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Advancement of deep learning in pneumonia/Covid-19 classification and localization:A systematic review with qualitative and quantitative analysis

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Chronic Diseases and Translational Medicine 2022年第3期8卷 154-171页

作者： Aakash shah Manan Shah Department of Computer Science&Engineering Instute of TechnologyNirma UniversityAhmedabadIndia Department of Chemical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagarIndia

Around 450 million people are affected by pneumonia every year,which results in 2.5 million deaths.Coronavirus disease 2019(Covid-19)has also affected 181 million people,which led to 3.92 million casualties.The chances of death in both of these diseases can be significantly reduced if they are diagnosed early.However,the current methods of diagnosing pneumonia(complaints+chest X-ray)and Covid-19(real-time polymerase chain reaction)require the presence of expert radiologists and time,respectively.With the help of deep learning models,pneumonia and Covid-19 can be detected instantly from chest X-rays or computerized tomography(CT)scans.The process of diagnosing pneumonia/Covid-19 can become faster and more widespread.In this paper,we aimed to elicit,explain,and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community-acquired pneumonia,viral pneumonia,and Covid-19 from images of chest X-rays and CT scans.Being a systematic review,the focus of this paper lies in explaining various deep learning model architectures,which have either been modified or created from scratch for the task at hand.For each model,this paper answers the question of why the model is designed the way it is,the challenges that a particular model overcomes,and the tradeoffs that come with modifying a model to the required specifications.A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal.Some tradeoffs cannot be quantified and,hence,they are mentioned explicitly in the qualitative analysis,which is done throughout the paper.By compiling and analyzing a large quantum of research details in one place with all the data sets,model architectures,and results,we aimed to provide a one-stop solution to beginners and current researchers interested in this field.

关键词： classification Covid-19 deep learning localization pneumonia

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State-of-the-art machine learning techniques for melanoma skin cancer detection and classification:a comprehensive review

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Intelligent Medicine 2023年第3期3卷 180-190页

作者： Harsh Bhatt Vrunda shah Krish shah Ruju shah Manan Shah Department of Computer Science Institute of TechnologyNirma UniversityAhmedabadGujaratIndia Dhirubhai Ambani Institute of Information and Communication Technology GandhinagarGujaratIndia Department of Chemical Engineering School of Energy TechnologyPandit Deendayal Energy UniversityGandhinagarGujaratIndia

Skin cancer is among the most common and lethal cancer types,with the number of cases increasing dramatically worldwide.If not diagnosed in the nascent stages,it can lead to metastases,resulting in high mortality rates.Skin cancer can be cured if detected early.Consequently,timely and accurate diagnosis of such cancers is currently a key research objective.Various machine learning technologies have been employed in computer-aided diagnosis of skin cancer detection and malignancy classification.Machine learning is a subfield of artificial intelligence(AI)involving models and algorithms which can learn from data and generate predictions on previously unseen data.The traditional biopsy method is applied to diagnose skin cancer,which is a tedious and expensive procedure.Alternatively,machine learning algorithms for cancer diagnosis can aid in its early detection,lowering the workload of specialists while simultaneously enhancing skin lesion diagnostics.This article presented a critical review of select state-of-the-art machine learning techniques used to detect skin cancer.Several studies had been collected,and an analysis of the performance of k-nearest neighbors,support vector machine,and convolutional neural networks algorithms on benchmark datasets was conducted.The shortcomings and disadvantages of each algorithm were briefly discussed.Challenges in detecting skin cancer were highlighted and the scope for future research was proposed.

关键词： Machine learning Skin cancer Classification

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Artificial intelligence and machine learning in drug discovery and development

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Intelligent Medicine 2022年第3期2卷 134-140页

作者： Veer Patel Manan Shah Ahmedabad International School AhmedabadGujaratIndia Department of Chemical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagarGujaratIndia

The current rise of artificial intelligence and machine learning has been significant.It has reduced the human workload improved quality of life significantly.This article describes the use of artificial intelligence and machine learning to augment drug discovery and development to make them more efficient and accurate.In this study,a systematic evaluation of studies was carried out;these were selected based on prior knowledge of the authors and a keyword search in publicly available databases which were filtered based on related context,abstract,methodology,and full text.This body of work supported the roles of machine learning and artificial intelligence in facilitating drug development and discovery processes,making them more cost-effective or altogether eliminating the need for clinical trials,owing to the ability to conduct simulations using these technologies.They also enabled researchers to study different molecules more extensively,without any trials.The results of this paper demonstrate the prevalent application of machine learning and artificial intelligence methods in drug discovery,and indicate a promising future for these technologies;these results should enable researchers,students,and pharmaceutical industry to dive deeper into machine learning and artificial intelligence in a drug discovery and development context.

