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A Meta-Cognitive Learning Algorithm for an Extreme Learning Machine Classifier
Savitha, R.,Suresh, S.,Kim, H. J. Springer Science + Business Media 2014 Cognitive computation Vol.6 No.2
This paper presents an efficient fast learning classifier based on the Nelson and Narens model of human meta-cognition, namely 'Meta-cognitive Extreme Learning Machine (McELM).' McELM has two components: a cognitive component and a meta-cognitive component. The cognitive component of McELM is a three-layered extreme learning machine (ELM) classifier. The neurons in the hidden layer of the cognitive component employ the q-Gaussian activation function, while the neurons in the input and output layers are linear. The meta-cognitive component of McELM has a self-regulatory learning mechanism that decides what-to-learn, when-to-learn, and how-to-learn in a meta-cognitive framework. As the samples in the training set are presented one-by-one, the meta-cognitive component receives the monitory signals from the cognitive component and chooses suitable learning strategies for the sample. Thus, it either deletes the sample, uses the sample to add a new neuron, or updates the output weights based on the sample, or reserves the sample for future use. Therefore, unlike the conventional ELM, the architecture of McELM is not fixed a priori, instead, the network is built during the training process. While adding a neuron, McELM chooses the centers based on the sample, and the width of the Gaussian function is chosen randomly. The output weights are estimated using the least square estimate based on the hinge-loss error function. The hinge-loss error function facilitates prediction of posterior probabilities better than the mean-square error and is hence preferred to develop the McELM classifier. While updating the network parameters, the output weights are updated using a recursive least square estimate. The performance of McELM is evaluated on a set of benchmark classification problems from the UCI machine learning repository. Performance study results highlight that meta-cognition in ELM framework enhances the decision-making ability of ELM significantly.
The Chicken Aorta as a Simulation-Training Model for Microvascular Surgery Training
Savitha Ramachandran,Christopher Hoe-Kong Chui,Bien-Keem Tan 대한성형외과학회 2013 Archives of Plastic Surgery Vol.40 No.4
As a technically demanding skill, microsurgery is taught in the lab, in the form of a course of variable length (depending on the centre). Microsurgical training courses usually use a mixture of non-living and live animal simulation models. In the literature, a plethora of microsurgical training models have been described, ranging from low to high fidelity models. Given the high costs associated with live animal models, cheaper alternatives are coming into vogue. In this paper we describe the use of the chicken aorta as a simple and cost effective low fidelity microsurgical simulation model for training.
Savitha Ramachandran,Yee-Siang Ong,Andrew YH Chin,In-Chin Song,Bien-Keem Tan 대한성형외과학회 2014 Archives of Plastic Surgery Vol.41 No.3
Microsurgery training in Singapore began in 1980 with the opening of the Experimental Surgical Unit. Since then, the unit has continued to grow and have held microsurgical training courses biannually. The road to becoming a full-fledged reconstructive surgeon requires the mastering of both microvascular as well as flap raising techniques and requires time, patience and good training facilities. In Singapore, over the past 2 decades, we have had the opportunity to develop good training facilities and to refine our surgical education programmes in reconstructive microsurgery. In this article, we share our experience with training in reconstructive microsurgery.
Ramachandran, Savitha,Ong, Yee-Siang,Chin, Andrew Y.H.,Song, In-Chin,Ogden, Bryan,Tan, Bien-Keem Korean Society of Plastic and Reconstructive Surge 2014 Archives of Plastic Surgery Vol.41 No.3
Microsurgery training in Singapore began in 1980 with the opening of the Experimental Surgical Unit. Since then, the unit has continued to grow and have held microsurgical training courses biannually. The road to becoming a full-fledged reconstructive surgeon requires the mastering of both microvascular as well as flap raising techniques and requires time, patience and good training facilities. In Singapore, over the past 2 decades, we have had the opportunity to develop good training facilities and to refine our surgical education programmes in reconstructive microsurgery. In this article, we share our experience with training in reconstructive microsurgery.
The Chicken Aorta as a Simulation-Training Model for Microvascular Surgery Training
Ramachandran, Savitha,Chui, Christopher Hoe-Kong,Tan, Bien-Keem Korean Society of Plastic and Reconstructive Surge 2013 Archives of Plastic Surgery Vol.40 No.4
As a technically demanding skill, microsurgery is taught in the lab, in the form of a course of variable length (depending on the centre). Microsurgical training courses usually use a mixture of non-living and live animal simulation models. In the literature, a plethora of microsurgical training models have been described, ranging from low to high fidelity models. Given the high costs associated with live animal models, cheaper alternatives are coming into vogue. In this paper we describe the use of the chicken aorta as a simple and cost effective low fidelity microsurgical simulation model for training.
