Latest news with #ANNs
Time of India
3 days ago
- Health
- Time of India
UAEU and IIT Madras Zanzibar create AI system to track and forecast malaria outbreaks
In a significant step forward for global public health, researchers from United Arab Emirates University (UAEU) and the Indian Institute of Technology Madras' Zanzibar campus have introduced a cutting-edge, data-driven framework that accurately models and forecasts malaria transmission. Tired of too many ads? go ad free now By integrating artificial intelligence with mathematical modelling, this new approach aims to support early intervention and improve disease control strategies in malaria-prone regions. A new approach to malaria modelling A collaborative research team led by Adithya Rajnarayanan, Manoj Kumar, and Professor Abdessamad Tridane has introduced a novel methodology that enhances how malaria outbreaks can be forecasted. Their work, published in Scientific Reports by Nature, presents a comprehensive model that combines artificial intelligence (AI) with classical epidemiological frameworks to simulate malaria dynamics with higher precision. The study, titled 'Analysis of a Mathematical Model for Malaria Using a Data-Driven Approach' , brings a fresh perspective to disease modelling. It incorporates temperature- and altitude-dependent variables into compartmental disease models, a method that makes the simulations more realistic and region-specific. This is particularly crucial for climate-sensitive and vulnerable areas where environmental factors heavily influence malaria transmission patterns. Technologies at the core: AI and dynamic systems To boost the predictive capability of their model, the researchers employed a suite of advanced AI tools. These included: Artificial Neural Networks (ANNs) Recurrent Neural Networks (RNNs) Physics-Informed Neural Networks (PINNs) Each of these tools was used to improve the accuracy of disease forecasting, enabling the model to detect patterns in the complex interplay between environmental conditions and malaria spread. Additionally, the study introduced Dynamic Mode Decomposition (DMD), a mathematical technique that helps break down complex systems into simpler, understandable components. Tired of too many ads? go ad free now This was used to create a real-time infection risk metric, offering public health officials a powerful resource for early detection and targeted response. Implications for global health Professor Abdessamad Tridane of UAEU emphasized the importance of this integration of AI with epidemiological modelling, stating: This research demonstrates the power of AI when combined with classical epidemiological models,' said Prof. Abdessamad Tridane of UAEU. 'By embedding environmental dependencies directly into the transmission functions, our model captures the complex, real-world behaviour of malaria spread, providing a more accurate and timely method for disease tracking.' The implications of this research are especially relevant for regions like sub-Saharan Africa, which accounts for 94% of global malaria cases. With over half a million malaria-related deaths each year, the need for accurate forecasting models is critical. This work offers a valuable step towards improved surveillance, early warning systems, and data-driven policymaking in the fight against malaria. Institutional collaboration and background This study represents a collaboration between two institutions that are expanding their global health research footprint: United Arab Emirates University (UAEU), established in 1976 in Al Ain, is the UAE's oldest public research university. Founded by Sheikh Zayed bin Sultan Al Nahyan, it offers a wide range of undergraduate and postgraduate programs across multiple disciplines. IIT Madras Zanzibar Campus, inaugurated in November 2023, is the first international campus of the Indian Institute of Technology Madras. Located in the Bweleo district of Zanzibar, Tanzania, the campus currently offers programs in Data Science and Artificial Intelligence. It aims to cater to a diverse student population from India, Tanzania, and other African nations, with plans to broaden its academic scope in the coming years.
