Google Drive Integration is an automation process that assists every salesforce and CRM-based company to automate the storing of the data in a personal/secure place such as Google Drive. Using this interface in salesforce, every week or month the data automatically uploads to the drive without the need for any local machine support. Every week or month current organization data will be stored in the drive for future purposes in an exact folder. This integration and uploading process is entirely in the background so it won’t cause any issues with the current processes. It will upload the data on a particular time basis. It won’t need any machines like a laptop or mobile to run the uploading process every time but only for the first time. Whenever it is uploaded the files to the drive it will notify the user who is in the salesforce by notification and intimate the user or any other responsible person via email in fact of any error occurs.

Keywords: e-commerce, information technology, customer satisfaction, business.

The possibility of precisely identifying people by ear images has become an interesting field within the biometric community in recent years. This is due to the unique characteristics of the human ear. A new deep learning architecture for ear recognition is proposed in this paper. The proposed method include a preprocessing stage for enhancing the important features of ear images using contrast-limited adaptive histogram equalization. A deep convolutional neural network classifier is used to classify the preprocessed ear images. The experimental results show that the proposed recognition system has recorded an overall 97.92 % testing accuracy.

Keywords: Deep learning, ear recognition, convolutional neural networks, features extraction, dropout.

: Sarcasm is a refined type of irony, used on social networking sites like Facebook and Twitter and microblogs. Typically, it is employed to impart tacit knowledge in a message. It is typically used to communicate implicit knowledge within an individual's message. For various reasons, such as criticism or satire, sarcasm can be used. But it's hard to remember even for humans. Consequently, it can be very helpful to recognize sarcastic comments in order to automatically increase feelings of data gathered from microblogging websites or social networks. Sentiment Analysis refers to the detection and compilation of Internet users' attitudes and views on a particular topic. In this article, we suggest a model-based method for detecting Twitter sarcasm. We offer four sets of features covering the various sarcasm forms that we have identified. We use tweets to describe sarcastic and non-sarcastic tweets. Our method proposed achieves a precision of 83.1% with a precision of 91.1%. We also investigate the meaning and importance of each of the suggested sets of characteristics. We stress the importance of pattern-based characteristics to identify sarcastic statements in particular.

Keywords:Twitter, sentiment analysis, sarcasm detection, machine learning.