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Transfer function adaptation for effective feature selection with the side-blotched lizard algorithm

Multimedia Tools and Applications 2024年 1-21页

作者： Eisa, Mohamed Abdel-lah, Marwa Farouk, Fifi Abdel-aal, Amr System and Information Technology Department Faculty of Management Technology and Information System Port Said University Port Said Egypt Department of Computer Science Faculty of Computer and Informatics Zagazig University Zagazig Egypt

Feature selection is a crucial preprocessing step in data mining and machine learning, enhancing model performance and computational efficiency. This paper investigates the effectiveness of the Side-Blotched Lizard Optimization Algorithm (SBLA) for feature selection by developing six novel variants: Sbla-s1, Sbla-s2, Sbla-s3, Sbla-v1, Sbla-v2, and Sbla-v3, each employing distinct S-shaped or V-shaped transfer functions to convert the continuous search space to a binary format. These variants were rigorously evaluated on nineteen benchmark datasets from the UCI repository, comparing their performance based on average classification accuracy, average number of selected features, and average fitness value. The results demonstrated the superiority of Sbla-s3, achieving an average classification accuracy of 92.8% across all datasets, a mean number of selected features of 20, and an average fitness value of 0.08. Furthermore, Sbla-s3 consistently outperformed six other state-of-the-art metaheuristic algorithms, achieving the highest average accuracy on sixteen out of nineteen datasets. These findings establish Sbla-s3 as a promising and effective approach for feature selection, capable of identifying relevant features while maintaining high classification accuracy, potentially leading to improved model performance in various machine learning applications. © The Author(s) 2024.

关键词： Heuristic algorithms

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Coexistence of Wi-Fi 6E with LTE-U/LAA in the 5 GHz Frequency Band 8th

Coexistence of Wi-Fi 6E with LTE-U/LAA in the 5 GHz Frequen...

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Ibrahim, Mohamed M. Azzuhri, Saaidal R. Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9789819932351

This work is how to achieve high user throughput with high performance in congested areas by amending the existing coexistence methods and enhancing the performance metrics by updating some input metrics such as duty cycle period, energy detection threshold, data transfer period, etc. The simple topology will verify the basic concept of LTE-U/LAA coexistence with Wi-Fi. Then the indoor-crowded coexistence scenarios of LTE-LAA/U and Wi-Fi 6E in the 5 GHz will be the main idea of this study using the enhanced CAT-4 LBT algorithm design. The proposed model evaluated the coexistence performance based on CDF of throughput and latency. The transport layer of wireless communication will play a worthy role. When using the TCP protocol, the Wi-Fi throughput and latency will be more effective than the LAA operator. When using the UDP protocol, the LTE-LAA network has better throughput and latency than the Wi-Fi network. The throughput and latency for LTE-U and Wi-Fi operators are roughly identical using the FTP protocol. Overall, the throughput improved by 20%, and an approximately 2 ms delay time of the handover compared to the previous research results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wi-Fi

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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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A systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

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IEEE Access 2024年 12卷 53408-53423页

作者： Por, Lip Yee Ng, Ian Ouii Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes. © 2013 IEEE.

关键词： Authentication

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MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

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Neural Computing and Applications 2024年第31期36卷 19777-19793页

作者： Pethuraj, Mohamed Shakeel Burhanuddin, M.A. Dzakiyullah, Nur Rachman Department of Information System Faculty of Computer and Engineering Universitas Alma Ata Yogyakarta Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Malaysia

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： computer aided analysis

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Securing Data in Multimode Fibers by Exploiting Mode-Dependent Light Propagation Effects

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Research 2023年第3期2023卷 669-682页

作者： Stefan Rothe Karl-Ludwig Besser David Krause Robert Kuschmierz Nektarios Koukourakis Eduard Jorswieck Jürgen W.Czarske TU Dresden Faculty of Electrical and Computer EngineeringLaboratory of Measurement and Sensor System Technique01062 DresdenGermany TU Braunschweig Faculty of Electrical EngineeringInformation TechnologyPhysicsInstitute for Communications Technology38106 BraunschweigGermany TU Dresden Faculty of Physics01062 DresdenGermany

