Multimodal information fusion for android malware detection using lazy learning

Multimedia Tools and Applications - Android has a large number of users that are accumulating with each passing day. Security of the Android ecosystem is a major concern for these users with the...     

Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

Image translation plays a significant role in realistic image synthesis, entertainment tasks such as editing and colorization, and security including personal identification. In Edge GAN, the major contribution is attribute guided vector that enables high visual quality content generation. This research study proposes automatic face image realism from freehand sketches based on Edge GAN. We propose a density variant image synthesis model, allowing the input sketch to encompass face features with minute details. The density level is projected into non-latent space, having a linear controlled function parameter. This assists the user to appropriately devise the variant densities of facial sketches and image synthesis. Composite data set of Large Scale CelebFaces Attributes (ClebA), Labelled Faces in the Wild (LFWH), Chinese University of Hong Kong (CHUK), and self-generated Asian images are used to evaluate the proposed approach. The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.     

Adaptive telecom churn prediction for concept-sensitive imbalance data streams

The Journal of Supercomputing - A key toward intelligent decision-making in industries lies in the ability to process and analyze vast quantities of business data. Concept drift and class imbalance...     

Phage-based technologies for highly sensitive luminescent detection of foodborne pathogens and microbial toxins: A review

Foodborne pathogens and microbial toxins are the main causes of foodborne illness. However, trace pathogens and toxins in foods are difficult to detect. Thus, techniques for their rapid and sensitive identification and quantification are urgently needed. Phages can specifically recognize and adhere to certain species of microbes or toxins due to molecular complementation between capsid proteins of phages and receptors on the host cell wall or toxins, and thus they have been successfully developed into a detection platform for pathogens and toxins. This review presents an update on phage-based luminescent detection technologies as well as their working principles and characteristics. Based on phage display techniques of temperate phages, reporter gene detection assays have been designed to sensitively detect trace pathogens by luminous intensity. By the host-specific lytic effects of virulent phages, enzyme-catalyzed chemiluminescent detection technologies for pathogens have been exploited. Notably, these phage-based luminescent detection technologies can discriminate viable versus dead microbes. Further, highly selective and sensitive immune-based assays have been developed to detect trace toxins qualitatively and quantitatively via antibody analogs displayed by phages, such as phage-ELISA (enzyme-linked immunosorbent assay) and phage-IPCR (immuno-polymerase chain reaction). This literature research may lead to novel and innocuous phage-based rapid detection technologies to ensure food safety.     

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.     

Programmed Editing of Rice (Oryza sativa L.) OsSPL16 Gene Using CRISPR/Cas9 Improves Grain Yield by Modulating the Expression of Pyruvate Enzymes and Cell Cycle Proteins

Rice (Oryza sativa L.) is one of the major crops in the world and significant increase in grain yield is constant demand for breeders to meet the needs of a rapidly growing population. The size of grains is one of major components determining rice yield and a vital trait for domestication and breeding. To increase the grain size in rice, OsSPL16/qGW8 was mutagenized through CRISPR/Cas9, and proteomic analysis was performed to reveal variations triggered by mutations. More specifically, mutants were generated with two separate guide RNAs targeting recognition sites on opposite strands and genomic insertions and deletions were characterized. Mutations followed Mendelian inheritance and homozygous and heterozygous mutants lacking any T-DNA and off-target effects were screened. The mutant lines showed a significant increase in grain yield without any change in other agronomic traits in T₀, T₁, and T₂ generations. Proteomic screening found a total of 44 differentially expressed proteins (DEPs), out of which 33 and 11 were up and downregulated, respectively. Most of the DEPs related to pyruvate kinase, pyruvate dehydrogenase, and cell division and proliferation were upregulated in the mutant plants. Pathway analysis revealed that DEPs were enriched in the biosynthesis of secondary metabolites, pyruvate metabolism, glycolysis/gluconeogenesis, carbon metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and citrate cycle. Gene Ontology (GO) analysis presented that most of the DEPs were involved in the pyruvate metabolic process and pyruvate dehydrogenase complex. Proteins related to pyruvate dehydrogenase E1 component subunit alpha-1 displayed higher interaction in the protein-protein interaction (PPI) network. Thus, the overall results revealed that CRISPR/Cas9-guided OsSPL16 mutations have the potential to boost the grain yield of rice. Additionally, global proteome analysis has broad applications for discovering molecular components and dynamic regulation underlying the targeted gene mutations.     

Modeling and minimization of FWM effects in DWDM-based long-haul optical communication systems

Photonic Network Communications - Optical communication systems (OCSs) mainly represent the backbone of modern long-haul communication networks because of low loss transmission over long distances...     

Polarization Agile Antenna for Underwater Communication Using Integrated Power Divider and Phase Shifter

Wireless Personal Communications - This paper presents a polarization agile antenna design that can be helpful for underwater communication, using the integrated structure of power divider and...     

Performance analysis of hybrid microchannel heat sink for varying nozzle geometry and layout on the basis of first- and second-law of thermodynamics

Journal of Mechanical Science and Technology - This paper investigates the enhanced cooling performance of a hybrid microchannel heat sink, consists of the microchannel, nozzle and pillars, in...     

Cobalt–Iron nanoparticles encapsulated in mesoporous carbon nanosheets: A one-pot synthesis of highly stable electrocatalysts for overall water splitting

Advancement of cost-effective, highly efficient and non-noble metal-based bifunctional electrocatalysts is considered an attractive approach to overcome the energy defect and environmental pollution challenges. Herein, this study presents a simple one-pot approach to synthesize cobalt-Iron nanoparticles encapsulated in mesoporous carbon nanosheets (Co₃Fe₇@CNSs) by the pyrolysis method. The Co₃Fe₇@CNSs-750/4h electrocatalyst exhibits a notable performance, low overpotential of 181 and 301 mV at a current density of 10 mA cm^?2 and small Tafel slope of 124.8 and 38.59 mV dec^?1, large active surface area 18.20 and 21.18 mF cm^?2, and low charge transfer resistance 4.92 and 9.42 Ω for hydrogen and oxygen evolution reactions, respectively, in 1.0 M KOH. Overall water splitting, with the set-up of two-electrode cells acquires the 10 mA cm^?2 of current density at 1.610 V in 1.0 M KOH. The combined structure of cobalt-iron nanoparticles encapsulated in carbon nanosheets; it could enhance the surface area and, provide more active sites that improve the overall catalytic activity. Not only this but also the synergistic effect due to different temperature treatments which significantly influenced the structural formation. However, the major involvement of this study is concerned with the production of economical non-noble metal-based electrocatalysts at an industrial scale for renewable energy to sustainability.