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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Azzuri, Puteri Nursyawati Suffian Sulaiman, Mohd Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia College of Computing Informatics and Mathematics Shah Alam Malaysia

ISBN: (纸本)9798350353853

Many people have chosen to use online-based shopping, which is easy, fast and more accessible to different types of products and services. Some of the popular e-commerce websites nowadays are Shopee, Lazada, eBay, Amazon and Alibaba. These websites serve as a good platform for people to find different kinds of products and services around the world. Most users are younger, ranging in age from 13 to 25 years old. However, some older people love to shop online. Most of them lack exposure to and education on how to tell the difference between a scam and a legitimate website. It is also one of humans' weaknesses to detect and identify a scam website if it looks the same as a legitimate website. Furthermore, there are numerous reports of scam attacks that result in significant financial losses each year. Scammers may target not just an individual but also a company. At times, the situation can worsen, potentially posing a threat to our families and lives. This study has proposed a web-based scam detector as a solution to verify the legitimacy of an e-commerce website. This prototype uses the Random Forest model as a classifier to detect scams. This is due to the good performance and high accuracy of detection compared to the other algorithms such as Decision Tree, Naïve Bayes and Support Vector Machine. The Random Forest model achieved 93% accuracy of detection and it is good enough to deploy on the real prototype. Additionally, this website offers guidance on identifying scam websites and the tactics they typically employ to lure victims. © 2024 IEEE.

关键词： Adversarial machine learning

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A Blind Digital Watermarking Approach Using Palmprint-Embedded Local Binary Patterns and Arnold Cat Map Transformations 6

A Blind Digital Watermarking Approach Using Palmprint-Embedd...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Ferik, Brahim Laimeche, Lakhdar Meraoumia, Abdallah Laouid, Abdelkader Chait, Khaled Alshaikh, Muath Tebessa Algeria El Oued Algeria Saudi Electronic University College of Computing And Informatics Department of Computer Science Saudi Arabia

ISBN: (纸本)9798350350265

The rapidly evolving healthcare domain and the rise of telemedicine and remote diagnostic techniques underscore the critical demand for secure, authentic, and reliable medical imaging solutions. This study introduces an innovative digital watermarking method designed to safeguard medical images against unauthorized alterations, employing biometric data - specifically, palmprints - as the watermark. Utilizing Local Binary Patterns (LBP) for feature extraction and combining Arnold Cat Map (ACM) with Discrete Cosine Transform (DCT) for image processing, this research implements a novel embedding strategy based on a 2 Least Significant Bits (2LSB) mechanism, facilitating a blind watermarking approach. The efficacy of the proposed method is validated through exceptional Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, achieving values of 51.21675 dB and 0.9999, respectively. These metrics provide substantial evidence of the technique's imperceptibility and robustness, employing state-of-the-art methodologies. The findings of this paper offer a compelling solution for enhancing the security of medical imagery within the telemedicine sector, promoting data integrity and supporting the verification and authentication processes. © 2024 IEEE.

关键词： Local binary pattern

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Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

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China Communications 2024年第9期21卷 11-26页

作者： Gao Ang Ren Xiaoyu Deng Bin Sun Xinshun Zhang Jiankang School of Electronic Information Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Near Ground Detection and Perception Technology Wuxi 214035China Department of Computing and Informatics Bournemouth UniversityUK

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals ***,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication *** the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert *** this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)*** to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to *** results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

关键词： covert communications deep reinforcement learning intelligent reflecting surface

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content 4

Using Machine Learning to Identify and Categorize Personally...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Arambawela, Milinda Aponso, Achala University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data. © 2024 IEEE.

关键词： Neural network models

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A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension

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IEEE Transactions on Neural Networks and Learning Systems 2023年第1期34卷 134-143页

作者： Zhou, Yiyi Ji, Rongrong Luo, Gen Sun, Xiaoshuai Su, Jinsong Ding, Xinghao Lin, Chia-Wen Tian, Qi Xiamen University Media Analytics and Computing Laboratory Xiamen361005 China Xiamen University School of Informatics Informatics and Communication Engineering Department Xiamen361005 China School of Informatics Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence Xiamen361005 China Xiamen University School of Informatics Xiamen361005 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Huawei Technologies Company Ltd. Cloud BU Shenzhen518129 China

Referring expression comprehension (REC) is an emerging research topic in computer vision, which refers to the detection of a target region in an image given a test description. Most existing REC methods follow a multistage pipeline, which is computationally expensive and greatly limits the applications of REC. In this article, we propose a one-stage model toward real-time REC, termed real-time global inference network (RealGIN). RealGIN addresses the issues of expression diversity and complexity of REC with two innovative designs: adaptive feature selection (AFS) and Global Attentive ReAsoNing (GARAN). Expression diversity concerns varying expression content, which includes information such as colors, attributes, locations, and fine-grained categories. To address this issue, AFS adaptively fuses features of different semantic levels to tackle the changes in expression content. In contrast, expression complexity concerns the complex relational conditions in expressions that are used to identify the referent. To this end, GARAN uses the textual feature as a pivot to collect expression-aware visual information from all regions and then diffuses this information back to each region, which provides sufficient context for modeling the relational conditions in expressions. On five benchmark datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, ReferIT, and Flickr30k, the proposed RealGIN outperforms most existing methods and achieves very competitive performances against the most advanced one, i.e., MAttNet. More importantly, under the same hardware, RealGIN can boost the processing speed by 10-20 times over the existing methods. © 2012 IEEE.

