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Image Splicing Forgery.Detection Using Feature-Based of Sonine Functions and Deep Features

Computers, Materials & Continua 2024年第1期78卷 795-810页

作者： ala’a R.Al-Shamasneh Rabha W.Ibrahim Department of Computer Science College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science and Mathematics Lebanese American UniversityBeirut11022801Lebanon Department of Mathematics Mathematics Research CenterNear East UniversityNicosia99138Turkey Information and Communication Technology.Research Group Scientific Research CenterAlayen UniversityDhi Qar64001Iraq

The growing prevalence of fake images on the Internet and social media makes image integrity.verification a crucial research *** of the most popular methods for manipulating digital images is image splicing,which involves copy.ng a specific area from one image and pasting it into *** were made to mitigate the effects of image splicing,which continues to be a significant research *** study.proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep *** stages make up the proposed *** first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced *** proposed Sonine functions-based feature extraction model reveals the spliced texture details by.extracting some clues about the probability.of image *** proposed model achieved an accuracy.of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy.of Sciences,Institute of Automation”which is a publicly.available dataset for forgery.*** experimental results show that,for image splicing forgery.detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy.precision,and ***,the obtained detection accuracy.attests to the benefit of using the Sonine functions alongside deep feature *** the regions or locations where image tampering has taken place is limited by.the *** research will need to look into advanced image analy.is techniques that can offer a higher degree of accuracy.in identify.ng and localizing tampering regions.

关键词： Image forgery image splicing deep learning Sonine functions

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer Sy.tem Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Sy.tems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology.has numerous applications in artificial intelligence including biometrics, security.authentication, law enforcement, and surveillance. Deep learning (DL) models, notably.convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily.fooled since they.o not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by.agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously.evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy.and performing well on unseen faces that have been blurred orrotated. Additionally. the suggested model outperformed the recently.released approaches on the COMSATS facedataset, achieving a high accuracy.of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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基于变革型领导力理论构建护士长领导力培训指标体系的研究

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护理管理杂志 2024年第3期24卷 257-261,271页

作者：亓琴沈庆荣马雪花郝文利 Minerva BDe ala 王甜泰安市中心医院护理部 271000 泰安市中心医院急诊监护室 271000 泰安市中心医院普通妇科 271000 泰安市中心医院胃肠外科 271000 菲律宾女子大学护理学院马尼拉市250030

目的基于变革型领导力理论构建护士长领导力培训指标体系,为提高护士长变革型领导力提供培训参考。方法基于变革型领导力理论,结合半结构式访谈结果及文献分析,拟订护士长变革型领导力培训课程初稿,对山东省16名护理管理、教学管理专家... 详细信息

目的基于变革型领导力理论构建护士长领导力培训指标体系,为提高护士长变革型领导力提供培训参考。方法基于变革型领导力理论,结合半结构式访谈结果及文献分析,拟订护士长变革型领导力培训课程初稿,对山东省16名护理管理、教学管理专家进行两轮函询,修订后形成护士长领导力培训指标体系。结果两轮问卷有效回收率均为100%,专家权威系数分别为0.853、0.891,肯德尔和谐系数分别为0.135、0.364;第2轮函询各条目的变异系数为0.000~0.156。最终确定的培训指标体系包含4个一级指标、13个二级指标和45个三级指标。结论护士长变革型领导力培训指标体系构建过程科学、可靠,内容全面,培训目标明确,能够满足护士长对变革型领导力的培训需求,可促进变革型领导力的发展。

关键词：变革型领导力理论领导力教学培训指标体系

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DNA Sequence Analy.is for Brain Disorder Using Deep Learning and Secure Storage

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Computers, Materials & Continua 2022年第6期71卷 5949-5962页

作者： ala Saleh Alluhaidan Departmemt of Information Sy.tems College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia

Analy.is of brain disorder in the neuroimaging of Magnetic Resonance Imaging(MRI),Positron Emission Tomography.PET),and Computed Tomography.CT)needs to understand the functionalities of the brain and it has been performed using *** learning algorithms have also been applied in genomics data *** brain disorder diseases of Alzheimer,Schizophrenia,and Parkinson are analy.ed in this *** main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging *** paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network(DBN)and securely.storing the image using a Deoxy.ibonucleic Acid(DNA)Sequence-based Joint Photographic Experts Group(JPEG)Zig Zag Encry.tion Algorithm(DBNJZZ).In this work,DBNJZZ implements an efficient and effective prediction model for disorders using the open-access datasets of Alzheimer’s Disease Neuroimaging Initiative(Adni),the Center for Biomedical Research Excellence(Cobre),the Open Access Series of Imaging Studies(Oasis),the Function Biomedical Informatics Research Network(Fbirn),a Parkinson’s dataset of 55 patients and 23 subjects with Parkinson’s sy.dromes(Ntua),and the Parkinson’s Progression Markers Initiative(Ppmi).This algorithm is implemented and tested using performance metric measures of accuracy.Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).DBNJZZ gives better performance with an accuracy.of 99.21%and also surpasses previous methods on other measures.

关键词： DBN Zig zag deep learning MAPE RMSE DNA genomics

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An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

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Computers, Materials & Continua 2023年第9期76卷 3443-3464页

作者： ala Kana Imtiaz Ahmad Department of Computer Engineering College of Engineering and PetroleumKuwait UniversityKuwait CityKuwait

A newly.proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by.apply.ng the Runge Kutta method as the key.search mechanism,has gained wider interest in solving optimization ***,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN *** work aims at filling this gap by.proposing an improved variant of the RUN algorithm called the *** size play. a vital role in both runtime efficiency.and optimization effectiveness of metaheuristic *** the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically.adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation *** addition,the proposed methodology.employ. an adaptive search step size technique to determine a better solution in the early.stages of evolution to improve the solution quality.fitness,and convergence speed of the original ***-RUN performance is analy.ed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original *** promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and *** results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality.solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent ***,with its higher computation efficiency.Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.

关键词： Optimization Runge Kutta(RUN) metaheuristic algorithm exploration exploitation population size adaptation adaptive search step size

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THEORETICAL AND NUMERICAL STUDy.OF THE BLOW UP IN A NONLINEAR VISCOELASTIC PROBLEM WITH VARIABLE-EXPONENT AND ARBITRARy.POSITIVE ENERGy

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Acta Mathematica Scientia 2022年第1期42卷 141-154页

作者： ala A.TalaHMEH Salim A.MESSAOUDI Mohamed alaHy.NE Mathematics Birzeit UniversityWest BankPalestine Mathematics University of SharjahP.O.Box 27272SharjahUAE Department of Mathematics RISEUniversity of SharjahP.O.Box 27272SharjahUAE

In this paper,we consider the following nonlinear viscoelastic wave equation with variable exponents:utt-△u+∫_(0)^(t)g(t-τ)△u(x,τ)dτ+μut=|u|^(p(x)-2)-u,whereμis a nonnegative constant and the exponent of nonlinearity.p(·)and g are given *** arbitrary.positive initial energy.and specific conditions on the relaxation function g,we prove a finite-time blow-up *** also give some numerical applications to illustrate our theoretical results.

关键词： nonlinear damping blow up finite time variable nonlinearity arbitrary.positive energy

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A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family.Classification

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Computers, Materials & Continua 2023年第7期76卷 1173-1193页

作者： Amjed Al Fahoum ala’a Zy.ut Hiam Alquran Isam Abu-Qasmieh Biomedical Sy.tems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Complex proteins are needed for many.biological *** amino acid chains reveals their properties and *** support healthy.tissue structure,phy.iology.and *** medicine and treatments require quantitative protein identification and *** technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains *** function inference from amino acid sequences is the main biological data *** study.analy.es whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily.s related protein families generate a solid vector representation.A coding technique for each sequence in each family.was devised using two representations to identify.each amino acid precisely.A bispectral analy.is converts encoded protein numerical sequences into images for better protein sequence and family.*** and validation employ.d 70%of the dataset,while 30%was used for *** paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by.a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all *** initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately.separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning *** single-stage technique had 64.2%+/-22.8%accuracy.63.3%+/-17.1%precision,and a 63.2%+/19.4%*** two-stage technique y.elded 92.2%+/-4.9%accuracy.92.7%+/-7.0%precision,and a 92.3%+/-5.0%*** work provides balanced,reliable,and precise forecasts for all families in all measures.