Time of India
3 days ago
- Health
- Time of India
UAEU and IIT Madras Zanzibar develop AI-based framework to forecast malaria outbreaks
Image: File In a significant step forward for global public health, researchers from United Arab Emirates University (UAEU) and the Indian Institute of Technology Madras' Zanzibar campus have introduced a cutting-edge, data-driven framework that accurately models and forecasts malaria transmission. By integrating artificial intelligence with mathematical modelling, this new approach aims to support early intervention and improve disease control strategies in malaria-prone regions. A new approach to malaria modelling A collaborative research team led by Adithya Rajnarayanan, Manoj Kumar, and Professor Abdessamad Tridane has introduced a novel methodology that enhances how malaria outbreaks can be forecasted. Their work, published in Scientific Reports by Nature, presents a comprehensive model that combines artificial intelligence (AI) with classical epidemiological frameworks to simulate malaria dynamics with higher precision. The study, titled 'Analysis of a Mathematical Model for Malaria Using a Data-Driven Approach' , brings a fresh perspective to disease modelling. It incorporates temperature- and altitude-dependent variables into compartmental disease models, a method that makes the simulations more realistic and region-specific. This is particularly crucial for climate-sensitive and vulnerable areas where environmental factors heavily influence malaria transmission patterns. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Retro Lanterns: A Halloween Staple topgadgetlife Shop Now Undo Technologies at the core: AI and dynamic systems To boost the predictive capability of their model, the researchers employed a suite of advanced AI tools. These included: Artificial Neural Networks (ANNs) Recurrent Neural Networks (RNNs) Physics-Informed Neural Networks (PINNs) Each of these tools was used to improve the accuracy of disease forecasting, enabling the model to detect patterns in the complex interplay between environmental conditions and malaria spread. Additionally, the study introduced Dynamic Mode Decomposition (DMD), a mathematical technique that helps break down complex systems into simpler, understandable components. This was used to create a real-time infection risk metric, offering public health officials a powerful resource for early detection and targeted response. Implications for global health Professor Abdessamad Tridane of UAEU emphasized the importance of this integration of AI with epidemiological modelling, stating: This research demonstrates the power of AI when combined with classical epidemiological models,' said Prof. Abdessamad Tridane of UAEU. 'By embedding environmental dependencies directly into the transmission functions, our model captures the complex, real-world behaviour of malaria spread, providing a more accurate and timely method for disease tracking.' The implications of this research are especially relevant for regions like sub-Saharan Africa, which accounts for 94% of global malaria cases. With over half a million malaria-related deaths each year, the need for accurate forecasting models is critical. This work offers a valuable step towards improved surveillance, early warning systems, and data-driven policymaking in the fight against malaria. Institutional collaboration and background This study represents a collaboration between two institutions that are expanding their global health research footprint: United Arab Emirates University (UAEU), established in 1976 in Al Ain, is the UAE's oldest public research university. Founded by Sheikh Zayed bin Sultan Al Nahyan, it offers a wide range of undergraduate and postgraduate programs across multiple disciplines. IIT Madras Zanzibar Campus, inaugurated in November 2023, is the first international campus of the Indian Institute of Technology Madras. Located in the Bweleo district of Zanzibar, Tanzania, the campus currently offers programs in Data Science and Artificial Intelligence. It aims to cater to a diverse student population from India, Tanzania, and other African nations, with plans to broaden its academic scope in the coming years.
Forbes
20-04-2025
- Health
- Forbes
The Meshing Of Minds And Machines Has Arrived
Brain computer interface in transhumanism connected hybrid mind Examining the mesh between humans and machines provides insight into the future. Science is already making significant progress in the development of brain/computer interface (BCI) technologies, such as brain mapping and neuromorphic circuits. A system that connects the brain directly to an external device is known as a brain-computer interface. These technologies gather brain impulses using sensors implanted in assistive devices, then use those signals to power external equipment. This implies that the conversion of brain impulses into various actions or even commands occurs without requiring human movement. BCIs then rely on brain activity that is recorded by a sensor and typically converted into digital form so that devices can interpret it. The goal of neuromorphic computing with BCI is to mimic the brain's energy efficiency and processing capacity. To achieve this, the system architecture must be redesigned to allow for in-memory computing (IMC), and electronic devices that simulate the actions of synapses and neurons must be created. Artificial intelligence, conceptual image. Neuromorphic Development BCIs have over a hundred years of history. Hans Berger discovered the brain's electrical activity in 1924. The first EEG recordings of