AIMC Topic: Deep Learning

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Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis.

Scientific reports Jul 1, 2025
Parkinson's Disease (PD) is a deteriorating condition that mostly affects older people. The lack of conclusive treatment for PD makes diagnosis very challenging. However, using patterns like tremors for early diagnosis, handwriting analysis has becom...
Image Processing, Computer-Assisted Humans Parkinson Disease Neural Networks, Computer Algorithms Early Diagnosis Deep Learning Handwriting
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FSID: a novel approach to human activity recognition using few-shot weight imprinting.

Scientific reports Jul 1, 2025
Accurate recognition of human activities from gait sensory data plays a vital role in healthcare and wellness monitoring. However, conventional deep learning models for Human Activity Recognition (HAR) often require large labeled datasets and extensi...
Gait Human Activities Algorithms Deep Learning Humans
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A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.

Scientific reports Jul 1, 2025
Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pathological assessments, prevent unnecessary polypectomies, and help avoid adverse events. Using a mu...
Adenomatous Polyps Narrow Band Imaging Deep Learning Colonic Polyps Male Colonoscopy Female Sensitivity and Specificity Colorectal Neoplasms Humans Artificial Intelligence
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Fusing satellite imagery and ground-based observations for PM air pollution modeling in Iran using a deep learning approach.

Scientific reports Jul 1, 2025
With the rapid advancement of urbanization and industrialization in cities, air pollution has become one of the significant environmental challenges and issues in many countries. The concentration of particulate matter with an aerodynamic diameter of...
Neural Networks, Computer Deep Learning Humans Iran Air Pollutants Air Pollution Satellite Imagery Particulate Matter Models, Theoretical Environmental Monitoring
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Depression detection methods based on multimodal fusion of voice and text.

Scientific reports Jul 1, 2025
Depression is a prevalent mental health disorder, and early detection is crucial for timely intervention. Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for auto...
Depression Voice Female Deep Learning Humans
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An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.

Scientific reports Jul 1, 2025
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...
Image Processing, Computer-Assisted Fetus Pregnancy Female Ultrasonography, Prenatal Deep Learning Breast Neoplasms Humans Neural Networks, Computer
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Leveraging federated learning and edge computing for pandemic-resilient healthcare.

Scientific reports Jul 1, 2025
The universal demand for the development and deployment of responsive medical infrastructure and damage control techniques, including the application of technology, is the foremost necessity that emerged immediately in the post-pandemic era. Numerous...
Humans Artificial Intelligence Pandemics SARS-CoV-2 Deep Learning Internet of Things COVID-19 Delivery of Health Care Contact Tracing Federated Learning
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Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.

Scientific data Jul 1, 2025
Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to assist in diagnosis and assessment, which can help improve clinical management and provide new idea...
Deep Learning Magnetic Resonance Imaging Humans Spondylosis Cervical Vertebrae Neck Pain Tomography, X-Ray Computed
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A deep learning approach to stress recognition through multimodal physiological signal image transformation.

Scientific reports Jul 1, 2025
Stress is widely acknowledged as a significant contributor to health issues. Recognizing stress involves assessing an individual's physiological and psychological responses to stressors, which is crucial for human well-being. Physiological signal-bas...
Stress, Psychological Image Processing, Computer-Assisted Markov Chains Humans Deep Learning
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ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.

Nature communications Jul 1, 2025
Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their...
Animals Deep Learning Humans Toxicity Tests, Acute Machine Learning
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