Tuning artificial intelligence to enrich quantum insights
This article provides a very brief introduction about traditional and quantum computers, an overview of quantum systems, quantum computation components and algorithms, quantum computing for artificial intelligence and artificial intelligence for quantum computing. Read more
3 Ways Big Data Is Helping To Shape Post-Pandemic Ecommerce
Following the COVID-19 pandemic, more people are connecting with eCommerce than ever before in the history of the internet. This is both good and bad news. Here are some emerging trends in eCommerce. Read more
Aurora Serverless Data Security and its Deployment
In this article, Data Security acts as an important segment. This consists of certain standard levels and various technologies that are used for data protection. This helps to secure data from accidental destruction and altering disclosures. Overall, in this article Aurora Serverless data security is majorly pointed. The AWS creates Aurora, which is nothing but a cloud-native execution of RDBMS. Finally, the implementation of Aurora is “serverless”. Read more
A Comparison between Logistic Regression and Decision Tree Methods for Predicting the Preterm Birth
Pre-term birth is a progressively more prevalent complex situation with various risk factors including environmental pollutants. We have been tested the logistic regression and decision tree classifier methods in this dataset and to evaluate the accuracy of the logistic regression and decision tree method through various indices. Read more
Adopting Qualified AI Algorithms to Optimize Regulated Pharma Manufacturing
Never has it been more important to ensure that the pharmaceutical productivity chain is functioning as optimally as possible. For the first time ever, artificial intelligence (AI) algorithms have been qualified, both for pharmaceutical as well as medical device manufacturing. Read more
Unstructured data provides equal risk and opportunities for businesses
This article aims at highlighting where unstructured data comes from, why it’s so hard to pin down, the risks of not securing unstructured data, and the rewards of bringing that data into a structured environment. Read more
Big Data & AI World: Finance & Banking Virtual Summit 27 October 2020
The Big Data & AI World: Finance & Banking Virtual Summit is taking place on 27 October 2020. It’s an interactive 1-day digital event, which consists of inspirational keynotes, case-study based presentations and fiery panel discussions. Read more
Poole Harbour goes live with an Artificial Intelligence world first
The first phase of Bournemouth, Christchurch and Poole (BCP) Council and Poole Harbour Commissioners’ innovative AI water quality sensing project, in partnership with UK AI company UnifAI Technology, has gone live this month. Read more
Modern Enterprise Data Management in Healthcare
There are proven data and information governance methods and frameworks in existence, but still today, the question is : are they capable enough to promise a fast-advancing but stable, secure and efficient framework for Data Management in an organization? Read more
Piecewise hazard model for under-five child mortality
The application of piecewise hazard model in mortality data becomes more useful in survival methods. This method is used to find the number and location cut points and to estimate the hazard model. The objective of this paper is to illustrate the piecewise constant hazard model and to find significant factors for under-five child mortality.
Understanding Imbalanced Datasets and techniques for handling them
Imbalanced datasets contain valuable data that is often lost in data techniques that aim to balance datasets. In this article, we will look at what an Imbalanced dataset is and various techniques that can be used to handle imbalanced datasets.
Real Time Data Analytics: Mitigating the Risks and the Challenges
While practical benefits of the Real-time data analytics over historic data analytics are obvious, it is also essential to understand and cater for its risks, challenges and limitations.
Understanding Buzzwords in Data Science
Have you come across Data Science jargon and buzzwords such as Artificial intelligence (AI); Machine Learning (ML); Deep Learning (DL); Neural Network (NN) etc.. want to know more about them and how each of them differ? If so then checkout this article, which will give you a brief idea what these are.
INNOVATING THE LEARNING EXPERIENCE THROUGH EDUCATIONAL TECHNOLOGY: A DIGITAL INSIGHT
The Next Generation Digital Learning Environment (NGDLE) is considered as a ecosystem - a learning situation comprising of learning apparatuses and segments that stick to normal guidelines. While the customary Learning Management System (LMS) gives regulatory capacities, the NGDLE is planned to straightforwardly bolster learning.
Knowing all about Outliers in Machine Learning
While working on various dataset to train a Machine Learning model. What is it, that you look for? What is the most important part of the EDA phase? There are certain things which, if they are not done in the EDA phase, can affect further statistical / Machine Learning modelling. One of the answers is to, find “Outliers”.
