A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors: A national cross-sectional and cohort study by Xingjian Xiao, Xiaohan Yi, Zumin Shi, Zongyuan Ge, Hualing Song, Hailei Zhao, Tiantian Liang, Xinming Yang, Suxian Liu, Bo Sun, Xianglong Xu

“…We employed nine machine learning algorithms to construct predictive models for gastric ulcers over the next seven years (2011–2018, with 1482 samples) and the next three years (2014–2018, with 2659 samples). …”
Get full text

Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis by Jimmy Londono, Shohreh Ghasemi, Altaf Hussain Shah, Amir Fahimipour, Niloofar Ghadimi, Sara Hashemi, Zohaib Khurshid, Mahmood Dashti

“…This meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. …”
Get full text

Reduction of artifacts associated with missing data in coherent diffractive imaging by Erik Malm, Yuriy Chushkin

Get full text

Neoantigen vaccines: advancing personalized cancer immunotherapy by Alaa A. A. Aljabali, Yassmen Hamzat, Alaa Alqudah, Lorca Alzoubi

Subjects: Get full text

Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model by Weichong Zhou, Xingfu Liao, Hui Shi, Mingfeng Wang, Yunchong Xiao, Xilu Yu, Yilong Wu, YanYi Liu, Yin Peng, Hai Su

Subjects: Get full text

One-point Method for Speed Strategy Optimization Based on Slope Simplification and Speed Limit Prediction by XING Jian, HAN Bing

Subjects: “…speed limit prediction…”
Get full text

Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma, Yue Zhou

“…Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. …”
Get full text

Efficient colour filter array demosaicking with prior error reduction by M.S. Safna Asiq, WR Sam Emmanuel

“…The proposed work introduces an error efficient demosaicking algorithm. The efficient prior error reduction technique helps to obtain better results. …”
Get full text

GRU-LSTM model based on the SSA for short-term traffic flow prediction by Changxi Ma, Xiaoyu Huang, Yongpeng Zhao, Tao Wang, Bo Du

Subjects: “…traffic flow prediction…”
Get full text

Enhancing sustainability in dairy industry: Blockchain-based waste reduction by Diana Hawashin, Khaled Salah, Raja Jayaraman, Raja Wasim Ahmad, Salil Kanhere, Ernesto Damiani

Get full text

Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm. by Xiaotong Bai, Yuefeng Zheng, Yang Lu, Yongtao Shi

“…Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. …”
Get full text

Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data by Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh

Get full text

Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction by Lihua Shen, Baorui Du, He Fan, Hailong Yang

Subjects: “…tool wear prediction…”
Get full text

Electricity Losses in Focus: Detection and Reduction Strategies—State of the Art by Daniela F. Niste, Radu Tîrnovan, Sorin Pavel, Horia Beleiu, Cziker Andrei, Marius Misaroș

Get full text

Surfactants Adsorption onto Algerian Rock Reservoir for Enhanced Oil Recovery Applications: Prediction and Optimization Using Design of Experiments, Artificial Neural Networks, and Genetic Algorithm (GA) by Kahina Imene Benramdane, Mohamed El Moundhir Hadji, Mohamed Khodja, Nadjib Drouiche, Bruno Grassl, Seif El Islam Lebouachera

“…Before training, 32 different ANN configurations were evaluated by varying learning algorithms, adaptation functions, and transfer functions. …”
Get full text

A Novel Grey Prediction Model: A Hybrid Approach Based on Extension of the Fractional Order Discrete Grey Power Model with the Polynomial-Driven and PSO-GWO Algorithm by Baohua Yang, Xiangyu Zeng, Jinshuai Zhao

“…Background: This study addresses the challenge of predicting data sequences characterized by a mix of partial linearity and partial nonlinearity. …”
Get full text

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer by Kaimin Hu, Yinan Wu, Yajing Huang, Meiqi Zhou, Yanyan Wang, Xingru Huang

“…Our classifier achieved a predicting accuracy of 81.25%, and 88.7% slides labeled as endocrine resistant were predicted as the mesenchymal-phenotype, while 75.6% slides labeled as sensitive were predicted as the epithelial-phenotype. …”
Get full text

Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa by Senthil Krishnamurthy, Oludamilare Bode Adewuyi, Emmanuel Luwaca, Mukovhe Ratshitanga, Prathaban Moodley

Subjects: “…Artificial intelligence-based predictive analytics…”
Get full text

Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey by Robert Chab, Fei Li, Sanjeev Setia

“…In this survey, we provide a comprehensive classification of GPU task scheduling approaches, categorized by their underlying algorithmic techniques and evaluation metrics. We examine traditional methods—including greedy algorithms, dynamic programming, and mathematical programming—alongside advanced machine learning techniques integrated into scheduling policies. …”
Get full text

Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency by Senthil G.A., Prabha R., Asha R.M., Suganthi S.U., Sridevi S.

“…Findings have indicated that the multi-algorithm approach not only promotes increased predictive capabilities and resource optimization but also raises food safety with the increased threats in agriculture.…”
Get full text