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11th IEEE International Conference on Software Defined Systems, SDS 2024

作者： Tahat, Dina Alfaisal, Raghad Tahat, Khalaf Salloum, Said Al Ain University Applied Sociology Department United Arab Emirates Universiti Pendidikan Faculty of Computing and Meta-Technology Tanjung Malim Malaysia United Arab Emirates University Yarmouk University Media & Creative Industries Jordan University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331518325

In an era where digital misinformation poses a severe challenge to societal trust, identifying fake news is imperative. This study employs a Long Short-Term Memory (LSTM) neural network to discern misinformation within a dataset of approximately 79,000 labeled news texts. Our LSTM model benefits from its aptitude for processing sequential data, crucial for understanding textual context. We evaluate our model's proficiency through accuracy, precision, recall, and F1 score, key metrics reflecting its classification capability. The model exhibits robust performance with a test accuracy of 94.09%, precision of 91.57%, recall of 95.45%, and an F1 score of 93.47%. These results underscore the potential of LSTM networks in automating fake news detection, which could significantly support the integrity of information consumption in the digital realm. © 2024 IEEE.

关键词： Long short-term memory

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A Novel Federated Learning Framework for Healthcare Applications Using Wearable Devices

A Novel Federated Learning Framework for Healthcare Applicat...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Rajesh Natarajan Sujatha Krishna Christodoss Prasanna Ranjith Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Al-Aqr 324 Shinas Oman Computer Science Department Messiah University Mechanicsburg PA USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

A key necessity in managing signals for processing and generating useful information in the healthcare domain is the use of many algorithms. Traditional computing methods have been successful but may not be effective in the future as secure privacy matters. This has led to the increasing popularity of machine learning technologies. However, we present a novel device for devices and sensors for federated learning (FL), which is aimed at establishing a new, safer framework. This pattern uses machine learning with a lot of emphasis on the availability of the devices. In such a case, models for the detection, monitoring, and management of diseases can be created. The FL approach is also believed to be applicable to several healthcare contexts.

关键词： Data privacy Accuracy Federated learning Biological system modeling Transfer learning Medical services Organizations Data models Sensors Biosensors

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A Platform for Automated Solar Data Analysis Using Machine Learning 9

A Platform for Automated Solar Data Analysis Using Machine L...

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9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

作者： Younis, Ola Tashtoush, Yahya Alomari, Mohammad Darwish, Omar Department of Computer Science University of Liverpool Liverpool United Kingdom Computer Science Department Jordan University of Science and Technology Jordan Applied Science Research Center Applied Science Private University Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti United States

ISBN: (纸本)9798350320480

This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system. © 2022 IEEE.

关键词： Data mining

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Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

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Computer Systems Science & Engineering 2023年第9期46卷 3303-3319页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable *** texts are generated in a natural communicative *** explosion of social media permitted individuals to spread data with minimal examination and filters *** to this,the old problem of fake news has *** has become an important concern due to its negative impact on the *** manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)*** perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite ***,a huge effort is required from the human side to generate the labelled training *** recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news *** unlock social media data,an automatic text classifier is highly helpful in the domain of *** current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)*** presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual *** attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding ***,the QCLSTM model is utilized for *** boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the *** proposed QCLSTM-FNC approach was experimentally validated against a benchmark *** QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

关键词： English corpus fake news detection social media natural language processing artificial intelligence deep learning

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Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第14期16卷 123-135页

作者： Maabreh, Majdi Obeidat, Ibrahim Elsoud, Esraa Abu Alnajjai, Asma Alzyoud, Rahaf Darwish, Omar Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI United States

Cyber attacks have increased in tandem with the exponential expansion of computer networks and network applications throughout the world. Fortunately, various machine/deep learning models have demonstrated excellent accuracy in predicting network attacks in the literature;nonetheless, having simple and understandable models might be a big benefit in network monitoring systems. In this study, we evaluate four feature selection algorithms to find the minimal set of predictive features of network attacks, seven classical machine learning algorithms, and the deep learning algorithm on one million random instances of the CSE-CIC-IDS2018 big data set for network intrusions. The feature selection algorithms highlighted the importance of features related to forwarding direction (FWD) and two flow measures (FLOW) in predicting the binary traffic type;benign or attack. Furthermore, the results revealed that not all features are required to build efficient ML/DL in detecting network attacks, four features unanimously selected by the feature selection algorithms were enough to build comparable ML models to those trained on all features. This might lead to models that are more suitable for deployment in terms of complexity, explainability, and scalability. Moreover, by selecting four unanimity features instead of all traffic features, the training time may be decreased by 10% to 50%. © 2022. All Rights Reserved.

