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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina University of Salford School of Science Engineering and Environment Manchester United Kingdom United Arab Emirates University Yarmouk University Media & Creative Industries Jordan United Arab Emirates University Media & Creative Industries Department United Arab Emirates Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798331542603

As hate speech is becoming common on social media platforms, it is important to detect, and curb hate speech in order to provide a better and safe environment online. Given the heavy usage of manual methods of hate speech detection, researchers started putting efforts in the direction of machine-learning-based automated methods sooner or later. Many available datasets and models on hate speech detection are largely inadequate for Arab hate speech because of the complexity of language and cultural nuances. In this paper, the researchers eased these difficulties, as they, used the K-Means to apply compilation for Arab hate speech in the L-HSAB dataset on the tweets. Methodology consisted of Prospecting and Pre-processing, Term Frequency Inverse Document frequency (TF-IDF) dimension reductions, through Principal Component Analysis (PCA), and assembly via K-Means. They helped to identify different sets of hate speech tweets and understand the common topics and topics. It has led to new understandings of how Arab hate speech flows on the Internet and now offers the potential for tailored interventions. Automated hate speech analysis via machine learning would allow policymakers to formulate tailored modification strategies focused on making the Internet safer and the community more harmonious. © 2024 IEEE.

关键词： Tweets

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Enhanced Diabetic Retinopathy Detection: An Explainable Semi-Supervised Approach Using Contrastive Learning

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Ali, Rashid Khan, Fiaz Gul Rehman, Zia Ur Kwak, Daehan Ali, Farman Comsats University Islamabad Department of Computer Science Abbottabad Campus KPK Abbottabad22060 Pakistan Kean University Department of Computer Science and Technology NJ07083 United States Sungkyunkwan University Department of Applied Ai School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Diabetic retinopathy (DR) is a leading cause of blindness and represents a critical challenge to global vision health. Early detection is essential to preventing irreversible eye damage. Automated medical image analysis plays a pivotal role in enabling timely diagnosis. However, the development of robust diagnostic models is challenged by the scarcity of labeled data and the prevalence of imbalanced and unlabeled datasets. Semi-supervised learning offers a potential solution by leveraging unlabeled data to enhance model performance. However, it is often limited by challenges such as unreliable pseudo-labeling, the exclusion of low-confidence data, and biases introduced by imbalanced datasets. To address these limitations, we propose a novel semi-supervised learning framework for DR detection that combines similarity and contrastive learning. Our approach utilizes class prototypes and an ensemble of classifiers to generate reliable pseudo-labels for unlabeled data. Unlike traditional methods that discard unreliable samples, our framework integrates them into the training process using contrastive learning. This allows us to extract valuable features and improve overall performance. Furthermore, we enhance the model's transparency and interpretability by incorporating the explainable AI technique GradCAM, which provides insights into the model's predictions for specific images. We evaluated the proposed method on the publicly available Kaggle DR dataset for diabetic retinopathy classification. Experimental results demonstrate that our approach achieves improved performance compared to existing semi-supervised learning methods. It also effectively leverages unreliable samples, highlighting its potential to advance DR diagnosis. © 2013 IEEE.

关键词： Contrastive Learning

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Multi-objective VNF Placement Optimization with NSGA-III 1st

Multi-objective VNF Placement Optimization with NSGA-III

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1st International Conference on Advances in computing Research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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V-CRYPT: A Secure Visual Cryptography System 7

V-CRYPT: A Secure Visual Cryptography System

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Nashrudin, Muhamad Ridhwan Bin Nasser, Abdullah B. Abdul-Qawy, Antar Shaddad H. Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Sumait University Facutly of Science Department of Science and Information Technology Zanzibar Tanzania United Republic of

ISBN: (纸本)9781665414074

Nowadays, peoples are very concerned about their data privacy. Hence, all the current security methods should be improved to stay relevant in this fast-growing technology world. Visual Cryptography (VC) is a cryptographic technique that using the image processing method. The implementation of VC can be varying and flexible to be applied to the system that requires an extra security precaution as it is one of the effective solutions in securing the data exchange between two or more parties. The main purpose of the development of V-CRYPT System is to improve the current VC technique and make it more complex in the encryption and decryption process. V-CRYPT system will let the user enter the key, then select the image that they want to encrypt, and the system will split the image into four shares: share0, share1, share2, share3. Each pixel of the image will be splatted into a smaller block of subpixels in each of the four shares and encrypted as two subpixels in each of the shares. The decryption will work only when the user selects all the shares, and the correct text key is entered. The system will superimpose all the shares and producing one perfect image. If the incorrect key is entered, the resulted image will be unidentified. The results show that V- CRYPT is a valuable alternative to existing methods where its security level is higher in terms of adding a secure key and complexity. © 2021 IEEE.

