Discriminant analysis of multi sensor data fusion based on percentile forward feature selection

Feature extraction is a widely used approach to extract significant features in multi sensor data fusion. However, feature extraction suffers from some drawbacks. The biggest problem is the failure to identify discriminative features within multi-group data. Thus, this study proposed a new discriminant analysis of multi sensor data fusion using feature selection based on the unbounded and bounded Mahalanobis distance to replace the feature extraction approach in low and intermediate levels data fusion. This study also developed percentile forward feature selection (PFFS) to identify discriminative features feasible for sensor data classification. The proposed discriminant procedure begins by computing the average distance between multi- group using the unbounded and bounded distances. Then, the selection of features started by ranking the fused features in low and intermediate levels based on the computed distances. The feature subsets were selected using the PFFS. The constructed classification rules were measured using classification accuracy measure. The whole investigations were carried out on ten e-nose and e-tongue sensor data. The findings indicated that the bounded Mahalanobis distance is superior in selecting important features with fewer features than the unbounded criterion. Moreover, with the bounded distance approach, the feature selection using the PFFS obtained higher classification accuracy. The overall proposed procedure is found fit to replace the traditional discriminant analysis of multi sensor data fusion due to greater discriminative power and faster convergence rate of higher accuracy. As conclusion, the feature selection can solve the problem of feature extraction. Next, the proposed PFFS has been proved to be effective in selecting subsets of features of higher accuracy with faster computation. The study also specified the advantage of the unbounded and bounded Mahalanobis distance in feature selection of high dimensional data which benefit both engineers and statisticians in sensor technolog

Optimization of Manpower a Case Study at Kilang Gula Felda Perlis Sdn. Bhd.

This thesis is the result of a research on the optimization of manpower in the manufacturing sector. A case study was performed at Kilang Gula Felda Perlis Sdn. Bhd. (KGFP) Chuping, Perlis. Basically, the objective is to identify the optimal number of permanent and temporary workers that would be allocated in the clarification, boiling, curing, and packing stations for morning, afternoon and night shifts. Three linear programming models were formulated using the optimization approach, by adapting earlier studies by Alfares, and Topaloglu and Ozkarahan, guided by other studies that are related to this research field. The findings of the models are able to improve the current allocation of workers by 30% to 36%. The computer package LINDO was used to attain the research objectives. The proposed models and their findings may offer good thoughts for the management of KGFP to improve the practiced human resource, mainly on manning the stations. As a return value, it would contribute to a significant cost saving. Also, the management may adapt the proposed models to solve similar problems in other departments in the company

Sensors closeness test based on an improved [0, 1] bounded Mahalanobis distance Δ2

Mahalanobis distance οଶ values are commonly in the range of 0 to ൅λ where higher values represent greater distance between class means or points. The increase in Mahalanobis distance is unbounded as the distance multiply.To certain extend, the unbounded distance values pose difficulties in the evaluation and decision for instance in the sensors closeness test.This paper proposes an approach to [0, 1] bounded Mahalanobis distance οଶ that enable researcher to easily perform sensors closeness test.The experimental data of four different types of rice based on three different electronic nose sensors namely InSniff, PEN3, and Cyranose320 were analyzed and sensor closeness test seems successfully performed within the [0, 1] bound

An Enhanced Random Linear Oracle Ensemble Method using Feature Selection Approach based on Naïve Bayes Classifier

Random Linear Oracle (RLO) ensemble replaced each classifier with two mini-ensembles, allowing base classifiers to be trained using different data set, improving the variety of trained classifiers. Naïve Bayes (NB) classifier was chosen as the base classifier for this research due to its simplicity and computational inexpensive. Different feature selection algorithms are applied to RLO ensemble to investigate the effect of different sized data towards its performance. Experiments were carried out using 30 data sets from UCI repository, as well as 6 learning algorithms, namely NB classifier, RLO ensemble, RLO ensemble trained with Genetic Algorithm (GA) feature selection using accuracy of NB classifier as fitness function, RLO ensemble trained with GA feature selection using accuracy of RLO ensemble as fitness function, RLO ensemble trained with t-test feature selection, and RLO ensemble trained with Kruskal-Wallis test feature selection. The results showed that RLO ensemble could significantly improve the diversity of NB classifier in dealing with distinctively selected feature sets through its fusionselection paradigm. Consequently, feature selection algorithms could greatly benefit RLO ensemble, with properly selected number of features from filter approach, or GA natural selection from wrapper approach, it received great classification accuracy improvement, as well as growth in diversity

A realization of classification success in multi sensor data fusion

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)

Principal Component Analysis – A Realization of Classification Success in Multi Sensor Data Fusion

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)

Competitive advantage through internal perception of organizational culture

Current business environment has become more complicated and advanced. Therefore, an organization should sustain its competitive advantage to ensure the business survival. An organization may enhance its business competitive advantage through their employees. This paper attempts to discuss relationship between the internal perceptions of organizational culture and strategic human resource management. A cross-sectional survey was performed to identify the current scenario of internal perception among 187 employees in Malaysian broadcasting industry. Research results showed that there were different means score in each dimension of the studied organizational culture and strategic human resource management.There exist significance correlations and regressions among the variables. The research findings suggest that the relationship between organizational culture and strategic human resource management influence the competitive advantage of the Malaysia broadcasting industry

Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together

A Bio-Inspired Herbal Tea Flavour Assessment Technique

Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers’ performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied

Artificial Odour Classification System

This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the classification rule.The common approach to deal with multicollinearity is feature extraction.However, the criterion used in extracting the raw features based on variances may not be appropriate for the ultimate goal of classification accuracy. Alternatively, feature selection method would be advisable as it chooses only valuable features. Two distance-based criteria in determining the right features for classification purposes, Wilk's Lambda and bounded Mahalanobis distance, are applied. Classification with features determined by bounded Mahalanobis distance statistically performs better than Wilk's Lambda.This chapter suggests that classification of e-nose with feature selection is a good choice to limit the cost of experiments and maintain good classification performance