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An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Computers, Materials & Continua 2023年第8期76卷 2061-2078页

作者： nouf Saeed Alotaibi Department of Computer Science College of Science and Humanities Al DawadmiShaqra UniversityShaqra11961Saudi Arabia

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in *** existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated *** problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system ***,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input ***,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked *** that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security *** prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data *** Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed *** suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

关键词： Intrusion detection system logistic tanh-based convolutional neural network classification(LTH-CNN) correlation coefficient based mayfly optimization(CC-MA) cyber security

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Enhanced Object Detection and Classification via Multi-Method Fusion

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Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia 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 Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Importance of Standardizing Analytical Characterization Methodology for Improved Reliability of the Nanomedicine Literature

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Nano-Micro Letters 2022年第11期14卷 18-32页

作者： Shahriar Sharifi nouf N.Mahmoud Elizabeth Voke Markita P.Landry Morteza Mahmoudi Department of Radiology and Precision Health Program Michigan State UniversityEast LansingMIUSA Faculty of Pharmacy Al-Zaytoonah University of JordanAmman 11733Jordan Department of Biomedical Sciences College of Health SciencesQU HealthQatar University2713 DohaQatar Department of Chemical and Biomolecular Engineering University of CaliforniaBerkeleyBerkeleyCAUSA Innovative Genomics Institute BerkeleyCAUSA California Institute for Quantitative Biosciences University of CaliforniaBerkeleyCAUSA Chan Zuckerberg Biohub San FranciscoCAUSA

Understanding the interaction between biological structures and nanoscale technologies,dubbed the nano-bio interface,is required for successful development of safe and efficient nanomedicine *** lack of a universal reporting system and decentralized methodologies for nanomaterial characterization have resulted in a low degree of reliability and reproducibility in the nanomedicine *** such,there is a strong need to establish a characterization system to support the reproducibility of nanoscience data particularly for studies seeking clinical ***,we discuss the existing key standards for addressing robust characterization of nanomaterials based on their intended use in medical devices or as *** also discuss the challenges surrounding implementation of such standard protocols and their implication for translation of nanotechnology into clinical ***,however,emphasize that practical implementation of standard protocols in experimental laboratories requires long-term planning through integration of stakeholders including institutions and funding agencies.

关键词： Characterization Nanomedicine Standard protocols Reproducibility Nanomedicine devices

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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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Robust Vehicle Detection Based on Improved You Look Only Once

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Computers, Materials & Continua 2023年第2期74卷 3561-3577页

作者： Sunil Kumar Manisha Jailia Sudeep Varshney nitish Pathak Shabana Urooj nouf Abd Elmunim Department of Computer Science Banasthali VidyapithRajasthan304022India Department of Computer Science&Engineering School of Engineering&TechnologySharda UniversityGreater Noida201310India Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110078India Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Vehicle detection is still challenging for intelligent transportation systems(ITS)to achieve satisfactory *** existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection *** to advancements in detection technology,deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing *** paper presents a robust vehicle detection technique based on Improved You Look Only Once(RVD-YOLOv5)to enhance vehicle detection *** proposed method works in three phases;in the first phase,the K-means algorithm performs data clustering on datasets to generate the classes of the ***,in the second phase,the YOLOv5 is applied to create the bounding box,and the Non-Maximum Suppression(NMS)technique is used to eliminate the overlapping of the bounding boxes of the ***,the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third *** simulation results show that the proposed method achieves better results when compared with other state-of-art techniques,namely LightweightDilated Convolutional Neural Network(LD-CNN),Single Shot Detector(SSD),YOLOv3 and YOLOv4 on the performance metric like precision,recall,mAP and *** simulation and analysis are carried out on PASCAL VOC 2007,2012 and MS COCO 2017 datasets to obtain better performance for vehicle ***,the RVD-YOLOv5 obtains the results with an mAP of 98.6%and Precision,Recall,and F1-Score are 98%,96.2%and 97.09%,respectively.

