There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric ...There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.展开更多
Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads ...Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.展开更多
Seeds of flax or linseed(Linum usitatissimum L.)are important nutraceutical foods with antioxidant,anti-inflammatory,estrogenic,laxative,and antibacterial properties.Flaxseed oil and seeds are the richest vegetarian s...Seeds of flax or linseed(Linum usitatissimum L.)are important nutraceutical foods with antioxidant,anti-inflammatory,estrogenic,laxative,and antibacterial properties.Flaxseed oil and seeds are the richest vegetarian source of omega-3 fatty acids.Consumption of flaxseeds helps in prevention and control of cardiovascular disease,neurodegenerative disorders,obesity,diabetes mellitus,polycystic ovary syndrome,gout,liver and kidney dysfunction,oxidative stress-related diseases,post-menopausal symptoms,osteoporosis,irritable bowel syndrome,dry eye disease,cystic fibrosis,diarrhea,and cancer,particularly of the mammary and prostate gland cancer.Of late,flaxseed is gaining more importance not only because of its industrial values but also due to its nutraceutical and pharmaceutical properties.The literature review was performed using PubMed,Scopus,PubMed Central,Google Scholar,and Web of Science from 1995 onwards.Data was also obtained from websites/books/book chapters.展开更多
文摘There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.
文摘Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.
文摘Seeds of flax or linseed(Linum usitatissimum L.)are important nutraceutical foods with antioxidant,anti-inflammatory,estrogenic,laxative,and antibacterial properties.Flaxseed oil and seeds are the richest vegetarian source of omega-3 fatty acids.Consumption of flaxseeds helps in prevention and control of cardiovascular disease,neurodegenerative disorders,obesity,diabetes mellitus,polycystic ovary syndrome,gout,liver and kidney dysfunction,oxidative stress-related diseases,post-menopausal symptoms,osteoporosis,irritable bowel syndrome,dry eye disease,cystic fibrosis,diarrhea,and cancer,particularly of the mammary and prostate gland cancer.Of late,flaxseed is gaining more importance not only because of its industrial values but also due to its nutraceutical and pharmaceutical properties.The literature review was performed using PubMed,Scopus,PubMed Central,Google Scholar,and Web of Science from 1995 onwards.Data was also obtained from websites/books/book chapters.
