Results 31 to 40 of about 6,983,756 (292)

Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection

Algorithms
Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide.
Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Rytis Maskeliūnas   +3 more
doaj   +1 more source

FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-Based Neuromorphic Continuous Gesture Recognition

Frontiers in Neuroscience, 2019
A neuromorphic vision sensors is a novel passive sensing modality and frameless sensors with several advantages over conventional cameras. Frame-based cameras have an average frame-rate of 30 fps, causing motion blur when capturing fast motion, e.g ...
Guang Chen, Guang Chen, Jieneng Chen, Marten Lienen, Jörg Conradt, Florian Röhrbein, Alois C. Knoll   +6 more
doaj   +1 more source

DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection

Big Data and Cognitive Computing
Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection.
Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius, Rytis Maskeliūnas   +3 more
doaj   +1 more source

Real-Time Camera Operator Segmentation with YOLOv8 in Football Video Broadcasts

AI
Using instance segmentation and video inpainting provides a significant leap in real-time football video broadcast enhancements by removing potential visual distractions, such as an occasional person or another object accidentally occupying the frame ...
Serhii Postupaiev, Robertas Damaševičius, Rytis Maskeliūnas   +2 more
doaj   +1 more source

Deriving real-time action systems with multiple time bands using algebraic reasoning [PDF]

, 2014
The verify-while-develop paradigm allows one to incrementally develop programs from their specifications using a series of calculations against the remaining proof obligations.
Derrick, J, Dongol, B, Hayes, IJ
core   +1 more source

GATransformer: A Graph Attention Network-Based Transformer Model to Generate Explainable Attentions for Brain Tumor Detection

Algorithms
Brain tumors profoundly affect human health owing to their intricacy and the difficulties associated with early identification and treatment. Precise diagnosis is essential for effective intervention; nevertheless, the resemblance among tumor forms often
Sara Tehsin, Inzamam Mashood Nasir, Robertas Damaševičius   +2 more
doaj   +1 more source

Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot

Frontiers in Neurorobotics, 2019
Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings.
Zhuangyi Jiang, Zhenshan Bing, Kai Huang, Alois Knoll   +3 more
doaj   +1 more source

A MDE-based optimisation process for Real-Time systems [PDF]

, 2010
The design and implementation of Real-Time Embedded Systems is now heavily relying on Model-Driven Engineering (MDE) as a central place to define and then analyze or implement a system.
Gilles, Olivier, Hugues, Jérôme
core   +1 more source

Why and When Are Evidence‐Based Interventions Adopted in Paediatric Supportive Care? A Qualitative Exploration of the Determinants of Photobiomodulation Implementation

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Oral mucositis is a common and debilitating side effect of childhood cancer and stem cell transplant treatments. It affects the quality of life of children and young people (CYP) and places a strain on services. Photobiomodulation is recommended for oral mucositis prevention in international guidance but is poorly implemented in UK ...
Claudia Heggie, Kara A. Gray‐Burrows, Peter F. Day, Bob Phillips, Carl A. Thompson   +4 more
wiley   +1 more source

Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121

Brain Sciences
Background/Objectives: This study investigates the classification of Major Depressive Disorder (MDD) using electroencephalography (EEG) Short-Time Fourier-Transform (STFT) spectrograms and audio Mel-spectrogram data of 52 subjects.
Musyyab Yousufi, Robertas Damaševičius, Rytis Maskeliūnas   +2 more
doaj   +1 more source