Articles tagged with Deep Learning
Researchers developed a compact, cost-effective diagnostic platform combining lensfree holography and deep learning for automated HER2 scoring. The system reached 84.9% accuracy for four-level HER2 classification and 94.8% accuracy for binary scoring, effectively lowering diagnostic risks.
Researchers used AI to analyze mammograms and found that women who developed breast cancer had increasing risk scores over time, while those who did not had stable scores. The study suggests that image-based AI risk scores can predict future breast cancer risk in women without a known genetic mutation or family history.
A University of Houston engineering professor developed a mathematical model to help decision-makers decide where to spend limited dollars on infrastructure resilience. The model accounts for real-world uncertainty and identifies critical assets to invest in, providing the greatest benefit before disaster strikes.
A Chinese research team has developed a deep learning model that can predict the South Indian Ocean Dipole (SIOD) seven months in advance, outperforming traditional dynamical forecasting systems. The model uses sea surface temperature and ocean heat content anomalies as inputs and automatically learns key features of ocean temperature ...
A groundbreaking technology called Time to Move (TTM) offers unprecedented control over object and character movement in AI-generated videos. TTM eliminates the need for complex infrastructure or training on millions of videos, making AI video creation more accessible.
A novel operator learning framework tackles the ill-posedness in inverse thermal problems by employing two synergistic deep learning components. The framework accurately estimates spatial material properties and filters high-frequency noise to project the final-state thermal field back to its initial distribution.
TurboLynx, developed by POSTECH researchers, analyzes complex, interconnected data up to 184 times faster than existing systems. The engine groups similar data together and processes them collectively, reducing unnecessary memory usage and enabling efficient analytical queries.
FireANTs, an open-source algorithm, combines AI optimization and geometry to quickly match complex medical images. The new method can accomplish what took weeks in minutes, detecting subtle changes that signal disease or cognitive decline, making it practical for clinical practice.
Researchers used deep learning to model energy release during r-process nucleosynthesis in hydrodynamic simulations, gaining new insights into element formation. The results suggest that r-process heating is an important effect that should be better accounted for in future modeling.
A new study found that AI-powered chatbots can make vaccine-hesitant parents more likely to say they will immunize their children against HPV, but no more than standard written public health materials. Additionally, the effects of the chatbots did not last longer than those of government health materials.
A new study published in the Journal of Big Data highlights the journal's emergence as a leading publication in data science and artificial intelligence research. The study found that JBD has become a central hub for high-impact research worldwide, with significant contributions from top researchers.
Binghamton University researchers have developed a new way to reduce troublesome fake information in AI chatbots, with high accuracy in identifying disease terms and drug names. The protocol harnesses multiple large language models to verify answers through 'voting', increasing confidence in the results.
A new framework, SUVA, enables organizations to measure and adjust AI chatbots' social preferences, improving their performance in customer complaints and other human-AI interactions. By understanding an LLM's existing tendencies, organizations can decide whether an available model already fits its values and usage scenarios.
A deep learning model combines knowledge from different catalyst families to identify a top-performing green hydrogen catalyst. The AI correctly predicted the activity ranking of 12 tested catalysts within a previously unexplored material family.
A new AI system, Empirical Research Assistance (ERA), can automatically write scientific software programs that outperform human-written ones. ERA combines a large language model with search strategies to explore and refine thousands of pieces of code, reducing the time required for exploration from months to hours or days.
Researchers have developed a framework called CHEEM that allows AI models to learn new tasks without losing performance on existing tasks. The framework improves adaptive intelligence by tailoring computational structure depending on task complexity.
A new model combines text mining and machine learning to extract service-specific aspects and customer actions from online reviews. The model effectively identifies core technical issues and user love for a platform, enabling targeted decisions for improvement. Researchers validated the model using 231,705 online reviews of Roblox.
Researchers developed a deep learning method that optimizes both material layout and fiber direction for continuous fiber composite structures, reducing design time by up to 99.7% while maintaining strong accuracy. The ResUNet-GAN framework produces high-performance structural layouts directly from design parameters.
Micro-gesture recognition is emerging as a key area of research in affective computing, focused on analyzing subtle, involuntary body movements that may reflect hidden emotional states. The field has expanded from RGB video to skeleton, audio, text, and privacy-preserving multimodal settings.
