Spectro-Temporal Modulations Incorporated Two-Stream Robust Speech Emotion Recognition

Information Science, Computer Science, Artificial Intelligence,
speech emotion recognition   spectro-temporal modulation features   robustness   deep learning   noisy environment  

Research on Two-Stream Robust Speech Emotion Recognition Based on Spectro-Temporal Modulation Features Academic Background Speech Emotion Recognition (SER) is a technology that identifies emotions by analyzing the emotional content in human speech. It has broad application potential in areas such as human-computer interaction, customer service mana...

Multi-Task Aquatic Toxicity Prediction Model Based on Multi-Level Features Fusion

Chemistry, Computational Chemistry, Environmental Chemistry, Engineering, Environmental Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health,
aquatic toxicity   deep learning   multi-task model   molecular fingerprints   molecular graph features  

Academic Background With the growing threat of organic compounds to environmental pollution, studying the toxic responses of different aquatic organisms to these compounds has become crucial. Such research not only helps assess the potential ecological impacts of pollutants on the overall aquatic ecosystem but also provides significant scientific f...

A Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
dynamic visual stimulus   style transfer   deep learning   vision science   spatiotemporal features   artificial vision   neuroscience  

Research Report on the Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation Academic Background The encoding and processing of visual information has been a significant focus in the fields of neuroscience and vision science. With the rapid development of deep learning techniques, investigating the similarities between arti...

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model

Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Life Sciences, Bioengineering,
pretrained model   protein essentiality   deep learning   biomedicine   biomarkers   cancer prognosis   microproteins  

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model Academic Background Human Essential Proteins (HEPs) are crucial for individual survival and development. However, experimental methods for identifying HEPs are often costly, time-consuming, and labor-intensive. Additionally, existing compu...

A Deep Learning Approach for Rational Ligand Generation with Toxicity Control

Chemistry, Medicinal Chemistry, Information Science, Artificial Intelligence, Life Sciences, Biochemistry,
deep learning   ligand generation   toxicity control   drug design   computational model   block-based generation   molecular optimization  

Latest Research on Deep Learning Applied to Target Protein Ligand Generation: Proposal and Validation of the DeepBlock Framework Background and Research Problem In the drug discovery process, finding ligand molecules that bind to specific proteins has always been a core objective. However, current virtual screening methods are often limited by the ...

Predicting Crystals Formation from Amorphous Precursors Using Deep Learning Potentials

Materials Science, Inorganic Non-Metallic Materials, Information Science, Computer Science, Artificial Intelligence,
deep learning   amorphous precursors   crystal formation   non-crystalline phases   polymorphs   materials science   energy landscape  

Predicting the Emergence of Crystals from Amorphous Precursors: Deep Learning Empowers Breakthroughs in Materials Science Background Introduction The process of crystallization from amorphous materials holds significant importance in both natural and laboratory settings. This phenomenon is widespread in various processes ranging from geological to ...

Residual-Dense Network for Glaucoma Prediction Using Structural Features of Optic Nerve Head

Information Science, Artificial Intelligence, Medicine, Ophthalmology, Medical Imaging,
glaucoma   residual-dense network   optic nerve head   structural features   deep learning   optic cup   optic disc  

Using Residual Dense Network (RD-Net) for Glaucoma Prediction Based on Structural Features of the Optic Nerve Head Background and Research Purpose Glaucoma is one of the leading causes of blindness worldwide, often referred to as the “silent thief of sight.” It is characterized by the progressive degeneration of the optic nerve head (ONH), resultin...

Boosting Few-Shot Semantic Segmentation with Prior-Driven Edge Feature Enhancement Network

Boosting Few-Shot Semantic Segmentation with Prior-Driven Edge Feature Enhancement Network

Information Science, Computer Science, Artificial Intelligence,
few-shot learning   semantic segmentation   edge features   prior information   deep learning   object boundaries   edge enhancement  

A New Approach to Enhance Few-Shot Semantic Segmentation: Prior-Driven Edge Feature Enhancement Network In the field of artificial intelligence, semantic segmentation is a core technology in computer vision that aims to assign semantic category labels to every pixel in an image. However, traditional semantic segmentation methods rely on large amoun...

Generative AI for Bone Scintigraphy Image Synthesis and Enhanced Deep Learning Model Generalization in Data-Constrained Settings

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
generative AI   bone scintigraphy   deep learning   data augmentation   multicenter   molecular imaging   medical imaging  

Breakthrough Applications of Generative Artificial Intelligence in Nuclear Medicine: Exploring the Potential of Synthetic Bone Scintigraphy Images and Their Application in Deep Learning Background and Research Questions In recent years, the rapid development of Artificial Intelligence (AI) has revolutionized medical imaging analysis. For instance, ...

PSMA PET/CT-based Multimodal Deep Learning Model for Accurate Prediction of Pelvic Lymph-Node Metastases in Prostate Cancer

Information Science, Artificial Intelligence, Medicine, Urology, Medical Imaging, Oncology,
prostate cancer   lymph node metastases   prostate-specific membrane antigen   PET/CT   deep learning  

In-depth Analysis of PSMA PET/CT-based Multimodal Deep Learning Model for Predicting Lymph Node Metastases in Prostate Cancer Patients Background Prostate cancer (PCA) is one of the most common malignant tumors in men and a leading cause of cancer-related deaths. In clinically localized prostate cancer patients, extended pelvic lymph node dissectio...