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26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Babic, Lepa Stoean, Ruxandra Cadjenovic, Jelena Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Department of Business Economics Belgrade Serbia University of Craiova Faculty of Sciences Craiova Romania Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798331532833

Main goal of the research presented in this manuscript is to analyze employees satisfaction by employing hybrid methods between machine learning and metaheuristics. Furthermore, the proposed work utilizes term frequency-inverse document frequency (TF-IDF), natural language processing (NLP) method, for the identification of important segments of the employees' statements towards their workplaces, while the extreme gradient boosting (XGBoost) model is used for predicting the outcomes. Since each machine learning technique should be calibrated to particular problem, the XGBoost has been optimized by a modified swarm metaheuristics based on red fox optimizer (RFO) algorithm and its performance is evaluated against other high-performing swarm intelligence approaches. The results indicate the superiority of proposed solution, which has been further evaluated with the Shapley additive explanations (SHAP) analysis. © 2024 IEEE.

关键词： Swarm intelligence

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Towards Digital Twin Integrated Border Control

Towards Digital Twin Integrated Border Control

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Tobing, Vianny Hamzah, Muzaffar Chew, Jackel Hamdan, Mosab Universiti Malaysia Sabah Faculty of Computing and Informatics Malaysia South East Technological University Walton Institute for Information and Communication System Science Waterford Ireland

ISBN: (纸本)9798331530303

As a promising enabling technology for digital transformation with the prime goal of solving numerous challenges, digital twin concept has gained significant recognition among researchers and practitioners, and has been adopted across many different industries. The importance of data to digital twin cannot be overstated. Data plays a critical role in constructing virtual models, building cyber-physical connections, and executing intelligent operations. Driven by data and model, the digital twin modeling is the core for accurate portrayal of its physical entity, which enables the digital twin to deliver a functional service in monitoring and prediction. Against this background, the paper suggests the adoption of digital twin within the border control domain for risk monitoring purposes. The monitoring function will identify, analyze, and respond to illegal activities at national entry points. The virtual data is collected from various sources of IoT devices. This research conducts a thorough review of existing literature and analysis on technology advancements to present the emerging trends and technology used in border control and subsequently proposed a model and framework of digital twin integrated border control that will support risk monitoring of border authority. Exploratory investigation was conducted in search for model and framework validation. Result of the survey shows that the majority of the respondents agreed with the idea of digital twin implementation within border control domain to support risk monitoring. The preliminary study proved that digital twin integration contributes to the effectiveness and efficiency of border protection routine. ©2024 IEEE.

关键词： Fog computing

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Cyberbully Detection by Using Machine Learning 4th

Cyberbully Detection by Using Machine Learning

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Tamring, Norazlinda Hung, Lai Po Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789819729760

Cyberbullying is a growing problem that affects mental health and academic achievement, especially among teenagers and young people. Detecting cyberbullying is challenging due to its complex and subjective nature, as well as the rapidly evolving technology and language. Victims have difficulty avoiding attacks, which can be as simple as malicious comments online. Effective models for detecting cyberbullying are needed to prevent such cases, but data extraction from social media is difficult due to privacy concerns and limited user information. The objective of this project is to enhance manual monitoring for cyberbullying on social networks and online platforms by researching and testing the most effective feature extraction method to be implemented. The project aims to achieve the following goals: preprocess the public dataset, design machine learning algorithm models with different feature extraction methods, evaluate the accuracy and performance of each algorithm, and create a simple interface to test sentences with the designed model. The classifiers used in the research are Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) that are optimized with two different feature extraction (TFIDF and Count Vectoriser). The classifier's evaluation is determined by accuracy, precision, recall, and f1-score. The classifier that obtained the highest performance is Linear SVM by using TFIDF. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phishing

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A Novel Framework for Preserving Patient Health Record in IoT Healthcare 3

A Novel Framework for Preserving Patient Health Record in Io...

