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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Nevien A. Mahdy Ahmed F. Elnokrashy D. A. Hammad Wael A. Mohamed Electrical Engineering Department Benha Faculty of Engineering Benha University Benha Egypt Computer Science Department Faculty of Information Technology and Computer Science Nile University Giza Egypt Basic Engineering Sciences Department Benha Faculty of Engineering Benha University Benha Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D.

关键词： Training Wearable Health Monitoring Systems Real-time systems Data models Glucose Diabetes Insulin

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Enhancing Healthcare Delivery Through IoT Applications in Remote Patient Monitoring and Telemedicine 3

Enhancing Healthcare Delivery Through IoT Applications in Re...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Yogi, Kottala Sri Gowda V, Dankan Manoj Kumar, S.B. Nithya, R. Begum, A.Yasmine Suganthi.n Symbiosis Institute of Business Management Hyderabad Symbiosis International University Department of Operations Pune India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Bgsit Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of Ece Karnataka 571448 India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamilnadu Namakkal India Mohan Babu University School of Engineering Department of Electronics and Communication Engineering Tirupati India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350364569

This paper presents how IoT plays a vital role in improving delivery of healthcare services especially through remote patient monitoring and telemedicine. The proposed system also employs IoT devices aimed at passing various health indicators like;pulse rate, blood glucose levels, blood pressure, and physical activity to continually and objectively provide real-time data in the body. This particular study, the participants with chronic diseases, normal population and elderly people also participated in the test of the system. Some of the findings include;it is possible to measure the daily steps corresponding to the a calorie expenditure, the sleep score ranging between 60-80 and oxygen saturation percentages ranging from 96%-99% in various postures. The system also proved the correlation between the percentage of variation in ambient temperature and the percentage of variation in the pulse rate which was quite sensitive to the changes in temperatures. Based on these observations, it is possible to conclude that IoT-based monitoring can improve patient's conditions as long as underlying issues will be detected at an early stage and necessary actions will be taken in time. The study also also demonstrates the application of telemedicine in making healthcare more accessible especially for the people from rural settings or in developing countries. The results show a good direction regarding the future of the system;however, security issues, privacy relating to data as well as integration are key hurdles in this connection. © 2024 IEEE.

关键词： Blood pressure

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Crime Type Prediction based on Various Occurrence using Parallel LSTM

Crime Type Prediction based on Various Occurrence using Para...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： M Saradha T Nithesh Priyan D U Shreeram S Viknesh Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well.

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Initiating a One Digital Health Unified Terminology (ODH-UT) to Facilitate Community Expansion

Initiating a One Digital Health Unified Terminology (ODH-UT)...

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34th medical Informatics Europe Conference, MIE 2024

作者： Bauberg, Helit Nehama, Dana Blinder, Constantine Davidman, Roy Cohen, Eliyahu Tamburis, Oscar Darmoni, Stefan J. Grosjean, Julien Benis, Arriel Department of Data Science Holon Institute of Technology Israel Faculty of Industrial Engineering and Technology Management Holon Institute of Technology Israel Institute of Biostructures and Bioimaging National Research Council Naples Italy Department of Biomedical Informatics Rouen University Hospital France LIMICS Laboratory of Medical Informatics and Knowledge Engineering in e-Health INSERM U1142 Sorbonne Université France Department of Digital Medical Technologies Science Holon Institute of Technology Israel

ISBN: (纸本)9781643685335

One Digital Health (ODH) merges the Digital Health and One Health approaches to create a comprehensive framework for future health ecosystems. In this rapidly evolving field, a standardized vocabulary is not just a convenience, but a necessity to ensure efficient communication. This research proposes the development of a "One Digital Health-Unified Terminology" (ODH-UT) to facilitate communication among researchers and practitioners in Digital Health and One Health, addressing this crucial need. © 2024 The Authors.

关键词： Electronic health record

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Secrecy Performance of Joint Antenna and User Selection in Cognitive Ambient Backscattering Communications 100

Secrecy Performance of Joint Antenna and User Selection in C...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Elbattrawy, Ahmed N. El-Malek, Ahmed H. Abd Rabia, Sherif I. Zahra, Waheed K. Basic and Applied Sciences Institute New Borg El-Arab City Alexandria21934 Egypt Egypt-Japan University of Science and Technology School of Electronics Communications and Computer Engineering New Borg El-Arab City Alexandria21934 Egypt Alexandria University Faculty of Engineering Department of Engineering Mathematics and Physics Alexandria21544 Egypt

ISBN: (纸本)9798331517786

In this work, we investigate the security performance of an underlay cognitive radio network (CRN) with ambient backscatter communication (AmBC), where a backscattering device (BD) shares the spectrum and the receivers with the secondary user. Different from the related work, we consider a secondary user transmitter (ST) with multiple antenna in an AmBC-CRN with multiple receivers and multiple passive eavesdroppers. The ST performs joint antenna-user selection to enhance its security performance and overcome the performance degradation caused by BD interference. Considering the Nakagami-m fading model, closed-form expressions are derived for the secrecy outage probability for both the ST and the BD transmissions. Monte Carlo simulations are performed to validate the derived closed-form expressions. Numerical results show that employing joint antenna-user selection enhances the ST security performance by exploiting antenna and user diversity. © 2024 IEEE.

关键词： Radio communication

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Asymptotic Stabilization of Passive Nonlinear Systems with Finite Countable Control Actions: mixed switching - nearest action control approach

Asymptotic Stabilization of Passive Nonlinear Systems with F...

