Latest news with #ArtificialNeuralNetwork
Time of India
14-05-2025
- Science
- Time of India
IISER-Bhopal team develops web-based solution to predict bacterial enzymes for pollutant breakdown
Bhopal: The widespread industrial growth and urbanisation have led to greater use of synthetic chemicals, including pesticides, fertilisers, and various plastics (PE, PET, PU, PVC). Poor waste management practices resulted in these chemicals and heavy metals building up in soil and water bioremediation methods require extensive laboratory work and costly analytical techniques. While natural microbial enzymes can break down complex pollutants, their identification using current methods is time-consuming and requires significant resources. A team in Bhopal plans to simplify and streamline the bioremediation process. XenoBug , a web-based solution developed by a team at IISER Bhopal , uses machine learning, neural networks, and chemo-informatics to predict bacterial metabolic enzymes capable of biodegrading specific contaminants. The system houses a comprehensive database containing approximately 3.3 million enzyme sequences from environmental metagenomes and 16 million enzymes from 38,000 bacterial genomes. The study was recently published in Nucleic Acids Research Genomics and Bioinformatics (2025). A team led by Dr Vineet Sharma, professor department of biological sciences in IISER has done the research work and the team also includes Dr Aditya S Malwe and Usha Longwani. The platform uses 6,814 enzyme substrates to train Random Forest and Artificial Neural Network classifiers. XenoBug operates through three distinct modules: Module-1 employs two multilabel classifiers for reaction class prediction, Module-2 uses six multilabel models for reaction subclass prediction, and Module-3 utilises structural similarity searches for complete reaction prediction. This tool helps identify bacterial enzymes for pollutant breakdown , determine bacterial groups and pathways linked to specific pollutant degradation, helping in developing effective bioremediation approaches. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like The Most Remarkable Oscar Outfits Ever Interesticle Undo It enhances knowledge of pollutant-bacterial interactions and their biodegradation research stands out for its extensive enzyme substrate database, coverage of environmental bacterial genomes and metagenomes, and proven effectiveness across various pollutant types, including pesticides, environmental contaminants, pharmaceutical products, and hydrocarbons. The predictive algorithms analyse chemical structures and connect them with possible degradative pathways, simplifying the discovery of new bioremediation system's database includes numerous environmental samples, providing reliable predictions for various pollutant types. It handles queries related to persistent organic pollutants, pharmaceutical compounds, industrial chemicals, and agricultural pesticides. The machine learning models, trained using verified enzyme-substrate interactions, deliver reliable predictions. XenoBug's modular design enables systematic analysis of degradation pathways. "The practical benefits include faster identification of suitable bacterial strains for bioremediation projects, reduced laboratory testing costs, and more targeted experimental designs," said Prof Sharma. Environmental scientists can use these predictions to develop more effective clean-up strategies for contaminated areas, he added. This computational approach provides insights that are typically challenging to obtain through conventional laboratory methods.
Associated Press
10-04-2025
- Business
- Associated Press
United States Artificial Neural Network Market Research 2024-2029 Featuring NVIDIA, IBM, Alphabet, Microsoft, Amazon, Synaptics, Intel, Meta Platforms, Salesforce, and C3.ai
The 'United States Artificial Neural Network Market by Region, Competition, Forecast and Opportunities, 2019-2029F' report has been added to offering. The United States Artificial Neural Network Market was valued at USD 88.01 million in 2023, and is projected to reach USD 160.52 million by 2029, rising at a CAGR of 10.37%. The United States Artificial Neural Network (ANN) market is experiencing rapid growth, driven by advancements in machine learning, artificial intelligence, and big data analytics. ANNs, which are computational models inspired by the human brain's structure and functioning, are increasingly being utilized across various industries for tasks such as image recognition, natural language processing, and predictive analytics. The integration of ANNs into business operations has enabled organizations to improve decision-making processes, enhance customer experiences, and streamline operations. As industries recognize the potential of ANNs to drive innovation, there is a growing demand for skilled professionals capable of developing and implementing these advanced technologies. Several factors contribute to the expanding ANN market in the U.S. One of the primary drivers is the increasing volume of data generated across sectors, which necessitates sophisticated analytical tools to derive actionable insights. ANNs excel at processing large datasets, enabling businesses to uncover patterns and trends that traditional analytical methods may overlook. Moreover, the proliferation of Internet of Things (IoT) devices has further amplified the data influx, creating a fertile environment for ANN adoption. The healthcare sector is one of the prominent beneficiaries of ANN technology, leveraging it for medical imaging analysis, patient diagnosis, and personalized treatment plans. Similarly, the financial services industry utilizes ANNs for fraud detection, credit scoring, and algorithmic trading, enhancing operational efficiency and risk management. Furthermore, the retail sector is harnessing ANNs to optimize inventory management, enhance customer segmentation, and improve sales forecasting, thereby boosting profitability. Despite the promising outlook, the U.S. ANN market faces challenges, including concerns over data privacy and