Emotional recognition of EEG signals utilizing residual structure fusion in bi-directional LSTM by Yue Xu, Yunyuan Gao, Zhengnan Zhang, Shunlan Du

“…The experimental results demonstrated that the novel neural network model proposed in this paper had outperformed currently available methods in emotion recognition tasks.…”
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A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale by Ziyao Zhao, Yi Zhang, Yi Zhang

“…Two forecasting methods, FM1 and FM2, and four predicting models, linear regression (LR), support vector machine (SVR), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA), were proposed to build models that can predict the parking occupancy of different parking lots. …”
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Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition by Patcharin Kamsing, Peerapong Torteeka, Wuttichai Boonpook, Chunxiang Cao

“…Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. …”
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Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation by Yongqi Guo, Yuxu Lu, Yu Guo, Ryan Wen Liu, Kwok Tai Chui

“…To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. …”
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Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

“…In addition, a Convolutional Neural Network (CNN) regression model was constructed and optimized with the Northern Goshawk Optimization (NGO) algorithm, resulting in a more precise CNN-NGO prediction model. …”
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Evaluation of Efficacy of Artificial Intelligence in Orthopantomogram in Detecting and Classifying Radiolucent Lesions by Sheetal Singar, Ajay Parihar, Prashanthi Reddy

“…Aim and Objective: The objective of our study was to build a convolutional neural network (CNN) model and detection and classification of benign and malignant radiolucent lesions in orthopantomogram (OPG) by implementing CNN. …”
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Optimization of Mix Proportions for Novel Dry Stack Interlocking Concrete Blocks Using ANN by Krishna Prakash A, Jane Helena H, Paul Oluwaseun Awoyera

“…This paper proposes novel concrete interlocking blocks made of fly ash and GGBS which are an alternative for the conventional concrete blocks. The artificial neural network (ANN) technique is used to estimate the mechanical strength of interlocking blocks and is verified with experimental investigation. …”
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Tea Disease Recognition Based on Image Segmentation and Data Augmentation by Ji Li, Chenyi Liao

“…Finally, an Inception Embedded Pooling Convolutional Neural Network (IDCNN) is developed for disease recognition. …”
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A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

“…A lightweight power quality disturbances (PQDs) recognition model that integrates convolutional neural network (CNN) and Transformer (CaT) is proposed to address the high number of parameters and computational complexity in existing deep learning-based models. …”
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Skin Lesion Image Segmentation Algorithm Based on MC-UNet by Guihua Yang, Bingxing Pan

“…Aiming at the situation of dermatoscopic images with fuzzy lesion boundaries, variable morphology and high similarity to background, this paper proposes a skin lesion segmentation algorithm that achieves higher segmentation accuracy by combining existing convolutional neural network methods. The algorithm begins by using a Multiscale Residual Block (MRB) with different-sized convolutional kernels to enlarge the receptive field and extract multi-scale features of dermatoscopic images. …”
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A Small Target Detection Method Based on the Improved FCN Model by Guofeng Ma

“…This study proposes an improved FCN model based on the full convolutional neural network (FCN) and applies it to the STD. The following is the central concept of the proposed method. …”
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Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision by Min Wang, Yanwen Zhang, Huiping Ye

“…By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. …”
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Robust identification method of website fingerprinting against disturbance by ZHANG Jingxi, LI Tengyao, TU Yukuan, LUO Xiangyang

“…With the matrix as input, a robust flow classifier with convolutional neural network was established. Through extensive experimental analysis on the dataset provided by DF, the accuracy under RF Countermeasure is 95.4%, which is 21.2% higher than RF. …”
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An Improved Predictor-Corrector Guidance Algorithm for Reentry Glide Vehicle Based on Intelligent Flight Range Prediction and Adaptive Crossrange Corridor by Mingjie Li, Chijun Zhou, Lei Shao, Humin Lei, Changxin Luo

“…In this paper, according to residual network (ResNet)’s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. …”
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Features of the restaurant market and consumer behavior in the Moscow restaurant business segment: study results by A. N. Timokhovich

“…At the second stage to identify the main trends of the Moscow restaurant market the method of content analysis of the Moscow restaurant business establishments sites with high rating indicators based on the Yandex neural network data was used. The main trends of the Moscow restaurant market are: restaurants’ focus on preparing healthy food and vegetarian cuisine; use of farm products and local ingredients in prepared dishes; technological innovations implementation that simplify consumer experience; focus on the principles of sustainable development and environmental friendliness in the business model; restaurant formats variety. …”
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Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission by Sandeep Sachdeva, Maninder Singh, U. P. Singh, Ajat Shatru Arora

“…To reduce the error of load forecasting, fuzzy method has been used with Artificial Neural Network (ANN) and OFDM transmission is used to get data from outer world and send outputs to outer world accurately and quickly. …”
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Classification and Analysis of <em>Agaricus bisporus</em> Diseases with Pre-Trained Deep Learning Models by Umit Albayrak, Adem Golcuk, Sinan Aktas, Ugur Coruh, Sakir Tasdemir, Omer Kaan Baykan

“…This research evaluates 20 advanced convolutional neural network (CNN) architectures for classifying mushroom diseases in <i>Agaricus bisporus</i>, utilizing a custom dataset of 3195 images (2464 infected and 731 healthy mushrooms) captured under uniform white-light conditions. …”
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Stage-Based Remaining Useful Life Prediction for Bearings Using GNN and Correlation-Driven Feature Extraction by Guangzhong Huang, Wenping Lei, Xinmin Dong, Dongliang Zou, Shijin Chen, Xing Dong

“…This paper presents a model that combines correlation analysis feature extraction with a Graph Neural Network (GNN)-based approach for bearing degradation stage classification and RUL prediction, aiming to achieve accurate bearing life prediction. …”
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Robust CNN for facial emotion recognition and real-time GUI by Imad Ali, Faisal Ghaffar

“…Utilizing a robust architecture of a convolutional neural network (CNN), we designed an efficacious framework for facial emotion recognition that predicts emotions and assigns corresponding probabilities to each fundamental human emotion. …”
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Hyperbolic Graph Convolutional Network Relation Extraction Model Combining Dependency Syntax and Contrastive Learning by Jinzhe Li

“…Based on the hyperbolic graph neural network, dependent syntactic information and information optimization strategies are introduced to solve the problem of word embedding concentration. …”
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