Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems by Maham Nayab, Asim Waris, Muhammad Jawad Khan, Dokhyl AlQahtani, Ahmed Imran, Syed Omer Gilani, Umer Hameed Shah

“…To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. …”
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Simultaneous detection of human neutrophil elastase and cathepsin G on a single substrate using a fluorometric quantum dots probe and chemometric models by Fátima A.R. Mota, Rafael C. Castro, David S.M. Ribeiro, João L.M. Santos, Ricardo N.M.J. Páscoa, Marieta L.C. Passos, M. Lúcia M.F.S. Saraiva

“…These second-order data were processed using various chemometric models, including unfolded partial least-squares with residual bilinearization (U-PLS/RBL), radial basis function artificial neural network (RBF-ANN), and partial least squares-discriminant analysis (PLS-DA), to guarantee a detailed and precise analysis. …”
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An Explainable Artificial Intelligence Model for the Classification of Breast Cancer by Tarek Khater, Abir Hussain, Riyad Bendardaf, Iman M. Talaat, Hissam Tawfik, Sam Ansari, Soliman Mahmoud

“…The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. …”
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The impact of dietary fiber on colorectal cancer patients based on machine learning by Xinwei Ji, Lixin Wang, Pengbo Luan, Jingru Liang, Weicai Cheng

“…Additionally, four machine learning models—Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM)—were developed based on nutritional and clinical indicators.ResultsIn the observation group, levels of procalcitonin (PCT), beta-endorphin (β-EP), C-reactive protein (CRP), interleukin-1 (IL-1), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) were significantly lower compared to the control group (p < 0.01). …”
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Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis by Ziyuan Jiang, Qinkai Han, Xueping Xu

“…The convolutional neural network (CNN), which can automatically extract features, is also adopted. …”
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Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture by Anju Das, N. Neelima, K. Deepa, Tolga Ozer

“…Based on the selected gene set, the Depth-wise Separable Convolutional Neural Network (DSCNN) is employed to categorize diverse cancerous and non-cancerous classes. …”
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Evaluation of keratometric and total corneal astigmatism measurements from optical biometers and anterior segment tomographers and mapping to reconstructed corneal astigmatism vect... by Achim Langenbucher, Nóra Szentmáry, Alan Cayless, Peter Hoffmann, Jascha Wendelstein, Seth Pantanelli

“…Feedforward shallow neural network (NET) and linear regression (REG) prediction models were derived to map the measured C0 and C45 power vector components to the respective recCP components.…”
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A hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention by Gang Chen, Shen Chen, Dong Li, Cai Chen

“…The proposed approach, termed KSC-ConvLSTM, integrates the k-nearest neighbors (KNN) algorithm, spatio-temporal attention (STA) mechanism, the residual block, and convolutional long short-term memory (ConvLSTM) neural network. The KNN algorithm adaptively selects highly correlated neighboring domains, while the residual block, enhanced with the STA mechanism, extracts spatial features from the input data. …”
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PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network by Jiwei Fan, Xiaogang Yang, Ruitao Lu, Xueli Xie, Siyu Wang

“…Finally, by sharing neural network weights, the integrated design of target recognition and image-matching localization algorithms is achieved. …”
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Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork by Xiaojing Tian, Jun Wang, Zhongren Ma, Mingsheng Li, Zhenbo Wei

“…In order to predict the pork proportion in adulterated mutton, multiple linear regression (MLR), partial least square analysis (PLS), and backpropagation neural network (BPNN) regression models were used, and the results were compared, aiming at building effective predictive models. …”
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Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation by Xiuxun Liu, Zuping Tang, Jiaolong Wei

“…In particular, the root mean square error of the presented method in the first dataset is 0.239 m, which is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>39.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>17</mn><mo>%</mo></mrow></semantics></math></inline-formula> lower than the current state-of-the-art long short-term memory network and convolutional neural network, respectively. The presented method can provide higher-precision estimated values for studying the GNSS positioning error estimation problem.…”
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Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review by Abid Ali, Hans-Peter Kaul

“…Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. …”
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Optimizing Breast Cancer Mammogram Classification Through a Dual Approach: A Deep Learning Framework Combining ResNet50, SMOTE, and Fully Connected Layers for Balanced and Imbalanc... by Abdullah Fahad A. Alshamrani, Faisal Saleh Zuhair Alshomrani

“…The framework incorporates a blockwise Convolutional Neural Network (CNN), utilizing VGG16 preprocessing for input standardization and ResNet50 for feature extraction. …”
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Multimodal consumer choice prediction using EEG signals and eye tracking by Syed Muhammad Usman, Shehzad Khalid, Aimen Tanveer, Ali Shariq Imran, Muhammad Zubair

“…Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. …”
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Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan by John Karongo, Joseph Ivivi Mwaniki, John Ndiritu, Victor Mokaya

“…We use five Machine Learning (ML) techniques, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGboost), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict 2021 end-of-season sorghum yield in conflict affected Upper Nile and Western Bahr El Gazal states. …”
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Image-based 3D mesoscopic modeling and thermo-mechanical properties prediction of SiC/SiC composites with different preforms by Han Zeng, Xin Jing, Yasong Sun

“…First, CT scanning images of 3D models are segmented using an efficient and accurate DL neural network. The fabric structures of the yarns are reconstructed and real 3D descriptions are generated. …”
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Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection by Urszula Jachymczyk, Paweł Knap, Krzysztof Lalik

“…The best results were achieved by a deep neural network trained on the full dataset, with accuracy, precision, recall, and F1 score of 97.30%, 97.23%, 97.23%, and 97.23%, respectively, while the top-performing ML model (a voting classifier trained on the reduced dataset) attained scores of 97.13%, 96.99%, 96.95%, and 96.94%. …”
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A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals by Dalin Yang, Trung-Hau Nguyen, Wan-Young Chung

“…Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. …”
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Utilizing deterministic smart tools to predict recovery factor performance of smart water injection in carbonate reservoirs by Ali Maghsoudian, Amin Izadpanahi, Zahra Bahmani, Amir Hossein Avvali, Ali Esfandiarian

“…In this paper, three predictive algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and multigene genetic programming (MGGP) are developed to predict the RF of smart water flooding in carbonate reservoirs. …”
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Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique by Sengottaiyan N., Ananthi J., Rajesh Sharma R., Hamsanandhini S., Sungheetha Akey, Chinnaiyan R., Ketema Adare Gemeda

“…These parameters are processed through a sophisticated neural network architecture designed to identify patterns and correlations that signify the likelihood of a forest fire. …”
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