关键词： Network security

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XAI Based Cattle Identification with YOLO and SIFT Technique

XAI Based Cattle Identification with YOLO and SIFT Technique

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Katharria, Aashu Pant, Millie Deep, Kusum Devi, Indu Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand Roorkee India Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand Roorkee India Indian Institute of Technology Roorkee Department of Mathematics Uttarakhand Roorkee India ICAR-National Dairy Research Institute Livestock Production Management Division Haryana Karnal132001 India

ISBN: (纸本)9798350391213

In precision livestock farming, accurate cattle identification is essential for enhancing animal welfare, health monitoring, and productivity, while also supporting traceability and minimizing false insurance claims. This paper presents a novel approach for cattle identification using muzzle prints, with a focus on both efficiency and explainability. Taxicab metric, employed for efficient annotation of muzzle patterns significantly reduces the labeling time for training of YOLOv8 model. YOLOv8 is utilized for detecting muzzle prints in images, followed by SIFT (Scale-Invariant Feature Transform) for feature extraction and matching. The incorporation of Explainable AI (XAI) methods, particularly Grad-CAM, further enhances the transparency and interpretability of the SIFT model. © 2024 IEEE.

关键词： Precision agriculture

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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Body Fat Prediction using Various Regression Techniques 2

Body Fat Prediction using Various Regression Techniques

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2nd IEEE International Conference on Advances in computing, Communication and applied Informatics, ACCAI 2023

作者： Mahesh, Nikhil Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Engineering Oman

ISBN: (纸本)9798350315905

Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study's results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction. © 2023 IEEE.

关键词： Forecasting

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Finite Difference Approximation with ADI Scheme for Two-dimensional Keller-Segel Equations

arXiv

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arXiv 2023年

作者： Lu, Yubin Chen, Chi-An Li, Xiaofan Liu, Chun Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States

Keller-Segel systems are a set of nonlinear partial differential equations used to model chemotaxis in biology. In this paper, we propose two alternating direction implicit (ADI) schemes to solve the 2D Keller-Segel systems directly with minimal computational cost, while preserving positivity, energy dissipation law and mass conservation. One scheme unconditionally preserves positivity, while the other does so conditionally. Both schemes achieve second-order accuracy in space, with the former being first-order accuracy in time and the latter second-order accuracy in time. Besides, the former scheme preserves the energy dissipation law asymptotically. We validate these results through numerical experiments, and also compare the efficiency of our schemes with the standard five-point scheme, demonstrating that our approaches effectively reduce computational costs. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy dissipation

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Analysing Student Depression through Cyber-Activity Patterns Using Heterogeneous Graph Attention Networks 1

Analysing Student Depression through Cyber-Activity Patterns...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Shukla, Tushar Dhar Vanyan, Diana Rizvi, Qaim Mehdi George, Geogen Bhosale, Aryaraj Dattatraya Bhoopathy, V. University Institute of Physical Education & Sports Chandigarh University Department of Physical Education Mohali India Kuban State University Krasnodar Russia Kolkata India University of Technology and Applied Sciences College of Computing and Information Sciences Middle East Ibri Oman Indian Institute of Information Technology Pune India Sree Rama Engineering College Department of Computer Science and Engineering Tirupathi India

ISBN: (纸本)9798350352931

This study focusses on the crucial matter of identifying student sadness by analysing patterns of cyberactivity. The technology utilizes Heterogeneous Graph Attention Networks (HGATs) to represent and examine intricate connections within the data. The dataset used for training and testing the model was obtained from Kaggle. The model achieved an accuracy of 9 6. 8 %, a precision of 9 5. 4 %, a recall of 94.7 %, and an AUC-ROC of 98.3 %. The findings of this study demonstrate a substantial improvement over current models, indicating the high efficacy of HGATs in accurately capturing complex patterns in cyber-activity data. The results indicate that this method can function as a potent instrument for identifying depression at an early stage, with wide-ranging implications for monitoring the mental health of students and implementing intervention techniques. © 2024 IEEE.

关键词： Graph neural networks

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