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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

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2873-2897页

作者： Yingui Qiu Shuai Huang Danial Jahed Armaghani biswajeet Pradhan Annan Zhou Jian Zhou School of Resources and Safety Engineering Central South UniversityChangsha410083China School of Civil and Environmental Engineering University of Technology SydneyNew South WalesAustralia Centre for Advanced Modelling and Geospatial Information Systems School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew SouthWalesAustralia Civil and Infrastructure Engineering Discipline School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVictoria3001Australia

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and *** the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction *** performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine *** previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of ***,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF *** the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,*** examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM *** of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied *** newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed ***,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHA

关键词： Tunnel boring machine random forest GOGHS optimization PSO optimization GA optimization ABC optimization SHAP

Genesis of Graphite in Betul Belt, Madhya Pradesh, India:Inferences Based on Petrographic and Other Studies

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Open Journal of Geology（地质学期刊） 2022年第11期

作者： rajendra kumar sharma biswajeet lenka syed ajaz ahmed

来源：博看期刊评论

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Energy: Preface

Geoscience Frontiers 2021年第6期12卷 354-355页

作者： biswajeet Pradhan Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-roGwangjin-guSeoul 05006Republic of Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityP.O.Box 80234Jeddah 21589Saudi Arabia

Energy is a vital commodity that sustains human lives,as well as economic *** challenges towards energy generation,demand and supply are plenty owing to the use of fossil fuels leading to climate change and environmental problems like water and air *** the increasing awareness over climate change,post Paris Agreement,the role of energy plays a key role towards achieving the proposed *** contributions in this Special Issue of Geoscience Frontiers on Energy includes 8 papers from esteemed research groups worldwide which explores,highlights and provide new insights towards the various aspects of energy.

关键词： commodity plenty Preface

Geoinformation techniques in natural hazard modeling:Preface

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Geoscience Frontiers 2015年第6期6卷 791-792页

作者： biswajeet Pradhan Department of Civil Engineering Faculty of EngineeringUniversity Putra Malaysia

There are number of geoinformation approaches and modeling tools to the numerical measurement of natural hazards,including direct and indirect heuristic approaches,and deterministic,probabilistic,statistical and data mining approaches（Pradhan and Buchroithner,2012;Pradhan et al.,2012,2014;Pradhan,2013）.In this thematic section of Geoscience Frontiers,a set of six contributions are assembled that provide a window on various parameters,

关键词： thematic probabilistic deterministic heuristic contributions assembled hazard landslide triggered holistic

Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud

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Journal of Rock Mechanics and Geotechnical Engineering 2018年第3期10卷 534-544页

作者： Mohammed Oludare Idrees biswajeet Pradhan Department of Civil Engineering Geospatial Information Science Research Centre(GISRC)Faculty of EngineeringUniversity Putra Malaysia School of Systems Management and Leadership Faculty of Engineering and Information Technology University of Technology Sydney

The use of terrestrial laser scanning（TLS） in the caves has been growing drastically over the last ***, TLS application to cave stability assessment has not received much attention of *** study attempted to utilize rock surface orientations obtained from TLS point cloud collected along cave passages to（1） investigate the influence of rock geostructure on cave passage development, and（2）assess cave stability by determining areas susceptible to different failure types. The TLS point cloud was divided into six parts（Entry hall, Chamber, Main hall, Shaft 1, Shaft 2 and Shaft 3）, each representing different segments of the cave passages. Furthermore, the surface orientation information was extracted and grouped into surface discontinuity joint sets. The computed global mean and best-fit planes of the entire cave show that the outcrop dips 290° with a major north-south strike. But at individual level, the passages with dip angle between 26° and 80° are featured with dip direction of 75°-322°. Kinematic tests reveal the potential for various failure modes of rock slope. Our findings show that toppling is the dominant failure type accounting for high-risk rockfall in the cave, with probabilities of 75.26%, 43.07%and 24.82% in the Entry hall, Main hall and Shaft 2, respectively. Unlike Shaft 2 characterized by high risk of the three failure types（32.49%, 24.82% and 50%）, the chamber and Shaft 3 passages are not suffering from slope failure. The results also show that the characteristics of rock geostructure considerably influence the development of the cave passages, and four sections of the cave are susceptible to different slope failure types, at varying degrees of risk.

