Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…Future research could explore the application of this method across additional levels of supply chain management.…”
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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…These outcomes underscore the effectiveness of combining machine learning with nature-inspired optimization algorithms to produce more accurate stock price forecasts. …”
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Machine learning approaches for grain seed quality assessment: a comparative study of maize seed samples in Malawi by Wisdom Richard Mgomezulu, Moses M. N. Chitete, Beston B. Maonga, Mthakati A. R. Phiri

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An Improved Framework for Content- and Link-Based Web-Spam Detection: A Combined Approach by Asim Shahzad, Nazri Mohd Nawi, Muhammad Zubair Rehman, Abdullah Khan

“…To detect the spam web pages, several researchers from industry and academia are working. …”
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Current status and outlook of UWB radar personnel localization for mine rescue by ZHENG Xuezhao, MA Jiawen, HUANG Yuan, LI Qiang, REN Jing, LIU Yu

“…Key challenges in mine rescue scenarios are identified: ① significant localization errors and limited effective detection range in thick, heterogeneous, and discontinuous media; ② weakened radar echoes and severe clutter interference under Non-Line-of-Sight (NLOS) conditions, leading to low-precision micro-motion target detection and large real-time errors for dynamic targets; ③ signal interference and occlusion effects among multiple targets degrading localization accuracy. Future research directions of UWB radar personnel localization technology for mine rescue operations are proposed: ① optimizing the UWB radar localization system by constructing cross-modal information fusion models and developing highly adaptive signal processing methods to enhance the system's adaptability to post-mining disaster environments; ② improving the applicability of combined static and dynamic target localization by developing hybrid localization algorithms that integrate Bayesian networks or deep belief networks to fuse static and dynamic target features and establishing state-switching-based comprehensive models; ③ improving UWB radar echo processing algorithms, combining adaptive beamforming technology, Multiple Input Multiple Output (MIMO) technology, and optimized K-means++ or entropy-based hierarchical analysis algorithms, effectively distinguishing multi-target position information, and validating their adaptability and reliability in complex environments through extensive simulation experiments.…”
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Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach by Rafael Travincas, Maria Paula Mendes, Isabel Torres, Inês Flores-Colen

“…This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. …”
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Enhancing Short-Term Wind Speed Prediction Based on Deep Learning With Ensemble Learning Model for Small Wind Turbine Applications by J. Sathyaraj, V. Sankardoss

“…This study discusses various deep learning (DL) algorithms for enhancing wind speed forecasting accuracy. …”
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Multi-label remote sensing classification with self-supervised gated multi-modal transformers by Na Liu, Ye Yuan, Guodong Wu, Sai Zhang, Jie Leng, Lihong Wan

“…With the rise of self-supervised learning (SSL) algorithms in recent years, RS researchers began to pay attention to the application of “pre-training and fine-tuning” paradigm in RS. …”
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Combining machine learning and single-cell sequencing to identify key immune genes in sepsis by Hao Wang, Linghan Len, Li Hu, Yingchun Hu

“…Next, a Biological association network was constructed, and five key hub genes (CD4, HLA-DOB, HLA-DRB1, HLA-DRA, AHNAK) were identified using a combination of three topological analysis algorithms (MCC, Closeness, and MNC) and four machine learning algorithms (Random Forest, LASSO regression, SVM, and XGBoost). immune cell distribution showed that the key genes correlated with multiple immune cell infiltrations. …”
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Software complex for simulation modelling of single nucleotide genetic polymorphism sites by M. M. Yatskou, D. D. Sarnatski, V. V. Skakun, V. V. Grinev

“…A comparative analysis of the most effective algorithms for identifying single nucleotide polymorphism sites was performed. …”
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A New Approach to the Criteria-Weighted Fuzzy Soft Max-Min Decision-Making Method and Its Application to a Performance-Based Value Assignment Problem by Samet Memiş, Serdar Enginoğlu

“…Finally, we provide the conclusive remarks and some suggestions for further research.…”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…This research sought to forecast concrete compressive strength through six machine learning (ML) algorithms namely Linear Regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boost (GB), Support Vector Machine (SVM), and Categorical Gradient Boost (CatBoost), and to examine the significance of the input factors on the concrete compressive strength. …”
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Modern possibilities for optimizing the calculation of intraocular lens optical power using deep machine learning capabilities by A.R. Vinogradov, B.G. Dzhashi, S.V. Balalin

“…Fyodorov National Medical Research Center «MNTK «Eye Microsurgery» developed a design project for the LensCalc software application and algorithms of its step-by-step operation. …”
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STUDY OF THE INFLUENCE OF CRACKS ON THE BEARING CAPACITY OF REINFORCED CONCRETE PLATES USING THE PRINS CALCULATION COMPLEX by K. R. Aydemirov, V. P. Agapov

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INVESTIGATION OF THE CARRYING CAPACITY OF REINFORCED CONCRETE SLABS WITH CRACKS AFTER THEIR REINFORCEMENT WITH COMPOSITE FABRICS BY THE FINITE ELEMENT METHOD USING THE PRINS COMPUT... by V. P. Agapov, K. R. Aydemirov

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Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection by Tuğçe Öznacar, Tunç Güler

“…Early detection is highly critical for increasing survival chances. This research aims to assess the feature extraction process from various machine learning techniques for better modelling of ovarian cancer and the selection process in ovarian cancer analysis. …”
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Rigorous and extensive accuracy assestment of automatically classified LiDAR data: a case study in the city of Milan, Italy by D. Lodigiani, V. M. Casella

“…LiDAR data filtering has been an active research area for nearly thirty years and continues to present significant challenges due to the increasing density of acquired LiDAR data. …”
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Predictive factors and nomogram for the risk of cytokine release syndrome with anti-CD3 × CD20 bispecific antibodies for Chinese patients by Peng Sun, Cui Chen, Bai-Tian Zhao, Bo-Yan Zhang, Hang Yang, Yu Wang, Fei Xu, Kang-Ming Huang, Yin-Ting Liu, Mei-Shuo Ouyang, Jia-Jia Huang, Pan-Pan Liu, Ying Guo, Zhi-Ming Li

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Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers by Victor Chang, Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra, Jiabin Luo

“…Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. …”
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