Pembelajaran IPA Berbasis Pendidikan Karakter dengan Huruf Braille untuk Siswa Difable Netra by Yosi Yulizah

“…Characteristics of science studies, C. Science learning, D. Science learning objectives, E. Children with special needs, F. …”
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DICTION: DynamIC robusT whIte bOx Watermarking Scheme for Deep Neural Networks by Reda Bellafqira, Gouenou Coatrieux

“…Deep neural network (DNN) watermarking is a suitable method for protecting the ownership of deep learning (DL) models. It secretly embeds an identifier within the model, which can be retrieved by the owner to prove ownership. …”
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Implementation framework for AI deployment at scale in healthcare systems by Hassan Sami Adnan, Amitis Shidani, Lei Clifton, Clare R. Bankhead, Rafael Perera-Salazar

“…This framework targets health systems that integrate multiple machine learning (ML) models with various modalities. This design thinking approach promotes clinical utility beyond model prediction, combining privacy preservation with clinical parameters to establish a reward function for reinforcement learning, ranking competing models. …”
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Adaptive weight optimization with large pretraining for pest detection by Kejian Yu, Wenwen Xu, Fuqin Geng, Yunzhi Wu

“…Accurate pest recognition and localization are achieved through an adaptive loss function, which optimizes the model’s performance across multiple tasks. …”
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Fetal Health Prediction From Cardiotocography Recordings Using Kolmogorov–Arnold Networks by W. K. Wong, Filbert H. Juwono, Catur Apriono, Ismi Rosyiana Fitri

“…KANs have recently been proposed asa powerful competitor to the conventional transfer function approach in feedforward neural networks. The proposed method leverages the powerful capabilities of KANs to model the intricate relationships within the CTG data, leading to improved classification accuracy. …”
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Complete Object-Compositional Neural Implicit Surfaces With 3D Pseudo Supervision by Wongyeom Kim, Jisun Park, Kyungeun Cho

“…Neural implicit surface reconstruction has recently emerged as a prominent paradigm in multi-view 3D reconstruction using deep learning. In contrast to traditional multi-view stereo methods, signed distance function (SDF)-based approaches leverage neural networks to effectively represent 3D scenes. …”
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Breeding evaluations in aquaculture using neural networks by Christos Palaiokostas

“…In addition, the effect of various hyperparameters of neural networks, such as the number of hidden layers, neurons per layer, activation function, learning rate, batch size, and regularisation techniques like dropout, were examined. …”
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Tropical Cyclone Surface Winds From Aircraft With a Neural Network by Alexander J. DesRosiers, Michael M. Bell, Jennifer C. DeHart, Jonathan L. Vigh, Christopher M. Rozoff, Eric A. Hendricks

“…The model is capable of learning physical relationships that are consistent with theoretical understanding of the TC boundary layer. …”
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Brain network analysis of benign childhood epilepsy with centrotemporal spikes: With versus without interictal spikes by Zhixing Hong, Dinghan Hu, Runze Zheng, Tiejia Jiang, Feng Gao, Jiajia Fang, Jiuwen Cao

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Cenobamate reduces epileptiform activity in the ex vivo F98 rat glioma model by Ferdinand Forberger, Fabiana Santana Kragelund, Katrin Porath, Rüdiger Köhling, Rüdiger Köhling, Timo Kirschstein, Timo Kirschstein, Falko Lange, Falko Lange

“…Therefore, for the first time, we evaluated the efficacy of cenobamate, a novel anticonvulsant shown to inhibit persistent sodium currents and modulate GABAA receptor function, in a preclinical model of glioma-associated epilepsy. …”
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A weighted difference loss approach for enhancing multi-label classification by Qiong Hu, Masrah Azrifah Azmi Murad, Azreen Bin Azman, Nurul Amelina Nasharuddin

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Research on Small-Target Detection of Flax Pests and Diseases in Natural Environment by Integrating Similarity-Aware Activation Module and Bidirectional Feature Pyramid Network Mod... by Manxi Zhong, Yue Li, Yuhong Gao

“…Additionally, this paper adopts the EIOU loss function to further optimize the model’s bounding box regression, reducing the distortion of bounding boxes caused by high sample variability. …”
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Developing a molecular diagnostic model for heatstroke-induced coagulopathy: a proteomics and metabolomics approach by Qingbo Zeng, Qingwei Lin, Longping He, Lincui Zhong, Ye Zhou, Xingping Deng, Nianqing Zhang, Qing Song, Qing Song, Jingchun Song, Jingchun Song

“…Multivariate and univariate statistical analyses identified differentially expressed proteins (DEPs) and metabolites (DEMs). Functional annotation and pathway enrichment analyses were performed using the GO and KEGG databases, and machine learning models were developed using candidate proteins selected by LASSO and Boruta algorithms to diagnose HSIC. …”
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A CASE STUDY OF A STUDENT PROJECT: USING ARTIFICIAL INTEL LIGENCE TO DEVELOP AN INDIVIDUAL PROGRAM TO STUDY ENGLISH by I.P. Ryabkova

“…ChatGPT successfully handled all the tasks set when testing these functions, unlike the Yandex system, which is based on the Russian language and generally performed less successfully or did not perform well in implementing the tested functions. …”
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Uczenie się dorosłych w hybrydowym modelu pracy by Marcin Rojek

“…This article presents part of the results of a study conducted in 2022/2023 on informal adult learning in a hybrid work model. This model frames the professional functioning of an increasing number of workers and is the source of their work experience. …”
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The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market by Vasileios Laitsos, Georgios Vontzos, Paschalis Paraschoudis, Eleftherios Tsampasis, Dimitrios Bargiotas, Lefteri H. Tsoukalas

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Using Machine Learning for the Precise Experimental Modeling of Catastrophe Phenomena: Taking the Establishment of an Experimental Mathematical Model of a Cusp-Type Catastrophe for... by Shaonan Zhang, Liangshan Xiong

“…It establishes the quantitative relationship between the actual catastrophe mathematical model and the canonical catastrophe mathematical model by assuming that the actual potential function is equal to the canonical potential function, and it uses a machine learning model to represent the diffeomorphism that can realize the error-free transformation of the two models. …”
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Investigating the Effects of Spectroscopic Method in Estimating Soluble Solid Content Values and Firmness of Cherries from an Environmental Point of View: Prediction of Environment... by Shirzad Naim, Shahgholi Gholamhossein, Ardabili Sina, Szymanek Mariusz

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RETRACTED ARTICLE: Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numeric... by Muhammad Naeem Aslam, Nadeem Shaukat, Arshad Riaz, Ilyas Khan, Shafiullah Niazai

“…The minimum fitness function value is 1.32 × 10−8 computed across these multiple runs. …”
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G3DC: A Gene-Graph-Guided Selective Deep Clustering Method for Single Cell RNA-seq Data by Shuqing He, Jicong Fan, Tianwei Yu

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