Deep learning for stage prediction in neuroblastoma using gene expression data by Aron Park, Seungyoon Nam

“…Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. …”
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A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263 by S. Lakshmana Kumar, V. Jacintha, A. Mahendran, R. M. Bommi, M. Nagaraj, Umamahesawari Kandasamy

“…The feed forward neural network (FFNN) was used to develop a predictive model. …”
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Using object-oriented databases in face recognition by X. C. Dong, V. I. Ionin

“…Using a convolutional neural network in the algorithm allows the transition from specific features of the image to more abstract details.…”
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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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Forecasting Directions, Dates, And Causes of Future Technological Revolutions concerning the Growth of Human Capital by Ning Chen, Masoud Vaseei, Ali Peivandizadeh

“…Next, research gaps were analyzed by using the artificial neural network clustering method and also by analyzing covered and uncovered compounds. …”
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Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network by Yi Zhao, Satish V. Ukkusuri, Jian Lu

“…The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. …”
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Adaptive Neural Control of Hypersonic Vehicles with Actuator Constraints by Changxin Luo, Humin Lei, Dongyang Zhang, Xiaojun Zou

“…Secondly, on the basis of the implicit function theorem, the radial basis function neural network (RBFNN) is introduced to approximate the uncertain items of the model. …”
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Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention by Biao Yang, Jinmeng Cao, Rongrong Ni, Ling Zou

“…We propose an anomaly detection approach by learning a generative model using deep neural network. A weighted convolutional autoencoder- (AE-) long short-term memory (LSTM) network is proposed to reconstruct raw data and perform anomaly detection based on reconstruction errors to resolve the existing challenges of anomaly detection in complicated definitions and background influence. …”
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Advanced Techniques and Antenna Design for Pulse Shaping in UWB Cognitive Radio by Lise Safatly, Mohammed Al-Husseini, Ali El-Hajj, Karim Y. Kabalan

“…The Parks-McClellan algorithm is employed, a neural network is trained, and a reconfigurable band stop filter is designed to generate an adaptive waveform with nulls at specific frequencies. …”
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Coordinated Control of Slip Ratio for Wheeled Mobile Robots Climbing Loose Sloped Terrain by Zhengcai Li, Yang Wang

“…To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system.…”
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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model by Nhat-Duc Hoang, Anh-Duc Pham, Quoc-Lam Nguyen, Quang-Nhat Pham

“…Based on experimental outcomes, prediction results of the GPR model are superior to those of the Least Squares Support Vector Machine and the Artificial Neural Network. Furthermore, GPR model is strongly recommended for estimating HPC strength because this method demonstrates good learning performance and can inherently express prediction outputs coupled with prediction intervals.…”
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An algorithm of blood typing using serological plate images by S.A. Korchagin, E.E. Zaychenkova, D.A. Sharapov, E.I. Ershov, Y.V. Butorin, Y.Y. Vengerov

“…The proposed recognition algorithm allows the alveolus boundaries to be accurately determined and the agglutination degree to be evaluated using a lightweight convolutional neural network. A unique dataset was collected with the independent assessment of agglutination degree conducted by medical experts. …”
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Command Filtering and Barrier Lyapunov Function-Based Adaptive Control for PMSMs with Core Losses and All-State Restrictions by Xiaoling Wang, Jinpeng Yu

“…To begin with, the RBF neural network technique is utilized to get close to the uncharted nonlinear terms which existed in PMSM’s mathematical model. …”
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images by Anne M. Davis, Asahi Tomitaka

“…The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. …”
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Image Sequence Fusion and Denoising Based on 3D Shearlet Transform by Liang Xu, Junping Du, Zhenhong Zhang

“…The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. …”
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Epilepsy: The Quintessential Pathology of Consciousness by Andrea Eugenio Cavanna, Fizzah Ali

“…This article provides a description of the phenomenology of ictal consciousness and reviews the underlying shared neural network, dubbed the 'consciousness system', which overlaps with the 'default mode' network. …”
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Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions by Hanbyul Lee, Junghyun Kim, Suyeon Kim, Jimin Yoo, Guang J. Choi, Young-Seob Jeong

“…After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. …”
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Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings by Mosbeh R. Kaloop, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, Mohamed Elsharawy

“…Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. …”
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Adaptive Finite-Time Fault-Tolerant Control for Half-Vehicle Active Suspension Systems with Output Constraints and Random Actuator Failures by Jie Lan, Tongyu Xu

“…Unknown functions and coefficients are approximated by the neural network (NN). Assisted by the stochastic practical finite-time theory and FTC theory, the proposed controller can ensure systems achieve stability in a finite time. …”
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Spam Email Detection using Naïve Bayes classifier by Wang Liansong

“…Various algorithms such as the tree-based model, support vector machine Algorithm, and Convolutional Neural Network have been explored in prior research to tackle this challenge. …”
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