AIMC Topic: Pattern Recognition, Automated

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ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature.

Journal of chemical information and modeling
The emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present i...

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

Medical & biological engineering & computing
Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LID...

A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes.

Artificial intelligence in medicine
OBJECTIVE: Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swa...

Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks.

BioMed research international
The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then perfo...

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection.

IEEE transactions on bio-medical engineering
OBJECTIVE: False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively add...

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical ...

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation.

Computational intelligence and neuroscience
This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm ...

Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.

BioMed research international
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detec...

Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means.

IEEE transactions on bio-medical engineering
OBJECTIVE: We introduce descriptor-based segmentation that extends existing patch-based methods by combining intensities, features, and location information. Since it is unclear which image features are best suited for patch selection, we perform a b...

Deep Neural Networks for Identifying Cough Sounds.

IEEE transactions on biomedical circuits and systems
In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem. A convolutional neural network and...