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UPSC IES/ISS 2023: Registration Starts on April 19; Check Eligibility Criteria,Syllabus, Posts & Salary

The Union Public Service Commission (UPSC) will commence the registration process for the Indian Economic Service/Indian Statistical Service (IES/ISS) on April 19, 2023. The deadline for registration is May 9th, 2023. Candidates must qualify both a written examination and an interview to be selected for the IES/ISS. ....

National Statistical Organization , Union Public Service Commission , Indian Economic Service Statistical , Operations Research , Indian Economic , Indian Statistical Service , Eligibilty Criteria , Viva Voce , Statistical Methods , Numerical Analysis , Computer Application , Data Processing , Statistical Inference , Hypothesis Testing , Official Statistics , Sampling Techniques , Applied Statistics , Vital Statistics , Survival Analysis , Clinical Trial , Quality Control , Salary Package , Higher Administrative Grade Officer , Principal Officer , Junior Time Scale , Assistant Director ,

Registration is now open for the 4th Annual AI in Drug Discovery Conference

SAE Media Group reports: The brochure is available to download for the 4th Annual AI in Drug Discovery Conference in London. ....

United Kingdom , City Of , Andrew Gibbons , Sarah Nobrega , Ssae Media Group , Media Group Conferences , Media Group , Drug Discovery Conference , Darren Green , Data Science , Drug Discovery Processes , Hypothesis Testing , Preclinical Screening , Machine Learning , Drug Discovery , Group Conferences , Ai Drug Discovery ,

"Improving Project Control by Utilizing Predictive Data Analytic Models" by Kamal Jaafar, Ahmad Aloran et al.

Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing collected data from archived projects can assist managers to envisage project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average - ARIMA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next 3 months. This research paper provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short term project progress in ord ....

Autoregressive Integrated Moving Average , Hypothesis Testing , Predictive Models , Project Management , Project Progress ,

"Framework for a Predictive Progress Model–case of infrastructure proje" by Kamal Jaafar, Mohamad Watfa et al.

Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing historical data from archived projects can assist managers in predicting project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average–ARIMùA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next three months. This research provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short-term project progress to ....

Autoregressive Integrated Moving Average , Data Analytics , Hypothesis Testing , Predictive Models , Project Management , Project Progress , Time Series ,

"False Discovery Rates to Detect Signals from Incomplete Spatially Aggr" by Hsin Cheng Huang, Noel Cressie et al.

There are a number of ways to test for the absence/presence of a spatial signal in a completely observed fine-resolution image. One of these is a powerful nonparametric procedure called enhanced false discovery rate (EFDR). A drawback of EFDR is that it requires the data to be defined on regular pixels in a rectangular spatial domain. Here, we develop an EFDR procedure for possibly incomplete data defined on irregular small areas. Motivated by statistical learning, we use conditional simulation (CS) to condition on the available data and simulate the full rectangular image at its finest resolution many times (M, say). EFDR is then applied to each of these simulations resulting in M estimates of the signal and M statistically dependent p-values. Averaging over these estimates yields a single, combined estimate of a possible signal, but inference is needed to determine whether there really is a signal present. We test the original null hypothesis of no signal by combining the (Formula pr ....

Middle East , Conditional Simulation , Nhanced False Discovery Rate , Hypothesis Testing , Small Area Data ,