Improving prediction of solar radiation using Cheetah Optimizer and Random Forest DOI Creative Commons
Ibrahim Al-Shourbaji, Pramod Kachare, Abdoh Jabbari

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0314391 - e0314391

Опубликована: Дек. 20, 2024

In the contemporary context of a burgeoning energy crisis, accurate and dependable prediction Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable generation. Machine Learning (ML) models have gained widespread recognition for their precision computational efficiency in addressing SR challenges. Consequently, this paper introduces innovative model, denoted Cheetah Optimizer-Random Forest (CO-RF) model. The CO plays pivotal role selecting most informative features hourly forecasting, subsequently serving inputs RF efficacy developed CO-RF model is rigorously assessed using two publicly available datasets. Evaluation metrics encompassing Mean Absolute Error (MAE), Squared (MSE), coefficient determination ( R 2 ) are employed validate its performance. Quantitative analysis demonstrates that surpasses other techniques, Logistic Regression (LR), Support Vector (SVM), Artificial Neural Network, standalone Random (RF), both training testing phases prediction. proposed outperforms others, achieving low MAE 0.0365, MSE 0.0074, 0.9251 on first dataset, 0.0469, 0.0032, 0.9868 second demonstrating significant error reduction.

Язык: Английский

Optimal time-frequency localized wavelet filters for identification of Alzheimer’s disease from EEG signals DOI
Digambar Puri, Jayanand P. Gawande, Pramod Kachare

и другие.

Cognitive Neurodynamics, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 9, 2025

Язык: Английский

Процитировано

3
STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network DOI
Pramod Kachare, Sandeep B. Sangle, Digambar Puri

и другие.

Cognitive Neurodynamics, Год журнала: 2024, Номер 18(5), С. 3195 - 3208

Опубликована: Июль 19, 2024

Язык: Английский

Процитировано

15
LEADNet: Detection of Alzheimer’s Disease Using Spatiotemporal EEG Analysis and Low-Complexity CNN DOI Creative Commons
Digambar Puri, Pramod Kachare, Sandeep B. Sangle

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 113888 - 113897

Опубликована: Янв. 1, 2024

Clinical methods for dementia detection are expensive and prone to human errors. Despite various computer-aided using electroencephalography (EEG) signals artificial intelligence, a consistent separation of Alzheimer's disease (AD) normal-control (NC) subjects remains elusive. This paper proposes low-complexity EEG-based AD CNN called LEADNet generate disease-specific features. employs spatiotemporal EEG as input, two convolution layers feature generation, max-pooling layer asymmetric redundancy reduction, fully-connected nonlinear transformation selection, softmax probability prediction. Different quantitative measures calculated an open-source dataset compare four pre-trained models. The results show that the lightweight architecture has at least 150-fold reduction in network parameters highest testing accuracy 98.75% compared investigation individual showed successive improvements selection separating NC subjects. A comparison with state-of-the-art models accuracy, sensitivity, specificity were achieved by model.

Язык: Английский

Процитировано

11
EEGConvNeXt: A Novel Convolutional Neural Network Model for Automated Detection of Alzheimer's Disease and Frontotemporal Dementia Using EEG Signals DOI

Madhav R. Acharya,

Ravinesh C. Deo, Prabal Datta Barua

и другие.

Computer Methods and Programs in Biomedicine, Год журнала: 2025, Номер 262, С. 108652 - 108652

Опубликована: Фев. 8, 2025

Язык: Английский

Процитировано

2
Explaining electroencephalogram channel and subband sensitivity for alcoholism detection DOI Creative Commons
Sandeep B. Sangle, Pramod Kachare, Digambar Puri

и другие.

Computers in Biology and Medicine, Год журнала: 2025, Номер 188, С. 109826 - 109826

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

1
Enhanced EEG-based Alzheimer’s disease detection using synchrosqueezing transform and deep transfer learning DOI

shraddha jain,

Ruchi Srivastava

Neuroscience, Год журнала: 2025, Номер 576, С. 105 - 117

Опубликована: Апрель 25, 2025

Язык: Английский

Процитировано

1
Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care DOI Creative Commons
Laith Abualigah, Saleh Ali Alomari,

Mohammad H. Almomani

и другие.

Journal of Translational Medicine, Год журнала: 2025, Номер 23(1)

Опубликована: Март 10, 2025

Abstract Background Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, scalability remain barriers to achieving optimal predictive performance. Methods This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) Deep Neural Networks (DNN) address these challenges. The was evaluated using two distinct datasets: MIMIC-IV, critical care database containing clinical data of critically ill patients, UK Biobank, which comprises genetic, clinical, lifestyle from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, AUROC, were used assess against traditional advanced ML models. Results proposed demonstrated superior compared classical Logistic Regression, Random Forest, Support Vector (SVM), Networks. For example, on Biobank dataset, model achieved an AUROC 0.96, significantly outperforming (0.92). also efficient, requiring only 32.4 s for training with low prediction latency, making it suitable real-time applications. Conclusions effectively addresses medicine, offering accuracy efficiency. Its robust across diverse datasets highlights its potential integration into decision support systems, facilitating personalized medicine improving patient outcomes. Future research will focus interpretability broader

Язык: Английский

Процитировано

0
A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection DOI Creative Commons

Ilknur Sercek,

Niranjana Sampathila, İrem Taşçı

и другие.

Cognitive Neurodynamics, Год журнала: 2025, Номер 19(1)

Опубликована: Май 10, 2025

Abstract Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: retrospectively analyzed the EEG records 134 and 113 non-AD patients. To generate multilevel features, discrete wavelet transform was used decompose input EEG-signals. devised novel quantum-inspired EEG-signal extraction function 7-distinct different subgraphs Goldner-Harary pattern (GHPat), selectively assigned specific subgraph, using forward-forward distance-based fitness function, each block textural extraction. extracted statistical features standard moments, which we then merged with features. Other components were iterative neighborhood component analysis selection, shallow k-nearest neighbors, as well majority voting greedy algorithm additional voted prediction vectors select best overall results. With leave-one-subject-out cross-validation (LOSO CV), our attained 88.17% accuracy. Accuracy results stratified by channel lead placement brain regions suggested P4 parietal region be most impactful. Comparison existing methods: The proposed outperforms methods achieving higher accuracy approach, ensuring robustness generalizability. Cortex maps generated that allowed visual correlation channel-wise various regions, enhancing explainability.

Язык: Английский

Процитировано

0
Diagnosis of Alzheimer's Disease Using Non-Linear Features of ERP Signals through a Hybrid Attention-Based CNN-LSTM Model DOI Creative Commons
Elias Mazrooei Rad, Sayyed Majid Mazinani, Seyyed Ali Zendehbad

и другие.

Computer Methods and Programs in Biomedicine Update, Год журнала: 2025, Номер 7, С. 100192 - 100192

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Optimized deep learning models for stress-based stroke prediction from EEG signals DOI Creative Commons

Sivasankaran Pichandi,

Gomathy Balasubramanian,

C. Venkatesh

и другие.

Deleted Journal, Год журнала: 2025, Номер 7(6)

Опубликована: Май 23, 2025

Язык: Английский

Процитировано

0