E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review by Abed Mutemi, Fernando Bacao

“…Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.…”
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PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts by Jordi Armengol-Estapé, Felipe Soares, Montserrat Marimon, Martin Krallinger

“…In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. …”
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Machine learning-assisted prediction of durability behavior in pultruded fiber-reinforced polymeric (PFRP) composites by Ammar A. Alshannaq, Mohammad F. Tamimi, Muath I. Abu Qamar

“…The results reveal that Decision Trees, Artificial Neural Networks, and Random Forests are the best models to predict behavior of pultruded composites under environmental exposure, which achieved R2 values of 0.9647, 0.9537, and 0.8970, respectively for the case of flexural strength. …”
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The Real-Time Prediction of Cracks and Wrinkles in Sheet Metal Forming According to Changes in Shape and Position of Drawbeads Based on a Digital Twin by Sarang Yi, Daeil Hyun, Seokmoo Hong

“…A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. …”
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First-principles based data-driven strain engineering for ferroelectrics via active machine learning: A nonlinear piezoelectric constitutive equation by Susumu MINAMI, Yasuaki MARUYAMA, Yoshimasa ABE, Tomohiro NAKAYAMA, Takahiro SHIMADA

“…Here, we developed a technical framework that enables efficient exploration of physical properties in the vast strain space based on machine learning (i.e., artificial neural networks), active learning, and high-throughput first-principles calculation. …”
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Exploring the Potential of Neural Networks to Predict Statistics of Solar Wind Turbulence by Daniel Wrench, Tulasi N. Parashar, Ritesh K. Singh, Marcus Frean, Ramesh Rayudu

“…Here, we study the utility of artificial neural networks (ANNs) to predict statistics of sparse time series. …”
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Systematic review of machine learning applications using nonoptical motion tracking in surgery by Teona Z. Carciumaru, Cadey M. Tang, Mohsen Farsi, Wichor M. Bramer, Jenny Dankelman, Chirag Raman, Clemens M. F. Dirven, Maryam Gholinejad, Dalibor Vasilic

“…From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. …”
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Investigation of boiler energy consumption in the gas refinery units using RSM ANN and Aspen HYSYS by Erfan Gholamzadeh, Ahad Ghaemi, Abolfazl Shokri, Bahman Heydari

“…Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered and examined. …”
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Enhancing Indoor mmWave Communication With ML-Based Propagation Models by Gustavo Adulfo Lopez-Ramirez, Alejandro Aragon-Zavala

“…We employ various ML models, including Artificial Neural Networks (ANNs), hybrid models integrating linear regression, ANNs, and Gaussian Processes, and Extreme Gradient Boosting (XGBoost), to predict and analyze the propagation loss in a controlled indoor setting. …”
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Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus by V Vinoth Kannan, Bhavesh Kanabar, J Gowrishankar, Ali Khatibi., Sarfaraz Kamangar, Amir Ibrahim Ali Arabi, Pushparaj Thomai, Jasmina Lozanović

“…Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. …”
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Ensemble Deep Learning Technique for Detecting MRI Brain Tumor by Rasool Fakhir Jader, Shahab Wahhab Kareem, Hoshang Qasim Awla

“…In recent years, a variety of computational algorithms for segmentation and classification have been developed with improved results to get around the issue. Artificial neural networks (ANNs) have the capability and promise to classify in this regard. …”
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Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin by iran salehvand, amir gandomkar, ebrahim fatahi

“…Due to the nonlinear behavior of rainfall, artificial neural networks were used for modeling. Factor analysis was used to determine the best architecture for entering the neural network. …”
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Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface by Alfredo Bonini Neto, Alexandre de Queiroz, Giovana Gonçalves da Silva, André Gifalli, André Nunes de Souza, Enio Garbelini

“…This study introduces an innovative approach using Artificial Neural Networks (ANN) combined with the graphical interface to predict complete curves of real and reactive power losses in power systems under various contingencies. …”
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Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy by Muhammad Shoaib Saleem, Naeem Abas

“…Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

“…In this study, we focus on CKD classification and creatinine prediction using three sets of features: at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. …”
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Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks by Oradee Srikimkaew, Saman Azhari, Deep Banerjee, Yuki Usami, Hirofumi Tanaka

“…The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.…”
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A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy by Şenol Bayraktar, Cem Alparslan, Nurten Salihoğlu, Murat Sarıkaya

“…Statistical analyses of the effects of V and f on thrust force (Fz), surface roughness (Ra), and torque (Mz) were performed using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Analysis of Variance (ANOVA). …”
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Daily reference evapotranspiration prediction using empirical and data-driven approaches: A case study of Adana plain by Semin Topaloğlu Paksoy, Deniz Levent Koç

“…The objective of this research was to examine the effectiveness of five different data-driven techniques, including artificial neural networks "multilayer perceptron" (ANN), gene expression programming (GEP), random forest (RF), support vector machine "radial basis function" (SVM), and multiple linear regression (MLR) to model the daily ET0. …”
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Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM by Igor Peško, Vladimir Mučenski, Miloš Šešlija, Nebojša Radović, Aleksandra Vujkov, Dragana Bibić, Milena Krklješ

“…The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. …”
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Künstliche Intelligenz im Englischunterricht – Grundwissen und Praxisbeispiele by Inez De Florio-Hansen

“…What are the meanings of fundamental concepts such as algorithms, machine learning, and artificial neural networks? What are the best practices for inputting prompts (prompt engineering), and how can prompt quality be improved to achieve desired outcomes? …”
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