Time Series Modelling of the Caspian Kutum (Rutilus frisii) Catch Using SARIMA Model by Fateh Moezzi, Hadi Poorbagher, Soheil Eagderi, Jahangir Feghhi

“…In the present study, we conducted a time-series analysis for catch data ofthe species over a decadal period.Material and Methods: The commercial catch data of Caspian Kutum, over the seine netfishing points of the northern coastal regions of Iran during catch seasons 2002/03 to 2011/12,were used as catch-per-unit-of-effort (CPUE). …”
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Prediction of Unconfined Compressive Strength of Expansive Soil Amended with Bagasse Ash and Lime Using Artificial Neural Network by D.R. Goutham, A.J. Krishnaiah

“…The models were evaluated and contrasted on the training dataset (70% data) and the testing dataset (30% residual data) using the coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) criteria. …”
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Modeling and Analyzing the Impact of Environmental Disturbances in Vessel Model Estimation by Beatriz Sanguino, Tongtong Wang, Guoyuan Li, Øyvind Kåre Kjerstad, Houxiang Zhang

“…To further assess their influence on the trajectory prediction error, Sobol sensitivity analysis is applied to determine which parameter variations most significantly affect trajectory accuracy. …”
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Encryption–decryption-based state estimation for nonlinear complex networks subject to coupled perturbation by Peixia Gao, Chaoqing Jia, Aozhan Zhou

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Estimation of Total Suspended Solids Concentration in Streams Using Regression and Artificial Neural Networks Methods by Betül Mete, Sinan Nacar, Adem Bayram, Osman Tuğrul Bak

“…In this study considering total suspended solids (TSS) parameter monitored in a stream watershed, the predictability of upstream values from downstream data was investigated using regression analysis, which were applied to linear, power, exponential, and quadratic functions, and artificial neural networks (ANNs) method. …”
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Plane tree risk assessment in urban space using Artificail Neural Network by Mozdeh Nafian, Mohsen Bahmani, Elham Ghehsareh Ardestani, Ali Soltani

“…The high coefficient of determination values of training, validation, verification, and finally, all neural network data (0.999, 0.949, 0.996, and 0.991) and the least mean square error values (training data = 0.052, verification 0.114, and validation = 0.044) indicated the accuracy and desirability of the ANN in the prediction of the risk classes for street side trees. …”
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Establishment of an Improved Elman Neural Network Model for Predicting the Corrosion Rate of 3C Steel in Marine Environment and Analysis of the Factors Affecting Model Accuracy by Wenbo Jin, Zhuo Chen, Wanying Liu, Qing Quan, Zongxiao Ren

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Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model by Bin Wang, Li Shao

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A novel and efficient digital image steganography technique using least significant bit substitution by Shahid Rahman, Jamal uddin, Hameed Hussain, Sabir Shah, Abdu Salam, Farhan Amin, Isabel de la Torre Díez, Debora Libertad Ramírez Vargas, Julio César Martínez Espinosa

“…Abstract Steganography is used to hide sensitive types of data including images, audio, text, and videos in an invisible way so that no one can detect it. …”
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Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network by Stefano A. Bini, MD, Nicholas Gillian, PhD, Thomas A. Peterson, PhD, Richard B. Souza, PhD, PT, Brooke Schultz, MS, ACE-CPT, Wojciech Mormul, MS, Marek K. Cichoń, MS, Agnieszka Barbara Szczotka, MS, Ivan Poupyrev, PhD

“…Methods: Data from 40 healthy participants performing fixed walking, stair climbing, and sit-to-stand tasks were collected using both 3D motion capture and IMUs. …”
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Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations by Xiaozhi Lu, Farag M. A. Altalbawy, Tarak Vora, R. Manjunatha, Debasish Shit, Shirin Shomurotova, Akshay Kumar, Atreyi Pramanik, Ajay Sharma, Raed H. C. Alfilh, Samim Sherzod, Mohammad Mahtab Alam

“…The dataset consists of 949 field records from Middle Eastern drilling sites, incorporating variables such as borehole diameter, drilling fluid viscosity, mud weight, solid content, and pressure differential. Initial data analysis included statistical evaluation, outlier detection using leverage diagnostics, and data normalization to ensure validity and consistency. …”
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Forecast generation model of municipal solid waste using multiple linear regression by J.A. Araiza-Aguilar, M.N. Rojas-Valencia, R.A. Aguilar-Vera

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CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification [version 1; peer review: 2 approved] by Luca Zammataro

“…Digital pathology methodologies, including image analysis, have improved cervical cancer diagnostics. …”
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Experimental and Computational Fluid Dynamics Simulation Study on the Performance of a Two-stroke Aviation Engine: A Comparative Analysis of Turbulence Models and Mesh Strategies by G. Coskun, Y. Delil, U. Demir

“…The numerical results were compared with experimental data to determine the most accurate simulation configuration. …”
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Development of a model for predicting money laundering rate by E. S. Anisimov, J. M. Beketnova

“…The article presents the results of data analysis using the method of least squares, calculating the mean squared error (MSE). …”
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Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

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Prediction of coalbed methane productivity based on neural network models by JIN Yi, ZHENG Chenhui, SONG Huibo, MA Jiaheng, YANG Yunhang, LIU Shunxi, ZHANG Kun, NI Xiaoming

“…Finally， according to the classification results， combined with the actual drainage data， the BP and LSTM neural network algorithms were used to predict the daily gas production of CBM wells.ResultsThe results show that： （1） Based on the grey correlation method model analysis， 10 parameters such as permeability， gas saturation and reservoir pressure gradient in the study area are the key factors affecting the gas production performance of coalbed methane； （2） Using fuzzy mathematics evaluation method to evaluate the enrichment of coalbed methane， the gas production effects of 34 wells in the study area is divided into three categories： favorable area， relatively favorable area and unfavorable area. （3）A coal reservoir daily gas production prediction model was established based on the LSTM algorithm， with a prediction error value between 4.06% and 14.79%， and the average error value of 11.09%. …”
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Early prediction of ransomware API calls behaviour based on GRU-TCN in healthcare IoT by Jueun Jeon, Seungyeon Baek, Byeonghui Jeong, Young-Sik Jeong

“…EPS-Ran predicted ransomware behaviours early with a low error rate even when the analysis time was reduced from 120 s to 30 s.…”
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Abnormal event detection based on local topology and l_1/2norm regularize by Qing YU, Ken CHEN, Meng LI, Fei LI

“…A new dictionary learning method was proposed by introducing a local topology term to describe structural information of video events and using the l_1/2norm as the sparsity constraint to the representation coefficients based on the traditional analysis dictionary learning method.In feature extraction,a histogram of interaction force(HOIF) containing rich motion information and a histogram of oriented gradient(HOG) containing texture information were merged.Then,the improved dictionary was used to train the feature data.Finally,the reconstruction error of the testing sample under the dictionary was used to determine whether the testing sample was an abnormal sample.Experiments on UMN show the high performance of the algorithm.Compared with the state-of-the-art algorithms,the analysis dictionary classification algorithm based on local topology and l_1/2norm has made more effective detection on the abnormal events in the crowd.…”
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Optimizing agricultural yield: a predictive model for profitable crop harvesting based on market dynamics by Nilesh P. Sable, Nilesh P. Sable, Vinod Kumar Shukla, Parikshit N. Mahalle, Vijayshri Khedkar

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