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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Clinical application of remote fetal heart rate monitoring system for AI early warning1 25

Clinical application of remote fetal heart rate monitoring s...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhuang, Chunyu Chen, Xiaojing Lin, Chunlan Han, Yan Zhang, Qingmei Du, Qiongliang Duan, Yucong Chen, Jiacheng Haikou Maternal and Child Health Care Hospital Nursing Department Hainan Haikou570203 China Haikou Maternal and Child Health Care Hospital Neonatology Hainan Haikou570203 China Haikou Maternal and Child Health Care Hospital Pediatric Intensive Care Unit Hainan Haikou570203 China School of Computer Science and Technology Hainan University Hainan Haikou570203 China Hainan General Hospital Department of Hepatobiliary and Pancreatic Surgery Hainan Haikou570311 China

ISBN: (纸本)9798350330014

To compare the safety, effectiveness and effect of the AI early warning system and the traditional fetal monitoring system. Methods: Establish and deploy AI remote fetal monitoring system;From January 1, 2022 to December 31, 2022, 440 pregnant women in the third trimester of pregnancy admitted to the Maternal and Child Health Hospital of Haikou City and meeting the admission criteria were selected and randomly assigned to the artificial intelligence early warning remote fetal visit group 220 cases and the traditional group 220 cases. In the AI group, remote fetal monitoring system was used for examination and treatment, while in the traditional group, pregnant women went to the hospital for examination and delivery according to the usual standard procedures. The effectiveness and safety of the AI-early warning impromptu fetal heart test system were observed, and the rates of perinatal adverse outcomes (low birth weight, perinatal death, premature delivery, neonatal asphyxia, cord blood oxygen saturation and fetal distress) of pregnant women in the two groups were compared. Results: In the test of artificial intelligence early warning fetal monitoring system, the AI group detected 332 normal fetal cardiogram cases of pregnant women, 206 suspected pathological changes in 18 cases;In the traditional group, 421 cases of normal fetal cardiogram were detected, 117 cases of suspected pathological changes were detected in 16 cases. The sensitivity and specificity of normal pregnant women (true positive was normal group, false negative was suspicious + lesion) were 81.55% and 96.98% respectively. The sensitivity and specificity of pregnant women with abnormal fetal monitoring map were 100% and 82%. The low birth weight rate was 6/220 in the AI group and 13/220 in the control group, with a total value of 2.695 (p value 0. 101). Perinatal death was 0 in both groups. The rate of preterm infants in AI group was 7/220, compared with 15/220 in control group, and peason2 was 3.062,

关键词： Obstetrics

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Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response 27th

Percolate: An Exponential Family JIVE Model to Design DNA-B...

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27th International Conference on Research in Computational Molecular Biology, RECOMB 2023

作者： Mourragui, Soufiane M. C. Loog, Marco van Nee, Mirrelijn de Wiel, Mark A van Reinders, Marcel J. T. Wessels, Lodewyk F. A. Computational Cancer Biology Division of Molecular Carcinogenesis Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Faculty of EEMCS Delft University of Technology Delft Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Epidemiology and Biostatistics Amsterdam Public Health research institute Amsterdam University medical centers Amsterdam Netherlands MRC Biostatistics Unit University of Cambridge Cambridge United Kingdom Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands

ISBN: (纸本)9783031291180

Motivation: Anti-cancer drugs may elicit resistance or sensitivity through mechanisms which involve several genomic layers. Nevertheless, we have demonstrated that gene expression contains most of the predictive capacity compared to the remaining omic data types. Unfortunately, this comes at a price: gene expression biomarkers are often hard to interpret and show poor robustness. Results: To capture the best of both worlds, i.e. the accuracy of gene expression and the robustness of other genomic levels, such as mutations, copy-number or methylation, we developed Percolate, a computational approach which extracts the joint signal between gene expression and the other omic data types. We developed an out-of-sample extension of Percolate which allows predictions on unseen samples without the necessity to recompute the joint signal on all data. We employed Percolate to extract the joint signal between gene expression and either mutations, copy-number or methylation, and used the out-of sample extension to perform response prediction on unseen samples. We showed that the joint signal recapitulates, and sometimes exceeds, the predictive performance achieved with each data type individually. Importantly, molecular signatures created by Percolate do not require gene expression to be evaluated, rendering them suitable to clinical applications where only one data type is available. Availability: Percolate is available as a Python 3.7 package and the scripts to reproduce the results are available here. © 2023, The Author(s).

