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International Conference on computer and Applications (ICCA)

作者： Fatma A. Madian Gamal A. Ebrahim Mostafa M. Salah Marwa A. Abdelaal Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Electrical Engineering Department Future University in Egypt Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%.

关键词： Crisis management computer viruses Simulation Decision making Time series analysis Organizations Predictive models Strategic planning Root mean square Long short term memory

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The Administration, Analysis, and Potential for Innovation of Big Data as it Relates to Present and Future uses in Healthcare

The Administration, Analysis, and Potential for Innovation o...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Somashekhara Reddy Santosh S Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore India

Vast volumes of info, or "big data," have amazing potential. Due of the immense potential it has, it has received particular attention during the last 20 years. To enhance the operations they deliver, several public and private companies produce, manage, and analyses big data. Official records, individual patient history, test findings, and world wide web of things-enabled devices are just a few of the big data sources used in the medical *** data pertinent to health care is also produced in great quantities by the physiological science field. To produce helpful information from this data, effective administration and assessment are necessary. Instead, trying to discover a remedy via big data analysis rapidly resembles trying to locate a haystack of needles. Every stage of processing large data comes with a unique set of difficulties that can only be overcome by adopting powerful processing technology for big data analysis. Medical practitioners must be fully provided with the architecture to regularly create and analyses big data in order to offer timely solutions for enhancing public wellness. By creating new opportunities for contemporary medical, effective big data administration, analysis, and interpretations may completely alter the game. That is precisely the reason why a variety of sectors, including that of the health care industry, are moving aggressively to transform this capability into improved financial and service benefits. The medical treatments and customised medicine may be revolutionized by contemporary healthcare organizations with a logic and facts of health care and biomedical data.

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Breast Cancer Detection in Mammogram Images using Machine Learning Methods and CLAHE Algorithm 5

Breast Cancer Detection in Mammogram Images using Machine Le...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Srivastav, Gaurav Rashid, Mamoon Singh, Richa Gehlot, Anita Sharma, Neha Computer Science & Engineering Datta Meghe Institute of Higher Education & Research Maharastra India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Computer Science & Engineering Krishna Institute of Engineering & Technology Gaziabad India Uttaranchal University Uttaranchal Institute of Technology Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350398267

Breast cancer is one of the most common cancer types. This is the second-leading cause of cancer-related death in women. It ranked 2nd according to available data lung cancers is the only one causing more causalities. It's critical to receive a breast cancer diagnosis quickly. The MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one's precision, recall, accuracy, and F-score. The analysis's findings were higher than 69.88%. © 2022 IEEE.

关键词： Machine learning

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Performance Impact of Immersion and Collaboration in Visual Data Analysis

Performance Impact of Immersion and Collaboration in Visual ...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Daniel Garrido João Jacob Daniel Castro Silva Department of Informatics Engineering Faculty of Engineering University of Porto and Artificial Intelligence and Computer Science Laboratory

Immersive Analytics is a recent field of study that focuses on utilizing emerging extended reality technologies to bring visual data analysis from the 2D screen to the real/virtual world. The effectiveness of Immersive Analytics, when compared to traditional systems, has been widely studied in this field’s corpus, usually concluding that the immersive solution is superior. However, when it comes to comparing collaborative to single-user immersive analytics, the literature is lacking in user studies. As such, we developed a comprehensive experimental study with the objective of quantifying and analysing the impact that both immersion and collaboration have on the visual data analysis process. A two-variable (immersion: desktop/virtual reality; number of users: solo/pair) full factorial study was conceived with a mixed design (within-subject for immersion and between subject for number of users). Each of the 24 solo and 24 pairs of participants solved five visual data analysis tasks in both a head-mounted display-based virtual world and a desktop computer environment. The results show that, in terms of task time to completion, there were no significant differences between desktop and virtual reality, or between the solo and pair conditions. However, it was possible to conclude that collaboration is more beneficial the more complex the task is in both desktop and virtual reality, and that for less complex tasks, collaboration can be a hindrance. system Usability Scale scores were significantly better in the virtual reality condition than the desktop one, especially when working in pairs. As for user preference, the virtual reality system was significantly more favoured both as a visual data analysis platform and a collaborative data analysis platform over the desktop system. All supplemental materials are available at https://***/k94u5/.

