Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Also, the findings emphasize the potential relevance of AI algorithms in addressing complex diagnostic challenges and supporting clinical decisions.This research lays the foundation for developing robust and generalizable models that can advance precision oncology in cancer diagnostics.…”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…Hyperparameter optimization techniques showed minimal improvement in model performance, suggesting that the transformations effectively captured the underlying relationships in the data. This research represents the first comprehensive application of ML techniques to WDE incubation period prediction, establishing a methodological framework that integrates experimental data, statistical analysis, and advanced ML algorithms. …”
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Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…However, most of the previous studies on fault analysis in power systems relied on proprietary data and lack of standardized benchmarks, hindering the comparison of algorithms and making performance more erratic. Moreover, the nonavailability of labeled data for all types of faults is the most problematic. …”
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An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction by Anuradha Yenkikar, Ved Prakash Mishra, Manish Bali, Tabassum Ara

“…Explainable Artificial Intelligence (XAI) techniques address this challenge by making AI-driven predictions more transparent, ensuring trust and better decision-making. This research integrates XAI techniques into a hybrid model that combines the powers of Random Forest (RF), Long Short-Term Memory (LSTM), and XGBoost algorithms by incorporating SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and Counterfactual Analysis for yield prediction. …”
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A Generic Modeling Method of Multi-Modal/Multi-Layer Digital Twins for the Remote Monitoring and Intelligent Maintenance of Industrial Equipment by Maolin Yang, Yifan Cao, Siwei Shangguan, Xin Chen, Pingyu Jiang

“…Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. …”
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A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation by Zhen Yang, Mingxuan Wang, Shikun Xie

“…This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. …”
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DBN-BAAE: Enhanced Lightweight Anomaly Detection Mechanism with Boosting Adversarial Autoencoder by Yanru Chen, Bei Wu, Wang Zhong, Yanru Guo, Dizhi Wu, Yi Ren, Yuanyuan Zhang

“…Effective anomaly detection is essential to safeguard these facilities and prevent costly disruptions. Although current research has advanced anomaly detection, it is still challenging for algorithms to be capable of effectively balancing the interplay between training speed, computational cost, and accuracy while simultaneously exhibiting robust stability and adaptability. …”
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AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes by José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon

“…Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. …”
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A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision by Rongyong Zhao, Feng Hua, Bingyu Wei, Cuiling Li, Yulong Ma, Eric S. W. Wong, Fengnian Liu

“…Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. …”
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Bearing Response Prediction in Hydrothermal Aged Carbon Fiber Reinforced Epoxy Composite Joints Using Machine Learning Techniques by Mohit Kumar, Govind Vashishtha, Babita Dhiman, Sumika Chauhan

“…The hydrothermal aging process can significantly affect the mechanical behavior of such composites, particularly in joint configurations. In this research, an innovative support vector regression approach is present that leverages machine learning algorithms to forecast the bearing response of CFREC joints after undergoing hydrothermal aging. …”
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The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications by Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo, Manfredo Atzori

“…This study is the first to thoroughly investigate the effects of EEG preprocessing in deep learning applications, drafting guidelines for future research. It evaluates the effects of varying preprocessing levels, from raw and minimally filtered data to complex pipelines with automated artifact removal algorithms. …”
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An Adaptive Optimization Method for Acoustic Temperature Measurement Topology Based on Multiple Sub-Objectives by Jialiang Zhu, Xinzhi Zhou, Hailin Wang, Yixiao Chen, Tao Xu, Zhengxi He

“…In this paper, based on the previous research on high-precision temperature field reconstruction algorithms, an adaptive optimization method of acoustic temperature measurement topology based on multiple sub-objectives is proposed. …”
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Mitigating Catastrophic Forgetting in Pest Detection Through Adaptive Response Distillation by Hongjun Zhang, Zhendong Yin, Dasen Li, Yanlong Zhao

“…Although knowledge distillation has been shown to effectively mitigate catastrophic forgetting, current research predominantly focuses on feature imitation, neglecting the extraction of potentially valuable information from responses. …”
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Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event by Francisca Santana-Robles, Eva Selene Hernández-Gress, Ricardo Martínez-López, Isidro Jesús González-Hernández

“…Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. …”
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A Custom Reinforcement Learning Environment for Hybrid Renewable Energy Systems: Design and Implementation by Dalton F. Guedes Filho, Marcelo A. Moret, Erick G. Sperandio Nascimento

“…We present HybridEnergyEnv, an open-source, Gym-style simulation environment designed for reinforcement learning (RL) research in hybrid renewable energy systems (HRES) combining wind, solar, and battery storage. …”
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Implementing and evaluating the quality 4.0 PMQ framework for process monitoring in automotive manufacturing by Fathy Alkhatib, Mohamed Allam, Vikas Swarnakar, Juman Alsadi, Maher Maalouf

“…The study relied on various ML algorithms, such as Decision Trees (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), to classify and predict defects in engine valves during manufacturing processes. …”
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Predicting Court Judgment in Criminal Cases by Text Mining Techniques by Mohammad Farhadishad, Mohammad Kazemifard, Zahra Rezaei

“…Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. …”
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Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data by Daoye Zhu, Boyong Xiao, Haoling Xie, Dong Li, Haitong He, Weixin Zhai

“…Identifying agricultural machinery operations is crucial for enhancing agricultural productivity and promoting the transition to data-driven agriculture. Current research focuses solely on administrative divisions, overlooking the links between machinery movement, natural spatial patterns, and spatiotemporal dependencies. …”
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Predictive framework of vegetation resistance in channel flow by Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

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Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features by Rodrigo Oliveira Almeida, Richardson Barbosa Gomes da Silva, Danilo Simões

“…In default mode, we ran 25 popular algorithms through the database and compared them based on accuracy and error metrics. …”
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