A modified scout bee for artificial bee colony algorithm and its performance on optimization problems

The artificial bee colony (ABC) is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. In this paper, artificial bee colony with the rate of change technique which models the behaviour of scout bee to improve the performance of the standard ABC in terms of exploration is introduced. The technique is called artificial bee colony rate of change (ABC-ROC) because the scout bee process depends on the rate of change on the performance graph, replace the parameter limit. The performance of ABC-ROC is analysed on a set of benchmark problems and also on the effect of the parameter colony size. Furthermore, the performance of ABC-ROC is compared with the state of the art algorithms

Multi objective machining estimation model using orthogonal and neural network

Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi orthogonal and NN modeling approach is tested on two types of electrical discharge machining (EDM) operations; Cobalt Bonded Tungsten Carbide (WC-Co) and Inconel 718 to observe the efficiency of proposed approach on different numbers of objectives. WC-Co EDM considered two objective functions and Inconel 718 EDM considered four objective functions. It is found that one hidden layer 4-8-2 layer recurrent neural network (LRNN) is the best estimation model for WC-Co machining and one hidden layer 5-14-4 cascade feed forward back propagation (CFBP) is the best estimation model for Inconel 718 EDM. The results are compared with trial-error approach and it is proven that the proposed modeling approach is able to improve the machining performances and works efficiently on two-objective problems

An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis

To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48 by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97 by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis

Antibiotic resistance and molecular typing among cockle (Anadara granosa) strains of Vibrio parahaemolyticus by polymerase chain reaction (PCR)-based analysis

Genomic DNA of Vibrio parahaemolyticus were characterized by antibiotic resistance, enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) and random amplified polymorphic DNA-polymerase chain reaction (RAPD-PCR) analysis. These isolates originated from 3 distantly locations of Selangor, Negeri Sembilan and Melaka (East coastal areas), Malaysia. A total of 44 (n = 44) of tentatively V. parahaemolyticus were also examined for the presence of toxR, tdh and trh gene. Of 44 isolates, 37 were positive towards toxR gene; while, none were positive to tdh and trh gene. Antibiotic resistance analysis showed the V. parahaemolyticus isolates were highly resistant to bacitracin (92 %, 34/37) and penicillin (89 %, 33/37) followed by resistance towards ampicillin (68 %, 25/37), cefuroxime (38 %, 14/37), amikacin (6 %, 2/37) and ceftazidime (14 %, 5/37). None of the V. parahaemolyticus isolates were resistant towards chloramphenicol, ciprofloxacin, ceftriaxone, enrofloxacin, norfloxacin, streptomycin and vancomycin. Antibiogram patterns exhibited, 9 patterns and phenotypically less heterogenous when compared to PCR-based techniques using ERIC- and RAPD-PCR. The results of the ERIC- and RAPD-PCR were analyzed using GelCompare software. ERIC-PCR with primers ERIC1R and ERIC2 discriminated the V. parahaemolyticus isolates into 6 clusters and 21 single isolates at a similarity level of 80 %. While, RAPD-PCR with primer Gen8 discriminated the V. parahaemolyticus isolates into 11 clusters and 10 single isolates and Gen9 into 8 clusters and 16 single isolates at the same similarity level examined. Results in the presence study demonstrated combination of phenotypically and genotypically methods show a wide heterogeneity among cockle isolates of V. parahaemolyticus

Utilization of filter feature selection with support vector machine for tumours classification

Due to rapid technology advancement, machine learning has been widely used for solving cancer classification problem. Classification performance is highly depending on the quality of input features. With an explosive increase number of features of high dimensional data, the occurrence of ambiguous samples and data redundancy directly leads to poor classification accuracy. Therefore, this paper presents a utilization of filter feature selection using four filter methods such as Information Gain, Gain Ratio, Chi-Squared and Relief-F by performing attribute rankings to remove the irrelevant and redundant features and evaluate the significance and correlation of input data. Then, the classification will be performed using Support Vector Machine (SVM) to measure the accuracy performance based on the number of selected features. The performance measurement will be validated on standard Breast Cancer datasets consisting of 286 instances obtained from the UCI repository. Evaluation metrics such as accuracy, sensitivity, specificity and Area under Receiver Operating Characteristic Curve (AUC) will be used to assess the performance of the SVM classifier using four different filter methods. Experimental result shows that Gain ratio improves the accuracy of SVM classification compared to Information Gain, Chi-Squared and Relief-F in classifying breast cancer data with only small number of features selected

Audio deformation based data augmentation for convolution neural network in vibration analysis

