Contact Us

By

 Feel free to share your views or suggestions with us through email or you can also comment down below. We would be happy to receive your feedback.

Email: scigaze@gmail.com

Comments

Post a Comment

Please do not use foul language while commenting. We will appreciate your suggestions and reviews in the comments.

Read Also

Big Bang and the Timeline of Universe

By
Image
Introduction: Whenever we start thinking about the universe and laws of nature that drive this beyond the imagination world, the first question stuck in our mind is, ‘How was the universe born?’ A few decades ago, this question was considered one of the most challenging questions to answer for the science community. But today, after a series of theories and predictions, we have indeed answered this up to a certain extent. The Big Bang Theory, a very famous term we all are familiar with. But what exactly is the Big Bang? And what happened during the formation of the universe? You will find the answers very soon. Singularity and the Big Bang Model: Big Bang theory is considered one of the most accurate theories for the birth of the universe. The theory is based on the equations of general relativity. According to Big Bang, initially, the universe was a highly dense energy state. The density and temperature of this initial energy state are beyond the imagination of humanity. The origin of...
Read more

How Visual Cortex inspired the Convolutional Neural Networks

By
Image
Introduction Since the 1960s, after the successful development (at least with respect to the existing technologies of that time) of the first perceptron by Frank Rosenblatt, researchers around the world have been trying to make computers more brain-like. In other words, they are constantly trying to develop programs and hardware that work much like our brains. However, creating a machine that thinks and programs like a human brain is still impossible, given the limitations of present technologies. But, we are successful in this direction up to an agreeable extent. The perceptron developed by Rosenblatt can be considered one of the first implementations of neural networks. It was a huge hardware model, rather than a Python program like today's perceptrons.  Rosenblatt's perceptron was a simple binary input-output system, with various limitations. It was not trainable for different patterns, rather but for simple image recognization. The major drawback was its inability to perfo...
Read more

Marine eDNA Analysis using DL techniques

By
Image
Introduction Environmental DNA (eDNA) is the genetic information or material released by an organism into its surroundings through activities like skin shedding, mucus, urine or other secretions. These materials contain DNA fragments that can be found in soil, water or air samples. The concept of eDNA is based on the fact that organisms constantly leave organic traces behind them. By analysing the eDNA, researchers can detect the presence of any species in that area. This method is a valuable technique for biodiversity monitoring and ecological research.    Unlike traditional methods that involve direct observation or capturing of organisms, eDNA analysis allows researchers to study biodiversity without directly interacting with the species. This non-invasive approach is particularly useful for studying elusive or endangered species. eDNA analysis has been applied in various ecosystems, including freshwater, marine, and terrestrial environments. It is used to study aquatic org...
Read more

Deep Neural Networks for ADMET properties' prediction

By
Image
Introduction The absorption, distribution, metabolism, excretion and toxicity properties of any compound contributes to its potential as an orally administered drug. A set of criteria defined by Rule of Five or the Pfizer's Rule of Five stats that any compound meeting these criteria are more likely to have ADMET properties. The importance of these properties has increased in modern medicinal chemistry and new machine learning techniques are employed to predict these properties. The predictive ADMET models have helped in discovery of small molecules with improved safety and dose. Deep Neural Networks are showing usefulness in such predictions due to improved computational efficiency, larger datasets and adaption of image processing in the chemistry. Early Models The early DNNs employed showed improved prediction performance. Many researchers raised concerned that this improvement is in fact has no relation with better computational capacity, rather it is result of mere memorizing of...
Read more