Nuzulha Khilwani Ibrahim

License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study

Most vehicle license plate recognition use neural network techniques to enhance its computing capability. The image of the vehicle license plate is captured and processed to produce a textual output for further processing. This paper reviews image processing and neural network techniques applied at different stages which are preprocessing, filtering, feature extraction, segmentation and recognition in such way to remove the noise of the image, to enhance the image quality and to expedite the computing process by converting the characters in the image into respective text. An exemplar experiment has been done in MATLAB to show the basic process of the image processing especially for license plate in Malaysia case study. An algorithm is adapted into the solution for parking management system. The solution then is implemented as proof of concept to the algorithm.

Threats Identification in Healthcare Information Systems Using Genetic Algorithm and Cox Regression

Threats to information security for healthcare information system increased tremendously. There are various factors contribute to information security threats, many researchers focused only to certain factors which interest them (e.g., virus attack). Certain factors which may be important remain unexplored. In addition, lack of tools and technologies directed to limited number of threats traced in healthcare system. Thus it introduces bias in threat analysis. This study explored the use of biological computational termed Genetic Algorithm (GAs) combined with Cox regression (CoRGA) in identifying a potential threat for healthcare system. The results show that variable described “misused of e-mail” is the major information security threats for healthcare system. Results were compared with manual analysis using the same data, and it is shows that GAs not just introducing new threats for healthcare system but it was similar with others threats proposed by previous researches.

The Utilization of Template Matching Method for License Plate Recognition: A Case Study in Malaysia

Automatic License plate detection and recognition system is special form of optical character recognition and has been an active research domain in image processing field. However, the accuracy is varied due to different styles of number plates endorsed. Besides, the characters on Malaysia license plate are in one or two lines. Thus, the proposed license plate recognition (LPR) technique of this research is able to achieve the best recognition performance based on Malaysia license plate vehicle registration number characters. This paper presents a study of applying the template matching method for character image recognition. The database of characters and license plate image has been created by collecting images from various type of car. The initial pre-processing involves image enhancement, binarization, filtering and segmenting of license plate. There are 100 license plates that contain 693 characters have been tested, and the result shown that 92.78 % of all characters is correctly recognized. Thus, template matching can be classified as one of the promising algorithm for recognizing Malaysia license plate.

Nurse scheduling with fairness criteria for public hospital

Nurses are the main players in healthcare organization who provide services to the community round the clock. Thus, nurse duty roster is one of the most important components in healthcare to make sure a hospital or clinic provides satisfaction to their patient. In usual practice, the duty roster is built manually or partly-automated, and the fairness criteria are often neglected. These situations may lead to dissatisfaction among nurses. In this paper, we report the results of our investigation upon the process of nurse scheduling done in few Malaysian public hospitals. We also conducted a survey to collect a list of the most desired fairness criteria in building the schedule. Results of survey are presented and discussed.

Enhancement of Nurse Scheduling Steps Using Particle Swarm Optimization

Allocating of working schedule, especially for shift approach is hard to ensure its fairness among them. In the case of nurse scheduling, to set up the time table for each available nurse is time consuming and complicated, which consider many factors including rules, regulation and human factor. Moreover, most nurses are women, which have personnel constraints and maternity leave factors. The undesirable schedule can affect the nurse productivity, social life and the absenteeism can significantly as well affect the patient’s life. This paper aimed to enhance the scheduling process by utilizing the particle swarm optimization in order to generate an intelligent nurse schedule. The result shows that the multiple initial schedules can be generated and can be selected with the lowest cost of constraint violation.

Quantitative measure in image segmentation for skin lesion images: A preliminary study

Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the readers intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted.

Prediction of the lithium-ion cell performance via concentration profile simulation

This paper presents the lithium-ion concentration profile simulation in the cathode for the half cell to predict the performance of the lithium-ion cell. By utilizing the mathematical model from the identified theoretical principles, the lithium-ion cell system in the solution phase is used to describe the discharge behavior of the rechargeable cell based on the simulated concentration profile. For this work, the cathode is the positive electrode employing LiMn2O4 as the active material. In addition, the numerical technique namely Finite Difference Method is used to solve the material balances equation of lithium-ion cell. This method is compared to other methods to identify the concentration profile. Nevertheless, the solutions in this paper merely focus on the concentration profile of lithium-ion in the cathode under galvanostatic discharge in the half cell.

The integration of simple Markov model in solving single line production system

Small and Medium Enterprise (SME) has limitations in capital to upgrade the production process especially in installing new machines in parallel line. Generally the machines are installed in single-line and not equipped with a modern technology. If any machines in the series are breakdown and stopped, the whole production system also stopped. By understanding the condition, most of SMEs struggle to reduce the overhead cost and increase the companys profit by escalating the availability of the machines. A simple yet powerful Markov model is used in this study to identify the prime cost involves in production line downtimes. The influence of the performance of a single machine on a line is investigated. A case study in the production of the tamarind juice drink, obviously when the failure rate of the machines is reduced, the cost of lost also can be reduced.

A review on the workload in the nurse rostering problem

Healthcare organization is very important for community in delivering a healthy life. Nurse is the person in charge that will handle the healthcare organization wisely. The nurses should get their duty roster with a wide hand to deliver a good service. There are some issue related to that needs to be classified such as the modes of operation and constraints. The most desirable environment for the nurses is the balance workload roster that will consider any preferences from them that is possible to achieve due to aim to give a respect and care about. Besides that, the solution approach that can be applied in their field must be clarified. In this paper, we will discuss the entire thing about the nurse rostering problem such as its constraints, the modes of operation, the process to complete a roster and the solution approach to this problem.

Neural network prognostics model for industrial equipment maintenance

This paper presents a new prognostics model based on neural network technique for supporting industrial maintenance decision. In this study, the probabilities of failure based on the real condition equipment are initially calculated by using logistic regression method. The failure probabilities are subsequently utilized as input for prognostics model to predict the future value of failure condition and then used to estimate remaining useful lifetime of equipment. By having a time series of predicted failure probability, the failure distribution can be generated and used in the maintenance cost model to decide the optimal time to do maintenance. The proposed prognostic model is implemented in the industrial equipment known as autoclave burner. The result from the model reveals that it can give prior warnings and indication to the maintenance department to take an appropriate decision instead of dealing with the failures while the autoclave burner is still operating. This significant contribution provides new insights into the maintenance strategy which enables the use of existing condition data from industrial equipment and prognostics approach