative Study of machine learning Algorithms for Salary Estimation 3

A Comparative Study of Machine Learning Algorithms for Salar...

引用

3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Mishra, Praveen Srivastava, Shivansh Gupta, Priyanshi Anand, Atul Gupta, Subhash Chandra Galgotias University Faculty of Computing Science Department Greater Noida203201 India Galgotias University Greater Noida203201 India

ISBN: (纸本)9781665438117

Employee recruitment is a main activity of any business for smooth conduction of the companies' production and competitiveness. Salary is also a motivational key parameter for any employee to fulfill his expectations, according to his qualification and technical skill. Our salary prediction system is aimed at providing better assistance to the college students regarding the salary that they can aspect after completing their course. Not only they will be able to get an idea of their deserving salary but also, they can get to know about the skills that they need to meet their professional goals. This will enhance the motivation of students who are enrolled in higher education institutes and provide better assistance also. Through this paper, we have tried to provide a system for salary prediction in which data mining technique is used. In this system the profile of student will be compared with graduated student. We have used data mining techniques for comparison as they perform best. We have also performed an experiment on student data set using 10-fold cross-validation. © 2021 IEEE.

关键词： machine learning

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Using SinaWeibo Microblogs to Identify Complaints of Food Customers 22

Using SinaWeibo Microblogs to Identify Complaints of Food Cu...

引用

3rd international conference on Control, Robotics and Intelligent System, CCRIS 2022

作者： Chen, Feng Qin, Weiguang Business School Ludong University Shandong Provincial Big Data Development and Innovation Laboratory of Public Resources Yantai Shandong China Business School Ludong University Yantai Shandong China

ISBN: (纸本)9781450396851

Monitoring and responding to complaints about food by consumers are critical for enterprises and governments. Here, we design a method to identify and analyze microblogs posted on SinaWeibo. We proposed a machine learning model to classify the microblogs. Our model used multimodal features and improved a term classification weighting method. We used LDA to identify the most common topics in complaints and summarized bloggers' complaint behavior. Our model obtained an optimal weighted F value of 0.942 using only 4 features. We found that linguistic and multimedia features were more important than social features. When expressing complaints, bloggers were more inclined to mention (@) other bloggers, and most bloggers (80.08%) used images or videos. However, the communication effect of complaint microblogs was even weaker than that of noncomplaint microblogs. Our proposed methods were effective social media monitoring and analysis tools. data mining of SinaWeibo may reduce information asymmetry and enhance the ability of governments to rapidly respond to community concerns and awareness. © 2022 ACM.

关键词： Blogs

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Automate Stock and Crypto Research Using Python and machine learning

Automate Stock and Crypto Research Using Python and Machine ...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Awasthi, Charu Bansal, Anmol Patel, Anurag Yash Shukla, Siddharth Singh, Puneeta Jss Academy of Technical Education Department of Information Technology Noida India

ISBN: (纸本)9798331519582

This paper focuses on automating the analysis of financial news for stocks and cryptocurrencies, thereby providing traders and analysts with actionable insights. In today's fast-paced markets, being up-to-date is the need of the hour;however, manual analysis takes time. To overcome this challenge, the paper employs Python and deep learning tools to make the process of collecting, summarizing, and conducting sentiment analysis on relevant news articles much easier. The key assets analyzed are popular stocks and cryptocurrencies, such as Tesla, Gamestop, Bitcoin, and Ethereum. The system uses Hugging Face's Pegasus model, which is a state-of-the-art transformer, to summarize lengthy financial news into concise, manageable summaries. This reduces the effort required to sift through large amounts of information while preserving essential details. Moreover, the system applies pre-trained sentiment analysis models to gauge the market's overall sentiment-positive, negative, or neutral-toward specific assets, thus making quick and informed trading decisions possible. The paper workflow includes automatically scraping web sources such as Google News and Yahoo Finance, cleaning and processing the data, and exporting results in structured CSV files for further analysis. The files include the ticker symbols, sentiment scores, confidence levels, URLs, and summary text. It is scalable and flexible so that users can input their stock tickers and run real-time analysis with changes in market conditions. Overall, this paper provides an efficient, end-to-end solution for financial news analysis, allowing users to make informed decisions with reduced time and effort on data gathering and interpretation. © 2025 IEEE.

