RNN

Visa’s Growing Services Business Infused with New AI-Powered Products

Retrieved on: 
Wednesday, March 27, 2024
Technology, Payments, Finance, Security, Fintech, Professional Services, Digital Cash Management, Digital Assets, Networks, Artificial Intelligence, VRM, RNN, SVP, Sale, AI, Intelligence, Deep learning, VAA, CNP, Investment, VDA, Fraud, Identity, Payment card

Today, Visa (NYSE:V), a leader in digital payments, announced continued expansion of its global value-added services business with the addition of three new AI-powered risk and fraud prevention solutions.

Key Points: 
  • Today, Visa (NYSE:V), a leader in digital payments, announced continued expansion of its global value-added services business with the addition of three new AI-powered risk and fraud prevention solutions.
  • The new products, part of the end-to-end Visa Protect suite, are designed to reduce fraud across immediate account-to-account and card not present (CNP) payments, as well as transactions both on and off Visa’s network.
  • Visa Protect is part of a broad suite of Visa value-added services that have grown to nearly 200 products, spanning five high-demand categories: Acceptance, Advisory, Issuing, Open Banking, and Risk and Identity.
  • Last year, Visa helped block $40 billion in fraudulent activity, nearly double from the year prior2.

WiMi Developed an LSTM-based Data Analysis System

Retrieved on: 
Monday, January 22, 2024
Cloud, RNN, Learning, Holography, Architecture, Natural language processing, Social media, Policy, Deep learning, Memory, Feedback, LSTM, Environment, Data analysis, Long short-term memory, Augmented reality, WIMI, Cryptocurrency

Traditional data analysis methods make it difficult to handle such complex data, but the LSTM algorithm can solve this problem.

Key Points: 
  • Traditional data analysis methods make it difficult to handle such complex data, but the LSTM algorithm can solve this problem.
  • The system uses a variety of data sources, including historical prices, transaction volumes, social media data, and more.
  • WiMi utilizes the LSTM algorithm to build an efficient data analysis system that is capable of deep learning from historical Bitcoin transaction data to extract key factors that influence price trends.
  • The system mainly includes the following modules:
    Data pre-processing: Processing raw data to ensure the quality of the data.

WiMi Announced Hybrid Recurrent Neural Network Architecture-Based Intention Recognition for Human–Robot Collaboration

Retrieved on: 
Monday, December 18, 2023
Cloud, Architecture, CNN, Classification, Probability, Research, LSTM, WIMI, Human, Augmented reality, Speech, Holography, GRU, RNN, Computer data storage

Hybrid recurrent neural network architecture is a model that combines recurrent neural network (RNN) and convolutional neural network (CNN).

Key Points: 
  • Hybrid recurrent neural network architecture is a model that combines recurrent neural network (RNN) and convolutional neural network (CNN).
  • Hybrid recurrent neural network combines the advantages of RNN and CNN, which can better capture sequence information and local features, and can better handle intention recognition for human-robot collaboration.
  • Using hybrid recurrent neural network architecture for human-robot collaboration intention recognition can greatly improve the efficiency and quality of human-robot collaboration.
  • WiMi will try to fuse multi-modal information into a hybrid recurrent neural network architecture-based human-robot collaboration intention recognition.

Global Water Treatment Market Insights and Forecast (2023-2028): Integration of AI, Growing Demand for IoT Solutions and Increasing Adoption of Nanotechnology - ResearchAndMarkets.com

Retrieved on: 
Thursday, November 23, 2023
Other Natural Resources, Natural Resources, Recursive neural network, Artificial intelligence, Water, CNN, Metal, DT, Recycling, Investment, Recurrent neural network, Water supply, Drinking water, Development finance institution, RNN, Water resources, Automation, Nutrient, Seawater, Nanotechnology, Growth, Pesticide, AI, UNICEF, Knowledge, CAF, Sewage treatment

So, rising concerns regarding ground water depletion, low availability of naturally available drinking water and increasing adoption of the concept of recycling & reusing water by companies will continue to boost the growth of global water treatment market.