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Quantum Channel Characteristics from the Point of View of Stability 28

Quantum Channel Characteristics from the Point of View of St...

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28th International Conference on Information, Communication and Automation Technologies, ICAT 2022

作者： Lauterbach, Filip Burdiak, Patrik Rozhon, Jan Dervisevic, Emir Slivova, Martina Plakalovic, Matej Mehic, Miralem Voznak, Miroslav Vsb - Technical University of Ostrava Department of Telecommunications Faculty of Electrical Engineering and Computer Science Ostrava708 00 Czech Republic University of Sarajevo Department of Telecommunications Faculty of Electrical Engineering Sarajevo71000 Bosnia and Herzegovina

ISBN: (纸本)9781665466929

The article presents a series of measurements conducted on the fully-operated Quantum Key Distribution system. These measurements primarily focus on the Quantum Bit Error Rate (QBER), which is the most important parameter of the quantum channel. This parameter was observed and measured for 16 days under the quantum channel's operating conditions to determine any correlations between the QBER and other quantum link parameters, such as secret key rate. A thorough statistical analysis of the measured data was performed as a part of this investigation and is presented in the paper. © 2022 IEEE.

关键词： Bit error rate

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Indoor Air Quality Monitoring system for Healthcare Facilities 7th

Indoor Air Quality Monitoring System for Healthcare Faciliti...

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7th International Conference on Advancements of Medicine and Health Care through Technology, MEDITECH 2020

作者： Puscasiu, Adela Fanca, Alexandra Valean, Honoriu Gota, Dan-Ioan Stan, Ovidiu Roman, Nicolae M. Clitan, Iulia Muresan, Vlad Faculty of Automation and Computer Science Department of Automation Technical University of Cluj Napoca Cluj Napoca Romania Faculty of Electrical Engineering Electrical Engineering and Measurements Department Technical University of Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9783030935634

The human body is not able to detect whether the air is polluted or not, which is why it is important to have a device that can measure the air quality, both in the closed environment and in nature. Thus, air pollution is defined as a harmful phenomenon to the ecological system and to the normal conditions of human existence and development when certain substances in the atmosphere exceed a certain concentration. The paper proposes an indoor air monitoring system for healthcare facilities, using an Arduino Mega2560 board, temperature sensor, humidity sensor, eCO2 (equivalent CO2 which designates global warming potential), TVOC (Total Volatile Organic Compounds) and atmospheric pressure sensor. The technologies used for the software implementation are Arduino IDE for the acquisition of data from sensors and Visual Studio for the graphical interface implementation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Air quality

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Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

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computers, Materials & Continua 2023年第6期75卷 5213-5228页

作者： Sobia Nawaz Sidra Rasheed Wania Sami Lal Hussain Amjad Aldweesh Elsayed Tag eldin Umair Ahmad Salaria Mohammad Shahbaz Khan Basic Health Unit Sumra Lodhran59320Pakistan Basic Health Unit 20-G ChishtianBahawalnagar62300Pakistan Doctor’s Hospital Lahore54590Pakistan Department of Computer Science&IT Neelum CampusThe University of Azad Jammu and KashmirAthmuqam13230Azad KashmirPakistan Department of Computer Science&IT King Abdullah CampusThe University of Azad Jammu and KashmirMuzaffarabad13100Azad KashmirPakistan College of Computer science and information technology Shaqra UniversityShaqra15273Saudi Arabia Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Department of Electrical Engineering University of Azad Jammu and KashmirChehla CampusMuzaffarabad13100Azad KashmirPakistan Department of Electrical Engineering Mirpur University of Science&TechnologyMirpurMuzaffarabad10250Azad KashmirPakistan Children’s National Hospital 111 Michigan AVE NWWashingtonDC20854USA

This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of *** this study,AI-based analysis tools were developed that can precisely classify COVID-19 lung *** available datasets of COVID-19(N=1525),non-COVID-19 normal(N=1525),viral pneumonia(N=1342)and bacterial pneumonia(N=2521)from the Italian Society of Medical and Interventional Radiology(SIRM),Radiopaedia,The Cancer Imaging Archive(TCIA)and Kaggle repositories were taken.A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was ***,the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning(ML)algorithms,which twice trained the learning *** ResNet101 with optimized parameters yielded improved performance to default *** extracted features from ResNet101 are fed to the k-nearest neighbor(KNN)and support vector machine(SVM)yielded the highest 3-class classification performance of 99.86%and 99.46%,*** results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of *** proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

关键词： COVID-19 deep learning(DL) lung infection convolutional neural network(CNN)

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Transcription for Thai Traditional Medicine Texts with Wav2Vec2

Transcription for Thai Traditional Medicine Texts with Wav2V...

