Academic Articles & Research Papers

Deep Learning With Edge Computing: A Review

Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in...

IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT.

Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computa...

A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method

Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...

Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural de...

Automation of urban technological census. The historical centre of Bethlehem

The research proposal reports the outcomes of a research track concerning the automation of the architectural census of technological elements in urban environments, aiming at the development of a monitoring and management system for the bu...

Deep learning for computational chemistry

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...

Deep Learning in Neuroradiology

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applic...

Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs

Abstract The application of deep learning models in resource-constrained environments requires a trade-off among accuracy, efficiency, and interpretability a trilemma frequently neglected in conventional knowledge distillation (KD) approach...

HIFU Tissue Degeneration Classification Based on Multifractal Detrending Fluctuation Analysis and Vision Transformer

For high-intensity focused ultrasound (HIFU) thermal ablation to be safe and effective, real-time, high-precision monitoring of tissue coagulative necrosis is essential. However, decoding the ultra-long, non-stationary radio frequency (RF) ...

A novel framework for spatio-temporal prediction of environmental data using deep learning

Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help ...

Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization Algorithm

Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder,...

Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations

Port congestion and prolonged ship waiting times pose challenges for global trade and increase operational costs and inefficiencies. In this study, a novel machine learning-based predictive approach was proposed to improve port operations b...

Machine Learning for Cultural Heritage: A Survey

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the unde...

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges fo...

Machine learning and deep learning

Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of a...

Artificial Intelligence for Intraoperative Guidance

OBJECTIVE: The aim of this study was to develop and evaluate the performance of artificial intelligence (AI) models that can identify safe and dangerous zones of dissection, and anatomical landmarks during laparoscopic cholecystectomy (LC)....

Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.

Convergence of Artificial Intelligence and Wearables in Strength Training and Performance Monitoring: A Scoping Review

Background: Strength training (ST) is essential for enhancing athletic performance and reducing injury risk, yet traditional monitoring relies heavily on subjective assessment, limiting objective and individualized evaluation. Objective: Th...

Identification of Aspergillus at section and species levels by artificial intelligence-based microscopic morphology image recognition

ABSTRACT Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phyl...

Integrating Merkle Trees with Transformer Networks for Secure Financial Computation

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of th...