关键词： Artificial intelligence Machine learning Drug discovery

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A comprehensive review of approaches to detect fatigue using machine learning techniques

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Chronic Diseases and Translational Medicine 2022年第1期8卷 26-35页

作者： Rohit Hooda Vedant Joshi Manan Shah Gandhinagar Institute of Technology Gujarat Technological UniversityGandhinagarGujaratIndia LJ Institute of Engineering and Technology Gujarat Technological UniversityAhmedabadGujaratIndia Department of Chemical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagarGujaratIndia

In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent.So,this study aimed to understand what is fatigue,its repercussions,and techniques to detect it using machine learning(ML)approaches.This paper introduces,discusses methods and recent advancements in the field of fatigue detection.Further,we categorized the methods that can be used to detect fatigue into four diverse groups,that is,mathematical models,rule-based implementation,ML,and deep learning.This study presents,compares,and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue.Finally,the paper discusses the possible areas for improvement.

关键词： deep learning driver monitoring fatigue detection healthcare machine learning

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A comprehensive review of machine learning techniques on diabetes detection

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 308-323页

作者： Toshita Sharma Manan Shah Department of Electronics and Communication Technology Nirma University382481 AhmedabadGujaratIndia Department of Chemical Engineering School of TechnologyPandit Deendayal Energy University382426 GandhinagarGujaratIndia

Diabetes mellitus has been an increasing concern owing to its high morbidity,and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties.Given the high prevalence,it is necessary to address with this problem effectively.Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors.Data mining techniques with algorithms such as-density-based spatial clustering of applications with noise and ordering points to identify the cluster structure,the use of machine vision systems to learn data on facial images,gain better features for model training,and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners.Machine learning classifiers such as support vector machines,logistic regression,and decision trees,have been comparative discussed various authors.Deep learning models such as artificial neural networks and recurrent neural networks have been considered,with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models.Various parameters such as the root-mean-square error,mean absolute errors,area under curves,and graphs with varying criteria are commonly used.In this study,challenges pertaining to data inadequacy and model deployment are discussed.The future scope of such methods has also been discussed,and new methods are expected to enhance the performance of existing models,allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

关键词： Machine learning Deep learning Health care Diabetes detection

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Emerging the dual string drilling and dual coil tubing drilling technology in a geothermal well applications

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Petroleum 2022年第1期8卷 102-109页

作者： Jainish Shingala Nikshit Vora Manan Shah School of Petroleum Technology Pandit Deendayal Energy UniversityGandhinagar382426India Department of Chemical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagar382426India

The challenges found in geothermal well drilling have forced the industry to develop new significant cost effective,time saver technologies to go for deeper geothermal resources beyond their traditional limits.To overcome these challenges,the new Dual String Drilling(DSD)technology which is based on penetration pathway of drilling fluid and rock cuttings during drilling a well.The principle of DSD employs drilling fluid flows through the annulus of inner and outer string pipe while cutting from the bottom of the well through inner pipe.The paper describes the novel Dual String Drilling(DSD)technology,predicting their occurrence and advantages on drilling of geothermal well.The outcome shows that with DSD approach a lot of time will be saved to circulate the kick out of the well.Additionally,the characteristic response of DSD enables to improve the hole cleaning capacity,to prevent pipe stuck,better well stability,reduction in torque and drag,to remove the dynamic equivalent circulating density gradient in geothermal wells.Moreover,the paper also depicts the system and method for dual coil tubing drilling of horizontal well which follows the same principle as DSD.The comparison of DSD with conventional drilling assesses that the DSD technology is affordable and appropriate for geothermal well drilling.

关键词： Dual string drilling Geothermal energy Kick detection

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Crime forecasting:a machine learning and computer vision approach to crime prediction and prevention

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 76-89页

作者： Neil shah Nandish Bhagat Manan Shah Department of Computer Engineering Sal Institute of Technology and Engineering ResearchAhmedabadGujarat 380060India Department of Chemical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagarGujarat 382426India

A crime is a deliberate act that can cause physical or psychological harm,as well as property damage or loss,and can lead to punishment by a state or other authority according to the severity of the crime.The number and forms of criminal activities are increasing at an alarming rate,forcing agencies to develop efficient methods to take preventive measures.In the current scenario of rapidly increasing crime,traditional crime-solving techniques are unable to deliver results,being slow paced and less efficient.Thus,if we can come up with ways to predict crime,in detail,before it occurs,or come up with a“machine”that can assist police officers,it would lift the burden of police and help in preventing crimes.To achieve this,we suggest including machine learning(ML)and computer vision algorithms and techniques.In this paper,we describe the results of certain cases where such approaches were used,and which motivated us to pursue further research in this field.The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques.The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect,prevent,and solve crimes at a much more accurate and faster rate.In summary,ML and computer vision techniques can bring about an evolution in law agencies.

