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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

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computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： Brain tumor deep learning ensemble detection healthcare

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Skin Disease Detection Using Convolutional Neural Network 10

Skin Disease Detection Using Convolutional Neural Network

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Priyanka, G. Dhanabal, D. Divya, D. Hemanth, M. Karthika, V. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

Skin conditions are common everywhere, and early and precise diagnosis is essential for successful treatment. This work gives a thorough investigation of the use of Python programming and machine learning techniques for the prediction of skin disorders. In the suggested method, several machine learning algorithms are trained and assessed using a wide dataset of dermatological photos. The study starts with preprocessing methods to get the data ready for modeling, such as picture improvement and feature extraction. Then, using well-known Python libraries like scikit-learn and TensorFlow, a variety of classification methods are constructed, including Support Vector Machines, Random Forests, and Convolutional Neural Networks. To maximize the models' performance, cross-validation techniques are used for training and fine-tuning. The findings demonstrate the usefulness of the created models in properly predicting skin disease classes. Performance parameters such as accuracy, precision, recall, and F1-score are presented and compared between algorithms. The suggested Python-based system has considerable potential for supporting dermatologists and healthcare practitioners in the early detection and management of skin problems. © 2024 IEEE.

关键词： Convolutional neural networks

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Lightweight image super-resolution reconstruction based on mixed attention and global inductive bias network

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Multimedia Tools and Applications 2024年 1-17页

作者： Cai, Yuxi Wen, Xiaopeng Wang, Tongguan College of Computer Science Sichuan University Chengdu China Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China College of Informatics Huazhong Agricultural University Wuhan China

Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability to model global information of hierarchical features, incomplete attention to information, and excessive parameters make some based on convolutional methods face challenges. To address these issues, this paper designs the lightweight image super-resolution reconstruction based on Mixed Attention and Global Inductive bias Network(MAGIN), which directly captures the global information of phased features from the horizontal and vertical directions respectively to produce global inductive bias information. Furthermore, the global spatial attention block is proposed, which can realize the perception of global feature space without redundant operation. To realize more comprehensive attention for features, a mixed attention block arises, which enables the network to attend to features across different dimensions: channel, spatial, and pixel levels. Extensive experimental results demonstrate that the MAGIN delivers more satisfactory results than some other advanced lightweight reconstruction algorithms on several standard test datasets under the condition of considering the network’s performance, parameter and computational complexity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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An Integrated Web-Based Platform for Enhanced college Placement Management and Student Engagement 10

An Integrated Web-Based Platform for Enhanced College Placem...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Prakash, D. Shyam Sarumathi, K.M. Dhanashree, R. Sachin Samuel, R. Preetham Murthy, B. Department of Computer Science and Business systems Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350384369

In the contemporary educational landscape, college placement plays a pivotal role in the transition of students from academia to the professional world. However, this phase is often plagued by challenges such as inefficient communication between students and placement cells, inadequate tracking of job applications, and the need for personalized guidance. This research paper introduces a pioneering web application tailored to reshape this process fundamentally. The proposed system leverages state-of-the-art web technologies, coupled with the intelligence of artificial intelligence (AI) algorithms, to create an interface that redefines the way students and placement cells collaborate. This transformative web application streamlines communication, enhances efficiency, and ultimately optimizes outcomes for both stakeholders. In this paper, we present a comprehensive exploration of the system's architecture, functionality, and potential impact on the higher education ecosystem. © 2024 IEEE.

关键词： Students

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A Hybrid Recurrent Neural Network with Mini-Batch Gradient Descent Method for Multiple Sclerosis Disease Prediction

A Hybrid Recurrent Neural Network with Mini-Batch Gradient D...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Kavipriya, E. Sasikala, S. Department of Computer Science With Cognitive Systems and AIML Hindusthan College of Arts & Science Tamil Nadu Coimbatore India

ISBN: (纸本)9789819795062

Machine Learning techniques on different sectors plays a vital role for prediction analysis. In healthcare sector, diagnosis support and prognostic evaluation has been done using various algorithms of machine learning. The Multiple Sclerosis (MS) is one of the chronic diseases which affects the central nervous system of human. There are multiple symptoms and disabilities indicate the issues of Multiple Sclerosis. This neurological disorder has various patterns and disease progression rate among worldwide. This causes a variety of symptoms including fatigue, numbness, visual difficulties, urinary incontinence, and difficulty moving and cognitive deficiencies. This paper analyzes the risk factors of Multiple Sclerosis and early prediction using Machine learning methods. This work proposed hybrid Recurrent Neural Network with mini-batch gradient descent method for Multiple Sclerosis prediction. The performance of this proposed method is evaluated and the achieved accuracy rate is 92%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1374-1390页

作者： Mamuna Fatima Muhammad Attique Khan Saima Shaheen Nouf Abdullah Almujally Shui‐Hua Wang Department of Computer Science HITEC UniversityTaxilaPakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Mathematics University of LeicesterLeicesterUK

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical *** deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few *** manual diagnosis of medical issues always requires an expert and is also ***,developing some computer diagnosis techniques based on deep learning is *** cancer is the most frequently diagnosed cancer in females with a rapidly growing *** is estimated that patients with BrC will rise to 70%in the next 20 *** diagnosed at a later stage,the survival rate of patients with BrC is ***,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature *** significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning *** experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation *** comparison depicts that the presented method surpasses the current state‐ofthe‐art approache

关键词： artificial intelligence artificial neural network deep learning,medical image processing,multi‐objective optimization

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Revolutionizing Road Maintenance: YOLO Based Pothole Detection System 10

Revolutionizing Road Maintenance: YOLO Based Pothole Detecti...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Saranya, E. Nivetha, R. Abirami, S. Mohaideen Arsath, M. Dharaneesh, S. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Potholes on roadways pose a significant risk to public safety, leading to numerous accidents and fatalities annually. In this research, we present a custom-trained machine learning model for real-time pothole detection using the You Only Look Once (YOLO) version 7 object detection algorithm. Our study aims to address the critical issue of pothole-related accidents by evaluating the effectiveness of YOLO V7 in identifying potholes under various road conditions and lighting scenarios. We compare YOLO V7 with a sequential Convolutional Neural Network (CNN) algorithm and demonstrate that YOLO V7 outperforms in real-time pothole detection. The system incorporates a camera that captures live footage and highlights potholes in real time, enhancing road safety. Our research showcases the versatility of YOLO V7 in detecting potholes in both daylight and low-light conditions. Furthermore, we explore the feasibility of deploying YOLO V7 on edge devices for real-time pothole detection from moving vehicles. The results affirm YOLO V7's high accuracy and fast processing capabilities, making it a suitable solution for improving road safety by alerting drivers to road hazards. This research contributes to enhancing road safety and reducing accidents caused by potholes, ultimately saving lives and minimizing road infrastructure damage. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

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computers, Materials & Continua 2023年第2期74卷 2443-2459页

作者： Amani Abdulrahman Albraikan Nadhem NEMRI Mimouna Abdullah Alkhonaini Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Melanoma remains a serious illness which is a common formof skin *** the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic *** recent advances in deep learning(DL)models find useful to examine the medical image and make proper *** this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is *** goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of *** ADL-MDC technique performs contrast enhancement and data augmentation at the initial ***,the k-means clustering technique is applied for the image segmentation *** addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction ***,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification *** exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several *** simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

关键词： Biomedical images dermoscopic images deep learning melanoma detection machine learning

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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computer Modeling in Engineering & sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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