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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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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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Challenges in Mobile Network Capacity Monitoring & Solutions to Improve Capacity Problems Using Various Parameters

Challenges in Mobile Network Capacity Monitoring & Solutions...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Vikas Gupta Ashish Kumar Chakraverti Manoj Kumar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Faculty of Engineering and Information Science University of Wollongong Dubai UAE

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper has an overview on study of traffic capacity of Mobile network parameter that affect the network utilization and network experience in Mobile network era., This capacity study & analytics provide a conception of how the various networks function, capacity parameters & constraints of different domain of Mobile Network like RAN (Radio access Network),CN (Core Network), IP & TX (Internet Protocol & Transport Network) & their associated capacity parameter have correlations in network utilization. It also has the access of Mobile Network KPIs detail, for readers to easily retrieve a variety of capacity parameters for effective capacity management. In order to manage end to end capacity requirement in telecom domain & can help for solving capacity problem with the help of various network parameters. These various capacity parameters can be used to generate systematic, rule-based decision which are standardized, more flexible, and faster to take decision on capacity expansion well in advance through the parametric behaviour. furthermore, the review concludes the published work their gaps and future work in the direction of improving Mobile network capacity management.

关键词： Systematics Protocols Correlation Reviews Internet Resource management IP networks Monitoring Radio access networks

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kumar, Brajesh Agrawal, Prateek Uike, Dipesh Lourens, Melanie Bhati, Indira Peter, M. Chandrakumar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India India Dr. Ambedkar Institute of management studies and research Maharashtra Nagpur India Durban University of Technology Faculty of Management Sciences South Africa Department of Management India PeriyarManiammai Institute of Science and Technology Department of Software Engineering Tamilnadu Thanjavur India

ISBN: (纸本)9798331543617

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems. © 2024 IEEE.

关键词： Predictive analytics

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Study of Photonic Platforms for On-Chip Refractive Index Sensing

Study of Photonic Platforms for On-Chip Refractive Index Sen...

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Photonics North

作者： Raghi El Shamy Mohamed A. Swillam Xun Li Department of Electrical and Computer Engineering Faculty of Engineering McMaster University Hamilton Canada Department of Physics School of Science and Engineering The American University in Cairo New Cairo Egypt

ISBN: (数字)9798350366365

ISBN: (纸本)9798350366372

Here we present a rigorous study for the different integrated photonic platforms that can be used for on-chip refractive index (RI) sensing. The study includes the widespread silicon photonics platform, the silicon nitride platform and the silica platform. We compare these three platforms according to five parameters that determine the performance and reliability of the RI sensor. Finally, we conclude with the optimum platform for on-chip RI sensing according to the available technology and the aimed application.

关键词： Refractive index Silicon nitride Silicon photonics System-on-chip Sensors Reliability

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Semantic Search Engine, Thai Traditional Medicine Knowledge Graph Application

Semantic Search Engine, Thai Traditional Medicine Knowledge ...

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International Electrical engineering Congress (iEECON)

作者： Natthinan Sakulpakdee Nattavee Narischat Kanabadee Srisomboon Wilaiporn Lee Luepol Pipanmekaporn Akara Prayote Department of Computer and Information Science Faculty of Apply 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

In this paper, a semantic information retrieval framework is presented to improve the precision of search results by concentrating on the context of concepts is presented. Instead of the keyword matching technique, the graph theory is used for leveraging the relationships and meanings of search terms. Information is represented as a knowledge graph for semantic searching performed by the inference engine and results are passed to the NLP technique for interpreting entity, relationship and property to their relevancy query. In the current system, we focused on healthcare system to helping the foundation recommendations of Thai Traditional Medicine. Once Thai Traditional Medicine contains a numerous of words which have the same meaning, the traditional keyword based cannot retrieve all relevant document. In our proposed semantic architecture, the query is performed an context-based understanding for retrieving all relevant document, therefore, it can retrieve the same number of documents for the queries which have the same meaning.

关键词： Knowledge engineering Electrical engineering Accuracy Semantic search Keyword search Knowledge graphs Medical services Information retrieval Graph theory Engines

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An Innovation Development of Decentralized Prove Piercing Data Recuperation Using Improved Red in Mobile Grid Computing

An Innovation Development of Decentralized Prove Piercing Da...

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

作者： Karthikeyan S Merin Thomas Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore India

The recent advancements in mobile computing have opened up the possibilities of decentralized data recovery in mobile grid computing. With the help of improved Red (Recovery of Erased Data) technique, data recovery can be done in a decentralized manner. This method is a combination of distributed computing, data replication, and data recovery algorithms which improves the data recovery process. The improved Red technique uses distributed computing to replicate the data and store it in multiple nodes in the grid. This helps to ensure that the data is distributed and available in multiple nodes for recovery. Data recovery is done by the Red algorithm which traverses the grid and identifies the nodes which have the data and then recovers it. The algorithm uses a technique called "greedy search" to identify the nodes which have the data. This helps to reduce the time and cost associated with the recovery process. The improved Red technique also makes use of data replication which helps to improve the data recovery process. The data is replicated and stored in multiple nodes which helps to improve the speed of data recovery. The replication process helps to ensure that the data is always available and can be recovered quickly. The improved Red technique also makes use of distributed computing to improve the scalability of the data recovery process. The distributed computing helps to ensure that the data recovery process can be done efficiently and quickly. The data recovery process can also be done on a large scale which helps to improve the speed of data recovery.

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Current Trends in Automated Test Case Generation 18

Current Trends in Automated Test Case Generation

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Potuzak, Tomas Lipka, Richard Department of Computer Science and Engineering/ Ntis - New Technologies for Information Society Faculty of Applied Sciences University of West Bohemia Univerzitni 8 European Center of Excellence Plzen306 14 Czech Republic European Center of Excellence/ Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitni 8 Ntis - New Technologies for the Information Society Plzen306 14 Czech Republic

ISBN: (纸本)9788396744784

The testing is an integral part of the software development. At the same time, the manual creation of individu-al test cases is a lengthy and error-prone process. Hence, an intensive research on automated test generation methods is ongoing for more than twenty years. There are many vastly dif-ferent approaches, which can be considered automated test case generation. However, a common feature is the generation of the data for the test cases. Ultimately, the test data decide the prog-ram branching and can be used on any testing level, starting with the unit tests and ending with the tests focused on the behavior of the entire application. The test data are also mostly independent on any specific technology, such as programming language or paradigm. This paper is a survey of existing literature of the last two decades that deals with test data generation or with tests based on it. This survey is not a systematic literature review and it does not try to answer specific scientific questions formulated in advance. Its purpose is to map and categorize the existing methods and to summarize their common features. Such a survey can be helpful for any teams developing their methods for test data generation as it can be a starting point for the exploration of related work. © 2023 Polish Information Processing Society.

关键词： Software testing

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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An Intelligent Data Analytics Approach for Crisis Management in Healthcare Systems

An Intelligent Data Analytics Approach for Crisis Management...

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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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