关键词： Emotion recognition

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3rd international conference on data Science, E-learning and Information Systems, data 2021

作者： Vangipuram, Sravan Kiran Appusamy, Rajesh Department of CSE Jain Deemed to be University Karnataka Bengaluru India Department of IT VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Jain Deemed to be University Karnataka Bengaluru Bengaluru India

ISBN: (纸本)9781450388382

An important observation which figures out when we look into several applications which are the result of applying data science, machine learning, and deep learning techniques is that most of these techniques are based on the concept of measuring similarity between any two vectors. These vectors may act as representatives for objects being considered. Similarity measurement thus gains a great importance in the design of machine learning or deep learning algorithms and techniques. In similar lines, when we are required to carry a supervised or unsupervised learning task, an algorithm is required to carry the task efficiently. Thus, in this paper, our objective is to outline various similarity measures that have been considered for carrying supervised or unsupervised learning tasks and also to throw light on different machine learning algorithms employed for supervised and unsupervised learning tasks from disease classification and prediction point of view and also interdisciplinary domains such as time series analysis, temporal data mining, medical data mining, and anomaly or intrusion detection. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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Recent Recommender Systems and Analysis of the machine learning Public Instances

Recent Recommender Systems and Analysis of the Machine Learn...

引用

international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Raja Marappan Saraswatikaniga R School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai M.Tech Computer Science and Engineering (Business Analytics) School of Computer Science and Engineering Vellore Institute of Technology Chennai

Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (ML) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in ML public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and ML techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99 % ) and the error rate occurs at (5%, 25 % ) for the ML public instances.

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Sentiment Analysis using machine learning 3

Sentiment Analysis using Machine Learning

引用

3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kalangi, Ruth Ramya Maloji, Suman Tejasri, N. Chand, P. Prem Ch, Vishnu Priya. Koneru Lakshmaiah Education Foundation CSE Guntur India Koneru Lakshmaiah Education Foundation ECE Guntur India

ISBN: (纸本)9781665438117

Twitter is one of the broadly used social networking structures with greater than 192 million each day lively customers and a 500 million Tweets. Basically, Twitter is reaching a large target audience, connects quite a few users and also gives a useful factor of view on an ongoing difficulty, based on their interest. This paper consists of sentiment evaluation on twitter statistics that is related to Airline critiques using machine learning, NLP. Basically customers and their rankings on the airlines they would choose for their journey are evaluated. Positive, negative and neutral opinions of the users are estimated based on their tweets in the twitter. data visualization which may include cleaning, checking and transforming the pattern is also used. Natural Language Processing (NLP) and machine learning are used on this data-driven model for prediction. machine learning techniques are applied to the corresponding twitter dataset containing reviews of Airlines to find out the necessary information and inference. These algorithms are used to extract the sentiments of the user and to discover future trends based on current usage. © 2021 IEEE.

关键词： data visualization

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An Artificial Voice Box that Makes Use of Unconventional Methods of machine learning 1

引用

3rd international conference on Information Technology, InCITe 2023

作者： Chadha, Raman Singla, Sanjay Singh, Nongmeikapam Thoiba Department of Computer Science Engineering UIE Chandigarh University Punjab India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

