Optimization of roundness error in deep hole drilling using cuckoo search algorithm

In the manufacturing industry, machining is a part of all manufacture in almost all metal products. Machining of holes is one of the most common processes in the manufacturing industries. Deep hole drilling, DHD is classified as a complex machining process .This study presents an optimization of machining parameters in DHD using Cuckoo Search algorithm, CS comprising feed rate (f), spindle speed (s), depth of hole (d) and Minimum Quantity Lubrication MQL, (m). The machining performance measured is roundness error, Re. The real experimentation was designed based on Design of Experiment, DoE which is two levels full factorial with an added centre point. The experimental results were used to develop the mathematical model using regression analysis that used in the optimization process. Analysis of variance (ANOVA) and Fisher‘s statistical test (F-test) are used to check the significant of the model developed. According to the results obtained by experimental the minimum value of Re is 0.0222μm and by CS is 0.0198μm. For the conclusion, it was found that CS is capable of giving the minimum value of Re as it outperformed the result from the experimental

Modeling and optimization of electric discharge machining performances using harmony search algorithm

Electric Discharge Machining (EDM) is one of the widely used non-conventional machining processes for complex and difficult-to-machine materials. EDM technology has been improve significantly and has been developed in many ideas especially in the manufacturing industries that yielded enormous benefits in economic as well as generating keen interest in research area. A major issue in EDM process is how to obtain accurate results of the machining performance measurement value at optimal point of cutting conditions. Thus, this study proposed harmony search algorithm approach for optimization of surface roughness (Ra) in die sinking electric discharge machining (EDM). The mathematical model was developed using regression analysis based on four machining parameters which are pulse on time, peak current, servo voltage and servo speed. The result shows that the optimal solutions for Ra can be found with the minimum values of 1.3031 μm

Computer aided learning knowledge among medical students in the Faculty of Medicine and Health Sciences, Universiti Putra Malaysia

Computer proficiency has become necessary in many areas of medicine, administration, clinical practices, research, as well as education. The need for greater competence in information and communication technologies (ICT) by doctors and medical students is increasingly recognised. Objectives: This study was undertaken to determine the knowledge, attitude and practices on ICT in the medical students of a local university. Methods: A cross-sectional descriptive study was conducted among medical students (Years 1-5) from December 2005 to May 2006 in Universiti Putra Malaysia. A self-administered questionnaire was used to collect data. Descriptive statistics were used to obtain frequencies for all variables studied. Results: There were 343 respondents aged 18-29 years old. The results showed 82.2% of the respondents (82.2%) were comfortable using computers after entering medical school and 89.2% believed that ICT and computers skills are important for doctors. About 81.3% of the respondents were aware of the role of ICT and computers in learning medicine, 90.4% had used presentation packages, and 83.4% used word processing and search engines as software tools. Conclusion: The findings of this study show that medical students are knowledgeable and do have skills in information technology (IT) and computers. They are also aware of the role of information technology (IT) and computers in medicin

Overview of PSO for Optimizing Process Parameters of Machining

In the current trends of optimizing machining process parameters, various evolutionary or meta-heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs

Feature selection of high dimensional data using Hybrid FSA-IG

Feature selection (FS) is a process of selecting a subset of relevant features depends on the specific target variables especially when dealing with high dimensional dataset. The aim of this paper is to investigate the performance comparison of different feature selection techniques on high dimensional datasets. The techniques used are filter, wrapper and hybrid. Information gain (IG) represents the filter, Fish Swarm Algorithm (FSA) represents metaheuristics wrapper and Hybrid FSA-IG represents the hybrid technique. Five datasets with different number of features are used in these techniques. The dataset used are breast cancer, lung cancer, ovarian cancer, mixed-lineage leukaemia (MLL) and small round blue cell tumors (SRBCT). The result shown Hybrid FSA-IG managed to select least feature that represent significant feature for every dataset with improved performance of accuracy from 4.868% to 33.402% and 1.706% to 25.154% compared to IG and FSA respectively

Consideration of canny edge detection for eye redness image processing: a review

Eye redness can be taken as a sign of inflammation which may suggest severity and progression of a specific disease. In image processing, there is apportioning a digital image into relevant features in sets of pixels where is called image segmentation. The image that consists of numerous parts of different colors and textures need to be distinguished in this process. In each digital image, the transformation of images into edges was using edge detection techniques. It represents the contour of the image which could be helpful to recognize the image as an object with its detected edges. The Canny edge detector is a standard edge detection algorithm for many years among the present edge detection algorithms. This paper focuses on important canny edge detection for detecting a region of interest (ROI) in eye redness images

Optimization of surface roughness in deep hole drilling using moth-flame optimization

This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four center point. Machining parameters involved are spindle speed, feed rate, depth of hole and minimum quantity lubricants (MQL) to obtain the minimum value for surface roughness. Results experiments are needed to go through the next process which is modeling to get objective function which will be inserted into the moth-flame optimization algorithm. The optimization results show that the moth-flame algorithm produced a minimum surface roughness value of 2.41μ compared to the experimental data. The value of machining parameters that lead to minimum value of surface roughness are 900 rpm of spindle speed, 50 mm/min of feed rate, 65 mm of depth of hole and 40 l/hr of MQL. The ANOVA has analysed that spindle speed, feed rate and MQL are significant parameters for surface roughness value with P-value <0.0001, 0.0219 and 0.0008 while depth of hole has P-value of 0.3522 which indicates that the parameter is not significant for surface roughness value. The analysis also shown that the machining parameter that has largest contribution to the surface roughness value is spindle speed with 65.54% while the smallest contribution is from depth of hole with 0.8%. As the conclusion, the application of artificial intelligence is very helpful in the industry for gaining good quality of products

Detecting SIM box fraud by using support vector machine and artificial neural network

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud. The classification uses nine selected features of data extracted from Customer Database Record. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training

Feature Selection with Harmony Search for Classification: A Review

In the area of data mining, feature selection is an important task for classification and dimensionality reduction. Feature selection is the process of choosing the most relevant features in a datasets. If the datasets contains irrelevant features, it will not only affect the training of the classification process but also the accuracy of the model. A good classification accuracy can be achieved when the model correctly predicted the class labels. This paper gives a general review of feature selection with Harmony Search (HS) algorithm for classification in various application. From the review, feature selection with HS algorithm shows a good performance as compared to other metaheuristics algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)