Efficient recognition of Parkinson’s disease mice on stepping characters with CNN by Yunsong Luo, Yiqi Huang, Guangzhan Fang, Ningping Tan, Yezhong Tang

“…By processing footprint images collected in the absence of light—employing numerical area summation for noise reduction, adaptive enhancement algorithms based on pixel values, and a high-accuracy Convolutional Neural Network algorithm. And integrating motion data analysis, we achieved effective fusion of footprint images and behavioral data. …”
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Wheat Futures Prices Prediction in China: A Hybrid Approach by Yunpeng Sun, Jin Guo, Shan Shan, Yousaf Ali Khan

“…This research investigates whether China wheat futures price can be predicted by employing artificial intelligence neural network. This would add to our knowledge whether wheat futures market is resourceful and would enable traders, sellers, and investors to improve cost-effective trading strategy. …”
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Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data by Pengfei Zhang, Zhenliang Ma, Xiaoxiong Weng

“…The isolation forest coupled with a neural network (NN) takes these features as inputs to detect the wrongly associated fare machines and infer the correct association stations. …”
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MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling by Wei Gu, Hongyan Xing, Tianhao Hou

“…Furthermore, we design an ensemble network based on SENet, ResNet, and the evolutionary convolutional neural network Squeeze Excitation Residual Network (SEResNet) to explore the hidden associations between different types of features from multiple perspectives. …”
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Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology by Haitao Chen

“…The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. …”
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Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning by Shaobin Ma, Lan Li, Chengwen Zhang

“…Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. …”
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Multi-Objective Optimal Design of Dropping Shock of Series Cushioning Packaging System by Yang Xue, Wei-Sheng Song, Hong-Tao Miao, Jing Wang, Yu Li

“…This paper adopts a BP neural network to develop a more precise constitutive relationship. …”
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Deep Learning Automated System for Thermal Defectometry of Multilayer Materials by A. S. Momot, R. M. Galagan, V. Yu. Gluhovskii

“…The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. …”
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Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism by Dong Yun Lee, Jang Yeop Kim, Soo Young Cho

“…To address this challenge, this study proposes a convolutional neural network (CNN)-based super-resolution architecture, utilizing a melanoma dataset to enhance image resolution through deep learning techniques. …”
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Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

“…At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, k‐nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non‐specific treatment response. …”
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The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution by Mohamed Khalid AlOmar, Faidhalrahman Khaleel, Abdulwahab Abdulrazaaq AlSaadi, Mohammed Majeed Hameed, Mohammed Abdulhakim AlSaadi, Nadhir Al-Ansari

“…In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. …”
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Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm by Yanhai Yang, Baitong Qian, Qicheng Xu, Ye Yang

“…The pavement degradation in each climatic zone was related to the climate characteristics of the region. Probabilistic neural network (PNN) and support vector machine (SVM) climate regionalization predictive models were established with MATLAB. …”
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Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment by Shifeng Niu, Guiqiang Li

“…From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). …”
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Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning by Shuai Liang

“…This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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Real-world pharmacovigilance analysis unveils the toxicity profile of amivantamab targeting EGFR exon 20 insertion mutations in non-small cell lung cancer by Jing Zhang, Wenjie Li

“…A comprehensive disproportionality analysis was performed, employing the reporting odds ratio (ROR), proportional reporting ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and the Bayesian confidence propagation neural network to calculate information components (ICs), to identify statistically significant adverse events. …”
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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. …”
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Multi-Scale Bilateral Spatial Direction-Aware Network for Cropland Extraction Based on Remote Sensing Images by Weimin Hou, Yanxia Wang, Jia Su, Yanli Hou, Ming Zhang, Yan Shang

“…Compared to other neural network models, MBSDANet achieves better accuracy with a precision of 0.9481, an IoU of 0.8937, and an F1 score of 0.9438.…”
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Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation by Jinfeng Wang, Muye Pang, Peixuan Yu, Biwei Tang, Kui Xiang, Zhaojie Ju

“…Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. …”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…Moreover, we propose an integrated end-to-end neural network learning framework based on one complete encoder-decoder architecture transformer model: Transfer Text-to-Text Transformer (T5), by learning the embedding vector representation space of conditional molecular properties to encode and guide the vector representation of SMILES sequences, resulting in the output of the final decoder block with a softmax output (maximum likelihood objective). …”
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