AIMC Topic: Algorithms

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Uncertainty Class Activation Map (U-CAM) Using Gradient Certainty Method.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering t...

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network.

Big data
At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial...

Deep transfer learning approaches for bleeding detection in endoscopy images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Wireless capsule endoscopy is a non-invasive, wireless imaging tool that has developed rapidly over the last several years. One of the main limiting factors using this technology is that it produces a huge number of images, whose analysis, to be done...

Development of a Real-Time Human-Robot Collaborative System Based on 1 kHz Visual Feedback Control and Its Application to a Peg-in-Hole Task.

Sensors (Basel, Switzerland)
In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system....

Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.

International journal of molecular sciences
Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-AT...

The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms.

Evolution; international journal of organic evolution
Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioral experiments. However, many phenotypes are complex. One example is coloration: camouflage aims to make detection harder, while conspicuous signal...

On the identification of thyroid nodules using semi-supervised deep learning.

International journal for numerical methods in biomedical engineering
Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or ...

An objective structural and functional reference standard in glaucoma.

Scientific reports
The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In the present study, we propose an objective definition of glaucomatous optic neuropathy (GON) using cle...

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

BioMed research international
Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many c...