Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency by Jonathan Tarquino, Jhonathan Rodríguez, David Becerra, Lucia Roa-Peña, Eduardo Romero

“…The herein introduced strategy presents an engineered feature representation, the region-attention embedding, which improves the deep learning classification performance of a cytomorphology with 21 bone marrow cell subtypes. …”
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A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer by Hisham Allahem, Sameh Abd El-Ghany, A. A. Abd El-Aziz, Bader Aldughayfiq, Menwa Alshammeri, Malak Alamri

“…Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. …”
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Complete Supply Chain Operations Measurement (C- SCOM): A Model Proposed for Measuring Manufacturing Supply Chain Performance by Tenaw Tegbar Tsega, Thoben Klaus-Dieter, Rao D.K. Nageswara, Bereket Haile Woldegiorgis

“…So far, several models for measuring supply chain performance (SCP) have been developed. …”
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An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body r... by Yi Chen, David Pasquier, Damon Verstappen, Henry C. Woodruff, Philippe Lambin

“…This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical data to predict 2-year survival in HCC patients treated with stereotactic body radiation therapy (SBRT). …”
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Establishment and validation of a combined diagnostic model for aldosterone-producing adenoma of the adrenal gland based on CT radiomics and clinical features by ZHANG Mingquan, LIU Jingjing, LIN Xin, FU Min, FENG Ying, CHEN Jingjing

“…Objective To establish a combined diagnostic model based on CT radiomics and clinical features, and to investigate the value of the model in the diagnosis of aldosterone-producing adenoma (APA) of the adrenal gland. …”
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Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models by Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo

“…To support frontline pediatricians with a more objective diagnostic tool, we developed and implemented KDpredictor, a machine learning-based model for KD risk identification. KDpredictor leverages only the routine blood test features, including complete blood count with differential count, C-reactive protein, and alanine aminotransferase. …”
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Temporal record linkage for heterogeneous big data records by Reham I. Abdel Monem, Ehab E. Hassanein, Ali Z. El Qutaany

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Prediction and Correction of Software Defects in Message-Passing Interfaces Using a Static Analysis Tool and Machine Learning by Norah Abdullah Al-Johany, Fathy Elbouraey Eassa, Sanaa Abdullah Sharaf, Amin Y. Noaman, Asaad Ahmed

“…This system predicts defects including deadlock, race conditions, and mismatch, by dividing the model into three stages: training, testing, and prediction. …”
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A Machine Learning-Based Intelligent Framework for Predicting Energy Efficiency in Next-Generation Residential Buildings by Hafiz Muhammad Shakeel, Shamaila Iram, Richard Hill, Hafiz Muhammad Athar Farid, Akbar Sheikh-Akbari, Farrukh Saleem

“…Further, machine learning models revealed that Random Forest, Gradient Boosting, XGBoost, and LightGBM deliver the lowest mean square error scores of 6.305, 6.023, 7.733, 5.477, and 5.575, respectively, and demonstrated the effectiveness of advanced algorithms in forecasting energy performance. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…The results show that the coupled CNN-LSTM model performs better than the flood predictions compared to the individual CNN or LSTM models under the longest foresight period of 25 h. …”
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Temporal Restriction and Interest for the Elderly on Cultural Participation. The Case of Spanish Performing Arts 2019 by Blas DÍaz León, Ignacio Martinez Fernandez, Luis Palma Martos

“…This paper discusses the relationship of cultural participation in performing arts with the manifested interest. Using the data set from the Cultural Habits and Practices Survey 2018-2019 a binary probit model has been applied for the analysis. …”
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Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit by Nadea Putri Nur Fauzi, Siti Khomsah, Aditya Dwi Putra Wicaksono

“…Maka penelitian ini bertujuan meningkatkan akurasi model klasifikasi algoritma Random Forest dengan menerapkan tuning parameter dan feature engineering yang lebih dalam. …”
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EmoBERTa–CNN: Hybrid Deep Learning Approach Capturing Global Semantics and Local Features for Enhanced Emotion Recognition in Conversational Settings by Mingfeng Zhang, Aihe Yu, Xuanyu Sheng, Jisun Park, Jongtae Rhee, Kyungeun Cho

“…Although existing deep learning and Transformer-based pretrained language models have shown remarkably enhanced performances, both approaches have inherent limitations. …”
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Evaluating the Performance of a Fake News Model on A Domain-Specific and Heterogeneous Dataset to Improve Detection by Georgina Obuandike, Emmy Danny Ajik, Faith Oluwatosin Echobu

“…However, while domain-specific models perform exceptionally well in their respective contexts, models trained on a diverse range of datasets exhibit greater generalizability across domains. …”
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Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma by Liyan Li, Xueying Wang, Zeming Tan, Yipu Mao, Deyou Huang, Xiaoping Yi, Muliang Jiang, Bihong T. Chen

“…Objectives: To develop and validate a prediction model based on brain MRI features to predict disease-free survival (DFS) and overall survival (OS) for patients with intracranial extraventricular ependymoma (IEE). …”
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Prediction of IPO performance from prospectus using multinomial logistic regression, a machine learning model by Mazin Fahad Alahmadi, Mustafa Tahsin Yilmaz

“…A total of twelve characteristics in two segments, namely 'prospectus characteristics' and 'financial ratios', were used as the features to assess IPO performance in the Saudi stock market in three categories: BELOWAVERAGE, AVERAGE, and ABOVE AVERAGE performance. …”
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Optimizing photovoltaic performance: Data-driven maximum power point prediction via advanced regression models by Maissa Farhat, Azzeddine Dekhane, Abdelhak Djellad, Maen Takruri, Aws Al-Qaisi, Oscar Barambones

“…By analyzing a dataset of irradiance, temperature, and PMPP measurements, the research compares the performance of these models in capturing complex nonlinear relationships between key variables. …”
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Additively manufactured hybrid auxetic structures for enhanced low frequency acoustic performance through experiments and modelling by Ali Bin Naveed, Aamir Mubashar, Muhammad Khizer Ali Khan, Adnan Munir, Kamran A. Khan

“…Sample fabrication was performed with fused deposition modeling (FDM), an Additive Manufacturing technique, using PLA as build material. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). …”
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Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from me... by Shao-Cai Wang, Sheng-Nan Yin, Zi-You Wang, Ning Ding, Yi-Ding Ji, Long Jin

“…Abstract Objective To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiological and clinical features in differentiating lipid-poor adrenal adenomas from metastatic tumors, and to explain the model’s prediction results through SHAP(Shapley Additive Explanations) analysis. …”
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