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Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

Computer Modeling in Engineering & Sciences 2022年第12期133卷 843-872页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan mazen ebraheem assiri Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Electronics&Telecommunication Vilad GhatDr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydney2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Universiti KebangsaanInstitute of Climate ChangeMalaysiaSelangor43600Malaysia Department of Computer Science LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Center for Applied Artificial Intelligence(SCAAI) LavaleSymbiosis International(Deemed University)Pune412115India

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent ***’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing *** learning models are at the forefront of this ***,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their *** a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep *** situation is substantially different in safety-critical *** lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this ***,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an *** lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment *** is the term introduced in the preceding *** AImodel’s black box nature will become explainable with these *** in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial *** models’explainable natures will help them commercially in diagnosis decisions in the medical *** paper explores the different frameworks for the explainability of AI models in the medical *** available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

关键词： Medical imaging explainability artificial intelligence XAI

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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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Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

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Computer Modeling in Engineering & Sciences 2023年第5期135卷 1053-1089页

作者： Sneha Basak Himanshi Agrawal Shreya Jena Shilpa Gite Mrinal Bachute Biswajeet Pradhan mazen assiri Computer Science and Information Technology Department Symbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South Wales2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Institute of Climate ChangeUniversity Kebangsaan MalaysiaBangiSelangor43600Malaysia

Speech recognition systems have become a unique human-computer interaction(HCI)*** is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation *** paper aims to present a retrospective yet modern approach to the world of speech recognition *** development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this *** review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal *** speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

关键词： Speech recognition automatic speech recognition(ASR) mel-frequency cepstral coefficients(MFCC) hidden Markov model(HMM) artificial neural network(ANN)

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