Asish Kondragunta

• Currently working as the marketing and hiring manager for the hive media company.
• Helped create company brand messaging, collateral materials, customer events, promotional strategies, generating leads, and email marketing.
• Boosted brand awareness and generated leads while managing internal and external marketing campaigns and programs.
• Utilized market trends and target audience statistics to effectively and appropriately market products.
• Executed search engine marketing, advertising and public relations campaigns to generate demand and sales leads.
• Worked with team of marketing professionals to build knowledge and skills.
• https://hivemedia.substack.com/about
• https://substack.com/profile/163038613-asish-kondragunta

• Worked alongside kailash in developing a working retractable web shooter by using blender software and put it up for sale on amazon
• Made an innovative modification on spool holders enabling us the create a Rolling Filament Spool Holder which is also put up for sale on amazon
• https://amzn.eu/d/88OtRPq

• Imported historical stock prices data from
  Kaggle
• Done changes in the dataset by data
  preprocessing.
• Preparation of training set and
  testing set
• Model building process of LSTM and adding
  dropout layers
• Model compiling process – We trained the
  LSTM model. For optimizer we used Adam
  and the loss function is the mean of squared
  errors between actual values and
  predictions.
• Model evaluation by applying RMSE – root
  mean squared error
• Model prediction
• Visualization of the model prediction

SEDMDroid - An enhanced stacking ensemble framework for android malware detection

The main objective to efficiently detect the malware in android from the dataset.Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.

• Data selection - First we take dataset which contains the information about android malware apps and permissions.
• Data preprocessing - The aim is to remove unwanted data from the dataset and prepare it for analysis.
• Splitting Dataset - The dataset is further divided into training and testing dataset. The separation of dataset is a crucial step in evaluating data mining models. PCA technique is further used for data exploration and reduction.
• Classification - XGBoost and Random forest algorithms are imposed to classify and predict the malware.