LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns by Mehran Motamedi, Reza Shidpour, Mehdi Ezoji

“…Abstract In this paper, we present a machine learning-based approach that leverages Long Short-Term Memory (LSTM) networks combined with a sliding window technique for feature extraction, aimed at accurately predicting point defect percentages in semiconductor materials based on simulated X-ray Diffraction (XRD) data. …”
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Enhancing Business Intelligence by Means of Suggestive Reviews by Atika Qazi, Ram Gopal Raj, Muhammad Tahir, Erik Cambria, Karim Bux Shah Syed

“…A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. …”
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Improving Text Recognition Accuracy for Serbian Legal Documents Using BERT by Miloš Bogdanović, Milena Frtunić Gligorijević, Jelena Kocić, Leonid Stoimenov

“…High-quality datasets, considered a prerequisite for many supervised machine learning algorithms, are often only available in very limited quantities. …”
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Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview by Paolo Visconti, Giuseppe Rausa, Carolina Del-Valle-Soto, Ramiro Velázquez, Donato Cafagna, Roberto De Fazio

“…Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management.…”
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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. …”
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Managing the University’s Educational Process Based on Predicting Students’ Academic Performance by E. V. Alikina, D. V. Maltsev

“…Methods of mathematical statistics were used to analyze the data: determining the type of data distribution using the Shapiro-Wilk test, establishing the presence of multicollinearity in the construction of multiple regression by the Pearson criterion, establishing correlation dependencies by Spearman’s rank correlation method. Machine learning methods are implemented in the Python programming language (v. 3.8) using the freely distributed Keras library.   …”
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The role of force and torque in friction stir welding: A detailed review by Mostafa Akbari, Milad Esfandiar, Amin Abdollahzadeh

“…A notable advancement in this field is the implementation of machine learning tools, which enhance the ability to predict potential weld defects and improve overall weld quality. …”
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Identification and Development of Synovial B-Cell-Related Genes Diagnostic Signature for Rheumatoid Arthritis by Jifeng Tang, Jinfang Xia, Huiming Sheng, Jinpiao Lin

“…B-cell-related differential expression genes (BRDEGs) were screened, and BRDEGs-based model was developed by machine learning algorithms and evaluated by an external validation set and clinical RA cohort, then biological functions were further analyzed. …”
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ECG-based transfer learning for cardiovascular disease: A scoping review by Sharifah Noor Masidayu Sayed Ismail, Siti Fatimah Abdul Razak, Nor Azlina Ab Aziz

“…Advancements in medical diagnosis using artificial intelligence have popularised transfer learning among researchers in machine learning and deep learning. This scoping review focuses on the application of ECG-based transfer learning for CVD identification, examining the most common types of CVD studied, the input formats used, frequently referenced databases, and the extent of transfer learning's application in diagnosing CVD through ECG analysis. …”
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Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images by Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Alberto Battistel, Paul David Docherty, Hisham ElMoaqet, Thomas Neumuth, Knut Moeller

“…Acquiring surgical data is challenging due to ethical constraints and the complexity of establishing data recording infrastructures. For machine learning tasks, there is also the large burden of data labelling. …”
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Cardiovascular health in perspective: a comprehensive five-year geodatabase of hospitalizations and environmental factors in Mashhad, Iran by Shahab MohammadEbrahimi, Mohammad Dehghan, Behzad Kiani

“…Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions. …”
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Advances and Mechanisms of RNA–Ligand Interaction Predictions by Chen Zhuo, Chengwei Zeng, Haoquan Liu, Huiwen Wang, Yunhui Peng, Yunjie Zhao

“…However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. …”
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Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning by YANG Yinghe, WEI Handi, FAN Dixia, LI Ang

“…The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. …”
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Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting by Juan Carlos Moreno Sánchez, Héctor Gabriel Acosta Mesa, Adrián Trueba Espinosa, Sergio Ruiz Castilla, Farid García Lamont

“…This study uses these data to develop a wheat grain yield (GY) prediction model, using machine learning techniques such as Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). …”
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CORE-MD clinical risk score for regulatory evaluation of artificial intelligence-based medical device software by Frank E. Rademakers, Elisabetta Biasin, Nico Bruining, Enrico G. Caiani, Rhodri H. Davies, Stephen H. Gilbert, Eric Kamenjasevic, Gearóid McGauran, Gearóid O’Connor, Jean-Baptiste Rouffet, Baptiste Vasey, Alan G. Fraser

“…Abstract The European CORE–MD consortium (Coordinating Research and Evidence for Medical Devices) proposes a score for medical devices incorporating artificial intelligence or machine learning algorithms. Its domains are summarised as valid clinical association, technical performance, and clinical performance. …”
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Temperature and Humidity Monitoring in Hydroponic Cultivation Based on Internet of Things: Dataset Development for Smart Agriculture by Simon Prananta Barus, Jeriko Ichtus Seo

“…Further research will integrate more monitoring parameters, conduct direct hydroponic cultivation trials, and apply artificial intelligence such as machine learning and deep learning to improve efficiency and effectiveness in hydroponic cultivation.…”
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Ensemble Classification Model With CFS-IGWO–Based Feature Selection for Cancer Detection Using Microarray Data by Pinakshi Panda, Sukant Kishoro Bisoy, Sandeep Kautish, Reyaz Ahmad, Asma Irshad, Nadeem Sarwar

“…Cancer is the top cause of death worldwide, and machine learning (ML) has made an indelible mark on the field of early cancer detection, thereby lowering the death toll. …”
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Distinguishing Reality from AI: Approaches for Detecting Synthetic Content by David Ghiurău, Daniela Elena Popescu

“…Key detection approaches reviewed include stylometric analysis, watermarking, pixel prediction techniques, dual-stream networks, machine learning models, blockchain, and hybrid approaches, highlighting their strengths and limitations, as well as their detection accuracy, independent accuracy of 80% for stylometric analysis and up to 92% using multiple modalities in hybrid approaches. …”
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Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm by Kerem Gencer, Gülcan Gencer

“…The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. …”
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Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear arrayScienceDB by Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon

“…To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. …”
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