AIMC Topic: Neural Networks, Computer

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Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Colorectal cancer is one of the common cancers in the United States. Polyps are one of the major causes of colonic cancer, and early detection of polyps will increase the chance of cancer treatments. In this paper, we propose a novel classification o...

Diagnosis of prostate cancer in a Chinese population by using machine learning methods.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
An early diagnosis of prostate cancer (PC) is key for the successful treatment. Although invasive prostate biopsies can provide a definitive diagnosis, the number of biopsies should be reduced to avoid side effects and risks especially for the men wi...

Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In recent years machine learning methods for human activity recognition have been found very effective. These classify discriminative features generated from raw input sequences acquired from body-worn inertial sensors. However, it involves an explic...

Increasing the learning Capacity of BCI Systems via CNN-HMM models.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Despite all the work in the Brain Computer Interface (BCI) community, one of the main issues that prevents it from becoming pervasive is the limitation on the number of commands with a satisfactory accuracy of detection. In this paper, we propose a s...

Gated Recurrent Neural Networks for EMG-Based Hand Gesture Classification. A Comparative Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Electromyographic activities (EMG) generated during contraction of upper limb muscles can be mapped to distinct hand gestures and movements, posing them as a promising modality for prosthetic and cybernetic applications. This paper presents a compara...

Nonlinear System Identification Based on Convolutional Neural Networks for Multiple Drug Interactions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In heart failure patients, hemodynamics can be regulated by therapeutic drugs. Although the cardiovascular responses to these drugs usually include nonlinearity and drug interactions, it is difficult to identify the characteristics of the dynamics un...

Automatic detection and classification of marmoset vocalizations using deep and recurrent neural networks.

The Journal of the Acoustical Society of America
This paper investigates the methods to detect and classify marmoset vocalizations automatically using a large data set of marmoset vocalizations and deep learning techniques. For vocalization detection, neural networks-based methods, including deep n...

Classification of producer characteristics in primate long calls using neural networks.

The Journal of the Acoustical Society of America
Primate long calls are high-amplitude vocalizations that can be critical in maintaining intragroup contact and intergroup spacing, and can encode abundant information about a call's producer, such as age, sex, and individual identity. Long calls of t...

Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.

The plant genome
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive natur...