Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

“…This article provides a critical analysis of the following cutting-edge methods: DTC with Space Vector Modulation (DTC-SVM), DTC based on Fuzzy Logic (DTC-FL), DTC using Artificial Neural Networks (DTC-ANN), DTC optimized by Genetic Algorithms (DTC-GA), DTC with Ant Colony Optimization (DTC-ACO), DTC with rooted tree optimization (DTC-RTO), Sliding Mode Control (DTC-SMC), and Predictive DTC (P-DTC). …”
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Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…For tracking tasks, transformer-based models like SiamFCA and FishTrack leverage hierarchical attention mechanisms and convolutional neural networks (CNNs) to achieve high accuracy and robustness in dynamic underwater environments. …”
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Estimation of minimum miscible pressure in carbon dioxide gas injection using machine learning methods by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Fatemeh Mohammadinia, Amirjavad Borhani

“…Furthermore, ML algorithms such as Artificial Neural Networks (ANN), Bayesian networks, Random Forest (RF), Support Vector Machine (SVM), LSBoost, and Linear Regression (LR) were employed to estimate MMP. …”
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Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset by Xiaoying Pan, Yizhe Zhao, Hao Chen, De Wei, Chen Zhao, Zhi Wei

“…To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. …”
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Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays by Nourhan Zayed, Nahed Tawfik, Mervat M. A. Mahmoud, Ahmed Fawzy, Young-Im Cho, Mohamed S. Abdallah

“…Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. …”
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DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification by Fei Pan, Dawei He, Pengjun Xiang, Mengdie Hu, Daizhuang Yang, Fang Huang, Changmeng Peng

“…The proposed method leverages the strengths of convolutional neural networks (CNNs) and transformers to extract both local and global features, effectively capturing spectral and image characteristics. …”
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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns, a self-attention mechanism to construct dynamic adjacency matrices that adapt over time, and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation. …”
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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway by Mohib Ullah, Haijun Qiu, Wenchao Huangfu, Dongdong Yang, Yingdong Wei, Bingzhe Tang

“…To address this, this study assessed the performance of six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF and CNN+CatBoost), and a Stacking Ensemble (SE) combining CNN, RF, and CatBoost in mapping landslide susceptibility along the Karakoram Highway in northern Pakistan. …”
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Digital mapping of soil organic carbon in a plain area based on time-series features by Kun Yan, Decai Wang, Yongkang Feng, Siyu Hou, Yamei Zhang, Huimin Yang

“…SOC prediction models were established using random forests (RF), backpropagation neural networks (BP), and support vector machines (SVM). …”
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MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling by Jihye Ahn, Hyesong Choi, Soomin Kim, Dongbo Min

“…In stereo matching, Convolutional Neural Networks (CNNs), a class of deep learning models designed to process grid-like data such as images, have traditionally served as the predominant architectures. …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. …”
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Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…The machining performance indicators of the first set are optimized using graphical method of contour plots. Artificial neural networks technique, which is well known for its versatility to model linear as well as non-linear data, is used to express the surface roughness as a function of tool geometrical variables. …”
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Few-shot Remote Sensing Imagery Recognition with Compositionality Inductive Bias in Hierarchical Representation Space by Shichao Zhou, Zhuowei Wang, Zekai Zhang, Wenzheng Wang, Yingrui Zhao, Yunpu Zhang

“…Different from the naive data-driven strategies mentioned above, we alternatively devote to delicate feature modeling by constraining the mapping behavior of deep neural networks. Specifically, we embed inductive bias of compositionality into hierarchical latent representation space, which operates on two aspects: 1) disentangled and reusable representation. …”
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Dissociation between Cerebellar and Cerebral Neural Activities in Humans with Long-Term Bilateral Sensorineural Hearing Loss by Xiao-Min Xu, Yun Jiao, Tian-Yu Tang, Jian Zhang, Chun-Qiang Lu, Ying Luan, Richard Salvi, Gao-Jun Teng

“…To further explore the role of long-term bilateral sensorineural hearing loss on cerebellar function, we investigated hearing loss-induced changes among neural networks within cerebellar subregions and the changes in cerebellar-cerebral connectivity patterns using resting-state functional MRI. …”
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Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion by Wenjun Du, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, Tuo Sun

“…This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. …”
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Application of Conversational AI Models in Decision Making for Clinical Periodontology: Analysis and Predictive Modeling by Albert Camlet, Aida Kusiak, Dariusz Świetlik

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Hybrid Analysis of Biochar Production from Pyrolysis of Agriculture Waste Using Statistical and Artificial Intelligent-Based Modeling Techniques by Hani Hussain Sait, Ramesh Kanthasamy, Bamidele Victor Ayodele

“…This study used response surface methodology (RSM) and artificial neural networks (ANNs) to optimize and predict the production of biochar from the pyrolysis of palm kernel shells. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…Additionally, the IDS employs a hybrid deep learning classifier that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture the smart grid data's spatial and temporal features. …”
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Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka by Helani Perera, Miyuru B. Gunathilake, Ravindu Panditharathne, Najib Al-mahbashi, Upaka Rathnayake

“…Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. …”
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Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework by Shaik Khasim Saheb, B. Narayanan, T.V. Narayana Rao

“…The proposed SC2SSP approach attains 3.101% and 7.12% higher accuracy; 24.13% and 13.04% higher precision compared with existing methods, like the Detection of COVID-19 from Chest X-ray Images Using Convolutional Neural Networks (Resnet50), Deep learning for automated recognition of covid-19 from chest X-ray images (VGGNet), respectively. …”
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