Building A Malware Finding System Using A Filter-Based Feature Selection Algorithm

Flexible Mutual Information Feature Selection is another supervised filter-based feature selection formula that has recently been proposed. With FMIFS, there's no doubt about it, MIFS and MMIFS are outdated. According to FMIFS, a revision to Battiti's formula would help cut down on redundancy among features. Redundancy parameters are no longer required in MIFS and MMIFS because of FMIFS. MIFS and MMIFS are unquestionably better alternatives to FMIFS. Based on the advice of FMIFS, Battiti's formula should be updated to minimize redundancy. In FMIFS, the redundant parameter is eliminated and it results in MIFS and MMIFS. None of the existing technologies are capable of fully safeguarding the internet software and operating networks against threats like DoS attacks, spyware, and adware. Incredible amounts of network traffic pose a major obstacle to IDSs. Our function selection formula contributed significantly more important functionality to LSSVM-IDS in regards to improving LSSVM-IDS' accuracy while minimizing the use of computation in comparison to other approaches. This feature selection method is especially suitable for features that are dependent on either a linear or nonlinear relationship. To provide accurate classification, we have provided a formula based on mutual knowledge, which mathematically selects the perfect function. Its utility is measured by taking into account the use of network intrusion detection. Data with redundant and irrelevant functionality has created a long-term traffic condition. It not only slows the overall classification process, but it also impedes classifiers from making correct decisions, specifically when handling large amounts of data

Deep Learning for User Behaviour Prediction Using Streaming Analytics

Streams of web user interactions reflect behaviour of customers or users of a web application through which a company is being operated online. The interactions may be in the form of visits to web components and even purchases made by users in case of e-Commerce applications. Modelling user behaviour can help the organizations to ascertain patterns of user behaviours and improve their products and services to meet their needs besides making promotional schemes. There are many existing methods for modelling user behaviour. However, of late, deep learning models are found to be more accurate and useful. In this paper a deep learning based framework is proposed for predicting web user behaviour from streams of user interactions. The framework is based on the mechanisms that exploit Recurrent Neural Network (RNN), one of the deep learning approaches, to learn from low-level features of sequential and streaming data. The mechanisms are used to model user interactions and predict the user behaviour with respect to purchasing items in future. In presence of plenty of items, item embeddings is explored for better results. In addition to this, attention mechanisms are employed to achieve RNN model interoperability. The empirical study revealed that the proposed framework is useful besides helping to evaluate different variants of attention mechanisms and item embeddings

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

Diversity of Sodium Transporter HKT1;5 in Genus Oryza

Asian cultivated rice shows allelic variation in sodium transporter, OsHKT1;5, correlating with shoot sodium exclusion (salinity tolerance). These changes map to intra/extracellularly-oriented loops that occur between four transmembrane-P loop-transmembrane (MPM) motifs in OsHKT1;5. HKT1;5 sequences from more recently evolved Oryza species (O. sativa/O. officinalis complex species) contain two expansions that involve two intracellularly oriented loops/helical regions between MPM domains, potentially governing transport characteristics, while more ancestral HKT1;5 sequences have shorter intracellular loops. We compared homology models for homoeologous OcHKT1;5-K and OcHKT1;5-L from halophytic O. coarctata to identify complementary amino acid residues in OcHKT1;5-L that potentially enhance affinity for Na+. Using haplotyping, we showed that Asian cultivated rice accessions only have a fraction of HKT1;5 diversity available in progenitor wild rice species (O. nivara and O. rufipogon). Progenitor HKT1;5 haplotypes can thus be used as novel potential donors for enhancing cultivated rice salinity tolerance. Within Asian rice accessions, 10 non-synonymous HKT1;5 haplotypic groups occur. More HKT1;5 haplotypic diversities occur in cultivated indica gene pool compared to japonica. Predominant Haplotypes 2 and 10 occur in mutually exclusive japonica and indica groups, corresponding to haplotypes in O. sativa salt-sensitive and salt-tolerant landraces, respectively. This distinct haplotype partitioning may have originated in separate ancestral gene pools of indica and japonica, or from different haplotypes selected during domestication. Predominance of specific HKT1;5 haplotypes within the 3 000 rice dataset may relate to eco-physiological fitness in specific geo-climatic and/or edaphic contexts