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Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

computers, Materials & Continua 2022年第1期70卷 1017-1032页

作者： Akashdeep Bhardwaj Ahmed A.Mohamed Manoj Kumar Mohammed Alshehri Ahed Abugabah School of Computer Science University of Petroleum&Energy Studies(UPES)Dehradun248007India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Technology Faculty of Computer and InformationAssiut UniversityAssiut71515Egypt Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Information System College of Technological InnovationZayed UniversityAbu DhabiUnited Arab Emirates

The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple *** have shut their borders,and their nationals have been advised to *** variety of responses to the pandemic has given rise to data privacy *** prevention and control strategies as well as disease control measures,especially real-time contact tracing for COVID-19,require the identification of people exposed to *** tracing frameworks use mobile apps and geolocations to trace ***,while the motive may be well intended,the limitations and security issues associated with using such a technology are a serious cause of *** are growing concerns regarding the privacy of an individual’s location and personal identifiable information(PII)being shared with governments and/or health *** study presents a real-time,trust-based contact-tracing framework that operateswithout the use of an individual’sPII,location sensing,or gathering GPS *** focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the *** research validates the trust-based framework using Bluetooth as practical and *** our proposed methodology,personal information,health logs,and location data will be secure and not *** research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

关键词： Privacy contact tracing mobile apps Bluetooth Covid epidemic

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Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications 40

Performance Analysis of IDS_MDL Algorithm to Predict Intrusi...

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40th National Radio Science Conference, NRSC 2023

作者： Mahmoud, Walaa Adel Fathi, Mohamad El-Badawy, Hesham Sadek, Rowayda Coll. of Comp. Information Technology Arab Academy for Science Technology and Maritime Transport Information System Department Cairo Egypt National Telecommunication Institute Cairo Egypt Helwan University Faculty of Computer and Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350311440

In the last year, many modern attacks have targeted Internet of Things (IoT) devices. IoT botnet attacks are the most common. Despite the plethora of detection and prevention systems for cyberattacks, a security mechanism for IoT settings is still required since these devices have certain restrictions that make it difficult to properly implement the detection system. These memory resources are limited to processing, storing, and calculating. An effective defense against cyberattacks is an intrusion detection system (IDS). To develop the rules for IDS_MDL-IoT, we used the contemporary botnet attack dataset. Because IoT devices have limited resources, it is difficult to employ all attack patterns in the initial anomaly dataset in traditional anomaly-based IDS. Therefore, in this study, the tested ML model resulting in NB has proven to be the best classification algorithm to classify the risk of attack with 99.7% accuracy predicting from a time-speed of 3 seconds, and the tested DL model resulting in RNN has proven to be the best classification algorithm to classify the risk of attack with 99% accuracy predicting from a time-speed of 8 seconds. © 2023 IEEE.

关键词： Machine learning

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GAN-Based Unsupervised Learning Approach to Generate and Detect Fake News

GAN-Based Unsupervised Learning Approach to Generate and Det...

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Bhardwaj, Pranjal Yadav, Krishna Alsharif, Hind Aboalela, Rania Anwar VIT Vellore Vellore India National Institute of Technology Kurukshetra Kurukshetra India Faculty of Computer and Information Technology Umm AL-Qura University Makkah Saudi Arabia Department of Information System King Abdulaziz University Rabigh Saudi Arabia

ISBN: (纸本)9783031220173

Social media has grown into an increasingly popular means of disseminating information. Its massive growth has given evolution to fake news in misinformation and rumors, spreading very quickly. These days the generation of fake news is not only limited to the traditional method but is also extended to deep learning-based methods. The characteristics of fake news generated from these algorithms are very much identical to original news, which makes existing supervised machine learning algorithms difficult to detect these machine-generated fake news. Motivated by the problem, we have brought a fully unsupervised approach based on Autoencoder and GAN. With the help of an autoencoder, we have generated the high dimensional feature vector of news sentences which is later used by generators in GAN to create machine-generated fake news. The generated fake news is then identified with the real news with the help of a discriminator. We have tested our approach with the news dataset that contains about 30,000 news headlines. The obtained experimental results suggest that our approach is very reliable and can be very helpful in automating fake news detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

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computer systems Science & Engineering 2023年第9期46卷 3303-3319页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia 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 UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable *** texts are generated in a natural communicative *** explosion of social media permitted individuals to spread data with minimal examination and filters *** to this,the old problem of fake news has *** has become an important concern due to its negative impact on the *** manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)*** perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite ***,a huge effort is required from the human side to generate the labelled training *** recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news *** unlock social media data,an automatic text classifier is highly helpful in the domain of *** current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)*** presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual *** attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding ***,the QCLSTM model is utilized for *** boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the *** proposed QCLSTM-FNC approach was experimentally validated against a benchmark *** QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

关键词： English corpus fake news detection social media natural language processing artificial intelligence deep learning

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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Improving Performance Fuzzy Methods in Stroke Disease Diagnosis systems with Certainty Factor

Improving Performance Fuzzy Methods in Stroke Disease Diagno...

