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

Computers, Mater.als & 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 copying 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 analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

关键词： Image forgery image splicing deep learning Sonine functions

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Fractional rényi Entropy Image Enhancement for Deep Segmentation of Kidney MrI

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Computers, Mater.als & Continua 2021年第5期67卷 2061-2075页

作者： Hamid A.Jalab ala’a r.al-shamasneh Hadil Shaiba rabha W.Ibrahim Dumitru Baleanu Faculty of Computer Science&Information Technology University of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Informetrics research Group Ton Duc Thang UniversityHo Chi Minh City758307Vietnam Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City758307Vietnam Department of Mathematics Cankaya UniversityBalgatAnkara06530Turkey Institute of Space Sciences Magurele-BucharestR76900Romania Department of Medical research China Medical UniversityTaichung40402TaiwanChina

recently,many rapid developments in digital medical imaging have made further contributions to health care *** segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical *** factors like image contrast and quality affect the result of image *** to that,image contrast remains a challenging problem for image *** study presents a new image enhancement model based on fractional rényi entropy for the segmentation of kidney MrI *** proposed work consists of two stages:enhancement by fractional rényi entropy,and MrI Kidney deep *** proposed enhancement model exploits the pixel’s probability representations for image *** fractional rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MrI *** the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MrI *** experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground *** is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.

关键词： Fractional calculus rényi entropy convolution neural networks MrI kidney segmentation

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A New Medical Image Enhancement algorithm Based on Fractional Calculus

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Computers, Mater.als & Continua 2021年第8期68卷 1467-1483页

作者： Hamid A.Jalab rabha W.Ibrahim ali M.Hasan Faten Khalid Karim ala’a r.al-shamasneh Dumitru Baleanu Faculty of Computer Science and Information Technology University MalayaKuala Lumpur50603Malaysia Nonlinear Dynamics research Center(NDrC) Ajman UniversityAjman346UAE College of Medicine Al-Nahrain UniversityBaghdad10001Iraq Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Cankaya UniversityBalgatAnkara06530Turkey Institute of Space Sciences Magurele-BucharestR76900Romania Department of Medical research China Medical UniversityTaichung40402Taiwan

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured *** captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis *** overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the *** proposed model dynamically enhances the image based on the image *** main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’***,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the ***,the contrast of the image is then adjusted to enhance the regions with low ***,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input *** were conducted on brain MrI,lungs CT,and kidney MrI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in *** obtained comparative results show that the proposed image enhancement model achieves the best BrISQUE and NIQE ***,this model improves the details of brain MrI,lungs CT,and kidney MrI scans,and could therefore potentially help the medical staff during the diagnosis process.

关键词： Fractional calculus image enhancement brain MrI lungs CT kidney MrI

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A review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and research Directions

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Tsinghua Science and Technology 2024年第4期29卷 1152-1180页

作者： Ahmad alzu’bi ala’a alomar Shahed alkhaza’leh Abdelrahman Abuarqoub Mohammad Hammoudeh Department of Computer Science Jordan University of Science and TechnologyIrbid 22110Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiff CF52YBUK Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran 31261Kingdom of Saudi Arabia

The healthcare industry is rapidly adapting to new computing environments and technologies. With academics increasingly committed to developing and enhancing healthcare solutions that combine the Internet of Things (IoT) and edge computing, there is a greater need than ever to adequately monitor the data being acquired, shared, processed, and stored. The growth of cloud, IoT, and edge computing models presents severe data privacy concerns, especially in the healthcare sector. However, rigorous research to develop appropriate data privacy solutions in the healthcare sector is still lacking. This paper discusses the current state of privacy-preservation solutions in IoT and edge healthcare applications. It identifies the common strategies often used to include privacy by the intelligent edges and technologies in healthcare systems. Furthermore, the study addresses the technical complexity, efficacy, and sustainability limits of these methods. The study also highlights the privacy issues and current research directions that have driven the IoT and edge healthcare solutions, with which more insightful future applications are encouraged.

关键词： edge computing fog computing data privacy healthcare systems intelligent edges

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CapsNet-Fr: Capsule Networks for Improved recognition of Facial Features

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Computers, Mater.als & 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 System 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 Systems 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 theydo 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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Self-management in patients with diabetic foot ulcer:a concept analysis

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Frontiers of Nursing 2024年第3期11卷 267-274页

作者： Hui-ren Zhuang Yong-Mei You Qin Liu Hai-Ping Yu Minerva B.De ala Shanghai East Hospital Nursing DepartmentShanghai 200123China Health School Attached to Shanghai University of Medicine&Health Sciences Shanghai 200237China Philippine Women’s University Manila 1004Philippines

Objective:This paper presents an analysis of the concept of patient ***:The present study conducted searches on various databases,including Wanfang,Sinomed,CNKI,Pub Med,Cochrane Library,Embase,Web of Science,and *** paper followed the Walker and Avant concept-analysis ***:Initially,899 pieces of literature were identified through the search process,and after screening,41 of them were ultimately included in the *** identified attributes of the concept included(1)capability,(2)decision making,and(3)*** antecedents were shaped by factors such as illness perception,self-efficacy,and family and *** consequences included(1)physiological effects,(2)psychological effects,and(3)social ***:The concept analysis of self-management in patients with diabetic foot ulcers(DFUs)not only aids in clinical practice and suppor ts interventions,but also contributes to the development of self-management *** common goal of clinical medical staff is to assist DFU patients in improving cognitive ability,making correct self-management decisions,and enhancing self-management behavior.

关键词： conception conceptual analysis DFU diabetic foot ulcer self-management

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

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Computers, Mater.als & Continua 2023年第7期76卷 1173-1193页

作者： Amjed al Fahoum ala’a Zyout Hiam alquran Isam Abu-Qasmieh Biomedical Systems 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,physiology,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 analyzes 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 analysis converts encoded protein numerical sequences into images for better protein sequence and family *** and validation employed 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 yielded 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 analysis 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, Mater.als & Continua 2023年第5期75卷 3971-3991页

作者： Isam Abu-Qasmieh Amjed al Fahoum Hiam alquran ala’a Zyout Biomedical Systems 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,physiology,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 analysis 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-nor.alized and nor.alized 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-nor.alized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after ***,the nor.alized 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 analysis using deep learning models outperforms standard machine learning methods and other deep learning

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

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Exact solutions of conformable time fractional Zoomeron equation via IBSEFM

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Applied Mathematics(A Jour.al of Chinese Universities) 2021年第4期36卷 554-563页

作者： Ulviye Demirbilek Volkan ala Khanlar r.Mamedov Department of Mathematics Faculty of Science and LettersMersin UniversityMersin 33343Turkey Department of Mathematics Faculty of Science and LettersIgdir UniversityIgdir 76000Turkey

The nonlinear conformable time-fractional Zoomeron equation is an important mod-el to describe the evolution of a single scalar *** this paper,new exact solutions of con-formable time-fractional Zoomeron equation are constructed using the Improved Bernoulli Sub-Equation Function Method(IBSEFM).According to the parameters,3D and 2D figures of the solutions are plotted by the aid of Mathematics *** results show that IBSEFM is an efficient mathematical tool to solve nonlinear conformable time-fractional equations arising in mathematical physics and nonlinear optics.

关键词： conformable time-fractional derivative zoomeron equation IBSEFM

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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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