Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate diabetes.This research aimed to develop an intelligent mobile application based on machine learning to determine ...Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate diabetes.This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical tests.This study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design.The Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster diagnosis.The Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes.The Doctor Consultation Module ensures free treatment related to diabetes.In this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&LightGBM.To evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,respectively.This manuscript presents some exploratory data analysis,including a correlation matrix and survey report.Moreover,the proposed solution can be adjustable in the daily activities of a diabetic patient.展开更多
Depression is a crippling affliction and affects millions of individuals around the world.In general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration wi...Depression is a crippling affliction and affects millions of individuals around the world.In general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient outcomes.However,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical considerations.Thus,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and predicted.The methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the RHT.In addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss function.Besides,the Features Importance Interpretation is also delineated in this research.These forecasts could help individuals connect with expert mental health specialists more quickly and easily.According to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately diagnosed.This study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree Classifier.But after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)algorithm.Besides,several factors are identified in terms of predicting depression through statistical data analysis.Though the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.展开更多
Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net...Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep learning.It is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.展开更多
This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three p...This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.展开更多
文摘Diabetes Mellitus is one of the most severe diseases,and many studies have been conducted to anticipate diabetes.This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic,pre-diabetic,or non-diabetic without the assistance of any physician or medical tests.This study’s methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design.The Diabetes Prediction Approach uses a novel approach,Light Gradient Boosting Machine(LightGBM),to ensure a faster diagnosis.The Proposed System ArchitectureDesign has been combined into sevenmodules;the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes.The Doctor Consultation Module ensures free treatment related to diabetes.In this research,90%accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor’s algorithm(KNN)&LightGBM.To evaluate the model’s performance,Receiver Operating Characteristics(ROC)Curve and Area under the ROC Curve(AUC)were applied with a value of 0.948 and 0.936,respectively.This manuscript presents some exploratory data analysis,including a correlation matrix and survey report.Moreover,the proposed solution can be adjustable in the daily activities of a diabetic patient.
文摘Depression is a crippling affliction and affects millions of individuals around the world.In general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient outcomes.However,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical considerations.Thus,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and predicted.The methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the RHT.In addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss function.Besides,the Features Importance Interpretation is also delineated in this research.These forecasts could help individuals connect with expert mental health specialists more quickly and easily.According to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately diagnosed.This study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree Classifier.But after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)algorithm.Besides,several factors are identified in terms of predicting depression through statistical data analysis.Though the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.
基金This study was funded by the Deanship of Scientific Research,Taif University Researchers Supporting Project number(TURSP-2020/348),Taif University,Taif,Saudi Arabia.
文摘Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep learning.It is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.
文摘This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.
