Recurrent neural network

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.

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

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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.

AI-based Digital Pathology / AI Pathology Market Distribution by Type of Neural Network, Type of Assay, Type of End-user, Area of Application, Type of Target Disease Indication and Key Geographies, 2022-2035

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Thursday, December 15, 2022
Laboratory, Cervical cancer, IARC, ER, Hospital, Colorectal cancer, Benchmarking, Diagnosis, Convolutional neural network, Breast cancer, Software, MENA, Workflow, PD-L1, Research, HER2, Gastrointestinal cancer, Collection, Type, Lung cancer, The Other End of the Line, International Agency for Research on Cancer, Intelligence, Disease, Organic, Artificial neural network, AI, Investment, Overalls, Geography, Marie Cassidy, Bangladesh Technical Education Board, Nature, Prostate cancer, Recurrent neural network, Hospital Insurance and Diagnostic Services Act, Medical imaging, Medical device, Pathology, Cancer, USD

View original content:https://www.prnewswire.com/news-releases/ai-based-digital-pathology--ai-...

Key Points: 
  • Amidst the ever-growing demand for pathology services, the simultaneous use of technological advances to automate and digitize healthcare procedures is growing.
  • To provide more context, AI-based digital pathology involves collection, management, analyzing and sharing of data (via digital slides) in a digital setting.
  • Moreover, AI-powered digital pathology solutions (digital pathology scanners and digital pathology software) allow pathologists to examine more cases and offer a precise diagnosis.
  • A general introduction to AI-based digital pathology, featuring information on artificial intelligence in digital pathology, workflow of AI-based digital pathology, applications of AI-based digital pathology solutions in the healthcare domain.

Artificial Intelligence (AI)-Based Digital Pathology Solutions Market to Record Rapid Growth with CAGR of 7.67% by 2030 - Exclusive Report by InsightAce Analytic

Retrieved on: 
Thursday, December 8, 2022
Laboratory, MiCK assay, Breast cancer, Oradel Industrial Center, Cleveland Clinic, Gastrointestinal cancer, Cancer, Pathai, Drug development, VNA, Digital pathology, Algorithm, Colorectal cancer, Diagnosis, Perception, Convolutional neural network, DP, Digital, Lung cancer, Lymph, AI, Cervical cancer, Pathology, Growth, Prostate cancer, Data management, Colorectal surgery, Type, Roche, Artificial neural network, Investment, Artificial intelligence, Marie Cassidy, Tribune, Workflow, Patient, History of Cleveland Clinic, Bangladesh Technical Education Board, Recurrent neural network, Artificiality, GE Healthcare, Medical device, Medical imaging, Research

According to the latest research by InsightAce Analytic, the global AI-Based Digital Pathology solutions market is expected to grow with a CAGR of 7.67% during the forecast period of 2022-2030.

Key Points: 
  • According to the latest research by InsightAce Analytic, the global AI-Based Digital Pathology solutions market is expected to grow with a CAGR of 7.67% during the forecast period of 2022-2030.
  • During the forecast period, digital pathology solutions will continue to drive market revenue growth due to the increasing use of digital pathology solutions in drug development and biomarker identification.
  • Additionally, a slow rate of technological advancement may hinder the growth of AI-based digital pathology solutions on the market.
  • Global AI-Based Digital Pathology Solutions Market, by Type of Neural Network, 2022-2030 (Value US$ Mn)

Artificial Intelligence (AI)-Based Digital Pathology Solutions Market to Record Rapid Growth with CAGR of 7.67% by 2030 - Exclusive Report by InsightAce Analytic

Retrieved on: 
Thursday, December 8, 2022
Laboratory, MiCK assay, Breast cancer, Oradel Industrial Center, Cleveland Clinic, Gastrointestinal cancer, Cancer, Pathai, Drug development, VNA, Digital pathology, Algorithm, Colorectal cancer, Diagnosis, Perception, Convolutional neural network, DP, Digital, Lung cancer, Lymph, AI, Cervical cancer, Pathology, Growth, Prostate cancer, Data management, Colorectal surgery, Type, Roche, Artificial neural network, Investment, Artificial intelligence, Marie Cassidy, Tribune, Workflow, Patient, History of Cleveland Clinic, Bangladesh Technical Education Board, Recurrent neural network, Artificiality, GE Healthcare, Medical device, Medical imaging, Research

According to the latest research by InsightAce Analytic, the global AI-Based Digital Pathology solutions market is expected to grow with a CAGR of 7.67% during the forecast period of 2022-2030.

