A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations by Muhammad Irfan Ishaq, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Nimra Saeed, Maaz Ansar

“…From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. To resolve this issue, this paper presents a unique hybrid Convolutional Neural Network (CNN) along with the Long Short Term Memory (LSTM) topology for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. …”
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Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning by Haixia Li, Qian Li, Chunlai Yu, Shanjun Luo

“…Moreover, the model accuracies (MLP and CNN) before and after variable screening showed noticeable changes. Conducting variable screening contributed to a substantial improvement in the accuracy of rice LAI estimation. …”
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Modeling energy consumption indexes of an industrial cement ball mill for sustainable production by Saeed Chehreh Chelgani, Rasoul Fatahi, Ali Pournazari, Hamid Nasiri

“…To fill the gap, this study developed a CL by examining different AI models (Random Forest, Support Vector Regression, Convolutional Neural Network, extreme gradient boosting, CatBoost, and SHapley Additive exPlanations) for modeling energy consumption indexes of a close ball mill circuit in a cement plant to address the effectiveness of operating variables. …”
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A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition by Guohua Li, An Liu, Huajie Shen

“…To address the problem of incomplete model inputs due to the presence of missing values, we propose to treat and describe the missing values as system-level variables and realize the alternate update of network parameters and dynamic filling of missing values through iterative learning among subnets. …”
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MSKFaceNet: A Lightweight Face Recognition Neural Network for Low-Power Devices by Peng Zhang, Qinghua Ma, Yi Li, Min Cui

“…First, we propose a novel lightweight convolutional neural network module called MSKFNet. MSKFNet adopts a bottleneck design and introduces variable kernel convolutions from VarKNet, combined with channel shuffle and structural re-parameterization techniques, establishing an efficient CNN module for embedded systems. …”
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H-ConvLSTM to Estimate Reference Evapotranspiration From Air Temperature and Relative Humidity by Abdul Haris, M. Marimin, Sri Wahjuni, Budi Indra Setiawan

“…These three algorithms have been extensively evaluated and validated due to their ability to predict and estimate ETo data using temperature (T), relative humidity (RH), and solar radiation (Rs) as variables. This paper presents a comparison of the results of the three algorithms using only two variables, namely temperature and relative humidity, without the inclusion of solar radiation variables. …”
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Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management by Meysam Alizamir, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim, Jeong Eun Lee

“…Furthermore, various configurations of the input variables were examined across seven distinct observational scenarios to identify the most significant predictive factors. …”
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A MFR Work Modes Recognition Method Based on Dual-Scale Feature Extraction by Zhiyuan Li, Xuan Fu, Chengjian Mo, Jianlong Tang, Ronghua Guo, Wenbo Li

“…The recognition method first obtains the variable-length sequence processing capability through pulse sequence segmentation. …”
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Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques by Satish Kumar, Sameer Sayyad, Arunkumar Bongale

“…The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. …”
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Analyzing spatiotemporal variation in suspended particulate matter in lakes using remote sensing by WEI Junyan, ZHAO Yiming, HAO Yanling, JIA Xiaoxue, MA Xinyan

“…The most influential variable was the B4·B5, followed by B4, B4+B5, and B5·(B4+B5). ③ From 2017 to 2023, annual average SPM concentrations ranged from 8.43 to 11.68 mg/L, showing a slight downward trend. …”
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Short-Term Target Maneuvering Trajectory Prediction Using DTW–CNN–LSTM by Haifeng Guo, Jinyi Yang, Xianyong Jing, Peng Zhang

“…Considering the characteristics of high noise, dynamic complexity, and variable data lengths inherent in short-range air combat scenarios, we employ dynamic time warping (DTW) to assess the similarity of 3D trajectory data. …”
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Research on the Construction of Crossborder e-Commerce Logistics Service System Based on Machine Learning Algorithms by Jinbo Xu, Shibiao Mu

“…Finally, through simulation experiments, a series of data processing work such as data outlier cleaning, sliding window construction features of data variables, and training set and test set division are designed to convert regression prediction problems into classification problems to predict commodity demand. …”
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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

“…In practical industrial environment, variable working condition can result in shifts in data distributions, and the labeled fault data in various working conditions is difficult to collect because rotating machines often works in normal status, and the insufficient labeled fault data brings data samples imbalance and performance degradation of intelligent fault diagnosis model. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…The input dataset is processed in tabular format using Microsoft Excel and includes variables such as topography, meteorology, soil characteristics, and human activity. …”
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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan, Ikram Din Ujjan

“…Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. …”
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A pyramid Gaussian pooling based CNN and transformer hybrid network for smoke segmentation by Guiqian Wang, Feiniu Yuan, Hongdi Li, Zhijun Fang

“…Abstract Visual smoke semantic segmentation is a challenging task due to semi‐transparency, variable shapes, and complex textures of smoke. To improve segmentation performance, a convolutional neural network and transformer hybrid network are proposed based on pyramid Gaussian pooling (PGP) for smoke segmentation. …”
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An attack detection method based on deep learning for internet of things by Yihan Yu, Yu Fu, Taotao Liu, Kun Wang, Yishuai An

“…However, current attack detection methods struggle to identify complex and variable attack methods, resulting in a high false positive rate. …”
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Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches by Yang Ximin

“…This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. …”
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