AIMC Topic: Neural Networks, Computer

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Evaluation of Machine Learning Techniques for Traffic Flow-Based Intrusion Detection.

Sensors (Basel, Switzerland)
Cybersecurity is one of the great challenges of today's world. Rapid technological development has allowed society to prosper and improve the quality of life and the world is more dependent on new technologies. Managing security risks quickly and eff...

Case Study: Improving the Quality of Dairy Cow Reconstruction with a Deep Learning-Based Framework.

Sensors (Basel, Switzerland)
Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers. With applications in animal h...

Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data.

Sensors (Basel, Switzerland)
Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, ...

Deep Learning in the Detection of Disinformation about COVID-19 in Online Space.

Sensors (Basel, Switzerland)
This article focuses on the problem of detecting disinformation about COVID-19 in online discussions. As the Internet expands, so does the amount of content on it. In addition to content based on facts, a large amount of content is being manipulated,...

A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection.

Sensors (Basel, Switzerland)
Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion d...

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks.

BMC biology
BACKGROUND: Deep-learning-based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of tr...

Low Complexity Binarized 2D-CNN Classifier for Wearable Edge AI Devices.

IEEE transactions on biomedical circuits and systems
Wearable Artificial Intelligence-of-Things (AIoT) devices exhibit the need to be resource and energy-efficient. In this paper, we introduced a quantized multilayer perceptron (qMLP) for converting ECG signals to binary image, which can be combined wi...

Audit lead selection and yield prediction from historical tax data using artificial neural networks.

PloS one
Tax audits are a crucial process adopted in all tax departments to ensure tax compliance and fairness. Traditionally, tax audit leads have been selected based on empirical rules and randomization methods, which are not adaptive, may miss major cases ...

Dead detector element detection in flat panels using convolutional neural networks.

Medical physics
BACKGROUND: Independent testing of image quality metrics is important to provide an unbiased determination of medical imaging performance. Due to the underreporting by vendors of dead detector elements, which are elements that do not function but may...

Develop a hybrid machine learning model for promoting microbe biomass production.

Bioresource technology
Since the cultivation condition of microbe biomass production (mycelia yield) involves a variety of factors, it's a laborious process to obtain the optimal cultivation condition of Antrodia cinnamomea (A. cinnamomea). This study proposed a hybrid mac...