Articles tagged with Artificial Neural Networks
Researchers use deep neural networks to recognize images transmitted over optical fibers, achieving high accuracy despite distortions caused by environmental factors. The technique has potential for improving endoscopic imaging in medical diagnosis and increasing the information-carrying capacity of fiber-optic telecommunication networks.
The UCLA-developed artificial neural network device can analyze large volumes of data and identify objects at the speed of light. It uses diffraction of light to process images without advanced computing programs or energy consumption.
A new 3D-printed optical deep learning network called Diffractive Deep Neural Network (D2NN) has been developed by Xing Lin and colleagues. This system processes information through layers of optically diffractive surfaces that work together to recognize handwritten digits with high accuracy.
Researchers developed an artificial synapse inspired by the human brain, which efficiently processes information and demonstrates excellent energy efficiency. This breakthrough could lead to the development of energy-efficient neuromorphic computing, revolutionizing AI devices and transforming industries.
Artificial neural networks can now be trained directly on an optical chip, paving the way for less expensive, faster, and more energy-efficient AI. This breakthrough enables complex tasks like speech or image recognition to be performed more efficiently.
A new approach uses machine learning to generate computer-generated X-rays to supplement real images, increasing the size of training sets for AI systems. This method improves classification accuracy for common and rare conditions by up to 40%, overcoming a challenge in applying artificial intelligence to medicine.
Researchers at Caltech developed an artificial neural network made of DNA that can accurately identify handwritten numbers. The network, designed by Kevin Cherry, uses a 'winner take all' competitive strategy and undergoes complex reactions to classify molecular information.
Scientists developed an AI program to categorize volcanic ash particle shapes, helping provide information on eruptions and aid hazard mitigation. The program achieved a success rate of 92% in classifying basal shapes with probability ratios.
A team at Plymouth University used artificial neural networks to classify five planets as potentially habitable, estimating a probability of life in each case. The technique may aid in selecting targets for future space missions with improved spectral detail.
Researchers developed a visible neural network, DCell, that uses real-world cellular behaviors and constraints to predict cellular growth. The system can simulate cellular growth nearly as accurately as a real cell grown in a laboratory.
Researchers at MIT have designed an artificial synapse that can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team found that their chip and its synapses could recognize samples of handwriting with 95% accuracy.
Researchers at the University of Michigan have created a new type of neural network made with memristors that can dramatically improve the efficiency of teaching machines to think like humans. The system, called reservoir computing, uses fewer nodes and requires less training time than traditional neural networks.
Researchers developed a neural network-based model to assess coastal communities' resiliency to hurricanes. The model forecasts storms in terms of impacts, rather than just wind speed, and has been tested during real-time storms, including Hurricane Harvey.
Researchers at Lobachevsky University are creating a neurochip that can transmit signals to healthy brain cells, potentially replacing damaged areas. The team aims to develop an artificial neural network of 100 nerve cells within three years.
A new algorithm, developed by a team at the University of Maryland, uses artificial neural networks to address multiple flaws in a single image. The algorithm can be trained on high-quality images and then applied to any image with imperfections.
Researchers at Lobachevsky University are developing a neural network prototype based on memristors that can analyze and classify living cell culture dynamics. The project aims to create compact electronic devices that function as part of bio-like neural networks in conjunction with living biological cultures.
Researchers developed an AI-based approach to extract lung nodules from chest CTs, improving diagnosis accuracy and reducing false positives. The method can be integrated into existing CADe systems and accommodate new data streams, potentially increasing the five-year survival rate for lung cancer patients.
The University of Texas at San Antonio is developing an artificial neural network called NFrame to monitor and detect 'bad behavior' in computer systems. The system will learn normal behaviors and flag anomalies, allowing it to predict potential issues and prevent security breaches.
Scientists at Stanford University and Sandia National Laboratories have developed an artificial synapse that mimics the human brain's efficient processing. This innovation could lead to the creation of more brain-like computers that can interpret visual and auditory signals with improved accuracy.
Researchers at Binghamton University developed a new multilevel input layer artificial neural network to predict flight delays. The model outperformed traditional networks in terms of accuracy and training time, predicting delay lengths with about 20% more accuracy than traditional models.
Researchers at the University of Southampton have demonstrated a nanoscale device called a memristor to power artificial neural networks, enabling efficient learning and pattern recognition. The study showcases the potential of memristive synapses in compact volumes and low energy costs.
A research group has made progress in obtaining bio-oils and raw materials from biomass using its patented reactor. Artificial neural networks are being used to calculate the gross calorific value of biomass, which is essential for designing and improving biomass pyrolysis, gasification, and combustion systems.
Researchers used a new artificial neural network method to simulate the atomic interactions of water molecules, explaining its melting temperature and density maximum. The study provides insights into the unusual properties of water, which cannot be understood solely on the basis of its chemical composition.
POSTECH researchers developed an organic nanofiber-based artificial synapse that emulates both important functions and energy consumption of biological synapses. The device enables high memory density and low energy consumption, potentially leading to advancements in AI computing and neuromorphic electronics.
New research reveals that hierarchy in biological networks arises due to cost constraints on connections, leading to more efficient networks. This finding may accelerate the development of complex computational brains in AI and robotics.
