Run your Payment Gateway on E2E Cloud

December 7, 2020

Payment gateway is a technology which enables the customers to make payment through their credit card or debit cards without making the sensitive information public. A payment gateway implements a high level of security which encrypts sensitive data and ensures that information is passed securely from the customer to the acquiring bank, via the merchant.

So in other words, the Payment gateway works in the middle between the customer and the service provider hence ensuring that payment is completed securely and seamlessly.

To deploy the payment gateway on a Cloud server, the prerequisite for the same is security as in a payment transaction sensitive information like card details are used to make the payment and to protect this sensitive information is the basic requirement for the payment gateway.

E2E Cloud can be chosen to run your payment gateway. The following features of E2E Cloud help you to decide which vendor to choose.

DDOS Protection - E2E Networks all the cloud machines are protected from the DDOS( Distributed Denial of Services) attack at the network level and E2E Networks ensures that clean traffic reaches the server and it stops all the suspicious traffic at the network level and hence creates the first line of defence from such attacks for the payment gateway server.

Tier3+ Certified Data Centre - E2E Networks have co-located all the servers in tier3+ certified data centre which comes with a very high degree of security along with a robust platform with N+1 redundancy.

Helps to make customer's environment PCI DSS Compliant - PCI DSS is the information security standard for organizations that ensure that all the companies that processes, stores and transmit credit card information maintain a secure environment. Many of the fintech companies like Instant Pay, SRS live and Verifone etc are using E2E Cloud services for running their payment gateway.

CDP Backup and Security - E2E Networks provides an automated backup solution for all of its cloud machines. The backup agent will give customers an option to take backup as per their convenience and can choose the backup frequency. By default, a backup agent takes backup every 6 hours and hence 4 times in 24 hours but customers can reduce the frequency if it is required at his end. Also, backup restoration is quite easy and in a single click, the backup can be restored in the same server or at different servers altogether.

BitNinja and Imunifi 360 Security shields - E2E Cloud also provides BitNinja and Imunifi 360 security shields to provide an additional layer of security to its servers. These security shields protect the servers from unwanted access through any port using honey trap techniques.

Dedicated Firewall - A firewall is a dedicated hardware that decides what services you want to expose publicly/Internet-facing and the rest can be hidden behind the firewall. E2E Cloud’s dedicated firewall makes sure that any kind of access to your servers is restricted to the outside world and applications that require access publicly can be exposed publicly and the rest of the servers can be restricted.

Enabling best practices at the customer's end - E2E Cloud recommends to implement best practices to improve the security of the customer's environment. For example, Always use SSH-based access to the server and remove password-based access altogether. Patches and upgrades come under the customer’s responsibility hence server admin makes sure that they regularly upgrade the server patches to avoid any kind of vulnerability.

These features on E2E Cloud along with best practices at the customer's end makes it an ideal platform to run your mission-critical payment gateway on E2E Cloud.

For more details, please visit: https://www.e2enetworks.com/product

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

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How can you create the ideal customer acquisition strategy for your business?

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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

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https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

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By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

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Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

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The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

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  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
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In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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