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7th IEEE information Technology International Seminar, ITIS 2021

作者： Pratama, Arista Ridwandono, Doddy Suryanto, Tri Lathif Mardi Safitri, Eristya Maya Khusna, Asmaul University of Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia

ISBN: (纸本)9781665408073

Smartphone technology in health is critical, especially in improving health services quality and quality. The use of smartphone technology in health services is called Mobile Health (M-Health). The application of M-health is one solution to improve the quality of public health services. The application of M-Health will make it easier for users to obtain health information and services. JKN Mobile is an M-Health application that provides health insurance services managed by the Indonesian government. The JKN Mobile application has several features such as documentation of medical records, viewing the number of bills to be paid, updating participant data, and conducting online doctor consultations. This study aims to analyze the service quality factors that affect satisfaction and continuous use of the JKN Mobile Application using the Service Quality of M-Health Model. This study uses quantitative methods by analyzing data using Structural Equation Modeling (SEM) involving 100 respondents using the JKN Mobile Application. The results showed that the variables of interaction quality and information quality as dimensions of service quality significantly influenced user satisfaction. information quality has a significant influence on sustainable use. Monetary cost does not significantly affect user satisfaction and continuous use of the JKN Mobile Application. © 2021 IEEE.

关键词： mHealth

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Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya Mohamed K.Nour Ahmed S.Salama Mohamed I.Eldesouki Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)*** presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

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Clustering Mechanism in Particle Swarm Optimization Algorithm for Data Aggregation

Clustering Mechanism in Particle Swarm Optimization Algorith...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Sharmin Sharmin Ismail Ahmedy Rafidah Md Noor Habibah Ismail Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Computer Science Unit Centre of Foundation Studies Univesrsiti Teknologi MARA Dengkil Selangor Malaysia

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes with distinct resources working together to achieve a common objective efficiently. A critical issue is extending the network lifetime of WSNs, especially for data transfer, as sensor nodes depend on their internal batteries to carry out essential functions and communicate with one another. Efficient energy use per sensor is essential for ensuring the overall energy efficiency of the network. Clustering has been the most energy-efficient approach but lacking a method for selecting the most effective cluster head results in decreased data aggregation efficiency, leading to the increased power consumption of sensor nodes. The project aims to enhance energy efficiency and extend network service life by using Particle Swarm optimization (PSO) for clustering in WSNs. PSO is used to select the cluster head based on member node distance from the base station and remaining energy. The proposed PSO technique was tested against Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Zonal Stable Election Protocol Z-SEP, and Extended Stable Election Protocol (EZ-SEP) and found to be more effective in reducing energy usage and prolonging the network’s lifespan. The recommended technique can extend the service life of WSNs and reduce energy consumption, ultimately leading to more cost-effective and eco-friendly solutions for various applications such as environmental monitoring, surveillance, and healthcare.

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Long Short-Term Memory and Word Embedding For Sentiment Analysis of User Review

Long Short-Term Memory and Word Embedding For Sentiment Anal...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Lia Silviana Erna Budhiarti Nababan Muhammad Zarlis Faculty Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information System Bina Nusantara University Jakarta Indonesia

Accuracy in training sentiment analysis models for large number of review datasets is affected by the correct classification of sentiment labels. Improving the accuracy of sentiment labels, and text representation also affects the performance of sentiment analysis models. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis, it is necessary to use the right labeling method and good text representation to be used as an embedding layer. This study proposes sentiment labeling using Lexicon and Long Short-Term Memory (LSTM) as well as used FastText as embedding words in sentiment classification. As a corpus, the InSet Lexicon Dictionary is employed for feature extraction. The sentiment data used is the reviews given by users on several applications provided on Google Play. The results showed that the LSTM network using Word-embedded FastText with a dimension of 300 words received a small error value of 0.111 with an accuracy of 95.55% for data labeled based on Lexicon.

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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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Intelligent Automation & Soft Computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

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Analyzing public perceptions toward COVID-19 Vaccination process using social media and machine learning 2021

Analyzing public perceptions toward COVID-19 Vaccination pro...

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7th International Conference on Arab Women in Computing, ArabWIC 2021

作者： Elhishi, Sara El-Deeb, Reem El-Gamal, Fatma El-Zahraa Ahmed Sak, Nehal A. El-Metwally, Sara Information System Department Faculty of Computers and Information Mansoura University Dakahlia Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Dakahlia Mansoura Egypt

ISBN: (纸本)9781450384186

Since the declaration of COVID-19 as a global pandemic, the world is disrupting socially and economically. COVID-19 vaccines are still in the human trials and the governments should understand the society attitudes towards the vaccinations acceptance/hesitancy in order to deliver more accurate plans and health messages. Social media represents a catalog of our daily-life communications and activities. In this paper, we utilized the power of social media and machine learning to gain insights and understand the public attitudes towards COVID-19 vaccinations. The peak of online vaccination conversation on a social media happened with the authorization of Moderna and Pfizer vaccines for the emergency usage. The sentiment analysis of the clustered tweets relevant to the vaccination topic reveals that most of the public opinions was neutral and target the understanding of the vaccination process, confirming its efficiency and safety, and the countries plans to distribute and secure doses for their residents. The second top sentiment analysis group has negative attitudes according to spreading claims of the vaccines productions, side effects, and the previously reported vaccinations trails in different historical pandemic periods. The reported analysis raised the emergency need for interactive communications with communities from different cultural and educational level to increase their vaccination awareness and validate the vaccines' associated news. The decision makers' deliverable speech should simplify the scientific terms, target the community fears and release any public concerns regarding the vaccination process and its distribution plans. © 2021 Association for Computing Machinery.

