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2022 International electrical engineering Congress, iEECON 2022

作者： Sonsilphong, Suphachoke Sonsilphong, Amornthep Hormdee, Daranee Khampitak, Kovit Khon Kaen University Data Science Health Informatics Program Academic Affairs Faculty of Medicine Khon Kaen Thailand Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Embedded System RD Group Department of Computer Engineering Faculty of Engineering Khon Kean Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two main phases, the training phase, and the real-time operating phase. In the training phase, a modified YOLOv4 algorithm is proposed to be adopted within the Darknet framework for training the weights of detection models. The best detection model is then selected to be assigned to the object detector in the real-time operating phase. The validation result of the detection model across the experiment datasets is 90% accuracy on average during the training phase. To validate the viability of real-time object detection, the proposed system was implemented in two test-cases of gynecologic surgery with the soft-body cadavers. © 2022 IEEE.

关键词： Laparoscopy

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Determination of Planck's Constant Using the Photoelectric Effect Experiment 11

Determination of Planck's Constant Using the Photoelectric E...

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11th National Physics Seminar, SNF 2022

作者： Fitri, U.R. Ziveria, M. Muhara, I. Department of Physics Education Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Information Systems Study Program Kalbis Institute of Technology and Business Jl. Pulomas Selatan No.22 Jakarta Indonesia Chemical Engineering Study Program Faculty of Industrial Technology Institut Teknologi Bandung West Java Bandung Indonesia

Planck's constant experiment has been carried out to determine the value of Planck's constant using the regression analysis method. The steps are carried out using the scientific process outlined in the practical work steps. The specification of the experimental equipment uses the Planck constant experimental set with 12v/35w tungsten halogen light, 15v output, +-0.2% accuracy, 220V power requirement, 0.5A fuse rating and red, yellow 1, yellow 2, and red color filters, green and blue. Experiments using the concept of the photoelectric effect phenomenon began by irradiating metallic materials with light using a filter. The wavelength (635, 570, 540, 500, 460) nm is the light filter variation. The wavelength that hits the metal material is selected by placing a light filter on the light propagation path toward the metal material. So the light that passes through the filter is only light with a certain wavelength. After the metal material is irradiated by light with a certain wavelength, then the stopping potential voltage measurement is carried out on the metal material irradiated by light. From the variation of the light filter used, it is possible to calculate Planck's constant using the regression method with the value obtained 6.26x1034. This value is very close to the actual Planck's constant value which is 6.62x1034 so an error value is obtained 4.23%. With 95.77% accuracy, Planck's constant value obtained from the calculation using the linear regression method can be said to be very close to the actual Planck's constant value. © Published under licence by IOP Publishing Ltd.

关键词： Photoelectricity

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Text-to-Speech User Interface for ChatGPT

Text-to-Speech User Interface for ChatGPT

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Electronic and electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Alfa Faridh Suni Aryo Baskoro Utomo Khoiruddin Fathoni Budiandra Yusuf Mahendra Izzati Gemi Seinsiani Ahmad Fashiha Hastawan Computer Engineering Universitas Negeri Semarang Semarang Indonesia Electrical Engineering Universitas Negeri Semarang Semarang Indonesia Information Technology Education Universitas Negeri Semarang Semarang Indonesia English Literature Study Program Universitas Negeri Semarang Semarang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

The emergence of OpenAI's ChatGPT is paying a lot of attention to Generative AI and its impact on Academic integrity. Generative AI is a system designed to generate content or output such as text, image, audio, simulation, video and code based on their respective training data. ChatGPT itself is in the form of a chatbot where users can communicate with AI through dialogue applications via text. GPT 3 API is the technology behind the ChatGPT. However, interaction via text is not as flexible as dialogue using voice. Therefore, speech-to-text technology is needed to interface between the user and the AI. Chatbot AI powered by GPT3 with the optional voice UI powered by Google Text to Speech clearly benefits accessibility and usability. The output of dialogue between human users and AI can be done through text or sound. This research attempts to implement a Text to Speech and vice versa Speech to Text as a User Interface on a Gen-AI using the GPT 3 API so that it will be more user-friendly and accessible. This research opens many possibilities for human-AI interaction (HAX) in implementing virtual assistants. A seamless dialogue between humans and AI will be our goal in the future.

关键词： electrical engineering Generative AI Virtual assistants Training data Chatbots Text to speech Internet Usability Intelligent systems Speech to text

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Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

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Review of mayflies (Insecta Ephemeroptera) as a bioindicator of heavy metals and microplastics in freshwater

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Science of the Total Environment 2025年 958卷 178057页

作者： Benhadji, Nadhira Kurniawan, Setyo Budi Imron, Muhammad Fauzul Institute of Technology and Life Sciences—National Research Institute Falenty Hrabska Avenue 3 Raszyn05-090 Poland Department of Chemical and Process Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor UKM Bangi43600 Malaysia Study Program of Environmental Engineering Department of Biology Faculty of Science and Technology Universitas Airlangga Campus C UNAIR Jalan Mulyorejo Surabaya60115 Indonesia Department of Water Management Faculty of Civil Engineering and Geosciences Delft University of Technology Stevinweg 1 CN Delft2628 Netherlands

