A hybrid bio-inspired and musical-harmony approach for machine loading optimization in flexible manufacturing system

Manufacturing industries are facing fierce challenges in handling product competitiveness, shorter product cycle time and product varieties.The situation demands a need to improve the effectiveness and efficiency of capacity planning and resource optimization while still maintaining their flexibilities.Machine loading - one of the important components of capacity planning is known for its complexity that encompasses various types of flexibilities pertaining to part selection, machine and operation assignment along with constraints.Various studies are done to balance the productivity and flexibility in Flexible Manufacturing System (FMS).From the literature, researchers have developed many approaches to reach a suitable balance of exploration (global improvement) and exploitation (local improvement).We adopt a hybrid of population approaches; hybrid constraint-chromosome genetic algorithm and harmony search algorithm (H-CCGaHs), to solve this problem that aims at mapping a feasible solution to the domain problem.The objectives are to minimize the system unbalance as well as to increase the through-put while satisfying the constrains such as machine time availability and tool slots.The proposed algorithm is tested for it performance on 10 sample problems available in FMS literature and compared with existing solution approaches

Cuckoo inspired algorithms for feature selection in heart disease prediction

Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets

Community-based mosquito surveillance: an automatic mosquito-on-human-skin recognition system with a deep learning algorithm

Public community engagement is crucial for mosquito surveillance programs. To support community participation, one of the approaches is assisting the public in recognizing the mosquitoes that carry pathogens. Therefore, this study aims to build an automatic recognition system to identify mosquitos at the public community level. We construct a customized image dataset consisting of three mosquito species in either damaged or un-damaged body conditions. To distinguish the mosquito in harsh conditions, we explore two state-of-the-art deep learning (DL) architectures: (i) a freezing convolutional base, with partial trainable weights, and (ii) training the entire model with most of the trainable weights. We project a weighted feature map on different layers of the model to visualize the morphological region used by the model in classification and compared it with the morphological key used by the expert

Classification of aromatic herbs using artificial intelligent technique

Herbs have unique characteristics such as colour, texture and odour. In general, herb identification is through organoleptic methods and is heavily dependent on botanists. It is becoming more difficult to identify different herb species in the same family based only on their aroma. It is because of their similar physical appearance and smell. Artificial technology, unlike humans, is thought to have the capacity to identify different species with precision. An instrument used to identify aroma is the electronic nose. It is used in many sector including agriculture. The electronic nose in this project was to identify the odour of 12 species such as lauraceae, myrtaceae and zingiberaceae families. The output captured by the electronic nose gas sensors were classified using two types of artificial intelligent techniques: Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). From the result, ANFIS has 94.8% accuracy compared with ANN at 91.7%

Purification and properties of polygalacturonase associated with the infection process of Colletotrichum truncatum CP2 in chilli

In this study, polygalacturonase enzyme produced by Colletotrichum truncatum CP2 was partially purified by aqueous two-phase system and the properties of this enzyme was characterized. The highest yield (57.4%) and purification fold (5.1) was obtained using 22% PEG 6,000/15% sodium citrate comprising crude load of 16% (w/w) at pH 7.0 with addition of 1.0% (w/w) sodium chloride. The partially purified PG remained active over a wide range of pH (2.5-6.0) and the optimum activity was obtained at pH 5.0. Incubation of the partially purified PG at 40 and 50 °C for 30 min caused the activity of PG to decrease up to 20% and 40%, respectively. However, no significant changes in the activity when the enzymes were incubated up to 4 h at 40 and 50 °C. The results from this study suggested that ATPS comprising of PEG and sodium citrate could be potentially used as an alternative method for purification of PG

Aromatic herbs classification by using discriminant analysis techniques

An electronic nose was used to distinguish between selected herb samples according to their family group species. This paper aims to evaluate the potential of using the electronic nose to characterize three groups of families of twelve herb species based on the discriminant analysis approach. The feature extraction involves the use of a signal processing technique that simplifies classification and yields optimal results. Two discriminant techniques: the principal component analysis (PCA) and the multiple discriminant analysis (MDA) were used to investigate the potential to distinguish herb species between several herbs within the same family group. The results showed that the twelve herb species can be better classified using the MDA method compared to the PCA method

Isolation, structure elucidation, identification and quantitative analysis of 1'-acetoxychavicol (ACA) from the roots of Chlorophytum boriviliuanum (safed musli)

