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Is AI leading to a reproducibility crisis in science?

Scientists worry that ill-informed use of artificial intelligence is driving a deluge of unreliable or useless research. Scientists worry that ill-informed use of artificial intelligence is driving a deluge of unreliable or useless research. ....

United States , Mountain View , France General , New Jersey , Menlo Park , Eteläuomen Läi , Edward Raff , Benjamin Haibe Kains , Veronika Cheplygina , Joseph Cohen , Viren Jain , Alex Trevino , Joaquin Vanschoren , Sanchari Dhar , Sayash Kapoor , Lorena Barba At George Washington University , Institute For Reproducible Research , French National Institute For Research , It University Of Copenhagen , Arvind Narayanan At Princeton University , Booz Allen Hamilton , Joelle Pineau At Mcgill University , Google Health , Google Health In Palo Alto , Eindhoven University Of Technology , University Of Toronto ,

Echo Chess: The Quest for Solvability

Let’s make Chess more fun in Single-Player. ....

United States , Genki Dama , Travelly Boi , Mark Rosewater , Convolutional Neural Network , Game Full Stack Development , Turn Based Strategy , Echo Chess , Mechanical Turk , Give Up , Difficulty Saw , Classic Mode , Endless Mode , Edwards Notation , New Formal , Pathfinding Algorithms , Depth First Search , Echo Chess Solvability , Hamiltonian Path Problem , Thoeretical Computer Science , Traveling Salesman Problem , Monte Carlo Tree Search , Remember Minesweeper , Game Over , Training Set , Validation Set ,

How AI could transform financial services in emerging markets

AI can allow financial services providers to make use of alternative data sources to assess creditworthiness. This could prove transformative. ....

Central African Republic , South Africa , United States , Mark Nasila , America Erica , First National Bank , World Bank , Saharan Africa , North Africa , Central African , Synthetic Minority Oversampling Technique , Partial Attribute Decorrelation , Capital Float , Life Plan ,

"Heart failure prediction with machine learning: A comparative study" by Jing Wang

Heart failure is a worldwide healthy problem affecting more than 550,000 people every year. A better prediction for this disease is one of the key approaches of decreasing its impact. Both linear and machine learning models are used to predict heart failure based on various data as inputs, e.g., clinical features. In this paper, we give a comparative study of 18 popular machine learning models for heart failure prediction, with z-score or min-max normalization methods and Synthetic Minority Oversampling Technique (SMOTE) for the imbalance class problem which is often seen in this problem. Our results demonstrate the superiority of using z-score normalization and SMOTE for heart failure prediction. ....

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