Vehicle Detection and Tracking Based on Improved YOLOv8 by Yunxiang Liu, Shujun Shen

“…Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. …”
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Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer by Emre Kilinc, Umut Orhan

“…Using the Ultrasound Penetration-based Digital Soil Texture Analyzer (USTA), this research combined ultrasound time series data with pH and EC measurements to predict sand, silt, and clay ratios through machine learning methods—support vector regression (SVR), Random Forest (RF), and multi-layer perceptron neural network (MLPNN). Simulations showed that RF yielded the best results, improving R2 values to 0.52, 0.33, and 0.31 for sand, silt, and clay, respectively. …”
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Vision transformers for automated detection of diabetic peripheral neuropathy in corneal confocal microscopy images by Chaima Ben Rabah, Ioannis N. Petropoulos, Rayaz A. Malik, Ahmed Serag

“…The ViT model's performance was also compared to ResNet50, a convolutional neural network (CNN) previously applied for DPN detection using CCM images. …”
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On the Analysis and Assessment of First-Order Group Contribution Models for the Calculation of Normal Boiling Point and Critical Properties of Pure Compounds by Vanessa Villazón-León, Adrián Bonilla-Petriciolet, Juan Carlos Tapia-Picazo

“…The performance of these models was characterized and compared for several compound families using a standardized approach to determine their group contributions and parameters. An artificial neural network model was also applied and assessed to improve the estimations obtained with the best group contribution models. …”
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Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling by Lu-Kai Song, Guang-Chen Bai, Cheng-Wei Fei, Jie Wen

“…To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. …”
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Oculomotor Plant Mathematical Model in Kalman Filter Form With Peak Velocity-Based Neural Pulse for Continuous Gaze Prediction by Dmytro Katrychuk, Dillon J. Lohr, Oleg V. Komogortsev

“…We additionally extend the prior work by evaluating the proposed model on the largest to date pool of 322 subjects against the naive zero displacement baseline and a long short-term memory (LSTM) neural network.…”
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Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model by Dechen Yao, Qiang Sun, Jianwei Yang, Hengchang Liu, Jiao Zhang

“…Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. …”
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Enhancing Image-Based JPEG Compression: ML-Driven Quantization via DCT Feature Clustering by Shahrzad Sabzavi, Reza Ghaderi

“…In this study, an auto-encoder neural network is utilized to extract DCT features from images. …”
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Wind Power Assessment Based on a WRF Wind Simulation with Developed Power Curve Modeling Methods by Zhenhai Guo, Xia Xiao

“…These approaches are improvements on the power curve modeling that is originally fitted by the single layer feed-forward neural network (SLFN) in this paper; in addition, a data quality check and outlier detection technique and the directional curve modeling method are adopted to effectively enhance the original model performance. …”
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The Reduced-Order Model for Droplet Drift of Aerial Spraying under Random Lateral Wind by Wencheng Li, Wenyun Wang, Xiaomao Huang, Chenyang Li

“…Based on the input and output dataset of CFD, the recursive algorithm including nonlinear autoregressive exogenous model and surrogate-based recurrence framework and the deep learning method for time-series prediction called long short-term memory neural network are used to build the efficient reduced-order model, respectively. …”
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A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods by Yonghui Tang, Fei Wu

“…In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). …”
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Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction by G. Kajeeth Kumar, S. Muthurajkumar

“…Additionally, we introduce the Fuzzy Temporal Optimized Convolutional Neural Network (FTOCNN) classifier that incorporates CGBO and fuzzy temporal rules to enhance detection accuracy. …”
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Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion by Yue Hou, Zhiyuan Deng, Hanke Cui

“…This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. …”
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Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands by Sergio Velázquez Medina, José A. Carta, Ulises Portero Ajenjo

“…A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. …”
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Spatiotemporal Traffic Flow Prediction with KNN and LSTM by Xianglong Luo, Danyang Li, Yu Yang, Shengrui Zhang

“…Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.…”
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A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion by Shuwei Guo, Yunyu Bo, Jie Chen, Yanan Liu, Jiajia Chen, Huimin Ge

“…In Stage 1, a deep feedforward neural network autoencoder reconstructs preprocessed multi-source heterogeneous indicators of human-vehicle-road-environment. …”
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End-to-End Semantic Leaf Segmentation Framework for Plants Disease Classification by Khalil Khan, Rehan Ullah Khan, Waleed Albattah, Ali Mustafa Qamar

“…Our model uses a deep convolutional neural network based on semantic segmentation (SS). The proposed algorithm highlights diseased and healthy parts and allows the classification of ten different diseases affecting a specific plant leaf. …”
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High-resolution hybrid TDM-CDM MIMO automotive radar by Zakaria Benyahia, Mostafa Hefnawi, Mohamed Aboulfatah, Hassan Abdelmounim, Jamal Zbitou

“…On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. …”
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Prediction of Passive Torque on Human Shoulder Joint Based on BPANN by Shuyang Li, Paolo Dario, Zhibin Song

“…Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. …”
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Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader by Qiang Liu, Songyong Liu, Qianjin Dai, Xiao Yu, Daoxiang Teng, Ming Wei

“…In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. …”
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