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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Heruatmadja, Chandra Hermawan Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Binus University Computer Science Jakarta Indonesia School of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia BINUS Business School Management Departement Jakarta Indonesia

ISBN: (纸本)9781665475174

Virtual Reality (VR) is a type of technology that enables users to interact each other inside artificial environments. While this technology makes life easier, it also creates new issues, particularly in the areas of security and authentication. In contrast to authentication techniques in general, where users can use a pin or password safely and securely, in VR this security model creates a new security loophole and the better authentication method and more secure authentication in virtual reality have both been the subject of numerous studies including using biometric. In order to learn more about biometric authentication, which is the most common issue in VR authentication research, this research is conducted in a systematic literature review to answer the research questions what biometric media, HeadMounted Display (HMD), also examines what machine learning and biometric media are frequently used, and how accurate biometric authentication can identify individual person, and in the end, to help future researchers to shorten the time in conducting research or have the right authentication method for a VR service. Result of the research found that to build biometric authentication model, researcher often use k-Nearest Network and Support Vector Machine (SVM) machine learning, and finger vein and hand movement are the most accurate biometric authentication among others. © 2023 IEEE.

关键词： Virtual reality

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Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

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computer systems Science & Engineering 2022年第2期40卷 691-702页

作者： Mohammed Baz Sabita Khatri Abdullah Baz Hosam Alhakami Alka Agrawal Raees Ahmad Khan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health *** paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare *** view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher *** the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be *** the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug *** paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic *** paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational *** paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

关键词： Artificial intelligence information privacy machine learning coronavirus(COVID-19) SARS-CoV-2 epidemic

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A novel CatractNetDetect deep learning model for effective cataract classification through data fusion of fundus images

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Discover Artificial Intelligence 2024年第1期4卷 54页

作者： Ismail, Walaa N. Alsalamah, Hessah A. Department of Management Information Systems College of Business Al Yamamah University Riyadh11512 Saudi Arabia College of Computer and Information Sciences King Saud University Riyadh4545 Saudi Arabia Computer Engineering Department College of Engineering and Architecture Al Yamamah University Riyadh11512 Saudi Arabia

Cataracts are common eye disorders characterized by the clouding of the lens, preventing light from passing through and impairing vision. Various factors, including changes in the lens’s hydration or alterations in its proteins, may contribute to their development. Regular eye examinations conducted by an ophthalmologist or optometrist are imperative for detecting cataracts and other ocular conditions early on. Manual checks by caregivers pose several problems, including subjectivity, human error, and a lack of expertise. Biomedical fusion involves combining or linking various characteristics specific to certain diseases from different medical imaging resources. The primary objectives of this approach in disease classification are to reduce the error rate and increase the number of retrieved features. The aim of this study is to evaluate the outcomes associated with fusing visual features related to left and right eye cataract characteristics. Additionally, we investigate the impact of limited variability in deep learning models, specifically in the classification of cataract fundus versus normal fundus images. To address this issue, this study introduces CataractNetDetect, an innovative multi-label deep learning classification system that fuses feature representations from pairs of fundus images (e.g., left and right eyes) for the automatic diagnosis of various ocular disorders. Our focus is on achieving improved performance by stacking discriminative deep feature representations to combine two fundus images into a unified feature representation. Several deep learning architectures are utilized as feature descriptors, including ResNet-50, DenseNet-121, and Inception-V3, enhancing the resilience and quality of representations. Fine-tuning of these DL architectures is conducted using the ImageNet dataset, followed by an integrated stacking approach combining ResNet-50, DenseNet-121, and Inception-V3 models. The model is trained on the publicly available ODIR-5k datas

关键词： Medical imaging

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Customer Segmentation for Targeted Marketing: A Comparative Analysis of Clustering Techniques 25

Customer Segmentation for Targeted Marketing: A Comparative ...

