Improving sporadic demand forecasting using a modified k-nearest neighbor framework
Forecasting sporadic or intermittent demand presents significant challenges in supply chain
management, primarily due to the frequent occurrence of zero demand values and the …
management, primarily due to the frequent occurrence of zero demand values and the …
COVID-19 chest X-ray image classification in the presence of noisy labels
X Ying, H Liu, R Huang - Displays, 2023 - Elsevier
Abstract The Corona Virus Disease 2019 (COVID-19) has been declared a worldwide
pandemic, and a key method for diagnosing COVID-19 is chest X-ray imaging. The …
pandemic, and a key method for diagnosing COVID-19 is chest X-ray imaging. The …
Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments
X Yan, Y Lu, Y Liu, M Jia - Measurement Science and …, 2023 - iopscience.iop.org
Due to rolling bearings usually operate under fluctuating working conditions in practical
engineering, the raw vibration signals generated by bearing faults have nonlinear and non …
engineering, the raw vibration signals generated by bearing faults have nonlinear and non …
Predicting the direction of financial dollarization movement with genetic algorithm and machine learning algorithms: The case of Turkey
M Bumin, M Ozcalici - Expert Systems with Applications, 2023 - Elsevier
Financial dollarization has many implications on the economy and the banking sector of
developing countries. High level of financial dollarization causes fragilities on the balance …
developing countries. High level of financial dollarization causes fragilities on the balance …
Efficient data dimensionality reduction method for improving road crack classification algorithms
FJ Rodriguez‐Lozano… - … ‐Aided Civil and …, 2023 - Wiley Online Library
Automatic crack classification plays an essential role in road maintenance. Using many
features for the classification is inefficient for implementing embedded systems with low …
features for the classification is inefficient for implementing embedded systems with low …
Accelerating kNN search in high dimensional datasets on FPGA by reducing external memory access
Implementing an efficient k-Nearest Neighbors (kNN) algorithm on FPGA is becoming
challenging due to the fact that both the size and dimensionality of datasets that kNN is …
challenging due to the fact that both the size and dimensionality of datasets that kNN is …
A novel approach based on machine learning and public engagement to predict water-scarcity risk in urban areas
Climate change, population growth and urban sprawl have put a strain on water supplies
across the world, making it difficult to meet water demand, especially in city regions where …
across the world, making it difficult to meet water demand, especially in city regions where …
An improved soft-YOLOX for garbage quantity identification
J Lin, C Yang, Y Lu, Y Cai, H Zhan, Z Zhang - Mathematics, 2022 - mdpi.com
Urban waterlogging is mainly caused by garbage clogging the sewer manhole covers. If the
amount of garbage at a sewer manhole cover can be detected, together with an early …
amount of garbage at a sewer manhole cover can be detected, together with an early …
Hybrid machine learning model for prediction of vertical deflection of composite bridges
A novel hybrid model, based on machine learning technique, for quick and accurate
prediction of the vertical deflection of steel–concrete composite bridges was developed. The …
prediction of the vertical deflection of steel–concrete composite bridges was developed. The …
Reconfigurable hardware implementation of K-nearest neighbor algorithm on FPGA
Abstract Nowadays, Machine Learning is commonly integrated into most daily life
applications in various fields. The K Nearest Neighbor (KNN), which is a robust Machine …
applications in various fields. The K Nearest Neighbor (KNN), which is a robust Machine …