Simultaneous Instance and Attribute Selection for Noise Filtering by Yenny Villuendas-Rey, Claudia C. Tusell-Rey, Oscar Camacho-Nieto

“…Removing or reducing noise can help classification algorithms focus on relevant patterns, preventing them from being affected by irrelevant or incorrect information. …”
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Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction by Yang Yue, Shu Li, Yihua Cheng, Lie Wang, Tingjun Hou, Zexuan Zhu, Shan He

“…While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. …”
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Time-Series Forecasting for Peak Hour Traffic Accidents by Md. Ferdousul Haque Shikder, Yili Tang, Majid Emami Javanmard

“…The results provide references and practical insights into the potential of leveraging advanced algorithms and techniques to tackle the dynamics of traffic accidents and improve road safety.…”
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Cell‐free epigenomes enhanced fragmentomics‐based model for early detection of lung cancer by Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song, Fei Zhao, Tiantian Gu, Zhe Feng, Haibo Wang, Bowen Li, Daoyun Wang, Bin Zhou, Chao Guo, Yuan Xu, Yang Song, Zhibo Zheng, Zhongxing Bing, Haochen Li, Xiaoqing Yu, Ka Luk Fung, Heqing Xu, Jianhong Shi, Meng Chen, Shuai Hong, Haoxuan Jin, Shiyuan Tong, Sibo Zhu, Chen Zhu, Jinlei Song, Jing Liu, Shanqing Li, Hefei Li, Xueguang Sun, Naixin Liang

“…Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low‐pass whole‐genome sequencing. Machine learning algorithms were then employed to integrate the multi‐omics data, aiding in the development of a precise lung cancer detection model. …”
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Improved Key Microbial Biomarker Discovery Using Ensemble Statistical Methods by Walter Pirovano, Yashjit Gangopadhyay, Mirna Lilian Baak, Christiaan Arie de Leeuw, Radhika Bongoni, Eline Suzanne Klaassens

“…However, advanced biostatistical analysis is required to properly combine microbiome taxonomic abundance data with phenotypical metadata and reliably predict disease states. While differential abundance analysis and machine-learning techniques are widely used to perform such analyses, selecting the best method is not trivial due to the complexity and specific characteristics of both the data and the algorithms. …”
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Comparing the Performance of Regression and Machine Learning Models in Predicting the Usable Area of Houses with Multi-Pitched Roofs by Leszek Dawid, Anna Marta Barańska, Paweł Baran

“…To address this problem, a study was conducted that used linear models, non-linear models and machine learning algorithms to calculate the floor area of buildings with complex multi-pitched roofs. …”
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Prediction of Graduate Career Relevance Based on Academic and Non-Academic Aspects using Machine Learning by Muhammad Yusuf Luthfi Ijlal, Arif Setiawan, Diana Laily Fithri

“…Three classification algorithms were applied: Decision Tree, Random Forest, and Support Vector Machine (SVM). …”
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Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma by Bin Zhu, Stephanie S. McHale, Michelle Van Scoyk, Gregory Riddick, Pei-Ying Wu, Chu-Fang Chou, Ching-Yi Chen, Robert A. Winn

“…Unsupervised machine learning algorithms stratified B/AA patients into groups with distinct survival outcomes, while supervised algorithms demonstrated a higher accuracy in predicting survival for B/AA LUAD patients compared to white patients.DiscussionIn total, this study explored OS-associated genes and pathways specific for B/AA LUAD patients. …”
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Enhancing Automated Maneuvering Decisions in UCAV Air Combat Games Using Homotopy-Based Reinforcement Learning by Yiwen Zhu, Yuan Zheng, Wenya Wei, Zhou Fang

“…However, relying solely on the terminal outcome of victory or defeat as the optimization target, but without incorporating additional rewards throughout the process, poses significant challenges for reinforcement learning due to the sparse reward structure inherent in these scenarios. While algorithms enhanced with densely distributed artificial rewards show potential, they risk deviating from the primary objectives. …”
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Current application of artificial intelligence in laparoscopic cholecystectomy by S.M. Chooklin, S.S. Chuklin

“…Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. …”
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Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. by Andrei Prodan, Valentina Tremaroli, Harald Brolin, Aeilko H Zwinderman, Max Nieuwdorp, Evgeni Levin

“…However, specialized software and algorithms are needed to convert raw sequencing data into biologically meaningful information (i.e. tables of bacterial counts). …”
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Nonintrusive Load Disaggregation Based on Attention Neural Networks by Shunfu Lin, Jiayu Yang, Yi Li, Yunwei Shen, Fangxing Li, Xiaoyan Bian, Dongdong Li

“…House1 and House5 are employed as the training set, while House2 data is reserved for testing. The proposed model achieves lower errors for all appliances compared to other algorithms. …”
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Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. …”
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AI-Driven Wearable Bioelectronics in Digital Healthcare by Guangqi Huang, Xiaofeng Chen, Caizhi Liao

“…However, their widespread adoption faces technical, ethical, and regulatory hurdles, such as data interoperability, privacy concerns, algorithmic bias, and the need for robust clinical validation. …”
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A machine learning-powered energy consumption prediction system with API by Toyeeb Adekunle Abd’Azeez, Lanre Olatomiwa

“…The lower MAPE and the higher R2 score indicate the superiority of the ExtraTreeRegressor over other algorithms. While energy consumption is characterised by high variance, our optimised model effectively interprets interactions between input features and predicts the equivalent energy consumed with a lower RMSE of 11.75. …”
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ILR-Net: Low-light image enhancement network based on the combination of iterative learning mechanism and Retinex theory. by Mohan Yin, Jianbai Yang

“…Aiming at the existing image enhancement algorithms tend to overly focus on increasing brightness, while neglecting the enhancement of color and detailed features. …”
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Tracking Secondary Raw Material Operational Framework—DataOps Case Study by Gabriel Pestana, Marisa Almeida, Nelson Martins

“…It enhances supply chain transparency through digital traceability, promotes predictive material sourcing, and streamlines logistics via algorithmic optimization. Challenges such as regulatory gaps and quality standards are addressed through standardized processes, open data governance, and innovative algorithms. …”
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Precision Agriculture: Utilizing Machine Learning for Accurate Plant Disease Diagnosis by Divya Banti, Tandan Gajendra

“…Mobile applications with ML algorithms also enable on the spot plant disease diagnosis from the smartphone camera, enabling site rather than later intervention. …”
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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability and clinical applicability. …”
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Hybrid Mode Sensor Fusion for Accurate Robot Positioning by Viktor Masalskyi, Andrius Dzedzickis, Igor Korobiichuk, Vytautas Bučinskas

“…Robotic systems are becoming increasingly crucial in applications requiring high precision. While a robot can operate using basic sensor feedback under controlled conditions, achieving micro-level accuracy requires more comprehensive data integration, especially in dynamic environments. …”
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