Researchers have developed a deep-learning framework to reconstruct a global, high-resolution nighttime light dataset from 1992 to 2024. The new product improves upon existing datasets by reducing saturation-related bias and better capturing temporal changes in urbanization, economic activity, and human development.
Researchers have developed SmartDJ, an AI-powered editor that allows users to reshape audio experiences with simple words. The system uses language models and diffusion models to interpret high-level requests and generate edited outputs.
Researchers developed a cancer assessment tool that can identify high-risk patients and specific cell populations linked to their risk. The tool, called scSurvival, predicts survival outcomes more accurately than traditional methods by analyzing single-cell data at cellular resolution.
Researchers propose a novel SENet-CNN-Transformer model to predict electric vehicle charging duration, outperforming existing models in accuracy and reducing training time. The approach combines data enhancement, channel attention, convolutional neural networks, Transformer modeling, and transfer learning to address real-world data sca...
A review article highlights a deep learning-driven CNN approach for detecting and classifying dynamic road obstacles, achieving high accuracy in obstacle identification and classification. The proposed architecture shows strong performance, but real-world deployment requires continued evaluation across larger and more varied scenarios.
Researchers developed a health-aware V2G strategy using reinforcement learning to optimize charging and discharging times, resulting in significant lifecycle cost savings ($1,539) and extended battery life (21 months). The study suggests electric bus charging stations can be promising platforms for scalable V2G services.
Researchers propose a personalized longitudinal motion planning policy combining reinforcement learning and imitation learning for intelligent vehicles. The approach adapts driving style to target drivers while meeting performance requirements, promoting human-like behavior and increasing acceptance.
Researchers propose an integrated eco-driving framework using deep reinforcement learning to optimize motion trajectory planning and energy management. The framework achieves substantial improvements in transverse-longitudinal comfort, energy economy, and power system health, while reducing hydrogen consumption and driving costs.
Researchers evaluate the effectiveness of Vision Transformers and convolutional neural networks for faster and more accurate defect detection in railway track fasteners. The study finds that transformer-based models outperform traditional CNNs, suggesting their potential value for predictive health management in rail networks.
Researchers used 3D imaging and artificial intelligence to analyze the microscopic structure of coral skeletons, revealing subtle changes caused by Stony Coral Tissue Loss Disease. The study found that Attention U-Net performed best in detecting differences between healthy and diseased corals.
Researchers developed a new training technique, HarmonyGNN, to improve the accuracy of graph neural networks in heterophilic graphs. The framework achieved state-of-the-art performance on four heterophilic graphs with accuracy improvements ranging from 1.27% to 9.6%.
Researchers identified patient-reported symptoms associated with GLP-1s, including menstrual changes, fatigue, and temperature-related complaints, that may not be fully captured in clinical trials or drug labeling. Nearly 4% of Reddit users reported reproductive symptoms, and fatigue was the second most common complaint.
Scientists at the University of Virginia Health System have developed a suite of AI-powered tools, called YuelDesign, YuelPocket and YuelBond, to transform how new drugs are created. These tools can design drug molecules tailored to fit their protein targets exactly, even accounting for protein flexibility.
Researchers surveyed how transformers are integrated into graph-based recommender systems, improving handling of long-range patterns, sparse interactions, and complex heterogeneous data. The study proposes a taxonomy and design strategies for transformer-based recommender systems across various tasks.
Researchers have developed a new artificial intelligence framework called CLAK that enables drones to localize themselves in GPS-denied environments using non-visual sensors such as LiDAR, barometric altitude, and inertial measurements. The model improves localization accuracy while remaining lightweight enough for practical deployment.
A team of researchers at Binghamton University has developed a method to pinpoint discoveries that reshaped the course of science. The new metric uses neural embedding to analyze approximately 55 million scientific papers and patents, identifying major breakthroughs and simultaneous discoveries with greater accuracy.
A new model combines multiple ways of analysing 3D data, integrating local and global perspectives to interpret complex environments more reliably. The system improves detection of small or partially visible objects in real-world situations, enhancing safety in autonomous systems.
A new deep learning model classifies Japanese Sue ware from 3D scans with high accuracy, using three-dimensional point clouds directly. The model achieved an overall accuracy of 93.2%, performing almost perfectly on visually distinct categories, while focusing on regions that may correspond to expert archaeologists' considerations.
