The Malvaceae is a large family of about 243 genera and 4225 species with medicinal and gardening importance. Palynological observations were carried out of 9 species collected from different localities of Purba Medinipur District during the period of February 2016 to December 2017. The present study reveals the pollen morphological characteristics, especially pollen shape classes like Oblate (1 species), Oblate-spheroidal (4 species), Subprolate (1 species) and Per-oblate (3 species) with diameter ranges between 35 µm (Urena lobata L.) to 115 µm (Malvaviscus arboreus Cav.). Pollen morphology is one of the most significant tool for the taxonomic study as well as systematics of plant taxa.

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In today era modern infrastructures and technologies are more prone to various types of accesses. A method that is commonly used for launching these types of attack is popularly known as malware i.e. viruses, Trojan horses and worms, which, when propagate can cause a great damage to commercial companies, private users and governments. The another reason that enhance malware to infect and spread very rapidly is high-speed Internet connections as it has become more popular now a days, therefore it is very important to eradicate and detect new (benign) malware in a prompt manner. Hence in this work, proposing three data mining algorithms to produce new classifiers with separate features: RIPPER, Naïve Bayes and a Multi Classifier system along with hybrid of clustering techniques and the comparison between these methods to predict which provides better results.