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An Innovative Bispectral Deep Learning Method for Protein Family Classification

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 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-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 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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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 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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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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Computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa isam abu qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** 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 *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** 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.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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Computer Systems Science & Engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie isam abu-qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** 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 *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** 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 *** 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 *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Combustion of Renewable Biogas Fuels

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Journal of Energy and Power Engineering 2015年第10期9卷 831-843页

作者： Chaouki Ghenai isam Janajreh Sustainable and Renewable Energy Department College of Engineering University of Sharjah Sharjah 27272 United Arab Emirates Mechanical Engineering Program MASDAR Institute of Science and Technology Abu Dhabi 54224 United Arab Emirates

Biogas fuel is a sustainable and renewable fuel produced from anaerobic digestion of organic matter. The biogas fuel is a flammable mixture of methane and carbon dioxide with low to medium calorific values. Biogas is an alternative to conventional fossil fuels and can be used for beating, transportation and power generation. CFD （computational fluid dynamic） analysis of the combustion performance and emissions of biogas fuel in gas turbine engines is presented in this study. The main objective of this study is to understand the impact of the variability in the biogas fuel compositions and lower heating values on the combustion process. Natural gas, biogas from anaerobic digester, landfill biogas, and natural gas/biogas mixture fuels combustion were investigated in this study. The CFD results show lower peak flame temperature and CO mole fractions inside the combustor and lower NOx emissions at the combustor exit for the biogas compared to natural gas fuel. The peak flame temperature decreases by 37% for the biogas landfill （COJCH4 = 0.89） and by 22% for the biogas anaerobic digester （CO2/CH4 = 0.54） compared to natural gas fuel combustion. The peak CO mole fraction inside the combustor decreases from 9.8 × 10-2 for natural gas fuel to 2.22 × 10-4 for biogas anaerobic digester and 1.32 × 10-7 for biogas landfill. The average NOx mole fraction at the combustor exit decreases from 1.13 × 10-5 for natural gas fuel to 0.40 × 10-6 for biogas anaerobic digester and 1.06 × 10-6 for biogas landfill. The presence of non-combustible constituents in the biogas reduces the temperature of the flame and consequently the NOx emissions.

关键词： Anaerobic digestion biogas non-premixed combustion NOx emissions CFD.

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Numerical Simulation of Char Particle Gasification

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Journal of Energy and Power Engineering 2015年第8期9卷 679-686页

作者： Syed Shabbar Raza isam Janajreh Rizwan Ahmed Ashjan AlKatheeri Waste to Energy Lab Masdar Institute of Science and Technology Abu Dhabi 54224 UAE Takreer Research Center Abu Dhabi 3593 UAE

A first principal modeling of the gasification of a char particle is performed using single step mechanism. The char particle is considered to be spherical in shape and only the physical and chemical properties can change in the radial direction. The carbon dioxide is used as the gasification agent that reacts with the char and form carbon monoxide. The presence of both solid and gaseous phase species makes the reaction heterogeneous. The char particle is considered with varying porosity that also allows the change in the surface area of the particle. A time invariant temperature and pressure profile is used at which the Arrhenius rate constant and diffusion is calculated. The mass conservation of model results in the form of two coupled partial differential and one ordinary differential equation. The equations are solved with a set of initial and boundary conditions using the bulk species concentration at the particle surface. A second order accurate central differencing scheme is used to discretize space while backward differencing is used to discretize time. Finally, the results are presented for the concentration distribution of CO and CO2 in radial direction with respect to time. It shows that, maximum concentration of CO is present at the center of the particle while the concentration gradient becomes higher near the particle surface. The nonlinear concentration trend due to the diffusion is effectively captured. The results show that, completed conversion of char depend upon the time provided for the reaction which can be reduced by decreasing the size of particle or increasing the reaction temperature. The sensitivity study of temperature and initial porosity also performed and showed that temperature has high impact on char conversion as compare to initial porosity.

关键词： Coal particle conversion biomass porosity CO2 gasification.

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A Study of Correlation of Serum Chromium Level with Glycosylated Haemoglobin (HbA1c), Total Cholesterol and Triglycerides, among Type 2 Diabetes Patients

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Open Journal of Blood Diseases 2019年第1期9卷 1-8页

作者： Hani M. Mohamed isam M. Sadik Abdelgadir Eltom Ahmed L. Osman Asaad MA. Babker Department of Clinical Chemistry College of Medical Laboratories Science University for Sciences and Technology Omdurman Sudan Department of Clinical Chemistry Faculty of Medical Laboratories Science Omdurman Islamic University Khartoum Sudan Department of Medical Laboratory Sciences College of Health Sciences Gulf Medical University Ajman UAE

Background: Diabetes mellitus is a common disease and it is a major cause of morbidity;several studies indicate that diabetes is a likely under reported cause of death. Chromium’s is important trace element to control diabetes mellitus and metabolism of carbohydrate, lipid and protein. Objective: The purpose of this study was to understand the relationship between serum chromium, with HbA1c, Total cholesterol and Triglycerides among type 2 diabetes patients among diabetic patients. Methodology: This is cross-sectional study done in Jabber abu Ezz Centre for treatment and care of diabetics in Khartoum—Sudan. Four hundred subjects were enrolled in this study;one hundred subjects were normal healthy as control group, and three hundred subjects diabetic patient type 2 as test group;demographic and biochemical data were collected;serum chromium, Glycosylated Haemoglobin (HbA1c), Total cholesterol Triglycerides, were determined by using NYCOCARD READER II, spectrophotometer (Biosystem 310) and spectrophotometer 210-VGP. Result: In this study there is significant parameters level means of FBS HbA1c, Total Cholesterol, Triglycerides and Chromium of the test groups when compared with healthy control groups subjects (P = 0.001, 0.018, 0.01, 0.011, 0.004), respectively. Significant negative correlation is between FBS, HbA1c, Total Cholesterol, Triglycerides and Chromium (r = ?0.555, P value = 0.003), (r = ?0.668, P value = 0.002), (r = ?0.335, P value = 0.004) and (r = ?0.774, P value = 0.002) respectively. Conclusion: There was significant correlation between serum Chromium level with fasting blood sugaer, Glycosylated Haemoglobin (HbA1c), Total cholesterol and Triglycerides among type 2 diabetes patients.

关键词： Type 2 Diabetes Mellitus Chromium Glycosylated Haemoglobin Total Cholesterol Triglycerides

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