In this article, Data Security acts as an important segment. This consists of certain standard levels and various technologies that are used for data protection. This helps to secure data from accidental destruction and altering disclosures. Overall, in this article Aurora Serverless data security is majorly pointed. The AWS creates Aurora, which is nothing but a cloud-native execution of RDBMS. Finally, the implementation of Aurora is “serverless”.READ MORE
One of the ways physicians can gain some forewarning of impending heart failure is through the detection of excess fluid in the lungs, and MIT researchers have developed a new machine learning tool that could offer them a helping hand. The algorithm is able to detect severe cases of this condition with a high level of accuracy, and the researchers behind it are hopeful it could be adapted to assist with the management of other conditions, too. Learn more here: https://bit.ly/2HQaKR4READ MORE
I have a Data Cloud Lab and Github too, Ready to contribute someone like you. please send me your resume at firstname.lastname@example.org. I will find out your solution. Thank youREAD MORE
Agree, with the time researchers has found various tools and techniques that today one can do real time data analysis, but again it depends on the person, how (s)he will be using the tools and techniques with respect to the creativity. As to achieve there could be multiple ways, to use right weights & parameters are the most effective part of it.Thanks for sharing this valuable information.READ MORE
Many times, I have seen data science professionals who know very well what is p-values but they struggle to explain it. I have also seen many professionals who don’t want to get into technical details but want to know more and understand why it is so important.READ MORE
That's a good article and very well explained.I was using EML without realizing it... in form of Random Forest Algorithm.READ MORE
It is really very excellent and eye-catching website. Thanks Nr. Chris for your kind gestures and support to grant me full membership status. I will try my level best to do some extraordinary things for Data Science. Inshaa Allah (God Willing).READ MORE
The Next Generation Digital Learning Environment (NGDLE) is considered as a ecosystem - a learning situation comprising of learning apparatuses and segments that stick to normal guidelines. While the customary Learning Management System (LMS) gives regulatory capacities, the NGDLE is planned to straightforwardly bolster learning.READ MORE
What is the correlation between Data Science and Econometrics.I have been thinking to find out if Data Science can stand alone without Econometrics. Will it rather be wise to say Econometrics forms a better modelling blocks on which Data Science thrive. I will like to know the correlation between this two fields. ThanksREAD MORE
One more thing which i missed in the article, that presence of outliers affects the distribution of the data. Let's examine what can happen to a data set with outliers. For the sample data set: 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4 We find the following: mean, median, mode, and standard deviation Mean = 2.58 Median = 2.5 Mode = 2 Standard Deviation = 1.08 If we add an outlier to the data set: 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 400 The new values of our statistics are: Mean = 35.38 Median = 2.5 Mode = 2 Standard Deviation = 114.74 It can be seen that having outliers often has a significant effect on the mean and standard deviation and hence affecting the distribution. We must take steps to remove outliers from our data sets. One of the easiest way to handle this is log transformation.Hope this information will help you.READ MORE
Very nice explanation of a very common problem. Examples are given in such a way that any one can understand easily. Also remedies for under fitting and over fitting are given in a structured manner.keep it up.READ MORE
Emotions and Intelligence are a co-related phenomenon; therefore emotions must be taken into consideration for designing truly intelligent agent. Emotional intelligence has emerged as an important area of research in artificial intelligence covering wide range of real-life domains. In this article, we will focus on emotional intelligence research with an emphasis on areas such as Emotion detection, Emotional agents, Text emotion detection, Modeling artificial agent’s environments.READ MORE
As the volumes of data increase each day, organisations are begining on Data Science and Data Analytics to make sense of it. This article is a basic introduction to those new to the fieldREAD MORE
Good day!I would like to ask for some ideas related to Health Informatics that I can pursue for my capstone project for my graduate-level Health Informatics and Health Information Management. My ideal project will be data analytics, but open to any health informatics-related project ideas. I appreciate your input.Be safe and stay healthy!DennisREAD MORE
This technology is very much within reach. When we do reach it, we’ll have a new, expansive field for human workers. Soon, it will be time to write a job description, but only once we figure out some crucial problems.READ MORE
The Select Committee on Artificial Intelligence outlined ways agencies can approach commercial cloud computing for research and development.READ MORE
Researchers have developed artificial intelligence technology that brings together imaging, processing, machine learning, and memory in one electronic chip, powered by light.READ MORE
As tech becomes more mainstream, journalists are challenged to report on complex subjects for their readers. Seasoned tech reporters offer advice on how to broach the topicREAD MORE
Among all the new innovations, the role of artificial intelligence (AI) has become more important. This technology helps businesses to save money as well as time whether it comes to marketing or operating various activities of the businesses. Here, we are going to show you how AI is very essential for modern businesses.READ MORE
Using analytics that can spot hidden connections between otherwise seemingly disconnected individuals means that next-generation know your customer (KYC) checks can more reliably flag the signs of deception and exploitation and escalate to law enforcement if necessary.READ MORE
One of the greatest barriers to adopting and scaling AI applications is the scarcity of varied, high-quality raw data. To overcome it, firms need to share their data. But the many regulatory restrictions and ethical issues surrounding data privacy pose a major obstacle to doing this.READ MORE
A new software application called the Smart Power Grid Simulator (Smart-PGSim) uses neural networks, a type of artificial intelligence (AI), to efficiently solve power grid simulations crucial for planning and optimizing electricity delivery.READ MORE
The research suggests that teaching materials science, mechanical engineering, computer science, biology and chemistry as a combined discipline could help students develop the skills they need to create lifelike artificially intelligent (AI) robots as researchers.READ MORE
As the shortage of skilled cybersecurity professionals intersects with the change and sophistication of the threat landscape, AI and ML will be leveraged to fill the gaps, according to a new Osterman survey and report.READ MORE
Artificial intelligence is continuously evolving and propagating across every industry. Here’s a look at the top 10 AI Research Labs in the world that are leading the research and development in AI and related technologies.READ MORE
Researchers with Pfizer and IBM developed an artificial intelligence (AI) model that they claim can predict the eventual onset of Alzheimer’s disease with 71% accuracy based on a language sample.READ MORE
Researchers at University of Maryland developed a machine learning algorithm that can infer the direction of the thermodynamic arrow of time in both macroscopic and microscopic processes. This algorithm, presented in a paper published in Nature Physics, could ultimately help to uncover new physical principles related to thermodynamics.READ MORE
A combination of machine learning and artificial intelligence has accelerated the design of making materials, including plastics, with properties that quickly degrade without harming the environment and super-strong lightweight plastics for aircraft and satellites that would one day replace the metals being used.READ MORE
The Data Science Foundation is committed to improving decision making. We believe that the use of data can improve decision making, but only if the social and environmental impact of decisions are evaluated alongside the knowledge and insight created though analytics. We work hard to help our members improve their decision making. To make good decisions today and to make better decisions tomorrow.
We help organisations new to evidence-based decision making; collect, process and analyse data. We help them appreciate the meaning hidden in their data so that they can understand more about the landscape in which they operate. We then help organisations who already have the expertise to collect data and create insight, to consider the moral implications of their actions. To think about the social and environmental impact of what the data might suggest is the best possible course of action.