关键词： Semantics

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Enhanced Parkinson's Disease Detection Using Neural Brain Image

Enhanced Parkinson's Disease Detection Using Neural Brain Im...

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2024 Multimedia University Engineering Conference, MECON 2024

作者： Gayathri Devi, T. Srinivasan, A. Sudha, S. Anuar, Khairil Srinivasa Ramanujam Centre SASTRA University Department of Electronics and Communication Engineering Kumbakonam India Multimedia University Department of Computing and Informatics Selagor Cyberjaya Malaysia

ISBN: (纸本)9798331530747

A brain disorder called Parkinson's disease (PD) is fairly frequent and damages cells that produce dopamine. Doctors can typically diagnose it by looking at movement problems and a patient's medical history. However, these traditional methods rely on interpreting subtle movements, which can be subjective and prone to error. This paper explores the application of deep learning for improved Parkinson's disease (PD) diagnosis using magnetic resonance imaging (MRI). This work proposes a novel method employing pre-trained convolutional neural networks (CNNs) - ResNet50 and VGG16 - alongside image resizing and Stochastic Gradient Descent with Momentum (SGDM) optimization. The models were trained and evaluated on an MRI dataset encompassing PD patients and healthy controls. The proposed method achieved outstanding accuracy in classifying Parkinson's disease. Notably, ResNet50 achieved 95.06% accuracy, while VGG16 reached an impressive 96.30% in correctly identifying PD compared to healthy participants. Additionally, the performance measures such as sensitivity, specificity and precision are assessed to provide a more comprehensive evaluation. This research demonstrates that deep learning techniques can effectively analyze MRI scans to detect Parkinson's disease. However, further studies are warranted to validate these findings on larger datasets and assess the model's generalizability for clinical use. © 2024 IEEE.

关键词： Convolutional neural networks

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Approximation Algorithms for Maximum Weighted Throughput on Unrelated Machines 26

Approximation Algorithms for Maximum Weighted Throughput on ...

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26th International Conference on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2023 and the 27th International Conference on Randomization and Computation, RANDOM 2023

作者： Karakostas, George Kolliopoulos, Stavros G. Department of Computing & Software McMaster University Hamilton Canada Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece

ISBN: (纸本)9783959772969

We study the classic weighted maximum throughput problem on unrelated machines. We give a (1 − 1/e − Ε)-approximation algorithm for the preemptive case. To our knowledge this is the first ever approximation result for this problem. It is an immediate consequence of a polynomial-time reduction we design, that uses any ρ-approximation algorithm for the single-machine problem to obtain an approximation factor of (1 − 1/e)ρ − Ε for the corresponding unrelated-machines problem, for any Ε > 0. On a single machine we present a PTAS for the non-preemptive version of the problem for the special case of a constant number of distinct due dates or distinct release dates. By our reduction this yields an approximation factor of (1 − 1/e) − Ε for the non-preemptive problem on unrelated machines when there is a constant number of distinct due dates or release dates on each machine. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Approximation algorithms

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APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR EPIDEMIC MODELING

APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE FOR EPIDEM...

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2024 Winter Simulation Conference, WSC 2024

作者： Villaplana, Hannah Danielle Ladera Kwak, Jaeyoung Lees, Michael H. Li, Hongying Cai, Wentong College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (纸本)9798331534202

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents’ decision-making using random forest model. © 2024 IEEE.

关键词： COVID-19

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Comparative Analysis of the K-Nearest Neighbor Method and Support Vector Machine in Human Fall Detection 3

Comparative Analysis of the K-Nearest Neighbor Method and Su...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Safira, Shaina Maulida Gunawan, Putu Harry Kurniawan, Wandi Yusuf Gede Karang Komala Putra, I. Reganata, Gde Palguna Kadek Winda Patrianingsih, Ni Gede Wahyu Surya Dharma, I. Kadek Arya Sugianta, I. Udhayana, Khadijah F. R. Telkom University School of Computing Bandung Indonesia Universitas Bali Internasional Informatics Department Denpasar Indonesia

ISBN: (纸本)9798350394559

Falling events occur every day, especially among elderly patients or patients in need of special care. To some extent, falling can cause serious injuries, both open and internal wounds. Therefore, fast and accurate technology is needed to detect whether someone has fallen, especially older adults. This research aims to find a solution for detecting fall movement using machine learning algorithms. In this research, the dataset was collected from 206 participants through the Phyphox application on smartphones. This app utilizes the accelerometer sensor to measure acceleration on each 3D axis (x, y, and z). Here, two proposed classification methods are used to detect the fall movement, i.e., K-Nearest Neighbor and Support Vector Machine. The accuracy of both models is calculated and compared in this paper. The research results show that the linear kernel Support Vector Machine (SVM) has a performance range of 77% to 95%. Meanwhile, the RBF kernel SVM demonstrates a performance range of 79% to 87%, and the polynomial order three of the SVM kernel displays a performance range of 51% to 87%. Moreover, the performance of the K-NN algorithm falls within the scope of 72% to 84%. Overall, the scenario employing the linear kernel Support Vector Machine (SVM) technique exhibits the highest level of efficacy, boasting an accuracy rate of up to 95%. © 2023 IEEE.

关键词： Support vector machines

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