Ashwini Kumar,Savitha Janakiraman,Lokesh Kyathsandra Nataraj 한국화학공학회 2021 Korean Journal of Chemical Engineering Vol.38 No.9
Hyaluronic acid finds its complete application in areas such as therapeutics, cosmetics, and as a health supplement. In the present investigation, standardization for the production of hyaluronic acid by Streptococcus equisimilus MK156140 in complex media was performed. Some of the selected physicochemical parameters such as pH, temperature, speed, incubation time, sucrose, yeast extract, and beef extract were screened using Plackett-Burman foldover design. Further, the screened parameters interaction was investigated using central composite design (CCD) and closely compared with OVAT studies. At a pH of 7.38, with beef extract, 12.15%, and yeast extract 7.64%, the observed yield was 7.16 g/L, which was in close line with the predicted value of 7.21 g/L.
Topological Indices and Bounds for the Energy of Fibonacci Graph
Anitha N.,Savitha H. C.,임동규 장전수학회 2021 Proceedings of the Jangjeon mathematical society Vol.24 No.1
Fibonacci sequence of numbers plays a significant role in communication networks, coding theory, encryption and many such areas. A Fibonacci graph Fd,2n is a d regular graph where d is such that Fd is the largest Fibonacci number less than or equal to n. In this paper we present few lower and upper bounds for the energy of the Fibonacci graph in terms of n, d and the determinant of the adjacency matrix, detA. We have also investigated some topological indices for the Fibonacci graph and the obtained results are tabulated.
Development of a Five-Day Basic Microsurgery Simulation Training Course: A Cost Analysis
Masha Singh,Natalia Ziolkowski,Savitha Ramachandran,Simon R Myers,Ali Mahmoud Ghanem 대한성형외과학회 2014 Archives of Plastic Surgery Vol.41 No.3
The widespread use of microsurgery in numerous surgical fields has increased the need for basic microsurgical training outside of the operating room. The traditional start of microsurgical training has been in undertaking a 5-day basic microsurgery course. In an era characterised by financial constraints in academic and healthcare institutions as well as increasing emphasis on patient safety, there has been a shift in microsurgery training to simulation environments. This paper reviews the stepwise framework of microsurgical skill acquisition providing a cost analysis of basic microsurgery courses in order to aid planning and dissemination of microsurgical training worldwide.
Fibonacci Graph and its Energies
Chandrashekar Adiga,Anitha N.,Savitha H. C. 장전수학회 2021 Advanced Studies in Contemporary Mathematics Vol.31 No.1
The energy of a graph is dened as the sum of absolute values of its eigenvalues. In this paper, we compute the spectrum and energy of the Fibonacci graph. Numerous matrices can be associated with a graph and their spectrums provide useful information about the graph. In recent times, various other graph energies are studied, based on eigenvalues of several graph matrices. In the present paper, we also establish relationship between the usual energy of the Fibonacci graph and other energies like Signless Lapalcian energy, Randic energy, maximum degree energy, common-neighborhood energy, 2-distance energy and Seidal energy.
Neha G. Wasnik,Mahalakshmi. Muthusamy,Savitha Chellappan,Veena Vaidhyanathan,Ramakrishna Pulla,Kalaiselvi Senthil,양덕춘 한국자원식물학회 2009 한국자원식물학회지 Vol.22 No.6
Adventitious root culture was established in the Jawahar variety of Withania somnifera using MS basal medium supplemented with 0.5 (mg/l) IAA and 2.0 (mg/l) IBA. Root tips from germinated seedlings, MS0 maintained plants and adventitious roots were maintained in suspension medium (1/2 MS basal medium supplemented with 3% sucrose) for a period of 1 to 6 months. The weight gain in roots was noted and the withanolides were extracted from the dry roots using solvents petroleum ether, 50% ethanol and chloroform. The withanolides in the chloroform fractions of all root samples analyzed were compared using thin layer chromatographic analysis. Withanolide content in adventitious root sample was found to be superior compared to other roots at any given point of time during the 6month growth period.HPLC analysis of in vitro adventitious roots showed the presence of a new compound.