Breast cancer classification based on hybrid CNN with LSTM model

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 5, 2025

Breast cancer (BC) is a global problem, largely due to shortage of knowledge and early detection. The speed-up process detection classification crucial for effective treatment. Medical image analysis methods computer-aided diagnosis can enhance this process, providing training assistance less experienced clinicians. Deep Learning (DL) models play great role in accurately detecting classifying the huge dataset, especially when dealing with large medical images. This paper presents novel hybrid model DL combined Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) binary breast on two datasets available at Kaggle repository. CNNs extract mammographic features, including spatial hierarchies malignancy patterns, whereas LSTM networks characterize sequential dependencies temporal interactions. Our method combines these structures improve accuracy resilience. We compared proposed other models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, RESNET-50. CNN-LSTM achieved superior performance accuracies 99.17% 99.90% respective datasets. uses prediction evaluation metrics accuracy, sensitivity, specificity, F-score, AUC curve. results showed that our classifiers others second dataset.

Язык: Английский

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The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review

Agriculture, Год журнала: 2024, Номер 14(8), С. 1225 - 1225

Опубликована: Июль 25, 2024

The potato is a key crop in addressing global hunger, and deep learning at the core of smart agriculture. Applying (e.g., YOLO series, ResNet, CNN, LSTM, etc.) production can enhance both yield economic efficiency. Therefore, researching efficient models for great importance. Common application areas chain, aimed improving yield, include pest disease detection diagnosis, plant health status monitoring, prediction product quality detection, irrigation strategies, fertilization management, price forecasting. main objective this review to compile research progress various processes provide direction future research. Specifically, paper categorizes applications into four types, thereby discussing introducing advantages disadvantages aforementioned fields, it discusses directions. This provides an overview describes its current stages chain.

Язык: Английский

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EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 18, 2024

This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve precision of categorizing eye states as either open (0) or closed (1). The evaluation proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination null values. MBER algorithm's binary format is specifically designed select features can significantly enhance accuracy classification. competing ones, namely, (BER), Particle Swarm Optimization (PSO), Whale Algorithm (WAO), Grey Wolf Optimizer (GWO) Genetic (GA) were evaluated predefined sets assessment criteria. statistical analysis employed ANOVA Wilcoxon signed-rank tests effectiveness significance compared other five algorithms. Furthermore, A series visual depictions presented validate robustness algorithm. Thus, outperformed optimizers on majority unimodal benchmark functions due these considerations. Different ML models used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, LR. KNN model achieved highest values Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), (0.9612), Sensitivity (0.970578) Specificity (0.949711). serves a fitness function optimized by utilization earth radius (MBER). Finally, state classification 96.12%

Язык: Английский

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Orthopedic disease classification based on breadth-first search algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 8, 2024

Orthopedic diseases are widespread worldwide, impacting the body's musculoskeletal system, particularly those involving bones or hips. They have potential to cause discomfort and impair functionality. This paper aims address lack of supplementary diagnostics in orthopedics improve method diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), particle swarm optimization (BPSO), grey wolf optimizer (BGWO), whale algorithm (BWAO) for feature selections, BBFS makes an average error 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, ET. dataset used contains 310 instances distinct features. Through experimentation, RF model led optimal outcomes during comparison remaining with accuracy 91.4%. parameters were optimized using four algorithms: BFS, PSO, WAO, GWO. To check how well works on dataset, this prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, AUC curve. results showed that BFS-RF can performance original classifier compared others 99.41% accuracy.

Язык: Английский

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Advancing educational data mining for enhanced student performance prediction: a fusion of feature selection algorithms and classification techniques with dynamic feature ensemble evolution

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 13, 2025

The integration of technology into educational institutions has led to the generation vast data, creating opportunities for Educational Data Mining (EDM) improve learning outcomes. This study introduces a novel feature selection model, "Dynamic Feature Ensemble Evolution Enhanced Selection" (DE-FS), which combines traditional methods such as correlation matrix analysis, information gain, and Chi-square with heat maps select most relevant features predicting student performance. core innovation DE-FS lies in its dynamic adaptive thresholding mechanism, adjusts thresholds based on evolving data patterns, addressing limitations static mitigating issues like overfitting underfitting. research makes three key contributions: it an advanced ensemble-based methodology, incorporates accuracy flexibility, demonstrates DE-FS's superior predictive performance across diverse datasets. results highlight ability adapt fluctuating enabling precise reliable predictions, supporting targeted interventions, improving resource allocation enhance personalized experiences.

Язык: Английский

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LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm

Network Modeling Analysis in Health Informatics and Bioinformatics, Год журнала: 2025, Номер 14(1)

Опубликована: Март 17, 2025

Язык: Английский

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Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines

Energy Science & Engineering, Год журнала: 2025, Номер 13(5), С. 2565 - 2584

Опубликована: Март 17, 2025

ABSTRACT The increasing scale of wind farms demands more efficient approaches to turbine monitoring and maintenance. Here, we present an innovative framework that combines enhanced kernel principal component analysis (KPCA) with ensemble learning revolutionize normal behavior modeling (NBM) turbines. By integrating random kitchen sinks (RKS) algorithm KPCA, achieved a 25.21% reduction in computational time while maintaining model accuracy. Our mixed approach, synthesizing LightGBM, forest, decision tree algorithms, demonstrated exceptional performance across diverse operational conditions, achieving R ² values 0.9995 primary testing. reduced mean absolute error by 25.1% percentage 33.4% compared conventional methods. Notably, when tested three distinct environments, the maintained robust ( > 0.97), demonstrating strong generalization capability. system automatically detects anomalies using 0.1% threshold, enabling real‐time 78 variables 136,000+ records. This scalable approach integrates seamlessly existing SCADA infrastructure, offering practical solution for large‐scale farm management. findings establish new paradigm monitoring, combining efficiency unprecedented accuracy prediction.

Язык: Английский

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Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 16, 2025

Air pollution poses a significant threat to public health and environmental sustainability, necessitating accurate predictive models for effective air quality management. This study uses machine learning techniques forecast through utilizing the annual AQI dataset obtained from U.S. Environmental Protection Agency (EPA). Feature selection (FS) was conducted using Binary version of Grey Wolf Optimizer (BGWO), Particle Swarm Optimization (BPSO), Whale Algorithm (BWAO), novel hybrid BPSO-BWAO approach identify most relevant features prediction. Among feature methods, BPSO achieved best Mean Squared Error (MSE) score 53.56, but with high variance, while BWAO demonstrated lower variance consistent results. The method emerged as optimal solution, achieving an MSE 53.93 improved stability set balance, selecting key such 'Days AQI,' 'Median CO,' NO2,' PM2.5,' 'Good_Days_Percent,' 'Unhealthy_Days_Percent.' Machine models, including Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector (SVM), Linear Regression (LR), were evaluated before after selection. model performance 53.93, R² 0.9710, reduced fitted time. Further optimization PSO-WAO enhanced RF performance, 51.82 0.9821, demonstrating efficacy hyperparameter tuning. concludes that significantly improve accuracy computational efficiency, offering robust framework forecasting.

Язык: Английский

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Prediction and evaluation of environmental quality for nursing sow buildings via multisource sensor information fusion

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 23, 2025

Язык: Английский

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Integrating CNN-based feature fusion and spatial attention for tuberculosis detection with Bayesian optimized XGBoost

International Journal of Data Science and Analytics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 28, 2025

Язык: Английский

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