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Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

Computers, Materials & Continua 2023年第4期75卷 393-407页

作者： Sachin Sharma Meena Malik Chander Prabha Amal Al-Rasheed Mona Alduailij sultan almakdi Department of CSE Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522302India Deparmtent of CSE Chandigarh UniversityMohaliPunjab140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran55461Saudi Arabia

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.

关键词： Image watermarking lifting wavelet transform discrete wavelet transform(DWT) firefly technique invariant moments

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Cryptanalysis of 2D-SCMCI Hyperchaotic Map Based Image Encryption Algorithm

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Computer Systems Science & Engineering 2023年第8期46卷 2401-2414页

作者： Mohammed S.Alshehri sultan almakdi Mimonah Al Qathrady Jawad Ahmad Department of Computer Science Najran UniversityNajran55461Saudi Arabia Department of Information Systems Najran UniversityNajran55461Saudi Arabia School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that,in addition to standard statistical evaluations,thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover,the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange,a few further improvement recommendations are also suggested.

关键词： Cryptanalysis hyperchaotic map image encryption chosen-plaintext attack cryptosystem

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Dissolution mechanism and kinetics ofβ(Mg_(17)Al_(12))phases in AZ91 magnesium alloy

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Journal of Magnesium and Alloys 2024年第4期12卷 1581-1592页

作者： sultan Alomairy Department of Physics College of ScienceTaif UniversityTaif 21944Saudi Arabia

In this study,the phase transformations,crystallization kinetics and dissolution mechanism ofβphase(Mg_(17)Al_(12))in magnesium alloy AZ91 were investigated by optical microscopy,X-ray diffraction,differential scanning calorimetry and differential dilatometry.The results indicate that this AZ91 alloy undergoes a phase transformation during aging,a discontinuous precipitation of theβphase(Mg_(17)Al_(12))at 150℃at the grain boundaries and another continuous at 350℃within the grains.The activation energy of the dissolution reaction of theβphase(Mg_(17)Al_(12))under non-isothermal conditions is 116.781 kJ/mol,while it is 129.7383 kJ/mol under isothermal conditions.The Avrami coefficient,n,relevant for the dissolution kinetics of theβphase(Mg_(17)Al_(12))is 1.152 and 1.211 in the non-isothermal and isothermal conditions respectively.The numerical coefficients m and Avrami n are 0.993 and 1.152.

关键词： AZ91 kinetics β(Mg_(17)Al_(12)) Activation energy Discontinuous precipitation Continuous precipitation DSC

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A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

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Computer Systems Science & Engineering 2023年第9期46卷 3017-3036页

作者： sultan almakdi Mohammed SAlshehri Yousef Asiri Mimonah Al Qathrady Anas Ibrar Jawad Ahmad Department of Computer Science Najran UniversityNajran61441Saudi Arabia Department of Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data.Medical picture encryption is a crucial step in many cloud-based and healthcare applications.In this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed cipher.The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process.The suggested cryptosystem enhances the security of the delivered medical images by performing many operations.To validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique characteristics.Several measures are used to evaluate the proposed cryptosystem,which all support its robust security.The simulation results confirm the supplied cryptosystem’s secrecy.Furthermore,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and IoMT.It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

关键词： Jigsaw transformation cryptosystem image encryption medical images Ikeda map chaotic system

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Effect of mitomycin C on the corneal endothelial cells of Saudi patients with myopia after transepithelial photorefractive keratectomy:a two-armed cohort study

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International Journal of Ophthalmology(English edition) 2024年第5期17卷 909-915页

作者： sultan H Alrashidi Department of Ophthalmology College of MedicineQassim UniversityBuraidah 51452Saudi Arabia

AIM:To study the effect of mitomycin C(MMC)applied during transepithelial photorefractive keratectomy(TPRK)on the corneal endothelium one week(W1)and three months(M3)after surgery and its determinants.METHODS:In this two-armed cohort study conducted in 2022,eyes treated with MMC during TPRK(group 1)were compared with eyes not treated with MMC(group 2).The corneal endothelial cell(EC)count,EC density(ECD;cells/mm2),average(μm2),standard deviation(μm2),coefficient of variation(CV%),ECmax,ECmin,and EC percentage of hexagonality were estimated at W1 and M3.The postoperative changes in the EC count in the two groups were compared and correlated with the other independent variables.RESULTS:Group 1 had 26 eyes,and group 2 had 78 eyes.All TPRK indices were significantly higher for the eyes in group 1 than for those in group 2.The MMC usage was not a significant predictor of the change in ECD(P=0.644),change in CV(P=0.374),and change in the percentage of hexagonality of EC(P=0.164)at W1.However,the use of MMC was a significant predictor of changes in CV(P=0.014)and the change in the percentage of hexagonality of EC(P=0.039)at M3.The duration of laser exposure and the size of the optical zone influenced the correlation of MMC use with the changes in EC indices,postoperatively.CONCLUSION:The use of MMC doesn’t affect ECD,CV,and percentage of hexagonality at W1 if other surgical indices are considered.At M3 after operating myopic eyes by TPRK,MMC significantly influence the CV and percentage of hexagonality.The duration of the laser application and the size of the optical zone should be considered when determining the effect of MMC on the EC indices.

