Popularity Prediction of Social Media Post Using Tensor Factorization

The traditional method of doing business has been disrupted by socialmedia. In order to develop the enterprise, it is essential to forecast the level ofinteraction that a new post would receive from social media users. It is possiblefor the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detectionstrategies that are user-based or population-based are unable to keep up with theseshifts, which leads to inaccurate forecasts. This work makes a prediction abouthow popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that istensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation bychoosing the top contents that were most like each other. The tensor is factorisedwith the application of the Adam optimization. It has been modified such that thebias is now included in the gradient function of A-PARAFAC, and the value ofthe bias is updated after each iteration. The prediction accuracy is improved by32.25% with this strategy compared to other state of the art methods.

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.

Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed.However,this progress has also led to security concerns related to the transmission of confidential data.Nevertheless,safeguarding these data during communication through insecure channels is crucial for obvious reasons.The emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation learning.According to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification direction.Through simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual quality.During the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.

Key molecules of Mucorales for COVID-19-associated mucormycosis:a narrative review

Mucormycosis is a lethal human disease caused by fungi of the order Mucorales.Mucormycosis is caused by fungi mainly belonging to the genera Mucor,Rhizopus,and Lichtheimia,all of which belong to the order Mucorales.The number of individuals with mucormycosis-causing disorders has increased in recent years,hence,leading to the spread of mucormycosis.Throughout the coronavirus disease 2019(COVID-19)pandemic,numerous cases of mucormycosis in COVID-19-infected patients have been reported worldwide,and the illness is now recognized as COVID-19-associated mucormycosis,with most of the cases being reported from India.Immunocompromised patients such as those with bone marrow sickness and uncontrolled diabetes are at a greater risk of developing mucormycosis.Genes,pathways,and other mechanisms have been studied in Mucorales,demonstrating a direct link between virulence and prospective therapeutic and diagnostic targets.This review discusses several proteins such as high-affinity iron permease(FTR1),calcineurin,spore coat protein(CotH),and ADP-ribosylation factors involved in the pathogenesis of mucormycosis that might prove to be viable target(s)for the development of novel diagnostic and therapeutic methods.

Figure of Merit Enhancement of Surface Plasmon Resonance Biosensor Using Ga-Doped Zinc Oxide in Near Infrared Range

This work presents a surface plasmon resonance biosensor for the figure of merit enhancement by using Ga-doped zinc oxide(GZO),i.e.,nanostructured transparent conducting oxide as plasmonic material in place of metal at the telecommunication wavelength.Two-dimentional graphene is used here as a biorecognition element(BRE)layer for stable and robust adsorption of biomolecules.This is possible due to stronger van der Waals forces between graphene’s hexagonal cells and carbon-like ring arrangement present in biomolecules.The proposed sensor shows improved biosensing due to fascinating electronic,optical,physical,and chemical properties of graphene.This work analyses the sensitivity,detection accuracy,and figure of merit for the GZO/graphene SPR sensor on using the dielectric layer in between the prism and GZO.The highest figure of merit of 366.7 RIU^(−1) is achieved for the proposed SPR biosensor on using the nanostructured GZO at the 3000 nm dielectric thickness.The proposed SPR biosensor can be used practically for sensing of larger size biomolecules with due availability of advanced techniques for the fabrication of the nanostructured GZO and graphene.