Improving with Hybrid Feature Selection in Software Defect Prediction by Muhammad Yoga Adha Pratama, Rudy Herteno, Mohammad Reza Faisal, Radityo Adi Nugroho, Friska Abadi

“…Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. …”
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Review of Research on Trajectory Prediction of Road Pedestrian Behavior by YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu

“…Firstly, it defines the core concepts of pedestrian trajectory prediction and conducts an in-depth analysis of the main prediction methods. …”
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Enhancing Explainability in Predictive Maintenance : Investigating the Impact of Data Preprocessing Techniques on XAI Effectiveness by Mouhamadou Lamine NDAO, Genane YOUNESS, Ndèye NIANG, Gilbert SAPORTA

“…Indeed, understanding the nuanced relationships between evaluation metrics is essential for a comprehensive and accurate assessment of explainability methods.…”
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Comparative Analysis of Several Models for Churning Customer Prediction by Tan Zhaoyuan

“…Results show that the LGBM Classifier with Borderline SMOTE achieves the highest accuracy (97.43%) and F1 score (0.9259), outperforming other methods. This approach effectively balances precision and recall, improving minority class prediction. …”
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Association of Model-Predicted Epigenetic Age and Female Infertility by Elena Pozdysheva, Vitaly Korchagin, Tatiana Rumyantseva, Daria Ogneva, Vera Zhivotova, Irina Gaponova, Konstantin Mironov, Vasily Akimkin

“…Background: To date, there are no precise clinical and laboratory methods to accurately predict the onset of fertility decline in women, with chronological age being the ultimate predictor. …”
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Explainable artificial intelligence for targeted protein degradation predictions by Francis J. Prael III, Jutta Blank, William C. Forrester, Lingling Shen, Raquel Rodríguez-Pérez

“…Herein, we present a novel application of XAI methods to targeted protein degradation (TPD) predictions. …”
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A Review of Link Prediction Algorithms in Dynamic Networks by Mengdi Sun, Minghu Tang

“…After summarizing and comparing the common datasets and evaluation indicators for dynamic network link prediction, we briefly review classic related algorithms in recent years, and classify them according to the network changes, sampling methods, underlying principles of algorithms, and other classification methods. …”
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Predicting survival in malignant glioma using artificial intelligence by Wireko Andrew Awuah, Adam Ben-Jaafar, Subham Roy, Princess Afia Nkrumah-Boateng, Joecelyn Kirani Tan, Toufik Abdul-Rahman, Oday Atallah

“…Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall survival (OS) and progression-free survival (PFS) as key measures to evaluate patients. However, predicting survival using traditional methods such as the Kaplan–Meier estimator and the Cox Proportional Hazards (CPH) model has faced many challenges and inaccuracies. …”
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Metric-based defect prediction from class diagram by Batnyam Battulga, Lkhamrolom Tsoodol, Enkhzol Dovdon, Naranchimeg Bold, Oyun-Erdene Namsrai

“…Many researchers have leveraged these metrics to predict defects using ML and DL methods, achieving state-of-the-art performance. …”
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Ensuring representative sample volume predictions in microplastic monitoring by Richard K. Cross, Sarah L. Roberts, Monika D. Jürgens, Andrew C. Johnson, Craig W. Davis, Todd Gouin

“…Rather, they are usually operationally defined based on their size, polymer and shape, dependent on the sample collection method and the analytical range of the measurement technique. …”
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Social Networks Link Prediction Based on Incremental Learning by Jian SHU, Zhichen CHEN

“…The heterogeneity and dynamic topology pose significant challenges for link prediction. Specifically, this study addresses three key issues: (1) an incremental update strategy for random walk sequences, (2) the extraction of the correlation of causal relationships, and (3) the construction of the mutual perceptron.Methods Therefore, an incremental learning social network link prediction (IL−SNLP) method is proposed. …”
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Predictive Machine Learning Approaches for Supply and Manufacturing Processes Planning in Mass-Customization Products by Shereen Alfayoumi, Amal Elgammal, Neamat El-Tazi

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Prediction of Air Quality Index Using Ensemble Models by Theresia Herlina Rochadiani

“…Traditional methods of monitoring air quality are inaccurate and time-consuming. …”
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Predicting sport event outcomes using deep learning by Jianxiong Gao, Yi Cheng, Jianwei Gao

“…This hybrid design enables the model to uncover nuanced feature interactions critical to outcome prediction. We evaluate our approach on a benchmark sports dataset, where it outperforms traditional machine learning methods and standard deep learning models in both accuracy and robustness. …”
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Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning by Jia Liu, Dan Zhu, Lingzhi Deng, Xiaoliang Chen

“…Objective We evaluated the predictive value of electrocardiogram (ECG)–derived features and developed an ML model to stratify HF risk. …”
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Using EEG Signals and AI to Predict Neurodegenerative Diseases by Juan Li, Dongyuan Zhang, Wei Lin, Wei Liu

“…We introduce the Adaptive Knowledge Integration Strategy (AKIS) to enhance model robustness by addressing modality-specific noise, data imbalance, and temporal consistency. Experimental evaluations on four benchmark datasets demonstrate that our method achieves superior prediction accuracy and interpretability compared to state-of-the-art approaches. …”
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ClusterEmbed: Lightweight Protein Structure Prediction on PCs by Yuan Chuxin

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Nomogram for prediction of plastic bronchitis in Chinese children with pneumonia by Xiaoqian Fang, Hemin Lu

“…Key indicators were identified using regression analysis, and a nomogram prediction model was developed. Its effectiveness was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and the bootstrap (BS) method.ResultsA total of 65 patients (13.3%) out of 487 had PB. …”
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Computational biology approach to predict molecular mechanism in cancer by Ansari Vikhar Danish Ahmad, Subur W. Khan, Qazi Yasar, Mohd Sayeed Shaikh, Mohd Mukhtar Khan

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Predictive modeling of oil rate for wells under gas lift using machine learning by Famin Ma, Farag M. A. Altalbawy, Pinank Patel, R. Manjunatha, Rishiv Kalia, Shoira Formanova, P. Raja Naveen, Kamal Kant Joshi, Aashna Sinha, Abdolali Yarahmadi Kandahari, Taqi Mohammed Khattab Al-Rubaye, Mohammad Mahtab Alam

“…This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. …”
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