Study on an Intelligent Prediction Method of Ticket Price in a Subway System with Public-Private Partnership by Shengmin Wang, Jun Fang, Lanjun Liu, Han Wu

“…To effectively cope with the effects of multiple influencing factors and strong nonlinearity among them, the mean impact value (MIV) method and the back-propagation (BP) feed-forward neural network improved by the sparrow search algorithm (SSA) are used in this study to develop an intelligent prediction model. …”
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Analysis and Risk Assessment of Corporate Financial Leverage Using Mobile Payment in the Era of Digital Technology in a Complex Environment by Wenjing Wei, Bingxiang Li

“…Combined with a single-layer neural network or CNN model, the comparison experiment is carried out in two ways. …”
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Automated Measurement of Air Bubbles Dispersion in Ice Cream Using Machine Learning Methods by Igor A. Korolev

“…The optimal number channels in the convolutional layers of a neural network with LeNet-type architecture was determined, which made it possible to classify images as spheres or non-spheres with an accuracy of ≥ 0.995. …”
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Wind turbine blade damage detection based on acoustic signals by Chenchen Yang, Shaohu Ding, Guangsheng Zhou

“…Considering the computational challenges of spectral subtraction under extreme noise intensities, a pretrained sound source separation neural network was used to distinguish between random wind noise and mechanical noise in wind turbine sound signals. …”
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Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance by Jirakorn Jamrasnarodom, Pharuj Rajborirug, Pises Pisespongsa, Kitsuchart Pasupa

“…This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. …”
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Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation by Li Hu, Xilingyuan Chen, Rentao Yu

“…In external validation, the AUC of the artificial neural network (ANN) model was the highest, 0.830, while the AUC of the logistic regression (LR) model was the lowest, 0.792. …”
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Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution by Bochao Jiang, Michael Dorosan, Justin Wen Hao Leong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Tiing Leong Ang

“…We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…Comparison with random forest, support vector machine, and neural network models showed that the BO-GBDT model achieved the highest accuracy and generalization, with an average relative error of 2.61%. …”
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A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias by Longjiang Song, Jiahao Liu, Leilei Wang, Ziyi Wang, Yibo Yuan

“…Specifically, we utilize precision satellite clock bias data from the International GNSS Service forecast experiments and assess the predictive effects of various models including backpropagation neural network (BPNN), wavelet neural network (WNN), long short-term memory (LSTM), and gated recurrent units (GRUs). …”
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Klasifikasi Penyakit Alzheimer Dari Scan Mri Otak Menggunakan Convnext by Yehezkiel Stephanus Austin, Haikal Irfano, Juan Young Christopher, Lintang Cahyaning Sukma, Octo Perdana Putra, Riyadh Ilham Ardhanto, Novanto Yudistira

“…Teknologi machine learning dan neural network dapat mendukung deteksi dini melalui penggunaan model ConvNeXt yang telah dilatih dengan metode transfer learning menggunakan bobot awal dari ImageNet, dan di-fine-tune untuk mengklasifikasikan empat tingkat keparahan Alzheimer berdasarkan hasil pemindaian MRI otak, yaitu Mild Demented, Moderate Demented, Non Demented, dan Very Mild Demented. …”
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Wind Energy Resource Prediction and Optimal Storage Sizing to Guarantee Dispatchability: A Case Study in the Kenyan Power Grid by Hampfrey Odero, Cyrus Wekesa, George Irungu

“…The historical weather data, namely wind speed, ambient temperature, relative humidity, wind direction, and generation output from LTWPP, are employed in the training, testing, and validation of the neural network. LTWPP and BESS are modelled in MATLAB R2016a software. …”
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Predicting Screening Efficiency of Probability Screens Using KPCA-GRNN with WP-EE Feature Reconstruction by Qingtang Chen, Yijian Huang

