In district-based multi-party elections, electors cast votes in their respective districts. In each district, the party with maximum votes wins the corresponding seat in the governing body. Election Surveys try to predict the election outcome (vote shares and seat shares of parties) by querying a random sample of electors. However, the survey results are often inconsistent with the actual results, which could be due to multiple reasons. The aim of this work is to estimate a posterior distribution over the possible outcomes of the election, given one or more survey results. This is achieved using a prior distribution over vote shares, election models to simulate the complete election from the vote share, and survey models to simulate survey results from a complete election. The desired posterior distribution over the space of possible outcomes is constructed using Synthetic Dirichlet Likelihoods, whose parameters are estimated from Monte Carlo sampling of elections using the election models. We further show the same approach can also use be used to evaluate the surveys - whether they were biased or not, based on the true outcome once it is known. Our work offers the first-ever probabilistic model to analyze district-based election surveys. We illustrate our approach with extensive experiments on real and simulated data of district-based political elections in India.

Glioblastoma brain tumors are highly malignant and often require early detection and accurate segmentation for effective treatment. We are proposing two deep learning models in this paper, namely UNet and Deeplabv3, for the detection and segmentation of glioblastoma brain tumors using preprocessed brain MRI images. The performance evaluation is done for these models in terms of accuracy and computational efficiency. Our experimental results demonstrate that both UNet and Deeplabv3 models achieve accurate detection and segmentation of glioblastoma brain tumors. However, Deeplabv3 outperforms UNet in terms of accuracy, albeit at the cost of requiring more computational resources. Our proposed models offer a promising approach for the early detection and segmentation of glioblastoma brain tumors, which can aid in effective treatment strategies. Further research can focus on optimizing the computational efficiency of the Deeplabv3 model while maintaining its high accuracy for real-world clinical applications. Overall, our approach works and contributes to the field of medical image analysis and deep learning-based approaches for brain tumor detection and segmentation. Our suggested models can have a major influence on the prognosis and treatment of people with glioblastoma, a fatal form of brain cancer. It is necessary to conduct more research to examine the practical use of these models in real-life healthcare settings.

Toronto Pearson is located in Mississauga, west of Toronto, in Ontario, Canada. It is Canada's largest airport and the sixth-most-connected airport in the world. "As the first airport in the world to test HEXWAVE, we see the potential benefits of utilizing this innovative solution as part of our broader airport security program following further testing and evaluation," said Dwayne MacIntosh, Director, Corporate Safety and Security, GTAA. "We were impressed with the HEXWAVE's seamless screening during beta testing and look forward to working with Liberty Defense on the enhanced detection the HEXWAVE would bring to the airport." HEXWAVE uses millimeter wave, advanced 3D imaging, and AI to detect all types of concealed metallic and non-metallic weapons and other prohibited items – without having to divest common items.

This article was published as a part of the Data Science Blogathon. This article will discuss some data science interview questions and their answers to help you fare well in job interviews. These are data science interview questions and are based on data science topics. Though some of the questions may sound basic, these are frequently asked in interviews. Most candidates overlook them and won't focus on the basics, and they face rejection in job interviews.

Ran Poliakine, Chairman and CEO and Erez Meltzer, Director, will host industry experts and Nanox team members to share the Company's AI vision of the integration of the AI technologies with the Nanox.ARC and Nanox.SOURCE NEVE ILAN, Israel, Sept. 24, 2021 (GLOBE NEWSWIRE) -- NANO-X IMAGING LTD ("Nanox" or the "Company," Nasdaq: NNOX), an innovative medical imaging technology company, today announced that Ran Poliakine, Chairman and Chief Executive Officer, and Erez Meltzer, Director (appointed CEO effective January 1, 2022), will host a Nanox AI Vision event. About Nanox: Nanox, founded by the serial entrepreneur Ran Poliakine, is an Israeli corporation developing a commercial-grade digital X-ray source designed to be used in real-world medical imaging applications. Nanox believes that its novel technology could significantly reduce the costs of medical imaging systems and plans to seek collaborations with world-leading healthcare organizations and companies to provide affordable, early detection imaging services for all. For more information, please visit www.nanox.vision. Forward-Looking Statements This press release may contain forward-looking statements that are subject to risks and uncertainties.

