Articles tagged with Machine Tools
A new AI-based tool estimates patient-specific cancer survival rates using machine learning, offering personalized prognosis for patients with breast, thyroid, and pancreatic cancers. The Cancer Survival Calculator differs from existing estimators by incorporating multiple factors beyond disease stage.
A proposed AI-centric medical curriculum aims to educate future healthcare practitioners in digital technology, with a focus on technical concepts, validation, ethics, and appraisal. The curriculum caters to varying student levels, from consumers to developers, promoting interprofessional collaboration and adaptable learning.
A new AI tool developed by Brazilian researchers can detect potentially cancerous lung nodules in CT reports, missing a crucial early diagnosis. The NLP tool achieved an accuracy rate of 97% in identifying suspicious nodules.
The Army Research Laboratory has chosen Texas A&M University for its High-Throughput Materials Discovery for Extreme Environments Center (HTMDEC). The center aims to develop novel materials for extreme conditions, reducing experimentation costs and duration. By leveraging machine learning, physics-based simulations, and collaboration, ...
A new model for producing human brown fat cells in vitro has been developed, providing a potential solution for treating obesity and type 2 diabetes. The researchers identified key cellular signaling cues that lead to brown adipocyte formation and successfully reproduced this process in human pluripotent stem cells.
The new tool, SnorCall, analyzes unsolicited calls to shed light on robocall trends and types of scams. It extracted information from over 232,000 robocalls, including phone numbers used in scams, helping regulators and law enforcement take action.
Insilico Medicine's inClinico platform uses generative AI to predict Phase II to Phase III clinical trial success with an accuracy of 79%. The tool has been validated in various studies and can provide valuable insights for investors and biotech/pharma companies.
A new open-source Python toolbox called simpleNomo has been made available, enabling the creation of nomograms directly from logistic regression coefficients. This facilitates the translation of research findings into practical use, particularly in resource-poor settings or areas without internet access.
A recent study involving over 500 participants found that humans can only detect speech deepfakes 73% of the time. While training participants showed minimal effects, automated detectors performed better and were comparable to human detection abilities.
De-Stijl, a machine learning-based tool, suggests color palettes and demonstrates their impact in different distributions. It removes tedious guesswork from graphic design, facilitating creativity.
A team of researchers at Texas A&M University is developing a new method for understanding metal behavior under extreme conditions using metal cutting, a traditional manufacturing tool. The process involves shearing or deforming the metal to extreme levels under high rates and can provide fundamental information on material strength an...
Washington State University engineers have created a way to 3D-print two types of steel in the same circular layer using two welding machines. The resulting bimetallic material proved stronger than either metal alone due to pressure caused between the metals as they cool together.
A chemist at the University of Kansas has developed a digital tool that can spot scientific text generated by ChatGPT with 99% accuracy. The tool, which was published in Cell Reports Physical Science, uses human insight and intuition to identify key differences between human-written and AI-generated texts.
A new study published in The Neuroradiology Journal introduces an artificial intelligence computer program that can accurately identify changes in brain structure resulting from repeated head injury. This AI tool uses machine learning to process magnetic resonance imaging (MRI) scans and distinguish between the brains of male athletes ...
A team of researchers has developed an AI tool called CRANK-MS that uses neural networks to analyze biomarkers in patients' bodily fluids and predict Parkinson's disease onset with an accuracy of up to 96%. The tool may help identify early warning signs for the disease, which can be challenging to diagnose.
Researchers at the University of Georgia have confirmed evidence of a previously unknown planet outside our solar system using machine learning tools. The discovery highlights the potential for artificial intelligence to enhance scientists' work and speed up analysis, with the potential to dramatically expand exoplanet discoveries.
A new machine learning model estimates optimal treatment timing for sepsis, taking into account vital signs and lab test results to predict patient survival. The model was trained on a dataset of over 14,000 individuals with sepsis and showed improved outcomes when actual treatment matched the recommended timeline.
Researchers review numerical simulations for ultra-precision diamond cutting, exploring properties and microstructures of workpiece materials and their impact on the cutting process. The study provides guidelines for numerical simulations to predict machining responses for various materials.
A team of researchers developed a model-free approach using deep reinforcement learning to optimize estimation of multiple parameters in quantum sensors. The protocol achieved significantly better estimations compared to nonadaptive strategies, demonstrating enhanced performance in resource-limited regimes.
A research team at Carnegie Mellon University has developed a machine learning method called SPICEMIX to analyze spatial transcriptomics data. The tool helps identify and understand gene expression patterns in cells, revealing new insights into brain cell types.
