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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

Computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair Ahmed Hanaa Fathi diaa s.lama abd elminaam Department of Computer science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAs Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information system Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DsⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DsⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DsⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan sahar A.El-Rahman Didi Rosiyadi diaa s.lama abd elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAs malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

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Computers, Materials & Continua 2021年第12期69卷 4129-4149页

作者： diaa salam abd elminaam Nabil Neggaz Ibrahim abdulatief Ahmed Ahmed El sawy Abouelyazed Department of Information systems Faculty of Computers and Artificial IntelligenceBenha University12311Egypt Department of Computer science Faculty of Computer ScienceMisr International UniversityEgypt Universite des sciences at de la Technologie d’Oran Mohamed Boudiaf USTO-MBBP 1505EL M’naouer31000 OranLaboratoire Signal Image Parole(SIMPA)Department d’informatiqueFaculte des Mathematiques et InformatiqueAlgerie Department of Computer science Faculty of Computers and Artificial IntelligenceBenha University12311Egypt Department of Computer science Obour High Institute for Computers and InformaticsEgypt

At present,the immense development of social networks allows generating a significant amount of textual data,which has facilitated researchers to explore the field of opinion *** addition,the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality *** study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence(sI)-based algorithm,Harris hawks algorithm,to select the most relevant *** experimental study has been tested on two datasets:Arabic Jordanian General Tweets and Opinion Corpus for *** terms of accuracy and number of features,the results are better than those of other sI based algorithms,such as grey wolf optimizer and grasshopper optimization algorithm,and other algorithms in the literature,such as differential evolution,genetic algorithm,particle swarm optimization,basic and enhanced whale optimizer algorithm,slap swarm algorithm,and ant–lion optimizer.

关键词： Arabic opinion mining Harris hawks optimizer feature selection AJGT and OCA datasets

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Micropropagation of Daylily(Hemerocallis fulva)from Crown-Tip Explants and Assessment of somaclonal Variation of in Vitro-Propagated Plants Using sCoT Markers

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Phyton-International Journal of Experimental Botany 2023年第7期92卷 2183-2196页

作者： Esraa E.shalan said s.soliman Ahmed A.Mahmoud Jameel M.Al-Khayri salha M.ALshamrani Fatmah A.safhi Areej s.Jalal diaa abd El-Moneim abdallah A.Hassanin Genetics Department Faculty of AgricultureZagazig UniversityZagazig44511Egypt Department of Agricultural Biotechnology College of Agriculture and Food SciencesKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biology College of ScienceUniversity of JeddahJeddah21959Saudi Arabia Department of Biology College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Plant Production (Genetic Branch)Faculty of Environmental and Agricultural SciencesArish UniversityEl-Arish45511Egypt

Determination of the somaclonal variation of in vitro-propagated plants is crucial to determine the appropriate micropropagation protocol and growth regulators for commercial scale *** this research,nine multiplication media(MM)augmented with different concentrations of 6-benzyl adenine(BA),Kinetin(Kin),and Thidiazuron(TDZ),Three rooting media(RM)supplemented with three levels ofα-naphthalene acetic acid(NAA)and three types of soil mixtures(v/v);Coco peat/Vermiculite/sand(CVs),Peat moss/Perlite/sand(PPs)and Peat moss/Perlite(PP)were used in the micropropagation protocol of daylily ***2 showed the maximum shoot length and the number of leaves,while MM9 showed the maximum number of *** RM1 showed the maximum root length and the number of *** acclimatization,CVs,PPs,and PP soil mixture showed similar performance except the CVs mixture showed lower performance regarding plant height and *** genetic fidelity of micropropagated plants was evaluated using start Codon Targeted(sCoT)*** sCoT primers amplified 51 scorable bands with an approximate range from 146 bp to 1598 bp *** one out of 51 loci were presented in the mother plants.40 loci were polymorphic,11 were monomorphic and 7 were *** amplification patterns of the micropropagated plants demonstrated genetic integrity to the mother plant ranging from 84.32 to 47.06 and somaclonal variations ranging from 52.94 with 5 mg/l BA pathway to 15.68 with 1mg/l TDZ pathway,thus demonstrating that the homogeneity and the variation of the micropropagated plants affected by the type and the quantity of the plant growth regulator used during multiplication *** research can be successfully used for other ornamental and medicinal plants’bulk multiplication,germplasm conservation,and future genetic improvement.

关键词： Genetic fidelity medicinal plants micropropagation molecular markers ornamental plants somaclonal variation

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Anatomical and Molecular Identification of Ornamental Plant Ficus ***

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Phyton-International Journal of Experimental Botany 2023年第5期92卷 1329-1347页

作者： Abtisam Binnoubah Rim Hamdy Osama G.Ragab Ahmed M.El-Taher Ahmed Abou El-Yazied Fatmah A.safhi Hala A.Elzilal Ashwaq T.Althobaiti salha M.ALshamrani diaa abd El Moneim Ahmed El-Banhawy Department of Botany Faculty of ScienceUniversity of SabrathaSabratha999116Libya Department of Botany Faculty of ScienceCairo UniversityGiza12111Egypt Department of Botany and Microbiology Faculty of Science(Boys Branch)Al-Azhar UniversityCairo11884Egypt Department of Agricultural Botany Faculty of AgricultureAl-Azhar UniversityCairo11884Egypt Department of Horticulture Faculty of AgricultureAin Shams UniversityCairo11566Egypt Department of Biology College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of science and Technology College of RanyahTaif UniversityTaif21944Saudi Arabia Department of Biology College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Biology College of ScienceUniversity of JeddahJeddah21959Saudi Arabia Department of Plant Production(Genetic Branch) Faculty of Environmental Agricultural SciencesArish UniversityEl-Arish45511Egypt Department of Botany and Microbiology Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

This present study includes twelve species that represent the Ficus genus,namely;aspera,carica,tinctoria ***,hirta,hispida,neriifolia,palmata,pumila,racemosa,septica,sur,and sycomorus,belonging to the Moraceae *** species samples were collected from various locations in *** study focused on the anatomical and molecular characteristics of mature foliage *** the identification and classification of taxa are highly dependent on the anatomical features of leaves,the anatomical characteristics were recorded in the form of a comparison between the examined plants in the data *** study aims to contribute to the identification of the studied species based on the anatomical details of the matured *** characterization includes the variations in upper and lower epidermal layers that are covered by a thin or thick cuticle;the number of palisade and spongy layers;crystals;secretory elements;lithocysts;the midrib zone has parenchyma associated with mechanical tissue,vascular system,and investigation of trichomes;on the other hand,in the current study,the phylogenetic analysis was conducted by using the ITs and 5.8 s *** the analysis of all the available data,it could be stated that there is an overall agreement with the anatomical character dendrogram.

关键词： ITs trichomes leaf anatomy Ficus moraceae

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