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On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

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Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.


Intel, Dell launch AI incubator lab at Arizona community college

ZDNet

Nate writes about the intersection of education and technology. He's also worked as a newspaper staff writer covering K-12 and higher education, business, local government, and public safety. Intel and Chandler-Gilbert Community College in Arizona recently announced the opening of a new on-campus artificial intelligence incubator lab. The lab is part of the AI for Workforce Program. Launched in 2020, the program's goals include helping students get workforce ready by equipping them with hands-on AI knowledge, skills, and experiences.


The future of work

#artificialintelligence

It can seem like "help wanted" signs are everywhere. But at the same time, unemployment in Arizona is the lowest it has been for more than a decade. This week, The Buzz focuses on the future of work in a tight labor market. "We're seeing a strong labor market but more people looking for jobs, maybe taking their time to look for that perfect job or that perfect industry they want to work in," said Jennifer Pullen, an economist at the University of Arizona's Eller College of Management. And while the city remains one of the more affordable places to buy a home for someone earning the median family income, wages have not increased at the same rate as home prices, she said.


AI beats top players at Bridge in two-day tournament

#artificialintelligence

In brief AI algorithms crushed eight world champions playing the card game Bridge, marking another milestone in machine learning systems becoming better than humans at specific games. Top Bridge players were invited to play against NooK, AI software developed by French startup NuukAI, in a tournament over two days in Paris. They battled against one another across 80 rounds, and the machine won 67 sets, beating humans at a rate of 83 per cent, according to The Guardian. NooK is made up of a combination of modern deep learning and older rule-based programmes. NuukAI's co-founder Jean-Baptiste Fantun said the company had developed the software over five years, and its decisions are easier to understand compared to today's black box-like systems.


Intel, Community College District in Arizona Launch First-of-its-Kind AI Lab

#artificialintelligence

Arnav Bawa, a student in the artificial intelligence program at Chandler Gilbert Community College, has developed an AI application to interpret EEG brain wave scans. The application can help predict brain seizures, so a patient can take medication or prevent injury from falling. William Glover, a student in the artificial intelligence program at Chandler Gilbert Community College, has developed an AI application for drones. The application can be used in indoor search and rescue situations. It uses AI to interpret a live video feed to look for and recognize people who may be trapped in a burning building.


How self-driving cars got stuck in the slow lane

The Guardian

"I would be shocked if we do not achieve full self-driving safer than a human this year," said Tesla chief executive, Elon Musk, in January. For anyone who follows Musk's commentary, this might sound familiar. In 2020, he promised autonomous cars the same year, saying: "There are no fundamental challenges." In 2019, he promised Teslas would be able to drive themselves by 2020 – converting into a fleet of 1m "robotaxis". He has made similar predictions every year going back to 2014.


'I'm the Operator': The Aftermath of a Self-Driving Tragedy

WIRED

Rafaela Vasquez liked to work nights, alone, buffered from a world she had her reasons to distrust. One Sunday night in March 2018, Uber assigned her the Scottsdale loop. She drove a gray Volvo SUV, rigged up with cameras and lidar sensors, through the company's garage, past the rows of identical cars, past a poster depicting a driver staring down at a cell phone that warned, "It Can Wait." The clock ticked past 9:15, and Vasquez reached the route's entry point. She flipped the Volvo into autonomous mode, and the car navigated itself through a blur of suburban Arizona, past auto dealers and Zorba's Adult Shop and the check-cashing place and McDonald's.


La veille de la cybersécurité

#artificialintelligence

To overcome the issue of data scarcity, University of Arizona researchers have devised a new method for automatically generating datasets with tagged radar data-camera images. It labels the radar point cloud using an object recognition algorithm (YOLO) on the camera image stream and an association technique (the Hungarian algorithm). The approach works on the idea of using an image-based object detection framework to automatically label the radar data instead of manually looking at images if the camera and radar were staring at the same item. The approach's co-calibration, grouping, and association capabilities are three distinguishing properties. The method co-calibrates a radar and its camera to identify how the location of an object detected by the radar would translate into digital pixels on a camera.


Two Men Identified As Authors Behind QAnon Discovered By Machine Learning

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Two teams of forensic linguists have been working to track down the identity of Q, an anonymous writer claiming to be a government insider who has been fueling the conspiracy movement QAnon since 2017. The teams have used a machine learning program to identify two men as the potential origin of Q. This research was publicized in a New York Times report Saturday, and named South African software developer Paul Furber as the most likely person behind the very first Q posts. They also alleged that Furber collaborated with Ron Watkins to compose messages under the pseudonym. Furber and Watkins were already known as prominent figures in the movement before this material was released, and Watkins recently announced his intent to run for Congress in Arizona.


QAnon founder may have been identified thanks to machine learning

Engadget

With help from machine learning software, computer scientists may have unmasked the identity of Q, the founder of the QAnon movement. In a sprawling report published on Saturday, The New York Times shared the findings of two independent teams of forensic linguists who claim they've identified Paul Furber, a South African software developer who was one of the first to draw attention to the conspiracy theory, as the original writer behind Q. They say Arizona congressional candidate Ron Watkins also wrote under the pseudonym, first by collaborating with Furber and then later taking over the account when it eventually moved to post on his father's 8chan message board. The two teams of Swiss and French researchers used different methodologies to come to the same conclusion. The Swiss one, made up of two researchers from startup OrphAnalytics, used software to break down Q's missives into patterns of three-character sequences.