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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Copy-Right protection of color videos using robust watermarking based geometrically invariant moments of fractional orders and logistic sine cosine chaotic map

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Multimedia Tools and Applications 2024年 1-26页

作者： Alsahafi, Yousef S. Darwish, Mohamed M. Hosny, Khalid M. Department of Information Technology Khulis College University of Jeddah Jeddah Saudi Arabia Department of Computer Science Faculty of Computers and Information Assiut University Assiut Egypt Department of Computer Science University College of Al Wajh University of Tabuk Tabuk Saudi Arabia Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig Egypt

Digital video watermarking research has yielded numerous promising breakthroughs in recent years. As more people grow interested in videos, the copyright protection of videos has become an urgent issue that must be addressed. Digital video watermarking is a significant method for copyright protection of videos because of the growing need to safeguard video data’s intellectual property. Some existing works cannot resist geometric attacks effectively, while others fail to address the balance between robustness and visual imperceptibility. Significant progress has been made in studying fractional orthogonal moments due to their geometric invariance and beneficial image description capabilities. For these reasons, this paper proposes a robust watermarking algorithm for color video using accurate quaternion fractional Gegenbauer moments (QFrGMs), which are constructed relying on a combination of quaternion theory and accurate fractional Gegenbauer moments (FrGMs). The proposed algorithm is designed to address existing deficiencies in some related works and improve resilience. This algorithm is imperceptibly invisible and resistant to various kinds of attacks. In the proposed algorithm, the watermark information is embedded into accurately selected coefficients of QFrGMs for the selected frame of the cover video. This algorithm is robust to geometric attacks due to the outstanding geometric invariance of QFrGMs. To ensure security and improve the suggested algorithm’s security, we employed a chaotic map termed a one-dimensional Logistic Sine Cosine (LSC) map with superior chaotic characteristics to scramble the watermark. Numerical experiments were performed regarding imperceptibility and robustness to test the effectiveness. The experimental results demonstrated that the proposed method provides high visual quality and robustness against common signal processing, geometric, frame averaging, frame swapping, and frame dropping attacks. Furthermore, the proposed algorithm e

关键词： Image watermarking

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A Secure and Efficient information Authentication Scheme for E-Healthcare System

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computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Mining Customer Churns for Banking Industry using K-means and Multi-layer Perceptron

Mining Customer Churns for Banking Industry using K-means an...

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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Khine, Saw Thazin Myo, Win Win University of Information Technology Faculty of Computer Science Yangon Myanmar University of Information Technology Faculty of Computing Yangon Myanmar

ISBN: (纸本)9781665435994

Customer churns prediction is a major concern for banking industries all over the world, and market development is happening a more considerable task, and a more important challenge is taking place in business growth. The revenue of the banking sector totally depends on its important customers. By reducing the churn customers, commercial banks have many benefits on both gaining more profits and improving core competitiveness among the competitors. In this study, a mining customer churns using K-means and Multi-Layer Perceptron (MLP) is proposed for forecasting of customer churns in the banking industry. Churn-Modelling dataset from Kaggle site is used in this study. To develop the customer churns prediction model, the dataset is first cleaned, preprocessed and then K-means is used to find similar customer groups. To improve cluster quality, Silhouette method is applied before K-means. After that, Multi-Layer Perceptron classifier is applied to predict whether each customer from each group can leave or not from a bank. The proposed model gets good accuracy with low training time in all customers groups. The results show that the proposed mining customer churns prediction is able to find a certain number of customer churns identifying customer behavior and churn happens. To compare with the proposed model, the methods of K-means + SVM and the previous study on the same dataset are used. © 2023 IEEE.

关键词： Forecasting

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OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

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computers, Materials & Continua 2023年第7期76卷 1267-1289页

作者： M.Muzaffar Hameed Rodina Ahmad Laiha Mat Kiah Ghulam Murtaza Noman Mazhar Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur46400Malaysia Department of Computer Science Bahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science Sukkur Institute of Business Administration UniversitySukkur65200Pakistan

Offline signature verification(OfSV)is essential in preventing the falsification of *** learning(DL)based OfSVs require a high number of signature images to attain acceptable ***,a limited number of signature samples are available to train these models in a real-world *** researchers have proposed models to augment new signature images by applying various ***,on the other hand,have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature ***,augmenting a sufficient number of signatures with variations is still a challenging *** study proposed OffSig-SinGAN:a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature *** proposed model is capable of augmenting better quality signatures with diversity from a single signature image *** is empirically evaluated on widely used public datasets;*** quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference,peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),and frechet inception distance(FID).Furthermore,various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy *** results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation *** improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.

关键词： Signature forgery detection offline signature verification deep learning image augmentation generative adversarial networks

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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A Hybrid Mel-Frequency Cepstrum Coefficients and Gated Recurrent Units Approach for Voiceprint Recognition 24

A Hybrid Mel-Frequency Cepstrum Coefficients and Gated Recur...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Teng, Jackson Horlick Ong, Thian Song Lim, Kian Ming Faculty of Information Science and Technology Multimedia University Melaka Malaysia School of Computer Science University of Nottingham Ningbo China

ISBN: (纸本)9798400718014

A voiceprint system recognizes an individual using their voice biometrics for person recognition purpose. Voiceprint offers notable advantages in convenience and cost since it only requires a microphone, a ubiquitous component in smartphones, making it highly practical for user identity management such as personalized customer support by recognizing the user using just their voice. This paper proposes a voiceprint recognition system to combine Mel-Frequency Cepstrum Coefficients (MFCC) and Gated Recurrent Units (GRU), which is termed MFCC-GRU method. The resulting mixture is extremely useful in voiceprint recognition, as it could accurately capture and evaluate the unique characteristics and temporal dynamics of an individual’s voice for identity verification. The proposed method is tested on three voiceprint datasets and achieving the highest accuracy of 98.17% at best. © 2024 Copyright held by the owner/author(s).

关键词： Biometrics

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Lung Cancer Detection Using Deep Learning and Explainable Methods 14

Lung Cancer Detection Using Deep Learning and Explainable Me...

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14th International Conference on information and Communication Systems, ICICS 2023

作者： Alomar, Ayah Alazzam, Moayed Mustafa, Hala Mustafa, Ahmad Jordan University of Science and Technology Faculty of Computer and Information Technology Irbid Jordan

ISBN: (纸本)9798350307870

Lung cancer is one of the most prevalent deadly diseases and it can extend to the rest of the human body. One way to detect it in CT scan images is by using deep learning models. Explaining these models by XAI techniques and radiologists make the results trusted for medical use. In this paper, the deep learning models inceptionV3and ResNet50 were used to classify CT scans of lungs for the presence of cancer. The models were trained on a Kaggle dataset that is pre-processed and augmented. It was able to accurately detect lung cancer in new patients. Additionally, an XAI model was used to explain the decision-making process of the deep learning model, providing insights into which features of the CT scan were most important for the model's diagnosis. ResNet50 achieved the highest performance with 100% accuracy on testing images, and InceptionV3 got 99.92%. LIME and GRAD-CAM explained the model's performance by highlighting the most important features for each model. On the other hand, radiologists provided insight on whether the deep learning and XAI models are correct by determining the cancer region in each image and point out the model's misclassification. The results show that the radiologist's diagnosis is important even though the model's accuracy is high. © 2023 IEEE.

关键词： computerized tomography

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