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International Conference on Informatics and Computing (ICIC)

作者： Rini Sovia Mutiara Salsabila Musli Yanto Information System Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Stroke is a disease caused by damage to brain cells due to impaired blood supply. Stroke has causes such as hypertension, cholesterol, arteriosclerosis, heart disease, diabetes, family history of stroke, and migraine. The purpose of this study is to develop a stroke diagnosis system by improving the performance of the fuzzy method with the performance of the Certainty Factor (CF) method. The resulting diagnosis system based on the performance of the fuzzy method enhanced with CF can present an effective and efficient diagnosis system in decision-making. The research dataset refers to the knowledge processed about stroke symptoms obtained from an expert. The results of testing the performance of the fuzzy method enhanced with CF in the diagnosis process have been able to provide the knowledge needed by patients in preventing stroke. The performance of fuzzy in the diagnosis system has also made predictions or early diagnoses so that solutions or ways to treat stroke are obtained optimally. Improving the performance of the fuzzy method with CF can present an effective and efficient system for diagnosing stroke. The accuracy of the diagnosis system produced by the fuzzy method based on testing presents results of $\mathbf{8 9 \%}$ in diagnosing stroke. The results of improving the performance of the fuzzy method with CF have also provided innovation in the Knowledge-Based system in the stroke diagnosis process. Based on these results, this study can contribute to helping medical personnel in providing alternative solutions for early prevention of increasing stroke incidence.

关键词： Technological innovation Accuracy Prevention and mitigation Knowledge based systems Decision making Migraine Stroke (medical condition) Medical diagnosis Personnel Testing

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Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

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2022 IEEE Symposium on Wireless technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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Information technology Readiness and Success In Sustainable Ecotourism Implementation

Information Technology Readiness and Success In Sustainable ...

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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Hartawan, Muhammad Syarif Maharani, Maya Dewi Dyah Krisnanik, Erly Rahman, Aedah Abd Krinadwipayana University Faculty of Engineering Jakarta Indonesia Sahid University Faculty of Environmental Engineering Jakarta Indonesia Universitas Pembangunan Nasional Veteran Jakarta Computer Science Faculty Departement Information System Jakarta Indonesia Asia e University Departement Information and Communication Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

The use of information technology plays an important and necessary role in the development of ecotourism institutional. Likewise, if the development and progress of information technology can be applied to the small and medium businesses related to the ecotourism industry then, the selling value of the ecotourism industry will develop and increase. This research was conducted to determine the level of readiness of the ecotourism industry in carrying out the sustainable implementation of information technology in managing the ecotourism industry. We developed a research model by combining and adapting the technology readiness model and the successful information technology development model for the development of the ecotourism industry in six ecotourism units in Indonesia. This quantitative study involved 300 SME workers and managers. Data was processed and analyzed using the Multi Dimensional Scaling method, with descriptive data entered into a spreadsheet application. According to the analysis performed for this study, the sustainability index value was 53.11%, which is in the range of (50-74.99) percent or is fairly sustainable. The leverage attribute refers to the use of social media to promote wild products in the ecotourism sector. Wild products are goods or services that are produced without causing environmental harm. © 2023 IEEE.

关键词： Human resource management

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Unraveling Academic Failure: Examining the Influence of Course Correlations on Student Performance Through Association Rules Algorithms

Unraveling Academic Failure: Examining the Influence of Cour...

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International Conference on computer and Applications (ICCA)

作者： Hana Safour Mansour Essgaer Almahdi Alshareef Computer Sciences Department Faculty of Sciences Sebha University Sebha Libya Information System Department Faculty of Information Technology Sebha University Sebha Libya Computer Sciences Department Faculty of Information Technology Sebha University Sebha Libya

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The field of information technology has witnessed rapid developments in data mining applications in education. In this context, association rule mining, one of the key aspects of data mining, has emerged as a specialized technique for extracting meaningful insights from sets of frequent items. Academic institutions face persistent challenges, including high failure rates and suboptimal academic achievement among students. This study examined the use of the Apriori algorithm to analyze data within an academic setting, focusing on students enrolled in the Department of Zoology at Sebha University. The primary objective was to study the effect of interconnectedness between academic courses on the overall academic performance of students in the Department of Zoology. The results revealed that high failure rates were observed consistently during the first two academic years. When the Arabic language course was linked to the botany course for second-semester students, an alarming failure rate of 80% occurred. Similarly, linking Arabic with Chemistry in the same semester resulted in a high failure rate of 75%. The English course during the same period showed a failure rate of 52%, which dropped significantly to 51% when linked to the zoology and chemistry courses. Conversely, linking other zoology courses to chemistry increased the failure rate by 59%, highlighting the subtle effects of course linkage on academic outcomes. These findings for the Department of Zoology are of fundamental importance to educators, providing valuable insights into improving student performance and identifying those who need additional support.

关键词： Schedules Chemistry Education Neural networks Zoology Focusing Association rule learning Information technology Random forests Faces

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