Automatic Recognition and Detection System Based on Machine Vision by Lu Wang, Lihong Cui

“…This paper combines machine vision with motion control theory and uses pulse coupled neural network (PCNN) edge detection and recognition algorithms to preliminarily design a set of machine vision automatic recognition and detection systems and carry out detection and recognition experiments on small parts such as relay covers. …”
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Statistical mechanics and machine learning of the α-Rényi ensemble by Andrew Jreissaty, Juan Carrasquilla

“…We conclude by performing a variational minimization of the α-Rényi free energy using a recurrent neural network (RNN) Ansatz where we find that the RNN performs well in two dimensions when compared to the Monte Carlo simulations. …”
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Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN by Mengmeng Huang, Fang Liu, Xianfa Meng

“…Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. …”
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Neural processing of naturalistic audiovisual events in space and time by Yu Hu, Yalda Mohsenzadeh

“…Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early cross-modal connections to build a biologically plausible model of audiovisual perception. …”
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Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree by Siyu Ji, Chenglin Wen

“…Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. …”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.…”
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A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods by Meng Meng, Kun Zhu, Keqin Chen, Hang Qu

“…With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. …”
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Machine learning-based analyzing earthquake-induced slope displacement. by Jiyu Wang, Niaz Muhammad Shahani, Xigui Zheng, Jiang Hongwei, Xin Wei

“…This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. …”
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Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment by Xiang Huang

“…Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. …”
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Dilated SE-DenseNet for brain tumor MRI classification by Yuannong Mao, Jiwook Kim, Lena Podina, Mohammad Kohandel

“…Abstract In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitation (SE) networks’ attention mechanisms. …”
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Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia. by Dyah Aryani Perwitasari, Imaniar Noor Faridah, Haafizah Dania, Didik Setiawan, Triantoro Safaria

“…There were significant differences in ALT and AST between good and poor adherence groups, especially in the female patients. The Neural Network and Random Forests were the most suitable models to predict tuberculosis patients' adherence with good Area Under The Curve (AUC).…”
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A comparative analysis for crack identification in structural health monitoring: a focus on experimental crack length prediction with YUKI and POD-RBF by Zenzen, Roumaissa, Ayadi, Ayoub, Benaissa, Brahim, Belaidi, Idir, Sukic, Enes, Khatir, Tawfiq

“…Comparative evaluations with conventional optimisation algorithms, namely Cuckoo, Bat, and Particle Swarm Optimisation, reveal similar Mean Percentage Error values but with increased result variability, whereas Deep Artificial Neural Network models with varied hidden layer sizes.…”
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Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS by Adel T. Abbas, Mohanad Alata, Adham E. Ragab, Magdy M. El Rayes, Ehab A. El Danaf

“…In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. …”
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Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil by Lei Yan, Yuting Zhu, Haiyan Wang

“…Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. …”
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Discrimination of Melanoma Using Laser-Induced Breakdown Spectroscopy Conducted on Human Tissue Samples by Muhammad Nouman Khan, Qianqian Wang, Bushra Sana Idrees, Geer Teng, Xutai Cui, Kai Wei

“…Chemometric methods, artificial neural network (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and partial least square discriminant analysis (PLS-DA) are used to build the classification models. …”
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Synergetic monitoring of pressure and temperature stimulations in multisensory electronic skin based on time decoupling effect by Zhiyi Gao, Ye Zhang, Zhenyu Hu, Dongdong Zhang, Shengbin Li, Huiyun Xiao, Ziyin Xiang, Dan Xu, Haifeng Zhang, Yuanzhao Wu, Yiwei Liu, Jie Shang, Runwei Li

“…More importantly, by equipping with a multilayer neural network, the evolution from tactile perception to advanced intelligent tactile cognition is demonstrated.…”
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COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings by Mohd Zulfaezal Che Azemin, Radhiana Hassan, Mohd Izzuddin Mohd Tamrin, Mohd Adli Md Ali

“…We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. …”
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AtOMICS: a deep learning-based automated optomechanical intelligent coupling system for testing and characterization of silicon photonics chiplets by Jaime Gonzalo Flor Flores, Jim Solomon, Connor Nasseraddin, Talha Yerebakan, Andrey B Matsko, Chee Wei Wong

“…This paper presents a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. …”
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Fault Diagnosis of Batch Reactor Using Machine Learning Methods by Sujatha Subramanian, Fathima Ghouse, Pappa Natarajan

“…Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. …”
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Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis by Jia Li, Pei Wu, Feilong Kang, Lina Zhang, Chuanzhong Xuan

“…The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. …”
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