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Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

What is Hadoop? | What is Hadoop Architecture? | HDFS Architecture | YARN Architecture | MapReduce | The Takeaway

Do you want to know more about the Hadoop Architecture in Big Data? HDFS, MapReduce, and YARN are the three important concepts of Hadoop. In this tutorial, you will learn the Apache Hadoop HDFS and YARN Architecture in details. 

Hadoop Architecture Tutorial YARN HDFS

What is Hadoop?

Hadoop is an open source framework that allows for the distributed processing of large datasets across clusters of computers using simple programming models. It is from Apache and is used to store process and analyze data which are very huge in volume. The summary of the Hadoop framework is as follows:Read More »Hadoop Architecture in Big Data: YARN, HDFS, and MapReduce

AWS vs Azure vs Google Cloud A Comparative Review

AWS vs Azure vs Google Cloud: A Comparative Review

A comparative review of the AWS vs Azure vs Google Cloud is very important in the modern context. Code free complete machine learning lifecycle is the key trend for the modern cloud computing platforms.

As companies are going for more on code free deep learning and other high-end IoT technologies, selecting suitable cloud platforms are vital for corporate growth. In this article, we’re going to help you decide between the three giants of cloud computing.

Best of AWS Azure and Google Cloud Platforms

Cloud computing Foundation

Before going into the prominence in the cloud market, Google, Amazon and Microsoft are the market leaders in their respective fields. Each has unique advantage. All these three major cloud providers have also attempted to create many general-purpose services that are relatively easy to use by the end users. 

Price, speed, performance, flexibility, and features are the five key criteria normally used to select the best cloud computing platform for your jobs. 

The cloud computing makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand for those features increases.

GPUs are the key hardwires and the processors in the cloud computing  of choice for many complex machine learning applications because they significantly reduce processing time.… Read more..

Key Artificial Intelligence Projects to Fight Against COVID-19https://amitray.com/artificial-intelligence-to-fight-against-covid-19/

Artificial Intelligence to Fight Against COVID-19

Key Artificial Intelligence Projects to Fight Against COVID-19

Dr. Amit Ray 
Compassionate  AI Lab 

In our Compassionate AI Lab, we broadly classified our fight against COVID 19 Artificial intelligence (AI) based research projects into six groups. They are AI for COVID vaccine development, AI for COVID drug discovery, AI for COVID diagnosis, AI for COVID testing, AI for COVID growth rate forecasting, and AI for social robots.

Researchers across the world are in search of urgent drugs and vaccines that can save millions of lives of infected people and perhaps prevent infections for the future generations. The exacerbated time, cost and high failure rate of traditional path of drug discovery and vaccine development has prompted the need for efficient use of machine learning techniques. In these projects, we are trying to solve one the most complex problems of humanity ever encountered 

1. AI for COVID 19: An Overview

The massive outbreak of the COVID-19 has prompted various scientists, researchers, laboratories, and organizations around the world to conduct large-scale research to help develop vaccines and other treatment strategies. Biology and medicine of coronavirus are data rich, complex, and often ill understood. Problems of this nature may be particularly well suited to deep learning techniques.… Read more..

Machine Learning to Fight Antimicrobial Resistance

Machine Learning to Fight Antimicrobial Resistance

The seven top machine learning projects to fight against antimicrobial resistance are explained. Antimicrobial resistance  is one of the key reasons of human sufferings in modern hospitals. Preventing microbes from developing resistance to drugs has become as important issue for treating illnesses across the world. Artificial Intelligence, machine learning, genomics and multi-omics data integration are the fast-growing emerging technologies to counter antimicrobial resistance problems. Here,  Dr. Amit Ray explains how these technologies can be used in seven key areas to counter antimicrobial resistance issues.

What’s Holding Back Machine Learning in Healthcare

What is holding back the large scale implementation of machine learning systems in healthcare and precision medicine? In this article Dr. Amit Ray, explains the key obstacles and challenges of  implementing large-scale machine learning systems in healthcare.   Dr. Ray argued that lack of deeper integration, incomplete understanding of the underlying molecular processes of disease it is intended to treat, may limit the progress of implementing large-scale machine learning based reliable systems in healthcare. Here, nine obstacles of present day machine learning systems in healthcare are discussed. 

What Holding Back Machine Learning in Healthcare

Machine Learning in Healthcare

Recently, machine learning algorithms, especially deep learning has shown impressive performance in many areas of medical science, especially in classifying imaging data in different clinical domains. In academic environment, Deep learning and Reinforcement learning methods of Artificial Intelligence (AI) has shown tremendous success in numerous clinical areas such as: Omics data integration (such as genomics, proteomics or metabolomics), prediction of drug-disease correlation based on gene expression, and finding combinations of drugs that should not be taken together. Deep learning is very successful in predicting cancer outcome based on tumour tissue images. Machine learning are used for medical decision support systems for ICU and critical care. Artificial Intelligence in Healthcare Current Trends discusses the current status of AI in healthcare. Read More »What’s Holding Back Machine Learning in Healthcare

7 Limitations of Molecular Docking & Computer Aided Drug Design and Discovery

7 Limitations of Molecular Docking & Computer Aided Drug Design and Discovery

Over the past decades, molecular docking has become an important element for drug design and discovery.  Many novel computational drug design methods were developed to aid researchers in discovering promising drug candidates. In the recent years, with the rapid development of faster architectures of  Graphics Processing Unit (GPU)-based clusters and better machine algorithms for high-level computations, much progress has been made in areas such as scoring functions,  search methods and ligand-receptor interaction for living cells and other approaches for drug design and discovery.  

