DEEP LEARNING-BASED SUPER-RESOLUTION TECHNIQUES: A COMPARATIVE ANALYSIS WITH RECENT INSIGHTS by Renuka Sambhaji Sindge, Maitreyee Dutta, Jagriti Saini

Get full text

Road Damage Detection Using Yolov9-Based Imagery by Febrian Akbar Azhari, Tatang Rohana, Kiki Ahmad Baihaqi, Ahmad Fauzi

“…A consistent decline in loss functions during training also signifies effective learning by the model. …”
Get full text

Comparison of Random Survival Forest Based‐Overall Survival With Deep Learning and Cox Proportional Hazard Models in HER‐2‐Positive HR‐Negative Breast Cancer by Wenqi Cai, Yan Qi, Linhui Zheng, Huachao Wu, Chunqian Yang, Runze Zhang, Chaoyan Wu, Haijun Yu

“…Evaluation revealed: The C‐index of the DeepSurv models constructed using the training set is greater than 0.8, performing better than both the RSF and CoxPH models. …”
Get full text

A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…The two LCA and FOA algorithms produced the best estimation, according to the evaluation criteria results. …”
Get full text

Gradient boosting for yield prediction of elite maize hybrid ZhengDan 958. by Oumnia Ennaji, Sfia Baha, Leonardus Vergutz, Achraf El Allali

“…Using a recent dataset of over 1700 maize yield data pairs, our evaluation included a spectrum of algorithms. Our results show robust prediction accuracy for all algorithms. …”
Get full text

Artificial intelligence in clinical decision support and the prediction of adverse events by S. P. Oei, T. H. G. F. Bakkes, M. Mischi, R. A. Bouwman, R. A. Bouwman, R. J. G. van Sloun, S. Turco

“…Additionally, emphasizing human judgment alongside AI integration is essential to mitigate the risks of deskilling healthcare practitioners. Ongoing evaluation processes and adjustments to regulatory frameworks are crucial for ensuring the ethical, safe, and effective use of AI in CDS, highlighting the need for meticulous attention to data quality, preprocessing, model training, interpretability, and ethical considerations.…”
Get full text

UNS Geo: LiDAR Dataset for point cloud classification in urban areas by M. Govedarica, G. Jakovljevic, I. Ruskoviski, V. Pajic

“…Publicly available annotated benchmark datasets have become the standard for the evaluation of algorithms' performance; however, most focus on data acquired from mobile or terrestrial laser scanners. …”
Get full text

Synthetic Data-Driven Approaches for Chinese Medical Abstract Sentence Classification: Computational Study by Jiajia Li, Zikai Wang, Longxuan Yu, Hui Liu, Haitao Song

“…MethodsWe developed 3 training datasets (dataset #1, dataset #2, and dataset #3) and a test dataset to evaluate our model. …”
Get full text

Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer by Denghui Liu, Lin Zhong, Haiyang Wu, Songyang Li, Yida Li

“…Abstract To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. …”
Get full text

Energy Minimization for Federated Learning Based Radio Map Construction by Fahui Wu, Yunfei Gao, Lin Xiao, Dingcheng Yang, Jiangbin Lyu

“…The DRL-based method enables the UAV to learn from its interaction with the environment and ascertain the most energy-efficient deployment locations through an evaluation of energy consumption during the training process. …”
Get full text

Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…The performance of the trained models can be evaluated using various metrics such as accuracy, precision, and recall. …”
Get full text

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control by Rohit Bokade, Xiaoning Jin

“…To address these limitations, we introduce PyTSC, a robust and flexible simulation environment that facilitates the training and evaluation of MARL algorithms for TSC. …”
Get full text

Research on Management Model Based on Deep Learning by Yuting Zhao

“…Absolute mean relative error (AMRE) and variance (R2) are used for evaluation in term of accuracy. The training system is used with three available datasets from big company, health issue, and industry. …”
Get full text

Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah by Evita Fitri

“…Evaluation for all the method applying Cross-Validation. …”
Get full text

Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism. by Yutong Liu, Shile Zhang

“…The research uses the Conference on Natural Language Learning (CoNLL)-2014 dataset as an experimental benchmark, which contains a rich collection of grammatical error samples and is a standard resource in linguistic research. During model training, the Adam optimization algorithm is employed, and the model's performance is enhanced by introducing a customized dependency self-attention mechanism for parameter optimization. …”
Get full text

Machine Learning-Based Seismic Response Prediction for Nuclear Power Plant Structures Considering Aging Deterioration by Hyunsu Kim, Soyeon Lee, Junsu Jang, Sihyeon An

“…A total of 500 artificial ground motions were created for time history analyses, and the analytical results were utilized to establish a database for training and testing ML models. Six ML algorithms, commonly employed in the structural engineering domain, were used to construct the seismic response prediction model. …”
Get full text

Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

“…The dataset was divided into training and testing subsets. Known precipitation types in the training set allowed us to fit classifying algorithms. …”
Get full text

A fair and efficient two-step procedure for sugarcane properties prediction based on near-infrared spectra by Luiz Alexandre Peternelli, Andréa Carla Bastos Andrade, Cristina Silva Dias, Reinaldo Francisco Teófilo

“…In the first step, the data was split into training and testing sets via the Kernard-Stone algorithm to determine the appropriate pretreatments for the NIR spectra. …”
Get full text

Design and implementation of quantum hippo inspired convolutional neural networks using parametric quantum circuits for an efficient lung cancer classification by S. Radhika, G. Sharada

“…However, training classical learning frameworks consumes a huge computational resources which leads to complexity and low diagnostic performance. …”
Get full text

Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems by Edson Ramiro Lucas Filho, George Savva, Lun Yang, Kebo Fu, Jianqiang Shen, Herodotos Herodotou

“…An extensive evaluation is performed with production traces provided by Huawei Technologies, showing that the models are practical, with low memory consumption (<1.3 MB) and low training delay (<1.8 ms per training instance), and can make accurate predictions online (0.98 F1 score and 0.07 MAE on average).…”
Get full text