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Measuring the percentage of brain concentration levels using Bi-LSTM algorithm

Procedia computer science 2023年 216卷 655-663页

作者： Felix Filipi Febrian Nugroho Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

In carrying out activities, humans need concentration so that their activities can run well, especially to carry out activities that require high concentration, such as taking exams in class, and working in the laboratory to make certain medicines. Humans generate electroencephalography (EEG) signals when carrying out activities that require concentration, this is very interesting to observe and analyze. The EEG signal data that produced by the human brain can be measured, so that the level of human concentration is known in carrying out an activity using a deep learning approach. The deep learning model that will be used to process EEG signal data is Bidirectional Long Short-Term Memory (BiLSTM), the result is that the algorithm can provide a scale of 1 to 100 for human concentration. The BiLSTM algorithm produces an accuracy rate of 82 percent for subject A data and produces a 93 percent accuracy rate for subject B data.

关键词： Human Concentration Electroencephalograph BiLSTM

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Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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Big data Mining and Analytics 2022年第1期5卷 64-78页

作者： Qixiang Shao Runlong Yu Hongke Zhao Chunli Liu Mengyi Zhang Hongmei Song Qi Liu Department of Anhui Province Key Laboratory of Big Data Analysis and Application(BDAA) School of Computer Science and TechnologyUniversity of Science and Technology of China Department of College of Management and Economics Tianjin University School of Management Hefei University of Technology Department of Information Technology China Merchants Bank

Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for *** real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the ***,extracting useful information from various characteristics of users and further predicting their purchase inclination are ***,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data *** this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related ***,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems *** addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature ***,we conduct extensive experiments on a real-world dataset provided by a famous fintech *** results clearly demonstrate the effectiveness of MFEM.

关键词： Intelligent Financial Advisor(IFA) potential client identification MultiTask Learning(MTL) feature selection

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Applying Natural Language Processing for Detecting Cybersecurity Threats Using Sentimental Analysis Techniques 5th

Applying Natural Language Processing for Detecting Cybersecu...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Ganesh, D. Sharma, Apurva Vignesh, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the ability to use herbal Language Processing (NLP) and Sentiment evaluation strategies to detect cybersecurity threats. We describe the traditional tactics and present-day tendencies in cybersecurity chance detection and discuss the demanding situations the technique confronts. We then present how NLP and Sentiment analysis can resource inside the detection technique, provide examples of ways NLP tools can assist in chance detection, and discuss how NLP can offer beneficial data in phrases of danger detection. We quickly review the strategies for classifying and verifying sentiment from textual statistics. Our findings propose that NLP and mawkish analysis can successfully discover cybersecurity threats and provide treasured insights for additional studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Development of Fuzzy Logic System for Directional Beamforming in Underwater Communication 5th

Development of Fuzzy Logic System for Directional Beamformin...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Krishnamoorthy, Ramkumar Agrawal, Jaishree Kaipa, Sandhya Agarwal, Trapty Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper describes the development of an advanced fuzzy logic system for directional beamforming in underwater communication systems. The fuzzy logic system utilizes a combination of fuzzy and proportional integral derivative controllers, state machines, and signal processing algorithms to create directional beamforming beams that align with the environment. The fuzzy logic system is able to detect changes in the environment, such as changes in water current, and adjust the beamforming beams accordingly. The fuzzy logic system is also optimized to adjust the beamforming beams dynamically. Experiments were carried out to evaluate the performance of the fuzzy logic system in terms of directionality, communication range, and robustness in changing environments. The results showed that the fuzzy logic system provided the best performance compared to all the other systems tested. This system provides an effective solution for directional beamforming in underwater communication systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Beamforming

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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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Real-Time Monitoring and Control Systems Using IoT-Integrated Wireless Sensor Networks-Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-Time Monitoring and Control Systems Using IoT-Integrate...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Swaminathan, Subbiah Poongavanam, N. Arun, M. Pushparani, S. Muthukrishnan, A. Johri, Prashant Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai602105 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Avadi Chennai India School of Computer Application and Technology Galgotias University India

ISBN: (纸本)9798331542344

This paper introduces a developed real-Time monitoring and control system using Internet of Things (IoT) incorporated Wireless Sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-Time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges;decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-Time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications. © 2024 IEEE.

关键词： Predictive analytics

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Instructor-inspired machine learning for robust molecular property prediction 24

Instructor-inspired machine learning for robust molecular pr...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Fang Wu Shuting Jin Siyuan Li Stan Z. Li Computer Science Department Stanford University School of Computer Science and Technology Wuhan University of Science and Technology School of Engineering Westlake University

ISBN: (纸本)9798331314385

Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical data is extremely laborious. To surmount this data sparsity challenge, we present an instructive learning algorithm named InstructMol to measure pseudolabels' reliability and help the target model leverage large-scale unlabeled data. InstructMol does not require transferring knowledge between multiple domains, which avoids the potential gap between the pretraining and fine-tuning stages. We demonstrated the high accuracy of InstructMol on several real-world molecular datasets and out-of-distribution (OOD) benchmarks.

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Harnessing Medical Big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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Application of AI/ML Algorithms for Enhancing Precision in Medical Diagnostic Processes 5th

Application of AI/ML Algorithms for Enhancing Precision in M...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Raghavendra, R. Bhatt, Rahul Yamanappa Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The utility of artificial intelligence (AI) and gadget getting to know (ML) algorithms to medical diagnostic processes has been developing in significance in recent years. AI/ML models were evolved to help clinical professionals in imparting correct and green diagnostic approaches. Those models are capable of providing precise and correct diagnoses at the same time as reducing the complexity of conventional decision making. AI/ML algorithms are getting used to discover styles in medical statistics that could in any other case be hard for human beings to come across or to offer contextual information to refine and enhance the accuracy of diagnoses. The benefits of using AI/ML algorithms for enhancing precision in medical diagnostic tactics consist of rapid and accurate prognosis, extended performance, and better clinical consequences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Analyzing Clinical data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care 5th

Analyzing Clinical Data for Improved Diagnosis Using AI/ML A...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Lakshmisha, S.K. Alam, Intekhab Bhuvana, J. Bhatt, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

This study analyzes the capability for using AI/ML algorithms to improve diagnostic accuracy in smart health care. AI/ML strategies have revolutionized clinical analysis by way of offering state-of-the-art and dependable techniques for studying clinical facts. Via using superior techniques such as machine learning, deep gaining knowledge of, reinforcement studying, temporal pattern recognition, and superior decision tree algorithms, AI/ML algorithms provide plenty extra accurate predictions of diseases and conditions from affected person records. Further, AI/ML algorithms can discover relationships between different clinical elements that aren't visible to medical doctors and clinicians. Through the use of AI/ML algorithms, doctors will be able to make greater correct diagnoses and provide higher remedies to patients. The consequences of this take a look at imply that the AI/ML algorithms tested own the ability to enhance prognosis in clever healthcare in phrases of accuracy, precision, do not forget, and cost-effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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