Unsupervised Boosted Fusion Network for Single Low-Light Image Enhancement by Jianfeng Zhang, Hengxuan Li, Zhanqiang Huo

“…The visual evaluation and quantitative experiments demonstrate that our proposed method outperforms the current state-of-the-art unsupervised low-light image enhancement methods. …”
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A Systematic Review of the Use of Deep Learning in Satellite Imagery for Agriculture by Brandon Victor, Aiden Nibali, Zhen He

“…So, this review also introduces a taxonomy of data input shapes and how they are interpreted in order to facilitate easier communication of algorithm types and enable quantitative analysis.…”
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MSRFSR: Multi-Stage Refining Face Super-Resolution With Iterative Collaboration Between Face Recovery and Landmark Estimation by Amir Hajian, Supavadee Aramvith

“…The results consistently demonstrate the superiority of our proposed Multi-Stage Refining Face Super-Resolution (MSRFSR) model over state-of-the-art methods through extensive quantitative and qualitative experiments on four datasets and both scales.…”
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Ischemic Stroke Lesion Segmentation on Multiparametric CT Perfusion Maps Using Deep Neural Network by Ankit Kandpal, Rakesh Kumar Gupta, Anup Singh

“…Automatic segmentation on CT images is challenging due to the poor contrast-to-noise ratio. Quantitative CT perfusion images improve the estimation of the perfusion deficit regions; however, they are limited by a poor signal-to-noise ratio. …”
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Modeling the train operation of the mainline locomotive by A. G. Silyuta, E. E. Kossov

“…To date, there is a large number of programs that simulate the train operation of the locomotive using the method of traction calculations. As input data in the program, information is entered about the track section, locomotive and train composition. …”
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MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling by Rahil Ashtari Mahini, Gerardo Casanola-Martin, Simone A. Ludwig, Bakhtiyor Rasulev

“…A python package was developed for this purpose, allowing users to compute 12 different mixture-descriptors to use as input for the generation of mixture-based Quantitative Structure-Activity/Property Relationship (mxb-QSAR/QSPR) machine learning models for predicting a range of chemical and physical properties across various complex systems.…”
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Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients by Su-Ying Guo, Xiu-Jun Gong

“…Comprehensive evaluations across various qualitative and quantitative metrics demonstrate its superior interpretability. …”
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Is the European Charter of Local Self-Government an Effective Instrument for the Protection of Local Autonomy in Poland? by Stanisław Zakroczymski

“…Design/methodology/approach: The research is based on the qualitative and quantitative empirical research of the case law and administrative practice with elements of doctrinal analysis. …”
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On a feedback of the share of avalanche alimentation with a glacier accumulation by V. V. Popovnin, Ya. Ye. Sergiyevskaya

“…Here, the conceptual basis of the required calculations is created, and the input information has been supplied for many years by the route snow-measuring profling performed in the spring close to the date of the maximum of seasonal snow reserves and accompanied by direct feld mapping. …”
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A method for benchmarking genetic screens reveals a predominant mitochondrial bias by Mahfuzur Rahman, Maximilian Billmann, Michael Costanzo, Michael Aregger, Amy H Y Tong, Katherine Chan, Henry N Ward, Kevin R Brown, Brenda J Andrews, Charles Boone, Jason Moffat, Chad L Myers

“…Abstract We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. …”
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Assessing green innovation efficiency and spatial characteristics of China’s high tech industry based on the three stage undesirable SBM model by Bo-Wen An, Pei-Yuan Xu, Long-Zhan Liu, Chun-Bo Li, Qiu-Ping Guo

“…The quantitative results show an increase in GIE from 0.350 to 0.566, with technological and environmental factors playing a significant role. …”
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Methodology for extracting narratives from social media big data by E. Yu. Petrov, A. Yu. Sarkisova, D. O. Dunaeva, A. S. Voronov, M. G. Myagkov

“…As a result of the study, a dataset was obtained, including 237 narratives.Conclusions and Relevance: the developed toolkit is universal: the methodology can be adapted to any relevant topic, requiring only adjustments to the input parameters of thematic modeling. The obtained dataset is planned to be introduced into scientific circulation as an up-to-date material for studying public opinion on vaccination in Russia. …”
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Development of an integrated ESG and climate risk assessment framework for semiconductors industries in Malaysia by Ola Y. Mohamed, Nabila F. Jamaludin

“…Despite the recent movement of manufacturers towards establishing their ESG data collection, a quantitative tool to compare their performance against standards is limited. …”
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A Simple Tool to Identify Representative Wind Sites for Air Pollution Modelling Applications by M. A. Elangasinghe, K. N. Dirks, N. Singhal, J. A. Salmond, I. Longley, V. I. Dirks

“…The simple SOSE model can therefore be used to complement regulatory meteorological guidelines by providing a quantitative assessment of wind site representativeness for air quality applications in complex urban environments.…”
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AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact. by Monica Golumbeanu, Olivier Briët, Clara Champagne, Jeanne Lemant, Munir Winkel, Barnabas Zogo, Maximilian Gerhards, Marianne Sinka, Nakul Chitnis, Melissa Penny, Emilie Pothin, Tom Smith

“…The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. …”
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MultiModal Emotional Recognition by Artificial Intelligence and its Application in Psychology by Seyed Sadegh Hosseini, Mohammad Reza Yamaghani

“…Methods: This research employs a correlation-based approach between emotions and input data, utilizing machine learning methods and regression analysis to predict a criterion variable based on multiple predictor variables (the emotional category as the criterion variable and the features, audio, image, and text variables as predictors). …”
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What Do Egypt’s New Urban Communities Need to Outperform? A Strategic Framework for Equitable Population Redistribution by Mohamed Kadry, Husam R Husain

“…Mixed-method analysis combined official statistics, spatial datasets and a questionnaire survey of fifty-four domain experts. Quantitative indicators show that, on average, NUCs have absorbed only 31 % of their planned residents; densities in five cities remain well below the UN-Habitat benchmark of 7,000 inh.…”
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SegAN for Recognition of Caries From 2D-Panoramic X-Ray Images by Parvathaneni Naga Srinivasu, Gorli L. Aruna Kumari, D. Jaya Kumari, Paolo Barsocchi, Akash Kumar Bhoi

“…Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. …”
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Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images. by Vahid Sadeghi, Alireza Mehridehnavi, Maryam Behdad, Alireza Vard, Mina Omrani, Mohsen Sharifi, Yasaman Sanahmadi, Niloufar Teyfouri

“…Applying the Bayes rule, the membership probability of every pixel of the input test image to each of the two classes is evaluated. …”
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Image dehazing based on double branch convolution and detail enhancement by ZHAI Fengwen, ZHU Yutong, JIN Jing

“…In the image dehazing model, we introduced the attention module composed of channel attention mechanism and pixel attention mechanism, which improves its feature extraction ability, suppresses the features that are not related to the current task and further reduces the color distortion and contrast in the image dehazing process. Then, we input the image dehazed with the image dehazing module into the detail enhancement module to further recover image details, so that the image is more similar to that in its real domain. …”
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