Informatics Publishing Limited

Neha Kumari , Akanksha Nagar , Sunil Kumar

Abstract

This paper presents the design and development of a context aware system using RFID technology. This system contains RFID tags RFID reader and a server as the processors and screen monitor at various locations. The objective of this system is to sense the location of the system and urgently deliver message to the user. This paper aims to design and implement an RFID-based reliable and efficient solution to sense the location of system. The wireless LANs are used to communication between the tag reader and the web server. The result of this survey has indicated that the proposed system is efficient and easy to use.

Keywords

RFID (Radio Frequency Identification), Ubiquitous Computing, EPC (Electronic Product Code) tag.

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Neha Kumari ¹, Satwinder Singh ¹

Affiliations
1 Department of Computer Science and Technology, Central University of Punjab, City Campus, Manasa Road, Bathinda – 151001, Punjab, IN

Abstract

Objectives: To build a model for the prediction of the code smells using the supervised learning techniques. The motive to build code smell prediction model is to propose a model with less false positive code smells. Also, the proposed model is cross validated using 10-fold cross validation Methods/Statistical Analysis: To build a smell prediction model, two code smell detection tools are used i.e. IPlasma and PMD. The metrics are extracted using understand and IPlasma. To achieve the above mentioned objective, two experiments are performed. One is using the code smells of the PMD and the other one is using the smells of the IPLasma. The smells of the PMD are associated with the metrics that are extracted using Understand. Then, the model is trained using the different supervised learning algorithms that are called classifiers i.e. Random Forest, Naïve Bayes and Kstar. Findings: In this research work, two experiments are performed. One is using the code smells of the PMD and other one is using the code smells of the IPlasma. From the results obtained i.e. with the code smells of the PMD, it is concluded that Random Forest predicts small number of false positive and false negative code smells as the precision and Recall of the Random Forest in each dataset is larger than the other two classifier’s. Moreover, the ROC value of Random Forest is higher in some datasets and in some datasets the ROC value of KStar is higher. The results obtained i.e. with the code smells of the IPlasma, it is concluded that again Random Forest predict code smells more correctly than the other two classifiers and give less number of false positive and false negative code smells. Moreover, there is an exception, there is one dataset in which Random Forest and KStar both shows 100% accuracy i.e., Precison and Recall both are equal to 1, which shows that both classifiers predicts no false positive and false negative code smells. Moreover, the ROC value of Random Forest is higher than the other two classifiers, even in some datasets it is equals to 1. Using this it is concluded that Random Forest gives the best code smell predicting model. Application/Improvements: The results of this experiments shows only one case where the false positive and false negative code smells are not predicted by the models. This can improve such that on apply each and every dataset it gives zero false positive code smells.

Keywords

Automatic Tools, Bad Code Smells, Code Smell, Code Smell Prediction, Supervised Learning Techniques

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Suxam ¹, Nitan Bharti ¹, Pooja Sharma ¹, Neha kumari ¹

Affiliations
1 Sri Sai College of Pharmacy, Badhani, Pathankot, Punjab., IN

Abstract

Drugs that shows complete oral absorption and good bioavailability are soluble in gastrointestinal tract or vice versa. Objective and Method: To enhance the solubility and dissolution of poorly water soluble drug itraconazole. Technique used- Microwave generated bionanocomposites technique. Carriers- Chitosan, &#946- cyclodextrin and Poly vinyl pyrolidine. Result and Conclusion: The prepared bionanocomposites was evaluated by UV spectroscopy, XRD study, In vitro drug release study and TEM. The results shows that in vitro data obtained for bionanocomposites of itraconazole showed increase solubility in phosphate buffer having pH= 6.8 and the method of preparation of itraconazole found to be simple, reproducible, provide good solubility and drug content. Also it is obtained that there was maximum 23 fold increase in solubility when Poly vinyl pyrolidine used as a carrier in 1:4 and hence considered as effective carrier for crystallization of itraconazole.

Keywords

Bionanocomposites, Nanocomposites, Itraconazole, polymer, solubility

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