Audio deformations in audio processing have proved ability in preserve semantic meaning for audio signal. Convolution Neural Network (CNN) is among deep learning model that requires huge dataset during training for excellence performance Thus, data augmentation (DA) method is used to overcome the problem of limited dataset number for vibration analysis. Several signal processing phases including segmentation and image converting need to be performed before the vibration signal can be used as input for CNN. In this research, audio-deformation based DA is proposed in generating the additional vibration signal dataset. The proses is start by encoding the raw vibration signal to audio signal format to enable the audio deformation process performing, then decoding back into new vibration signal. Speed and amplify transformation are selected for audio deformation process. The new vibration data set of bearing fault detection problem are used for training CNN to validate the proposed approach. The results obtained from 13 experiments setting have shown that the proposed DA able to increase the accuracy of training for CNN until 13% compared with the previous DA method

Ergogenic, anti-diabetic and antioxidant attributes of selected Malaysian herbs: characterisation of flavonoids and correlation of functional activities

In the present work, aqueous ethanolic (60% ethanol) extracts from selected Malaysian herbs including Murraya koenigii L. Spreng, Lawsonia inermis L., Cosmos caudatus Kunth, Piper betle L., and P. sarmentosum Roxb. were evaluated for their ergogenic, anti-diabetic and antioxidant potentials. Results showed that the analysed herbs had ergogenic property and were able to activate 5'AMP-activated protein kinase (AMPK) in a concentration dependant manner. The highest AMPK activation was exhibited by M. koenigii extract which showed no significant (p > 0.05) difference with green tea (positive control). For anti-diabetic potential, the highest α-glucosidase inhibition was exhibited by M. koenigii extract with IC50 of 43.35 ± 7.5 μg/mL, which was higher than acarbose (positive control). The determinations of free radical scavenging activity and total phenolics content (TPC) indicated that the analysed herbs had good antioxidant activity. However, C. caudatus extract showed superior antioxidant activity with IC50 against free radical and TPC of 21.12 ± 3.20 μg/mL and 221.61 ± 7.49 mg GAE/g, respectively. RP-HPLC analysis established the presence of flavonoids in the herbs wherein L. inermis contained the highest flavonoid (catechin, epicatechin, naringin and rutin) content (668.87 mg/kg of extract). Correlations between the analyses were conducted, and revealed incoherent trends. Overall, M. koenigii was noted to be the most potent herb for enhancement of AMPK activity and α-glucosidase inhibition but exhibited moderate antioxidant activity. These results revealed that the selected herbs could be potential sources of natural ergogenic and anti-diabetic/antioxidant agents due to their rich profile of phenolics. Further analysis in vivo should be carried out to further elucidate the mechanism of actions of these herbs as ergogenic aids and anti-diabetic/antioxidant agents

Neural network corner detection of vertex chain code

This paper presents a Neural Network Classifier to be implemented in corner detection of chain code series. The classifier directly uses chain code which is derived using Vertex chain code as training, testing and validation set. The steps of developing Neural Network Classifier are included in this paper. Comparison results between Vertex chain code Neural Network Classifier with other computational corner detection are presented to show the reliability of the proposed classifier. This paper ends with the discussions on the implementation of proposed neural network in corner detection of chain code series. Experimental results have shown that the proposed network has good robustness and detection performance. This makes this method a great choice for machine vision

Neural network in corner detection of vertex chain code series

This paper presents a Neural Network Classifier to be implemented in corner detection of chain code series. The classifier directly uses chain code which is derived using Freeman chain code as training, testing and validation set. The steps of developing Neural Network Classifier are included in this paper. Comparison results between Neural Network Classifier corner detection and other computational corner detection are presented to show the reliability of the proposed classifier. This paper ends with the discussions on the implementation of proposed neural network in corner detection of chain code series. Experimental results have shown that the proposed network has good robustness and detection performance. This makes this method a great choice for machine vision

Concentration measurements of bubbles in a water column using an optical tomography system

Optical tomography provides a means for the determination of the spatial distribution of materials with different optical density in a volume by non-intrusive means. This paper presents results of concentration measurements of gas bubbles in a water column using an optical tomography system. A hydraulic flow rig is used to generate vertical air–water two-phase flows with controllable bubble flow rate. Two approaches are investigated. The first aims to obtain an average gas concentration at the measurement section, the second aims to obtain a gas distribution profile by using tomographic imaging. A hybrid back-projection algorithm is used to calculate concentration profiles from measured sensor values to provide a tomographic image of the measurement cross-section. The algorithm combines the characteristic of an optical sensor as a hard field sensor and the linear back projection algorithm