Heavy metal and microplastic pollutions are prevalent in freshwater ecosystems, with many freshwater bodies being contaminated by one or both of these pollutants. Recent studies reported extreme detections of Cd, Pb and Zn, high concentrations of Cr, Pb and Cu and microplastics acting as vectors of pollutants, including heavy metals. Mayflies can serve as bioindicators of heavy metal contamination in freshwater ecosystems because changes in their community structure, physiology, and behaviour can reflect and help predict the concentrations of metals in these environments. This review discusses the ecological alterations induced by tissue metal concentration in mayflies and other macroinvertebrates. As sensitive taxa to heavy metal contamination, mayflies can reflect the impacts of this pollution through their ethology and relationship to the substrate, highlighting issues such as eutrophication, alterations in community structure, inhibitory effects and sediment toxicity. Mayflies are also highly affected by microplastic exposure, which leads to ingestion, bioaccumulation, biomagnification, habitat and community alteration, behavioural changes, physiology alteration and toxicity. Mayflies bioindication metrics for assessing the impact of heavy metals and microplastics include the examination of community alteration, functional feeding behaviour, molecular structure, dietary and toxicity impacts, bioaccumulation and biomagnification and biomarkers. Current challenges for the utilization of mayflies as bioindicators include temporal variations in sensitivity, lack of universally recognised protocols and need for standardised protocols for microplastic analysis. Additionally, the applicability of mayflies as bioindicators may vary across different ecosystems, emphasising the need for selecting suitable indicators that align with the unique characteristics of the ecosystem. © 2024 The Authors

关键词： Macroinvertebrates

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Decision Tree for Boosting Firewall Accuracy: Optimizing Action Classification for Enhanced Cybersecurity

Decision Tree for Boosting Firewall Accuracy: Optimizing Act...

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International Conference on Business and Industrial Research (ICBIR)

作者： Prasert Teppap Wirot Ponglangka Prasert Luekhong Doctor of Engineering Program in Electrical Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand Faculty of Engineering Rajamangala University of Technology Lanna Chiang Rai Thailand College of Integrated Science and Technology Rajamangala University of Technology Lanna Chiang Mai Thailand

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

This study aims to categorize many “actions” types presented in the comprehensive dataset, known as “Internet Firewall Data.” We employed this dataset to train and assess several models, including KNN, NB, RF, DL, and DT, aiming to identify a highly accurate classification model for firewall log data. Through 10-fold cross-validation, we determined that the RF and DT models achieved robust performance, with accuracies of 99.86% and 99.85%, respectively. Moreover, the NB and DT models exhibited the quickest processing times, measured at 0.22 s and 0.35 s, respectively. These findings underscore the substantial impact of reducing the number of “actions” on the model's capacity for discernment and precision. This paper offers insights and recommendations valuable to both cybersecurity professionals and researchers, advocating for the consolidation of firewall action groups to enhance manageability. Simplifying firewall configurations not only bolsters security but also diminishes operational complexities.

关键词： Radio frequency Accuracy Firewalls (computing) Focusing Nearest neighbor methods Particle measurements Data models Internet Complexity theory Decision trees

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Twenty years of e-learning in health science: A bibliometric

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Procedia Computer Science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

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A Slot Antenna Array With Low Cross-Polarization Level Based on a Novel Substrate Integrated Coaxial Line Feeding Structure

A Slot Antenna Array With Low Cross-Polarization Level Based...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Shuhao Shen Kang Zhou Chunli Dong School of Electrical Information and Electronics Engineering Shanghai Jiao Tong University Shanghai China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

A slot antenna array with low cross-polarization level based on a novel substrate integrated coaxial line (SICL) feeding structure is proposed in this paper. The longitudinal slots are etched on the top layer as the radiation part, thus a broadside radiation characteristic can be achieved. Two sets of SICL are arranged on both sides to feed the quasi-substrate integrated double line (SIDL), and the length of the quasi-SIDL is adjusted to obtain the optimal slot etching scheme. A slot antenna array covering 27.1-27.6 GHz is designed and verified. The simulated results show that the peak gain is more than 8.6 dBi, while the cross-polarization level is better than 43.9 dB. The excellent radiation performance makes it a strong competitor for millimeter-wave (mm-wave) wireless applications.

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Low-Resolution Face Recognition for CCTV and Edge-Powered Smart Attendance Systems

Low-Resolution Face Recognition for CCTV and Edge-Powered Sm...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Nico Surantha Edward Yose Sani Muhamad Isa Department of Electrical Electronics and Communication Engineering Faculty of Science and Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Attendance systems have become more modern, and one of the biometric systems without physical contact is face recognition. However, many face-based attendance systems still carry out attendance individually and cannot detect multiple faces simultaneously. In addition, capturing facial data in real-time is still a challenge because the relatively large distance between the camera and the individual reduces the ability to recognize faces. The general solution is to use super-resolution to generate better-quality faces while maintaining the main facial recognition features. One technique still being researched is super-resolution generative adversarial networks (SRGAN). SRGAN can enlarge the resolution of captured images and maintain image quality sufficient for face recognition. The attendance system can be easily integrated into edge devices such as the Jetson Nano. This paper proposes automatic and effective attendance systems with the super-resolution technique to detect and recognize faces in low-resolution input. The experimental results show that using face data capture with a resolution of 40 × 40 pixels and a four-fold magnification results in a resolution of 160 × 160 pixels. Combining Face SRGAN with FaceNet architecture as the basis of face recognition can achieve an accuracy rate of 78.19% and an F1-Score of 81.13% with an average processing time of 1.61 seconds per frame on a PC and 14.55 seconds per frame on a Jetson Nano at an average of face recognition per frame of as many as up to 8 faces simultaneously.

关键词： Support vector machines Image quality Accuracy Face recognition Image edge detection Superresolution Generative adversarial networks

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