Machine Learning Technologies is one of the key fields that impact the most advanced technologies in modern society. The tremendous growing demands on the related fields, such as adaptive systems, business intelligence, intelligent control, and intelligent and knowledge based system, require a highly interdisciplinary investigation of this field with other subjects and a deeply solid study in its theoretic foundation. Under these circumstances, 5th International Conference on Machine Learning Technologies (ICMLT 2020) will be convened from 19 to 21 June 2020.
Proceeding Downloads
The use of machine learning methods for fast estimation of CO2-brine interfacial tension: A comparative study
The CO2-brine interfacial tension (IFT) is key to designing the CO2 injection into underground saline aquifers in order to reduce CO2 and slow global temperature increase. Laboratory measurement of CO2-brine IFT is usually time-consuming and requires an ...
Efficient Logistic Regression with L2 Regularization using ADMM on Spark
Linear classification has demonstrated success in many areas of applications. Modern algorithms for linear classification can train reasonably good models while going through the data in only tens of rounds. However, large data often does not fit in the ...
Comparative Analysis Using Supervised Learning Methods for Anti-Money Laundering in Bitcoin
With the advance of Bitcoin technology, money laundering has been incentivised as a den of Bitcoin blockchain, in which the user's identity is hidden behind a pseudonym known as address. Although this trait permits concealing in the plain sight, the ...
An intelligent teaching assistant system using deep learning technologies
In this paper, we describe an intelligent teaching assistant system using deep learning technologies. A few works had been done on building intelligent assistant for teachers before and our job is novel. The main challenge in this area is the ...
Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain
Graph networks are extensively used as an essential framework to analyse the interconnections between transactions and capture illicit behaviour in Bitcoin blockchain. Due to the complexity of Bitcoin transaction graph, the prediction of illicit ...
Method for Detecting Android Malware Based on Ensemble Learning
In recent years, we have become increasingly dependent on smart devices. Android is an operating system mainly used on mobile devices, where hundreds of millions of users can download various apps through many application stores. Under these ...
Performance Evaluation and Machine Learning based Thermal Modeling of Tilted Active Tiles in Data Centers
Thermal management system of data center continuously face a lot of challenges, because data center industry has seen a boom growth in power density. In this paper we proposed the Tilted Active Tiles (TATs) to improve the local cold air supply and ...
An LSTM-Based Method for Detection and Classification of Sensor Anomalies
Most existing machine learning (ML) based solutions for anomaly detection in sensory data rely on carefully hand-crafted features. This approach has a fundamental limitation since it is often application-specific and requires considerable human effort ...
A method of intrusion detection based on Attention-LSTM neural network
Recently, network attacks with complex types have occurred more frequently than before, and traditional detection algorithms cannot meet current needs. For this reason, an intrusion detection method based on Attention- Long Short Term Memory (LSTM) ...
Prediction of Soybean Yield using Self-normalizing Neural Networks
Nowadays, agriculture around the world is facing severe challenges because of global warming and rapid population growth. In order to maximize the agricultural production and minimize the environmental degradation at the same time, careful land-use ...
Speeding Up Deep Convolutional Neural Networks Based on Tucker-CP Decomposition
Convolutional neural networks (CNNs) have made great success in computer vision tasks. But the computational complexity of CNNs is huge, which makes CNNs run slowly especially when computational resources are limited. In this paper, we propose a scheme ...
Data Assimilation by Artificial Neural Network using Conventional Observation for WRF Model
In this paper, artificial neural network(ANN) are introduced to data assimilation for WRF model, which is a mesoscale complex model. A particle swarm optimization optimized Multilayer Perception data assimilation (MLP-PSO-DA) model is proposed in order ...
YU-net Lung Segment Image Preprocess Methods Used for Common Chest Diseases Prediction
With the availability of large-scale data set of X-ray images and development of CNNs(Convolutional Neural Networks), using CNNs assist diagnose become more and more popular. But training CNNs using global image may be affected by the excessive ...
Top-down Feature Aggregation Block Fusion Network for Salient Object Detection
The emergence of deep neural networks and full convolutional neural networks has brought great progress to salient object detection. In this paper, we propose a new type of deep full convolutional neural network structure, named top-down feature ...
Domain Adaptation Based Person-Specific Face Anti-spoofing Using Color Texture Features
Face anti-spoofing technology is indispensable for the face recognition system, which is vulnerable to malicious spoofing attacks such as printed attacks and replayed video attacks. In this paper, we focus on more challenging cross-database ...
Meta Learning for Few-Shot Joint Intent Detection and Slot-Filling
Intent detection and slot filling are the two main tasks in natural language understanding module in goal oriented conversational agents. Models which optimize these two objectives simultaneously within a single network (joint models) have proven ...
Application of Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation in Public Transport of Ulaanbaatar City, Mongolia
Ulaanbaatar is considered as one of the most congested cities in the world. The public transport system of a city is a great indicator of its relative level of development. This article applies a combined model composed of the Analytic Hierarchy Process ...
On the Application of Computer War Chess Technology in the Support of Military Supplies
This paper introduces the concept of computer war chess system and the JTLS war chess system of the U.S. Army. Starting from the application of the current war chess system in the military demand, it puts forward the concept of the war chess simulation ...
Application of virtual machine in Quartermaster training
With the progress and development of technology, virtual machine gradually appears in people's vision and is widely used in various fields. Based on the analysis of a series of problems in Quartermaster training, this paper puts forward a comprehensive ...
A Trajectory-based Deep Sequential Method for Customer Churn Prediction
Customer churn prediction is a pivotal issue in business marketing. Many researches have been pursuing more efficient features and techniques for it. Rapid growth of mobile Internet devices has generated large amounts of customer trajectory data, which ...
Solar Power Prediction in IoT Devices using Environmental and Location Factors
Energy-harvesting IoT nodes need to conserve their energy to remain operating without interrupting. By predicting input power supply, IoT nodes could appropriately schedule or adjust data transmission interval to match available energy for lasting ...
An artificial immune based dynamic forensics model for distributed anonymous network
The success of blockchain technology makes more and more applications begin to use distributed anonymous network. However, the decentralized, anonymous, and distributed features of the distributed anonymous network also create conditions for many ...
A Short Text Classification Approach with Event Detection and Conceptual Information
Text classification is an elementary task in Natural Language Processing (NLP). Existing methods, such as Long Short-Term Memory Networks (LSTM) and Attention Mechanism have recently achieved strong performance on multiple NLP related tasks. However, in ...
Byte Visualization Method for Malware Classification
The exponential increase in the number of malware stems from the fact that attackers often create malware variants with automated tools. And automated tools generally tend to reuse similar function modules. It is essential, therefore, that security ...
Index Terms
- Proceedings of the 2020 5th International Conference on Machine Learning Technologies