A Survey on Multimedia Content Protection Mechanisms

Cloud computing has emerged to influence multimedia content providers like Disney to render their multimedia services. When content providers use the public cloud, there are chances to have pirated copies further leading to a loss in revenues. At the same time, technological advancements regarding content recording and hosting made it easy to duplicate genuine multimedia objects. This problem has increased with increased usage of a cloud platform for rendering multimedia content to users across the globe. Therefore it is essential to have mechanisms to detect video copy, discover copyright infringement of multimedia content and protect the interests of genuine content providers. It is a challenging and computationally expensive problem to be addressed considering the exponential growth of multimedia content over the internet. In this paper, we surveyed multimedia-content protection mechanisms which throw light on different kinds of multimedia, multimedia content modification methods, and techniques to protect intellectual property from abuse and copyright infringement. It also focuses on challenges involved in protecting multimedia content and the research gaps in the area of cloud-based multimedia content protection

A Novel Skin Disease Detection Technique Using Machine Learning

Skin sicknesses present critical medical care difficulties around the world, requiring precise and opportune location for successful therapy. AI became promising stuff for computerizing the discovery and characterization of skin illnesses. This study presents a clever methodology that uses the choice tree strategy for skin sickness location. In computerized location, we utilize an exhaustive dataset containing different skin sickness pictures, including melanoma, psoriasis, dermatitis, and contagious diseases. Dermatologists skillfully mark the dataset, guaranteeing solid ground truth for precise grouping. Preprocessing strategies like resizing, standardization, and quality improvement are applied to set up the symbolism for the choice tree calculation. Then, we remove applicable elements from the preprocessed pictures, enveloping surface, variety, and shape descriptors to catch infection explicit examples successfully. The choice tree model is prepared utilizing these removed elements and the named dataset. Utilizing the choice tree's capacity to learn progressive designs and choice principles, our methodology accomplishes an elevated degree of exactness in grouping skin sicknesses. Extensive experiments and evaluations on a dedicated validation set demonstrate the effectiveness of our decision tree-based method, achieving a classification accuracy of 96%. Our proposed method provides a reliable and automated solution for skin disease detection, with potential applications in clinical settings. By enabling early and accurate diagnoses, our approach has the capacity to improve patient outcomes, trim down healthcare overheads, and alleviate the burden on dermatologists

In silico approach towards the identification of potential inhibitors from Curcuma amada Roxb against H. pylori: ADMET screening and molecular docking studies

Introduction: The present study attempts to identify potential targets of H. pylori for novel inhibitors from therapeutic herb, mango ginger (Curcuma amada Roxb.). Methods: Crystal structure of all the selected drug targets obtained from Protein Data Bank (PDB) were subjected to molecular docking against a total of 130 compounds (found to have biological activity against H. pylori) were retrieved from public databases. Compounds with good binding affinity were selected for Prime MM-GBSA rescoring and molecular dynamics (MD) simulation. Final list of compounds were taken for ADMET predictions. Results: Based on binding affinity denoted by glide score and ligand efficiency, mango ginger compounds were found selective to shikimate kinase and type II dehydroquinase through hydrogen bonding and salt bridge interactions. Stability of the interactions and free energy calculations by Prime MM-GBSA results confirmed the affinity of mango ginger compounds towards both shikimate kinase and type II dehydroquinase. From the above results, 15 compounds were calculated for ADMET parameters, Lipinski’s rule of five, and the results were found promising without any limitations. MD simulations identified gentisic acid as hit compound for shikimate kinase of H. pylori. Conclusion: Current study could identify the in silico potential of mango ginger compounds against shikimate kinase and type II dehydroquinase targets for H. pylori infections and are suitable for in vitro and in vivo evaluation

Not Available

Not AvailableNot AvailableNot Availabl

Not Available

Not AvailableNot AvailableNot Availabl

Not Available

Not AvailableNot AvailableNot Availabl

Not Available

Not AvailableNot AvailableNot Availabl

Single-step synthesis of chemically cross-linked polysilastyrene and its conversion to β -silicon carbide

A new method for chemically cross-linking polysilastyrene using divinylbenzene as the cross-linking agent is reported. The procedure involves a single-step synthesis using the alkali-metal sodium to promote the polymerization of dimethyldichlorsilane in the presence of the comonomers phenylmethyldichlorosilane and divinylbenzene. The cross-linked polymer can be readily converted to β-SiC on pyrolysis at 1500° C. The β -SiC obtained by this procedure is nanocrystalline and has a grain-size distribution of 8-20 nm