关键词： Machine learning Computer vision Crime forecasting

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Comparative analysis of breast cancer detection using machine learning and biosensors

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Intelligent Medicine 2022年第2期2卷 69-81页

作者： Yash Amethiya Prince Pipariya Shlok Patel Manan Shah Dharmsinh Desai University NadiadGujaratIndia Department of Chemical Engineering School of Technology Pandit Deendayal Energy UniversityGandhinagarGujaratIndia

Breast cancer is a widely occurring cancer in women worldwide and is related to high mortality.The objective of this review was to present several approaches to investigate the application of multiple algorithms based on machine learning(ML)approach and biosensors for early breast cancer detection.Automation is needed because biosensors and ML are needed to identify cancers based on microscopic images.ML aims to facilitate self-learning in computers.Rather than relying on explicit pre-programmed rules and models,it is based on identifying patterns in observed data and building models to predict outcomes.We have compared and analysed various types of algorithms such as fuzzy extreme learning machine-radial basis function(ELM-RBF),support vector machine(SVM),support vector regression(SVR),relevance vector machine(RVM),naive bayes,k-nearest neighbours algorithm(K-NN),decision tree(DT),artificial neural network(ANN),back-propagation neural network(BPNN),and random forest across different databases including images digitized from fine needle aspirations of breast masses,scanned film mammography,breast infrared images,MR images,data collected by using blood analyses,and histopathology image samples.The results were compared on performance metric elements like accuracy,precision,and recall.Further,we used biosensors to determine the presence of a specific biological analyte by transforming the cellular constituents of proteins,DNA,or RNA into electrical signals that can be detected and analysed.Here,we have compared the detection of different types of analytes such as HER2,miRNA 21,miRNA 155,MCF-7 cells,DNA,BRCA1,BRCA2,human tears,and saliva by using different types of biosensors including FET,electrochemical,and sandwich electrochemical,among others.Several biosensors use a different type of specification which is also discussed.The result of which is analysed on the basis of detection limit,linear ranges,and response time.Different studies and related articles were reviewed and analysed systematically,and those publis

关键词： Breast cancer Detection Machine learning Biosensors

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Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

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Visual Computing for Industry,Biomedicine,and Art 2020年第1期3卷 289-306页

作者： Binny Naik Ashir Mehta Manan Shah Department of Computer Engineering Indus UniversityAhmedabadGujarat 382115India Department of Chemical Engineering School of TechnologyPandit Deendayal Petroleum UniversityGandhinagarGujarat 382007India

Alzheimer’s disease(AD)is the most common type of dementia.The exact cause and treatment of the disease are still unknown.Different neuroimaging modalities,such as magnetic resonance imaging(MRI),positron emission tomography,and single-photon emission computed tomography,have played a significant role in the study of AD.However,the effective diagnosis of AD,as well as mild cognitive impairment(MCI),has recently drawn large attention.Various technological advancements,such as robots,global positioning system technology,sensors,and machine learning(ML)algorithms,have helped improve the diagnostic process of AD.This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls.Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

关键词： Machine learning Support vector machine Alzheimer

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Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 152-164页

作者： Dhruvil shah Devarsh Patel Jainish Adesara Pruthvi Hingu Manan Shah Department of Computer Engineering Vishwakarma Government Engineering CollegeAhmedabadGujarat 382424India Department of Chemical Engineering School of TechnologyPandit Deendayal Energy UniversityGandhinagarGujarat 382426India

Although the education sector is improving more quickly than ever with the help of advancing technologies,there are still many areas yet to be discovered,and there will always be room for further enhancements.Two of the most disruptive technologies,machine learning(ML)and blockchain,have helped replace conventional approaches used in the education sector with highly technical and effective methods.In this study,a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees.The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation,the problems of further counterfeiting and insecurity in the student achievements can be avoided.Further,ML models will be used to train and predict valid data.This system will provide the university with an official decentralized database of student records who have graduated from there.In addition,this system provides employers with a platform where the educational records of the employees can be verified.Students can share their educational information in their e-portfolios on platforms such as LinkedIn,which is a platform for managing professional profiles.This allows students,companies,and other industries to find approval for student data more easily.

关键词： Machine learning Blockchain Education

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