AIMC Topic: Algorithms

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Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response.

Computational and mathematical methods in medicine
In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be proces...

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark.

Computational intelligence and neuroscience
Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heightened risk, and end-stage renal disease, which can be feasibly avoidable by early detection and treatm...

Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training.

Computational intelligence and neuroscience
Nowadays, China's sports industry has attained effective development, but the athlete's efficiency in the training process is too complex to have a scientific guarantee. Machine learning technology's help in guiding the sports training process has be...

Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning.

Computational intelligence and neuroscience
In order to improve the effect of cross-border e-commerce intelligent information recommendation, this paper applies deep learning to the intelligent information processing and intelligent recommendation of e-commerce and proposes an improved version...

A Study on the Application of Distributed System Technology-Guided Machine Learning in Malware Detection.

Computational intelligence and neuroscience
In recent years, with the development of information technology, the Internet has become an essential tool for human daily life. However, as the popularity and scale of the Internet continue to expand, malware has also emerged as an increasingly wide...

A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network.

Network (Bristol, England)
The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothes...

Model-driven deep unrolling: Towards interpretable deep learning against noise attacks for intelligent fault diagnosis.

ISA transactions
Intelligent fault diagnosis (IFD) has experienced tremendous progress owing to a great deal to deep learning (DL)-based methods over the decades. However, the "black box" nature of DL-based methods still seriously hinders wide applications in industr...

Finite-time and sampled-data synchronization of complex dynamical networks subject to average dwell-time switching signal.

Neural networks : the official journal of the International Neural Network Society
This study deals with the finite-time synchronization problem of a class of switched complex dynamical networks (CDNs) with distributed coupling delays via sampled-data control. First, the dynamical model is studied with coupling delays in more detai...

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection.

Sensors (Basel, Switzerland)
We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential...

Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field.

Sensors (Basel, Switzerland)
The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classifica...