Key Points: 
  • According to the latest research by InsightAce Analytic, the global AI-Based Digital Pathology solutions market is expected to grow with a CAGR of 7.67% during the forecast period of 2022-2030.
  • During the forecast period, digital pathology solutions will continue to drive market revenue growth due to the increasing use of digital pathology solutions in drug development and biomarker identification.
  • Additionally, a slow rate of technological advancement may hinder the growth of AI-based digital pathology solutions on the market.
  • Global AI-Based Digital Pathology Solutions Market, by Type of Neural Network, 2022-2030 (Value US$ Mn)

GBT Seeking to Develop a New LSTM (Long/Short Term Memory) RNN (Recurrent Neural Network)

Retrieved on: 
Wednesday, January 8, 2020
Artificial neural networks, Articles, Artificial intelligence, Academic disciplines, Computational neuroscience, Emerging technologies, Recurrent neural network, Long short-term memory, RNN, RNN, Neural network, Bastiaan Quast

AI, for both mobile and fixed solutions, announced that it is now working on the development of a new LSTM (Long/Short Term Memory) RNN (Recurrent Neural Network).

Key Points: 
  • AI, for both mobile and fixed solutions, announced that it is now working on the development of a new LSTM (Long/Short Term Memory) RNN (Recurrent Neural Network).
  • GBT targets AI-controlled communications markets which are constantly expanding, and to address this expansion, GBT is designing a new architecture of RNN that is based on a proprietary LSTR (Long/Short Term Memory) algorithm.
  • The new RNN is designed to store and distribute vast amounts of information aboutpast and present events efficiently.
  • The new RNN memory method is being designed to create a dynamic long-short term memory network based on database logistics and multiplicative interactions.

GBT Commences Development of Graph Neural Network (GNN) Based Autonomous Machine

Retrieved on: 
Wednesday, November 6, 2019
Computational neuroscience, Articles, Academic disciplines, Emerging technologies, Artificial intelligence, Artificial neural networks, Technology, Cybernetics, Neural network, Recurrent neural network, Deep learning

AI, for both mobile and fixed solutions, announced that is now working on Graph Neural Network (GNN) based autonomous machine algorithms development, as part of the research to enhance its Avant!

Key Points: 
  • AI, for both mobile and fixed solutions, announced that is now working on Graph Neural Network (GNN) based autonomous machine algorithms development, as part of the research to enhance its Avant!
  • that are leveraging neural nets to process a huge amount of data, specifically for autonomous machines applications.
  • The new technology is based on a Graph Neural Network (GNN) - a type of Neural Network which directly operates on the Graph structure, modeling the dependencies between nodes in a graph and enabling advanced algorithms in a supervised manner.
  • The data will be analyzed in multiple layers of a neural network to produce probabilities of a certain outcome.

CEVA Introduces WhisPro™, Neural Network-Based Speech Recognition Technology For Voice Assistants and IoT devices

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Monday, January 7, 2019
Artificial neural networks, Computational neuroscience, Applied mathematics, Artificial intelligence, Computing, Computational statistics, Econometrics, Networks, Neural network, Speech recognition, Recurrent neural network, CEVA, Inc.

WhisPro operates in tandem with CEVA's ClearVox front-end voice processing software technology and offers its customers a robust CEVA DSP-based hardware and software solution for speech recognition.

Key Points: 
  • WhisPro operates in tandem with CEVA's ClearVox front-end voice processing software technology and offers its customers a robust CEVA DSP-based hardware and software solution for speech recognition.
  • This holistic integration of voice pre-processing and neural network algorithms delivers a high recognition rate of more than 95%, while consuming minimal computing resources and power.
  • Based on speech AI technology and optimized specifically for edge devices, WhisPro's scalable Recurrent Neural Network technology is capable of recognizing multiple trigger phrases, simultaneously.
  • CEVA will be demonstrating the WhisPro technology in its private meeting suite at CES 2019, January 8-11, 2019.

Lucd Announces Ground Breaking Advancements in Reservoir Computing

Retrieved on: 
Monday, November 12, 2018
Artificial neural networks, Computational neuroscience, Applied mathematics, Artificial intelligence, Cybernetics, Neuroscience, Computational statistics, Deep learning, Reservoir computing, Recurrent neural network, Neural network, Recursive neural network

Lucd has implemented a key approach to Reservoir Computing, called Echo State Neural Networks , using its patent pending Distributed Optimistic System .

Key Points: 
  • Lucd has implemented a key approach to Reservoir Computing, called Echo State Neural Networks , using its patent pending Distributed Optimistic System .
  • Reservoir computing (RC) is an alternative to Deep Learning Recurrent Neural Networks (RNNs), which are critical to breakthroughs in applications such as natural language processing.
  • As an alternative to Deep Learning, Reservoir Computing is transforming artificial intelligence (AI) because it takes less time to train highly accurate models.
  • Lucd is at booth #3775 and will be showcasing its Reservoir Computing results.