Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Rolando Marcel Torres Castillo, Siva Avudaiappan

“…The records were divided into training set (900 records = 80%) and validation set (235 records = 20%) following standard partitioning pattern reported in the literature. The kNN and AdaBoost, with SSE of 1408.5 MPa2 and 1397 MPa2 respectively and a tie Accuracy of 95.5% and R2 of 0.985 showed the best models suggesting excellent model performance while GMDH-NN showed the worst. …”
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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…Likewise, cytokines such as IL-10, TGF-β, RORγ, and IL-21 exhibited abnormal expression patterns in UC tissues. WGCNA identified three immune cell-associated gene modules, among which the MEblue, MEturquoise, and MEgrey modules were highly correlated with aberrant immune cells. …”
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On the Effectiveness of Automatic Code Generation for Synthetic Dataset Creation by Josh Mitchell, Varghese Mathew Vaidyan, Yong Wang

“…This paper compares synthetic and real-world code datasets for machine learning applications in cybersecurity by examining the relationships between machine code and Low-Level Virtual Machine Intermediate Representation (LLVM IR). …”
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Rationale for granulometric medium characteristics under vibration processing of parts with small grooves and holes by M. A. Tamarkin, E. N. Kolganova, M. A. Yagmurov

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Conflict evidence combination rule based on multi-dimensional weighted evidence optimization method and its applications in pattern recognition by Fuhai Xi, Mengli Mei, Shi Yang, Hang Guo, Min Yu

“…Abstract In complex pattern recognition scenarios, multi-source information fusion faces critical challenges in handling conflicting evidence. …”
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Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation by Min Jeong, Hyeongmo Koo

“…However, differences in the estimated global trends and spatio-temporal interactions resulting from applying machine learning may influence the spatio-temporal variation patterns of the kriging results. …”
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A Machine Learning-Based Approach to Analyze and Visualize Time-Series Sentencing Data by Eugene Pinsky, Kandaswamy Piranavakumar

“…This work proposes a machine learning approach to associate patterns with clusters. …”
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Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques by Janerra D. Allen, Sravani Varanasi, Fei Han, L. Elliot Hong, Fow-Sen Choa

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Machine learning in stream and river water temperature modeling: a review and metrics for evaluation by C. R. Corona, T. S. Hogue, T. S. Hogue

“…Most recently, the use of artificial intelligence, specifically machine learning (ML) algorithms, has garnered significant attention and utility in hydrologic sciences, specifically as a novel tool to learn undiscovered patterns from complex data and try to fill data streams and knowledge gaps. …”
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Predicting financial default risks: A machine learning approach using smartphone data by Shinta Palupi, Gunawan, Ririn Kusdyawati, Richki Hardi, Rana Zabrina

“…This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. …”
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Artificial Intelligence—What to Expect From Machine Learning and Deep Learning in Hernia Surgery by Robert Vogel, Björn Mück

“…In contrast, DL, a subset of ML, generally leverages unlabeled, raw data such as images and videos to autonomously identify patterns and make intricate deductions. This process is enabled by neural networks used in DL, where hidden layers between the input and output capture complex data patterns. …”
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Identification of Relevant ECG Features for Epileptic Seizure Prediction Using Interpretable Machine Learning by Azra Abtahi, Philippe Ryvlin, Amir Aminifar

“…We employ SHAP (SHapley Additive exPlanations), an interpretability framework, to interpret the prediction patterns. Based on our analysis, multifractality features are among the most important features in seizure prediction, capturing patterns that are not captured by the HRV and Lorenz features.…”
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Detecting Fraudulent Transaction in Banking Sector Using Rule-Based Model and Machine Learning by Cut Dinda Rizki Amirillah

“…The results showed that the IF model could detect anomalous patterns not covered by RBM, thereby improving the accuracy of fraud transaction identification. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…Motion data collected from the system was analyzed to extract distinct angle variation patterns associated with different batting shots. These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity by Doris Klingelhöfer, Markus Braun, Janis Dröge, David A. Groneberg, Dörthe Brüggmann

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Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach by Tianyi Li, Mingfeng Shang, Shian Wang, Raphael Stern

“…The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles.…”
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High-throughput behavioral screening in Caenorhabditis elegans using machine learning for drug repurposing by Antonio García-Garví, Antonio-José Sánchez-Salmerón

“…However, these methods present certain limitations in detecting subtle and non-linear patterns. In this study, we propose a high-throughput screening method based on machine learning, using classifiers that provide a recovery percentage as a measure of treatment effect. …”
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Driven early detection of chronic kidney cancer disease based on machine learning technique. by Wafa Almukadi, Sayed Abdel-Khalek, Adel A Bahaddad, Ahmed Mohammed Alghamdi

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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). …”
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Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques by Surafiel Habib Asefa, Yaregal Assabie

“…It is also an under-resourced language, presenting significant challenges for natural language processing tasks like machine translation. The primary challenges include the scarcity of parallel data, which increases the risk of overfitting and limits the model’s ability to generalize effectively, and the complex morphology of Amharic, which further complicates learning patterns in translation tasks. …”
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