Multimode fibers hold great promise to advance data rates in optical communications but come with the challenge to compensate for modal crosstalk and mode-dependent losses,resulting in strong *** holographic measurement of the transmission matrix enables not only correcting distortions but also harnessing these effects for creating a confidential data connection between legitimate communication parties,Alice and *** feasibility of this physical-layer-security-based approach is demonstrated experimentally for the first time on a multimode fiber link to which the eavesdropper Eve is physically *** the proper structured light field is launched at Alice’s side,the message can be delivered to Bob,and,simultaneously,the decipherment for an illegitimate wiretapper Eve is *** a real communication scenario,we implement wiretap codes and demonstrate confidentiality by quantifying the level of *** to an uncoded data transmission,the amount of securely exchanged data is enhanced by a factor of *** complex light transportation phenomena that have long been considered limiting and have restricted the widespread use of multimode fiber are exploited for opening new perspectives on information security in spatial multiplexing communication systems.

关键词： distortion destroyed holographic

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Enhancing Children's Health Awareness through Narrative Game-Based Learning using Digital technology: A Framework and Methodology 9

Enhancing Children's Health Awareness through Narrative Game...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Muniandy, Nithiyaa Kamsin, Amirrudin Sabri, Mas Idayu Faculty of Computer Science & Information Technology University Malaya Kuala Lumpur Department of Computer System & Technology Malaysia

ISBN: (纸本)9798350304084

Effective health education and awareness initiatives are essential in a world facing growing concerns about children's well-being and rising unhealthy behaviours. This research presents a Narrative Game-Based Learning (N-GBL) framework designed to enhance children's health awareness, addressing critical gaps in their health education. Through a systematic literature review following the PRISMA methodology, this study identifies 13 essential components that form the foundation of the N-GBL framework. These components are structured around the Input-Process-Output (IPO) model, emphasizing narratives, game mechanics, and user engagement strategies. The core purpose of the N-GBL framework is to deepen children's understanding of the consequences of their choices, fostering informed decision-making and to empower them to make healthier choices. This research contributes to developing effective health awareness games tailored to children's learning preferences, bridging existing gaps in their health education. The N-GBL framework offers a revolutionary approach to health education for children, significantly enhancing health awareness and reducing health issues resulting from unhealthy behaviours. Future work will involve expert validation and practical testing in primary schools to transform health education for children. © 2023 IEEE.

关键词： Decision making

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Constructing a hybrid activation and parameter-fusion based CNN medical image classifier

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International Journal of Information technology (Singapore) 2024年第5期16卷 3265-3272页

作者： Maree, Mohammed Zanoon, Tareq Dababat, Alya’ Awwad, Mays Department of Information Technology Faculty of Information Technology Arab American University 13 Zababdeh Jenin Palestine Department of Computer System Engineering Faculty of Engineering Arab American University Jenin Palestine

In recent years, numerous deep learning models for medical image classification have emerged, with varying accuracies influenced by factors like image quality, content, and the convoluted low-level features. In this article, we aim to identify key features that enhance the precision of convolutional neural networks (CNNs), which are widely employed in this domain. We empirically demonstrate that parameters like activation functions, image augmentation, convolutional layers, filter sizes, and max pooling significantly impact precision, while others like hidden layers, epochs, and batch sizes affect quality and runtime negatively. To validate our proposal, we use a publicly-available dataset of colored Breast Cancer images which comprises 27,751 images distributed over three sub-datasets as follows: 4162 testing images (2980 no cancer and 1182 with cancer), 19,426 training images (13,911 no cancer and 5515 with cancer), and 4163 validation images (2981 no cancer and 1182 with cancer). Multiple experimental runs were conducted, and the outcomes of each run were recorded, taking into account various evaluation metrics and components of the CNN architecture. Results demonstrate that the highest CNN classifier’s accuracy was achieved when coupling RelU and Sigmoid activation functions using a 3 × 3 filter with a batch of size 32 and 20 epochs and 2 hidden layers. A Similar accuracy rate (83.55%) has also been achieved when coupling ReLU and Sigmoid activation functions using the DensNet-201 pre-trained CNN model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Breast cancer CNN Image classification Multi-level architecture Neural networks Parameter fusion

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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