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

Research and modelling of surface roughness, cutting forces and I-kaz coefficients for S42C in turning using response surface methodology

This paper presents the optimization in machining processes on the cutting parameters for the S45C in turning process using the response surface method (RSM). The experimental work conducted investigates the influence of cutting parameters on statistical analysis of signals and surface quality. The paper also presents a statistical analysis of signal processing. The cutting force was measured during machining using the Kistler 9129AA dynamometer to monitor the force signals and the data was analyzed using the I-kazTM method of statistical analysis. This statistical analysis was used to assess the effect of force signals during the machining process. The RSM models for Ra and Rz, and I-kaz coefficients (Z) have been developed with ANOVA and multiple regression equations. The models also were compared and validated with the predicted and measured of Ra and Rz values, and I-kaz coefficients. The optimal configuration of cutting parameters was observed at 200 m/min, 0.1 mm/rev and 0.521 mm with desirability of 95.9%. It is observed that the models developed are suggested to be utilized for predicting surface roughness values and I-kaz coefficients for the machining of S45C steel

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

Research of chatter suppression in turning operation with process damping using stability lobe diagram

This paper presents a chatter detection technique based on the stability of the measured Ra and Rz values of process damping and surface roughness in low cutting speed activities. In practice, process damping during machining procedures is hard to predict and identify due to the model and technique of limitation. The impact of cutting conditions on process damping in turning with P20 steel pre-hardened metal in terms of cutting velocity, feed rate and cutting depth was explored by the Stability Lobe Diagram method. A CNC turning machine was used in dry turning procedures with carbide insert. The highest and minimum value of natural frequencies and damping ratios were evaluated by modal testing and the stability lobe diagram analysis was applied. It is concluded that in the same region of the Stability Lobe Diagram, the chatter and measured surface roughness values were correlated and shown to have strong consensus

Modification of Thin Film Composite Nanofiltration Membrane using Silver Nanoparticles: Preparation, Characterization and Antibacterial Performance

This paper reports on preparation of polyamide membrane with addition of silver nanoparticles (AgNPs). AgNPs act as antibacterial agents that are less susceptible to membrane’s biofouling by interfacial polymerization (IP) method. AgNPs was synthesized via green route which has been reported previously. Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR), Field emission scanning electron microscopy (FESEM), contact angle measurement, and Energy dispersive X-ray spectrometer (EDX) were carried out to characterize the morphology of the prepared membrane samples. Inhibition zone of E. Coli bacteria was used to study the antibacterial behavior of the membranes. As a result, FTIR spectra clearly present the peak of primary amide, secondary amides, carboxyl group and etc. In addition, from the FESEM images, it could be seen that relatively regular and spherical shape AgNPs were formed. Contact angle results revealed that the PA membrane is more hydrophilic than that of the PES membrane. EDX spectra shows a peak that confirmed the presence of AgNPs on PA/Ag membrane. In antibacterial test, the PA membrane alone could not inhibit the growth of E.Coli. Membranes with 10 ml and 15 ml loading of AgNPs added to the M-Phenylenediamine (MPD) monomer for the IP process were not enough to kill the E.Coli bacteria. The addition of 20ml of AgNPs to MPD monomer however showed an interesting result as we can clearly observe the inhibition zone in the diameter of 1 mm around the circle of the membrane indicate that all bacteria were totally killed

Further insights into the syndrome of prolapsing non-coronary aortic cusp and ventricular septal defect.

Ventricular septal defect (VSD) with prolapse of the right coronary cusp and aortic regurgitation can be managed surgically with the anatomical correction technique. However when the VSD is located underneath the non coronary cusp surgical management differs due to anatomical constraints and secondary pathological changes seen in the non coronary cusp. It is therefore important that the location of the VSD and the morphology of prolapsing cusp be characterised preoperatively in order to plan appropriate surgical repair. We present a case study in which we discuss the salient differences in the surgical management of the prolapsing right and the prolapsing non coronary cusps