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European Control Conference (ECC)

作者： Arvind Ragghav V Muhammad Zaki Almuzakki Bayu Jayawardhana Arun D. Mahindrakar Department of Electrical Engineering IC&SR IIT Madras Indian Institute of Technology Madras Chennai India Department of Computer Science Faculty of Science and Computer Universitas Pertamina Indonesia Faculty of Science and Engineering Engineering and Technology Institute Groningen University of Groningen Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper studies the global asymptotic stabilization of passive nonlinear systems with finite, countable control actions. We show that for nonlinear passive systems that are large-time norm observable and admit a finite control input set whose convex hull contains the origin, the origin can be globally asymptotically stabilized and locally exponentially stabilized by means of relaxed control and nearest-action control approaches. In particular, we improve on a recent result of practical stabilization via nearest-action control by utilizing switching controllers that can synthesize extra control actions from an existing control input set. Three switching methodologies are proposed to enlarge the control set and enable global asymptotic and local exponential stabilization. These three methodologies vary in the cardinality of the expanded control set. These methods are validated in numerical simulations where a comparison of the convergence rate is provided.

关键词： Europe Switches Control systems Numerical simulation Nonlinear systems Convergence

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Integrating NLP for Intelligent Cybersecurity Risk Detection from Bulletins

Integrating NLP for Intelligent Cybersecurity Risk Detection...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mondal, Kalimuddin Ebenezar, U. Samson Karthikeyan, L. Gururama Senthilvel, P. Preetham, B.S. Simats Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha University Department of Computer Science and Engineering Chennai India Ms Ramaiah Institute of Technology Department of Medical Electronics Bangalore India

ISBN: (纸本)9798331538538

The rate of developed threats increases and poses challenges when it comes to the recognition of the risks and measures to be taken. Current cybersecurity risk assessment is mainly done using word-of-mouth through reviewed security announcements, vulnerable product or service reports, and threat intelligence data feeds, all of which are time-consuming and often contain human errors. The use of Natural Language Processing (NLP) in cybersecurity brings the efficiency of automation where text information is analyzed with the aim of identifying and categorizing threats in real time. In this paper, NLP techniques including Named Entity Recognition (NER), sentiment analysis and topic modelling are used to analyze the security bulletins and determine the security threats. Supervised, semi-supervised, and unsupervised learning methods of the NLP system can bene fit to mine adequate security-related text data, identify suitable patterns and or make forecasts of susceptible vulnerabilities. The proposed framework is primarily a framework to improve threat intelligence through vulnerability, attack vectors, and related systems connection. In addition, NLP-based risk identification also has several advantages: it increases the accuracy of risk identification and decreases the time for risk detection, as well as strengthens cybersecurity infrastructure. The paper demonstrates that by applying the technique of IBM security bulletins, organizations can reduce the risks and improve the controls effectively. The paper concludes with areas of future work in enhancing the application of NLP techniques in cybersecurity with an agreement on the idea of adaptive models that reflects the dynamic and ever-changing nature and trends of cyber threats. © 2025 IEEE.

关键词： Deep learning

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Health Recommender System for Maternal Care Implementation Challenges: A Qualitative Analysis of Physicians' Perspective 14

Health Recommender System for Maternal Care Implementation C...

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14th International Conference on Advanced computer science and Information Systems, ICACSIS 2022

作者： Priambodo, Rinto Handayani, Putu Wuri Sensuse, Dana Indra Suryono, Ryan Randy Kautsarina Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia Ministry of Communication and Information Technology Jakarta Indonesia

ISBN: (数字)9781665489362

ISBN: (纸本)9781665489362

The World Health Organization (WHO) strategy for Ending Preventable Maternal Mortality (EPMM) accommodates guidelines and objectives to lower the level of the global Maternal Mortality Ratio (MMR). Regular antenatal care (ANC) visits could increase maternal satisfaction, hence activities in ANC include risk identification, prevention, and management of pregnancy-related diseases, which could lead to maternal death. The health recommender system in healthcare helps patients and health professionals perform early predictions and receive meaningful recommendations based on patients' up-to-date health records. However, implementations of this application in maternal care are still few. Therefore, we conducted a semi-structured interview to five physicians working in maternal care to explore the challenge. The interview data were afterwards analyzed using qualitative content analysis. This qualitative study has identified six major challenges related to technological issues, such as the ability to display critical information, simplicity, compatibility, correctness, consistency, and fulfillment of functionalities required to adhere to maternal care guidelines, as well as challenges related to regulations and policies. This study contributes knowledge about the implementation of health recommender systems for maternal care and provides recommendations to application vendors and health regulators for the implementation of this system in Indonesia. © 2022 IEEE.

关键词： Recommender systems

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Trans-Compiler-Based Database Code Conversion Model for Native Platforms and Languages 11th

Trans-Compiler-Based Database Code Conversion Model for Nati...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Barakat, Rameez Radwan, Moataz-Bellah A. Medhat, Walaa M. Yousef, Ahmed H. School of Information Technology and Computer Science Nile University Giza Egypt Center for Informatics Science Nile University Giza Egypt Department of Computer and Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of computers and artificial intelligence Benha University Banha Egypt

ISBN: (纸本)9783031215940

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler Based Android to iOS Converter (TCAIOSC) was proposed to convert the front-end and back-end code of Android Java applications to iOS applications. Since databases are essential for mobile applications, this paper proposes a new database code conversion model based on trans-compilation and pattern matching. It proposes a model that can be used to support database code conversion between native languages and platforms and applies the proposed model to support the conversion of Firebase Firestore database code from Android (Java) to iOS (Swift) using TCAIOSC. The enhanced tool’s results show high accuracy for the converted database code and a noticeable improvement in the overall conversion results for TCAIOSC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pattern matching

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