关键词： transepithelial photorefractive keratectomy mitomycin-C corneal endothelium

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A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

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Computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri sultan almakdi Oumaima Saidani Nazik Alturki Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

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Forecasting Mental Stress Using Machine Learning Algorithms

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Computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi sultan almakdi Mohammed Alshehri M.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the world.In general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient outcomes.However,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical considerations.Thus,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and predicted.The methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the RHT.In addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss function.Besides,the Features Importance Interpretation is also delineated in this research.These forecasts could help individuals connect with expert mental health specialists more quickly and easily.According to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately diagnosed.This study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree Classifier.But after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)algorithm.Besides,several factors are identified in terms of predicting depression through statistical data analysis.Though the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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Computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan sultan almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable farming.Deep Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf diseases.However,current DL methods often require substantial computational resources,hindering their application on resource-constrained devices.We propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this.The Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification.The proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet approach.More specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato diseases.The model could be used on mobile platforms because it is lightweight and designed with fewer layers.Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Dm-Health App:Diabetes Diagnosis Using Machine Learning with Smartphone

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Computers, Materials & Continua 2022年第7期72卷 1713-1746页

作者： Elias Hossain Mohammed Alshehri sultan almakdi Hanan Halawani Md.Mizanur Rahman Wahidur Rahman Sabila Al Jannat Nadim Kaysar Shishir Mia Department of Software Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science Najran UniversityNajran55461Saudi Arabia Department of Computer Science and Engineering Rajshahi University of Engineering and TechnologyRajshahi6204Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of Computer Science and Engineering World University BangladeshDhaka1230Bangladesh

Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate diabetes.This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical tests.This study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design.The Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster diagnosis.The Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes.The Doctor Consultation Module ensures free treatment related to diabetes.In this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&LightGBM.To evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,respectively.This manuscript presents some exploratory data analysis,including a correlation matrix and survey report.Moreover,the proposed solution can be adjustable in the daily activities of a diabetic patient.

关键词： Machine learning diabetes-prediction support vector machine(SVM) LightGBM eHealth ROC-AUC

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Antimicrobial,antibiofilm,angiogenesis,anti-inflammatory,and wound healing activities of zinc nanoparticles green synthesized using Ferula macrecolea extract

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Asian Pacific Journal of Tropical Biomedicine 2024年第6期14卷 259-268页

作者： sultan F.Alnomasy Department of Medical Laboratories Sciences College of Applied Medical SciencesShaqra UniversityAl-Quwayiyah 19257Saudi Arabia

Objective:To assess the antimicrobial,antibiofilm,anti-inflammatory,angiogenic,and wound healing activities of zinc nanoparticles(ZNPs)green synthesized using Ferula macrecolea extract.Methods:The green synthesis was conducted using the precipitation method.Then,the minimum inhibitory concentration(MIC),minimum bactericidal concentration(MBC),and minimum biofilm inhibition concentration 50%(MBIC50)of ZNPs against Staphylococcus aureus(S.aureus)and Pseudomonas aeruginosa(P.aeruginosa)were evaluated.The effects of ZNPs on the gene expressions of Staphylococcus spp.[intracellular adhesion A(icaA)and D(icaD)]and P.aeruginosa(rhlI and rhlR)were investigated using quantitative real-time PCR.In addition,the effects of ZNPs on wound healing,angiogenesis,and anti-inflammatory markers were assessed.Results:The green-synthesized ZNPs demonstrated significant antimicrobial efficacy against S.aureus and P.aeruginosa.The biofilm formation in S.aureus and P.aeruginosa was also inhibited by ZNPs with MBIC50 values of 3.30μg/mL and 2.08μg/mL,respectively.Additionally,ZNPs downregulated the expression of biofilm-related genes icaA,icaD,rhlI,and rhlR in the tested bacteria.They also demonstrated promising in vitro wound healing effects by promoting fibroblast cell proliferation and wound closure in a dose-dependent manner.A significant increase in the expression of HLA-G5 and VEGF-A genes as well as a marked decrease in the expression of NF-κB,IL-1β,and TNF-αgenes were observed in cells treated with ZNPs compared to the control group(P<0.05).Conclusions:ZNPs display promising antibacterial effects against S.aureus and P.aeruginosa and wound-healing effects by inhibiting biofilm formation,inducing angiogenesis,and reducing inflammation.However,further studies must be conducted to specify the accurate mechanisms of action and toxicity of ZNPs.

关键词： Wound healing Antibacterial Angiogenesis Nanomedicine Ferula macrecolea Inflammation

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