这是我们最新精选的有关神经网络的全球英文出版物和专利，涉及许多科学在线期刊，分类并侧重于神经网络、人工神经元、纪元、神经架构、机器学习、深度学习和支持向量机。
A study on consumer behavior pattern recognition in a low-carbon economy based on deep learning
Published on 2026-07-08 by @OXFORD
Abstract: AbstractThis paper proposes a novel low-carbon consumer behavior recognition method based on multilevel deep learning. By leveraging an Adaptive Temporal Convolutional Network (ATCN), a Mixed Prior Variational Autoencoder (MP-VAE), and a context-aware gated fusion mechanism, the model dynamically adapts to interindividual heterogeneity and temporal dynamics. The ATCN models multiscale behavior patterns, the MP-VAE captures heterogeneous latent motivational factors, and the fusion mechanism integ[...]
Our summary: This study introduces a method for recognizing consumer behavior in a low-carbon economy using multilevel deep learning techniques. It employs an Adaptive Temporal Convolutional Network and a Mixed Prior Variational Autoencoder to model behavior patterns and motivational factors. The approach enhances robustness through uncertainty-aware fusion and adaptive imputation, demonstrating effectiveness in real-world applications.
deep learning, consumer behavior, low-carbon economy, pattern recognition
Publication
Deep learning-based big data analysis and state sensing research on power grid
Published on 2026-07-03 by @OXFORD
Abstract: AbstractA multidimensional analysis framework for user behavior characteristics is constructed, integrating load curve shape features, influencing factors, and adjustability evaluation indicators within the context of power big data. Secondly, a two-stage clustering mechanism combining K-means and self-organizing map is designed, and a BP neural network is introduced to refine the clustering results, improving classification accuracy and model robustness. The results demonstrate that the method [...]
Our summary: A multidimensional analysis framework for user behavior characteristics in power big data is developed. A two-stage clustering mechanism combines K-means and self-organizing maps with BP neural networks to enhance classification accuracy. The method shows strong generalization ability and practical value in identifying power usage states and adapting to user behavior differences.
deep learning, big data, clustering, power grid
Publication
Behavioral imitation with artificial neural networks leads to personalized models of brain dynamics during videogame play
Published on 2026-07-02 by @MIT
Abstract: AbstractVideogames provide a promising framework to understand brain activity in a rich, engaging, and active environment, in contrast to mostly passive tasks currently dominating the field, such as image viewing. Analyzing videogames neuroimaging data is, however, challenging, and relies on time-intensive manual annotations of game events, based on somewhat arbitrary rules. Here, we introduce an innovative approach using Artificial Neural networks (ANN) and brain encoding techniques to generate[...]
Our summary: This study introduces a method using artificial neural networks to analyze brain activity during videogame play. It demonstrates that subject-specific imitation models can better predict individual brain dynamics compared to traditional approaches. The framework enhances brain encoding in complex, interactive environments, offering new avenues for cognitive neuroscience research.
Neural networks, brain dynamics, imitation learning, videogame analysis
Publication
Vehicle damage validation using symmetry-based deep learning
Patent published on the 2026-07-02 in US under Ref US20260188012 by UVEYE LTD [IL] (Navot Shira [il], Moskovitz Yonatan [il], Orr Itai [il], Gazit Shirel [gb], Morgenstein Barr Shaked [il], Hever Amir [us], Orfaig Roy [il])
Abstract: A computer-implemented method and a system for temporal damage consistency validation, comprising receiving, at one or more processors, a current vehicle scan containing image data and damage detections for a vehicle, retrieving historical damage data for the vehicle from previous scans;, projecting geometric positions of the previously detected damage regions onto corresponding locations in the current vehicle scan using a geometric transformation that accounts for vehicle positioning differenc[...]
Our summary: This method validates vehicle damage by comparing current scans with historical data. It uses deep learning to match projected damage positions with current detections. The system classifies damage states based on similarity scores from feature vectors.
Deep learning, vehicle damage, image data, geometric transformation
Patent
Machine learning model security at a processor
Patent published on the 2026-07-02 in US under Ref US20260187254 by ADVANCED MICRO DEVICES INC [US] (Nadarajah Kathirkamanathan [ca], Blinzer Paul [us], Sanghai Kaushal Amolak [us], Subramaniam Akila [us])
Abstract: A processor protects a machine learning model (MLM) from unauthorized access. The processor employs a neural processing unit (NPU) to execute the MLM and implements decryption and encryption processes to decrypt the MLM and re-encrypt the MLM at different points along MLM storage and execution paths. Furthermore, the processor executes the encryption and decryption processes at different processing units and processing engines, thereby reducing the ability of malicious software to access the MLM[...]
Our summary: A processor secures a machine learning model (MLM) using a neural processing unit (NPU). It employs encryption and decryption processes during MLM storage and execution. The processor also protects NPU buffers from unauthorized access to enhance security.
machine learning, model security, neural processing unit, encryption
Patent
Apparatuses, systems, and computer program products for transformation and mass exportation of enterprise insights data sets via an enterprise insight
Patent published on the 2026-07-02 in US under Ref US20260187570 by ATLASSIAN PTY LTD [AU] (Jadav Sanjaykumar [us], Hartsock Melissa [us], Johnson Nicholas Xavier [us], Kelly Joshua [us], Nguyen Michael Minh [us], Guimbellot David [us], Palepu Soundarya [us])
Abstract: An enterprise insights data set transformation apparatus comprising one or more processors and one or more memories storing instructions that are operable, when executed by the one or more processors, to cause the enterprise insights data set transformation apparatus to: access an enterprise insights data set from a selected enterprise tenant of an external software platform; transform the enterprise insights data set to define a flattened structure thereby generating a restructured enterprise i[...]
Our summary: The apparatus processes enterprise insights data sets from external platforms. It transforms the data into a flattened structure for easier analysis. The restructured data is then stored in a data warehouse for training machine learning models.
data transformation, enterprise insights, machine learning, data warehousing
Patent
Explainable ai system for icu diagnosis prediction
Patent published on the 2026-07-02 in WO under Ref WO2026143167 by NORTHEASTERN UNIV [US] (Amal Saeed [us])
Abstract: An explainable artificial intelligence system for early prediction of missed severe diagnoses in intensive care unit patients is provided. The system comprises a data processing module configured to receive and process electronic health record data including patient demographics, vital signs, laboratory results, and diagnostic codes. A machine learning module with ensemble, deep learning, and large language models analyzes processed data to predict likelihood of severe diagnoses including acute [...]
Our summary: The system predicts severe diagnoses in ICU patients using electronic health record data. It employs ensemble, deep learning, and large language models for analysis. An explainability module offers interpretable explanations and feature importance rankings for predictions.
Explainable AI, ICU diagnosis, machine learning, feature importance
Patent
Training device, program, system, and training method
Patent published on the 2026-07-02 in WO under Ref WO2026140096 by SOFTBANK CORP [JP] (Kawai Yuto [jp])
Abstract: Provided is a training device comprising: a training execution unit that generates, by machine learning, an autoencoder, which includes an encoder and a decoder and in which an input to the encoder is a bit string to be transmitted from one communication device to another communication device via a wireless environment, a latent space representation generated by the encoder is a signal to be included in a radio wave propagating in the wireless environment in order for the one communication devic[...]
Our summary: The training device utilizes a machine learning-generated autoencoder for efficient data transmission. It includes an encoder that processes bit strings and a decoder that reconstructs them after noise interference. The system facilitates communication between devices in a wireless environment.
machine learning, autoencoder, wireless communication, signal processing
Patent