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25th International Multi Topic Conference, INMIC 2023

作者： Ali, Rasikh Farhat, Tayyaba Abdullah, Sanya Akram, Sheeraz The Superior University Faculty of Computer Science and Information Technology Lahore54000 Pakistan College of Computer and Information Sciences Information Systems Department Riyadh12571 Saudi Arabia

ISBN: (纸本)9798350317701

In the contemporary landscape of data-driven decision-making, businesses are increasingly harnessing customer segmentation as a strategic tool for tailoring their marketing endeavors. his research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to investigate customer segmentation for targeted marketing. It encompasses phases such as data understanding, preprocessing, modeling, evaluation, deployment, and monitoring. Our study applies K-Means and Hierarchical Clustering algorithms to create customer segments. While Hierarchical Clustering provides a visually insightful segmentation structure, K-Means excels in terms of the Silhouette score, a crucial clustering metric. Overall, K-Means Clustering emerges as the superior choice due to its interpretability and comprehensive utility. This research contributes to data-driven marketing by offering insights for businesses seeking to enhance marketing strategies, elevate customer engagement, and boost revenue. © 2023 IEEE.

关键词： K-means clustering

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Ransomware Classification Using Machine Learning 32

Ransomware Classification Using Machine Learning

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Majd, Nahid Ebrahimi Mazumdar, Torsha California State University Department of Computer Science and Information Systems San Marcos United States

ISBN: (纸本)9798350336184

The rise of ransomware has emerged as a pressing concern for the technology industry, demanding prompt action to prevent monetary and ethical exploitation. Therefore, an accurate approach is imperative to identify and thwart such attacks effectively. Most of the prior ransomware detection techniques either are signature-based, which are inefficient to identify new ransomware, or utilize a dynamic analysis, which are complicated and computationally expensive. This paper proposes a feature selection-based framework along with different machine learning and deep learning algorithms that can effectively detect ransomware based on features extracted from the files. We performed various experiments beginning with filter, wrapper and embedded methods of feature selection and then applied Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Extreme Gradient Boost (XGB) and Multi-layer Perceptron (MLP) on a ransomware dataset that contains the features and label from files. The experimental results demonstrate that RF and MLP classifiers with ANOVA filter method of feature selection outperform other methods in terms of accuracy, precision, and recall. © 2023 IEEE.

关键词： Network security

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MpiPython: Prospects for Node Performance 6

MpiPython: Prospects for Node Performance

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6th International Conference on information and computer Technologies, ICICT 2023

作者： Nava, Judah Lee, Hanku Minnesota State University Moorhead Computer Science and Information Systems MoorheadMN United States

ISBN: (纸本)9798350300956

Python as an interpreted language is limited in performance by its ability to optimize code. With it being a high-level programming language, it's still a strong choice for data scientists to learn and use. If Python could be optimized for parallel programming, its full potential in parallel and cloud computing environments could be achieved. mpiPython is a message-passing module that gives Python the ability to be used in SPMD (Single Program Multiple Data) environments. In this paper, we review basic features of mpiPython, including its runtime communication libraries and design strategies. mpiPython also has new features to help mpiPython programmers, that includes simplifying traditional MPI initialization. During the development of mpiPython, we realized that individual node performance of mpiPython is uncertain and critical. mpiPython node performance will be analyzed within the benchmarks. © 2023 IEEE.

关键词： Python

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Contemporary Impact of Cyberbullying on Youngsters: An Appraisal of Psychological, Emotional, Physical Health, and Academic Implications 19

Contemporary Impact of Cyberbullying on Youngsters: An Appra...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Faith, Fatokun B. Long, Zalizah Awang Hamid, Suraya Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science IT Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Cyberbullying is now a serious issue in contemporary times. The impact of cyberbullying on youngsters is of enormous concern in the public health sector as well as in the cybersecurity field. Several researchers have discussed the impact of cyberbullying from diverse perspectives, however, there is still a large gap in finding the paramount impact of cyberbullying in contemporary times, as well as providing solutions to eradicate this menace, especially as it deals with the negative effects of technology which is emerging daily. This study reviews via a scoping evidence approach suggesting the impact of cyberbullying on youngsters, focusing on the psychological, emotional, physical health, and academic implications. Findings from studies revealed that most youngsters face more of the psychological and emotional consequences of cyberbullying as compared to others discussed in the literature. Interestingly, there were also revelations of how cyberbullying had negative influences on the physical health and academic achievements of youngsters. These findings thus instigate the need for novel approaches that can help curb the rampancy of cyberbullying as well as ensure that decision-makers and stakeholders in the respective institutions put these impacts of cyberbullying into consideration when designing cybersecurity policies to ensure that victims of cyberbullying are assisted to recover, put in place technological solutions that can track and punish offenders and also reduce the rate of cyberbullying victimization. © 2025 IEEE.

