International Journal of Intelligent Information and Database Systems (6 papers in press)
Impacts of Feature Selection on Classification of Individual Activity Recognitions for Prediction of Crowd Disasters
by Ali Selamat, Fatai Sadiq, Ondrej Krejcar, Roliana Ibrahim
Abstract: We examined possibility of feature selection using Statistical Based Time Frequency Domain (SBTFD) extracted features for human activity recognitions. This is to reduce the dimension of features space, remove redundant features to improve accuracy and minimize false negative alarm for crowd disasters. For this, we analyzed and classified 54 SBTFD features obtained from 22,350 instances comprising of climb down, climb up, peak shake while standing, standing, still, and walking; as classes V1, V2, to V8, respectively. The individual activity recognition dataset (D1) were collected from 20 students in a well-known institution in Malaysia. In addition, a similar dataset (D2) from repository was used. The dataset contains 250,936 instances from 9 users for smartphone accelerometer signals. Both datasets were subjected to Minimum Redundancy Maximum Relevance (MRMR), correlation and chi-square techniques to filter the relevant SBTFD features to select effective features to reduce the dimension. Based on the selected features, we applied 10-fold cross validation using WEKA with Random Forest (RF), J48, Sequential Minimal Optimization (SMO) and Naive Bayes (NB) classifiers to classify and predict abnormality behaviour classes V1 to V8. We achieved an excellent accuracy and reduce false negative rate to safe human lives from crowd disasters with 7 features of MRMR using RF.
Keywords: Statistical Based Time Frequency Domain (SBTFD); human activity recognitions; Minimum Redundancy Maximum Relevance (MRMR); chi-square; dimensional reductions.
An approach oriented viewpoints for cooperative information system eliciting requirements
by Kahina Kessi, Zaia Alimazighi, Mourad Chabane Oussalah
Abstract: Cooperative Information System (CIS) is a complex system, that involves the cooperation of several stakeholders sharing a common purpose, and each one is distinguished by his own viewpoint on the system. This makes its development more difficult. The successful design of a CIS is then mainly based on the definition of the requirements engineering phase. In software engineering domain, especially in requirements engineering domain, Viewpoint and abstraction level are two important concepts introduced to reduce systems complexity. They can handle the increasing complexity of today's enterprise and information systems by breaking down the system into several viewpoints where each viewpoint can be decomposed into abstraction levels to have more or less details. Complete requirements for the system are collected from the combination of the requirements derived from the different viewpoints.\
In this paper, we present an approach oriented viewpoints/abstraction levels which defines the necessary concepts to elicit the requirements of a CIS. In doing so, a model driven method is proposed in order to develop a CIS modeling tool. This method consists first of a conceptual modeling, where we propose a meta-model oriented viewpoints/abstraction levels which decomposes a CIS according to its different viewpoints. We then propose the modeling tool VpCIS (Viewpoints for Cooperative Information System) generated from the meta-model using Polarsys (an open source based on Eclipse) and some integrated plug-ins.
Keywords: viewpoints; abstraction level; needs analysis; requirements engineering; cooperative information system.
Special Issue on: Evolutionary Algorithms in Intelligent Systems
Object tracking using the particle filter optimized by the improved artificial fish swarm algorithm
by Zhi-Gao Zeng, Haixing Bao, Zhiqiang Wen, Wenqiu Zhu
Abstract: In particle filter algorithm, the weight values of particles will gradually decrease as the increase of iteration times and the variance of the weight values of the particles will increase. This will lead to an increase in the deviation between the estimated state and the true state. In order to deal with this problem, an improved particle filter algorithm is proposed in this paper. That is, an improved artificial fish swarm optimization algorithm is used to optimize the traditional particle filter. In the improved particle filter algorithm, the resampled particles will be driven to the region with high likelihood function to increase the weight values of the particles. Thus, the estimated state is closer to the real state. Experiment results show the advantage of our new algorithm over a range of existing algorithms.
Keywords: object tracking; particle filter; artificial fish swarm algorithm.
Solve the IRP Problem with an Enhanced Discrete Differential Evolution Algorithm
by Shi Cheng, Zelin Wang
Abstract: The inventory -routing problem is a NP hard problem. It is difficult to find the optimal solution in polynomial time. Many scholars have studied it in many years. This paper analyzes the inventory-routing optimization problem, and comprehensive differential evolution algorithm is good performance in solving combinatorial optimization problems. The differential evolution algorithm was improved to make it be suitable for solving discrete combination optimization problems. In order to improve the performance of the differential evolution algorithm to solve the inventory routing problem, this paper puts forward dynamic adjustment of mutation factor and crossover factor of the differential evolution. It is proved by numerical experiments that the proposed algorithm has certain performance advantages, and it also proves that the improved algorithm can improve the performance of the algorithm by dynamic adjustment of the mutation factor and crossover factor.
Keywords: differential evolution algorithm; inventory routing problem; mutation factor; crossover factor.
Inventory routing optimization using differential evolution with feasibility checking and local search
by Hu Peng, Changshou Deng
Abstract: The inventory routing problem (IRP) is to minimize inventory and transportation costs simultaneously for increasing profitability of the system. However, the two costs are conflicting in most case and hard to solve. As a promising evolutionary algorithm, differential evolution (DE) has been successfully applied to solve many real-world optimization problems, but we found that it is not used to optimize the IRP. In this paper, for the first time, we utilize the DE algorithm to optimize the one-to-many IRP where a product is shipped from supplier to a set of retailers over a planning period. In the proposed DEIR algorithm, the solution feasible checking method, the local search method and the optimal routing method based on DE are designed to suit the IRP solving. The computational tests have been conducted on 50 benchmark instances. Experimental results and comparison with different parameter settings have proved that the proposed algorithm is competitive.
Keywords: Differential evolution; Inventory routing problem; Feasible checking; Local search.
Hybrid Fireworks Algorithm with Differential Evolution Operator
by JINGLEI GUO, Wei Liu, Ming Liu, Shijue Zheng
Abstract: As a population-based intelligence algorithm, fireworks algorithm simulates the fireworks explosion process to solve optimization problem. A comprehensive study on enhanced fireworks algorithm (EFWA) reveals that the explosion operator generates too much sparks for the best firework limits the exploration ability. A hybrid version of EFWA (HFWA_DE) is proposed by adding the differential evolution (DE) operator. In HFWA_DE, the population is divided into two subpopulations, then each subpopulation evolves with FWA operator and DE operator separately and exchanges the elitist individual. Experiments on 20 well-known benchmark functions are conducted to illustrate the performance of HFWA_DE. The results turn out HFWA_DE outperforms some state-of-the-art FWAs on most testing functions.
Keywords: Fireworks Algorithm; DE operator; explosion; exploitation; exploration.