Employee's knowledge is an organization asset. Turnover may impose apparent and hidden costs and irreparable damages. To overcome and mitigate this risk, employee's condition should be monitored. Due to high complexity of analyzing well-being features, employee's turnover predicting can be delegated to machine learning techniques. In this paper, we discuss employee's attrition rate. Three different supervised learning algorithms comprising AdaBoost, SVM and RandomForest are used to benchmark employee attrition accuracy. Attained models can help out at establishing predictive analytics.

More than 40% of all employees were thinking about leaving their jobs at the beginning of 2021, and as the year went on, workers quit in unprecedented numbers.1 Between April and September 2021, more than 24 million American employees left their jobs, an all-time record.2 As the Great Resignation rolls on, business leaders are struggling to make sense of the factors driving the mass exodus. More importantly, they are looking for ways to hold on to valued employees. To better understand the sources of the Great Resignation and help leaders respond effectively, we analyzed 34 million online employee profiles to identify U.S. workers who left their employer for any reason (including quitting, retiring, or being laid off) between April and September 2021.3 The data, from Revelio Labs, where one of us (Ben) is the CEO, enabled us to estimate company-level attrition rates for the Culture 500, a sample of large, mainly for-profit companies that together employ nearly one-quarter of the private-sector workforce in the United States.4 Monthly research-based updates on what the future of work means for your workplace, teams, and culture. While resignation rates are high on average, they are not uniform across companies.

Online human-behavior experimentation is pervasive, manifold, and unavoidable. Leading digital companies routinely conduct over 1,000 A/B tests every month with millions of users. Online labor markets boast hundreds of thousands of workers to hire for crowdsourcing tasks, including experimentation and beta-testing. Outside industry, academic researchers utilize online labor markets to run behavioral experiments that span from cooperation games to protein folding tasks. Hidden behind a deceiving façade of simplicity, implementing a human-behavior experiment for unbiased statistical inference is a task not to be taken lightly.

Retired Col. Douglas MacGregor has been a vocal proponent of withdrawing from Afghanistan, Syria and South Korea; Jennifer Griffin reports. As it stands, more than 55,000 active-duty U.S. soldiers – wracked by war wounds and injuries – are deemed non-deployable. In 2018, more than half of all active-duty soldiers sustained some form of physical trauma – with over 70% diagnosed as lower extremity micro-traumatic musculoskeletal (MSK) or "overuse" injuries. And aside from the gaping hole it leaves in the defense and security arena, the medical costs related to MSK ailments across all military branches cost the U.S. taxpayer more than $575 million per year. The U.S Marines using the Sparta Science system were taken at the School of Infantry – West Training Command at MCB Camp Pendleton, California. But in a bid to solve the impasse and cut down on costs, the Department of Defense is turning to an emerging new force plate and machine learning technology – from Sparta Science – to pinpoint potential problem points to prevent future maladies, zap the attrition rate and increase physical readiness.

In this paper, we introduce a non-linear Lanchester model of NCW-type and investigate an optimization problem for this model, where only the Red force is supplied by several supply agents. Optimal fire allocation of the Blue force is sought in the form of a piece-wise constant function of time. A threatening rate is computed for the Red force and each of its supply agents at the beginning of each stage of the combat. These rates can be used to derive the optimal decision for the Blue force to focus its firepower to the Red force itself or one of its supply agents. This optimal fire allocation is derived and proved by considering an optimization problem of number of Blue force troops. Numerical experiments are included to demonstrate the theoretical results.

Compared to other industries, in life sciences putting a new product to market is nothing short of a miracle, as it comes with extremely high costs and risk. It is estimated that getting a drug to market bears a cost in excess of $2 billion, with approximatively 60% linked to clinical development. It takes around 15 years to develop a new potential treatment, with no certainty of it ever getting to patients. The attrition rate is extremely high, with an estimated 15% of potential drugs in development successfully reaching the launch phase. To reduce the cost and increase the probability of success, life sciences would seem a model industry to benefit from the implementation of artificial intelligence tools -- and yet as an industry we are still lagging behind others in taking advantage of the benefits of advanced AI. Why? Let's get back to some basics.

Barcelona today is a global hot-spot for technology and innovation. The city is a magnet for international and local entrepreneurs, innovators and start-ups, making waves across the digital and tech sectors. Business evolution is Barcelona's calling card. Change is all around us and work has never seemed so exciting. How we bank (N26), buy food (Glovo) find our homes (Badi) have all evolved through technology.

For decades, earning your way onto an elite special operations unit has largely been about your performance on aptitude tests and during physically challenging assessments, but in the very near future, the Marine Corps' special operations unit known as the Raiders, or MARSOC, may be looking to computers to help identify the best candidates. MARSOC is currently experimenting with the idea that machine learning could be used to identify and track factors that lead to success for MARSOC applicants. By first identifying the unique traits that seem to make up the most ideally suited operator, the same practice could be employed on incoming classes -- sifting through Marines that don't possess the best traits for an operator and identifying those that are particularly well suited for the rigors of the special operations community. Over this past summer, SOCOM officials began collecting and assessing a wide range of data points recorded during the selection process for the Marine Corps Forces Special Operations Command. "It's really going to be our first experiment. It's exciting," said David Spirk, SOCOM's chief data officer.

Michael Ringman, CIO at contact center and IT services provider Telus International, has his hands full. Ringman, who reports to the CEO, oversees the company's internal IT operations, with a team of about 300. He also helps lead a team of 3,000 who provide expertise to customers in the throes of digital transformation efforts. And he oversees a majority of Telus International's data scientists, who are experimenting with advanced analytics techniques such as machine learning. What exactly is digital transformation?

A recent study released by development consulting firm Banyan Global sheds light on Microwork and Impact Sourcing highlighting the different sides of the industry by looking at what clients want out of service providers and how to ensure that workers are being offered steady work with future opportunity. Microwork is a series of small tasks which together constitute a larger project. A recent study released by development consulting firm Banyan Global sheds light on Microwork and Impact Sourcing highlighting the different sides of the industry by looking at what clients want out of service providers and how to ensure that workers are being offered steady work with future opportunity. Microwork is a series of small tasks which together constitute a larger project. Impact Sourcing, also known as socially responsible outsourcing, refers to the creation of employment for high potential but disadvantaged people in the services sector via contract work.