关键词： Electronic health record

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Aabbir: A New Solution for children with speech and intellectual disabilities.

Aabbir: A New Solution for children with speech and intellec...

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Zagrouba, Rachid Alroqaya, Lojien Aldraiweesh, Lujain Almegdad, Zaynab Alotaibi, Hala Alsomaly, Yasmin Imam Abdulrahman Bin Faisal University Department of Computer Information Systems Dammam Saudi Arabia

ISBN: (纸本)9798350322972

By enabling us to express our needs and experiences to one another, communication helps us connect with people in daily life and fosters the development of relationships. While some groups in our society have trouble communicating, many of them are unable to express themselves. the absence of instruments to facilitate successful communication among individuals with disabilities and ease both their own and their communicators' everyday lives. In this paper, we suggest a unique approach to helping kids with speech and intellectual challenges. Certain children and people with speech and intellectual disabilities who are unable to utilize speech to connect with their partners, such as parents, coworkers, or friends, can benefit from AAC (augmentative and alternative communication) technology. AAC software is user-friendly, high-quality, and open source, however, Arabic-speaking people do not commonly support it. We proposed a new solution for communication apps based on AAC technology using the Firebase for backend and the Flutter application builder for the front end. Our project strives to offer a dependable and trustworthy application in the sector of augmented and alternative technologies. Our project center on putting in place a system that makes it easier for persons with complex communication requirements to communicate. The program makes sure that it satisfies the requirements for accessibility, availability, and dependability. One of the research's shortcomings, though, is the lack of Arabic-supporting software. © 2023 IEEE.

关键词： Open source software

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Spam SMS Classification Using Machine Learning 32

Spam SMS Classification Using Machine Learning

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Majd, Nahid Ebrahimi Hanchate, Mandar Shivaji California State University San Marcos Department of Computer Science and Information Systems United States

ISBN: (纸本)9798350336184

Over the past few years, the use of emails and text messages has drastically increased. Short Message Service (SMS) on cellphone providers and related apps, like Whatsapp, is one of the best and fastest ways to communicate among users. SMSs are used and sent globally for personal and business purposes. However, alongside safe SMSs, the users may receive fraudulent Spam SMSs, which could cause security issues and inconvenient for the users. Numerous Spam messages are being sent daily for both personal and professional benefits. Accurately identifying Spam SMS is a challenge. The objective of this research is to build a model utilizing machine learning and deep learning to understand the semantics of SMSs and classify them to either Spam or non-Spam (Ham). We used a pre-trained BERT model and combined it with several machine learning and deep learning models. The results indicated that BERT+SVC and BERT+BiLSTM performed the best with 99.10% and 99.19% accuracies respectively on the test dataset. © 2023 IEEE.

关键词： Deep learning

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Intelligent Cybersecurity Classification Using Chaos Game Optimization with Deep Learning Model

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computer systems Science & Engineering 2023年第4期45卷 971-983页

作者： Eatedal Alabdulkreem Saud S.Alotaibi Mohammad Alamgeer Radwa Marzouk Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariJhnujhunuRajasthanIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent *** Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective identification and classification of *** addition,the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification *** this background,the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning(ICC-CGODL)*** goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon ***,ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform *** addition,Bidirectional Gated Recurrent Unit(BiGRU)model is utilized for detection and classification of ***,CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work.A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.

关键词： Deep learning chaos game optimization cybersecurity chaos game optimization cyberattack

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