the ethical implications of AI technologies. Organizations must navigate regulatory frameworks while ensuring transparency in their use of ANN systems. Additionally, the complexity of developing and training ANN models requires substantial investments in technology and expertise, which can be a barrier for smaller firms. Component Insights Solutions segment dominated in the United States Artificial Neural Network market in 2023, driven by several key factors that highlight the growing demand for comprehensive and tailored artificial intelligence applications across various industries. Organizations increasingly recognize the transformative potential of ANNs in solving complex problems, leading to a surge in investments in ready-to-use solutions that integrate seamlessly into existing workflows. One of the primary reasons for the dominance of the Solutions segment is the rapid pace of digital transformation across sectors such as healthcare, finance, retail, and manufacturing. Companies are actively seeking solutions that can harness the power of ANNs to enhance decision-making, automate processes, and improve customer experiences. For instance, in healthcare, ANN solutions are being employed for predictive analytics, patient diagnosis, and personalized treatment plans, streamlining operations and improving patient outcomes. Similarly, in the financial sector, ANNs facilitate real-time fraud detection and risk assessment, enhancing operational efficiency and safeguarding against potential threats. The increasing complexity of data and the need for real-time processing drive organizations to adopt complete ANN solutions rather than relying on isolated tools. These solutions offer end-to-end capabilities, including data preprocessing, model training, and deployment, enabling businesses to achieve faster results and maximize their return on investment. Additionally, the availability of cloud-based ANN solutions has further accelerated adoption by allowing organizations to access advanced capabilities without significant upfront infrastructure investments. The growing emphasis on customization and scalability in ANN applications supports the Solutions segment's growth. Organizations require flexible solutions that can be adapted to their unique operational requirements and can scale as their needs evolve. This trend highlights the importance of vendors that offer tailored ANN solutions that can cater to specific industry challenges, thus fostering deeper partnerships and long-term relationships between solution providers and businesses. Regional Insights Northeast dominated the United States Artificial Neural Network market in 2023, driven by several strategic factors that position it at the forefront of AI innovation and implementation. One of the primary reasons for this dominance is the concentration of leading technology firms, research institutions, and universities in the region. Cities such as New York, Boston, and Philadelphia are home to numerous tech startups and established companies focused on AI and machine learning. This concentration fosters collaboration between industry and academia, leading to advancements in ANN technologies and applications. Additionally, the Northeast region boasts a robust financial services sector, which increasingly relies on ANNs for various applications, including risk assessment, fraud detection, and algorithmic trading. Major banks and financial institutions in cities like New York utilize sophisticated neural networks to analyze vast amounts of data, optimize operations, and enhance decision-making processes. This sector's demand for cutting-edge AI solutions drives investment in ANN technologies and contributes significantly to the region's market growth. The presence of a skilled workforce also plays a crucial role in the Northeast's dominance. The region is known for its educational institutions, such as MIT, Harvard, and various state universities, which produce a steady stream of graduates proficient in AI and machine learning. This talent pool supports the development and implementation of ANN technologies across diverse industries, including healthcare, manufacturing, and retail. Moreover, the Northeast's strong venture capital ecosystem further fuels growth in the ANN market. Investors are actively seeking opportunities in AI-driven startups, providing the necessary funding to innovate and scale. This investment culture encourages the development of novel ANN solutions that cater to industry-specific challenges, ensuring sustained growth and competitiveness. Key Attributes: Report Attribute Details No. of Pages 88 Forecast Period 2023 - 2029 Estimated Market Value (USD) in 2023 $88.01 Million Forecasted Market Value (USD) by 2029 $160.52 Million Compound Annual Growth Rate 10.3% Regions Covered United States Report Scope: Key Players Profiled in this United States Artificial Neural Network Market Report NVIDIA Corporation IBM Corporation Alphabet Inc. Microsoft Corporation Inc. Synaptics Incorporated Intel Corporation Meta Platforms, Inc. Salesforce, Inc. Inc. United States Artificial Neural Network Market, By Component: Solutions Platform/API Services United States Artificial Neural Network Market, By Application: Image Recognition Signal Recognition Data Mining Others United States Artificial Neural Network Market, By Deployment Mode: Cloud On-Premises United States Artificial Neural Network Market, By Organization Size: Small & Medium-Sized Enterprises Large Enterprises United States Artificial Neural Network Market, By Industry Vertical: BFSI Retail & Ecommerce IT & Telecom Manufacturing Healthcare & Life Sciences Others United States Artificial Neural Network Market, By Region: Northeast Southwest West Southeast Midwest For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. View source version on CONTACT: Laura Wood, Senior Press Manager [email protected] For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900 KEYWORD: UNITED STATES NORTH AMERICA SOURCE: Research and Markets Copyright Business Wire 2025. PUB: 04/10/2025 07:20 AM/DISC: 04/10/2025 07:21 AM