brain waves were produced as a result of his investigations, which used electrodes to record electrical activity from the human scalp. He accomplished the first non-invasive BCI-assisted robot control in 1988. Cyberkinetics' BrainGate project successfully controlled a prosthetic hand in 2005. A comprehensive timeline of BCI can be found at: The history of Brain-Computer Interfaces (BCIs) - Timeline - RoboticsBiz In 2018, research funded by the Defense Advanced Research Projects Agency (DARPA) proved that a person with a brain chip could pilot a swarm of drones using signals from the brain. There have been various studies and experiments since then, and no doubt, science combining neural networks and artificial intelligence is on a path to enhance and even upgrade human cognitive capabilities. We could implant nanochips into our brains in the future to enhance our cognitive abilities and enable intelligent data uploads. Advancements in brain/computer interface technologies are progressing rapidly in 2025. There is a breakthrough that is impacting the meshing of mind and machine. When used unconventionally, a single, conventional silicon transistor can simulate a biological neuron and synapse, according to research from the National University of Singapore (NUS). This study, led by Associate Professor Mario Lanza of NUS's College of Design and Engineering's Department of Materials Science and Engineering, suggests scalable, energy-efficient hardware for artificial neural networks (ANNs). "We need hardware that is both scalable and energy-efficient to enable true neuromorphic computing, where microchips behave like biological neurons and synapses," Professor Lanza stated. The Neuralink logo on a laptop arranged in New York, US, on Wednesday, Jan. 31, 2024. Elon Musk said ... More that the first human patient has received a brain implant from his startup Neuralink Corp., a significant step forward for the company that aims to one day let humans control computers with their minds. Photographer: Gabby Jones/Bloomberg Elon Musk has been a pioneer in the neuromorphic field. The core business that develops Elon Musk's brain-computer interface (BCI) technology is Neuralink, which he created in 2016. To improve human potential and restore freedom for people with disabilities, Neuralink is developing implanted brain-computer interfaces (BCIs) that allow direct brain-to-computer communication. To help people with paralysis, the technique uses a surgical robot to implant gadgets in the brain. The procedure allows users to operate computers or other equipment with their thoughts. A 30-year-old man from Arizona, USA, named Noland Arbaugh became the first person to receive a brain chip implant from Neuralink, marking a significant milestone in neurotechnology. After a diving accident in 2016, Arbaugh suffered a paralysis below the shoulders. The BBC claimed that since receiving the chip in January 2024, the outcomes have been nothing short of remarkable. Arbaugh is now able to use a brain-computer interface (BCI) to operate a computer with just his thoughts thanks to this technology. Recalling his early battles with paralysis, he remarked, "You just have no control, no privacy, and it's hard." However, he was able to control a computer cursor after the surgery by simply considering moving his fingers. An article in Frontiers in Science, which involved cooperation between scientists, institutes, and academics, further highlights the promise of the human-computer interface. "We can imagine the possibilities of what may come next with the human brain machine interface," the conclusion reads. Neural nanorobotics-based human brain-computer interface systems could boost human intelligence and learning by giving people quick access to all the knowledge available in the cloud. Furthermore, it could elevate fully immersive virtual and augmented reality to previously unheard-of heights, allowing users to express themselves more completely and have more meaningful experiences. By addressing new difficulties for the human species, these improvements may help humanity adjust to emerging artificial intelligence systems and human augmentation technologies. * Please see Frontiers | Interface between Human Brain and Cloud ( Additionally, there is hope for a quantum brain made of intelligent material that can change physically to learn. In their pursuit of this "quantum brain," physicists have made significant progress. They have shown that they can replicate the independent actions of neurons and synapses in the brain as well as pattern and link a network of individual atoms. Refer to The Initial Steps Toward a Quantum Brain: An Intelligent Substance That Acquires Knowledge by Changing Itself Physically ( Future applications of brain-computer interfaces (BCIs) may enable instant communication, thought transfers, dream recording, and AI-consciousness integration. While these advancements hold potential for human augmentation, they also raise significant ethical concerns related to cyborg rights and the regulation of super AI. Additionally, cybersecurity and privacy issues are critical, as BCIs directly interact with brain impulses and could be susceptible to misuse or compromise. As this technology becomes more widespread, protecting user data and ensuring ethical usage will become increasingly imperative. Human-machine interaction is here, despite technological, security, and ethical challenges. It will shape our future and could define the Fifth Industrial Revolution. The key will be steering its applications with a focus on a positive impact that enhances lives.
