A group of researchers from the Boston University School of Public Health and the VA Boston Healthcare System utilized machine learning to streamline the diagnosis tool for post-traumatic stress disorder (PTSD). According to their new study, released in the journal Assessment, some of the questions imposed in the Structural Clinical Interview for the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (SCID-5) could be eliminated, leading to more relevancy of the veteran population. "Our study is only a first step--but an important one, because it shows that machine learning methods can be used to help inform efforts to make care more efficient, without sacrificing or degrading the quality of care provided," said co-author Jaimie Graudus, in a news release. The new research included data from the SCID-5 assessments related to more than 1,200 military soldiers, half of which were male and the rest female, who served during the Afghanistan and Iraq conflicts. The use of random forests, a form of machine-learning system, was also incorporated into the study.
American troops targeted with rocket and drone attacks, Lucas Tomlinson reports from the Pentagon. The death toll from a fire that swept through a hospital coronavirus ward climbed to 92 on Tuesday, Iraq's state news agency reported, as anguished relatives buried their loved ones and lashed out at the government over the country's second such disaster in less than three months. Health officials said scores of others were injured in the blaze that erupted Monday at al-Hussein Teaching Hospital in Nasiriyah. The tragedy cast a spotlight on what many have decried as widespread negligence and mismanagement in Iraq's hospitals after decades of war and sanctions. Prime Minister Mustafa al-Kadhimi convened an emergency meeting and ordered the suspension and arrest of the health director in Dhi Qar provice, the hospital director and the city's civil defense chief.
In late June, after Iranian-backed militias launched a drone attack against U.S. troops in Iraq, U.S. fighter jets responded by dropping bombs on the militias' facilities in Iraq and Syria. A Pentagon spokesman said the bombing was meant as "a clear and unambiguous deterrent message"--i.e., don't attack us again, or we'll attack you again. And yet, on Wednesday, less than two weeks after the bombings, the same militia fired 14 rockets at an Iraqi air base, which was hosting U.S. forces. It seems the "deterrent message" didn't get through. Then again, deterrent messages often don't get through.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A drone struck in the "vicinity" of the Erbil air base where U.S. troops are housed in the northern region of Iraq Tuesday, but no injuries or structural damage have been reported, according to Pentagon officials. "We are aware of reporting of a UAS [unmanned aircraft system] incident in the vicinity of Erbil, Iraq," Pentagon Spokesperson Commander Jessica McNulty told Fox News. "At this time, initial reports indicate no structural damage, injuries or casualties."
Thousands of members of Iraq's Hashed al-Shaabi paramilitary alliance gathered in Baghdad on Tuesday to mourn comrades killed in US air strikes along the Syrian border. The American raids early Monday sparked an exchange of fire between pro-Iranian militias and the US-led coalition in eastern Syria, and heightened fears of a new US-Iran escalation amid ongoing efforts to revive Tehran's 2015 nuclear deal with major powers. With chants of "death to America" and "vengeance for the martyrs", the Hashed members massed in Freedom Square near the Iraqi capital's high-security Green Zone where the US embassy is located. Security forces were deployed in large numbers, sealing off the Green Zone after a string of recent incursions by armed groups backed by Washington's arch-enemy Tehran. Mourners march with a banner showing (L to R) Iraq's slain Hashed al-Shaabi commander Abu Mahdi al-Muhandis, other slain members of the group and Iraq's top Shiite cleric Grand Ayatollah Ali al-Sistani, during a symbolic funeral in the Iraqi capital Photo: AFP / Sabah ARAR Several high-ranking Hashed figures took part in the symbolic funeral, including its top commander Faleh Al-Fayyadh and Hadi al-Ameri, head of one of its main factions, the Badr Organisation.
U.S. troops in eastern Syria came under rocket attack Monday, with no reported casualties, one day after U.S. Air Force planes carried out airstrikes near the Iraq-Syria border against what the Pentagon said were facilities used by Iran-backed militia groups to support drone strikes inside Iraq. Iraq's military condemned the U.S. airstrikes, and the militia groups called for revenge against the United States. Pentagon Press Secretary John Kirby said the militias were using the facilities to launch unmanned aerial vehicle attacks against U.S. troops in Iraq. It was the second time the administration has taken military action in the region since Biden took over earlier this year. There was no indication that Sunday's attacks were meant as the start of a wider, sustained U.S. air campaign in the border region.
The U.S. military, under the direction of President Joe Biden, carried out airstrikes against what it said were "facilities used by Iran-backed militia groups" near the border between Iraq and Syria, drawing condemnation from Iraq's military and calls for revenge by the militias. Pentagon Press Secretary John Kirby said the militias were using the facilities to launch unmanned aerial vehicle attacks against U.S. troops in Iraq. It was the second time the Biden administration has taken military action in the region since he took over earlier this year. Kirby said the U.S. military targeted three operational and weapons storage facilities on Sunday night -- two in Syria and one in Iraq. He described the airstrikes as "defensive," saying they were launched in response to the attacks by militias.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Israel shared three cell phone numbers used by Qasem Soleimani with U.S. intelligence in the hours before American drones unleashed Hellfire missiles on the Iranian general last year, Yahoo News reported Saturday. The revelation sheds new light on the role that Israel played in the killing of Soleimani, who the State Department says was responsible for hundreds of U.S. troop deaths as the head of the Revolutionary Guard's elite Quds Force. The drone strike occurred shortly after midnight on Jan. 2, 2020, as Soleimani and his entourage were leaving Baghdad's international airport.
This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation (EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.
In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence.