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34th Medical Informatics Europe Conference, MIE 2024

作者： Frohner, Matthias Treml, Lilly Maria Latikaynen, Daniel Goldmann, Annemarie Leipold, Stefan Loidl, Herwig Urbauer, Philipp Department Life Science Engineering University of Applied Sciences Technikum Wien Austria LOIDL Consulting & IT Services GmbH Austria Steszgal Informationstechnologie GmbH Austria Department Computer Science University of Applied Sciences Technikum Wien Austria

ISBN: (纸本)9781643685335

The Austrian research project Linked Care explored digitalization in mobile care, focusing on streamlining the medication process to save nursing staff time. A FHIR R5-based workflow was developed to support medication ordering by nurses, prescriptions by practitioners, and dispensing by pharmacies. Key FHIR resources were profiled and published in an HL7 Austria Member Implementation Guide (IG). The IG includes specifications and technical details for implementation and was the first member-contributed IG approved by the HL7 Austria FHIR community in early 2024. These specifications are now being implemented and will be tested in late 2024. © 2024 The Authors.

关键词： Specifications

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Leveraging Ensemble Techniques for Enhanced Eye State Classification with EEG Data 2

Leveraging Ensemble Techniques for Enhanced Eye State Classi...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Priyadharshini Jayadurga, N. Chandralekha, M. Saleem, Kashif Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India King Saud University College of Applied Studies and Community Service Department of Computer Sciences and Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350371314

Brain-computer Interaction (BCI) is an indispensable component in the constantly evolving field of human-machine interaction, offering direct interaction between the human brain and external devices. The acquisition of electroencephalogram, or EEG, signals by BCI devices is vital for this connection. The objective of this study is to differentiate eye states (open and closed) with the goal of shedding light on the consequences of artifacts when performing various BCI tasks. The background study explores the key concepts of BCI, emphasizing the critical role that EEG signals play in the intricate relationship between humans and computers. Within the BCI framework, the problem statement accentuates the importance of eye state classification across tasks. In an effort to improve classification accuracy and eliminate artifacts, this research strategically integrates techniques through the implementation of an extensive approach and assessments of multiple ensemble strategies. The findings and analysis reveal the efficiency of ensemble approaches in understanding eye states, hence improving BCI applications and expanding human-machine interaction. © 2024 IEEE.

关键词： Electroencephalography

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Development of ANFIS Controller for Trajectory Tracking Control Using ROBO2L MATLAB Toolbox for KUKA Industrial Robot via RSI 21

Development of ANFIS Controller for Trajectory Tracking Cont...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Mousa, Mohamed Al-Mahasneh, Ahmad Baniyounis, Mohammed Biesenbach, Rolf Falkenhain, Jan Philadelphia University Mechatronics Engineering Department Amman Jordan Institute for Automation and Industry 4.0 Bochum University of Applied Sciences Bochum Germany Bochum University of Applied Sciences Electrical Engineering and Computer Science Department Bochum Germany

ISBN: (纸本)9798350374131

A new control scheme is proposed in the MATLAB/Simulink environment based on ANFIS technology using ROBO2L MATLAB toolbox with RSI to control the motion of the real KUKA robot. In detail, the control system has two subsystems designed for trajectory tracking error regulation in closed-loop control. One is a fuzzy logic controller, and the other is an ANFIS controller. These two controllers were used to control the velocity of the KUKA robot located inside the Hochschule Bochum laboratory. The testing process for these controllers was done by switching between FLC and ANFIS controllers and then studying each response separately. The results were compared with a linear controller produced in Hochschule Bochum experiments. The ROBO2L MATLAB toolbox provides many functions, such as sending and receiving positions and rotation for the end effector via UDP/IP communication, which RSI established to provide remote control for the robot using a PC. This work mainly emphasizes the ability to track the trajectories supported by the robotics toolbox in smooth motion. After observing the simulation results, the comparison between the linear, FLC, and ANFIS controllers is confirmed. The performance of the proposed approach is efficient for trajectory tracking control for the manipulator. © 2024 IEEE.

关键词： Controllers

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Automated Radiology Report Generation from Chest X-ray Scans Using Deep Learning 6th

Automated Radiology Report Generation from Chest X-ray Scans...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Veer, V. Druva Priya, S. Baghavathi Tamilselvi, M. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India Information Technology Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9789819778614

A chest X-ray is a common diagnostic tool for many thoracic illnesses. Interpreting these images and coming up with accurate diagnostic results is a difficult and time-consuming task for radiologists. Recent results using deep learning approaches to automate the analysis of chest X-rays are promising. This paper provides a comprehensive description of deep learning techniques used to chest X-ray processing for the diagnosis of several diseases, including TB, pneumonia, and COVID-19. This paper provide a summary of the primary deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models, that are used for disease classification, localization, segmentation, and report generation from chest X-rays. This paper also discuss explainability techniques that are used to offer transparency and increase trust in model predictions. Along with existing issues, future directions are taken into consideration, such as multimodal learning, few-shot learning, and model evaluation. This paper explains the model which takes less time and give the optimal results. In order to facilitate future research into developing AI systems that are more accurate, understandable, and advantageous from a medical standpoint, this article provides an overview of the most current developments in deep learning techniques for automated chest X-ray diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Impact of Time Resolution and Window Length on the Capture of Frequency Variations and Events

Impact of Time Resolution and Window Length on the Capture o...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Polajžer, Boštjan Mohammadi, Younes Saremi, Amin University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia Umeå University Department of Applied Physics and Electronics Umeå Sweden

ISBN: (纸本)9789531842976

Power-quality standards provide limited guidance on frequency quality for short time scales, such as less than one hour. Capturing frequency variations and events requires high time resolutions, e.g., 0.1 seconds or less, resulting in significant data storage requirements. However, power-quality monitors typically report averaged values at intervals of 10 minutes, 15 minutes, or one hour, depending on the disturbance type. To address these challenges, we propose calculating statistic indices from high-resolution data within a 15-minute or one-hour window length, thus avoiding storing high-resolution data. We apply the basic statistic indices to frequency data measured in Finland for a single day in June 2023, demonstrating their effectiveness in capturing frequency variations and two significant events during that day. © 2024 IEEE.

关键词： Time measurement

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Robust asymmetric Cherenkov radiation in tilted anisotropic medium

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science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 46-51页

作者： Shuwen Xue Yadong Xu Linkang Han Zhi Ning Chen Qiaoliang Bao Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyJiujiang Research InstituteXiamen UniversityXiamen 361005China School of Physical Science and Technology Institute of Theoretical and Applied PhysicsSoochow UniversitySuzhou 215006China Department of Electrical and Computer Engineering National University of SingaporeSingapore 8117583Singapore Institute of Energy Materials Science(IEMS) University of Shanghai for Science and TechnologyShanghai 200093China

Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.

关键词： asymmetric Cherenkov radiation intrinsic losses long-distance radiation

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Ultra-Low Power Designs and Strategies for Next-Generation Sensor Networks 7

Ultra-Low Power Designs and Strategies for Next-Generation S...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Priyanka Revathi, V. Hameed, Ali Abduhussien Gupta, Manish Yadav, Suman Avdhesh Rao, Bolleddu Devananda Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India Iilm University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

Energy-efficient design and operation are essential for next-generation sensor networks to persist and require less maintenance. This paper describes a novel energy-efficient sensor network. It uses superior data routing and aggregation. Three novel algorithms-EARA, DFAA, and Adaptive Data Sampling-make this strategy practicable. EARA sends data with little energy, DFAA collects all data to eliminate duplicates, and ADSA adjusts sample rates based on relevance and variability. Each piece of software sends, manages, and changes sample rates with minimal energy. This innovative energy-saving strategy is tested against static-duty cycling, non-adaptive data aggregation, fixed-rate data sampling, high-energy data transmission, centralized data routing, and unoptimized sensor node location. The recommended solution outperforms network lifetime, energy usage, data accuracy, latency, scalability, and dependability. Energy savings, ease of use, affordability, adaptability, long-lastingness, and network lifespan are usually better with the proposed response. Scatter plots illustrate how much better the proposed method saves energy and works properly. The EARA, DFAA, and ADSA algorithm-based techniques offer great potential for energy-efficient sensor networks. This strategy stabilizes and prolongs sensor network configurations by optimizing data pathways, aggregating data, and adaptively sampling data. The results show that it works, making it a novel strategy for the future generation of sensor networks that demand higher performance and less energy. © 2024 IEEE.

关键词： Adaptive Data Sampling Data Aggregation Data Management Energy Consumption Energy Efficiency Energy-Aware Routing Network Lifetime Performance Evaluation Sensor Networks Sustainability

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning 2

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

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