Key Points: 
  • So, rising concerns regarding ground water depletion, low availability of naturally available drinking water and increasing adoption of the concept of recycling & reusing water by companies will continue to boost the growth of global water treatment market.
  • So with increasing competition and reduced margins, the companies aren't seeking such substantial investments needed to build water treatment plants, impeding the growth of global water treatment market.
  • Therefore, increasing integration of AI in water treatment processes is expected to boost the growth of global water treatment market in the forecasted period.
  • The market for water treatment has been fragmented due to the presence of numerous international, regional and local players in the market.

WiMi Developed "HoloMuxAI: Deep Learning Assisted Holographic Polarization Multiplexing" Technology

Retrieved on: 
Friday, November 24, 2023
Cloud, Deep learning, Acquisition, Natural language processing, CNN, Augmented reality, Virtual reality, Algorithm, Holography, RNN, Next, Hypersurface, WIMI, Architecture, Security printing, Medical imaging

BEIJING, Nov. 24, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed the technology "HoloMuxAI: deep learning assisted holographic polarization multiplexing", which is an innovation based on the framework algorithms of unsupervised deep learning computer-generated holography.

Key Points: 
  • BEIJING, Nov. 24, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed the technology "HoloMuxAI: deep learning assisted holographic polarization multiplexing", which is an innovation based on the framework algorithms of unsupervised deep learning computer-generated holography.
  • WiMi's HoloMuxAI: deep learning-assisted holographic polarization multiplexing combines deep learning and hologram processing to simplify the design and generation of polarization multiplexed holograms.
  • Deep learning: This is the core of the technology and includes a trained deep learning neural network.
  • WiMi's HoloMuxAI: deep learning-assisted holographic polarization multiplexing is a revolutionary technology that breaks through the limitations of traditional hologram technology through a deep learning approach.

WiMi Announced Multi-Level Feature Fusion Algorithm Based on Convolutional Neural Networks

Retrieved on: 
Tuesday, November 21, 2023
Cloud, WIMI, GCN, Classification, Holography, RNN, Learning, Natural language processing, Research, Augmented reality, CNN, Algorithm, 3D printing

The multilevel feature fusion algorithm studied by WiMi adopts a deep network structure, and gradually extracts the high-level features of the image through multiple convolutional and pooling operations, to better express the semantic information of the image.

Key Points: 
  • The multilevel feature fusion algorithm studied by WiMi adopts a deep network structure, and gradually extracts the high-level features of the image through multiple convolutional and pooling operations, to better express the semantic information of the image.
  • The multi-level feature fusion algorithm, as an improved CNN model, has important applications in the field of image processing.
  • The current multi-level feature fusion algorithm mainly focuses on the fusion of shallow and mid-level features, and in the future, WiMi will further explore deeper feature fusion, such as the fusion of higher-level features, to improve the algorithm's performance and expressive ability.
  • The attention mechanism is introduced into the multilevel feature fusion algorithm to improve the network's ability to perceive and utilize key features.

Water Treatment Market Poised to Reach $325.41 Billion by 2023: Uncovering Key Trends & Challenges

Retrieved on: 
Monday, November 20, 2023
Recursive neural network, Artificial intelligence, Pressure, Water, CNN, Water quality, Metal, DT, Recycling, Urbanization, Investment, Application, Co-production (public services), Recurrent neural network, Water supply, Drinking water, Disinfectant, Veolia, Development finance institution, RNN, Water resources, Fresh water, Automation, Nutrient, Seawater, Swimming, Nanotechnology, Sewage, Growth, Pesticide, Population growth, AI, UNICEF, Knowledge, Public, CAF, Sewage treatment

The global water treatment market is expected to reach US$325.41 billion in 2023, progressing at a CAGR 7.20% over the projected period.

Key Points: 
  • The global water treatment market is expected to reach US$325.41 billion in 2023, progressing at a CAGR 7.20% over the projected period.
  • So with increasing competition and reduced margins, the companies aren't seeking such substantial investments needed to build water treatment plants, impeding the growth of global water treatment market.
  • Therefore, increasing integration of AI in water treatment processes is expected to boost the growth of global water treatment market in the forecasted period.
  • Services water treatment market is both the largest and the fastest growing segment of global water treatment market owing to increasing manufacturing activities, rapid urbanization, increasing company focus on building technologically advanced water treatment facilities and growing need for maintenance of existing sewage treatment plants.

