IEEE

Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation

Abstract: The programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more ...
IEEE

LSTA-CNN: A Lightweight Spatiotemporal Attention-Based Convolutional Neural Network for ASD Diagnosis Using EEG

Abstract: Electroencephalography (EEG) is an effective assessment tool to identify autism spectrum disorders with low cost, and deep learning has been applied in EEG analysis for extracting meaningful ...
IEEE

Deep Learning-Based Malaria Detection: A Robust CNN Framework for Accurate and Automated Diagnosis

Abstract: This research suggests a strong framework for automated malaria detection using a Convolutional Neural Network (CNN) model. The dataset, sourced from Kaggle, consists of 27,558 ...
IEEE

DIFTransNet: Dual-Branch Interactive Fusion Network With CNN and Multiscale Transformer for Infrared Small Target Detection

Abstract: Recent advances in hybrid architectures combining convolutional neural networks (CNNs) and transformers have demonstrated significant potential in infrared small target detection (IRSTD).
Scientific Research Publishing

Enhancing MRI Image Quality through Deep CNN-Augmented Denoising: A Comparative Study of Standard and Hybrid Filters ()

1 Department of Computer Science and Informatics, University of Nairobi, Nairobi, Kenya. 2 Department of Computer Science, Mountains of the Moon University, Fort Portal, Uganda. Magnetic Resonance ...
IEEE

Privacy-Preserving Autism Detection Using Federated CNN-LSTM Networks: A Spatiotemporal Deep Learning Framework for Decentralized Behavioural Diagnosis

Abstract: Early detection of autism spectrum disorder (ASD) is essential for effective intervention but remains limited by privacy concerns, fragmented data sources, and constrained access to ...
IEEE

PLI and CNN-BiLSTM: An Enhanced Data Augmentation and Deep Learning Approach for Defect Recognition in Spiral Welded Pipe Based on Ultrasonic Guided Waves

Abstract: Defect detection in urban heat pipes is critical for ensuring system safety and reliability, where Ultrasonic Guided Wave (UGW) technology plays a pivotal role. However, the complex guided ...
eLife

Comprehensive Neural Representations of Naturalistic Stimuli through Multimodal Deep Learning

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors found that models based on ...
IEEE

Deep Learning Approaches to Babylonian Numeral Recognition: A CNN-SVM Hybrid Model

Abstract: This paper presents a novel deep learning framework for classifying Babylonian numerals by integrating Convolutional Neural Networks (CNNs) with a hybrid CNN-SVM model. The core ...
IEEE

Deep Learning Models for Precision Agriculture: Evaluating CNN Architectures for Accurate Plant Disease Detection

Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
IEEE

Enhanced Eye Disease Detection with YOLOv10 and Convolutional Neural Networks (CNN) in Deep Learning

Abstract: In this study, we present an eye disease detection and prevention solution based on deep learning techniques, CNN and YOLOv10. The main objective of this system is to detect multiple ...
IEEE

Deep Neural Networks-Based Hypersonic Vehicle Reentry Impact Point Prediction With Fixed-Length Lightweight Training Data

Abstract: Hypersonic vehicles play a key role in aerospace applications with their speed, maneuverability, and stealth. Accurate Impact point prediction is crucial for improving system performance.