International Journal of Knowledge Engineering and Data Mining (5 papers in press)
Gender Classification based on similarity features through SURF and SVM
by G. Kishore, Babureddy M
Abstract: The recognisable proof of people in view of their biometric body parts, for example, face, fingerprint, walk, iris, and voice, assumes an imperative part in electronic applications and has turned into a prominent territory of research in image pre-processing. It is likewise a standout amongst the best utilisations of computer-human interaction and understanding. Out of all the previously mentioned body parts, the face is one of most well known qualities in view of its extraordinary feature. In reality, people can process a face in an assortment of approaches to characterise it by its personality, alongside various different attributes. In this paper, we proposed a new algorithm to extract the facial features using SURF algorithm, features are invariant to extract affine transformations are extracted from each face using speeded up robust features (SURF) method (Morteza and Yousefi, 2011) and shows best accuracy on real-time face images compared with different licence datasets like ORL database and FGNet database and with different training ratios by using SVM algorithm (Rahman et al., 2013; Moghaddam and Yang; 2000; Swaminathan, 2000).
Keywords: Biometrics; Facial Features; Gender classification; SURF; SVM.
Selection of Optimal Hot Extrusion Processing Parameters for AA6061using Fuzzy AHP & TOPSIS
by Sarojini Jaji
Abstract: The need for improving the various material processing techniques for aluminium alloys has been felt due to their applications in various key industries. Due to ease of formability and low cost of aluminium alloys, extrusion has gained great popularity in recent years. Since the improper selection of processing parameters leads to poor quality and quantity, in the present work, an attempt was made to simulate hot direct extrusion of AA6061 alloy using DEFORM-3D, and the results were analysed using a hybrid multi-criteria decision making technique, a combined fuzzy AHP and TOPSIS approach to select the optimal combination of hot extrusion processing parameters. AHP is used to prioritise the evaluation criteria and the TOPSIS method used to rank the process parameters combination based on the simulation results.
Keywords: hot extrusion; simulation; processing parameters; MCDM; Fuzzy AHP; TOPSIS.
SharY: A Dynamic Ridesharing and Carpooling Solution Using Advanced Optimized Algorithm
by K.M. Mehedi Hasan Sonet, Md. Mustafizur Raman, Shoumik Rahman Rahman, M. Rashedur Rahman
Abstract: Getting into a public transportation is now very difficult in the city of Dhaka. Moreover, they are overcrowded and getting public bus on time is also very difficult. The problem of other ride sharing services currently available in Dhaka is that if a person reserves a car, then other passengers cannot avail the car. Our main aim is to develop a match making algorithm by which a host (who offers a ride) can take multiple clients (passengers) from multiple routes efficiently without having to compromise fare, distance and other basic preferences. As in our proposed method, most of the cars offering a ride will carry passenger(s) from the host's route or multiple routes, the road utilisation will be much more effective.
Keywords: Ridesharing; Carpooling; Profit Maximization; Ride Matching; Car and Traffic.
The Discovery of Normality of Body Weight Using Principal Component Analysis: A Comparative Study on Machine Learning Techniques Using Different Data Pre-Processing Methods
by Madasamy Sornam, Meharunnisa M
Abstract: In data mining, feature selection plays an important role in finding the most important predictor variables (or features) that explain a major part of the variance of the response variable is a key to identify and build high performing models. In this proposed work, primary data is used to identify the normality/ abnormality of body weight. The missing data has been imputed by predictive mean matching (PMM) method. Efforts are made to reduce the dimensions of the data before classification using principal component analysis (PCA). The principal components obtained are passed as input to the supervised learning algorithm such as na
Keywords: Missing Data Imputation; Predictive mean matching method; Pre-Processing techniques; Principal Component Analysis (PCA).
Real-world Credit Scoring: A comparative study of Statistical and Artificial Intelligent Methods
by Mohammad Shamsu Uddin, Zhou Ying, Tabassum Habib, Guotai Chi
Abstract: Credit scoring is an integral and crucial part of any lending process that any little development in it can reduce huge potential losses of financial organisations. The assessment of model performance varies because of different performance measures under a variety of circumstances on different nature of datasets. Therefore, this study employed six well-known classification approaches on six real-world credit datasets for comprehensive assessment by combining ten representative performance criterion. The experimental outcomes, statistical significance test and the estimated cost of prediction error confirm the marginal superiority of logistic regression (LR) and TreeNet over CART and MARS, being more robust compared to other two approaches LASSO and RF.
Keywords: Credit scoring; Performance measures; Statistical method; Artificial Intelligence (AI).