A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy

Machine Learning and Knowledge Extraction, Год журнала: 2025, Номер 7(1), С. 4 - 4

Опубликована: Янв. 7, 2025

Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, infrastructural factors. This study introduces an advanced hybrid model combining gradient boosting machine (GBM) neural network (NN) address these complexities. Unlike traditional approaches, this leverages GBM handle structured data features NN extract deeper relationships. The was evaluated against various baseline learning deep models, including random forest, CNN, LSTM, CatBoost, TabNet, using metrics such as RMSE, MAE, R2, MAPE. + achieved superior performance, with lowest RMSE of 0.3332, R2 0.9673, MAPE 7.0082%. also revealed significant insights into urban indicators, 10% improvement in air quality correlating 5% increase happiness. These findings underscore potential models analytics, offering both predictive accuracy actionable for planners.

Язык: Английский

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ViT-CB: Integrating hybrid Vision Transformer and CatBoost to enhanced brain tumor detection with SHAP

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 100, С. 107027 - 107027

Опубликована: Окт. 24, 2024

Язык: Английский

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Hybrid ViT-CapsNet Framework for Brain Tumor Diagnosis Using Biomedical MRI

Biomedical & Pharmacology Journal, Год журнала: 2025, Номер 18(December Spl Edition), С. 99 - 119

Опубликована: Янв. 20, 2025

Brain tumor identification through Bio-medical magnetic resonance imaging (MRI) presents a critical challenge in diagnostic imaging, where high accuracy is essential for informed treatment planning. Traditional methods face limitations segmentation precision, leading to increased misdiagnosis risks. This study introduces hybrid deep-learning model integrating Vision Transformer (ViT) and Capsule Network (CapsNet) improve brain classification accuracy. The aims enhance sensitivity specificity categorization. Utilising the BRATS2020 dataset, which comprises 6,000 MRI scans across four classes (meningioma, glioma, pituitary tumor, no tumor), dataset was divided into an 80-20 training-testing split. Data pre-processing included scaling, normalization, feature augmentation robustness. ViT-CapsNet assessed alongside individual ViT CapsNet performance using accuracy, recall, F1-score, AUC-ROC metrics. achieved of 90%, precision recall 89%, F1-score 89.5%, outperforming models. yielded 4-5% improvement types, with notable gains gliomas tumors. Unlike prior methods, achieving 88% our demonstrates superior 90%. approach offers promising solution more accurate detection. Future research could explore refining fusion techniques, advanced interpretability expanding model’s application various clinical environments.

Язык: Английский

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

Computers in Biology and Medicine, Год журнала: 2025, Номер 191, С. 110166 - 110166

Опубликована: Апрель 17, 2025

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets do not combine different feature extraction techniques better classification. To address these challenges, we propose a robust explainable stacking ensemble model multiclass tumor that combines EfficientNetB0, MobileNetV2, GoogleNet, Multi-level CapsuleNet, using CatBoost as the meta-learner improved aggregation classification accuracy. This approach captures complex characteristics while enhancing robustness The proposed integrates CapsuleNet within framework, utilizing to improve We created two large by merging data from four sources: BraTS, Msoud, Br35H, SARTAJ. tackle applied Borderline-SMOTE augmentation. also utilized methods, along with PCA Gray Wolf Optimization (GWO). Our was validated through confidence interval analysis statistical tests, demonstrating superior performance. Error revealed misclassification trends, assessed computational efficiency regarding inference speed resource usage. achieved 97.81% F1 score 98.75% PR AUC M1, 98.32% 99.34% M2. Moreover, consistently surpassed state-of-the-art CNNs, Vision Transformers, other methods classifying across individual datasets. Finally, developed web-based diagnostic tool enables clinicians interact visualize decision-critical regions scans Explainable Artificial Intelligence (XAI). study connects high-performing AI real clinical applications, providing reliable, scalable, efficient solution

Язык: Английский

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Lung Disease Detection Using Scale-Invariant Weighted Ensemble Neural Architecture

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 83 - 94

Опубликована: Янв. 1, 2025

Язык: Английский

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A Vision Transformer-Based Intelligent System For Brain Tumor Diagnosis

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 642 - 656

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence-Assisted Design of Nanomedicines for Breast Cancer Diagnosis and Therapy: Advances, Challenges, and Future Directions

BioNanoScience, Год журнала: 2025, Номер 15(3)

Опубликована: Май 21, 2025

Язык: Английский

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Attention-Driven Feature Fusion Synergised ViT and CNN Architectures for Brain Tumor Classification

Advances in medical diagnosis, treatment, and care (AMDTC) book series, Год журнала: 2025, Номер unknown, С. 133 - 162

Опубликована: Фев. 14, 2025

Classification of brain tumors has emerged as a major issue medical diagnostics. In this research, we propose model that integrates both CNN and Vit architectures, combined with an attention-based feature fusion mechanism to improve MRI data classification. The proposed successfully the capability Convolutional Neural Networks (CNNs) in capturing localized features Vision Transformers (ViTs) global contextual information. Multi-scale extraction is performed using DenseNet-201, followed by tokenization embedding transformation for input into ViTs. enables network dynamically integrate at different granularities enhance model's classify High-Grade Gliomas (HGG), Low-Grade (LGG), non-tumor regions. Experimental results demonstrate superior accuracy 97%, sensitivity, specificity across modalities, showcasing robustness efficiency automated tumor diagnosis.

Язык: Английский

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Explainable AI and vision transformers for detection and classification of brain tumor: a comprehensive survey

Artificial Intelligence Review, Год журнала: 2025, Номер 58(9)

Опубликована: Июнь 4, 2025

Язык: Английский

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Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

Algorithms, Год журнала: 2024, Номер 17(6), С. 252 - 252

Опубликована: Июнь 7, 2024

The increasing growth in knowledge about the functioning of nervous system mammals and humans, as well significant neuromorphic technology developments recent decades, has led to emergence a large number brain–computer interfaces neuroprosthetics for regenerative medicine tasks. Neurotechnologies have traditionally been developed therapeutic purposes help or replace motor, sensory cognitive abilities damaged by injury disease. They also potential memory enhancement. However, there are still no fully neurotechnologies neural capable restoring expanding functions, particular memory, humans. In this regard, search new technologies field restoration functions is an urgent task modern neurophysiology, neurotechnology artificial intelligence. hippocampus important brain structure connected information processing brain. aim paper propose approach based on deep networks prediction hippocampal signals CA1 region received biological input CA3 region. We compare results two widely used architectures: reservoir computing (RC) long short-term (LSTM) networks. proposed study can be viewed first step complex development neurohybrid chip, which allows one restore rodent hippocampus.

Язык: Английский

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