Background and Objectives: Carbapenems have been the choice of antibiotics for the treatment of infections caused by multidrug-resistant bacteria. The main objective of this study was to determine the prevalence of carbapenemase (blaVIM and bla ) producing isolates among Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Materials and Methods: A total of 1,151 clinical samples were collected from the patients visiting Annapurna Neurological Institute and Allied Science and Annapurna Research Centre, Kathmandu, between June 2017 and January 2018. Antibiotic susceptibility testing (AST) was performed on the Enterobacteriaceae, P. aeruginosa and A. baumannii isolates using the Kirby-Bauer disk diffusion method. The modified Hodge test (MHT) was performed on the carbapenem-resistant isolates to confirm carbapenemase production. DNA was extracted and then screened for blaVIM and blaIMP genes by multiplex PCR. Results: Of the total 1,151 clinical samples, 253 (22.0%) showed positive growth. Of them, 226 (89.3%) were identified as Enterobacteriaceae, P. aeruginosa, and A. baumannii. Among the 226 isolates, 106 (46.9%) were multidrug-resistant. Out of the 106, 97 (91.5%) isolates showed resistance to at least one of the carbapenem used. Among the 97 carbapenem-resistant isolates, 67 (69.1%) showed the modified Hodge test (MHT) positive results. bla isolates respectively using multiplex PCR assay. Conclusion: This study determined a high prevalence of MDR and carbapenem resistance among Enterobacteriaceae, P. aeruginosa, and A. baumannii as detected by the presence of blaVIM and blaIMP genes. This study recommends the use of rapid and advanced diagnostic tools along with conventional phenotypic detection methods in the clinical settings for early detection and management of drug-resistant pathogens to improve treatment strategies.

With the advancement in the web technology it is considered as one of the vast repository of information. However this information is in the hidden form. Various data mining techniques need to be applied for extracting the meaningful information from the web. In this paper the various techniques are discussed that have been used by many researchers for extracting the information and also shown the disadvantages with the existing approaches. The paper put forward a novel concept of mining the association rule from the web data by using Quine-McCluskey algorithm. This algorithm is an optimization technique over the existing algorithm like Apriori, reverse Apriori, k-map. This paper exhibits the working of the Quine-McCluskey algorithm that can extract the frequently accessed web pages with minimum number of candidate sets generation. However the limitation of Quine-McCluskey algorithm is that it cannot find the infrequent patterns.

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Abstract-This paper proposed an effective algorithm for mining frequent sequence patterns from the web data by applying association rules based on Apriori, known as Advanced Reverse Apriori Algorithm (ARAA). It also shows the limitation of existing Apriori and Reverse Apriori Algorithm. Our approach is based on the reverse scans. An experimental work is performed that shows that proposed algorithm works better than the existing two algorithms. The advantages of ARAA are that it can deeply reduce the multiple scans for frequent sequence pattern generation which results in less processing overhead. A comparative study performed on all three approaches shows that our algorithm improve the mining process significantly as compared to Apriori and Reverse Apriori based mining algorithms especially for the all database. The advantages of ARAA are reduced execution time and increase throughput.

Phishing refers to the fraudulent attempt to obtain sensitive information such as a user's username and password, as well as details about a checking account or credit card, for the purpose of using that information for malevolent purposes. It's possible that phishing scams are the most common form of cybercrime utilised today. Phishing attacks can be launched against victims in a variety of contexts, including the online payment industry, webmail, financial institutions, file hosting or cloud storage, and many more. Phishing may be detected quite effectively through the use of machine learning. Additionally, it eliminates the problem that was caused by the prior method. This study focuses on the application of machine learning technology to the problem of identifying phishing URLs. Specifically, it extracts and compares numerous characteristics of real and fraudulent URLs. By utilising the Support Vector Machine technique as well as the Random Forest technique, the project intends to identify URLs that lead to phishing websites.