Hybrid Analysis of Biochar Production from Pyrolysis of Agriculture Waste Using Statistical and Artificial Intelligent-Based Modeling Techniques by Hani Hussain Sait, Ramesh Kanthasamy, Bamidele Victor Ayodele

“…This study used response surface methodology (RSM) and artificial neural networks (ANNs) to optimize and predict the production of biochar from the pyrolysis of palm kernel shells. …”
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Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka by Helani Perera, Miyuru B. Gunathilake, Ravindu Panditharathne, Najib Al-mahbashi, Upaka Rathnayake

“…Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. …”
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Quantification of modal mineralogy in molybdenite-bearing drill-core samples by laser-induced breakdown spectroscopy by Jonnathan Álvarez, Germán Velásquez, Iván Arévalo, Jorge Yáñez, Claudio Sandoval-Muñoz, Benjamín Sepúlveda

“…The selected spectral signals are defined as “mineralogical patterns”, which are processed using supervised chemometrics methods, such as artificial neural networks, to enable an automated mineral classification. …”
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Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms by Huseyin Kunt, Zeki Yetgin, Furkan Gozukara, Turgay Celik

“…Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. …”
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Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…The machining performance indicators of the first set are optimized using graphical method of contour plots. Artificial neural networks technique, which is well known for its versatility to model linear as well as non-linear data, is used to express the surface roughness as a function of tool geometrical variables. …”
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Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques by Gunho Jung, Sun-Yong Choi

“…Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. …”
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SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence by G. Sathish Kumar, E. Suganya, S. Sountharrajan, Balamurugan Balusamy, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan

“…The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets. …”
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Neural network quantification for solar radiation prediction: An approach for low power devices by Brenda Alejandra Villamizar-Medina, Angelo Joseph Soto Vergel, Byron Medina-Delgado, Darwin Orlando Cardozo-Sarmiento, Dinael Guevara-Ibarra, Oriana Alexandra Lopez-Bustamante

“… Accurate solar radiation prediction leverages various machine learning techniques, with artificial neural networks (ANN) being the most common and precise due to their ability to detect and learn relationships between meteorological variables and solar radiation. …”
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Components and predictability of pollutants emission intensity by Z. Farajzadeh, M.A. Nematollahi

“…For this purpose, two well-known artificial neural networks, multilayer perceptron, and wavelet-based neural network were applied to forecast the emission intensity of the selected pollutants and their components.FINDINGS: The emission intensity of nitrogen oxides and sulphur dioxide illustrated a decreasing trend. …”
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Deteksi Cepat Kadar Alkohol Pada Minuman Kopi dengan Metode Dielektrik dan Jaringan Syaraf Tiruan by Sucipto Sucipto, Yuyun Rohmawati, Dyah Ayu Widyaningrum, Danang Triagus Setiyawan

“…The purpose of this study was to estimate the alcohol content and pH of coffee drinks based on the bioelectric of material and Artificial Neural Networks (ANN). The back propagation algorithm was used to connect the input of bioelectric properties and output of prediction of alcohol content and pH in liqueur coffee. …”
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Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods by Ahmad Almadhor, K. Mallikarjuna, R. Rahul, G. Chandra Shekara, Rishu Bhatia, Wesam Shishah, V. Mohanavel, S. Suresh Kumar, Sojan Palukaran Thimothy

“…The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). …”
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Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures by Hany A. Dahish, Ahmed D. Almutairi

“…The performance of the created models was compared to experimental data and earlier developed models: fuzzy logic models, artificial neural networks, genetic algorithms, and water cycle algorithms, using several evaluation metrics. …”
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Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach by Tanmoy Mazumder, Md. Mustafa Saroar

“…By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

“…This article provides a critical analysis of the following cutting-edge methods: DTC with Space Vector Modulation (DTC-SVM), DTC based on Fuzzy Logic (DTC-FL), DTC using Artificial Neural Networks (DTC-ANN), DTC optimized by Genetic Algorithms (DTC-GA), DTC with Ant Colony Optimization (DTC-ACO), DTC with rooted tree optimization (DTC-RTO), Sliding Mode Control (DTC-SMC), and Predictive DTC (P-DTC). …”
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Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms by Yucong Gu, Kaiwen Wang, Zhengyu Zhang, Yi Yao, Ziming Xin, Wenjun Cai, Lin Li

“…The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. …”
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Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability by Muhammad Paend Bakht, Mohd Norzali Haji Mohd, Babul Salam KSM Kader Ibrahim, Nuzhat Khan, Usman Ullah Sheikh, Ab Al-Hadi Ab Rahman

“…Their performance was then validated against commonly used artificial neural networks (ANN) and support vector machines (SVM) using multiple evaluation metrics including prediction accuracy, error rates, and interpretability. …”
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A Novel Model Using ML Techniques for Clinical Trial Design and Expedited Patient Onboarding Process by Iyer A, Narayanaswami S

“…Five ML models—XGBoost, Random Forest, Support Vector Classifier (SVC), Logistic Regression, and Decision Tree—were applied to both datasets, alongside Artificial Neural Networks (ANN) for the second dataset. Model performance was evaluated using precision, recall, balanced accuracy, ROC-AUC, and weighted F1-score, with results averaged across k-fold cross-validation.Results: In the first phase, XGBoost and Random Forest emerged as the best-performing models across all five subsets, achieving an average balanced accuracy of 0.71 and an average ROC-AUC of 0.7. …”
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Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian... by Somayyeh Noei Teymoordash, Hoda Zendehdel, Ali Reza Norouzi, Mahdis Kashian

“…Most studies agree that Artificial Neural Networks (ANN) and Machine Learning (ML) models outperform conventional statistics in predicting postoperative outcomes.…”
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TOPS-speed complex-valued convolutional accelerator for feature extraction and inference by Yunping Bai, Yifu Xu, Shifan Chen, Xiaotian Zhu, Shuai Wang, Sirui Huang, Yuhang Song, Yixuan Zheng, Zhihui Liu, Sim Tan, Roberto Morandotti, Sai T. Chu, Brent E. Little, David J. Moss, Xingyuan Xu, Kun Xu

“…Abstract Complex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. …”
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