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International Electrical engineering Congress (iEECON)

作者： Jettasic Popun Wilaiporn Lee Akara Prayote Kanabadee Srisomboon Luepol Pipanmekaporn Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Recently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Thai Traditional Medicine (TTM) contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Thai Traditional Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this paper, we propose an end-to-end speech recognition approach for Thai Traditional Medical texts leveraging self-supervised learning (SSL) to overcome the limitations of limited labeled data. We utilize a transformer-based model pre-trained (XLSR-Wav2Vec) on unlabeled TTM texts speech data using SSL techniques and fine-tune it with a small dataset of labeled TTM texts audio-text pairs. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 1,800 training sentences and 200 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Thai Traditional Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations.

关键词： Training Surveys Vocabulary Accuracy Terminology Self-supervised learning Speech enhancement Transformers Automatic speech recognition Speech to text

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The Design and implementation of a double layered Security Network with an Authentication system through Block chain Technology 3

The Design and implementation of a double layered Security N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shakeel, Mohammed Krishnammal, P. Muthu Vekariya, Vipul Akram, Shaik Vaseem Kumar, Parvathapuram Pavan Kapila, Dhiraj Invertis University Faculty of Computer Applications Bareilly India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravati India Parul University Department of PIET India Uttaranchal University Uttaranchal Institute of Technology India St. Martin's Engineering College Department of ECE Telangana Secunderabad India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

With more and more connected devices joining IoT networks, protecting their communications is becoming more difficult. The distributed ledger technology known as blockchain has the potential to solve the security issues plaguing 5G-enabled IoT networks. This article provides a layered blockchain security strategy to protect Internet of Things networks and make their deployment simpler. Using clustering, the complex multi-layer structure is simplified. The K-unknown clusters in the IoT network are generated by use of a hybrid Evolutionary Computation Methodology involving Simulated Annealing and Genetic Algorithms. The chosen cluster chiefs have authority over local authentication and permissions. By creating a private blockchain network on a local level, cluster administrators and critical nodes can streamline their interactions. This type of blockchain also facilitates authentication on a network, but it also adds an extra layer of security and integrity. The proposed framework makes use of Hyperledger Fabric, an open-source implementation of Distributed ledger technology. The encryption of data transmitted between nodes is ensured by the use of a global distributed ledger technology, which is instated in the base stations. As opposed to previously disclosed methods, simulation results show that the proposed clustering algorithm is effective. Finally, it is shown that the envisaged portable cryptocurrency architecture achieves a better latency/capacity tradeoff than a conventional global blockchain. © 2023 IEEE.

关键词： Blockchain

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An Intelligent Innovation of Electrochemical Energy Based Smart Industrial Automation

An Intelligent Innovation of Electrochemical Energy Based Sm...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： N. Shantha Kumar Karthikeyan S Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore India

Electrochemical energy based smart industrial automation uses electrochemical energy generated from chemical reactions to power and control industrial processes. This energy is generated through the use of electrolyte and electrodes, which convert chemical energy into electrical energy. This energy can then be used to power and control industrial machines, such as industrial robots and automated assembly lines. Additionally, this energy can be used to monitor, regulate, and control processes, such as temperature and pressure, within an industrial environment. This technology offers numerous advantages over traditional methods of powering and controlling industrial operations. Firstly, it is more efficient and cost effective than traditional methods. Secondly, it has the potential to reduce energy consumption, improve efficiency, and increase productivity. Finally, it is environmentally friendly, as it does not produce harmful emissions or waste materials. The use of electrochemical energy based smart industrial automation is the way of the future for industrial operations. The potential of this technology is immense, and the advantages it can bring to industrial operations are numerous. With the right implementation and application, this technology has the potential to revolutionize how industrial processes are operated and managed.

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