关键词： Gene expression

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Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features

arXiv

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arXiv 2024年

作者： Tshimula, Jean Marie Nkashama, D'Jeff K. Muabila, Jean Tshibangu Galekwa, René Manassé Kanda, Hugues Dialufuma, Maximilien V. Didier, Mbuyi Mukendi Kalala, Kalonji Mundele, Serge Lenye, Patience Kinshie Basele, Tighana Wenge Ilunga, Aristarque Mayemba, Christian N. Kasoro, Nathanaël M. Kasereka, Selain K. Mikese, Hardy Tardif, Pierre-Martin Frappier, Marc Kabanza, Froduald Chikhaoui, Belkacem Wang, Shengrui Sumbu, Ali Mulenda Ndona, Xavier Intudi, Raoul Kienge-Kienge Global Department of Computer Science University of Kinshasa Congo Department of Computer Science Université de Sherbrooke Canada LISV-UVSQ Université Paris-Saclay France University of Klagenfurt Austria Canada Biomedical Research Unit Hospital Monkole Kinshasa Democratic Republic Congo University of Florida United States School of Electrical Engineering and Computer Science University of Ottawa Canada Karlstad University Sweden Institut Supérieur Pédagogique de Kikwit Congo Applied Artificial Intelligence Institute TELUQ University Canada Faculty of Psychology and Education Sciences University of Kinshasa Congo Harrisburg University of Science and Technology United States School of Criminology University of Kinshasa Congo

The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language Models (LLMs) and psycholinguistic features. We investigate the intersection of psychology and cybersecurity, discussing how LLMs can be employed to analyze textual data for identifying psychological traits of threat actors. We explore the incorporation of psycholinguistic features, such as linguistic patterns and emotional cues, into cybersecurity frameworks. Our research underscores the importance of integrating psychological perspectives into cybersecurity practices to bolster defense mechanisms against evolving threats. © 2024, CC BY.

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Bilingual Fake News Detection: Leveraging Deep Learning for Arabic and English Media

Bilingual Fake News Detection: Leveraging Deep Learning for ...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Nour Mostafa Diaa Salama AbdElminaam Mariam Mohamed Abouelwafa Ahmed Ehab Ibrahim Mohmed Amira Alaa Saber College of Engineering and Technology American University of the Middle East Kuwait Jadara Research Center Jadara University Irbid Jordan Faculty of Computers Misr International University Egypt MEU Research Unit Middle East University Amman Jordan Faculty of Computer Science Misr International University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Nowadays, the news through this mass technology has a great impact on the information we share, and with this huge number of users, platforms have become a generator of a huge amount of news. They may contain claims such as political propaganda or aggressive content like bullying or harassment phrases. Sharing such fake news results to many struggles like hate speeches and aggressive actions in society so identifying fake and real news is a necessary requirement. Providing such a requirement in multiple languages is important and in this research, our aim is to develop a system able to detect fake news in both Arabic and English language based on textual features. The difference between the two datasets is the preprocessing phase Arabic needs more preprocessing due to the punctuation. This paper uses deep learning and machine learning algorithms for the detection of fake news in Arabic and English. For the English dataset, machine learning models such as gradient boost, logistic regression, decision tree, and random forest were employed, along with deep learning models like GAN, RNN, and LSTM. We employed random forest, decision tree, and logistic regression as machine learning models and LSTM, GAN, and RNN as deep learning models for the Arabic dataset. According to experimental results, the Arabic dataset yields the highest accuracy of 85% for LSTM and GAN, while the English dataset yields the highest accuracy of 99% for gradient boosting

关键词： Deep learning Logistic regression Accuracy Machine learning algorithms Generative adversarial networks Feature extraction Decision trees

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Innovations, Difficulties, and Approaches for Next-Generation Cybersecurity: Protecting the Digital Future

Innovations, Difficulties, and Approaches for Next-Generatio...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： C. V. Guru Rao Nur Mohammad Ali Chisty Shashi Kant Mishra Manoj Sathe Samreen Rizvi Mukesh Soni School of CS & AI SR Universtity Warangal Telangana Additional Superintendent of Police (Cyber Crime Wing) Anti-Terrorism Unit Bangladesh Police Dhaka Bangladesh Guru Nanak Institute of Technology Hyderabad Telangana Computer Science PES Modern Institute of Business Studies Pune India Information Technology UP India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Pune Tathawade

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

There has never been a greater pressing need for strong and sophisticated cybersecurity measures as our society gets more digitally integrated. This abstract explores the landscape of next-generation cybersecurity, addressing the innovations, difficulties, and approaches essential for safeguarding the digital future. Innovations in cybersecurity are vital for staying ahead of the evolving threat landscape. The advent of artificial intelligence (AI) and machine learning has revolutionized the field, allowing for proactive threat detection, rapid response, and adaptive defences. Blockchain technology is another noteworthy innovation, providing a decentralized and tamper-resistant framework for securing sensitive data and transactions. The integration of these technologies represents a paradigm shift in cybersecurity strategies, emphasizing a more dynamic and intelligent approach. However, with innovation comes a new set of challenges. The increasing sophistication of cyber threats poses difficulties for traditional security measures. Advanced persistent threats (APTs), zero-day exploits, and ransomware attacks require adaptive and resilient defense mechanisms. Additionally, the interconnected nature of modern systems raises concerns about supply chain attacks and the potential for cascading vulnerabilities. Addressing these difficulties requires a holistic and collaborative approach that spans technology, policy, and education. Approaches to next-generation cybersecurity must prioritize collaboration between industry, government, and academia. Public-private partnerships play a crucial role in sharing threat intelligence, best practices, and resources. Standardization of cybersecurity practices and regulations helps establish a baseline for security across different sectors. Moreover, user education and awareness programs are essential for creating a cyber-resilient society. In conclusion, the digital future demands innovative, adaptive, and collaborative approaches t

关键词： Industries Technological innovation Education Government Supply chains Collaboration Threat assessment

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Efficient and Non-Invasive Rice Moisture Quantification Using Coupled Lines Microwave Sensor Design

Efficient and Non-Invasive Rice Moisture Quantification Usin...

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International Conference on Information technology (INCIT)

作者： Pratya Phanomupatum Ravee Phromloungsri Decha Wongpatsa Tiwat Pongthavornkamol Phatcharanat Saeng-On Somchat Sonasang Electronics Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Faculty of Agriculture and Technology Nakhon Phanom University Nakhon Phanom Thailand Location and Automatic Identification System Research Team Location and Automatic NECTEC National Science and Technology Development Agency Pathum Thani Thailand Electrical Elements and Wireless Devices Unit (EEWDU) Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This research presents the design, simulation, and experimental validation of a novel microwave sensor for noninvasive moisture measurement in rice. The sensor employs coupled microstrip lines fabricated on an FR4 substrate, designed to operate at a center frequency of 900 MHz with a quarter-wavelength configuration. The Advanced Design System software was utilized for the simulation, where key parameters such as S 21 and phase response were analyzed across a frequency range of 0.1 to 3 GHz. The sensor was fabricated and tested using a Vector Network Analyzer, where the moisture content in rice samples was varied incrementally, and the sensor’s performance was evaluated based on the changes in transmission characteristics. The results demonstrated the sensor’s potential for accurate moisture detection in rice, showcasing its feasibility as a noninvasive measurement technique in agricultural applications. This study contributes to the development of efficient, real-time moisture measurement technologies in the food processing industry.

关键词： Microwave measurement Sensitivity Moisture measurement Moisture Network analyzers Microwave sensors Vectors Real-time systems System software Substrates

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Experimental Evaluation of a Passive Upper Limb Exoskeleton for High Voltage Live-Line Operations

Experimental Evaluation of a Passive Upper Limb Exoskeleton ...

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International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Dongwei Zhao Song Wang Shouqian Sun Xuequn Zhang Kimmo Vänni College of Computer Science and Technology Zhejiang University Hangzhou China Innovation Center of Yangtze River Delta Zhejiang University Jiaxing China Hangzhou Kuntai Robotics Co. Ltd. Hangzhou China Smart Research Unit Häme University of Applied Sciences Hämeenlinna Finland

ISBN: (数字)9798331540241

ISBN: (纸本)9798331540258

In high-voltage live-line work, workers often need to hold insulating tubes and other tools for extended periods, leading to upper limb muscle strain, especially among inexperienced workers. Reducing muscle load can effectively prevent such hazards. This paper validates a newly designed passive upper limb exoskeleton through workspace analysis, biomechanical simulation, and filed experiment. The results demonstrate that the exoskeleton meets the movement space requirements of live-line workers. In the simulation environment, we made the model perform the lead connection task, the average muscle activation of the deltoid muscle when wearing the exoskeleton is reduced by 34.07%–55.56% compared to without the exoskeleton, and the average muscle activation of the trapezius muscle is reduced by 19.40%–25.63%. In live-line working experiments, the muscle activation of subjects decreased by 38.33% in the insulating protective cover construction task, 33.49% in the lead connection task, and 23.69% in the climbing down the pole task. Meanwhile, wearing the exoskeleton did not negatively impact task efficiency. These findings suggest that the exoskeleton can significantly reduce muscle strain without compromising productivity during high-voltage live-line work.

关键词： Biomechanics Productivity Human-machine systems Exoskeletons High-voltage techniques Muscles Lead Hazards Strain Load modeling

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Panel: Faculty Leadership

Panel: Faculty Leadership

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Frontiers in Education (FIE) Conference

作者： Lina Karam Georges Zissis Jason Yao Jill Nelson Minho Jo Rafal Sliz Michele Nogueira Steve Watkins Huihui Wang School of Electrical Computer and Energy Engineering Arizona State University TEMPE AZ USA Science and Engineering Univestité Toulouse III - Paul Sabatier Toulouse France Department of Engineering East Carolina University Greenville NC USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Computer Convergence Software Korea University Sejong City S. Korea Optoelectronics and Measurement Techniques Unit University of Oulu Oulu Finland Department of Computer Science Federal University of Minas Gerais Belo Horizonte MG Brazil Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Computer Science and Cybersecurity St. Bonaventure University St. Bonaventure USA

This faculty leadership panel will be held jointly by the IEEE Education Society (IEEE EdSoc) and IEEE Educational Activities Board (EAB) Faculty Resources Committee (FRC). The panel intends to provide participants introductory knowledge and skills. Two moderators and four panelists will focus on discussing their various academic leadership experiences and practices. The panel will be held in an interactive way so that the participants will have enough time to ask questions or communicate with the panelists. It is expected that this joint panel may potentially help to build up networks and mentorships.

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