AIMC Topic: Machine Learning

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Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery.

Computational intelligence and neuroscience
In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The...

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

Computational and mathematical methods in medicine
OBJECTIVE: Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diast...

Machine learning-assisted non-target analysis of a highly complex mixture of possible toxic unsymmetrical dimethylhydrazine transformation products with chromatography-mass spectrometry.

Chemosphere
Unsymmetrical dimethylhydrazine (UDMH) is a toxic and environmentally hostile compound that was massively introduced to the environment during previous decades due to its use in the space and rocket industry. The compound forms multiple transformatio...

Protocol to predict mechanical properties of multi-element ceramics using machine learning.

STAR protocols
Identifying and designing high-performance multi-element ceramics based on trial-and-error approaches are ineffective and expensive. Here, we present a machine-learning-accelerated method for prediction of mechanical properties of multi-element ceram...

Protein-Ligand Docking in the Machine-Learning Era.

Molecules (Basel, Switzerland)
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function....

SALT: transfer learning-based threat model for attack detection in smart home.

Scientific reports
The next whooping revolution after the Internet is its scion, the Internet of Things (IoT), which has facilitated every entity the power to connect to the web. However, this magnifying depth of the digital pool oil the wheels for the attackers to pen...

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation.

Computational intelligence and neuroscience
As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poor...

All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks.

Computational intelligence and neuroscience
Coronary heart disease (CHD) has become one of the most serious public health issues due to its high morbidity and mortality rates. Most of the existing coronary heart disease risk prediction models manually extract features based on shallow machine ...

An analysis of the influence of transfer learning when measuring the tortuosity of blood vessels.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Convolutional Neural Networks (CNNs) can provide excellent results regarding the segmentation of blood vessels. One important aspect of CNNs is that they can be trained on large amounts of data and then be made available, fo...

Leveraging ShuffleNet transfer learning to enhance handwritten character recognition.

Gene expression patterns : GEP
Handwritten character recognition has continually been a fascinating field of study in pattern recognition due to its numerous real-life applications, such as the reading tools for blind people and the reading tools for handwritten bank cheques. Ther...