Fadratul Hafinaz Hassan

Swap-Based Discrete Firefly Algorithm for Traveling Salesman Problem

Firefly algorithm (FA) is an emerging nature-inspired algorithm which has been used to solve discrete optimization problems such as traveling salesman problem (TSP). However, during the discretization of firefly algorithm, one of the FA’s characteristics, i.e. the movement of a dimmer firefly towards a brighter firefly is unapparent as the movement are random. Thus, in this paper, the usage of swap operation as the movement strategy is proposed. The proposed algorithm, Swap-based Discrete Firefly Algorithm (SDFA), is then integrated with Nearest-Neighborhood initialization, reset strategy and Fixed Radius Near Neighbor 2-opt operator (FRNN 2-opt). The proposed algorithm is tested on 45 TSP instances and is compared with several states-of-the-art algorithm. The findings of this research show that the proposed algorithm performs competitively compared to the Discrete Firefly Algorithm, the Discrete Cuckoo Search, the Discrete Bat Algorithm, the Hybrid Genetic Algorithm and the Discrete Bacterial Memetic Evolutionary Algorithm. On average, SDFA reports a percentage deviation of 0.02% from known optimum for TSP instances with dimension range from 14 to 318 cities.

Incorporating Genetic Algorithm Operators in Optimization Spatial Layout Design

Spatial layout design application tools had become the in demand tools as the computational customization had become increasingly significant in this 21st century era. The users prefer to personalize their own space by customizing, modifying and planning based on their personal specification or preferences. However, these application tools usually highlighted the personal preferences without taking account on the universal design criteria that will cause some issues on the space utilization and security. Hence, this research paper had proposed the composition of objective functions for the users customization and the universal design criteria with the incorporation of Genetic Algorithm (GA) operators to optimize the floor plan design. At the end of this research study, the optimization of spatial layout design using GA operators was proven to be able to design the fittest spatial layout design with 94% of fitness function value that almost satisfies the designated objective functions.

Incorporating Cellular Automaton based Microscopic Pedestrian Simulation and Genetic Algorithm for Spatial Layout Design Optimization

Autonomous spatial layout design had become the prominent applications for early planning process for a space arrangement. The available spatial layout design applications had focused on the structural and functional demands for a trendy, cultural influences and scalable spatial design. The previous research on the autonomous spatial layout design had proved that the Genetic Algorithm (GA) with full-fledged of operators are able to design an optimal spatial layout that is scalable for the space utilization. However, in these recent years, due to many occurrences of emergency incidents, the security assurance of the pedestrian to evacuate from the spatial layout during panic situation had become the main focus in designing a spatial layout. Hence, this research had proposed the pedestrian movement simulation using Cellular Automata (CA) with the Moore Neighborhood transition movement direction as the objective function for optimizing the GA operators based spatial layout design. The results have shown that the CA based pedestrian movement simulation was able to optimize the GA operators based spatial layout design in designing a feasible and scalable space arrangement with low-risk of pedestrian casualties.

Predicting Risk of Getting Smoking-Related Cancer: A Comparison of Three Prediction Models

Smoking1 brings the biggest cause of cancer and deaths every year. It is essential for the smoker to aware of the bad effect of smoking and quit smoking. Therefore, cancer prediction tools are used to help in early diagnosis of cancer for the smoker for them to change their lifestyle to lower the risk of getting cancer in the future. Recently, there are many research study on early detection and diagnosis of cancer by using machine learning techniques. The cancer prediction algorithms that discussed here are decision tree algorithm, linear regression algorithm and support vector machine algorithm. These algorithms are widely used in the development of cancer prediction model. The strengths, limitations, and accuracy of each cancer prediction model are compared and analysed. In conclusion, linear regression algorithm shown the best result for the cancer prediction based on the analysis done on each cancer prediction algorithm.

Features of microscopic pedestrian movement in a panic situation based on cellular automata model

Pedestrian movement is the one of the subset for the crowd management under simulation objective. During panic situation, pedestrian usually will create a microscopic movement that lead towards the self-organization. During self-organizing, the behavioral and physical factors had caused the mass effect on the pedestrian movement. The basic CA model will create a movement path for each pedestrian over a time step. However, due to the factors immerge, the CA model needs some enhancement that will establish a real simulation state. Hence, this concept paper will discuss on the enhanced features of CA model for microscopic pedestrian movement during panic situation for a better pedestrian simulation.Pedestrian movement is the one of the subset for the crowd management under simulation objective. During panic situation, pedestrian usually will create a microscopic movement that lead towards the self-organization. During self-organizing, the behavioral and physical factors had caused the mass effect on the pedestrian movement. The basic CA model will create a movement path for each pedestrian over a time step. However, due to the factors immerge, the CA model needs some enhancement that will establish a real simulation state. Hence, this concept paper will discuss on the enhanced features of CA model for microscopic pedestrian movement during panic situation for a better pedestrian simulation.

Predictive analysis effectiveness in determining the epidemic disease infected area

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this pr...