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
Yahoo
09-03-2025
- Business
- Yahoo
Lowe's (LOW) Launches Mylow: AI-Powered Virtual Advisor in Home Improvement
We recently published a list of . In this article, we are going to take a look at where Lowe's Companies, Inc. (NYSE:LOW) stands against other AI news updates investors should not miss. Once a niche concept, generative artificial intelligence has become an essential tool in various industries. The revolutionary technology is transforming healthcare, agriculture and the auto industry while also giving rise to game-changing innovation in the tech industry. Likewise, a study by Grand View Research indicates the global artificial intelligence market is poised to grow at a compound annual growth rate of 36.6% to $1.81 trillion between 2024 and 2030. Breakthrough innovations, bullish investments, and increasing adoption across industries are catalysts fueling robust growth. Furthermore, the progress made in artificial neural networks (ANN) has dramatically enhanced the integration and utilization of artificial intelligence across various industries. The diverse applications of ANNs, such as image recognition, natural language processing, predictive modeling, autonomous driving, and healthcare diagnostics, have been driving market expansion. For example, auto companies are employing ANN to refine autonomous driving functionalities, focusing on object detection, path planning, and decision-making processes. The growing accessibility and availability of historical datasets have also accelerated the pace of advancements in artificial intelligence. Now, more unstructured datasets are available to researchers thanks to the development of cloud computing. Furthermore, as AI advances with each iteration, innovation has been spurred by the potential of next-generation computing power. Amid the robust growth and game-changing innovations, artificial intelligence has become a key technology battleground between the US and China. The emergence of breathtaking AI models from China, such as DeepSeek, has once again underscored how nations are racing to challenge the US regarding AI dominance. The enduring geopolitical rivalry and economic competition between China and the United States began to extend into artificial intelligence approximately ten years ago. However, this competition has significantly escalated with the swift rise of DeepSeek and other Chinese generative AI companies. The recent achievements of these Chinese firms have also prompted inquiries regarding the efficacy of strategies like export controls in curbing the technological advancements of foreign adversaries. 'China's been doing AI for a very long time … and is probably just as good as the US or anybody else. It's as simple as that,' said Alan Pelz-Sharpe, founder of AI analyst firm Deep Analysis. China's leading entrepreneurs and company executives, including the founders of DeepSeek and a robotics startup, have already held discussions with Chinese leader Xi Jinping. The discussions concerned maintaining a sense of national duty as they advance their technological initiatives. This gathering highlights China's strategic preparations to compete with the United States in a technology sector expected to drive the next industrial revolution. Furthermore, China has announced its commitment to nurturing emerging technologies, particularly open-source architectures for chip design. For years, China has sought to establish a semiconductor ecosystem centered around RISC-V, a significant open-source architecture, in an effort to diminish its dependence on technologies dominated by the United States. On the other hand, the United States is shifting from close research collaboration with China towards a military competition likely to diminish or terminate cooperation, according to Jennifer Lind, an associate professor of government at Dartmouth College. While tensions with China began to escalate during former President Barack Obama's tenure due to the increasing assertiveness of the Chinese government, Lind anticipates that the relationship will deteriorate further under President Trump as both nations compete in technological advancements. For this article, we selected AI stocks by going through news articles, stock analysis, and press releases. These stocks are also popular among hedge funds in Q4 2024. Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter's strategy selects 14 small-cap and large-cap stocks every quarter and has returned 373.4% since May 2014, beating its benchmark by 218 percentage points (). A family excitedly browsing through the aisles of a home improvement retail Companies, Inc. (NYSE:LOW) is a home improvement retailer that offers a line of products for construction, maintenance, repair, remodeling, and decorating. It also provides home improvement products, such as appliances for seasonal and outdoor living. On March 5, the company affirmed its push into conversational AI with the launch of Mylow, its first AI-powered home improvement virtual advisor. Developed in partnership with OpenAI, Mylow is designed to provide real-time answers to home improvement questions. It will also provide project guidance, complementing more than 300,000 associates. With the ability to customize suggestions according to location and budget, the AI assistant can assist clients in locating and acquiring the right equipment and supplies. 'The development and introduction of Mylow exemplifies the tech-forward vision of the Lowe's brand,' said Lowe's Senior Vice President of Technology, Chandhu Nair. 'We're doubling down with emerging technology collaborators like OpenAI to solve problems for our customers and because we want the home improvement experience at Lowe's to be a cut above.' Overall, LOW ranks 1st on our list of AI news updates investors should not miss. While we acknowledge the potential of LOW as an investment, our conviction lies in the belief that AI stocks hold greater promise for delivering higher returns and doing so within a shorter time frame. If you are looking for an AI stock that is more promising than LOW but that trades at less than 5 times its earnings, check out our report about the . READ NEXT: 20 Best AI Stocks To Buy Now and Complete List of 59 AI Companies Under $2 Billion in Market Cap Disclosure: None. This article is originally published at Insider Monkey.
