Academic Articles & Research Papers

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...

Application of visual transformer in renal image analysis

Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with th...

Unmixing-Guided Convolutional Transformer for Spectral Reconstruction

Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models excl...

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eli...

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...

A deep-learning system to classify lung X-ray images into normal/pneumonia class

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) ...

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a g...

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has...

Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems

The intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. A misuse IDS is a stable method that can achieve high attack detection rates (ADR) while keeping false alarm rates under...

Machine learning for biochemical engineering: A review

The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical eng...

Availability-Aware Multitask Learning for Robust Clinical Prediction from Incomplete Electronic Health Records

This study presents NORIA, an availability-aware multitask learning framework for clinical prediction from incomplete electronic health records (EHRs). We analyzed a public dataset of 77,724 patients and 107 numerical features. To prevent t...

Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand

Pore pressure is an essential parameter for establishing reservoir conditions, geological interpretation and drilling programs. Pore pressure prediction depends on information from various geophysical logs, seismic, and direct down-hole pre...

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering...

Upper Limb Movement Prediction Based on Segmented sEMG Signals

Motion intent recognition research is one of the key challenges in achieving human-robot collaboration in rehabilitation robots. In the traditional method, intention recognition is performed based on the complete sEMG, however, due to the m...

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical...

Machine Learning and Deep Learning

Now-a-days artificial intelligence has become an asset for engineering and experimental studies, just like statistics and calculus. Data science is a growing field for researchers and artificial intelligence, machine learning and deep learn...

Machine Learning for Engineers

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering probl...

Machine Learning in Chemical Engineering: A Perspective

Abstract The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML)...

Multi-Chain Semi-Markov Analysis of Intrapartum Cardiotocography.