Marina Yusoff

Optimization approaches for macroscopic emergency evacuation planning: A survey

Emergency evacuation planning is essential in disaster management. Numerous evacuation model and optimization algorithms have been developed and implemented for various types of emergencies mainly in natural and man-made disaster. This paper reviews such approaches focusing on macroscopic in emergency evacuation planning. The strength and weaknesses of algorithms and models are described. Variables considered in algorithms such as number of evacuee and evacuation time have been seen important to be applied to real evacuation scenario. Most of optimization algorithms demonstrate promising results in minimizing travelling time from the affected area to safety. Thus, algorithms should be enhanced to consider different scales of evacuation in different type of disasters. With the provision of efficient evacuation planning, it would help relevant authorities to reduce risk disaster management by offering a tool to support evacuation processes.

Solving vehicle assignment problem using evolutionary computation

This paper examines the use of evolutionary computation (EC) to find optimal solution in vehicle assignment problem (VAP) The VAP refers to the allocation of the expected number of people in a potentially flooded area to various types of available vehicles in evacuation process A novel discrete particle swarm optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position A min-max approach is based on minimum capacity and maximum capacity of vehicles We analyzed the performance of the algorithms using evacuation datasets The quality of solutions were measured based on the objective function which is to find a maximum number of assigned people to vehicles in the potentially flooded areas and central processing unit (CPU) processing time of the algorithms Overall, DPSO provides an optimal solutions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP.

Towards emotional control recognition through handwriting using fuzzy inference

Emotion control is one of personality characteristics that can be detected through handwriting or graphology. One of the advantages is it may help the counselor that has difficulties in identifying the emotion of their counselee. This study is to explore the fuzzy technique for feature extraction in handwriting and then identify the emotion of person. This study uses baseline or slope of the handwriting in determining the level of emotion control whether it is very low, low, medium, high or very high, through Mamdani inference.

A discrete particle swarm optimization with random selection solution for the shortest path problem

This article proposes a discrete particle swarm optimization (DPSO) for solution of the shortest path problem (SPP). The proposed DPSO adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Detailed descriptions of the new solution and the DPSO algorithm are elaborated. Computational experiments involve an SPP dataset from previous research and road network from Malaysia. The DPSO is compared with a genetic algorithm (GA) using naive and new solution mapping. The results indicate that the proposed DPSO is highly competitive and shows good performance in both fitness value and processing time.

An Improved Discrete Particle Swarm Optimization in Evacuation Planning

In any flash flood evacuation operation, vehicle assignment at the inundated areas is vital to help eliminate loss of life. Vehicles of various types and capacities are used to evacuate victims to relief centers. This paper examines combinatorial optimization approach with the objective function to assign a specified number of vehicles with maximum number of evacuees to the potential inundated areas. Discrete particle swarm optimization (DPSO) and improved DPSO are proposed and experimented on. Results are presented and compared. Improved DPSO with the proposed min-max approach yields better performance for all four testing categories. Experimenting on a large number of evacuees could further improve the performance of the DPSO.

DPSO based on a min-max approach and clamping strategy for the evacuation vehicle assignment problem

Abstract The most challenging task during a flood event is to evacuate people from the affected areas to safer locations. The difficulty of this task is attributed to an uneven distribution of vehicles, a lack of timely assistance, a deficiency in decision making and poor coordination at the operational level. Although it is important to identify flood-prone areas, the assignment of vehicles to the appropriate evacuation routes is of primary importance. The latter consideration is a crucial determinant of the number of people who are saved in any emergency evacuation. This paper proposes an improved discrete particle swarm optimisation (DPSO) algorithm for solving the evacuation vehicle assignment problem (EVAP). Discrete particle positions are proposed that support the implementation of this DPSO. A min-max approach is used for the initial calculations of particle positions for the EVAP. This algorithm was computationally tested using different numbers of potentially flooded areas and compared with an average DPSO approach and with genetic algorithms. The findings of this study reveal that DPSO with a min-max approach that incorporates a new velocity clamping procedure offers good performance with respect to maximising the number of individuals who can be evacuated by vehicles from diverse locations for situations involving various numbers of potential flooded areas.

A Review on Recent Traffic Congestion Relief Approaches

Traffic congestion is a global and crucial problem due to increase in demand and limited network transportation structure. Complex traffic structure, imbalanced traffic flow, and uncertain event like road accident are among of the identified factors that may cause traffic congestion. Traffic congestion problem causes delay in travelling, increase in travelling cost and accident rate and may affect air pollution. Numerous approaches have been developed and proposed in solving these problems. Among them are traffic signal controls, congestion pricing, turning restrictions and traffic routing. Various methods have been employed include mathematical model, heuristic and meta-heuristics. This paper presents a review on recent approaches that have been proposed for traffic congestion problem and methods used for implementation. This review provides a bigger picture on recent approaches; provide a comparison and suggestion for future research in traffic congestion relief.

Dermatology diagnosis with feature selection methods and artificial neural network

Dermatology or skin disease is one of the popular diseases among other diseases these days. The features similarities between different types of skin diseases make diagnosis of skin diseases very complex. A patient needs dermatologist that has a sound and vast good experience in skin diseases in order to give precise results at the right time. This paper elaborates a prototype with back propagation neural network (BPNN) to assist the dermatologist. This prototype improves expert diagnosis method in term of time efficiency and diagnosis accuracy. The use of two feature selection methods namely Correlation Feature Selection (CFS) and Fast Correlation-based Filter (FCBF) help by providing a smaller number of features with greater accuracy and faster response time. The adjustment of parameter in BPNN gives good performance. The findings show that FCBF method offers the shortest elapsed time and highest result compared to CFS method and the full features with an accuracy of 91.2%.

A multi-valued discrete particle swarm optimization for the evacuation vehicle routing problem

An optimal evacuation route plan has to be established to overcome the problem of poor coordination and uneven distribution of vehicles before or during disaster. This article introduces the evacuation vehicle routing problem (EVRP) as a new variant to the vehicle routing problem (VRP). EVRP is a process of moving vehicles from a vehicle location to the potentially flooded area (PFA), and from PFA to relief center using a number of capacitated vehicles. This paper examines the application of a multi-valued discrete particle swarm optimization (DPSO) for routing of vehicles from vehicle location to PFA. A solution representation is adopted and modified from the solution of the shortest path problem (SPP) to accommodate this problem. Experimental results were tested based on the objective function of finding a minimum total travelling time using datasets from a flash flood evacuation operation. DPSO was found to yield better results than a genetic algorithm (GA).

Online Slant Identification Algorithm Using Vector Rules

Signatures are among the most widely accepted personal attributes for identity verification. There are a lot of features that can be discovered in signature which are either dynamic or static features type. An algorithm needs to be designed to extract these signature features. Online system uses pressure sensitive tablets to capture signature of individual as they sign thus analysis can be done directly and immediately. This research explored slant feature algorithm since signature is usually slanted due to the mechanism of handwriting and the human personality. The proposed algorithm are used to formulate the Signature Extraction Features System (SEFS) which provides a set of tools that allow the users to extract slant features in signature automatically for analysis purposes. Twenty individuals from different background are randomly selected to have their signature taken. Their signatures are captured on a tablet and the SEFS would than gather and store the raw data. The image of the signature that is created by the SEFS would be used as samples for the questionnaire to identify the features of slant, where the questionnaires are given to human expert for evaluation. The results from the SEFS are compared with the result from the questionnaire. Results produced by the algorithm for slant extraction shows 85% identical answers compared to the outcome by human expert. These show that the algorithm proposed are promising for further exploration.