SLC3A2 as a key anoikis−related gene for prognosis and tumor microenvironment remodeling in melanoma by Xiaojin Liu, Jiaheng Xie, Yingying Xiao

“…A total of 150 anoikis-related genes were identified, and 101 machine learning algorithms and their combinations (including Cox regression, random survival forest, and gradient boosting machine) were systematically evaluated to identify the optimal prognostic model. …”
Get full text

Forecasting monthly residential natural gas demand in two cities of Turkey using just-in-time-learning modeling. by Burak Alakent, Erkan Isikli, Cigdem Kadaifci, Tonguc S Taspinar

“…In the current study, the historical monthly NG consumption data between 2014 and 2024 provided by SOCAR, the local residential NG distribution company for two cities in Turkey, Bursa and Kayseri, was used to determine out-of-sample monthly NGD forecasts for a period of one year and nine months using various time series models, including SARIMA and ETS models, and a novel proposed machine learning method. …”
Get full text

Noise Reduction Methods in the Vehicle Industry: Using Vibroacoustic Simulation for Sustainability by Krisztián Horváth, Ambrus Zelei

“…Capturing the current momentum of the industry, machine learning capabilities in vibroacoustic models can help engineers identify sources and eliminate or mitigate noise in the early design phase. …”
Get full text

A tied-weight autoencoder for the linear dimensionality reduction of sample data by Sunhee Kim, Sang-Ho Chu, Yong-Jin Park, Chang-Yong Lee

“…Abstract Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. …”
Get full text

Deep learning-based recognition model of football player’s technical action behavior using PCA–LBP algorithm by Hongtao Chen, Zhengbai Lin, Quan Xu

Get full text

Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals by Ahmed Shaheen, Liang Ye, Chrishni Karunaratne, Tapio Seppänen

Get full text

A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection by Rodrigo Colnago Contreras, Monique Simplicio Viana, Victor José Souza Bernardino, Francisco Lledo dos Santos, Önsen Toygar, Rodrigo Capobianco Guido

Get full text

Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach by José E. Teixeira, Pedro Afonso, André Schneider, Luís Branquinho, Eduardo Maio, Ricardo Ferraz, Rafael Nascimento, Ryland Morgans, Tiago M. Barbosa, António M. Monteiro, Pedro Forte

“…Optimizing recovery is crucial for maintaining performance and reducing fatigue and injury risk in youth football players. This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological features. …”
Get full text

ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction by Aneta Kolackova, Viet Anh Phan, Jan Jerabek, Sergey Andreev, Jiri Hosek

“…PESBiU 2.0 uses granular interval datasets and machine learning (ML) models to predict traffic loads and optimize power states. …”
Get full text

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…<h4>Introduction</h4>Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. …”
Get full text

Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review by José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, Eduardo Maio, Eduardo Maio, Eduardo Maio, Pedro Afonso, Pedro Afonso, Samuel Encarnação, Samuel Encarnação, Samuel Encarnação, Guilherme F. Machado, Guilherme F. Machado, Ryland Morgans, Tiago M. Barbosa, Tiago M. Barbosa, António M. Monteiro, António M. Monteiro, Pedro Forte, Pedro Forte, Pedro Forte, Ricardo Ferraz, Ricardo Ferraz, Luís Branquinho, Luís Branquinho, Luís Branquinho, Luís Branquinho

“…By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and time-series techniques. …”
Get full text

A deep operator network for Bayesian parameter identification of self-oscillators by Tobias Sugandi, Bayu Dharmaputra, Nicolas Noiray

Get full text

Classification of Nitrogen-Efficient Wheat Varieties Based on UAV Hyperspectral Remote Sensing by Yumeng Li, Chunying Wang, Junke Zhu, Qinglong Wang, Ping Liu

Get full text

Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning meth... by Feng Wang, Ruilin Liang, Shuyue Li, Meiyan Xiang, Weihao Yang, Miao Lu, Yingqiang Song

“…To explore the impact of environmental factors on soil organic carbon (SOC) with machine learning (ML) model is of great significance for mitigating climate change and soil carbon sequestration and emission reduction. …”
Get full text

Land Cover Transformations in Mining-Influenced Areas Using PlanetScope Imagery, Spectral Indices, and Machine Learning: A Case Study in the Hinterlands de Pernambuco, Brazil by Admilson da Penha Pacheco, João Alexandre Silva do Nascimento, Antonio Miguel Ruiz-Armenteros, Ubiratan Joaquim da Silva Junior, Juarez Antonio da Silva Junior, Leidjane Maria Maciel de Oliveira, Sylvana Melo dos Santos, Fernando Dacal Reis Filho, Carlos Alberto Pessoa Mello Galdino

“…This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas.…”
Get full text

Effective multimodal hate speech detection on Facebook hate memes dataset using incremental PCA, SMOTE, and adversarial learning by Emmanuel Ludivin Tchuindjang Tchokote, Elie Fute Tagne

“…To effectively address class imbalance and improve classification accuracy, our hybrid model combines ResNet for image processing with RoBERTa for text analysis, leveraging Synthetic Minority Over-sampling Technique (SMOTE) and Incremental Principal Component Analysis (PCA) combined with adversarial machine learning techniques. …”
Get full text

Design of reinforcement learning based robust μ-synthesis controller for single phase grid-connected VSI by P. Shambhu Prasad, Alivelu M. Parimi

“…The order of the controller has been reduced with a balanced model reduction approach. A novel methodology of tuning the weighting functions of the controller with advanced machine learning-based reinforcement learning has been adapted and performance specifications of the controller have been studied with tuned weighting functions. …”
Get full text

Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation by Amirreza Kandiri, Ramin Ghiasi, Maria Nogal, Rui Teixeira

“…The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. …”
Get full text

Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…The utilized techniques in this paper comprise the Multimodal Deep Boltzmann Machine (MDBM), Multimodal Variational Autoencoder (MVAE) and Attention-based Fusion Networks, all of which fully utilize the learned representations. …”
Get full text

Lithology Recognition Research Based on Wavelet Transform and Artificial Intelligence by FANG Dazhi, MA Weijun, YAN Xu, MAO Zheng, GAO Yang

“…After noise reduction, the XGBoost model performed the best, with test set accuracy, recall, and F1 score all reaching 0.998. …”
Get full text