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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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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm 5

Improving bot detection of fake followers on Twitter via a h...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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Enhancing Preschool Literacy: A Machine Learning Approach to Sinhala Alphabet Recognition

Enhancing Preschool Literacy: A Machine Learning Approach to...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： De Silva, D.I. Athukorala, K.S.N. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka UvaWellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798331543617

This paper proposes a new tool to help preschool children learn the Sinhala alphabet, as it employs the K-Nearest Neighbors algorithm for handwritten Sinhala character recognition and prediction. The interactive platform also addresses the challenges that arise from conventional classroom learning systems, such as the lack of quick feedback, one-off learning, and inflexibility for individual learners. A crucial aspect of the Sinhala script is that it contains fifty-six characters, making character recognition particularly tedious for preschool children. The K-Nearest Neighbors algorithm appears to fill that gap impressively, as it can determine the handwriting of individuals and convert it into usable pieces for students. Usability testing indicated that preschoolers achieved a defined accuracy threshold of 90%, while the model's accuracy was 87%. These indicators provide internal evidence of the tool's actual utility in classroom interactions. The present study highlights the cultural relevance of HCI applications for children in early years education and advances the fields of machine learning systems and the preservation of the Sinhala language. The outcomes of this research demonstrate the technical correctness of the solution and its potential usefulness in applying this method to other languages and educational systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Romanow, Dominik Grzesik, Piotr Benecki, Pawel Niyomugaba, Alexandre Habimana, Theodore Kostrzewa, Daniel Tokarz, Krzysztof Hung, Che-Lun Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Ines Ruhengeri - Institute of Applied Sciences Department of Computer Science Rwanda Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei Taiwan

ISBN: (纸本)9798350362480

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models. © 2024 IEEE.

关键词： Data streams

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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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Generative AI-Enabled Quantum Encryption Algorithm for Securing IoT-Based Healthcare Application Using Blockchain

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IEEE Internet of Things Journal 2025年第13期12卷 24541-24551页

作者： Prajapat, Sunil Kumar, Pankaj Das, Ashok Kumar Muhammad, Ghulam Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics Dharamshala176215 India International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of

关键词： Quantum computers

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C3BAM-XAI: Convolutional Block Attention Module Enhanced Explainable Artificial Intelligence-Based Parkinson’s Disease Stage Classification

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Cognitive Computation 2025年第3期17卷 1-19页

作者： Abbas, Muhammad John Khan, Muhammad Attique Hamza, Ameer Alsenan, Shrooq Rehman, Aleesha Baili, Jamel Zhang, Yudong Department of Computer Science HITEC University Taxila47080 Pakistan Center of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas51368 Lithuania Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Mang Pakistan Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Informatics University of Leicester Leicester United Kingdom

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes significant impairment in neurons, physiological structures, and behavior of people. However, these changes are very subtle in the early stages of PD, making diagnosis and treatment challenging. To overcome such challenges, we proposed a novel convolutional neural network (CNN) architecture called C3BAM-XAI that is based on the convolutional block attention module (CBAM) enhanced CNN and explainable artificial intelligence (XAI). In the proposed architecture, the data imbalance issue was resolved at the initial stage by employing augmentation techniques for the training data. After that, the proposed CNN architecture is designed with CBAM. The CBAM architecture is based on two sub-modules—the channel attention module and the spatial attention module. The input of the initial convolutional layer is passed to the channel attention module, which is further passed to the spatial attention module for more critical information extraction. For smooth training, hyperparameters are selected using Nadam Optimization (NO) instead of manual assignment. In the testing phase, features are extracted from the dense layer and passed to several neural network classifiers. In addition to this, the designed model is interpreted using the explainable AI technique to see the predicted label and insight decision features. The experimental process was performed on the publically available PD Kaggle dataset and obtained an improved accuracy of 93.33%. In addition, several ablation studies were conducted, and it is concluded that the CBAM not only improved accuracy but also made this model a reliable tool for clinical diagnosis, both in terms of accuracy and interpretability. The results thus open new avenues toward applying deep learning models practically for applications in the medical domain and early-stage detection of Parkinson’s disease, thereby helping to improve patient outcomes. © The Author(s), under

关键词： Convolutional neural networks

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Zero-Shot Sentiment Analysis Exploring BART Models 8

Zero-Shot Sentiment Analysis Exploring BART Models

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350373615

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique. © 2023 IEEE.

关键词： Sentiment analysis

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