AIMC Topic: Neural Networks, Computer

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Improving Wearable-Based Activity Recognition Using Image Representations.

Sensors (Basel, Switzerland)
Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increa...

Smart Contract Vulnerability Detection Model Based on Multi-Task Learning.

Sensors (Basel, Switzerland)
The key issue in the field of smart contract security is efficient and rapid vulnerability detection in smart contracts. Most of the existing detection methods can only detect the presence of vulnerabilities in the contract and can hardly identify th...

Neural Network Modeling and Dynamic Analysis of Different Types of Engine Mounts for Internal Combustion Engines.

Sensors (Basel, Switzerland)
This paper presents the results of studies on reducing the amount of vibrations in different frequency ranges generated by a combustion engine through the use of different types of engine mounts. Three different types of engine supports are experimen...

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Scientific reports
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quanti...

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Scientific reports
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an...

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.

BMC cancer
BACKGROUND: We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a model that combined deep learning with a mul...

Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.

Contrast media & molecular imaging
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have uti...

Identification and Prediction of Chronic Diseases Using Machine Learning Approach.

Journal of healthcare engineering
Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is diff...

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.

Computational intelligence and neuroscience
Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroen...

Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory.

PloS one
The early detection of acute myocardial infarction, which is caused by lifestyle-related risk factors, is essential because it can lead to chronic heart failure or sudden death. Echocardiography, among the most common methods used to detect acute myo...