AI Medical Compendium Topic

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Deep Learning Approach to UAV Detection and Classification by Using Compressively Sensed RF Signal.

Sensors (Basel, Switzerland)
Recently, the frequent occurrence of the misuse and intrusion of UAVs has made it a research challenge to identify and detect them effectively, and relatively high bandwidth and pressure on data transmission and real-time processing exist when sampli...

Cross subkey side channel analysis based on small samples.

Scientific reports
The majority of recently demonstrated Deep-Learning Side-Channel Analysis (DLSCA) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the size of the training set is limited, as in...

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.

PloS one
The classification of biological sequences is an open issue for a variety of data sets, such as viral and metagenomics sequences. Therefore, many studies utilize neural network tools, as the well-known methods in this field, and focus on designing cu...

DeepLumina: A Method Based on Deep Features and Luminance Information for Color Texture Classification.

Computational intelligence and neuroscience
Color texture classification is a significant computer vision task to identify and categorize textures that we often observe in natural visual scenes in the real world. Without color and texture, it remains a tedious task to identify and recognize ob...

Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm.

Sensors (Basel, Switzerland)
Anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) w...

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....