关键词： Rock geostructure Remote sensing Terrestrial laser scanning (TLS) Geospatial information system (GIS) Slope stability

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

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Geoscience Frontiers 2021年第2期12卷 625-637页

作者： Husam A.H.Al-Najjar biswajeet Pradhan Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and ITUniversity of Technology Sydney2007NSWAustralia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209Neungdong-roGwangin-guSeoul05006Republic of Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityP.O.Box 80234Jeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600UKMBangiSelangorMalaysia

In recent years,landslide susceptibility mapping has substantially improved with advances in machine ***,there are still challenges remain in landslide mapping due to the availability of limited inventory *** this paper,a novel method that improves the performance of machine learning techniques is *** proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of *** this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked ***,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron *** show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM *** models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,*** using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,*** the additional samples improved the test accuracy of all the models except *** a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is

关键词： Landslide susceptibility Inventory Machine learning Generative adversarial network Convolutional neural network Geographic information system

Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz,southwest Iran

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Geoscience Frontiers 2020年第3期11卷 783-792页

作者： Amir Karimian Torghabeh biswajeet Pradhan Ashkan Jahandari Department of Earth Sciences Faculty of SciencesShiraz UniversityShirazIran The Centre for Advanced Modeling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and ITUniversity of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong ro Gwangjin-guSeoul05006South Korea Department of Geology Shahid Bahonar University of KermanKermanIran

Geogenic dust is commonly believed to be one of the most important environmental problems in the Middle *** present study investigated the geochemical characteristics of atmospheric dust particles in Shiraz City(south of Iran).Atmospheric dust samples were collected through a dry collector method by using glass trays at 10 location sites in May *** composition was analysed through inductively coupled plasma optical emission *** data showed that the dustiest days were usually in spring and summer,particularly in April.X-ray diffraction analysis of atmospheric dust samples indicated that the mineralogical composition of atmospheric dust was calcite+dolomite(24%)>palygorskite(18%)>quartz(14%)>muscovite(13%)>albite(11%)>kaolinite(7%)>gypsum(7%)>zircon=anatase(3%).The high occurrence of palygorskite(16%-23%) could serve as a tracer of the source areas of dust storms from the desert of Iraq and Saudi Arabia to the South of *** electron microscopy indicated that the sizes of the collected dust varied from 50 μm to0.8 μm,but 10 μm was the predominant *** atmospheric dust collected had prismatic trigonal-rhombohedral crystals and semi-rounded irregular ***,diatoms were detected in several samples,suggesting that emissions from dry-bed lakes,such as Hoor Al-Azim Wetland(located in the southwest of Iran),also contributed to the dust *** trajectory simulations were performed at the date of sampling by using the NOAA HYSPLIT *** showed that the sources of atmospheric dust in the study area were the eastern area of Iraq,eastern desert of Saudi Arabia,Kuwait and Khuzestan *** Ca/Al ratio of the collected samples(1.14) was different from the upper continental crust(UCC) value(UCC=0.37),whereas Mg/A1(0.29),K/Al(0.22) and Ti/Al(0.07) ratios were close to the UCC value(0.04).This condition favours desert calcisols as the main mineral dust *** of the crustal enrichment factor(EF

关键词： Dust storm Atmospheric dust Heavy metals Diatom Palygorskite Provenance

Natural Hazards and Social Vulnerability of Place: The Strength-Based Approach Applied to Wollongong, Australia

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International Journal of Disaster Risk Science 2019年第3期10卷 404-420页

作者： Robert I.Ogie biswajeet Pradhan Smart Infrastructure Facility University of WollongongNSW 2522Australia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneyUltimoNSW 2007Australia Department of Energy and Mineral Resources Engineering Choongmu-gwanSejong UniversitySeoul 05006Republic of Korea

Natural hazards pose significant threats to different communities and various places around the *** to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques,however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index(SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.

关键词： Australia Disaster vulnerability Natural hazard Placed-based model Sense of community Strength-based approach