Researchers from SDSU discovered surprising similarities among ancient writing systems from Africa and the Caucasus region. The study suggests the Armenian alphabet may be more closely related to the ancient Ethiopic writing system than previously thought, revealing possible cultural contact and influence between regions.
The International Telecommunication Union (ITU) will host the seventh AI for Good Global Summit from 7 to 10 July 2026 at Geneva’s Palexpo convention centre. The summit aims to guide the future of artificial intelligence and unlock its potential to serve humanity.
Researchers created a new method combining scientific tests and artificial intelligence to differentiate recycled plastic from new plastic. The tool, developed by University at Buffalo researchers, can analyze samples and predict the percentage of recycled content with over 97% accuracy.
Researchers at North Carolina State University have identified key components in large language models that ensure safe responses to user queries. They've developed a new technique to improve LLM safety while minimizing the alignment tax, which allows AI systems to provide safe responses without affecting performance.
Researchers developed a new multiview DNN structure to capture complex 3D anatomy and physiology from multiple imaging views, improving diagnostic accuracy for cardiovascular conditions. The approach demonstrated better performance than single-view DNNs and provided a viable alternative for other medical imaging modalities.
A team of researchers at MSU used machine learning to predict how chemicals will influence gene expression, leading to the discovery of promising compounds for the treatment of liver cancer and a chronic lung disease. The study results from years of interdisciplinary work across multiple disciplines and institutes.
Researchers developed a computational tool that infers telomere length from structural changes in cells and tissues captured in medical biopsies. The TLPath model accurately predicts telomere length, providing new opportunities for studying human aging.
A new framework, DUPGT-CDR, uses gating networks to effectively incorporate both positive and negative feedback in cross-domain recommendation systems, achieving lower prediction errors and improved convergence speed. The framework offers more precise product recommendations and personalized learning resources across various domains.
Researchers developed Zephyrus, an AI agent capable of analyzing and answering questions in natural language about weather and climate data. The agent can handle language-based queries, translating them into code and generating plain language answers.
Emerging research across conceptual frameworks, biomarker science, digital phenotyping, and artificial intelligence synthesizes a translational pathway toward a more biologically grounded and clinically useful approach to psychiatric diagnosis. The current system falls short due to standardized clinical language and lack of biological ...
Researchers created a novel approach for simultaneous ERG, PanCK, and H&E image generation from label-free tissue sections, enhancing vascular invasion assessment accuracy and efficiency. The virtual multiplexed immunostaining method overcomes traditional IHC limitations, such as section-to-section variability and tissue loss.
The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) is developing machine learning solutions with industry partners to improve public health, traffic systems, and more. By bridging the gap between messy reality and mathematical models, ALIVE is creating intelligent visual systems that can handle real-world conditions.
Researchers at Kobe University developed an AI model that can diagnose acromegaly with high sensitivity and specificity using only pictures of the back of the hand and clenched fist. This approach holds promise for disease screening, particularly in rural or resource-constrained areas where access to specialists may be limited.
A global consortium created an exam with 2,500 questions spanning multiple subjects to assess AI capabilities. Current AI models consistently fail the exam, highlighting gaps in their understanding. The project aims to provide a long-term benchmark for evaluating advanced AI systems and demonstrate the importance of human expertise
Researchers developed a new method called Learn-to-Steer, which analyzes internal attention patterns of image-generation models to guide their placement according to user instructions. The approach improved accuracy in understanding spatial relationships by up to 61% in existing trained models.
Researchers have developed DEGU, a tool that improves the accuracy and efficiency of deep neural networks in predicting genomic experiment results. DEGU reduces the size of models while maintaining predictive capabilities, making it easier to understand uncertainty and drive reliable discoveries.
Researchers at UCSF and Wayne State University found that generative AI tools can perform orders of magnitude faster than human teams in analyzing health data. Junior researchers paired with AI generated viable prediction models in minutes, outperforming experienced programmers in hours or days.
SeaCast consistently outperforms the Copernicus operational model over a 10-day forecast horizon and extends predictions to 15 days, generating forecasts in just 20 seconds using a single GPU. This advancement enables rapid 'what-if' scenario testing and probabilistic ensemble forecasts.