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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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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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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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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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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A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying by Xin Xu, Ying Sun, Yuanyuan Yin, Yiwei Xue, Fangyan Ma, Chao Song, Hang Yin, Liqing Zhao

“…According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. …”
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An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model by Kaikai Wang, Chun’an Tang, Ke Ma, Xintang Wang, Qiang Li

“…The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation-Maximum (EM-GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), na&#x00EF;ve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention by Kavi Priya S, Pon Karthika K, Jayakumar Kaliappan, Senthil Kumaran Selvaraj, Nagalakshmi R, Baye Molla

“…The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. …”
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Estimation of Bearing Capacity of Strip Footing Rested on Bilayered Soil Profile Using FEM-AI-Coupled Techniques by Ahmed M. Ebid, Kennedy C. Onyelowe, M. Salah

“…Multiple numerical data were generated for the case under study and artificial intelligence (AI)-based techniques; generalized reduced gradient (GRG), genetic programming (GP), artificial neural network (ANN), and evolutionary polynomial regression (EPR) were used to predict the UBC. …”
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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python by Polina Lemenkova

“…Image processing using Machine Learning (ML) and Artificial Neural Network (ANN) methods was investigated by employing the algorithms of Geographic Resources Analysis Support System (GRASS) Geographic Information System GIS with embedded Scikit-Learn library of Python language. …”
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Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). …”
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A Metaheuristic Approach to Detecting and Mitigating DDoS Attacks in Blockchain-Integrated Deep Learning Models for IoT Applications by Manal Alkhammash

“…Besides, the MHADMA-BCIDL technique employs an attention-based convolutional neural network with bi-directional long short-term memory (CNN-BiLSTM-Attention) method for the detection and classification of attacks. …”
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Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset by Xuhui Song, Linyuan Fan

“…The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. …”
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