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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Abuain, Tarik Abuain, Waleed AbdelKarim Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia Taibah University College of Science and Computer Engineering Yanbu Al-Madinah Al-Munawwarah Saudi Arabia

ISBN: (纸本)9798350367492

The paper uses the SOAR analysis paradigm to investigate the impact of Large Language Models (LLMs) such as ChatGPT on crucial healthcare industry stakeholders. The background of the study highlights the growing application of LLMs in healthcare, aiming to enhance diagnostic accuracy, patient engagement, and educational outcomes. The method involves conducting a SOAR (Strengths, Opportunities, Aspirations, Results) analysis to assess the influence of LLMs on healthcare providers, management, patients, and medical students. The objective is to understand how LLMs can transform healthcare by identifying strengths, opportunities, and aspirations and utilizing these insights for strategic planning. The results show that LLMs integration improves efficiency, decision-making, patient engagement, and educational outcomes, although challenges that require further investigation persist. The conclusion emphasizes the need for careful implementation and ongoing research to fully harness the potential of LLMs in revolutionizing the healthcare industry. © 2024 IEEE.

关键词： Strategic planning

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Application of Blockchain Technologies to Improve the Quality of Microgrid Network Management 5

Application of Blockchain Technologies to Improve the Qualit...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Khilenko, Volodymyr Bielokha, Halina Denysiuk, Sergii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9798350339239

The use of blockchain technologies and Smart contracts to improve the quality of Microgrid network management is considered. The necessity of using a special mathematical apparatus for system analysis is noted, taking into account the nonlinear nature of dynamic processes in the network and, in particular, the presence of sharply changes in the load. The advantages and disadvantages of the approach known in the literature are analyzed, when the search for the optimal power distribution among generators to ensure the minimum costs of the Microgrid is carried out on the basis of nonlinear programming methods. Graphs of transient processes in the network are given and their corresponding analysis is carried out. © 2023 IEEE.

关键词： Smart contract

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K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

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computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

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The Deep Learning-based Intelligent System for Extracting Information from e-KTP 2

The Deep Learning-based Intelligent System for Extracting In...

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2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Fuadi, Zaki Muchtar, Kahlil Subianto, Muhammad Riza, Hammam Oktiana, Maulisa Nizamuddin Universitas Syiah Kuala Department of Informatics Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Jakarta10340 Indonesia

ISBN: (纸本)9798350317503

This research implements an Intelligent Information System for Text Recognition on Indonesian Identity Cards (eKTP) based on Deep Learning architecture. The aim of this research is to develop a system capable of accurately and efficiently detecting and recognizing text on e-KTP images. The first phase of this research involves selecting ideal images from the e-KTP dataset. Subsequently, pre-processing is conducted to crop the image sides and merge them with background images to create a more diverse dataset and obtain the e-KTP image masks as training data. After selecting the ideal images, the total training data amounts to 144 images. The U-Net architecture is chosen as the deep learning method for image segmentation in this research, while for text detection and recognition, two neural networks, CRAFT and TRBA (TPS-ResNet-BiLSTM-Attention), are employed. The testing, based on accuracy values, Dice coefficients, and IoU for the segmentation process, results in a U-Net model accuracy of 99.52%. Text detection and recognition follow confidence values. © 2024 IEEE.

关键词： Intelligent systems

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Design and construction of a medium chamber for a tissue bioreactor system 22

Design and construction of a medium chamber for a tissue bio...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Kolembusova, Petra Ferencik, Norbert Steingartner, William Hudak, Radovan Sedlakova, Veronika Stefanovic, Branko Technical University of Košice Dept. of Biomedical Engineering Košice Slovakia Technical University of Košice Dept. of Computer and Informatics Košice Slovakia

ISBN: (纸本)9798350317206

In the field of tissue engineering and regenerative medicine, understanding the intricate properties and behaviors of cells and tissues within their natural environment is fundamental for translating theory into practical applications. This scientific article introduces the development of a functional medium chamber. The design of this chamber was driven by the need to align it with the volume requirements and specified conditions of bioreactor systems. The objective was to acquire new practical and theoretical insights in the realms of tissue engineering, 3D printing, and bioreactor technology. The emphasis throughout the project was on simplicity in design, with the aim of enabling easy construction and disassembly of the model. This initiative not only involved the creation of a chamber design but also encompassed the 3D printing process and extended functional testing over the long term. The design parameters, including the closing mechanism and chamber shape, were thoughtfully selected to enhance versatility, making the chamber adaptable for various bioreactor systems in the future. Subsequently, the printed chamber underwent rigorous testing using water as the culture medium, laying the groundwork for potential advancements in bioreactor technology and tissue cultivation. © 2024 IEEE.