Chlorophytum borivilianum (safed musli) is a medicinally important plant. Its roots are being employed in folk medicine. Presently, the crude extract of C. borivilianum has been consumed for the treatment such as anti-diabetic, anti-aging, anti-oxidant, anti-ulcer and anti-inflammatory and previous studies have been carried out to further confirm these remarkable bioactivities of C. borivilianum. In this research, 1’-acetoxychavicol acetate (ACA) was isolated from the roots of C. borivilianum. The structure of ACA was elucidated based on the spectral data of 1H NMR, 13C NMR, DEPT, COSY, HMBC, HMQC and also based on the comparison with the previous literature data. ACA was isolated in an isocratic elution that eluted with hexane and ethyl acetate in the ratio of 10:0.25. In the HPLC analysis, the separation of the crude methanol extract was completed within 20 min and the retention time of ACA in the sample was 7.31 min. The regression equation of the calibration curve was developed and the correlation coefficient was found to be 0.991. This is the first report regarding the presence of ACA in C. borivilianum as well as its genus. For the first time, a high performance liquid chromatographic (HPLC) method with photodiode array detection was developed for the quantitative determination and identification of ACA

Isolation, structure elucidation, identification and quantitative analysis of 1’-acetoxychavicol (ACA) from the roots of chlorophytum boriviliuanum (safed musli)

Chlorophytum borivilianum (safed musli) is a medicinally important plant. Its roots are being employed in folk medicine. Presently, the crude extract of C. borivilianum has been consumed for the treatment such as anti-diabetic, antiaging, anti-oxidant, anti-ulcer and anti-inflammatory and previous studies have been carried out to further confirm these remarkable bioactivities of C. borivilianum. In this research, 1’-acetoxychavicol acetate (ACA) was isolated from the roots of C. borivilianum. The structure of ACA was elucidated based on the spectral data of 1H NMR, 13C NMR, DEPT, COSY, HMBC, HMQC and also based on the comparison with the previous literature data. ACA was isolated in an isocratic elution that eluted with hexane and ethyl acetate in the ratio of 10:0.25. In the HPLC analysis, the separation of the crude methanol extract was completed within 20 min and the retention time of ACA in the sample was 7.31 min. The regression equation of the calibration curve was developed and the correlation coefficient was found to be 0.991. This is the first report regarding the presence of ACA in C. borivilianum as well as its genus. For the first time, a high performance liquid chromatographic (HPLC) method with photodiode array detection was developed for the quantitative determination and identification of ACA

E-nose herbs recognition system based on artificial neural network technique

Electronic sensing technology intervention was intended to overcome human's physical limitation. It has developed and greatly advanced over the decade. This project emphasizes on characterizing herbs species based on unique of herbs odor. E-nose system in this project consist an array of commercial gas sensor which detects gas through an increase in electrical conductivity when reducing gases are absorbed on the sensor's surface. Data obtained from sensors array are classified using Artificial Neural Network (ANN) technique. The E-nose system with five sensors has the highest capability in classifying herbs sample. Accuracy in classifying the correct herbs increases with number of the sensors used. Results show that sensitivity of E-nose towards herbs classification increases with higher number of sensors

Effects of Palm Oil Mill Effluent (POME) Anaerobic Sludge From 500m3 of Closed Anaerobic Methane Digested Tank on Pressed-Shredded Empty Fruit Bunch (EFB) Composting Process

In this study, co-composting of pressed-shredded empty fruit bunches (EFB) and palm oil mill effluent (POME) anaerobic sludge from 500 m3 closed anaerobic methane digested tank was carried out. High nitrogen and nutrients content were observed in the POME anaerobic sludge. The sludge was subjected to the pressed-shredded EFB to accelerate the co-composting treatment. In the present study, changes in the physicochemical characteristics of co-composting process were recorded and evaluated. The co-composting treatment was completed in a short time within 40 days with a final C/N ratio of 12.4. The co-composting process exhibited a higher temperature (60 - 67℃) in the thermophilic phase followed by curing phase after four weeks of treatment. Meanwhile, pH of the composting pile (8.1 - 8.6) was almost constant during the process and moisture content was reduced from 64.5% (initial treatment) to 52.0% (final matured compost). The use of pressed-shredded EFB as a main carbon source and bulking agent contributed to the optimum oxygen level in the composting piles (10 - 15%). The biodegradation of composting materials is shown by the reduction of cellulose (34.0%) and hemicellulose (27.0%) content towards the end of treatment. In addition, considerable amount of nutrients and low level of heavy metals were detected in the final matured compost. It can be concluded that the addition of POME anaerobic sludge into the pressed-shredded EFB composting process could produce acceptable and consistent quality of compost product in a short time