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A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 593-631页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Abdullah Alamri Chang-Wook Lee Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)PuneMaharashtra412115India Electronics&Telecommunication Dr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Artificial Intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India Symbiosis centre of Applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India centre for advancedmodelling and geospatial information systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyAustralia Earth Observation centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Science Education Kangwon National UniversityChuncheon-si24341Korea

Infection of leukemia in humans causes many complications in its later *** impairs bone marrow’s ability to produce *** diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this *** binary classification is employed to distinguish between normal and leukemiainfected *** addition,various subtypes of leukemia require different *** sub-classes must also be detected to obtain an accurate diagnosis of the type of *** entails using multi-class classification to determine the leukemia *** is usually done using a microscopic examination of these blood *** to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for *** utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of *** advanced deep-learning methods are also *** to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding *** study introduces a deep learning-machine learning combined approach for leukemia *** uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning *** transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature *** extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia *** the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the *** d

关键词： Leukemia diagnosis deep learning machine learning random forest XGBoost

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Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques

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Geoscience Frontiers 2023年第1期14卷 118-132页

作者： Ratiranjan Jena Biswajeet Pradhan Mansour Almazroui Mazen Assiri Hyuck-Jin Park centre for advanced modelling and geospatial information systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyAustralia Center of Excellence for Climate Change Research Department of MeteorologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-roGwangjin-guSeoul 05006Korea Earth Observation centre Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia

Australia is a relatively stable continental region but not tectonically inert,having geological conditions that are susceptible to liquefaction when subjected to earthquake ground *** hazard assessment for Australia was conducted because no Australian liquefaction maps that are based on modern Al techniques are currently *** this study,several conditioning factors including Shear wave velocity(Vs30),clay content,soil water content,soil bulk density,soil thickness,soil pH,distance from river,slope and elevation were considered to estimate the liquefaction potential index(LPI).By considering the Probabilistic Seismic Hazard Assessment(PSHA)technique,peak ground acceleration(PGA)was derived for 50 yrs period(500 and 2500 yrs return period)in ***,liquefaction hazard index(LHI)(effects based on the size and depth of the liquefiable areas)was estimated by considering the LPI along with the 2%and 10%exceedance probability of earthquake ***,ground acceleration data from the Geoscience Australia projecting 2%and 10%exceedance rate of PGA for 50 yrs were used in this study to produce earthquake induced soil liquefaction hazard ***,deep neural net-works(DNNs)were also exerted to estimate liquefaction hazard that can be reported as liquefaction hazard base maps for Australia with an accuracy of 94%and 93%,*** per the results,very-high liquefaction hazard can be observed in Western and Southern Australia including some parts of *** research is the first ever country-scale study to be considered for soil liquefaction hazard in Australia using geospatial information in association with PSHA and deep learning *** study used an earthquake design magnitude threshold of Mw 6 using the source model *** resulting maps present the earthquake-triggered liquefaction hazard and are intending to establish a conceptual structure to guide more detailed investigations as may be requir

关键词： Earthquake induced liquefaction PSHA Deep learning GIS Australia

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GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

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Journal of Mountain Science 2019年第3期16卷 595-618页

作者： Alireza ARABAMERI Biswajeet PRADHAN Khalil REZAE Masoud SOHRABI Zahra KALANTARI Department of Geomorphology Tarbiat Modares University centre for advanced modelling and geospatial information systems (CAMGIS) Faculty of Engineering and ITUniversity of Technology Sydney Department of Energy and Mineral Resources Engineering Choongmu-gwanSejong University Faculty of Earth Sciences Kharazmi University Department of Civil Engineering- geotechnics Islamic Azad university of Urmia Stockholm University Department of Physical Geography and Bolin Centre for Climate Research

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

关键词： Landslide susceptibility GIS Remote sensing Bivariate model Multivariate model Machine learning model

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Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

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Geoscience Frontiers 2020年第2期11卷 613-634页

作者： Ratiranjan Jena Biswajeet Pradhan Ghassan Beydoun Nizamuddin Ardiansyah Hizir Sofyan Muzailin Affan centre for advanced modelling and geospatial information systems(CAMGIS) School of InformationSystems and ModellingUniversity of Technology SydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Choongmu-gwanSejong University209 Neungdong-roGwangjin-guSeoul05006South Korea Department of Informatics Syiah Kuala UniversityBanda AcehIndonesia Department of Statistics Syiah Kuala UniversityBanda AcehIndonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban ***,earthquake risk assessment(ERA)is indispensable in disaster *** is an integration of the extent of probability and vulnerability of *** study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA *** aim of the study is to quantify urban population risk that may be caused by impending *** model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating *** is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic *** risk map is subsequently created by combining the probability,hazard,and vulnerability ***,the risk levels of various zones are *** validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk *** findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.

关键词： Earthquake Hazard Vulnerability Risk GIS ANN–AHP

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Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

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Computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari Hiep Van Le Binh Thai Pham Krishna Kant Singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia centre for advanced modelling and geospatial information systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 *** World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,***,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely ***19 is a major threat worldwide due to its ability to rapidly *** vaccines are yet available for *** objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of *** applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the *** also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the *** study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

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