Patients who are subjected to intensive medical treatments in order to identify voice problems may suffer a considerable amount during the diagnostic process. As a result, automated speech recognition and methods for disorder diagnosis have garnered a lot of attention in recent years, and both of these approaches have been effective. Voice recordings were collected from the Saarbrucken Voice database to help with the completion of this inquiry. To de-noise and remove any potential noise, the signals are first preprocessed using a hybrid Wiener filter discrete wavelet transform (HWFDWT). Melanie Frequency Cestrum Cat Swarm Optimization is used to extract features while accounting for coefficients. Finally, the features are arranged by running the data through the modified optimized back propagation network (MOBPN) for the categorization of disordered voices. In all aspects of precision, recall, F1-score, and timespan, the classification strategy exceeds the present Support Vector machine (SVM) and Back Propagation Neural Network (BPNN) methodologies. Due to the cognitive speech system, people who lack the ability to speech can now convey their feelings and ideas to others in a form that can now communicate their thoughts and feelings to others in a way that is simple to grasp. It is a device that can record the electrical pulses the brain emits and then transform those records into synthetic speech. Provide a thorough explanation of the idea or solution you intend to develop. After recording the brain's electrical activity, it will be sent to a synthesizer for further processing. When it has completed decoding the signal, the synthesizer will transform it into a voice. After the voice has been decrypted, it is sent into an artificial voice box for further processing. Brain signals are employed to create an artificial voice. This voice is processed before being transmitted through the box. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte

关键词： Electroencephalography

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Comparative Analysis of machine learning Algorithms for Predicting Undergraduate Academic Performance

Comparative Analysis of Machine Learning Algorithms for Pred...

引用

Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Euis Sitinur Aisyah Danny Manongga Ade Iriani Sugeng Santoso Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Accurate prediction of student academic performance is crucial for educational institutions and parents to implement proactive intervention strategies. This study employs machine learning algorithms to predict the graduation outcomes of undergraduate students, focusing on the effectiveness of various algorithms. We used the Random Forest algorithm and compared its performance with Decision Tree, Nave Bayes, Artificial Neural Network, and Support Vector machine, utilizing the Orange data mining tool. Our evaluation metrics included Area Under Curve (AUC), accuracy, F1-score, precision, and recall, applied to a dataset of 387 students from the class of 2017 who graduated on time in 2021 and 2022. The features considered were ID, study program, age, sex, GPA of the first semester and GPA of the second semester. The Random Forest algorithm achieved the highest accuracy at 95%. This research contributes to the early identification of students at risk of academic failure and highlights the most effective machine learning algorithm for predicting academic outcomes in higher education.

关键词： Support vector machines machine learning algorithms Accuracy Education Predictive models Prediction algorithms Robustness Socioeconomics Random forests Testing

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The mining Algorithm of Association Rules is Comprehensively Evaluated to Improve the Quality of Talent Training

The Mining Algorithm of Association Rules is Comprehensively...

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Advancement in Technology (ICONAT), international conference for

作者： Chen Xin QiGao Department of Management and Economics JingDeZhen Ceramic University jingdezhen Jiangxi China

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Cultivating intelligence is an important part of the process of data analysis and research. Therefore, it is necessary to conduct a comprehensive evaluation of human resource *** talent training quality research is critical in the comprehensive evaluation of undergraduate majors to improve talents, however it has an issue with erroneous performance positioning. The typical Digital mining algorithm is unable to address the talent training issue in the comprehensive evaluation of undergraduate majors to improve talents, and the result is insufficient. As a result, a Association rule mining algorithm-based the Comprehensive Evaluation of Undergraduate Majors to Improve the Quality of Talent Training is provided, and the the Comprehensive Evaluation of Undergraduate Majors to Improve the Quality of Talent Training is assessed. To begin, the association rule theory is used to discover the influencing elements, and the indicators are split based on the talent training quality research’s needs to decrease interference factors in the talent training quality research. The association rule theory is then used to create a Association rule mining algorithm talent training quality research scheme, and the outcomes of the talent training quality research are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Association rule mining algorithm outperforms the standard Digital mining algorithm in terms of talent training quality research accuracy and time of influencing variables. The results show that the analysis of intelligent methods is needed to improve the recognition accuracy of talent training. Therefore, the results of the study show that the accuracy of talent identification can be improved by between 10% and 15%.

关键词： Training Accuracy data analysis Simulation Interference Association rule learning Internet Reliability MATLAB Standards

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