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3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Pothureddy, Priyanka Patnam, Sai Suraksha Gadda, Parimala Deepthi G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

As artificial intelligence, the Internet of Things (IoT), and next-generation mobile communication continue to evolve at a rapid pace, an increasingly popular application called the Internet of Medical Things (IoT) has arisen to provide ease and practicality in healthcare. Nonetheless, privacy preservation issues are brought up by accessing patient medical information inside the IoT framework, which calls for the creation of safe and private-preserving solutions. To address these issues, we provide in this study an IoT-Framework for preserving patient health record (FFPPHR) based on ELGamal blind signatures. We provide an enhanced privacy protection medical record searching technique (FFPPHR) based on the elliptic curve discrete logarithm problem (ECDLP) and point out security vulnerabilities in the current FFPPHR. Our security research shows that by utilizing the hard problem assumption of ECDLP, the FPMR guarantees identity privacy and accuracy. Furthermore, the use of Blind ECDSA Signatures improves data security. Performance assessments confirm the FFPPHR efficiency, and theoretical findings agree with simulations. The suggested FFPPHR approach yields lower time costs when compared to state-of-the-art techniques, which makes it a potential option for safe and effective medical record searches in IoT contexts. © 2024 IEEE.

关键词： Electronic health record

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Hand Sign Detection and Voice Conversion for the Hearing and Speech Impaired Using Convolutional Neural Networks 4th

Hand Sign Detection and Voice Conversion for the Hearing and...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Belgaum, Mohammad Riyaz Sowmya, Kurni Sireesha, Kuruva Priyanka, Tatagari Tony Vyshnavi, Uppara Uravakonda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9789819994410

Sign Language Recognition (SLR) tries to convert sign language into written or spoken form to enable communication between a deaf-mute person and a normal person. The task of communicating with disabled and impaired persons is extremely challenging, owing to its complexity and variety of hand motions that have a major societal impact. In order to depict sign language motions, SLR approaches are increasingly being used. These classification models are based on hand-crafted characteristics. Building reliable features that can adapt to the diverse range of hand signs is nonetheless challenging. To address this issue, the authors proposed a special 3D convolutional neural network (CNN) to ease the communication between normal and mute communities. Our proposed model eliminates the requirement to create features by automatically extracting discriminative spatial–temporal features from the actual livestream without any prior information. To improve performance, the 3D CNN is fed with multiple channels of video feeds that include colour, depth, trajectory information, joint body locations, and depth clues. We illustrate the proposed model's superiority to the conventional methods based on hand-crafted characteristics by testing them against a real dataset collected using a depth camera. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Evaluating the Utilization of Grammatical Structures of Arabic Language in Hiding Bits

Evaluating the Utilization of Grammatical Structures of Arab...

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2024 Advances in Science and Engineering Technology International Conferences, ASET 2024

作者： Rihan, Yazen Altaweel, Ala Khattab, Khattab Kamel, Ibrahim University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah United Arab Emirates Ondokuz Mayis University Faculty of Ilahiyat Samsun Turkey

ISBN: (纸本)9798350344134

Nowadays, there is massive volume of data transmitted constantly through digital mediums. This data faces the risks of various cyber-attacks like eavesdropping, traffic analysis, packet injection, and packet dropping. There has been several security mechanisms to secure sensitive data and ensure their confidentiality, integrity, and availability. Indeed, Encryption, which is the process of converting the plain-text data into a non-understandable form known as the cipher-text, can secure the sensitive data. However, Encryption techniques are costly in terms of computational resources. Steganography, on the other hand, attempts to exploit the redundancy in data representation and structure to hide secret information within the data. Text steganography refers to the art of hiding secret information inside long passages of text. This paper proposes a new text steganography technique that exploits grammatical structures of Arabic language. Specifically, the paper exploits the redundancy in the language structure of Arabic grammar. The paper also presents the results of applying the proposed technique on a large dataset of articles from different categories such as politics, sports, and religion. The conducted experiments measure the number of secret bits that can be hidden in 1000 words for each category. The results show that some steganography rules can perform differently when annlied to different categories of text. © 2024 IEEE.

