Decision trees [3, 4, 8, 31, 34, 40] and systems of decision rules [6, 7, 11, 14, 33, 34, 35, 36] are widely used as classifiers, knowledge representation tools, and algorithms. They are known for their interpretability in data analysis [10, 15, 23, 41]. Investigating the relationship between these two models is an important task in computer science. Converting decision trees into decision rule systems is a well-known and simple process [37, 38, 39]. This paper focuses on the inverse transformation problem, which is not trivial. The research related to this problem encompasses several directions: Two-stage construction of decision trees. This approach involves building decision rules based on input data, followed by the construction of decision trees or decision structures (which are generalizations of decision trees) using the generated rules. The benefits of this two-stage construction method are explained in [1, 2, 17, 18, 19, 20, 21, 22, 42].

Google has responded to last week's rumors that it may sunset its Stadia gaming service this year: "Stadia is not shutting down," the official Stadia Twitter account told a concerned fan in a tweet spotted by PC Gamer. "Rest assured we're always working on bringing more great games to the platform and Stadia Pro." I mentioned in my story about Xbox's streaming adventures into Samsung TVs that it's all been a little too quiet on the Stadia front when rival game streaming options have upped their game. Hopefully, Google has some exciting plans to reveal sooner rather than later. Hitting the Books: How Moderna dialed in its vaccine to fight COVID's variants Cellular service is coming to New York's subway tunnels, but it's going to take a while With horror stories of hackers selling personal information, it can be stressful to send your smartphone for repair. Yes, you could wipe your phone, but then you have to go through the hassle of restoring it afterward.

Ho cited a question on Tia's intake form that asks if a patient has ever experienced sexual abuse or assault, both of which have been linked to a wide range of health problems. Northwell is improving its data on maternal health in Black women by working with 100 faith-based collaborators in Black communities, said Mark Solazzo, president of strategic initiatives and chief operating officer. Data related to a patient's neighborhood of residence and employer type should also be included, said Empire BlueCross BlueShield CEO Alan Murray, adding that insurance companies can work with their employer clients to make those datasets more robust. Download Modern Healthcare's app to stay informed when industry news breaks. Stakeholders at different points of care then need to better connect that data to paint a complete picture of patients' health that all their providers can access and use to inform care decisions, Murray said. However, those datasets need to be accompanied by context on how the data was compiled and labeled to result in better AI models, said Mark Michalski, worldwide head of healthcare and life sciences for Amazon Web Services.

In August, 28-year-old Grant Michalski was implicated as part of a ring of men sharing images and videos of a young girl, the daughter of one of the ring's members, being sexually abused. The FBI arrived at Michalski's home with the authority to require him to unlock his iPhone X using the phone's Face ID feature. It was the first search warrant of its kind. From schools to summer camps to baseball stadiums to, of course, phones, biometric technology is everywhere. It's how we sort photos, buy beer at baseball games, and board flights.

The FBI has ordered a suspect to unlock his iPhone X using the facial recognition feature in the first case known worldwide of authorities using Apple's face ID technology to pry into devices. The incident occurred in Columbus, Ohio, when the FBI entered the home of Grant Michalski, 28, on August 10 while investigating him for child abuse. An agent told Michalski's to put his face to the phone and once inside uncovered salacious chats that helped charge him with receiving and possessing child pornography, according toForbes. Forbes obtained court documents which revealed special agent David Knight entered Michalski's home with a search warrant and forced the suspect to unlock his phone with his face. This allowed Knight to go through the contents of Michalski's device, including online chats and photos.

You lock your phone so other people can't access it. But how you lock your phone is an important factor in whether law enforcement can compel you to unlock it. Apple's year-old Face ID system is no exception. On Sunday, Forbes reported the first known example of law enforcement anywhere using a suspect's face to unlock a phone during an investigation. The question of whether cops can force someone to unlock their phone in the US for a search hinges on Fifth Amendment protects against self-incrimination--that no one "shall be compelled in any criminal case to be a witness against" themselves. Privacy advocates argue that this extends to the act of unlocking a phone, or generally decrypting data on a device.

In what may be a world first, the FBI has forced a suspect to unlock his iPhone X using Apple's Face ID feature. Agents in Columbus, Ohio entered the home of 28-year-old Grant Michalski, who was suspected of child abuse, according to court documents spotted by Forbes. With a search warrant in hand, they forced him to put his face on front of the device to unlock it. They were then able to freely search for his photos, chats and any other potential evidence. The FBI started investigating Michalski after discovering his ad on Craigslist titled "taboo."

With advances in artificial intelligence (AI), computing power and data science, we have entered a new era of medicine. There's a tremendous opportunity to improve the quality and efficiency of care, and prevention and prediction for an individual are finally going to be possible. Clinicians are inundated with and asked to integrate a tremendous amount of data into clinical practice. Until recently, the tools to harness vast quantities of information in a meaningful way to help patients have not been available. AI helps solve that problem.

Artificial intelligence is beginning to reshape healthcare and life sciences. And one application of AI, deep learning, is coming into its own. Deep learning is a type of machine learning based on data representations rather than task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Much of the excitement around AI today is fundamental because of three ingredients: the development of algorithms that make artificial neural networks, the increasing supply of digital data that now can be created, and, critically, the "GPU" chip architecture – it stands for graphics processing unit – pioneered by vendor NVIDIA, said Mark Michalski, MD, executive director of the Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science. "GPU chips are different than the CPU chips that run many of our computers today in that they solve many simple problems simultaneously, as opposed to one big problem at a time, like CPUs," Michalski explained.

Technology is all about disruption, but a doctor operating on a patient doesn't want to be "disrupted." Digital solutions that clinicians are eager to embrace won't lead to additional clicks, require extensive training, or feature some flashy object that's distracting to the task at hand. The best tools will simplify, prioritize and organize. They will take the clinician away from administrative or mundane tasks and bring them closer to the patient. AI, done right, achieves these objectives.