A large number of successful applications have been reported using a variety of docking techniques. However, despite their success in academic environment for concept validation, their real life application is very limited. There are many obstacles and number of issues remain unsolved. In this article Dr. Amit Ray, explains the key obstacles and challenges of molecular docking methods for developing efficient computer aided drug design and discovery (CADD) methods.   Dr. Ray argued incomplete understanding of the underlying molecular processes of the disease it is intended to treat may limit the progress of drug discovery. Here, the seven limitations of present CADD methods are discussed. 

 7 obstacles of Molecular docking & Computer aided drug design

In vivo, In vitro and In silico:  Experimentation for Drug Discovery 

Experimentation for Drug Discovery pathways are classified into three groups: in vivo, in vitro and in silico. … Read more..

AI for Balance-Control Fall Detection of Elderly People

Artificial Intelligence for Balance Control and Fall Detection of Elderly People

Artificial Intelligence for Balance Control of Elderly People

Designing automated balance control system for elderly people is one of the key project  of our Compassionate AI Lab. Here, Dr. Amit Ray discuses about one of the recent project of AI using deep learning algorithms for automatic balance control of elderly people. He explains how machine learning algorithms can be used to study and improve the dynamical properties of postural stability of elderly people. The project focuses on how image recognition, human-body joint dynamics, and path navigation methods of artificial intelligence can be used  to eliminate the imbalance, fall and injury of elderly people or for physically challenged people.

AI for Balance-Control Fall Detection of Elderly People

Compassionate Artificial Intelligence can be used for helping elderly people in many ways. Here, we discuss about one of our recent project of using AI & deep learning techniques for automatic balance control. The machine learning algorithms are used to improve dynamical properties of postural stability. In this project AI based machine learning algorithms are used to find the insights into the person specific postural strategies for older adults in order to adapt to the postural challenges during sleeping, standing, turning and walking. To study the body movement behavior of elderly people accurately, it is necessary to observe and record their movement trajectory and joint movements quantitatively and precisely in three dimensions.… Read more..

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence to Combat Antibiotic Resistant Bacteria – tools, techniques, models, scopes and challenges are discussed. Antibiotic resistance bacteria is one of the key research area of our Compassionate AI Lab. Dr. Amit Ray explains how artificial intelligence can be used in combating these superbugs. Antibiotic resistance bacteria is becoming world’s biggest health crisis. We discussed here multi-agent deep reinforcement learning models for predicting behavior of bacteria and phages in multi-drug environments.  We call this model as DeepCombat. 

Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Antibiotic resistant bacteria are bacteria that are not controlled or killed by antibiotics. They are able to survive and even multiply in the presence of an antibiotic.  These bacteria currently kill an estimated 700,000 people globally each year – a death toll which could rise to 10 million a year by 2050 if we don’t act [1]. The main difficulty is that the bacteria are changing fast. They changing faster than we can change the drugs in response.

 Artificial intelligence is showing alternative means of fighting these deadly infections and killer bacteria. Multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, thousands of lives lost. Artificial Intelligence for healthcare is progressing at an exponential rate.  We are evaluating here, the role of artificial intelligence in fighting these superbugs.  Especially, the use of AI for intelligent Phage therapy.Read More »Artificial Intelligence to Combat Antibiotic Resistant Bacteria

Artificial Intelligence and Blockchain for Precision Medicine

Here, we discussed the scopes and implementation issues of artificial intelligence and blockchain for precision medicine. Evidence-based medicine is gradually shifting from therapy to prevention and towards individually tailored precision medicine systems. Where, artificial intelligence can be used to automatically detect problems and threats to patient safety, such as patterns of sub-optimal care or outbreaks of hospital-acquired illness. Artificial intelligence can be used to prevent the issues like drug-interaction, over-diagnosis, over-treatment and under-treatment. It can be used more effectively to solve the problems of antibiotic resistant bacteria.

Artificial Intelligence in Precision Medicine

Artificial Intelligence in Precision Medicine

Artificial intelligence in precision medicine is a revolutionary new approach advancing health and wellness, knowledge, and health care delivery to maximize the quality of life for all over a lifetime. The main concept of precision medicine is providing health care which  is individually tailored on the basis of a person’s genes, lifestyle and environment. With the advances in genetics,  artificial intelligence and the growing availability of health data, present an opportunity to make precise personalized patient care a clinical reality. 

It is like cricket. No two cricket ball deliveries, players, — or patients — are exactly alike. No two games or diseases are exactly the same. To win the game every ball, every delivery needs unique strategy. Precision medicine is like that. No two diseases are same, so the treatments will be different and unique.  

AI with precision medicine is a part of artificial intelligence in health care. It brings together innovations in genomics, metabolomics, mobile health, biomedical data sciences, imaging, social engagement and networking, communication, and environmental sciences to make diagnostics, therapeutics, and prevention more individualized, proactive, predictive, and precise. 

Precision medicine often involves the application of panomic analysis and systems biology to analyze the cause of an individual patient’s disease at the molecular level and then to utilize targeted treatments (possibly in combination) to address that individual patient’s disease process.… Read more..