关键词： Image-processing K-means clustering CNN YOLOv5 loss function deep-learning

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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Computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

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Computers, Materials & Continua 2023年第9期76卷 3707-3725页

作者： Asifa Mehmood Qureshi nouf Abdullah Almujally Saud S.Alotaibi Mohammed Hamad Alatiyyah Jeongmin Park Department of Creative Technologies Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-siGyeonggi-do15073Korea

Road congestion,air pollution,and accident rates have all increased as a result of rising traffic density andworldwide population *** the past ten years,the total number of automobiles has increased significantly over the *** this paper,a novel method for intelligent traffic surveillance is *** proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five *** improve the results,mean-shift clustering was applied to the segmented ***,the pixels given the label for the vehicle were extracted and blob detection was applied to mark each *** the validation of each detection,a vehicle verification method based on the structural similarity index is *** tracking of vehicles across the image frames is done using the Identifier(ID)assignment technique and particle ***,vehicle counting in each frame along with trajectory estimation was done for each *** proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone(VAID),0.86 over AU-AIR,and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)dataset during the experimental *** proposed system can be used for several purposes,such as vehicle identification in traffic,traffic density estimation at intersections,and traffic congestion sensing on a road.

关键词： Traffic surveillance multi-label segmentation random forest particle filter computer vision

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Furuncular myiasis by Wohlfahrtia magnifica(Diptera:Sarcophagidae)in a healthy child

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Asian Pacific Journal of Tropical Medicine 2022年第2期15卷 87-89页

作者： Majed Hamdi Wakid Angham Ahmed Almakki nouf Saleh Alsahaf Mohammed Ali Almatrafi King Abdulaziz University Faculty of Applied Medical SciencesDepartment of Medical Laboratory TechnologyJeddahSaudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddahSaudi Arabia Department of Laboratory and Blood Bank Security Forces HospitalMakkahSaudi Arabia Medical College Umm Al Qura UniversityMakkahSaudi Arabia Department of Pediatrics Umm Al Qura UniversityMakkahSaudi Arabia Department of Pediatrics Security Forces HospitalMakkahSaudi Arabia

Rationale:Human myiasis is the invasion of tissue or organs by fly *** could be obligatory,facultative,or *** concerns:A 4-year-old Saudi boy complained of fever over the past three days with multiple inflamed painful dermal furuncles and worms-like ***:Furuncular obligatory myiasis caused by Wohlfahrtia ***:Maggots were removed for *** wounds were cleaned with antiseptic *** and oral antibiotics were ***:Seven days later,the wounds completely ***:Although several reports correlated human myiasis with old age,low health status,mental retardation,and low socioeconomic status,but the patient in our case was a healthy child from a family with good socioeconomic status,good hygiene,no history of diseases or mental disability,but traveled to a village where the climate is suitable for fly breeding.

关键词： Furuncule Myiasis Wohlfahrtia magnifica Maggots Saudi Arabia

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Choroidal thickness measurements in young Saudi adult population: a cross-sectional study

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International Journal of Ophthalmology(English edition) 2023年第11期16卷 1814-1819页

作者： Mohammed M Althomali Ahmed A Alharbi nouf M Albnayan Abdulaziz M Alkhudhair Muteb K Alanazi Optometry Department College of Applied Medical SciencesKing Saud UniversityRiyadh 11362Saudi Arabia

AIM:To determine the choroidal thickness(CT)in young healthy Saudi adults using spectral-domain optical coherence tomography(SD-OCT)with an automated CT segmentation ***:Fifty-eight young adults(total of 116 eyes),39 males and 19 females participated in this study between the ages of 18 and 38y(mean 22.65±3.9y).All participants underwent ophthalmic screening examination,including the SD-OCT for measurements of CT in each quadrant egmented into five eccentric regions starting from the foveal region up to 4.5 mm towards the ***:The choroid was thickest in the foveal region(central 1 mm,300±60μm)and began to progressively thinner beyond the parafovea(1.5–2.5 mm,284±67μm)towards the peripheral region(3.5–4.5 mm from the fovea,254±83μm).The superior choroid showed the thickest profile(309±57μm),while the nasal choroid exhibited the thinnest(229±76μm).The rate of the thinning with increasing eccentricity was more predominant in the nasal choroid,which thinned from the foveal region(294±58μm)to the peripheral region(158±55μm).The superior and inferior choroid did not show a statistically significant thinning with eccentricity(all P>0.05).There was no statistically significant difference in the CT between gender,age,and laterality of the eyes(all P>0.05).A significant association of myopia with thinner subfoveal choroid was observed(Pearson’s,r=0.37),and regression analysis showed that a 10.3μm choroidal thinning for each diopter increase of ***:CT profile depends on eccentric and the *** CT profile across the measured area in the young Saudi adult population is comparable to other previous *** error is critical for CT evaluation.

关键词： choroidal thickness optical coherence tomography young adults Saudi population

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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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