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6th international conference on Computational Intelligence and Networks, CINE 2024

作者： Swain, Debabrata Kumar, Manish Swain, Nirmal Keshari Kulkarni, Chinmay Bhilare, Amol Pandit Deendayal Energy University Computer Science and Engineering Department Gujarat India Pandit Deendayal Energy University Electronics and Communication Engineering Department Gujarat India Vardhaman College of Engineering Department of Information Technology Telengana India University of Cumberlands Department of Information Technology WilliamsburgKY United States Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331516796

Epigenetic modifications, like N6methyladenine (6 mA), regulate genetic configuration and impact the cellular tasks. Recognizing the dissemination of 6mA sites within the rice genome is required for deciphering its influence on plant progression. In the proposed work, a detailed investigation of 6 mA sites in the rice genome using advanced artificial intelligence (AI) techniques. In this research AI models are trained on vast genetic data to correctly categorize 6 mA sites. The classification result has shown the detailed useful implication of 6 mA adjustments, shedding light on possible controlling elements and their roles in important biological procedures. © 2024 IEEE.

关键词： Genome

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Exploring the Potential of IoT, Machine Learning, and Cloud Computing in Smart Home Applications

Exploring the Potential of IoT, Machine Learning, and Cloud ...

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2024 international conference on Advances in Computing research on science engineering and Technology, ACROSET 2024

作者： Mathur, Garima Savita, Pankaj Gaur, Neeraj Vishwakarma, Bhavana Tripathi, Vaibhav Sagar Institution of Science and Technology Computer Science & Engineering Bhopal India Itm University Computer Science & Engineering Gwalior India Oriental Institute of Science & Technology CSE-AIML Bhopal India

ISBN: (纸本)9798350388800

This study investigates using machine learning (ML), the Internet of Things (IoT), and cloud computing to predict cardiovascular diseases. Integrating these advanced technologies in healthcare enables the collection, analysis, and real-time response to patient data, enhancing early diagnosis and personalised treatment plans. This paper presents various case studies demonstrating the successful application of ML algorithms, IoT devices, and cloud platforms in cardiovascular disease prediction. The findings highlight significant improvements in prediction accuracy, patient monitoring, and overall healthcare efficiency. Additionally, the study addresses the challenges, opportunities, and future research directions in the field, aiming to provide a comprehensive understanding of the potential of these technologies in transforming cardiovascular healthcare. © 2024 IEEE.

关键词： Cloud Computing Intelligent Homes IoT Machine Learning

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CEBRA Method: Decoding Brain Activity for advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual international conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Human Motion Tracking: A Comparative Analysis of Recent Advancements

Human Motion Tracking: A Comparative Analysis of Recent Adva...

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2024 international conference on advanced Robotics and Intelligent Systems, ARIS 2024

作者： Lee, Yan-Ru Shaw, Jin-Siang Lee, Fu-Shin National Taipei University of Technology Department of Computer Science & Information Engineering Taipei Taiwan National Taipei University of Technology Department of Mechanical Engineering Taipei Taiwan Living Huafan University Department of Smart New Taipei City Taiwan

ISBN: (纸本)9798350362572

This paper explores the advancements in human motion-tracking technology within the past three years, analyzing research that presents novel methods for capturing and analyzing human movement. We examine the methodologies employed by each approach, including sensor-based systems, radar technology, Wi-Fi signal utilization, lightweight networks for real-time action recognition, and a system for joint camera localization and human motion tracking. This paper compares each surveyed research's advantages, disadvantages, and limitations, highlighting their practical implications and potential applications. Finally, this review discusses the future directions and challenges within human motion tracking. © 2024 IEEE.

关键词： Wi-Fi

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AI-Driven Smishing Detection in Android Devices Using TinyBERT and Aquila Optimization 27

AI-Driven Smishing Detection in Android Devices Using TinyBE...

