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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sadia Abdulhalim Rakif Pathan Belkacem Chikhaoui Department of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar Department of Science and Technology Institute of Applied Artificial Intelligence TELUQ University Montreal Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper proposes a new approach based on sentiment analysis for predicting the relevance of religious beliefs in contemporary relationships within the context of online dating. The motivation for this research lies in understanding how shared values, common missions, and rituals in various religions might influence decision-making in relationships, the study aims to predict users’ religious beliefs based on OkCupid profiles. Our approach starts first by 1) extracting relevant features using sentiment analysis from text data, and 2) developing machine learning models based on state-of-the-art algorithms such as random forests, logistic regression, and decision trees. The results obtained demonstrated the effectiveness of our proposed approach in accurately predicting religious beliefs and its superiority compared to the state-of-the-art methods. Through this investigation, the paper seeks insights into the interplay between religious beliefs and romantic choices.

关键词： Sentiment analysis Logistic regression Machine learning algorithms Shape Predictive models Feature extraction Prediction algorithms Decision trees Information technology Random forests

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman Akour Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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A Fast Exponential Analysis and Variable Projection Based Method for Linear Array Synthesis

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IEEE Antennas and Wireless Propagation Letters 2025年第6期24卷 1527-1531页

作者： Sengupta, Ramonika Knaepkens, Ferre Cuyt, Annie Prinsloo, David S. Schäfer, Thomas Smolders, A. Bart Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Faculty of Applied Engineering University of Antwerp Groenenborgerlaan 171 Antwerp2020 Belgium Computing Science and Mathematics Division University of Stirling Scotland StirlingFK9 4LA United Kingdom ASTRON Dwindloo Oude Hoogeveensedjik 4 7991 PD Netherlands SATCUBE Nellickevägen 22 Gothenburg41263 Sweden

This paper presents a fast exponential analysis and variable projection method to reduce the number of elements in linear phased arrays. The numerical examples given in this work demonstrate a reduction of approximately 20% or 10% in the number of elements, with only a slight decrease in directivity of 0.63 dBi observed for a scan range of [−45◦, 45◦]. Compared to the combined matrix pencil and Tabu search algorithm, the proposed technique converges in fewer iterations and exhibits ≈ 5 dB reduction in maximum side lobe level. © 2002-2011 IEEE All rights reserved.

关键词： Antenna phased arrays

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A Comprehensive Survey of Techniques for Lung Cancer Diagnosis and Prediction

A Comprehensive Survey of Techniques for Lung Cancer Diagnos...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Danish Jamil Syed Mehr Ali Shah Fatma Mahfoodh Mohammed Al-Jarwani Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan College of Computing and Information Sciences University of Technology and Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

Despite significant advancements, lung cancer remains a formidable global health challenge, necessitating effective diagnostic and prognostic methodologies. This survey paper examines current literature to identify and evaluate methods and models utilized for lung cancer diagnosis and prognosis, aiming to enhance early detection strategies for improved patient outcomes. The research addresses critical questions: What are the current methods and models employed for lung cancer diagnosis and prognosis, their strengths, limitations, and potential for future improvement? It investigates specific applications of deep learning architectures such as CNN, GoogleNet, VGG-16, U-Net, and machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models in lung cancer detection. Methodologically, a systematic review across key databases using relevant keywords was conducted to synthesize findings. The study highlights the strengths and limitations of existing approaches and identifies gaps in model interpretability, real-world validation, and integration of diverse techniques. Conclusions underscore the transformative potential of advanced computational methods in enhancing lung cancer diagnosis and prognosis, proposing avenues for future research to address current challenges and improve patient care.

关键词： Surveys Deep learning Machine learning algorithms Accuracy Computational modeling Transfer learning Lung cancer Medical services Prognostics and health management Systematic literature review

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Diffeomorphic Measure Matching with Kernels for Generative Modeling

arXiv

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arXiv 2024年

作者： Pandey, Biraj Hosseini, Bamdad Batlle, Pau Owhadi, Houman Department of Applied Mathematics University of Washington SeattleWA United States Department of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States

This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration. A theoretical analysis of the proposed method is presented, giving a priori error bounds in terms of the complexity of the model, the number of samples in the training set, and model misspecification. An extensive suite of numerical experiments further highlights the properties, strengths, and weaknesses of the method and extends its applicability to other tasks, such as conditional simulation and *** Codes 35Q68 49Q22 62F15 68T07 62R07 Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Solving 2 by 2 Grid Sudoku Problem using Grover’s Algorithm with Intel Quantum SDK

Solving 2 by 2 Grid Sudoku Problem using Grover’s Algorithm...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Tejas Shinde Yaknan John Gambo Kevin Rasch Helena Liebelt Rui Li High Performance Computing and Quantum Computing Deggendorf Institute of Technology Deggendorf Germany Quantum Computing Research Intel Inc Hillsboro Oregon US Department of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum Computers can do “multiple” tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover’s Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover’s algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover’s Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once.

