Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…Decisions at the early optimization stage are critical for the success of the drug candidate but are constrained due to the limited knowledge of the antibody and target pharmacology. In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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Hybrid Time Series Methods and Machine Learning for Seismic Analysis and Volcano Eruption Predict by Fridy Mandita, Ahmad Ashari, Moh. Edi Wibowo, Wiwit Suryanto

“…In response to this, the study presents a novel hybrid time series approach, integrated with machine learning techniques, to enhance the identification and classification of seismic events associated with volcanic eruptions. …”
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Postprocessing East African Rainfall Forecasts Using a Generative Machine Learning Model by Bobby Antonio, Andrew T. T. McRae, David MacLeod, Fenwick C. Cooper, John Marsham, Laurence Aitchison, Tim N. Palmer, Peter A. G. Watson

“…Improved forecasts could reduce the effects of extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning (ML)‐based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). …”
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Machine Learning Inspired Design of Complex-Shaped GaN Subwavelength Grating Reflectors by Onoriode N. Ogidi-Ekoko, Wen Liang, Haotian Xue, Nelson Tansu

“…We present the machine learning inspired design of two types of GaN zero contrast subwavelength gratings (SWGs): a polynomial-shaped grating and, for reference, a conventional rectangular grating, using the differential evolution algorithm to optimize the designs for high broadband reflectivity and separately for large fabrication tolerance. …”
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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus by Marco Tjakra, Kristína Lidayová, Christophe Avenel, Christel A.S. Bergström, Shakhawath Hossain

“…Abstract Biosimilar artificial mucus models that mimic native mucus facilitate efficient, lab-based drug diffusion studies, addressing the costly and challenging preclinical phase of drug development, especially for nano- and micro-scale particle-based colonic drug delivery. This study presents a machine-learning-driven framework that integrates microrheological features into diffusional fingerprinting to characterize nano- and micro-scale particle diffusion patterns in mucus and assess the effect of mucus microrheology on such movements. …”
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Optimization of deep learning architecture based on multi-path convolutional neural network algorithm by Chuan Zhou, Yan Liu, Xinghan An, Xiyao Xu, Hao Wang

“…Therefore, the study provides a critical reference for the optimization and practical application of MSCNN, contributing to the application research of deep learning in complex tasks.…”
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FedDB: A Federated Learning Approach Using DBSCAN for DDoS Attack Detection by Yi-Chen Lee, Wei-Che Chien, Yao-Chung Chang

“…Traditional machine learning approaches raise privacy concerns when handling sensitive data. …”
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Development of metacognitive skills of university students as a resource for understanding texts in the learning process by T.V. Borzova, E.N. Kameneva-Liubavskaia

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LMD, e- learning et évaluation par e-portfolio : mythe ou réalité ? by Fatiha FERHANI

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Selecting Observationally Constrained Global Climate Model Ensembles Using Autoencoders and Transfer Learning by Chibuike Chiedozie Ibebuchi, Oluwaferanmi Akinyemi, Itohan‐Osa Abu

“…Accurately representing these modes in Global Climate Models (GCMs) is crucial for assessing model performance and reducing uncertainty in future climate projections. In this study, we present a novel approach utilizing autoencoder neural networks (AEs) combined with transfer learning to evaluate the representation of monthly sea level pressure (SLP) modes over North America across five GCMs: the Geophysical Fluid Dynamics Laboratory Climate Model (GFDL‐CM4), Centro Euro‐Mediterraneo sui Cambiamenti Climatici Climate Model (CMCC‐CM2‐SR5), Canadian Earth System Model Version 5 (CanESM5), Institut Pierre‐Simon Laplace Climate Model (IPSL‐CM6A‐LR), and Hadley Center Global Environment Model Version 3 (HadGEM3‐GC31‐LL). …”
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VSRDiff: Learning Inter-Frame Temporal Coherence in Diffusion Model for Video Super-Resolution by Linlin Liu, Lele Niu, Jun Tang, Yong Ding

“…However, the inherent randomness of diffusion models presents significant challenges for controlling content. …”
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Activity recognition in motor-manual cross-cutting operations by machine learning on multimodal data by Stelian Alexandru Borz, Tomi Kaakkurivaara, Gabriel Osei Forkuo, Nopparat Kaakkurivaara

“…Advancements in machine learning and innovative data loggers present opportunities to reconsider and enhance these methods. …”
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hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction by Giulia Cisotto, Giulia Cisotto, Alberto Zancanaro, Italo F. Zoppis, Sara L. Manzoni

“…Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. …”
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Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms by Zhichao Wang, Long Cheng, Guanghui Li, Huiyan Cheng

“…Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning–derived immune-related biomarkers. …”
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Association between school learning models and psychological and social health visits to the emergency room by Kit Knier, Aliza Weinman, Aidan Mullan, Meghan Cain, Sara Hevesi, Venkatesh R. Bellamkonda

“…Methods We analyzed presentations to one emergency department in a mid‐sized Midwestern city. …”
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Classifying and Detecting Live Insects with Computationally Effective Deep Learning Object Detection Models by Arumuga Arun Rajeswaran, Karthik Katara, Yoganand Selvaraj, Ranjithkumar Sundarasamy

“…A wide range of applications in computer vision has proven that deep learning approaches are highly effective and popular. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…The client selection problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. …”
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E-learning in elementary schools: educational system change during COVID-19 pandemic by S. Rafiqa, E. Boeriswati, H. Usman

“…Future studies could be generalised by these research findings to other populations in different learning contexts, and the findings presented in thisarticle should be further explored. …”
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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

“…Hybrid models combining hydrological models with machine learning improve discharge simulation and offer better interpretability than standalone machine learning models. …”
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#GAMEADDICTED: A Machine Learning Framework for Digital Game Addiction Detection and Early Intervention by Esra Kahya Ozyirmidokuz, Bekir Asim Celik, Eduard Alexandru Stoica

“…The study employed machine learning models to process unstructured data collected from online game platforms between May 1, 2023, and June 1, 2023. …”
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