Category: New Realities through Artificial Intelligence


Asset Allocation Applying Multi-Objective Particle Swarm Optimization (MOPSO)

Portfolio optimization is an important problem in finance. Its goal is to discover an efficient frontier which shows highest expected return on each level of portfolio variance. The problem has multiple objectives and its search space is large. Multi-objective particle swarm optimization is a multi-objective optimization method, developed from particle swarm optimization by applying non-dominated


Analysis of Time Series Mining in Manufacturing Problems

Recently, almost all modern manufacturing operations rely on automatic tools. These automatic tools react with materials used and physical events. Physical events are determined by data patterns that definable by domain experts. However, when problems that have not predefined occurred, it will cause critical errors especially in manufacturing plants. Furthermore, with machine complexity and new


Analysis of Uncertainty in Time Series Data: Issues and Challenges

This paper reviews issues and challenges of uncertainty in time series data. The aim of uncertainty analysis is to determine the ways of how to deal with uncertain data in order to gain knowledge, fit low dimensional model, and do prediction. So as to build an efficient predictive tool, uncertainty in data could not be


Performance Evaluation of TCP/IP vs. OpenFlow in INET Framework Using OMNeT++, and Implementation of Intelligent Computational Model to Provide Autonomous Behaviour

Analysing the performance of transmitting data from a source to a certain destination is an interesting task. One of the most reliable networking protocol suites is the Transport Control Protocol and the Internet Protocol (TCP/IP), which will be studied against a new management paradigm called Software Defined Networking (SDN). SDN is an emerging programmable network


Applying the ICA-Based Approach to Detect Faults in Processes

To save time, cost and labor, there are many studies that have been conducted about the detection of faults in industrial processes. Most of the previous studies used only Independent Component Analysis (ICA) or Principal Component Analysis (PCA) for detection, but they can not form close enough boundaries to reject outliers. This paper proposes an