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Surya Neeragatti,Ranjit Kumar Dehury 한국경영정보학회 2023 Asia Pacific Journal of Information Systems Vol.33 No.4
This study focuses on the adoption of Healthcare Information System (HIS) in India’s healthcare services, which has led to an increased use of HIS software for managing patient information in hospitals. The study aims to evaluate the factors that influence hospital workers’ satisfaction with HIS usage and its impact on their intention to continue in the use of HIS. Primary data was collected through a survey questionnaire from 265 hospital workers. A new framework was developed, and Structural Equation Modeling (SEM) was used for analysis. Sensitivity analysis was also conducted on demographic data using an Artificial Neural Network (ANN) approach. The results indicated that all hypotheses were significant (p < 0.05). Effort expectancy was the most significant factor influencing hospital workers’ satisfaction (p < 0.01). Sensitivity analysis showed that education (Model-A) and experience in use of HIS (Model-B) were the most important factors. The study contributes by proposing a new theoretical framework and extending the previous research on HIS usage satisfaction. Overall, the study highlights the importance of easiness and usefulness in predicting HIS usage satisfaction.
Tripathy, Suman Kumar,De, Umasankar,Dehury, Niranjan,Pal, Satyanarayan,Kim, Hyung Sik,Patra, Srikanta The Royal Society of Chemistry 2014 Dalton Transactions Vol.43 No.39
<P>Phpy bridged homodinuclear Ru–Ru (<B>1</B>) and heterodinuclear Ir–Ru complexes (<B>2</B>) have been developed. Complex <B>2</B> induces autophagy towards the cisplatin resistant human breast cancer (MCF7) cell line, whereas <B>1</B> is inactive.</P> <P>Graphic Abstract</P><P>Heterodinuclear Ir–Ru (<B>2</B>) with polypyridyl based phpy ligand shows autophagy induced cell death, whereas homodinuclear Ru–Ru (<B>1</B>) is inactive. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c4dt01033g'> </P>
Tripathy, Suman Kumar,De, Umasankar,Dehury, Niranjan,Laha, Paltan,Panda, Manas Kumar,Kim, Hyung Sik,Patra, Srikanta The Royal Society of Chemistry 2016 Dalton Transactions Vol.45 No.38
<P>Six mononuclear Ir complexes (1-6) using polypyridyl-pyrazine based ligands (L-1 and L-2) and {[cp*IrCl-(mu-Cl)](2) and [(ppy)(2)Ir(mu-Cl)](2)} precursors have been synthesised and characterised. Complexes 1-5 have shown potent anticancer activity against various human cancer cell lines (MCF-7, LNCap, Ishikawa, DU145, PC3 and SKOV3) while complex 6 is found to be inactive. Flow cytometry studies have established that cellular accumulation of the complexes lies in the order 2 > 1 > 5 > 4 > 3 > 6 which is in accordance with their observed cytotoxicity. No changes in the expression of the proteins like PARP, caspase 9 and beclin-1, Atg12 discard apoptosis and autophagy, respectively. Overexpression of CHOP, activation of MAPKs (P38, JNK, and ERK) and massive cytoplasmic vacuolisation collectively suggest a paraptotic mode of cell death induced by proteasomal dysfunction as well as endoplasmic reticulum and mitochondrial stress. An intimate relationship between p53, ROS production and extent of cell death has also been established using p53 wild, null and mutant type cancer cells.</P>
A condensed polynomial neural network for classification using swarm intelligence
Dehuri, S.,Misra, B.B.,Ghosh, A.,Cho, S.B. Elsevier Science, B.V 2011 Applied soft computing Vol.11 No.3
A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very complex. Unlike PNN the proposed network needs to generate the partial description for a single layer. The discrete PSO (DPSO) is used to select a relevant set of PDs as well as features with a hope to get better accuracy, which are in turn fed to the output neuron. The weights associated with the links from hidden to output neuron is optimized by PSO for continuous domain (CPSO). Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of this model both in processing time and accuracy, is encouraging for harnessing its power in domain with large and complex data particularly in data mining area.
Bakshi, S.,Jagadev, A.K.,Dehuri, S.,Wang, G.N. Elsevier Science, B.V 2014 Applied soft computing Vol.15 No.-
Recommendation system has been a rhetoric area and a topic of rigorous research owing to its application in various domains, from academics to industries through e-commerce. Recommendation system is useful in reducing information overload and improving decision making for customers in any arena. Recommending products to attract customers and meet their needs have become an important aspect in this competitive environment. Although there are many approaches to recommend items, collaborative filtering has emerged as an efficient mechanism to perform the same. Added to it there are many evolutionary methods that could be incorporated to achieve better results in terms of accuracy of prediction, handling sparsity as well as cold start problems. In this paper, we have used unsupervised learning to address the problem of scalability. The recommendation engine reduces calculation time by matching the interest profile of the user to its partitioned and even smaller training samples. Additionally, we have explored the aspect of finding global neighbours through transitive similarities and incorporating particle swarm optimization (PSO) to assign weights to various alpha estimates (including the proposed α<SUB>7</SUB>) that alleviate sparsity problem. Our experimental study reveals that the particle swarm optimized alpha estimate has significantly increased the accuracy of prediction over the traditional methods of collaborative filtering and fixed alpha scheme.