“…The results show that WP-EE-GRNN achieves superior prediction accuracy compared to box dimension (d)-GRNN, box dimension-back propagation neural network (BPNN), and d-weighted least squares support vector machine, WP-d-GRNN, WP-EE-BPNN, EMD-EE-GRNN, and VMD-EE-GRNN. …”
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Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation by Shubo Wang, Haisheng Yu, Xuehui Gao, Na Wang

“…This model is incorporated into an augmented neural network (NN) to account for the unknown nonlinearities. …”
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Gold Price Forecasting: A Novel Approach Based on Text Mining and Big-Data-Driven Model by Poor Saeed Kian, Fattahi Shahram, Hajian Mohsen

“…To explain the gold price prognosis, the convolutional neural network (CNN) also uses linguistic criteria for news items about gold. …”
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Deep belief network-based link quality prediction for wireless sensor network by Lin-lan LIU, Jiang-bo XU, Yue LI, Zhi-yong YANG

“…After analyzing the existing link quality prediction models,a link quality prediction model for wireless sensor network was proposed,which was based on deep belief network.Support vector classification was employed to estimate link quality,so as to get link quality levels.Deep belief network was applied in extracting the features of link quality,and softmax was taken to predict the next time link quality.In different scenarios,compared with the model of link quality prediction based on logistic regression,BP neural network and Bayesian network methods,the experimental results show that the proposed prediction model achieves better precision.…”
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Machine Learning and Deep Learning Optimization Algorithms for Unconstrained Convex Optimization Problem by Kainat Naeem, Amal Bukhari, Ali Daud, Tariq Alsahfi, Bader Alshemaimri, Mousa Alhajlah

“…The convergence dynamics of convex optimization, is explored analyzing classical algorithms and contemporary neural network (NN) methodologies. The study concludes with a comparative assessment of these algorithms performance metrics and their respective strengths and weaknesses.…”
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Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map by Mehdi Nikoo, Łukasz Sadowski, Faezehossadat Khademi, Mohammad Nikoo

“…The obtained model was compared with linear regression (LR) and nonlinear regression (NonLR) models and also the radial basis function (RBF) of a neural network. It was concluded that the SOFM, when optimized with the GA, has more strength, flexibility, and accuracy.…”
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Survey of research on application of heuristic algorithm in machine learning by Yanping SHEN, Kangfeng ZHENG, Chunhua WU, Yixian YANG

“…Aiming at the problems existing in the application of machine learning algorithm,an optimization system of the machine learning model based on the heuristic algorithm was constructed.Firstly,the existing types of heuristic algorithms and the modeling process of heuristic algorithms were introduced.Then,the advantages of the heuristic algorithm were illustrated from its applications in machine learning,including the parameter and structure optimization of neural network and other machine learning algorithms,feature optimization,ensemble pruning,prototype optimization,weighted voting ensemble and kernel function learning.Finally,the heuristic algorithms and their development directions in the field of machine learning were given according to the actual needs.…”
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An Efficient Algorithm for Recognition of Human Actions by Yaser Daanial Khan, Nabeel Sabir Khan, Shoaib Farooq, Adnan Abid, Sher Afzal Khan, Farooq Ahmad, M. Khalid Mahmood

“…Image moments which are translation, rotation, and scale invariant are computed for a frame. A dynamic neural network is used to identify the patterns within the stream of image moments and hence recognize actions. …”
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Sentiment analysis of telecom official micro-blog users based on LSTM deep learning model by Xin CAI, Jingsheng LOU

“…As an internet media,China Telecom official micro-blog is an important channel for the company to publish information and get feedback from users.Users’ comments on telecom official micro-blog messages reflect different attitudes towards telecom brand,products and services.The message content and comment data of the micro-blog was crawled,and the Word2vec was used to express the text information after data cleaning,and the deep learning platform was chosen to carry out the positive and negative emotional classification of the user interaction text based on the LSTM deep neural network model,and sentiment analysis of telecom official micro-blog users was realized.…”
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