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27th international conference on advanced Communications Technology, ICACT 2025

作者： Gaurav, Akshat Gupta, Brij B. Chui, Kwok Tai Ronin Institute MontclairNJ United States Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Hong Kong Metropolitan University Hong Kong

ISBN: (纸本)9791188428137

The increasing prevalence of smishing (SMS phishing) attacks on mobile devices poses significant security risks, particularly for Android users. Traditional spam detection techniques often fail to accurately distinguish between legitimate and malicious messages due to the complexity and contextual nature of smishing. In this paper, we propose an AI-driven smishing detection approach using TinyBERT for feature extraction and Aquila Optimization (AO) to optimize a deep learning model. With AO-tuned hyperparameters, the proposed model achieved an accuracy of 96.81%, outperforming standard models like GRU, LSTM, and SVM. Our approach offers a robust, efficient solution for Android smishing detection. Copyright 2025 Global IT research Institute (GIRI). All rights reserved.

关键词： Phishing

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Methods for Developing Effective Models of Biomedical Processes in MATLAB 17

Methods for Developing Effective Models of Biomedical Proces...

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17th IEEE international conference on advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Kolisnyk, Kostyantyn Kolisnyk, Viktoriia Wik, Torsten National Technical University 'Kharkiv Polytechnic Institute' Dept. Industrial and Biomedical Electronics Kharkov Ukraine Kharkiv National University of Radio Electronics Dept. Media Engineering and Information Radioelectronic Systems Kharkiv Ukraine Institutionen for elektroteknik Chalmers tekniska hogskola Dept. Systems and Control Technology Electrical Technology Gothenburg Sweden

ISBN: (纸本)9798331520564

Mathematical modeling in the study of dynamic processes in biomedicine is currently widely used. The use of mathematical statistics and formal logic methods in the analysis of complex biomedical processes is especially important for increasing the efficiency of biomedical research. Taking into account the established growth of the technical base of research, the development of new technologies and technical means for obtaining and processing statistical biomedical information, the use of mathematical modeling methods is a fairly promising direction in scientific and practical work. In this paper the authors studies the possibility of improving the quality of biomedical process modeling using the MATLAB software environment. The research was carried out on real statistical material obtained by the authors during a full-scale experiment to study the impact of a complex of external factors on patients. The results obtained in this work can be used in the implementation of scientific and applied research of biomedical processes. © 2024 IEEE.

关键词： MATLAB

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th international conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Diabetes Prediction: Machine Learning Models Enhanced by Harmony Search Optimization 1

Diabetes Prediction: Machine Learning Models Enhanced by Har...

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1st international conference on advanced Computing and Emerging Technologies, ACET 2024

作者： Vennu, Mohith Kokkirala, Ajay Roa Saketh Reddy, Donthi Dwivedi, Anshu Kumar Sr University School of Computer Science and Artificial Intelligence Warangal India Sr University School of Computer Science and Artificial Intelligence Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350367720

The Harmony Search (HS) algorithm has been used in many applications, including engineering, excavation, epidemiology, object recognition, stock price prediction, genetic analysis, and structural design optimization, all derived from the improvisation process in musicians. The study covers the application of the HS algorithm for a predictive model of diabetes with its parameter tuning and optimization process of the model. In performing the research, a dataset will be used that contains data of information regarding demographic data, clinical metrics, and lifestyle attributes of people with and without diabetes. The most important features such as age, BMI, blood pressure, and glucose level will be treated as input variables. The HS algorithm will optimize the model parameters to develop the best model for the process of prediction of diabetes. The research has four parts: preparing, choosing features, training models, HS-driven upgrade, and checking results. The best model with K-NN beat all basic models in accuracy, sensitivity, and specificity. This shows good risk evaluation and early diabetes detection. This article contributes new contributions to emerging areas of diabetes prediction through novel avenues in model optimization. Specifically, this shows the efficacy of the HS algorithm, which improves the performance of predictive models. In that sense, this improves the ability to do an accurate and reliable risk evaluation of diabetes in clinical applications. © 2024 IEEE.

关键词： Clinical research

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm 27

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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27th international conference on advanced Communications Technology, ICACT 2025

作者： Alsheavi, Amar N. Wang, Xingfu Zhao, Wei Hawbani, Ammar Alhusaini, Naji Ahmed, Ibrahim Abdulrab Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Computer and Information Engineering Chuzhou University Anhui Chuzhou239000 China Computer Department Applied College Najran University Najran66462 Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen518060 China Faculty of Science Zagazig University Zagazig44519 Egypt

ISBN: (纸本)9791188428137

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Ant colony optimization

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