关键词： Bioreactors

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

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computer Systems Science & engineering 2023年第11期47卷 2513-2528页

作者： Adil Hussain Kashif Naseer Qureshi Khalid Javeed Musaed Alhussein Department of Transportation Engineering Chang’an UniversityXi’anChina Department of Electronic&Computer Engineering University of LimerickLimerickV94 T9PXIreland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

Information and communication technologies are spreading rapidly due to their fast proliferation in many *** number of Internet users has led to a spike in cyber-attack incidents.E-commerce applications,such as online banking,marketing,trading,and other online businesses,play an integral role in our *** Intrusion Detection System(NIDS)is essential to protect the network from unauthorized access and against other *** existing NIDS systems are based on the Backward Oracle Matching(BOM)algorithm,which minimizes the false alarm rate and causes of high packet drop *** paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and *** address the existing system issues,this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network *** proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD *** experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17%detection rate and a 0.22%lower false alarm rate than the existing solution.

关键词： E-commerce NIDS security algorithm network applications CIA detection

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A Procedure with MySQL Python for Developing Data Warehouse and Analytics Using Data from a Greek Soft Drinks Company

A Procedure with MySQL Python for Developing Data Warehouse ...

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International Conference on Applied Mathematics & computer Science (ICAMCS)

作者： Sotirios Tsakiridis Alkiviadis Tsimpiris Athanasios Angeioplastis Nikolaos Papaioannou Paris Mastorocostas Dimitrios Varsamis Dept. Computer Informatics and Telecommunications Engineering International Hellenic University Serres Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798350363807

ISBN: (纸本)9798350363814

The research focuses on the process of creating a data warehouse to meet the decision-making needs of a Greek beverage company. The data cover the period from 2018 to 2022. The developed data warehouse schema follows the star schema, with one fact table and five-dimension tables. Based on this structure, a web application was developed in PHP, which displays reports, charts, and analyses on the company's performance through interactive queries. Additionally, prediction functions tailored to the company's data were developed in Python. The process followed, the queries for creating the data warehouse, as well as the commands for creating analyses and predictions, serve as a useful guide for other companies that wish to upgrade the capabilities of their ERP systems.

关键词： Costs Data analysis Profitability Decision making Stars Companies Production Data warehouses Strategic planning Market research

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Diesel Engine Fault Detection using Deep Learning Based on LSTM 7

Diesel Engine Fault Detection using Deep Learning Based on L...

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7th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2023

作者： Naryanto, Rizqi Fitri Delimayanti, Mera Kartika Naryaningsih, Agustien Warsuta, Bambang Adi, Rizky Setiawan, Bima Aji Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia National Research and Innovation Agency Fisheries Reseach Center Indonesia Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia

ISBN: (纸本)9798350385526

This work aims to create a deep learning model utilizing Long Short-Term Memory (LSTM) as a classification model to detect and diagnose potential problems in diesel engines. The default dataset comprises 3,500 data entries and four distinct dataset types. White noise in the dataset is attributed to additive Gaussian white noise (AWGN). The main contributions of this study comprise developing a classification model aimed at identifying damage in diesel engines, as well as evaluating the performance of the Long Short-Term Memory (LSTM) method. The K-Fold cross-validation technique was utilized, where the value of k was set at 5. This study examines the damage classification in diesel engines using four datasets: default 0dB Noise, 15dB Noise, 30dB Noise, and 60dB Noise. The K-Fold Cross Validation methodology was employed, utilizing a value of k=5. The training and validation processes were conducted iteratively, employing a learning rate of 0.00005 during the training phase. The training phase utilized a mini-batch size of 128, and the maximum number of epochs was set at 10,000. The results indicate that the dataset containing 60dB noise consistently demonstrates the highest level of accuracy, with an average accuracy of 97.91% across the five folds and the efficacy of Long Short-Term Memory (LSTM) models in classification tasks is improved in the presence of white noise. © 2023 IEEE.

关键词： Long short-term memory

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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