DeepWalk Based Influence Maximization (DWIM): Influence Maximization Using Deep Learning

Big Data and artificial intelligence are used to transform businesses.Social networking sites have given a new dimension to online data.Social media platforms help gather massive amounts of data to reach a wide variety of customers using influence maximization technique for innovative ideas,products and services.This paper aims to develop a deep learning method that can identify the influential users in a network.This method combines the various aspects of a user into a single graph.In a social network,the most influential user is the most trusted user.These significant users are used for viral marketing as the seeds to influence other users in the network.The proposed method combines both topical and topological aspects of a user in the network using collaborativefiltering.The proposed method is DeepWalk based Influence Maximization(DWIM).The proposed method was able tofind k influential nodes with computable time using the algorithm.The experiments are performed to assess the proposed algorithm,and centrality measures are used to compare the results.The results reveal its performance that the proposed method canfind k influential nodes in computable time.DWIM can identify influential users,which helps viral marketing,outlier detection,and recommendations for different products and services.After applying the proposed methodology,the set of seed nodes gives maximum influence measured with respect to different centrality measures in an increased computable time.

A review of recent research progress on the effect of external influences on tropical cyclone intensity change

Over the past four years,significant research has advanced our understanding of how external factors influence tropical cyclone(TC)intensity changes.Research on air-sea interactions shows that increasing the moisture disequilibrium is a very effective way to increase surface heatfluxes and that ocean salinity-stratification plays a non-negligible part in TC intensity change.Vertical wind shear from the environment induces vortex misalignment,which controls the onset of significant TC intensification.Blocking due to upper-level outflow from TCs can reduce the magnitude of vertical wind shear,making for TC intensification.Enhanced TC-trough interactions are vital for rapid intensification in some TC cases because of strengthened warm air advection,but upper-level troughs are found to limit TC intensification in other cases due to dry midlevel air intrusions and increased shear.Aerosol effects on TCs can be divided into direct effects involving aerosol-radiation interactions and indirect effects involving aerosol-cloud interactions.The radiation absorption by the aerosols can change the temperature profile and affect outer rainbands through changes in stability and microphysics.Sea spray and sea salt aerosols are more important in the inner region,where the aerosols increase precipitation and latent heating,promoting more intensification.For landfalling TCs,the intensity decay is initially more sensitive to surface roughness than soil moisture,and the subsequent decay is mainly due to the rapid reduction in surface moisturefluxes.These new insights further sharpen our understanding of the mechanisms by which external factors influence TC intensity changes.

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.