Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 8, 2025

Heart disease is a category of various conditions that affect the heart, which includes multiple diseases influence its structure and operation. Such may consist coronary artery disease, characterized by narrowing or clotting arteries supply blood to heart muscle, with resulting threat attacks. rhythm disorders (arrhythmias), valve problems, congenital defects present at birth, muscle (cardiomyopathies) are other types disease. The objective this work introduce Greylag Goose Optimization (GGO) algorithm, seeks improve accuracy classification. GGO algorithm's binary format specifically intended choose most effective set features can classification when compared six optimization algorithms. bGGO algorithm for selecting optimal enhance accuracy. phase utilizes many classifiers, findings indicated Long Short-Term Memory (LSTM) emerged as classifier, achieving an rate 91.79%. hyperparameter LSTM model tuned using GGO, outcome alternative optimizers. obtained highest performance, 99.58%. statistical analysis employed Wilcoxon signed-rank test ANOVA assess feature selection outcomes. Furthermore, visual representations results was provided confirm robustness effectiveness proposed hybrid approach (GGO + LSTM).

Language: Английский

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Breast cancer classification based on hybrid CNN with LSTM model

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 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.

Language: Английский

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

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 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%

Language: Английский

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Orthopedic disease classification based on breadth-first search algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 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.

Language: Английский

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Optimizing Potato Leaf Disease Recognition: Insights DENSE-NET-121 and Gaussian Elimination Filter Fusion

Heliyon, Journal Year: 2025, Volume and Issue: unknown, P. e42318 - e42318

Published: Jan. 1, 2025

Language: Английский

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Remote sensing and artificial intelligence: revolutionizing pest management in agriculture

Frontiers in Sustainable Food Systems, Journal Year: 2025, Volume and Issue: 9

Published: Feb. 26, 2025

The agriculture sector is currently facing several challenges, including the growing global human population, depletion of natural resources, reduction arable land, rapidly changing climate, and frequent occurrence diseases such as Ebola, Lassa, Zika, Nipah, most recently, COVID-19 pandemic. These challenges pose a threat to food nutritional security place pressure on scientific community achieve Sustainable Development Goal 2 (SDG2), which aims eradicate hunger malnutrition. Technological advancement plays significant role in enhancing our understanding agricultural system its interactions from cellular level green field for benefit humanity. use remote sensing (RS), artificial intelligence (AI), machine learning (ML) approaches highly advantageous producing precise accurate datasets develop management tools models. technologies are beneficial soil types, efficiently managing water, optimizing nutrient application, designing forecasting early warning models, protecting crops plant insect pests, detecting threats locusts. application RS, AI, ML algorithms promising transformative approach improve resilience against biotic abiotic stresses sustainability meet needs ever-growing population. In this article covered leveraging AI RS data, how these enable real time monitoring, detection, pest outbreaks. Furthermore, discussed allows more precise, targeted control interventions, reducing reliance broad spectrum pesticides minimizing environmental impact. Despite data quality technology accessibility, integration holds potential revolutionizing management.

Language: Английский

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

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 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.

Language: Английский

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Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 10, 2025

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems cities. It has worldwide economic consequences. change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With focus on Al-Qassim Region, Saudi Arabia, assesses temperature, air dew point, visibility distance, atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) reduce dataset imbalance. The CNN-GRU-LSTM was compared 5 classic regression models: DTR, RFR, ETR, BRR, K-Nearest Neighbors. Five main measures were evaluate performance: MSE, MAE, MedAE, RMSE, R². After Min-Max normalization, split into training (70%), validation (15%), testing (15%) sets. paper shows beats standard methods in all four climatic scenarios, R² values 99.62%, 99.15%, 99.71%, 99.60%. Deep predicts climate well can guide environmental policy urban development decisions.

Language: Английский

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IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks

Engineering Technology & Applied Science Research, Journal Year: 2024, Volume and Issue: 14(6), P. 18929 - 18934

Published: Dec. 2, 2024

The rapid expansion of artificial intelligence (AI) integrated with the Internet Things (IoT) has fueled development various smart devices, particularly for city applications. However, heterogeneity these devices necessitates a robust communication network capable maintaining consistent traffic flow. This paper employs Machine Learning (ML) models to classify continuously received parameters from diverse IoT identifying necessary adjustments enhance performance. Key parameters, such as packet data, are transmitted through gateways via specialized tools. Six different ML techniques default were used: Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Stochastic Gradient Descent Classifiers (SGDC), environment (IoT / non IoT). models' performance was evaluated in real-time laboratory comprising 38 vendors following metrics: Accuracy, F1-score, Recall Precision. RF model achieved highest Accuracy 95.6%. Also Binary Particle Swarm Optimizer (BPSO) used across RF. results demonstrated that BPSO-RF hyperparameter optimization enhanced 95.6% 99.4%.

Language: Английский

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