关键词： Globin-like superfamily numerical encoding bispectral analy.is classification model deep convolutional neural network

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An Innovative Bispectral Deep Learning Method for Protein Family.Classification

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Computers, Materials & Continua 2023年第5期75卷 3971-3991页

作者： Isam Abu-Qasmieh Amjed Al Fahoum Hiam Alquran ala’a Zy.ut Biomedical Sy.tems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Proteins are essential for many.biological *** example,folding amino acid chains reveals their functionalities by.maintaining tissue structure,phy.iology.and *** that quantifiable protein characteristics are vital for improving therapies and precision *** automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence *** protein function from amino acid sequences is crucial in *** study.considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily.to generate a vector *** power of two representations was used to identify.each amino acid,and a coding technique was established for each sequence ***,the encoded protein numerical sequences are transformed into an image using bispectral analy.is to identify.essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding ***,the dataset was split 70/30 for training and ***,the dataset was utilized for 70%training,15%validation,and 15%*** suggested methods are evaluated using accuracy.precision,and *** non-normalized method had 70%accuracy.72%precision,and 71%recall.68%accuracy.67%precision,and 67%recall after ***,the normalized approach without validation had 92.4%accuracy.94.3%precision,and 91.1%*** showed 90%accuracy.91.2%precision,and 89.7%*** that both algorithms outperform the *** paper presents that bispectrum-based nonlinear analy.is using deep learning models outperforms standard machine learning methods and other deep learning

关键词： Globin-like superfamily numerical encoding bispectral analy.is classification model deep convolutional neural network(CNN)

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Review in life cy.le assessment of biomass conversion through py.oly.is-issues and recommendations

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Green Chemical Engineering 2022年第4期3卷 304-312页

作者： Zhaozhuo y. Haoxiang mab Xiangjun Liu Miao Wang Junqi Wang School of Human Settlements and Civil Engineering Xi'an Jiaotong UniversityXi'an710049China Deep Sea Engineering Division Institute of Deep-Sea Science and EngineeringChinese Academy of SciencesSanya572000China

Biomass,widespread and carbon-neutral energy.can provide electric energy.and replace fossil fuel-derived *** is the main way.of converting biomass to different bioenergy.products with the consumption of material and energy.which will cause environmental *** confirm the actual environmental impact of biomass conversion,life cy.le assessment(LCA)is used for analy.ing the *** to choosing different LCA methods,the results for the same thing in different reports will show obvious *** review is devoted to providing recommendations on how to handle methodological issues when analy.ing LCA study.by.which researchers can better realize the similarities and differences in biomass conversion *** this review,multiple clarifications and methodological recommendations on the four steps of the LCA process(including goal and scope definition,life cy.le inventory.life cy.le impact assessment,and interpretation)are ***,the LCA results are discussed sy.tematically.in which the global warming potential gets extra ***,different biomass feedstocks are divided into agricultural residues,forest residues,and microalgae ***,the current challenges and future framework of biomass conversion are expounded in detail from the perspective of LCA.

关键词： Life cy.le assessment Py.oly.is Global warming potential Agricultural residues Forest residues Microalgae

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Airstacknet: A Stacking Ensemble-Based Approach for Air Quality.Prediction

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Computers, Materials & Continua 2023年第1期74卷 2073-2096页

作者： Amel Ksibi Amina Salhi ala Saleh Alluhaidan Sahar A.El-Rahman Department of Information Sy.tems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

The quality.of the airwe breathe during the courses of our daily.lives has a significant impact on our health and well-being as ***,personal air quality.measurement remains *** this study.we investigate the use of first-person photos for the prediction of air *** main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution *** consists of two lay.rs and four regression models,where the first lay.r generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second lay.r computes the final prediction from the meta-data of the first lay.r using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality.Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only.can effectively.improve air pollution prediction performance by.overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.

关键词： Personal air quality prediction airstacknet ensemble learning feature extraction stacking

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