Global AI in Pathology Market Analysis Report 2023-2028: Transforming Diagnostic Decision-Making with Advanced Computational Tools with Drug Discovery Dominating

Retrieved on: 
Tuesday, November 7, 2023
Hologic, RNN, Artificial neural network, Therapy, Drug discovery, Marie Cassidy, Toxicology, Region, HIV disease progression rates, R, Software, Workflow, Growth, GAN, Diagnosis, Medicine, Hospital, USD, CNN, Algorithm, Component, AI, Investment, Prohibition, Patient, Patient safety, Medical device, Medical imaging, Fine chemical, Health care, Research, Pathology, Biotechnology, Prognosis, Education

This market focuses on harnessing AI algorithms, machine learning methodologies, and advanced computational tools to augment and automate various facets of the pathology workflow.

Key Points: 
  • This market focuses on harnessing AI algorithms, machine learning methodologies, and advanced computational tools to augment and automate various facets of the pathology workflow.
  • Within the AI in pathology market, firms engage in the development, deployment, and utilization of AI technologies meticulously crafted for pathology applications.
  • In 2022, the drug discovery segment held the largest share of the AI in pathology market.
  • These factors contribute to the substantial share of the drug discovery application segment in the AI in pathology market.

AI in Pathology Market Research Report 2023-2028: Focus on Drug Discovery, Diagnosis, Prognosis, Workflow, Education, Pharma, Biotech, Hospital Labs, &m Research - ResearchAndMarkets.com

Retrieved on: 
Monday, November 6, 2023
Technology, Artificial Intelligence, Hologic, RNN, Artificial neural network, Therapy, Drug discovery, Marie Cassidy, Toxicology, Region, HIV disease progression rates, R, Software, Workflow, Growth, GAN, Diagnosis, Medicine, Hospital, USD, CNN, Algorithm, Component, AI, Investment, Prohibition, Patient, Patient safety, Medical device, Medical imaging, Fine chemical, Health care, Research, Pathology, Biotechnology, Prognosis, Education

This market focuses on harnessing AI algorithms, machine learning methodologies, and advanced computational tools to augment and automate various facets of the pathology workflow.

Key Points: 
  • This market focuses on harnessing AI algorithms, machine learning methodologies, and advanced computational tools to augment and automate various facets of the pathology workflow.
  • Within the AI in pathology market, firms engage in the development, deployment, and utilization of AI technologies meticulously crafted for pathology applications.
  • In 2022, the drug discovery segment held the largest share of the AI in pathology market.
  • These factors contribute to the substantial share of the drug discovery application segment in the AI in pathology market.

WiMi Developed a Deep Learning-Based Approach to Personalized Video Recommendations

Retrieved on: 
Friday, October 13, 2023
Cloud, Online, Holography, Deep learning, Algorithm, RNN, Social network analysis, Intelligence, Virtual reality, WIMI, Communication, CNN, Heart, Recursive neural network, SGD, Augmented reality, AR, Social, Learning, LSTM, Social media, Research, Overfitting, Video game, Mobile phone, Online shopping

BEIJING, Oct. 13, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed a personalized video recommendation system based on deep learning according to the development needs of the industry, providing new ideas and directions for the research of personalized video recommendation under deep learning.

Key Points: 
  • BEIJING, Oct. 13, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed a personalized video recommendation system based on deep learning according to the development needs of the industry, providing new ideas and directions for the research of personalized video recommendation under deep learning.
  • In personalized video recommendation, different types of neural network models are used to model the association between the user and the video.
  • Feature Representation Learning: In a personalized video recommendation system, effective feature representations are critical to the performance of the model.
  • WiMi's deep learning-based personalized video recommendation technology solves information overload, personalizes user needs, improves user experience, and promotes market development in the online video industry.