Smart Shift Decision Method Based on Stacked Autoencoders by Zengcai Wang, Yazhou Qi, Guoxin Zhang, Lei Zhao

“…The stacked autoencoder (SAE) algorithm, a type of artificial neural network, is used in this study to predict shifting gear timing on the basis of throttle percentage, vehicle velocity, and acceleration. …”
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A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum by Guangliang Pan, Jun Li, Fei Lin

“…In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. …”
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Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction by Bo Wang, Liming Zhang, Hengrui Ma, Hongxia Wang, Shaohua Wan

“…Then, based on the long short-term memory depth neural network time series prediction, parallel long short-term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user-driven behavioral data to achieve vertical interaction between source and load. …”
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Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon by Mengfan Wang, Lixin Zhang, Changxin Fu

“…For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). …”
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MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction by Jihong Wang, Ruijia He, Xiaodan Wang, Hongjian Li, Yulei Lu

“…The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. …”
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Cross-ViT based benign and malignant classification of pulmonary nodules. by Qinfang Zhu, Liangyan Fei

“…There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus more on the local features of pulmonary nodules and lack the extraction of global features of pulmonary nodules. …”
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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System by Warapon Chinsatit, Takeshi Saitoh

“…This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. …”
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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods by Pragya Kashyap, Kalbhavi Vadhi Raj, Jyoti Sharma, Naveen Dutt, Pankaj Yadav

“…Next, benchmarking was performed across six different supervised-classification algorithms viz. logistic-regression, naïve-bayes, random-forest, extreme-gradient-boost (XGBoost), k-nearest neighbor, and deep neural network. Noteworthy, XGBoost, with an accuracy of 76.25%, and AUROC (area-under-receiver-operating-characteristic) of 0.81 with 69% specificity and 76% sensitivity, outperform the other five classification algorithms using LDA-transformed features. …”
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SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm by Yancheng Song, Jiaqi Lu, Huai Zhang, Guangjun Liu

“…The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). …”
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Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering by Ma Hyunjun, Kim Jin-Soo, Choe Jong-Ho, Park Q-Han

“…The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green’s function calculation is used to check the validity of Born approximation. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…This paper analyzes various Convolutional Neural Network (CNN) models, including VGG16, ResNet50, DenseNet121, and EfficientNet, exploring their integration within the FL framework to enhance diagnostic accuracy while preserving patient data privacy. …”
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Predictive Modeling of Fracture Behavior in Ti6Al4V Alloys Manufactured by SLM Process by Mohsen Sarparast, Majid Shafaie, Mohammad Davoodi, Ahmad Memaran Babakan, Hongyan Zhang

“…The research explores the impact of Artificial Neural Network (ANN) architecture, specifically hidden layers and neurons, on predicting fracture parameters. …”
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Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions by Kamilya Smagulova, Mohammed E. Fouda, Ahmed Eltawil

“…In addition, it reviews the available solutions designed to mitigate the impact of these challenges, including emerging temperature-resilient Deep Neural Network (DNN) training methods. Our work also provides a summary of the techniques and their advantages and limitations.…”
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A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport by Yingmiao Qian, Shuhang Chen, Jianchang Li, Qinxin Ren, Jinfu Zhu, Ruijia Yuan, Hao Su

“…The mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. …”
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A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny by HU Wei, ZHAO Jumin, LI Dengao

“…[Methods] By employing a lightweight convolutional neural network as the feature extraction backbone and integrating multi-branch reparameterized convolution blocks, this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…The research investigates the effectiveness of different architectural configurations, including variations in fuzzy layer membership functions (triangular, trapezoidal, Gaussian) and neural network architectures (1D ANN, 2D ANN, 1D LSTM, 2D LSTM). …”
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Online multi‐object tracking based on time and frequency domain features by Mahbubeh Nazarloo, Meisam Yadollahzadeh‐Tabari, Homayun Motameni

“…The features are given for learning vector quantization, which is a supervised artificial neural network (ANN). It is used to classify the dataset. …”
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Artificial Intelligence-Based Real-Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks by Yu Li, Peihua Liu

“…Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron (MLP) model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. …”
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Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN by Ali Reza Ghanizadeh, Mansour Fakhri

“…Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. …”
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Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models by Laurentiu Fara, Alexandru Diaconu, Dan Craciunescu, Silvian Fara

“…This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. …”
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