Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis by Abdel Fattah Azzam, Ahmed Maghrabi, Eman El-Naqeeb, Mohammed Aldawood, Hewayda ElGhawalby

“…Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. …”
Get full text

Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends by Lina Elmoiz Alatabani, Elmustafa Sayed Ali, Rania A. Mokhtar, Rashid A. Saeed, Hesham Alhumyani, Mohammad Kamrul Hasan

“…In this paper, some concepts related to deep learning networks will be discussed as one of the uses of machine learning in IoV systems, in addition to studying the effect of neural networks (NNs) and their types, as well as deep learning mechanisms that help in processing large amounts of unclassified data. …”
Get full text

Automated Particle Shape Identification and Quantification for DEM Simulation of Rockfill Materials in Subgrade Construction by Hao Bai, Xiangyu Hu, Ruidong Li, Fei Chen, Zhiyong Liao

“…This study first identifies the subgrade rockfill particle contour by machine learning algorithms, including AdaBoost, Cascade, and sliding windows. …”
Get full text

The Gust Factor Models Involving Wind Speed and Temperature Profiles for Wind Gust Estimation by Haichuan Hu, Chuanhai Qian, Shibo Gao

“…A unified upper-level gust impact model was developed through multiple regression (GF-L) and machine learning (GF-M) methods based on data from these stations to improve gust estimation accuracy. …”
Get full text

Graph-Based Data Analysis for Building Chemistry–Phase Design Rules for High Entropy Alloys by Scott R. Broderick, Stephen A. Giles, Debasis Sengupta, Krishna Rajan

“…The number and types of phases formed in high entropy alloys (HEAs) have significant impacts on the mechanical properties. While various machine learning approaches were developed for predicting whether an HEA is single or multiphase, changes in chemistry and/or composition can lead to other changes across length scales, which affect material performance. …”
Get full text

Creating non-fungible token (NFT)-backed emoji art from user conversations on blockchain by Maedeh Mosharraf, Mohammad Hossein Khorrami

“…It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments. …”
Get full text

ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE by WANG XiaoHui, WANG GuangBing, XIANG JiaWei, HUANG Zhen, SUI GuangZhou

“…According to characteristics of the bearing signal,the second-order cyclic demodulation information was introduced into machine learning,and a multi-domain kernel extreme learning machine（MKELM） based on the combination of cyclic autocorrelation（CAF） frequency domain features and time domain features（TD） was proposed to accurately identify the bearing status.The algorithm constructed a CAF function based on the second-order cyclic characteristics of the bearing signal to extract the cyclic frequency domain features of the samples,then combined them with the time domain feature quantities of the samples.The matching factors of multi-domain feature vectors was designed to fuse TD and CAF feature vectors; finally,the fused CAF-TD sample features was input into the kernel extreme learning machine for cluster regression.The spindle bearing experimental results show that the cyclic frequency domain statistics extracted based on CAF can sensitively reflect the signal characteristics.Compared with the classic classifier,the CAF-TD multi-domain kernel extreme learning machine can extract more feature information from limited samples and obtain more accurate diagnostic result.…”
Get full text

Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey by Giovanni Battista Gaggero, Paola Girdinio, Mario Marchese

“…Anomaly detection methods increasingly rely on Machine Learning techniques, that represent a game-changer tool for data analysis. …”
Get full text

Development of malaria diagnosis with convolutional neural network architectures: a CNN-based software for accurate cell image analysis. by Emrah ASLAN

“…The traditional method of diagnosis relies on experts examining red blood cells under a microscope and is inefficient as it is dependent on expert knowledge and experience. Nowadays, machine learning methods that provide high accuracy are increasingly used in disease detection. …”
Get full text

Investigating the electric vehicle adoption initiatives for achieving sustainable development goals by Shashi Kant Tripathi, Ravi Kant, Ravi Shankar

“…A novel research framework of sentence boundary extraction, a machine learning approach, and multi-criteria decision-making is proposed to achieve the research objective. …”
Get full text

Application of Computer-Based Techniques for Monitoring Animal Health, Behavior and Welfare: A Review by A. S. Famuyiwa, O. P. Dosunmu, D. Jimi-Olatunji

“…Computerized monitoring systems, including sensors, wearables, and artificial intelligence, provide continuous, real-time data that enhances the accuracy and efficiency of tracking animal welfare indicators such as stress, disease, and environmental comfort. Advances in machine learning, IoT, and blockchain have further expanded capabilities, enabling predictive insights and ensuring data security and transparency. …”
Get full text

