The stochastic contextual bandit problem, recognized for its effectiveness in navigating the classic exploration-exploitation dilemma through ongoing player-environment interactions, has found broad applications across various industries. This utility largely stems from the algorithms’ ability to accurately forecast reward functions and maintain an optimal balance between exploration and exploitation, contingent upon the precise selection and calibration of hyperparameters. However, the inherently dynamic and real-time nature of bandit environments significantly complicates hyperparameter tuning, rendering traditional offline methods inadequate. While specialized methods have been developed to overcome these challenges, they often face three primary issues: difficulty in adaptively learning hyperparameters in ever-changing environments, inability to simultaneously optimize multiple hyperparameters for complex models, and inefficiencies in data utilization and knowledge transfer from analogous tasks. To tackle these hurdles, this paper introduces an innovative transfer learning-based approach designed to harness past task knowledge for accelerated optimization and dynamically optimize multiple hyperparameters, making it well-suited for fluctuating environments. The method employs a dual Gaussian meta-model strategy—one for transfer learning and the other for assessing hyperparameters’ performance within the current task —enabling it to leverage insights from previous tasks while quickly adapting to new environmental changes. Furthermore, the framework’s meta-model-centric architecture enables simultaneous optimization of multiple hyperparameters. Experimental evaluations demonstrate that this approach markedly outperforms competing methods in scenarios with perturbations and exhibits superior performance in 70% of stationary cases while matching performance in the remaining 30%. This superiority in performance, coupled with its computational efficiency on par with existing alternatives, positions it as a superior and practical solution for optimizing hyperparameters in contextual bandit settings.

Collecting poverty data through the National Socio-Economic Survey (SUSENAS) demands significant time, costs, and human resources. To enable more efficient policy-making, predicting the poverty rate before the release of Statistics Indonesia (BPS) data is essential. This research compares day and night satellite images to predict per capita expenditure in East Java, Indonesia, which has the highest number of poor people. The satellite images are processed using a transfer learning approach that employs a pretrained Convolutional Neural Network (CNN) model with VGG-16 architecture as a feature extractor. These extracted features are then used as independent variables to predict East Java's per capita expenditure using Support Vector Regression (SVR) with RBF and polynomial kernels. The findings indicate that night images are more reliable than day images, with the best model being a combination of transfer learning and the SVR polynomial kernel using night images. The prediction mapping aligns well with the unmodeled night image, demonstrating the effectiveness of this approach in predicting per capita expenditure.

In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. Among these techniques, the use of Convolutional Neural Networks (CNNs) combined with ensemble methods is particularly promising. To investigate the feasibility of detecting fires using video streams from Unmanned Aerial Vehicles (UAVs), the lightweight CNN architecture MobileNetV2 was utilized for real-time detection. Several experiments were conducted on the DeepFire dataset, which comprises an equal number of images with and without fire, to evaluate MobileNetV2's performance. Notably, the architecture's linear bottlenecks and the efficient use of inverted residuals ensure high accuracy without compromising on feature extraction capabilities. For a comprehensive assessment, MobileNetV2 was benchmarked against other models, including DenseNet121, EfficientNetV2S, and VGG16. Accuracy was enhanced by averaging predictions through methods such as voting or summing results. As documented in the literature, MobileNetV2 consistently outperforms other architectures in computational efficiency and provides an excellent balance between efficiency and the quality of learned features over multiple epochs. This study underscores the suitability of MobileNetV2 for real-time applications on drones, particularly for the detection of forest fires in resource-constrained environments. The results show that MobileNetV2 achieves the highest accuracy (0.994), sensitivity (0.994), and specificity (0.998) among the tested models, with low standard deviations across all metrics. In contrast, EfficientNetV2S exhibited the lowest accuracy and sensitivity, both at 0.779, with a specificity of 0.829. The ensemble (Sum) method achieved an average accuracy of 0.989, sensitivity of 0.989, and specificity of approximately 0.988. Therefore, MobileNetV2 not only delivers the highest accuracy and stability but also demonstrates that the choice of ensemble method significantly affects the results.

