In these days, when complex, IT-controlled systems have found their way into many areas, models and the data on which they are based are playing an increasingly important role. Due to the constantly growing possibilities of collecting data through se...
The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distribute...
Road transport is the principal means of transporting freight and passengers in most developing countries, but several factors, both alone and in conjunction, contribute to increased inefficiency, risk and instability in the sector. The main factors...
The utilization of robust, pre-trained foundation models enables simple adaptation to specific ongoing tasks. In particular, the recently developed Segment Anything Model (SAM) has demonstrated impressive results in the context of semantic segmentati...
When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images th...
Data transmission requirements are a major constraint for mission design and can increase mission complexity significantly. Thus, reducing the amount of data required to be transmitted is key. In this work, the reference scenario of the European Spac...
Due to industrial demands to handle increasing amounts of training data, lower the cost of computing one model at a time, and lessen the ecological effects of intensive computing resource consumption, the job of speeding the training of deep neural n...
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain–computer interfaces. The proposed method is configur...
In order to increase the effectiveness of model training, data reduction is essential to data-centric Artificial Intelligence (AI). It achieves this by locating the most instructive examples in massive datasets. To increase data quality and training...
Land surface temperature (LST) is an important parameter in determining surface energy balance and a fundamental variable detected by the advanced geostationary radiation imager (AGRI), the main payload of FY-4A. FY-4A is the first of a new generatio...
This paper addresses the issue of abnormal fluctuations in brake pipe pressure causing variations in braking force, or even forced stops, in heavy-haul trains. A multi-parameter synchronous acquisition monitoring device has been designed to collect r...
The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier witho...
Machine learning methods were applied to data with an atmospheric corrosion monitoring sensor based on strain measurements to improve the evaluation accuracy of the thickness reduction of a low-carbon steel plate due to atmospheric corrosion. Monitor...
Personalized healthcare recommender systems are increasingly being deployed in edge AI environments through wearable devices. In such environments, cloud servers leverage high-performance GPUs to train base models, which are then optimized for data r...
Glaucoma management relies on lowering intraocular pressure (IOP), but determining the target reduction at presentation is challenging, particularly in normal-tension glaucoma (NTG). We developed and internally validated a neural network regression m...
Federated Learning (FL) has been proposed as a new machine learning paradigm to ensure data privacy by training the model in a decentralized manner. However, FL is challenged by device heterogeneity, asymmetric data contribution, and imbalanced datas...
The effectiveness of deep neural network models is intricately tied to the distribution of training data. However, in pose estimation, potential discrepancies in root joint positions and inherent variability in biomechanical features across datasets...
In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-ste...
Training deep learning models requires having the right data for the problem and understanding both your data and the models’ performance on that data. Training deep learning models is difficult when data are limited, so in this paper, we seek...
High-dimensional surrogate modeling with limited high-fidelity data poses a major challenge in uncertainty quantification. Classical supervised dimension reduction methods often fail in this setting due to insufficient accurate observations, while lo...
We have adapted, implemented and trained the Learned Primal Dual algorithm suggested by Adler and Öktem and evaluated its performance in reconstructing projection data from our PET scanner. Learned Primal Dual reconstructions are compared to Max...
We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing th...
Deep learning networks might require re-training for different datasets, consuming significant manual labeling and training time. Transfer learning uses little new data and training time to enable pre-trained network segmentation in relevant scenario...
The primary aim of this study was to contribute to the development of multilingual automatic speech recognition for lower-resourced Turkic languages. Ten languages—Azerbaijani, Bashkir, Chuvash, Kazakh, Kyrgyz, Sakha, Tatar, Turkish, Uyghur, an...
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resour...
Background/Objectives: Obesity has been linked to cardiometabolic alterations and deteriorated body composition. Gallic acid, a polyphenol with antioxidant properties, may influence these parameters; however, there is limited clinical data. The aim o...
The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Al...
In this paper, a semisupervised speech data extraction method is presented and applied to create a new dataset designed for the development of fully bilingual Automatic Speech Recognition (ASR) systems for Basque and Spanish. The dataset is drawn fro...
Recent advancements in deep learning have led to various challenges, one of which is the issue of data privacy in training data. To address this issue, federated learning, a technique that merges models trained by clients on servers, has emerged as a...
X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a comput...
An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that ma...
This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and trav...
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (n...
Occupancy detection for large buildings enables optimised control of indoor systems based on occupant presence, reducing the energy costs of heating and cooling. Through machine learning models, occupancy detection is achieved with an accuracy of ove...
Train running safety is considered one of the key criteria for advanced highway trains and bogies. While a number of existing research studies have focused on its measurement and monitoring, this study proposes a new and effective train running a saf...
Background: Structured exercise as part of lifestyle modification plays an important role in the improvement of non-alcoholic fatty liver disease (NAFLD); however, its effectiveness has been shown to vary. This systematic review with meta-analysis in...
Large scale testing of newly developed Li-ion cells is associated with high costs for the interested parties, and ideally, testing time should be kept to a minimum. In this work, an ageing model was developed and trained with real data from a large-s...
Critical velocity is very important for smoke control in longitudinally ventilated subway tunnel fires. Numerical investigations are conducted in this paper to study the impacts of metro train blockages on critical velocity in sloping subway tunnel f...
Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural...
Long-Term Evolution for Metro (LTE-M) is adopted as the data communication system in urban rail transit to exchange bio-direction train-wayside information. Reliable data communication is essential in LTE-M systems for ensuring trains’ operatio...
In the course of basketball training, a large number of basketball action data are generated according to the athletes’ body movements. Due to the low precision of the basketball action data processed by the traditional method in basketball tec...
In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysic...
In the growing field of Natural Language Processing (NLP), transformers have become excessively large, pushing the boundaries of both training and inference compute. Given the size and widespread use of these models, there is now a strong emphasis on...
Retrieving waveguiding properties of plasmonic metal nanowires (MNWs) through numerical simulations is time- and computational-resource-consuming, especially for those with abrupt geometric features and broken symmetries. Deep learning provides an al...
The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. However, retraining regression models with lookbac...
Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local...
Efficient deep learning systems are increasingly essential for automated quality inspection in the agri-food industry. This work systematically investigates the impact of optimized data-loading and augmentation pipelines on both training efficiency a...
In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the...
This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algori...
In resource-constrained environments, distributed split learning allows for collaborative training; however, the system suffers from high communication overhead and is sensitive to system heterogeneity. Despite advances in IoT data reduction and dist...
of 45