关键词： Electronic data interchange

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Predictive Modelling of Protest Event Signatures: Analyzing Temporal Dynamics and Digital Activism Discourse Across Global Movements

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Journal of Social computing 2025年第1期6卷 43-50页

作者： Amila Chethana Nanayakkara B. T. G. Samantha Kumara R. M. Kapila Tharanga Rathnayaka Center for Computer Studies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

This research introduced a predictive modeling framework to analyze the temporal dynamics of social media discourse during three global movements: the Mahsa Amini Protests (2022), South African Unrest (2021), and the Black Lives Matter Movement (2020). By utilizing Twitter data, the study developed a Protest Social Media Archetype to capture the evolution of tweet activity during key protest periods, identifying common patterns and notable variations in public engagement. Techniques such as LOESS regression and correlation analysis were used to model fluctuations in online activity and assess the impact of social media on public mobilization during socio-political unrest. The findings revealed distinct temporal signatures for each movement, showing similarities in initial engagement and differences in sustained activity across various contexts. These insights underscore the role of digital platforms in protest organization and global solidarity, offering a framework for future studies on digital activism and protest dynamics.

关键词： Analytical models Social computing Fluctuations Social networking (online) Blogs Refining Organizations Predictive models Real-time systems Mathematics

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Exploring the Synergy of Artificial and Computational Intelligence for Technological Advancements 1

Exploring the Synergy of Artificial and Computational Intell...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Palarimath, Suresh Maran, Pyingkodi Valsalan, Prajoona Wilfred Blessing, N.R. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331532956

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things. © 2024 IEEE.

关键词： Artificial intelligence computational intelligence Internet of things

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Toward Anomaly Detection in the Blockchain Using Numerical Association Rule Mining

Toward Anomaly Detection in the Blockchain Using Numerical A...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Renata Kramberger Iztok Fister Aida Kamišalić Department of Information Technology and Computing Zagreb University of Applied Sciences Zagreb Croatia Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

With the global increase in the popularity of cryptocurrencies, the need for anomaly detection and fraudulent behavior is reaching an all-time high. In our paper, we propose a novel method of anomaly detection with the use of Numerical Association Rule Mining with Differential Evolution. The experiment was conducted by using the Dogecoin blockhain, and the dataset contained all of the transactions from one month. Our results contained 303 rules, with the best fitness function value of 0.8.

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models.

关键词： Analytical models Load forecasting Biological system modeling Visual analytics Decision making Predictive models Data models Planning Forecasting Load modeling

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Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

Machine Learning and Network Traffic to Distinguish Between ...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

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Deciphering of solvent fraction, thermal relaxation and cubic autocatalysis in unsteady nonlinear radiative ternary nanoliquid flow over a slow rotating disk

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Multiscale and Multidisciplinary Modeling, Experiments and Design 2025年第6期8卷 1-15页

作者： Kumar, Thanesh Deevi Reddy, Prabhakara Reddy Reddy, Anitha Deevi Remidi, Srinivas Chohan, Jasgurpreet Singh Kedia, Ankit Mathematics and Computing Skills Unit University of Technology and Applied Sciences - Nizwa Nizwa Oman School of Mechanical Engineering Rayat Bahra University Mohali India Faculty of Engineering Sohar University Sohar Oman Department of Mechanical and Aerospace Engineering NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India

The present study explores the consequence of homogeneous–heterogeneous chemical reactions on the unsteady flow of Boger ternary nanoliquid via a slow rotating disk with the porous medium, suction, and non-linear thermal radiation. Additionally, the temperature equation is modelled using the Cattaneo–Christov heat flux theory. Fluid flow past revolving disk concepts are utilized in various technical applications, including gas turbine engines, flywheels, gears, and brakes. The analysis considers titanium dioxide, cobalt ferrite, and magnesium oxide nanoparticles that are suspended in water. Using similarity variables, the governing partial differential equations (PDEs) are converted to dimensionless ordinary differential equations (ODEs). Further, the resultant ODEs are numerically solved using the finite difference method (FDM). The impact of several parameters on the velocity, thermal, and concentration profiles are depicted with the graphical illustrations. The velocity profiles decline as the ratio of relaxation time and porous parameter values rise. An increase in the solvent fraction parameter enhances the velocity profiles. The velocity profiles diminish as the suction parameter increases. As the radiation parameter values increase, the temperature profile rises. The concentration profile diminishes as the intensity of the homogeneous and heterogeneous reaction parameters escalates. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Stream flow

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