Dental caries is arguably the most persistent dental condition that affects most people over their lives. Carious lesions are commonly diagnosed by dentists using clinical and visual examination along with oral radiographs. In many circumstances, dental caries is challenging to detect with photography and might be mistaken as shadows for various reasons, including poor photo quality. However, with the introduction of Artificial Intelligence and robotic systems in dentistry, photographs can be a helpful tool in oral epidemiological research for the assessment of dental caries prevalence among the population. It can be used particularly to create a new automated approach to calculate DMF (Decay, Missing, Filled) index score. In this paper, an autonomous diagnostic approach for detecting dental cavities in photos is developed using deep learning algorithms and ensemble methods. The proposed technique employs a set of pretrained models including Xception, VGG16, VGG19, and DenseNet121 to extract essential characteristics from photographs and to classify images as either normal or caries. Then, two ensemble learning methods, E- majority and E-sum, are employed based on majority voting and sum rule to boost the performances of the individual pretrained model. Experiments are conducted on 50 images with data augmentation for normal and caries images, the employed E-majority and E-sum achieved an accuracy score of 96% and 97%, respectively. The obtained results demonstrate the superiority of the proposed ensemble framework in the detection of caries. Furthermore, this framework is a step toward constructing a fully automated, efficient decision support system to be used in the dentistry area.

This bibliometric survey examines 200 highly cited publications spanning from 2009 to 2025, retrieved from the Scopus database using the search strategy targeting neural technology and human augmentation. The analysis reveals a transformative evolution from foundational neuroscience discoveries to sophisticated artificial intelligence-driven augmentation systems. Key findings indicate that deep learning architectures, particularly convolutional neural networks (CNNs), generative adversarial networks (GANs), and transfer learning approaches have become dominant methodological frameworks across 78% of analyzed publications. Data augmentation emerges as a critical enabling technology, appearing in over 35% of publications, addressing the persistent challenge of limited labeled data in biomedical applications. The United States, China, Germany, India, and South Korea represent the most productive nations, with intensifying international collaboration networks from 2015 onward. Thematic clustering identifies five major research domains: (1) brain-computer interfaces and neural signal decoding, (2) deep learning for medical image analysis, (3) sensory augmentation and assistive technologies, (4) data augmentation and synthetic data generation, and (5) ethical and regulatory frameworks for human enhancement. Emerging trends include transformer-based architectures, self-supervised learning, federated learning for privacy-preserving applications, and the convergence of neurotechnology with IoT-enabled wearable systems. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent research gaps including clinical translation barriers and dataset standardization, and proposes future directions for responsible neural augmentation technology development.

The combination of machine learning (ML) and neural networks (NN), specifically deep learning (DL), is making a big breakthrough to breast cancer studies. This scientometrics survey studies 200 highly cited publications to map the intellectual landscape and studies trends in this dynamic field. The survey discloses a dominant concentration on computer-aided diagnosis (CAD) systems using convolutional neural networks (CNNs) for the classification of breast cancer from different imaging modalities, including mammography, histopathology, ultrasound, and magnetic resonance imaging (MRI). Key survey directions identified include: (1) the development of comprehensive deep learning techniques for image-based detection and classification; (2) the application of transfer learning to resolve data scarcity; (3) the combination of multi-omics and clinical data for personalized prognosis and treatment prediction; and (4) the exploration of explainability and robustness in ML-driven clinical tools. This study synthesizes the methodological advancements, sheds light on the evolution from traditional machine learning to deep learning, and surveys the challenges associated with data heterogeneity, model interpretability, and clinical integration. By giving a structured overview of the seminal work and emerging paradigms, the study serves as a reference for graduate students and other interested parties to have a better understanding about the current state and future trajectories of AI in breast oncology.

Bidirectional Encoder Representations from Transformers (BERT) has become a paradigm shift in natural language processing (NLP). This scientometric study analyzes a curated dataset of 200 highly-cited Scopus publications to map the intellectual landscape and research trajectories catalyzed by BERT and other transformer models. The study discloses a quick evolution from foundational architectural innovations and pre-training paradigms to widespread domain adaptation, rigorous model optimization for efficiency, and critical examination of model capabilities and societal effects. The literature shows BERT's role as a foundational model, successfully implemented in diverse fields such as biomedicine, education, and software engineering, while simultaneously spurring substantial research into compression, quantization, and efficient inference. A parallel and influential strand of studies emerged concentrated on “BERTology”, giving the linguistic knowledge and biases encoded within these models, and addressing ethical concerns regarding their deployment. The study synthesizes these developments, presenting how BERT not only set new performance benchmarks but also built a new paradigm for transfer learning and spurred a self-critical research community, ultimately paving the way for the present era of large language models.