Machine Learning for Precision Agriculture: Predicting Persimmon Peak Harvest Dates and Yield Using Meteorological Data by Atsushi Okayama, Atsushi Yamamoto, Yutaka Matsuno, Masaomi Kimura

“…This study investigates the effectiveness of artificial-intelligence-driven models to accurately forecast the timing and yield of persimmon harvests, using meteorological data alongside historical harvest records. …”
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Preliminary exploration and application research on the model of gathering distillate according to the quality based on Fourier transform near infrared spectroscopy by LIAO Li, ZHANG Guiyu, ZOU Yongfang, ZHU Xuemei, PENG Houbo, ZHANG Wei, LI Yan

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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

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Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches by Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Heru Agus Santoso

“…In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. …”
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Nonlinearity modeling for online estimation of industrial cooling fan speed subject to model uncertainties and state-dependent measurement noise by Peng Chao-Chung, Tsai Min-Che, Chen Tsai-Ying

“…Simulation results indicate that the root-mean-square errors are reduced from 1.3393 with the traditional UKF to 0.7485 with the parameter update mechanism. …”
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A Novel Statistical Approach to Obtain the Best Visibility Slice in MRI Sequence of Brain Tumors by Hassan Al-Yassin, Mohammed A. Fadhel, Omran Al-Shamma

“…With the progress of the use of artificial intelligence in the medical field, the detection of brain tumors has become one of the researchers’ interests. …”
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Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models by Hongxia Ma, Jiandong Liu, Jia Zhang, Jiandong Huang

“…The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. …”
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Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System by Salah Al-Zubaidi, Jaharah A. Ghani, Che Hassan Che Haron

“…Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. …”
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Boosting Aeroponic System Development with Plasma and High-Efficiency Tools: AI and IoT—A Review by Waqar Ahmed Qureshi, Jianmin Gao, Osama Elsherbiny, Abdallah Harold Mosha, Mazhar Hussain Tunio, Junaid Ahmed Qureshi

“…This review further examines artificial intelligence (AI) and the Internet of Things (IoT) in aeroponics. …”
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Reconstruction of U.S. Regional-Scale Soybean SIF Based on MODIS Data and BP Neural Network by YAO Jianen, LIU Haiqiu, YANG Man, FENG Jinying, CHEN Xiu, ZHANG Peipei

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Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation by Wei-Jong Yang, Chih-Chen Wu, Jar-Ferr Yang

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Neural Machine Translation of Seismic Ambient Noise for Soil Nature and Water Saturation Characterization by José Cunha Teixeira, Ludovic Bodet, Agnès Rivière, Santiago G. Solazzi, Amélie Hallier, Alexandrine Gesret, Sanae El Janyani, Marine Dangeard, Amine Dhemaied, Joséphine Boisson Gaboriau

“…Validation demonstrates high accuracy, with a normalized root‐mean‐square error of 8%, while delivering fast insights into subsurface conditions. …”
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An Assessment of a Proposed Hybrid Neural Network for Daily Flow Prediction in Arid Climate by Milad Jajarmizadeh, Sobri Harun, Mohsen Salarpour

“…Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. …”
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Monitoring of Transformer Hotspot Temperature Using Support Vector Regression Combined with Wireless Mesh Networks by Naming Zhang, Guozhi Zhao, Liangshuai Zou, Shuhong Wang, Shuya Ning

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“E-Motional Navigators”: Italian Adaptation and Validation of the Socio-Emotional e-Competencies Questionnaire (e-COM) by C. Cricenti, G. Lausi, B. Barchielli, E. Mari, J. Burrai, A. M. Giannini, A. Quaglieri

“…Factor analyses confirmed the original five-factor structure, which showed excellent fit indices (i.e., root mean square error of approximation (RMSEA), Tucker–Lewis index (TLI), and comparative fit index (CFI)). …”
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A Mechanism-aid Deep Learning Method for Li-ion Battery State-of-charge Estimation by Zhanghua Xiao, Cheng Ji, Fangyuan Ma, Jingde Wang, Wei Sun, Chi Zhai

“…The rapid development of data science and artificial intelligence provides a new solution for battery SOC estimation. …”
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Yield Response of Different Rice Ecotypes to Meteorological, Agro-Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting a... by Md. Sabbir Ahmed, Md. Tasin Tazwar, Haseen Khan, Swadhin Roy, Junaed Iqbal, Md. Golam Rabiul Alam, Md. Rafiul Hassan, Mohammad Mehedi Hassan

“…The result shows that the root mean squared error (RMSE) of three different ecotypes are in between 9.38% and 24.37% and that of R-squared values are between 89.74% and 99.13% on two different machine learning algorithms. …”
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Streamlining Chemistry Learning Process: Applying Lean Methodologies to Minimize Waste in High School Education by Astuti Fatma Hermining, Sholihah Gustania Eka Putri

“…This study explores the application of lean education in the chemistry learning process to identify inefficiencies, understand root causes, and propose solutions to enhance learning efficacy. …”
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Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning by Doan Perdana, I Made Arya Indra Tanaya, Abdul Aziz Marwan, Fityanul Akhyar

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Using SDPC for Visual Exploratory Analysis of Semiconductor Production Line Sensor Data by Xinxiao Li, Xian-Hua Han, Yongqing Sun

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