Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation by Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth, Benkeser David

“…Here, we propose several methods that build on existing meta-learning algorithms to produce CATE estimates that can be represented as trees. …”
Get full text

Parallel Multi-Scale Semantic-Depth Interactive Fusion Network for Depth Estimation by Chenchen Fu, Sujunjie Sun, Ning Wei, Vincent Chau, Xueyong Xu, Weiwei Wu

“…Although self-supervised algorithms can reduce the dependence on labeled data, the performance is still affected by scene occlusions, lighting differences, and sparse textures. …”
Get full text

Spatial and Temporal Interplay Between Oceanic Circulation and Biological Production in Shaping Carbon Export Off the California Coast by M. Messié, C. L. Huffard, M. R. Stukel, H. A. Ruhl

“…Abstract A major challenge in understanding the oceanic carbon cycle is estimating the sinking flux of organic carbon exiting the sunlit surface ocean, termed carbon export. Existing algorithms derive carbon export from satellite ocean color, but neglect spatiotemporal offsets created by the temporal lag between production and export, and by horizontal advection. …”
Get full text

DynseNet: A Dynamic Dense-Connection Neural Network for Land–Sea Classification of Radar Targets by Jingang Wang, Tong Xiao, Kang Chen, Peng Liu

“…However, existing radar target detection algorithms predominantly achieve binary detection (i.e., determining the presence or absence of a target) and are unable to accurately classify target types. …”
Get full text

The Alliance of BIM and Artificial Intelligence: Challenges for a Reinvented Future – The State of the Art by Ellyssa Abdelmoula, Meriem Zammel, Najla Allani

“…The study advocates the adoption of standards protocols and unified application programming interfaces (APIs) to improve data exchange between BIM and AI systems. It also highlights the importance of data encryption, AI skills development and a comprehensive training framework within organizations to ensure successful implementation of AI-integrated BIM. …”
Get full text

Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture by Mohammad Hossein Modirrousta, Parisa Forghani Arani, Reza Kazemi, Mahdi Aliyari‐Shoorehdeli

“…Given this, it is glaringly obvious that algorithmic tools with strong detection performance and dependability are required for a variety of attack types. …”
Get full text

Breaks in the Arctic ice cover: from observations to predictions by A. A. Ershova, L. N. Dyment, T. A. Alekseeva

“…Processing of big volumes of satellite data and creating climatological datasets on breaks became possible owing to the development of algorithms for automatic identification of the ice breaks in images. …”
Get full text

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam by Tuan Vu Dinh, Nhat-Duc Hoang, Xuan-Linh Tran

“…Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. …”
Get full text

Bibliometric analysis and review of AI-based video generation: research dynamics and application trends (2020–2025) by Wei Xie, Anshu Hu, Qing Xie, Junjie Chen, Ruoyu Wan, Yuhan Liu

“…Our findings the rise of specific architectures, pinpoint high-activity application areas, reveal evolving thematic clusters, and also remarks persistent challenges, including technical hurdles and critical ethical issues such as deepfake detection, algorithmic transparency, and data privacy. …”
Get full text

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

“…Consequently, the number of large microbiome-related clinical trials has also significantly increased. However, advanced biostatistical analysis is required to properly combine microbiome taxonomic abundance data with phenotypical metadata and reliably predict disease states. …”
Get full text

Machine Learning for Anomaly Detection in Blockchain: A Critical Analysis, Empirical Validation, and Future Outlook by Fouzia Jumani, Muhammad Raza

“…Although blockchain provides multiple advantages, it has also been subject to some malicious attacks, such as a 51% attack, which is considered a potential risk to data integrity. …”
Get full text

Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…Based on performance measures, the proposed BPNN-SPA achieved better accuracy than existing algorithms.…”
Get full text

Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research by Sobia Iftikhar, Sania Bhatti, Zulfiqar Ali Bhatti, Mohsin Ali Memon, Faisal Memon

“…The results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms. …”
Get full text

Model Design for Personnel Selection with Data Mining Approach (Case Study: A Commerce Bank of Iran) by Adel Azar, Parviz Ahmadi, Mohammad Vahid Sebt

“…Rules Derivation has been accomplished by the QUEST, CHAID, C5.0 and CART algorithms. The objective and the appropriate algorithms are determined based on seemingly “irrelevant” components, which the Commerce Bank Human Resources management experts described. …”
Get full text

Mathematics teachers’ perceptions of computational thinking and its integration into mathematics education by Muhammad Zuhair Zahid

“…Computational thinking is a problem-solving process involving abstraction, algorithmic thinking, automation, debugging, decomposition, and generalisation. …”
Get full text

Design and optimization of distributed energy management system based on edge computing and machine learning by Nan Feng, Conglin Ran

“…These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. …”
Get full text

Performance of Machine Learning Classifiers for Diabetes Prediction by Mijala Manandhar, Shaikat Baidya, Babalpreet Kaur, Katia Atoji

“…Logistic Regression and Multilayer Perceptron also showed robust results, but SGD was superior in most metrics. …”
Get full text

Nonintrusive Load Disaggregation Based on Attention Neural Networks by Shunfu Lin, Jiayu Yang, Yi Li, Yunwei Shen, Fangxing Li, Xiaoyan Bian, Dongdong Li

“…Nonintrusive load monitoring (NILM), also known as energy disaggregation, infers the energy consumption of individual appliances from household metered electricity data. …”
Get full text

A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. …”
Get full text

Seismic Events Prediction Using Deep Temporal Convolution Networks by Yue Geng, Lingling Su, Yunhong Jia, Ce Han

“…Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. …”
Get full text