Application of Spatial Transcriptomics in Digestive System Tumors by Bowen Huang, Yingjia Chen, Shuqiang Yuan

“…This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research.…”
Get full text

An Importance Sampling Method for Generating Optimal Interpolation Points in Training Physics-Informed Neural Networks by Hui Li, Yichi Zhang, Zhaoxiong Wu, Zhe Wang, Tong Wu

“…The application of machine learning and artificial intelligence to solve scientific challenges has significantly increased in recent years. …”
Get full text

Strong wind is one of the important factors that trigger landslides by Yuan-Chien Lin, Jui-Yun Hsieh, Hua-San Shih, Wen-Hsin Wang

“…The significance of the combined rain–wind influence on landslides is examined using Mann–Whitney U test and 3D histogram, and a Random Forest machine learning model is constructed to predict the occurrences of landslides based on factors, such as heavy rain, strong winds, traditional geological conditions, and topographical factors. …”
Get full text

An empty-nest power user identification method based on weighted random forest algorithm by Zimeng LU, Jiayi CHEN, Jing LI, Yue XIE, Xinli JIANG, Lei HAN, Qian GUO

“…In view of the lack of effective technical means for the identification of empty-nesters by the government and the society,an empty-nesters prow user identification method based on weighted random forest algorithm was proposed.Firstly,some accurate labels of empty-nest users were obtained through questionnaires,and electricity characteristic library was drawn from three aspects:electricity consumption level,electricity consumption fluctuation and electricity consumption trend.Due to the data imbalance between empty-nest and non-empty-nest users,the weighted random forest algorithm was used to improve the data sensitivity phenomenon of machine learning.Finally,the algorithm model was put online in the power company’s acquisition system.The 2 000 unknown users of various types were identified,among which the identification accuracy of empty-nest users was 74.2%.The results show that the identification of empty-nesters from the perspective of electricity consumption can help power grid companies to understand the personalized and differentiated needs of empty-nesters,so as to provide users with more sophisticated services,and also assist the government and society to carry out assistance work.…”
Get full text

Novel and cost-effective CNC tool condition monitoring through image processing techniques by Alireza Falah, Mátyás Andó

“…Furthermore, it sets the stage for future investigations in this domain, indicating the possibility for enhancements through machine learning and an expanded application of these monitoring techniques.…”
Get full text

Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain by Ming YANG, Xuexian HU, Qihui ZHANG, Jianghong WEI, Wenfen LIU

“…Federated learning is a new distributed machine learning technology, where training tasks are deployed on user side and training model parameters are sent to the server side.In the whole process, participants do not need to share their own data directly, which greatly avoids privacy issues.However, the trust relationship between mobile users in the learning model has not been established in advance, there is hidden safety when users perform cooperative train with each other.In view of the above problems, a federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain was proposed.The scheme allowed the server side to use subjective logic models to evaluate the reputation of participating mobile users and provided them with credible reputation opinions sharing environment and dynamic access strategy interface based on the technique of smart contract of blockchain.Theoretical and experimental analys is results show that the scheme can enable the server side to select reliable users for training.And it can achieve more fair and effective reputation calculations, which improves the accuracy of federated learning.…”
Get full text

Study on the Impact of LDA Preprocessing on Pig Face Identification with SVM by Hongwen Yan, Yulong Wu, Yifan Bo, Yukuan Han, Gaifeng Ren

“…In this study, the implementation of traditional machine learning models in the intelligent management of swine is explored, focusing on the impact of LDA preprocessing on pig facial recognition using an SVM. …”
Get full text

A scaling law to model the effectiveness of identification techniques by Luc Rocher, Julien M. Hendrickx, Yves-Alexandre de Montjoye

“…We then generalize the model to forecast how κ scales from small-scale experiments to the real world, for exact, sparse, and machine learning-based robust identification techniques. …”
Get full text

Pansharpening Techniques: Optimizing the Loss Function for Convolutional Neural Networks by Rocco Restaino

“…Pansharpening is a traditional image fusion problem where the reference image (or ground truth) is not accessible. Machine-learning-based algorithms designed for this task require an extensive optimization phase of network parameters, which must be performed using unsupervised learning techniques. …”
Get full text