A systematic review of cutting friction behaviors in the metal cutting process reveals its significant impact on tool wear and surface quality. The study contributes to the development of high-quality cutting technology by understanding cutting friction mechanisms, simulation technologies, and anti-friction strategies.
A study published in the Canadian Journal of Cardiology found that smartwatch health apps detecting atrial fibrillation generated a high rate of false positives and inconclusive results, especially in patients with certain cardiac conditions. Better algorithms and machine learning may help improve the accuracy of these devices.
Researchers developed a universal screening tool for IPF that can alert primary care physicians to its possible presence, enabling earlier diagnosis and treatment. The Zero-burden Co-Morbidity Risk Score for IPF (ZCoR-IPF) algorithm uses existing patient records to identify patients at risk of developing the disease.
Researchers discovered a pattern of DNA mutations that links bladder cancer to tobacco smoking using a powerful new machine learning tool. The tool identified four mutational signatures, including one tied to tobacco smoking, which could lead to more customized treatments for patients with specific cancers.
Researchers developed an AI-based tool to calculate harmful effects of medicines, reducing risks of confusion, blurred vision, and falls. The International Anticholinergic Cognitive Burden Tool (IACT) provides a more accurate scoring system, supporting personalized medicine approaches.
A new method using machine learning corrects damaged DNA and unveils true mutation processes in tumour samples, helping early cancer detection and accurate diagnosis. The tool predicted over 90% of developing cancer processes, offering a significant advancement in cancer patient care.
Researchers used artificial intelligence to demonstrate the correlation between cytoskeleton organisation and nuclear position in eukaryotic cells. The study successfully predicted the presence and location of nuclei in over 8,000 cells with high accuracy, transforming the way scientists approach complex biological systems.
Researchers used machine learning to predict protein adsorption onto polymer brush films, identifying key film characteristics that impact adsorption. The study found hydrophobicity index to be the most critical parameter, with thickness and density also playing a significant role.
A UTSA professor will use a five-year $550,000 grant to study natural language processing and develop NLP models tailored to specific population groups. The goal is to improve the accuracy and relevance of these models in everyday applications.
A new machine learning-based AI tool has been developed to aid doctors in distinguishing between tropical diseases such as dengue and malaria. The tool has shown promising results in improving diagnosis accuracy.
A new machine-learning tool can speed up the diagnosis of psoriatic arthritis by identifying patients up to 4 years prior to a clinician's diagnosis. The tool, PredictAI, analyzed medical records and accurately identified 32-51% of confirmed PsA patients.
CHOP researchers developed CancerVar, an artificial intelligence-empowered platform for interpreting somatic cancer mutations. The tool provides standardized procedures for assessing the clinical impacts of over 13 million somatic cancer mutations.
Researchers at West Virginia University are using machine learning and geographic information systems to identify areas with low COVID-vaccine uptake. They aim to pinpoint counties with increased risk of outbreaks, predict where testing is most crucial, and develop targeted interventions to increase testing rates. By acknowledging comm...
Researchers at the University of Minnesota Medical School developed a COVID-19 prediction model that performed well across gender, race, and ethnicity for three different outcomes. The logistic regression algorithm created to predict severe COVID-19 facilitated shared decision-making with patients regarding discharge, reducing undue de...
A new machine learning model, RefMap, has identified 690 genetic risk factors for motor neurone disease, a five-fold increase from previous estimates. This discovery could lead to the development of new treatments and personalized medicine for patients with MND.
Researchers from South Ural State University and international universities reviewed over 200 sources to identify parameters that extend tool life in superalloys. The study suggests various methods, including tool tip texturing, flood cooling, and hybrid machining, to reduce wear and improve surface integrity.
A team of scientists from Tokyo University of Science has developed a machine learning-based tool to predict thermoacoustic oscillations in engines. The tool uses dynamical systems theory and can classify combustion into three states, identifying pressure fluctuations that indicate future combustion oscillations.
The WVU-led Dolly Sods GPU cluster enables researchers to accelerate computational research in fields like drug development, interstellar phenomena, and biometrics. The cluster will facilitate the analysis of massive datasets and enable real-time processing of signals from satellites in space.
A new machine learning model developed by Timothy Chan accurately predicts whether immune checkpoint blockade will be effective in patients with various cancers. The tool assesses multiple patient-specific factors, including tumor mutational burden and chemotherapy history, to predict response and survival outcomes.