关键词： COVID-19

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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments

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Alexandria Engineering Journal 2025年 127卷 239-250页

作者： Alzakari, Sarah A. Aljebreen, Mohammed Asiri, Mashael M. MANSOURI, Wahida Alahmari, Sultan Alqahtani, Mohammed Sorour, Shaymaa Bedewi, Wafi Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Community College King Saud University P.O. Box 28095 Riyadh11437 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and arts Turaif Northern Border University Arar91431 Saudi Arabia Cybersecurity Institute Riyadh Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Management Information Systems School of Business King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Smart cities have attracted extensive coverage from multidisciplinary studies, and many artificial intelligence (AI) solutions have been designed. Conversely, cybersecurity has constantly been a crucial issue and is becoming gradually dangerous in smart cities. The attack defence models are inappropriate for perceiving multistep assaults as the recognition rules are restricted, and efficacy is partial due to many false security alarms. Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). The foremost intention of this study is to improve cybersecurity detection and classification in smart city environments. To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. Primarily, data normalization utilizing min-max normalization is implemented. Next, the CCPOA-HDLM method utilizes ISSA based feature selection method to select optimum features. The CCPOA-HDLM technique employs a hybrid of the MCNN-RNN model for the cybersecurity detection and classification process. Moreover, the hyperparameter range of the hybrid of DL techniques occurs using the CPO technique. The experimental validation of the CCPOA-HDLM approach is performed on the UNSW-NB15 dataset, and the outcome portrayed a superior accuracy value of 99.04 % over other recent approaches under various measures. © 2025 The Authors

关键词： Convolutional neural networks

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A comparative analysis of MQTT and IoT application protocols 2

A comparative analysis of MQTT and IoT application protocols

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2nd IEEE International Conference on Electronic Engineering, ICEEM 2021

作者： Al Enany, Marwa O Harb, Hany M. Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Al-Azhar University Faculty of Engineering Systems and Computers Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Choosing the appropriate IoT protocol for a certain application was and still a critical issue for IoT applications, as protocols vary in behavior in different applications and under different network status. IoT application protocols have a recent focus of attention especially those protocols for the low-powered IoT applications and constrained devices. A brief survey of IoT layers and applied protocols for each layer and their applications were presented, besides, a detailed survey of IoT protocols with a presentation of the main researches that have been done to evaluate their performance. As a use case for this comparative study, the MQTT protocol was chosen because of its simplicity and its publish /subscribe model to be evaluated and compared against other application protocols. © 2021 IEEE.

关键词： Surveys

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Prediabetes Detection Using Non-Laboratory Data with Extreme Gradient Boosting Algorithm

Prediabetes Detection Using Non-Laboratory Data with Extreme...

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International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Muhammad Qurhanul Rizqie Iche A Liberty Indri S Septadina Pacu Putra Dian Palupi Rini Department of Informatics Faculty of Computer Science Universitas Sriwijaya South Sumatera Indonesia Department of Public Health and Community Medicine Faculty of Medicine Universitas Sriwijaya South Sumatera Indonesia Department of Anatomy Faculty of Medicine Universitas Sriwijaya South Sumatera Indonesia Department of Information System Faculty of Computer Science Universitas Sriwijaya South Sumatera Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Diabetes Mellitus (DM), characterized by high blood glucose levels, is a dangerous condition that leads to severe long-term health issues. Meanwhile, prediabetes is a condition where individuals have an increased risk of developing type 2 DM. Early detection of prediabetes is crucial for effective DM management. Traditional detection relies on medical records and family history, but laboratory tests are not always practical. This study proposes using non-laboratory data, processed by the Extreme Gradient Boosting (XGBoost) algorithm, to identify individuals with prediabetes. The dataset used in this research includes attributes like blood pressure, weight, height, waist size, food intake, and age. Preprocessing involved removing irrelevant data and transforming numeric data into categories. The XGBoost model, applied to this dataset, achieved 0.91 for average accuracy, precision, recall, and F1-score. AUC values of the model is 0.93, demonstrating its effectiveness in distinguishing between normal and prediabetic individuals. These results suggest the potential for non-laboratory data and XGBoost in early prediabetes detection, aiding in timely intervention and improved health outcomes.

关键词： Measurement Accuracy Hospitals Boosting Prediction algorithms Numerical models Diabetes History Informatics Testing

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