Academic Articles & Research Papers

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the ef...

Bi-Objective Portfolio Optimization Under ESG Volatility via a MOPSO-Deep Learning Algorithm

In this paper, we tackle a bi-objective optimization problem in which we aim to maximize the portfolio diversification and, at the same time, minimize the portfolio volatility, where the ESG (Environmental, Social, and Governance) informati...

Deep Learning in Medical Ultrasound Analysis: A Review

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variabil...

Deep learning for undersampled MRI reconstruction

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is u...

SPAF-SegNeXt: Prior-Guided Attention and Frequency-Spatial Hybrid Fusion for Multimodal Sugarcane Segmentation in Remote Sensing

Accurate monitoring of the spatial extent of sugarcane cultivation is critical for optimizing land-use efficiency and regulating the sugar industry. However, sugarcane cultivation areas are characterized by complex topography and fragmented...

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrat...

Deep-learning-based ghost imaging

In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep...

Spectral imaging with deep learning

The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational sp...

Deep Learning-Based Land Cover Extraction from Very-High-Resolution Satellite Imagery for Assisting Large-Scale Topographic Map Production

The demand for large-scale topographic maps in Indonesia has significantly increased due to the implementation of several government initiatives that necessitate the utilization of spatial data in development planning. Currently, the nation...

Application of Deep Learning in Food: A Review

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...

Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenu...

PharmaNet Deep: Real-Time Pharmaceutical Defect Detection Using Defect-Guided Feature Fusion and Uncertainty-Driven Inspection

Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy,...

Deep learning in optical metrology: a review

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, n...

Deep learning for water quality

Intelligent Distribution-Network Fault Handling via Cross-Modal Semantic Fusion and Knowledge-Graph Reasoning

Power distribution network fault handling imposes stringent requirements on both response timeliness and decision reliability. The high penetration of distributed renewable generation, the widespread deployment of power-electronic devices, ...

CT-based three-dimensional virtual reduction with implant templating for tibial plateau fractures involving the posterolateral region: a retrospective cohort study of early postoperative CT reduction quality.

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...

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-craft...

Advancements in 7T magnetic resonance diffusion imaging: Technological innovations and applications in neuroimaging

Abstract The development of 7‐Tesla (7T) magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths, especially in neuroscience research. This review investigates wh...

Machine Learning

Emotion recognition is an important research topic.Physiological signals seem to be an appropriate way for emotion recognition and specific sensors are required to collect these data.Therefore, laboratory sensors are commonly used while the...