New feature comes on the heels of VERB's Attribution feature announced in May 2021 for the verbLIVE livestream ecommerce platform NEWPORT BEACH, Calif. and SALT LAKE CITY, July 26, 2021 (GLOBE NEWSWIRE) -- Verb Technology Company, Inc. (Nasdaq: VERB) ("VERB" or the "Company"), a leader in interactive video-based sales enablement applications, including interactive livestream ecommerce, webinar, CRM and marketing applications for entrepreneurs and enterprises, today announced that it is introducing A I capabilities to its sales enablement platform. The new feature set called "Pulse" is the first iteration of VERB's artificial intelligence initiatives designed to make it easy for anyone to sell, giving pros and newbies alike a real competitive advantage. The new feature will be available in August 2021. Designed by sales people for sales people, Pulse helps automate management of their customer relationships and interactions. Based on prior activities and behavior, Pulse guides users through behavior-driven prompts, reminders, and suggested actions for specific customers.

If you do not have the time to read the full article, consider reading the 30 seconds version. If you have Machine Learning (ML) pipelines in production, you have to worry about backward compatibility of changes made to the pipeline. It may be tempting to increase test coverage, but a high test coverage cannot guarantee that your recent changes have not broken the pipeline or generated low quality results. To do that, you need to develop end-to-end tests that can be executed as part of the continuous integration pipelines. Developing such a test requires sampling the dataset that powers the pipeline from a run that produces acceptable result and on which you have an in-depth knowledge.

Predictiv AI Inc. (TSXV: PAI) (OTC: INOTF) (FSE: 71TA) ("Predictiv AI" or the "Company"), a software and solutions provider in the artificial intelligence markets is pleased to announce its dynamic new partnership with the Waterloo Artificial Intelligence Institute ("Waterloo.AI") at the University of Waterloo. This partnership will allow Predictiv AI's subsidiary, AI Labs Inc. ("AI Labs"), to pursue its various innovations through leveraging Waterloo.AI as an extension of the Predictiv AI team. The collaboration will allow access to the world's top artificial intelligence resources, creating much greater bandwidth in ideation, research, and development of solutions for real-world problems. Predictiv AI also looks forward to participating in and supporting Waterloo.AI events and introducing other synergistic partners to the program. Beyond having access to an unparalleled talent pool of academics and their teams for research and development, the partnership also provides for round table participation amongst Waterloo.AI partners.

SANTA CLARA, CALIF., Nov. 30, 2020 – ServiceNow (NYSE: NOW) today announced it has signed an agreement to acquire Element AI, a leading artificial intelligence (AI) company with deep AI capabilities and some of the world's brightest AI minds. Element AI will significantly enhance ServiceNow's commitment to build the world's most intelligent workflow platform, enabling employees to work smarter and faster, streamline business decisions, and unlock new levels of productivity. A pioneer in the AI industry, Element AI has world‑class scientists and practitioners who will bring expertise in applying modern AI to text and language, chat, images, search, question response, and summarization and will accelerate AI innovation natively in the Now Platform. Element AI Co‑founder and Lead Fellow, Dr. Yoshua Bengio, a winner of the 2018 ACM A.M. Turing Award for his pioneering contributions to modern AI, will serve as a technical advisor for ServiceNow. With the acquisition of Element AI, ServiceNow will create an AI Innovation Hub in Canada to accelerate customer‑focused AI innovation in the Now Platform. The new investment deepens ServiceNow's commitment to the Canadian market, which has long been a leader in AI research and represents one of the world's most significant locations for AI talent.

Machine learning is a pioneer subset of Artificial Intelligence, where Machines learn by itself using the available dataset. For the optimization of any machine learning model, an acceptable loss function must be selected. A Loss function characterizes how well the model performs over the training dataset. Loss functions express the discrepancy between the predictions of the model being trained and also the actual problem instances. If the deviation between predicted result and actual results is too much, then loss function would have a very high value.