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USAA invests in AI tech vendor CognitiveScale, implements its products
Banking Technology USAA, a banking and insurance provider that focuses on the US military personnel and their families, has made a “strategic investment” in machine intelligence software vendor CognitiveScale. Alongside USAA, other latest investors in CognitiveScale are … |
June 14, 2017 at 04:53AM
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IJCAI is coming up and there are some really interesting papers cropping up in the proceedings. I’ve been excited to read this one (warning: pdf download) in particular for a couple of weeks now.
The authors present an approach to both interpreting beginner-level probability problems in natural language and then solving these problems through probabilistic programming.
It’s quite a simple concept but the performance is quite impressive and it’s certainly an intuitive approach which I always favour.
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June 14, 2017 at 01:32AM
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Based on a study of more than 30,000 elementary, middle, and high school students conducted in winter 2015–16, researchers found that elementary and middle school students scored lower on a computer-based test that did not allow them to return to previous items than on two comparable tests—paper- or computer-based—that allowed them to skip, review, and change previous responses.
June 13, 2017 at 09:25PM
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Based on a study of more than 30,000 elementary, middle, and high school students conducted in winter 2015–16, researchers found that elementary and middle school students scored lower on a computer-based test that did not allow them to return to previous items than on two comparable tests—paper- or computer-based—that allowed them to skip, review, and change previous responses.
June 13, 2017 at 08:39PM
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The opening chords of Creedence Clearwater Revival’s “Bad Moon Rising” rocked a hotel ballroom in New York City as a nattily dressed British man strode onstage several weeks before last fall’s U.S. election.
June 13, 2017 at 01:57AM
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The opening chords of Creedence Clearwater Revival’s “Bad Moon Rising” rocked a hotel ballroom in New York City as a nattily dressed British man strode onstage several weeks before last fall’s U.S. election.
June 13, 2017 at 01:57AM
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A new system that learns how to grasp objects has been developed by a team of researchers.
June 12, 2017 at 04:35AM
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A new system that learns how to grasp objects has been developed by a team of researchers.
June 12, 2017 at 04:35AM
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A new system that learns how to grasp objects has been developed by a team of researchers.
June 12, 2017 at 04:35AM
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A new system that learns how to grasp objects has been developed by a team of researchers.
June 12, 2017 at 04:35AM
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Publication date: Available online 10 June 2017
Source:Artificial Intelligence in Medicine
Author(s): Alvaro Garcia-Martinez, José MarÃa Vicente Samper, José MarÃa Sabater-Navarro
Background and objectivesOn occasions, a surgical intervention can be associated with serious, potentially life-threatening complications. One of these complications is a haemorrhage during the operation, an unsolved issue that could delay the intervention or even cause the patient’s death. On laparoscopic surgery this complication is even more dangerous, due to the limited vision and mobility imposed by the minimally invasive techniques.MethodsIn this paper it is described a computer vision algorithm designed to analyse the images captured by a laparoscopic camera, classifying the pixels of each frame in blood pixels and background pixels and finally detecting a massive haemorrhage. The pixel classification is carried out by comparing the parameter B/R and G/R of the RGB space colour of each pixel with a threshold obtained using the global average of the whole frame of these parameters. The detection of and starting haemorrhage is achieved by analysing the variation of the previous parameters and the amount of pixel blood classified.ResultsWhen classifying in vitro images, the proposed algorithm obtains accuracy over 96%, but during the analysis of an in vivo images obtained from real operations, the results worsen slightly due to poor illumination, visual interferences or sudden moves of the camera, obtaining accuracy over 88%. The detection of haemorrhages directly depends of the correct classification of blood pixels, so the analysis achieves an accuracy of 78%.ConclusionsThe proposed algorithm turns out to be a good starting point for an automatic detection of blood and bleeding in the surgical environment which can be applied to enhance the surgeon vision, for example showing the last frame previous to a massive haemorrhage where the incision could be seen using augmented reality capabilities.
June 11, 2017 at 04:43PM
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Publication date: Available online 10 June 2017
Source:Artificial Intelligence in Medicine
Author(s): Chih-Jen Tseng, Chi-Jie Lu, Chi-Chang Chang, Gin-Den Chen, Chalong Cheewakriangkrai
Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory.
June 11, 2017 at 04:43PM
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