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All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution.

The Data mining algorithms have become vital to researchers in science, engineering, medicine, business, search and security domains. In recent years, there has been a terrific raise in the size of the data being collected and analyzed. Classification is the main difficulty faced in data mining. In a number of the solutions developed for this problem, most accepted one is Decision Tree Classification (DTC) that gives high precision while handling very large amount of data. This paper presents VLSI implementation of flexible architecture for Decision Tree classification in data mining using c4.5 algorithm.

In today's open market, e-commerce has developed its strategy to process various business applications. There applications are developed with the platforms like Magento, Zen Cart, Prestashop, Spree etc., to run a successful online store. These are also used for customer e-commerce apps and require a scalable database for storing the data. The NoSQL database provides an efficient storage access and processing environment with horizontally scalable and replicable strategy over RDBMS. Because of its schema less structure, NoSQL databases provides access to the users to dynamically change the data model applications. Due to this characteristics, NoSQL databases are being used in real time analysis. In order to build a successful online and scalable store, in this study, we choose the database as Document-Oriented NoSQL database for the inclusion of e-commerce workflow. As a new finding, in this study, we will ascertain design and working strategy of e-commerce based on MongoDB, which is a widely used Document-Oriented NoSQL database to process large scale business applications.