ANALYSIS OF THE DEVELOPMENT OF VIRTUAL FAN COMMUNITIES MEDIA SPACES by A. N. Timokhovich, O. I. Nikuradze

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Advanced KPI framework for IVF pregnancy prediction models in IVF protocols by Sergei Sergeev, Iuliia Diakova

“…Abstract The utilization of neural networks in assisted reproductive technology is essential due to their ability to process complex and multidimensional data inherent in IVF procedures, offering opportunities for clinical outcome prediction, personalized treatment implementation, and overall advancement in fertility treatment. …”
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A Hybrid MIL Approach Leveraging Convolution and State-Space Model for Whole-Slide Image Cancer Subtyping by Dehui Bi, Yuqi Zhang

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AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation by Marta Narigina, Agris Vindecs, Dušanka Bošković, Yuri Merkuryev, Andrejs Romanovs

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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. …”
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Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques by B.P. Pradeep kumar, Pramod K.B. Rangaiah, Robin Augustine

“…The approach focuses on noise reduction using median filtering and optic disc segmentation utilizing the U-Net and U-Net+ architectures. Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. …”
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Recent advances in deep learning for protein-protein interaction: a review by Jiafu Cui, Siqi Yang, Litai Yi, Qilemuge Xi, Dezhi Yang, Yongchun Zuo

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The results of using machine learning technology for intraocular lenses optical power calculation by A.A. Arzamastsev, O.L. Fabrikantov, S.V. Belikov, N.A. Zenkova

“…Purpose. To evaluate possibility of using the mathematical models obtained as a result of deep learning of artificial neural networks (ANN-models) to predict the optical power of modern intraocular lenses (IOL). …”
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A Comparative Study of a Deep Reinforcement Learning Solution and Alternative Deep Learning Models for Wildfire Prediction by Cristian Vidal-Silva, Roberto Pizarro, Miguel Castillo-Soto, Ben Ingram, Claudia de la Fuente, Vannessa Duarte, Claudia Sangüesa, Alfredo Ibañez

“…This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. The models were trained and evaluated using historical data from Chile (2000–2023), including wildfire occurrences, meteorological variables, topography, and vegetation indices. …”
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Efficient Segmentation Using Attention-Fusion Modules With Dense Predictions by Serdar Erisen

“…Dense predictions are deployed to augment the efficacy of dense-attention-boosting modules and dense-attention-fusion networks, enabling the extraction of global context feature maps in the encoder. …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

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CapsuleThermNet: A CNN-CapsuleNet Architecture for Early Detection of Suicide Risk in Depressed Patients Using Thermal Facial Imaging by Eddy Muntina Dharma, Harjanto Prabowo, Agung Trisetyarso, Tjhin Wiguna

“…CapsuleThermNet is designed by integrating CNN with Capsule Networks (CapsNet). In this architecture, CNNs are responsible for extracting spatial features from thermal facial images, while CapsNet captures hierarchical relationships and performs classification tasks. …”
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HLSK-CASMamba: hybrid large selective kernel and convolutional additive self-attention mamba for hyperspectral image classification by Xiaoqing Wan, Yupeng He, Feng Chen, Ziqi Sun, Dongtao Mo

“…CNNs, while effective, often struggle to adequately capture intricate semantic features, and increasing network depth leads to significantly higher computational costs. …”
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Nipah virus: a summary for clinicians by Corri B. Levine, Lauren M. Sauer, Susan L. F. McLellan, Jared D. Evans, On behalf of the State of the Science Working Group of the National Ebola Training and Education Center’s (NETEC’s) Special Pathogens Research Network (SPRN)

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Net2Brain: a toolbox to compare artificial vision models with human brain responses by Domenic Bersch, Domenic Bersch, Martina G. Vilas, Martina G. Vilas, Sari Saba-Sadiya, Timothy Schaumlöffel, Timothy Schaumlöffel, Kshitij Dwivedi, Christina Sartzetaki, Radoslaw M. Cichy, Radoslaw M. Cichy, Radoslaw M. Cichy, Gemma Roig, Gemma Roig

“…To address these challenges, we present Net2Brain, a Python-based toolbox that provides an end-to-end pipeline for incorporating DNNs into neuroscience research, encompassing dataset download, a large selection of models, feature extraction, evaluation, and visualization. …”
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Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears by Jhonathan Sora-Cardenas, Wendy M. Fong-Amaris, Cesar A. Salazar-Centeno, Alejandro Castañeda, Oscar D. Martínez-Bernal, Daniel R. Suárez, Carol Martínez

“…The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. …”
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Transition resistance influence of the upper structure track ballast on the rail-to-earth potential at AC railway sections by A. A. Krylov, I. A. Rebrov, A. V. Rudashevskaya, R. A. Rudashevskiy, E. D. Khar'kovskaya

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Impact of Normalization Techniques on Synthetic Load Profile Generation Using Deep Generative Models by Luis H. T. Bandoria, Walquiria N. Silva, Madson C. De Almeida

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WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

“…In our efficient and novel framework, we integrate an oversampling strategy that uses Generative Adversarial Networks (GANs) to overcome the difficulties introduced by imbalanced datasets, and we use the Random Forest (RF) importance algorithm to select a subset of features that best represent the dataset to reduce the dimensionality of a training dataset. …”
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Development of AI-Enhanced Smile Design Software for Ultra-Customized Aesthetic Outcomes by Lamia Mohsin, Najd Alenezi, Yara Rashdan, Aldana Hassan, Muneera Alenezi, Mohammad Khursheed Alam, Nor Farid Bin Mohd Noor, Fatema Akhter

“…Materials and Methods: A prototype AI-enhanced smile design software was developed using a combination of convolutional neural networks for facial analysis and generative adversarial networks for creating customized smile designs. …”
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