Suman Deb – författare

The objections faced by the pharmaceutical sales professionals are different from the objections faced by the sales professionals of automobile, FMCG or electronic goods. The basic difference in pharmaceutical selling is; customers (doctors) are not buyers (patients). And thus the patterns of objections are also different. Thereby handling those objections is also different. Moreover, there is no specific guideline, literature or book written on the different objections faced by the pharmaceutical sales professionals. This has motivated me to write a book on objection handling, particularly for pharmaceutical sales professionals.In this book, etiology of objection, attitude towards objection, types of objection, strategy for handling objection, techniques of handling objection SLUAAC Technique, methods of handling objection have explained elaborately. And lastly 22 commonly encountered objections and how to handle those objections have been discussed. In this book more applied aspects have bben incorporated; because books meant for practical use should contain much of applied discussion.

This volume constitutes the refereed proceedings of the Third International Conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2020, held in Agartala, India, in December 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full papers and 4 short papers were carefully reviewed and selected from 113 submissions. The papers are organised according to the following topics: computational intelligence, security, and internet of things.

This volume constitutes the refereed proceedings of the Third International Conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2020, held in Agartala, India, in December 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full papers and 4 short papers were carefully reviewed and selected from 113 submissions. The papers are organised according to the following topics: computational intelligence, security, and internet of things. 

Technical Report from the year 2019 in the subject Engineering - Robotics, grade: 9, , language: English, abstract: Robotic agents can be made to learn various tasks through simulating many years of robotic interaction with the environment which cannot be made in case of real robots. With the abundance of a large amount of replay data and the increasing fidelity of simulators to implement complex physical interaction between the robots and the environment, we can make them learn various tasks that would require a lifetime to master. But, the real benefits of such training are only feasible, if it is transferable to the real machines. Although simulations are an effective environment for training agents, as they provide a safe manner to test and train agents, often in robotics, the policies trained in simulation do not transfer well to the real world. This difficulty is compounded by the fact that oftentimes the optimization algorithms based on deep learning exploit simulator flaws to cheat the simulator in order to reap better reward values. Therefore, we would like to apply some commonly used reinforcement learning algorithms to train a simulated agent modelled on the Aldebaran NAO humanoid robot. The problem of transferring the simulated experience to real life is called the reality gap. In order to bridge the reality gap between the simulated and real agents, we employ a Difference model which will learn the difference between the state distributions of the real and simulated agents. The robot is trained on two basic tasks of navigation and bipedal walking. Deep Reinforcement Learning algorithms such as Deep Q-Networks (DQN) and Deep Deterministic Policy Gradients(DDPG) are used to achieve proficiency in these tasks. We then evaluate the performance of the learned policies and transfer them to a real robot using a Difference model based on an addition to the DDPG algorithm.

This volume constitutes the refereed proceedings of the Second International Conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2019, held in Agartala, India, in December 2019.The 31 full papers and 6 short papers were carefully reviewed and selected from 153 submissions. The papers are organised according to the following topics: Computational Intelligence, Security, Internet of Things. Papers from the extended track are also presented in the volume.