Researchers developed AI software to teach a quadrocopter to autonomously recognize and follow forest trails. The drone was able to find the correct direction in 85% of cases, outperforming humans who guessed correctly 82% of the time. This breakthrough enables drones to complement rescue teams and accelerate searches for missing people.
Darwin is a neuromorphic hardware co-processor developed by Chinese researchers that enables efficient execution of Spiking Neural Networks in resource-constrained embedded systems. It supports flexible configuration and has potential applications in intelligent hardware systems, robotics, brain-computer interfaces, and more.
PhD student Elise Hampton uses AI to analyze thousands of galaxy spectra, identifying the most turbulent and messy galaxies. Her goal is to understand how galaxies form, live, and die, and how different processes compete in these galactic systems.
Researchers at UC Santa Barbara demonstrate a simple artificial neural circuit that performs image classification, using memristor technology to achieve brain-like efficiency. The breakthrough has potential applications in medical imaging, navigation systems, and search technologies.
A study published in PLOS Computational Biology found that an artificial deep neural network performs as well as the primate brain at object recognition. This achievement could pave the way for significant advancements in artificial intelligence and our understanding of primate visual processing.
Researchers developed a neuromorphic system that can carry out complex sensorimotor tasks in real time, exhibiting cognitive abilities. The system combines artificial neurons into networks that implemented neural processing modules, closely resembling mammalian brain structures.
Artificial neural networks (ANNs) improve patient survival prediction in advanced brain cancers, with a pooled voting method correctly predicting risk of death within one year in 84% of patients. A new proposal calls for medical professionals and specialty societies to play an increased role in evaluating 'off-label' uses of medications.
A team of researchers has developed an artificial neural network model to predict the location of fossil sites. The software uses satellite imagery and maps to identify productive areas in the Great Divide Basin, Wyoming, and has already accurately pinpointed 79% of known fossil sites.
Researchers at Caltech created an artificial neural network out of DNA, exhibiting brain-like behavior by recalling memories based on incomplete patterns. The DNA-based neural network consists of four artificial neurons made from 112 distinct DNA strands and demonstrated correct responses in a mind-reading game.
A team of researchers has developed a new model that uses artificial neural networks to predict adverse drug reactions (ADRs) among 10,000 observations with 99.87% accuracy. The technology has the potential to save lives by identifying potential ADRs at an early stage of drug development and marketing.
A team of chemists used artificial neural networks to analyze tea leaves' mineral content and identify the type of tea. The technique achieved a high accuracy rate, allowing for clear differentiation between white, green, black, Oolong, and red tea varieties.
NeuFlow is a new supercomputer that processes tens of megapixel images in real time, allowing for rapid object recognition. The system has the potential to enable self-driving cars by recognizing various objects on the road, such as other cars, people, and stoplights.
Researchers developed an artificial neural network (ANN) to evaluate symptoms and predict endocarditis diagnoses. The ANN achieved high accuracy in distinguishing between infected and non-infected cases, eliminating the need for invasive transesophageal echocardiography.
Researchers developed a neural network system to classify music genres, such as cha-cha-cha, jive, and tango, with varying degrees of success. The approach combines the strengths of two existing methods and uses a neural network to analyze beat and tempo, outperforming other classification techniques.
Researchers have developed an analytical method using Empirical Mode Decomposition (EMD) to classify a wider range of heart sounds than skilled physicians can. The EMD system, trained with AI algorithms, outperforms conventional methods in identifying murmurs and other anomalies.
A study published in the World Journal of Gastroenterology found that artificial neural networks can accurately predict thyroid disease in patients with atrophic body gastritis. The analysis of 253 ABG patients revealed a high accuracy rate of 76%, correctly identifying 82% of patients with thyroid disease.
A new theory proposes that learning skills, such as flying, accelerates the evolution of innate abilities in birds by creating a latent memory that reduces the need for future generations to learn. This is achieved through the use of distributed representations in neural networks, which allows for faster evolution of adaptive behaviors.
Jeff Elman's work in connectionism and artificial neural networks has led to breakthroughs in speech perception, language processing, and cognition. His creation of the TRACE model and Simple Recurrent Network has been widely used to simulate human behavior.
Ames Laboratory researchers have created an AI-powered system that can detect secret files hidden in digital images using steganalysis. The system, utilizing artificial neural networks (ANNs), has been trained on a database of over 10,000 images and achieved high accuracy rates.
A new online tool provides personalized 10-year survival predictions for prostate cancer patients, taking into account age, race, co-morbidities, and treatment type. The model also highlights the significant impact of co-morbidities on long-term survival rates.
A prototype system using NIST-patented microheater technology and artificial neural networks can reliably identify trace amounts of toxic gases. The sensors can detect compounds like sulfur-mustard gas and nerve agents at levels below 1 part per million.
Researchers have developed an AI system that can recognize various marine species, including fish and potential threats. The system, called Fetch2, has successfully identified two species - jacks and sharks - using side scan sonar data and neural networks, paving the way for autonomous surveillance of coastlines and harbors.
Researchers at Pacific Northwest National Laboratory are developing TEDANN to predict failures and abnormal operations in M1 Abrams main battle tanks' turbine engines. The technology uses diagnostic engineering, artificial neural networks, and model-based decision algorithms to enhance tank readiness while reducing costly engine failures.