Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma. by Caiyun Yao, Maotong Hu, Lingxia Zhou, Hui Chen, Yang Cao

“…The two pathomics models were constructed to predict CLCA1 expression in COAD based on pathological image features using the random forest (RF) and XGBoost machine learning algorithms. The RF pathomics model demonstrated superior predictive performance, achieving area under the curve (AUC) values of 0.846 and 0.776 in the training and validation cohorts, respectively, and was selected for further analysis. …”
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Aberrant gene expression prediction across human tissues by Florian R. Hölzlwimmer, Jonas Lindner, Georgios Tsitsiridis, Nils Wagner, Francesco Paolo Casale, Vicente A. Yépez, Julien Gagneur

“…Abstract Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. …”
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X-ray modalities in the era of artificial intelligence: overview of self-supervised learning approach by Ivan Martinović, Shitong Mao, Mehdy Dousty, Wuqi Li, Milena Đukanović, Errol Colak, Ervin Sejdić

“…Self-supervised learning enables the creation of algorithms that outperform supervised pre-training methods in numerous computer vision tasks. …”
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A Survey on the Deep Learning-Based Mismatch Removal: Principles and Methods by Shiyu Chen, Cailong Deng, Yong Zhang, Yong Wang, Qixin Zhang, Zhimin Zhou

“…Due to the inherent limitations of matching algorithms and the complexities associated with image contents, mismatches are inevitable and can have detrimental effects on downstream tasks in computer vision and remote sensing. …”
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Enhancing highway security and wildlife safety: Mitigating wildlife–vehicle collisions with deep learning and drone technology by Nandutu Irene, Atemkeng Marcellin, Okouma Patrice, Mgqatsa Nokubonga, Fendji Jean Louis Ebongue Kedieng, Tchakounte Franklin

“…Here, we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. …”
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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. …”
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ML-Based Control Strategy for PHEV Under Predictive Vehicle Usage Behaviour by Aleksandr Doikin, Aleksandr Korsunovs, Felician Campean, Oscar García-Afonso, Enrico Agostinelli

“…This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. In particular, the RusBoosted ensemble classifier performed consistently well across the heterogeneous dataset (volume of data for training and variable imbalance in the datasets, reflecting the natural variability in the vehicle usage profiles), providing sufficiently accurate predictions for the proposed EMS strategy. …”
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A deep learning software tool for automated sleep staging in rats via single channel EEG by Andrew Smith, Snezana Milosavljevic, Courtney J. Wright, Charlie A. Grant, Ana Pocivavsek, Homayoun Valafar

“…Abstract Poor quality and poor duration of sleep have been associated with cognitive decline, diseases, and disorders. …”
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Unsupervised Class Generation to Expand Semantic Segmentation Datasets by Javier Montalvo, Álvaro García-Martín, Pablo Carballeira, Juan C. SanMiguel

“…Our approach aims to improve the performance of unsupervised domain adaptation methods by introducing novel samples into the training data without modifications to the underlying algorithms. …”
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Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting? by Nouf Alturayeif, Jameleddine Hassine

“…We conclude that existing ML-based approaches can effectively mitigate the challenges associated with limited data availability.…”
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Navigating ethical minefields: a multi-stakeholder approach to assessing interconnected risks in generative AI using grey DEMATEL by Sridhar Jonnala, Sridhar Jonnala, Nisha Mary Thomas, Nisha Mary Thomas, Sarthak Mishra

“…Through a comprehensive literature review and expert validation across three key stakeholder groups (AI developers, end users, and policymakers), we identified and analyzed 14 critical ethical challenges across the input, training, and output modules, including both traditional and emerging risks, such as deepfakes, intellectual property rights, data transparency, and algorithmic bias. …”
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Development of Political Statistics: Conceptual Foundations for the Formation of a System of Indexes by L. Yu. Arkhangelskaya, V. N. Salin

“…The conceptual basis for structuring sections of the PS, identifying a specific subject, method and research objectives, defining a system of indexes within each section of the PS and the relationships between these modules are presented in the form of an information and logical diagram of subsystems of indexes with the definition of algorithms for their calculation and data sources for their construction.   …”
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The Role of Artificial Intelligence in Sports Analytics: A Systematic Review and Meta-Analysis of Performance Trends by Przemysław Pietraszewski, Artur Terbalyan, Robert Roczniok, Adam Maszczyk, Kajetan Ornowski, Daria Manilewska, Szymon Kuliś, Adam Zając, Artur Gołaś

“…Sixteen peer-reviewed studies spanning 13 distinct sports disciplines were included, employing a variety of AI techniques—from classical machine learning algorithms to advanced deep learning and computer vision models. …”
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Postauthorization safety study of the DPP-4 inhibitor saxagliptin: a large-scale multinational family of cohort studies of five outcomes by Qing Liu, Sean Hennessy, Qufei Wu, K Rajender Reddy, Arlene M Gallagher, Kevin Haynes, David J Margolis, Brian L Strom, Vincent Lo Re, M Elle Saine, Craig W Newcomb, Jason A Roy, Serena Cardillo, Stephen E Kimmel, Peter P Reese, Andrea J Apter, Harshvinder Bhullar, Daina B Esposito, Dean M Carbonari

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Neural network-based snow depth retrieval from AMSR-2 brightness temperatures using ICESat-2 measurement as ground truth by Sunny Sun-Mack, Yongxiang Hu, Xiaomei Lu, Yan Chen, Ali Omar

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Detection of Parabolic Antennas in Satellite Inverse Synthetic Aperture Radar Images Using Component Prior and Improved-YOLOv8 Network in Terahertz Regime by Liuxiao Yang, Hongqiang Wang, Yang Zeng, Wei Liu, Ruijun Wang, Bin Deng

“…In order to tackle the challenges associated with component identification in satellite ISAR imagery, this study employs the Improved-YOLOv8 model, which was developed by incorporating the YOLOv8 algorithm, an adaptive detection head known as the Dynamic head (Dyhead) that utilizes an attention mechanism, and a regression box loss function called Wise Intersection over Union (WIoU), which addresses the issue of varying sample difficulty. …”
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Comment on “Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation” by Hinpetch Daungsupawong, Viroj Wiwanitkit

“…By incorporating advanced imaging techniques and machine learning algorithms, researchers can improve the accuracy and efficiency of diagnostic processes. …”
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Lightweight CNN-based seizure classification via leveraging chimera states in iEEG recordings by Fatemeh Azad, Saeed Bagheri Shouraki, Soheila Nazari, Mansun Chan

“…Epileptic seizures, a chronic brain disorder characterized by groups of neurons sending incorrect signals, result in recurring seizures associated with chimera states—the simultaneous presence of synchronized and desynchronized neural activity. …”
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Texture Analysis of T2-Weighted Images as Reliable Biomarker of Chronic Kidney Disease Microstructural State by Marcin Majos, Artur Klepaczko, Katarzyna Szychowska, Ludomir Stefanczyk, Ilona Kurnatowska

“…The aim of this study is to create an algorithm that can categorize CKD patients into active and non-active phases on the basis of MRI texture analysis and compare the results with histopathological examinations. …”
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