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A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis

computer Modeling in Engineering & sciences 2024年第12期141卷 2575-2608页

作者： Hussain AlSalman Taha Alfakih Mabrook Al-Rakhami Mohammad Mehedi Hassan Amerah Alabrah Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective *** deep learning has significantly advanced the analysis of mammographic images,challenges such as low contrast,image noise,and the high dimensionality of features often degrade model *** these challenges,our study introduces a novel method integrating Genetic Algorithms(GA)with pre-trained Convolutional Neural Network(CNN)models to enhance feature selection and classification *** approach involves a systematic process:first,we employ widely-used CNN architectures(VGG16,VGG19,MobileNet,and DenseNet)to extract a broad range of features from the Medical Image Analysis Society(MIAS)mammography ***,a GA optimizes these features by selecting the most relevant and least redundant,aiming to overcome the typical pitfalls of high *** selected features are then utilized to train several classifiers,including Linear and Polynomial Support Vector Machines(SVMs),K-Nearest Neighbors,Decision Trees,and Random Forests,enabling a robust evaluation of the method’s effectiveness across varied learning *** extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy,from 83.33%to 89.58%,underscoring the method’s *** detailing these steps,we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging.

关键词： Deep learning convolution neural network(CNN) support vector machine(SVM) genetic algorithmic(GA) breast cancer an optimized smart diagnosis

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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Hyperparameter Optimization for Convolutional Neural Networks using the Salp Swarm Algorithm

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Informatica (Slovenia) 2023年第9期47卷 133-144页

作者： Abdulsaed, Entesar H. Alabbas, Maytham Khudeyer, Raidah S. Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

Convolutional neural networks (CNNs) have exceptionally performed across various computer vision tasks. However, their effectiveness depends heavily on the careful selection of hyperparameters. Optimizing these hyperparameters can be challenging and time-consuming, especially when working with large datasets and complex network architectures. In response, we propose a novel approach for hyperparameter optimization in CNNs using the Salp Swarm Algorithm (SSA). Based on the natural behavior of mollusks, SSA mimics the collective intelligence that governs feeding and navigation. Taking advantage of SSA's unique properties, our research thoroughly explores the hyperparameter space. This exploration aims to identify the algorithm that maximizes CNNs performance. This paper presents the architecture of the SSA-based framework for hyperparameter optimization and compares it to other established optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). We also present experimental results using the MNIST and fashion MNIST datasets, achieving an impressive classification accuracy of 99.46% for MNIST and 94.53% for fashion-MNIST. This case study not only contributes to the fields of deep learning and hyperparameter optimization by demonstrating the effectiveness of SSA in optimizing CNNs, but it also provides benefits to researchers and practitioners who are looking for optimal hyperparameter configurations for CNNs in a variety of computer vision applications. We also evaluate the scalability and robustness of our proposed method in the context of different CNNs structures. The insights we gained highlight SSA's potential for addressing challenges related to hyperparameter optimization. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Detection of IoT Botnet Cyber Attacks Using Machine Learning

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Informatica (Slovenia) 2023年第6期47卷 55-64页

作者： Khaleefah, Alaa Dhahi Al-Mashhadi, Haider M. College of Computer Science and Information Technology Computer Information Systems Department University of Basrah Basrah Iraq

As of 2018, the number of online devices has outpaced the global human population, a trend expected to surge towards an estimated 80 billion devices by 2024. With the growing ubiquity of Internet of Things (IoT) devices, securing these systems and the data they exchange has become increasingly complex, especially with the escalating frequency of IoT botnet attacks (IBA). The extensive data quantity and pervasive availability provided by these devices present a lucrative prospect for potential hackers, further escalating cybersecurity risks. Hence, one of the paramount challenges concerning IoT is ensuring its security. The primary objective of this research project is the development of a robust, machine learning algorithm-based model capable of detecting and mitigating botnet-based intrusions within IoT networks. The proposed model tackles the prevalent security issue posed by malicious bot activities. To optimize the model's performance, it was trained using the BoT-IoT dataset, employing a diverse range of machine learning methodologies, including linear regression, logistic regression, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) models. The efficacy of these models was evaluated using the F-measure, yielding results of 98.0%, 99.0%, 99.0%, and 99.0% respectively. These outcomes substantiate the models' capacity to accurately distinguish between normal and malicious network activities. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Anomaly detection

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Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

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computers, Materials & Continua 2023年第5期75卷 3065-3089页

作者： Ali Arshad Muhammad Nadeem Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Zaid Alzaid Rana Alabdan Badr Almutairi Sultan Almotairi Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic university of MadinahMedinah42351Saudi Arabia Department of Information Systems Faculty of Computer and Information Sciences CollegeMajmaah University11952Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah 11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit

关键词： Cryptography symmetric cipher text encryption matrix cipher encoding decoding hill matrix 64-bit encryption

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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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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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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