Optimizing Wind Power Forecasting with RNN-LSTM Models through Grid Search Cross-validation

Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер unknown, С. 101054 - 101054

Опубликована: Ноя. 1, 2024

Язык: Английский

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Heart disease prediction using image segmentation Through the CNN model

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Год журнала: 2023, Номер unknown

Опубликована: Янв. 19, 2023

One of the most fatal diseases is heart disease. This a condition that affects big portion world's population. When we examine death rate and enormous number people who suffer from disease, it becomes clear how critical early detection disease is. There are numerous established approaches for predicting such sickness, but they do not appear to be adequate. an immediate need medical diagnosis system can anticipate on provide more accurate than standard as Logistic Regularization, Lasso, Elastic Network, Group Lasso regularisation. Nowadays, machine learning gaining lot traction. Convolutional Neural Networks (CNNs) utilised in this paper create stage prediction diagnosis. CNN receives 13 clinical features input. The trained using modified back propagation training approach. During testing, was discovered predicts absence presence cardiac with greater 95 percent accuracy. In investigation, present CardioHelp, method uses deep algorithm known convolutional neural networks predict whether or patient will have cardiovascular (CNN). order model temporal data, suggested technique makes use HF prediction. We compiled dataset used cutting-edge algorithms compare our findings. results were promising.

Язык: Английский

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$$D_MD_RDF$$: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection

Soft Computing, Год журнала: 2024, Номер 28(19), С. 11393 - 11420

Опубликована: Авг. 5, 2024

Abstract Diabetes mellitus is one of the most common diseases affecting patients different ages. can be controlled if diagnosed as early possible. One serious complications diabetes retina diabetic retinopathy. If not early, it lead to blindness. Our purpose propose a novel framework, named $$D_MD_RDF$$ DMDRDF , for and accurate diagnosis The framework consists two phases, detection (DMD) other retinopathy (DRD). novelty DMD phase concerned in contributions. Firstly, feature selection approach called Advanced Aquila Optimizer Feature Selection ( $$A^2OFS$$ xmlns:mml="http://www.w3.org/1998/Math/MathML">A2OFS ) introduced choose promising features diagnosing diabetes. This extracts required from results laboratory tests while ignoring useless features. Secondly, classification (CA) using five modified machine learning (ML) algorithms used. modification ML proposed automatically select parameters these Grid Search (GS) algorithm. DRD lies 7 CNNs reported concerning datasets shows that AO reports best performance metrics process with help classifiers. achieved accuracy 98.65% GS-ERTC model max-absolute scaling on “Early Stage Risk Prediction Dataset” dataset. Also, datasets, AOMobileNet considered suitable this problem outperforms CNN models 95.80% “The SUSTech-SYSU dataset”

Язык: Английский

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A Novel Vit-Based Multi-Scaled and Rotation-Invariance Approach for Precise Differentiation Between Meningioma and Solitary Fibrous Tumor

Опубликована: Май 27, 2024

Язык: Английский

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Solving the maximum cut problem using Harris Hawk Optimization algorithm

PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0315842 - e0315842

Опубликована: Дек. 30, 2024

The objective of the max-cut problem is to cut any graph in such a way that total weight edges are off maximum both subsets vertices divided due edges. Although it an elementary partitioning problem, one most challenging combinatorial optimization-based problems, and tons application areas make this highly admissible. Due its admissibility, solved using Harris Hawk Optimization algorithm (HHO). Though HHO effectively some engineering optimization sensitive parameter settings may converge slowly, potentially getting trapped local optima. Thus, additional operators used solve problem. Crossover refinement modify fitness hawk they can provide precise results. A mutation mechanism along with adjustment operator has improvised outcome obtained from updated hawk. To accept potential result, acceptance criterion been used, then repair applied proposed approach. system provided comparatively better outcomes on G-set dataset than other state-of-the-art algorithms. It 533 cuts more discrete cuckoo search 9 instances, 1036 PSO-EDA 14 1021 TSHEA instances. But for four lower TSHEA. Besides, statistical significance also tested Wilcoxon signed rank test proof superior performance method. In terms solution quality, MC-HHO produce quite competitive when compared related

Язык: Английский

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