关键词： Data integrity

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Optimizing the Classification Performance by Fine-Tuning the Machine Learning Hyperparameters and Utilizing PCA and RFE Feature Selection Methods 4th

Optimizing the Classification Performance by Fine-Tuning the...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Adam, Alfrendo Alvian Alfred, Rayner Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia

ISBN: (纸本)9789819729760

Breast cancer is among the most common and potentially fatal cancers, especially among women. Breast cancer usually has no obvious early signs, and doctors sometimes have trouble differentiating benign and malignant tumors. Thus, with the help of appropriate Machine Learning (ML) techniques, breast cancer patients can be classified more accurately and help doctors make diagnosis decisions. The research gaps in this topic are that there is no standard ML hyperparameter setting and feature selection method used to improve the ML algorithm performance in breast cancer classification. This paper aims to identify the best hyperparameters settings, identify the best feature selection methods and compare them with past research papers. The experiments conducted in this paper can be divided into two: the first experiment is to identify the best hyperparameter settings, and the second is to identify the best feature selection methods. In this paper, several algorithms are used that includes Logistic Regression (LR), Decision Tree (DT), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Hard Voting, and Artificial Neural Network (ANN) to model the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, the feature selection method uses Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). For comparison with past research, research papers that have used the same dataset and algorithms will be selected to ensure the comparison is fair and has no bias. Based on the results obtained in this paper, Adaboost, combined with the optimized decision tree, is found to be the best algorithm due to high accuracy and sensitivity compared to the other algorithms, which are 98.2% and 99.6%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Automatic Deployment to Kubernetes Cluster by Applying a New Learning Tool and Learning Processes 10

Automatic Deployment to Kubernetes Cluster by Applying a New...

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Golis, Tomáš Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia

The rapid growth and widespread adoption of containerization technologies, such as Docker, and the increasing popularity of Kubernetes as a container orchestration platform have significantly shaped the landscape of modern software development and deployment. Containers have created a paradigm shift in application packaging, offering a consistent and portable environment across diverse infrastructure setups. Meanwhile, Kubernetes has emerged as the dominant choice for managing containerized applications at scale, providing features such as automated scaling, load balancing, and fault tolerance. However, despite the numerous advantages containers and Kubernetes offer, deploying applications to a Kubernetes cluster often poses challenges due to the steep learning curve and complex configuration requirements. To address these challenges, this paper aims to introduce a comprehensive learning tool that automates and simplifies the deployment process on Kubernetes clusters. © 2023 Copyright for this paper by its authors.

关键词： CI/CD Container orchestration Docker image Kubernetes

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Modeling heterogeneous behaviors with different strategies in a terrorist attack

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Virtual Reality & Intelligent Hardware 2023年第4期5卷 351-365页

作者： Le BI Tingting LIU Zhen LIU Jason TEO Yumeng ZHAO Yanjie CHAI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Science&Technology Ningbo UniversityCixi 315300China Faculty of Computing&Informatics Universiti Malaysia SabahKota Kinabalu 88000Malaysia

Existing simulations of terrorist attacks do not consider individual *** overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist *** constructed an emotional model that integrated personality and visual perception for *** emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by *** demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

关键词： Terrorist attack simulation Computer animation Big Five personality Intelligent decision making Heterogeneous behaviors

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Covariance Analysis in Selected OOP Languages 47

Covariance Analysis in Selected OOP Languages

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47th ICT and Electronics Convention, MIPRO 2024

作者： Sirotić, Zlatko Sovilj, Siniša Oršulić, Matija Pripužić, Krešimir Istra Tech D.o.o. Pula Croatia Juraj Dobrila University Of Pula Faculty Of Informatics Pula Croatia University Of Zagreb Faculty Of Electrical Engineering And Computing Zagreb Croatia

ISBN: (纸本)9798350382495

This study aims to analyze covariance with non-generic parameters across seven renowned statically-typed object-oriented languages: C++, Eiffel, Java, C#, Scala, Kotlin, and Swift, presented in chronological order from oldest to newest. In the original examples, an attempt is made to apply covariance to two interconnected superclass/subclass pairs, specifically the Patient/HeartPatient and FoodForPatients / FoodForHeartPatients classes. Within a method of the Patient class, a parameter of the FoodForPatients class (or type) is introduced. We asked whether in the aforementioned languages, it is possible to create an overridden method in the HeartPatient subclass that utilizes the FoodForHeartPatients class in place of the FoodForPatients class. It is essential to emphasize that while the utilization of generic parameters and their covariance represents a viable solution, it is not considered the optimal one due to significant practical drawbacks. © 2024 IEEE.

关键词： Polymorphism

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