This is our latest selection of worldwide publications and patents in english on Neural Networks, between many scientific online journals, classified and focused on neural network, artificial neuron, epoch, neural architecture, machine learning, deep learning and support vector machine.
Large-Scale Monitoring of Potatoes Late Blight Using Multi-Source Time-Series Data and Google Earth Engine
Published on 2025-03-11 by Zelong Chi, Hong Chen, Sheng Chang, Zhao-Liang Li, Lingling Ma, Tongle Hu, Kaipeng Xu, Zhenjie Zhao @MDPI
Abstract: Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on a large scale. The method combines unsupervised and supervised machine learning algorithms. To improve the monitoring accuracy of the PLB regression model, the study used the K-Means algorithm in conjunction with morphological operations to identify potato growth areas. Input data consisted of monthly NDVI from Sentinel-2 an[...]
Our summary: Methodology to improve potato late blight identification on a large scale using multi-source time-series data and machine learning algorithms. Validation results show high accuracy with an F1 score of 0.95 and a validation RMSE of 20.50. The study presents a large-scale map of PLB distribution in China with an accuracy of 10 m.
monitoring, potato late blight, machine learning, Google Earth Engine
Publication
Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting
Published on 2025-03-11 by Yuyang Zhang, Lei Cui, Wenqiang Yan @MDPI
Abstract: Electricity demand is driven by a diverse set of factors, including fluctuations in business cycles, interregional dynamics, and the effects of climate change. Accurately quantifying the impact of these factors remains challenging, as existing methods often fail to address the complexities inherent in these influences. This study introduces a time series forecasting model based on Kolmogorov–Arnold Networks (KANs), integrated with three advanced neural network architectures, Tempor[...]
Our summary: Integrating Kolmogorov-Arnold Networks with advanced neural network architectures improves accuracy, robustness, and adaptability in electricity demand forecasting. The study introduces a model based on KANs integrated with TCN, BiLSTM, and Transformer to address the complexities in forecasting UK electricity demand, showcasing the potential of KANs in predictive analytics.
Kolmogorov-Arnold Networks, Time Series Prediction Framework, Electricity Demand Forecasting, Neural Network Architectures
Publication
Named Entity Recognition in Online Medical Consultation Using Deep Learning
Published on 2025-03-11 by Ze Hu, Wenjun Li, Hongyu Yang @MDPI
Abstract: Named entity recognition in online medical consultation aims to address the challenge of identifying various types of medical entities within complex and unstructured social text in the context of online medical consultations. This can provide important data support for constructing more powerful online medical consultation knowledge graphs and improving virtual intelligent health assistants. A dataset of 26 medical entity types for named entity recognition for online medical consultations is fi[...]
Our summary: Named entity recognition in online medical consultation addresses the challenge of identifying medical entities in unstructured text, constructing knowledge graphs, and improving virtual health assistants. The proposed deep learning approach outperforms the state-of-the-art method in identifying 26 medical entity types with an average F1 score of 85.47%, supporting real-time intelligent medical decision-making.
Named Entity Recognition, Online Medical Consultation, Deep Learning, Knowledge Graphs
Publication
Detection and Quantification of Vegetation Losses with Sentinel-2 Images Using Bi-Temporal Analysis of Spectral Indices and Transferable Random Forest Model
Published on 2025-03-11 by Alicja Rynkiewicz, Agata Ho?ci?o, Linda Aune-Lundberg, Anne B. Nilsen, Aneta Lewandowska @MDPI
Abstract: The precise spatially explicit data on land cover and land use changes is one of the essential variables for enhancing the quantification of greenhouse gas emissions and removals, which is relevant for meeting the goal of the European economy and society to become climate-neutral by 2050. The accuracy of the machine learning models trained on remote-sensed data suffers from a lack of reliable training datasets and they are often site-specific. Therefore, in this study, we proposed a method that [...]
Our summary: Detection and quantification of vegetation losses using bi-temporal analysis of spectral indices and transferable random forest model, improving accuracy and efficiency of greenhouse gas emissions quantification for climate goals.
Sentinel-2 Images, Bi-Temporal Analysis, Spectral Indices, Random Forest Model
Publication
Takagi–Sugeno–Kang Fuzzy Neural Network for Nonlinear Chaotic Systems and Its Utilization in Secure Medical Image Encryption
Published on 2025-03-11 by Duc Hung Pham, Mai The Vu @MDPI
Abstract: This study introduces a novel control framework based on the Takagi–Sugeno–Kang wavelet fuzzy neural network, integrating brain imitated network and cerebellar network. The proposed controller demonstrates high robustness, making it an excellent candidate for handling intricate nonlinear dynamics, effectively mapping input–output relationships and efficiently learning from data. To enhance its performance, the controller’s parameters are fi[...]
Our summary: Novel control framework based on Takagi-Sugeno-Kang wavelet fuzzy neural network, high robustness, parameters fine-tuned using Lyapunov stability theory, superior learning capabilities and outstanding performance metrics, synchronization technique applied to secure transmission of medical images, experimental results confirm effectiveness and reliability.
Takagi-Sugeno-Kang, Fuzzy Neural Network, Secure Medical Image Encryption, Nonlinear Chaotic Systems
Publication
Predictive Modelling of Alkali-Slag Cemented Tailings Backfill Using a Novel Machine Learning Approach
Published on 2025-03-11 by Haotian Pang, Wenyue Qi, Hongqi Song, Haowei Pang, Xiaotian Liu, Junzhi Chen, Zhiwei Chen @MDPI
Abstract: This study utilizes machine learning (ML) techniques to predict the performance of slag-based cemented tailings backfill (CTB) activated by soda residue (SR) and calcium carbide slag (CS). An experimental database consisting of 240 test results is utilized to thoroughly evaluate the accuracy of seven ML techniques in predicting the properties of filling materials. These techniques include support vector machine (SVM), random forest (RF), backpropagation (BP), genetic algorithm optimization of BP[...]
Our summary: Utilizing ML techniques to predict CTB performance, evaluating accuracy of 7 ML techniques, developing dynamic growth model
Predictive Modelling, Machine Learning, Alkali-Slag Cemented Tailings, Backfill
Publication
User interface for machine learning model restructuring
Patent published on the 2025-03-06 in WO under Ref WO2025048813 by STEM IA INC [US] (Goldstein Adam Julian Cisneros [us])
Abstract: Described is a system for a user interface enabling restructuring of a machine learning model by accessing a machine learning model, generating a user interface depicting a graphical representation of the machine learning model, presenting the user interface to a user. The system receives a selection from the user of the user-selectable interface element, and in response to the selection from the user of the user-selectable interface element: restructures the internal architecture of the machine[...]
Our summary: System enabling user interface for restructuring machine learning model, generating graphical representation, and presenting to user.
User interface, machine learning model, restructuring, graphical representation
Patent
Changing node clusters in artificial intelligence models
Patent published on the 2025-03-06 in WO under Ref WO2025048809 by STEM AI INC [US] (Goldstein Adam Julian Cisneros [us])
Abstract: Described is a system for dynamic changing a number of groups of nodes in a machine learning model by accessing an artificial intelligence model, the artificial intelligence model being configured to both modify the network of the artificial intelligence model and perform an inference in response to receiving input data, receiving, by the artificial intelligence model, the input data, and processing the received input data using the artificial intelligence model. The processing of the input data[...]
Our summary: System for dynamic changing node clusters in artificial intelligence models, modifying network based on cluster values, generating inference output.
node clusters, artificial intelligence models, machine learning, dynamic changing
Patent
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