Enhancing fingerprint identification using Fuzzy-ANN minutiae matching by S.P. Singh, Dinesh Kumar Nishad, Saifullah Khalid

“…The system's core lies in its ability to train an artificial neural network to learn an improved similarity function for minutiae matching. …”
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State of health estimation of individual batteries through incremental curve analysis under parameter uncertainty by Yue Zhao, Qian Li, Xiaohui Li, Ge Zhang, Hang Shi, Qinghua Li

“…Subsequently, a method is proposed to fuse these HIs using an artificial neural network to achieve precise SOH estimation. …”
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Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion by Xin Kang Li, Li Jun Tang, Ze Ying Li, Dian Qiu, Zhuo Ling Yang, Xiao Yi Zhang, Xiang-Zhi Zhang, Jing Jing Guo, Bao Qiong Li

“…The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. …”
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Teknologi Irigasi Cerdas pada Sistem Irigasi Drip dengan Algoritma Ant Colony Optimization by Abdul Haris, Nabilla Anggraini, Hengki Sikumbang

“…Banyak peneliti yang telah melakukan kajian dan inovasi di bidang ini untuk menghasilkan irigasi yang baik dan optimal, antara lain dengan mengimplementasikan gabungan Internet of Things (IoT) sebagai infrastruktur, Fuzzy Logic dan Artificial Neural Network (ANN) sebagai algoritma untuk menentukan waktu buka tutup dari Solenoid Valve dalam pengaturan distribusi air.  …”
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A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates by Mohammad Zakwan, Majid Niazkar

“…In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. …”
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Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites by Halil Ibrahim Kurt, Murat Oduncuoglu

“…The extensive experimental results were taken from literature and modeled with artificial neural network (ANN). The feed forward (FF) back-propagation (BP) neural network (NN) was used to predict the dry sliding wear behavior of UHMWPE composites. …”
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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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Evaluating Machine Learning Models for Prostate Cancer Classification Using Gene Expression Profiles from DNA Microarrays by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…These methods were combined with classifiers such as K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Tree Classifier (DTC), Naïve Bayes (NB), and Artificial Neural Network (ANN). Our results demonstrated that the best combination was the Signal to Noise Ratio with Linear Discriminant Analysis, achieving a classification accuracy of 95% using only six genes. …”
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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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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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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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ANN Model-Based Simulation of the Runoff Variation in Response to Climate Change on the Qinghai-Tibet Plateau, China by Chang Juan, Wang Genxu, Mao Tianxu, Sun Xiangyang

“…To identify the impacts of climate change in the runoff process in the Three-River Headwater Region (TRHR) on the Qinghai-Tibet Plateau, two artificial neural network (ANN) models, one with three input variables (previous runoff, air temperature, and precipitation) and another with two input variables (air temperature and precipitation only), were developed to simulate and predict the runoff variation in the TRHR. …”
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Prediction Model of Corrosion Current Density Induced by Stray Current Based on QPSO-Driven Neural Network by Chengtao Wang, Wei Li, Gaifang Xin, Yuqiao Wang, Shaoyi Xu

“…The QPSO algorithm was employed to optimize the updating process of weights and biases in the artificial neural network (ANN). The results show that the accuracy of the proposed QPSO-NN model is better than the model based on backpropagation neural network (BPNN) and particle swarm optimization-neural network (PSO-NN). …”
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Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods by Elaheh Konoz, Amir H. M. Sarrafi, Alireza Feizbakhsh, Zahra Dashtbozorgi

“…A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. …”
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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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A Qualitative Approach to Universal Numerical Integrators (UNIs) with Computational Application by Paulo M. Tasinaffo, Luiz A. V. Dias, Adilson M. da Cunha

“…Abstract Universal Numerical Integrators (UNIs) can be defined as the coupling of a universal approximator of functions (e.g., artificial neural network) with some conventional numerical integrator (e.g., Euler or Runge–Kutta). …”
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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. …”
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Fault Diagnosis of Power Transformers With Membership Degree by Enwen Li, Linong Wang, Bin Song

“…Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. …”
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