AIMC Topic: Data Collection

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Practical foundations of machine learning for addiction research. Part II. Workflow and use cases.

The American journal of drug and alcohol abuse
In a continuum with applied statistics, machine learning offers a wide variety of tools to explore, analyze, and understand addiction data. These tools include algorithms that can leverage useful information from data to build models; these models ca...

Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach.

Sensors (Basel, Switzerland)
Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-o...

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal.

Sensors (Basel, Switzerland)
In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals' characteristics at multiple Anchor Points (APs). We us...

State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere.

Sensors (Basel, Switzerland)
Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct and train five convolutional neural network (CNN) models: DeepLab, fully convolutional DenseNet24 (FC-...

Classification of ransomware using different types of neural networks.

Scientific reports
Malware threat the security of computers and Internet. Among the diversity of malware, we have "ransomware". Its main objective is to prevent and block access to user data and computers in exchange for a ransom, once paid, the data will be liberated....

A BERT based dual-channel explainable text emotion recognition system.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel dual-channel system for multi-class text emotion recognition has been proposed, and a novel technique to explain its training & predictions has been developed. The architecture of the proposed system contains the embedding modu...

Deep learning-based methods for natural hazard named entity recognition.

Scientific reports
Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide...

BC-DUnet-based segmentation of fine cracks in bridges under a complex background.

PloS one
Crack is the external expression form of potential safety risks in bridge construction. Currently, automatic detection and segmentation of bridge cracks remains the top priority of civil engineers. With the development of image segmentation technique...

A zeroing neural dynamics based acceleration optimization approach for optimizers in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
The first-order optimizers in deep neural networks (DNN) are of pivotal essence for a concrete loss function to reach the local minimum or global one on the loss surface within convergence time. However, each optimizer possesses its own superiority a...

Short-Text Classification Detector: A Bert-Based Mental Approach.

Computational intelligence and neuroscience
With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the ...