Data Science & AI based optimal advancement in scientific programming

June 21, 2022

Many scientists and researchers nowadays spend as much time staring at a computer screen as they do staring at their research lab samples. For example, geologists' job is to study geological formations but in such technological advancement days they spend a huge proportion of their time staring at a computer screen, and many modern hydrologists spend considerably more time developing code and scraping databases than wading in rivers. 

This is science in the twenty-first century. The research process is getting more efficient, speedier, and repeatable. It's a world where scientists from all disciplines use the same tool: scientific programming. 

This blog will introduce scientific programming and will brief you on why scientists nowadays are learning to code and to reap the benefits of clean and reproducible coding practices for open science.

This blog also focuses on the practical elements of data science and artificial intelligence in scientific programs, such as optimizations, deep learning, recommendation systems, and real-world applications.

What is Scientific Programming?

Scientific programming has a basic description that encompasses a wide range of applications and businesses. But in simple words, it is defined as the use of a computer program, leveraging computing power and utilizing algorithms for scientific study.

AI and data science optimizing scientific programming

1.  Optimizing the Borrowing Limit and Interest Rate using Artificial intelligence: 

Artificial intelligence systems may optimize specific parameters in challenges characterized by data flows. AI employs a three-layer BP neural network algorithm to estimate the borrowing limit and interest rate when consumers use a P2P online service to borrow money. Given the restricted information, this technique gives a fresh emphasis to borrowers to estimate and optimize the borrowing limit and interest rate. Furthermore, both parameters are optimized using an algorithmic approach in which a neural network and a genetic algorithm collaborate to address single-target and dual-target programming optimization issues. The success rate here is evaluated on real-world data to determine its suitability as a high-accuracy prediction approach.

2.  Making Use of Image Visual Features in a Content-Based Recommender System:

Data and intelligent algorithms for collaborative filtering address the challenge of researching latent information in huge datasets, from which recommender systems make predictions or suggestions based on the users' preferences. This work's knowledge field is highly important currently since many online systems record useful information about users' behavior while seeking things. These systems include not just e-commerce platforms, but also movies and academic databases.

Although recommender systems primarily evaluate user-item rating data, this study adds item hybrid characteristics based on image visual features to offer a unique recommendation model that may also be used in rating-based recommender situations. This model is very beneficial in sparse data settings, where it outperforms other standard techniques.

3. An Artificial Bee Colonies algorithm with Random Location Updating:

The algorithm named artificial bee colony  (ABC) is a novel metaheuristic algorithm inspired by the complex honey gathering technique of bees. Such an optimization approach has been widely and effectively employed to solve complicated optimization issues in a variety of application sectors. The ABC algorithm's core may be tweaked to improve the exploration phase and, as a consequence, boost convergence speed and solution quality. For that purpose, the original perturbation function is updated by incorporating random location updates, which can broaden the search range of novel solutions and increase the algorithm's exploration capabilities.

4. A Novel Nonlinear Continuous Optimization Algorithm: Application to Feed-Forward Neural Network Training:

Artificial neural networks are a critical approach in the field of artificial intelligence; they have played a significant role in solving a variety of classification, prediction, optimization, and identification challenges. However, the successful operation of neural networks is unquestionably dependent on good training, which is often carried out using the well-known backpropagation technique. To estimate the network heights, the algorithm computes the sum square error(gradient of error). Using this strategy, however, often entails delayed convergence and slipping into local minima. Metaheuristics are frequently used to address this issue. Also, a modified particle swarm optimization (PSO) method can be used for training multilayer feed-forward artificial neural networks.

The key difference between the original PSO and this one is that the utilized algorithm employs many swarms rather than a single one as in the classic PSO. This allows one to limit the number of particles leaving the search space while also strengthening the local search of each particle. Findings have shown that suggested methods have improved the accuracy of the classification done by these multilayer feed-forward neural networks. 

5. The Use of Polyhedral Conic Functions in Text Classification and Comparative Analysis:

The purpose of classifying texts into predetermined classifications is the work in the field of text categorization Because of the massive rise in internet data over the previous few years, this subject of expertise is particularly interesting nowadays. In a recent study, many common supervised algorithms, including logistic regression, SVMs or support vector machines, and Bayesian Networks, have been introduced to deal with the challenge. On this premise, the scientists investigate polyhedral conic function (PFC) approaches as supervised classification functions in addition to typical supervised procedures. They specifically propose using PCFs to handle binary and multiclass text classification challenges. The performance is assessed by solving the complex structure of real-world datasets from the literature and analyzing f-measure, accuracy, and execution time. In conclusion, the classification algorithms based on PFCs produce 2 Scientific Programming more promising outcomes than typical supervised algorithms.

6. Railway Subgrade Defect Automatic Recognition Method Based on Faster R-CNN Improvements:

Because of the variation in defect shape and size, as well as the amount of data produced by measuring systems, such as the vehicle-mounted ground penetrating radar (GPR), which is the most important technology today, defect detection is a difficult process. Because of this variability, most efforts in this area concentrate on classic machine-learning algorithms, where feature representation fails for subgrade faults. Furthermore, while deep-learning algorithms were launched in the railway industry, they were not used to detect subgrade faults. Based on this, the authors suggest a deep-learning technique for detecting errors in the GPR profile. To that aim, they offer a method for using quicker R-CNN to detect railway subgrade problems automatically. Experiments in a real-world context indicate that the idea outperforms a classic strategy based on a support vector machine and a histogram of directed gradients.

7. High-Frequency Trading in the Emerging Indian Stock Market:

The purpose of high-frequency trading is to develop, construct, and test a completely autonomous system that can function in a tiny market with highly concentrated ownership, such as the Indian stock market. A high-frequency trading system is demonstrated using powerful computer tools and represented as an NP-Complete issue. Separate tests on the developed algorithms are performed, analyzing the return i.e. profitability that may be fitted to some of the most recent weeks, months, and terms of real market data. The usage of particle swarm optimization as an optimization technique is demonstrated to be an effective solution since it can optimize a collection of different variables but is limited to a certain domain, resulting in a significant improvement in the final solution.

Conclusion:

From geologists to zoologists, all scientists may profit from scientific programming. A researcher may dramatically boost the rate and reproducibility of their work by using Data Science & AI-based optimal advancement in scientific programming. While people are undoubtedly superior to computers in some areas, computers were built to do complex computations, store data, and analyze outcomes. In the coming future Scientists will utilize data and artificial intelligence algorithms at an increased level to automate operations that would otherwise take a long time, be laborious, error-prone, and difficult for humans to complete.

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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.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

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

  • Define what your goals are

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 –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
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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

https://www.helpscout.com/customer-acquisition/

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

https://blog.hubspot.com/service/customer-acquisition

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The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

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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:

  • Input

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.

  • Output

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

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • 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.

  • Practical applications and use cases

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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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.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

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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