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| 1 | Variable coefficient equations of the Kadomtsev–Petviashvili hierarchy: multiple soliton solutions and singular multiple soliton solutions显示文摘 | H M Jaradat Safwan Al-Shara Fadi Awawdeh Marwan Alquran | 2012 | Physica Scripta2012，，3： | 1 |
| 2 | A variety of physical structures to the generalized equal-width equation derived from Wazwaz-Benjamin-Bona-Mahony model显示文摘In this paper we are interested in investigating the physical shape-changed propagations to the generalized Equal-Width equation through studying the explicit solutions of Wazwaz-Benjamin-Bona-Mahony model.Both models are of considerable importance in many disciplines of research,including ocean engineering and science,and describe the propagation of equally-width waves.We highlight the effect of the coefficients of both nonlinearity and dispersion terms on changing the physical shape of both models by implementing the new exponential-expansion scheme.2D and 3D graphical plots are provided to validate the findings of the paper. | Imad Jaradat Marwan Alquran | 2022 | Journal of Ocean Engineering and Science2022，7，3： | 0 |
| 3 | Pulmonary Diseases Decision Support System Using Deep Learning Approach显示文摘Pulmonary diseases are common throughout the world,especially in developing countries.These diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently COVID-19.In general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists.In recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases.In this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was analyzed.To the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)images.All models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross validation.Using individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors. | Yazan Al-Issa Ali Mohammad Alqudah Hiam Alquran Ahmed Al Issa | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 4 | New topological and non-topological unidirectional-wave solutions for the modifie d-mixe d KdV equation and bidirectional-waves solutions for the Benjamin Ono equation using recent techniques显示文摘In this paper,new explicit unidirectional wave solutions for the modified-mixed KdV equation and bidi-rectional waves for the Benjamin Ono equation are studied.New extension of the rational sine-cosine and sinh-cosh methods are used.In addition,an extension of the recent polynomial function method is applied as well.Different types of topological and non-topological soliton solutions are extracted to the proposed models and categorized by providing 2D and 3D graphs.Finally,some physical properties of the new bidirectional waves solutions to the Benjamin Ono model are discussed. | Marwan Alquran Mohammed Ali Hammad Jadallah | 2022 | Journal of Ocean Engineering and Science2022，7，2： | 0 |
| 5 | New physical structures and patterns to the optical solutions of the nonlinear Schrödinger equation with a higher dimension显示文摘It is commonly recognized that,despite current analytical approaches,many physical aspects of nonlinear models remain unknown.It is critical to build more efficient integration methods to design and construct numerous other unknown solutions and physical attributes for the nonlinear models,as well as for the benefit of the largest audience feasible.To achieve this goal,we propose a new extended unified auxiliary equation technique,a brand-new analytical method for solving nonlinear partial differential equations.The proposed method is applied to the nonlinear Schrödinger equation with a higher dimension in the anomalous dispersion.Many interesting solutions have been obtained.Moreover,to shed more light on the features of the obtained solutions,the figures for some obtained solutions are graphed.The propagation characteristics of the generated solutions are shown.The results show that the proper physical quantities and nonlinear wave qualities are connected to the parameter values.It is worth noting that the new method is very effective and efficient,and it may be applied in the realisation of novel solutions. | Karmina K Ali Abdullahi Yusuf Marwan Alquran Sibel Tarla | 2023 | Communications in Theoretical Physics2023，75，8： | 0 |
| 6 | Breather waves, analytical solutions and conservation laws using Lie–Bäcklund symmetries to the (2 + 1)-dimensional Chaffee–Infante equation显示文摘The(2+1)-dimensional Chaffee–Infante has a wide range of applications in science and engineering,including nonlinear fiber optics,electromagnetic field waves,signal processing through optical fibers,plasma physics,coastal engineering,fluid dynamics and is particularly useful for modeling ion-acoustic waves in plasma and sound waves.In this paper,this equation is investigated and analyzed using two effective schemes.The well-known tanh-coth and sine-cosine function schemes are employed to establish analytical solutions for the equation under consideration.The breather wave solutions are derived using the Cole–Hopf transformation.In addition,by means of new conservation theorem,we construct conservation laws(CLs)for the governing equation by means of Lie–Bäcklund symmetries.The novel characteristics for the(2+1)-dimensional Chaffee–Infante equation obtained in this work can be of great importance in nonlinear sciences and ocean engineering. | Abdullahi Yusuf Tukur Abdulkadir Sulaiman Alrazi Abdeljabbar Marwan Alquran | 2023 | Journal of Ocean Engineering and Science2023，8，2： | 0 |
| 7 | Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study显示文摘Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas. | Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 8 | Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach显示文摘Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women. | Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 9 | A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification显示文摘Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treatments require quantitative protein identification and function.Despite 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 unsolved.Protein function inference from amino acid sequences is the main biological data challenge.This 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 discrimination.Training and validation employed 70%of the dataset,while 30%was used for testing.This 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 phases.The 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 classification.The single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%F1-score.The two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%F1-score.This work provides balanced,reliable,and precise forecasts for all families in all measures.It ensured that the new model was resilient to family variances and provided high-scoring results. | Amjed Al Fahoum Ala’a Zyout Hiam Alquran Isam Abu-Qasmieh | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 10 | An Innovative Bispectral Deep Learning Method for Protein Family Classification显示文摘Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein characteristics are vital for improving therapies and precision medicine.The 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 data.Inferring protein function from amino acid sequences is crucial in biology.This 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 representation.The power of two representations was used to identify each amino acid,and a coding technique was established for each sequence family.Subsequently,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-normalized and normalized encoding techniques.Initially,the dataset was split 70/30 for training and testing.Correspondingly,the dataset was utilized for 70%training,15%validation,and 15%testing.The suggested methods are evaluated using accuracy,precision,and recall.The non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after validation.Meanwhile,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%recall.Validation showed 90%accuracy,91.2%precision,and 89.7%recall.Note that both algorithms outperform the rest.The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture.They offered the best inference performance as the proposed approach improves categorization and prediction.Several instances show successful multi-class prediction in molecular biology’s massive data. | Isam Abu-Qasmieh Amjed Al Fahoum Hiam Alquran Ala’a Zyout | 2023 | Computers, Materials & Continua2023，，5： | 0 |
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