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An Energy Efficient IPv6 Packet Delivery Outline for Industrial IoT Over G.9959 Protocol Based Wireless Sensor Network (WSN)

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SN Computer Science 2024年第7期5卷 813页

作者： Veena, R.S. Khambra, Geetanjli Sayal, Rishi Singh, Nagendra Dhandayuthapani, V. Bala Sar, Sumit Kumar Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Kanakapura Road Karnataka Bengaluru India Department of Computer Science and Applications The Bhopal School of Social Sciences MP Bhopal India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telangana Hyderabad 501506 India Department of Electrical and Electronics Engineering Trinity College of Engineering and Technology Telangana Karimnagar 505001 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science BIT Durg Chattisgarh Durg India

The world is witnessing a significant paradigm shift in the delivery and management of industrial services, driven by the need for automated manufacturing in industries such as mining. This transformation is pivotal as it leverages the Internet to interconnect people and devices, giving rise to a new field known as the Industrial Internet of Things (IIoT). IIoT represents the fusion of IoT’s computational capabilities with the principles of the industrial revolution, facilitating extensive scalability through the transition from IPv4 to IPv6. A crucial challenge within IIoT is the battery life of sensor nodes, which is directly influenced by the efficiency of their operating modules and protocols. Various protocols have been tested within IIoT-based Wireless Sensor Networks (WSNs) to address this issue. However, these protocols often fall short in terms of data rate performance, which is essential for supporting the diverse range of IIoT applications and services. This manuscript investigates the IPv6 packet delivery rate, focusing on energy efficiency and delay. The research identifies the G.9959 protocol as superior to IEEE 802.15.4 for IIoT adoption. Additionally, it examines the impact of bandwidth variation on energy efficiency. Through comprehensive simulations, the proposed scheme demonstrates superior performance compared to existing protocols, highlighting its potential for enhancing IIoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： G.9959 IIoT IPv6 OPD WSN

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Enhanced chaos-based image compression-encryption algorithm utilizing 2D compressive sensing and genetic algorithm optimization

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Multimedia Tools and Applications 2024年 1-38页

作者： Nanfak, Arnaud de Dieu Nkapkop, Jean Mvogo Ngono, Joseph Tabekoueng Njitacke, Zeric Lessouga Etoundi, Christophe Magloire Effa, Joseph Yves Laboratory of Electronic Electrical Engineering Automation and Telecommunication National Higher Polytechnic School of Douala University of Douala PO Box 2701 Douala Cameroon Technology and Applied Sciences Laboratory University Institute of Technology University of Douala P.O. Box 8698 Douala Cameroon Applied Computing Laboratory Faculty of Science University of Douala P.O. Box 2701 Douala Cameroon University of Buea P.O. Box 63 Buea Cameroon Department of Physics Faculty of Science University of Ngaoundere P.O. Box 454 Ngaoundere Cameroon

This paper introduces an innovative image compression and encryption algorithm that leverages the genetic algorithm (GA), compressive sensing (CS), and a newly developed hyperchaotic map. In this algorithm, the GA is employed to optimize the compression parameters, including those related to generating the measurement matrix and performing thresholding operation. The optimization objective is centred around on the Peak-Signal-to-Noise-Ratio (PSNR) of the reconstructed image. The encryption part involves employing the Arnold transform and byte-wise XOR operator for confusion and diffusion, respectively. The generation of the measurement matrix and chaotic sequences for encryption is facilitated by a novel two-dimensional fractional-Sine–Cosine (2DFSC) hyperchaotic map introduced in this research. Simulation outcomes demonstrate dynamic parameter values adjustments through optimization, resulting in improved decompression efficiency across different compression rates and image types. Assessment of security and decompression performance confirms the effectiveness of the proposed algorithm, showing a good compression effect, a good reconstruction efficacity even for low compression ratio, good speed and high security compared to some recent similar systems. This work may therefore be suitable to compress and secure data in wireless body area network applications, including those related to the internet of medical things. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Impact of User Innovativeness on Metaverse Adoption 25

Impact of User Innovativeness on Metaverse Adoption

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25th International Arab Conference on Information technology, ACIT 2024

作者： Salloum, Said Al-Maroof, Rana Saeed Tahat, Khalaf Alfaisal, Raghad Tahat, Dina University of Sharjah Health Economic and Financing Group Sharjah United Arab Emirates Al Buraimi University College English Language & Linguistics Department Al Buraimi Oman University of Jordan Digital Media Department Jordan Universiti Pendidikan Faculty of Computing and Meta-Technology Sultan Idris Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798331540012

the study focuses on the integration of the metaverse to enhance online learning platforms, particularly among Omani students. The research evaluates the acceptance of the metaverse using a conceptual model that emphasizes key adoption properties such as user mobility, users' accessibility and users' and ubiquity. The novelty of the paper lies in introducing the users' innovativeness as a moderator which positively influences other variables. The results indicate a favorable impact of the moderator on the mentioned variables. The practical implication of the study suggests that authorities in Oman should recognize the metaverse's significance as a transformative tool for online learning. Furthermore, managerial implications recommend the implementation of the metaverse in governmental sectors to enhance the learning environment in the Sultanate of Oman. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic classification of histology images into normal/cancer class with pre-trained CNN 8

Automatic classification of histology images into normal/can...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Achuthan, Geetha Kadry, Seifedine Suresh Manic, K. Meqdad, Maytham N. Department of Electrical and Communication Engineering National University of Science and Technology Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Al-Mustaqbal University College Babil Hillah Iraq

Deep-Learning-Scheme (DLS) based medical data assessment has been widely employed in recent years due to its improved accuracy. Our goal is to study the performance of the pre-trained DLS on RGB-scale breast-histology images. The implemented idea holds these phases;(i) Data collection, pre-processing and resizing, (ii) Training the DLS with chosen test-pictures, (iii) Testing and validating the performance of the DLS with 5-fold cross-validation. This investigation considered the breast-histology pictures for the study and binary classification is employed to achieve Normal/Cancer class grouping of images. The proposed work compared the classification performance of AlexNet, VGG16 and *** experimental outcome of this study authenticates that the AlexNet with the Random-Forest (RF) classifier helps to get a higher classification accuracy (>87%) compared to VGG16 and VGG19. © Published under licence by IOP Publishing Ltd.

关键词： Histology

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