A new study at Columbia University Mailman School of Public Health uses machine learning to predict successful opioid dispensing models in U.S. counties. The analysis reveals that prescription drug monitoring program access provisions are the most consistent predictors of high-dispensing and high-dose dispensing counties.
The emergency food management sector is ill-prepared for digital disaster management due to the lack of effective digital tools. A recent study found that existing digital strategies have not been proven effective or tested, leaving personnel with no guidance on potential best practices and the impact of digital tools.
Researchers developed a simple screening tool for cervical myelopathy (CM) using machine learning and finger motion analysis with a non-contact sensor. The tool has shown high accuracy in detecting CM, outperforming specialist diagnoses based on physical findings.
A study found that Airbnb's Smart Pricing algorithm narrowed the racial revenue gap, but only for Black hosts who adopted it. The algorithm increased monthly occupancy and overall revenue for Black hosts by nearly 9% and $13.92 a night.
The University of Huddersfield's Centre for Precision Technologies will receive £3m funding to drive advancements in machinery design and performance. The project aims to grow the UK's advanced machinery capability to a £2 billion export capacity within ten years, creating over 30,000 high-value manufacturing sector jobs.
A Cornell University-led team developed a machine learning tool called Correlation Convolutional Neural Networks (CCNN) to parse quantum matter and make distinctions in the data. CCNN can identify relationships among microscopic properties that are impossible to determine at the scale of quantum systems.
Researchers at UCSF have developed a method to detect complex fetal heart defects in utero, improving detection rates from 30-50% to 95%. The technique combines routine ultrasound imaging with machine learning computer tools to mimic clinicians' tasks.
Researchers developed a holographic endoscope made of single-hair thin optical fibers to reconstruct images of macroscopic objects at larger imaging distances. The tool sheds light on biological processes occurring at the macromolecular and subcellular levels, allowing for better treatment of severe brain diseases like Alzheimer's.
The cosmetic industry is shifting towards automation, AI, and machine learning for fast and smart formulation development. Key companies and start-ups will share their experiences and tools for optimizing product formulations sustainably.
A new study developed a transparent and reproducible machine learning tool, TL-Lite, to facilitate analysis of health information. The tool can be used in clinical forecasting, predicting trends and outcomes in individual patients, and is particularly useful for organizing clinical data into meaningful visualizations.
Researchers developed a machine learning tool to analyze brain scans and identify risk for earlier diagnosis and treatment. The tool accurately identified individuals with schizotypal personality traits at high risk of developing schizophrenia.
Researchers found a correlation between extraverts and the use of positive emotion words and social process words. The study aims to improve machine learning approaches for consumer marketing using well-founded linguistic predictors.
A study by MIT and Max Planck Institute found that people perceive AI as creative genius or tool depending on information presented. The researchers manipulated language to change degree of humanization, influencing recognition and responsibility assigned to humans behind AI systems.
Researchers at Saarland University have developed microlandscaped abrasive tools with structured surfaces made from cemented carbides, enabling precise grinding results. The tools are created using laser surface texturing and can be replicated in large numbers using electrochemical machining.
Researchers found that hiring algorithms are often opaque and biased, with few vendors disclosing concrete information on validation and mitigation. The study encourages transparency and conversation around ethical decision-making in pre-employment assessments using machine learning.
Researchers have successfully demonstrated how machine-learning tools can improve the stability of light beams' size for experiments by adjusting parameters that largely cancel out fluctuations. The technique has been shown to reduce beam width errors from a few percent down to 0.4 percent, with submicron precision.
Researchers developed a system called Northstar that runs on touchscreens and enables users to manipulate datasets, uncover trends and patterns. The new component VDS instantly generates machine-learning models to run prediction tasks, democratizing data science and making it accessible to everyone.
A recent study from the University of Waterloo found that measuring AI's ability to learn is challenging due to the complexity of tasks. The researchers discovered that no mathematical method can determine whether an AI-based tool can handle a task or not, even with precise task descriptions.
A new machine learning predictive tool, FORECasT, enables scientists to predict the exact mutations resulting from CRISPR-Cas9 gene editing, saving time and resources. The tool was developed using a massive dataset of 40,000 DNA sequences and analysis of over 1 billion DNA sequences.
A new AI model has been developed that can predict the risk of death in patients with heart disease more accurately than medical experts. The model was trained on electronic health data from over 80,000 patients and identified new variables that doctors hadn't considered.
The US military is developing a machine-learning tool to help primary care doctors better detect suicide risk and respond more effectively, with an 80-90% accuracy rate. The tool will be integrated into the Navy's electronic health records and provide real-time alerts to doctors.