BACKGROUND Mild cognitive impairment (MCI) is an etiologically heterogeneous condition that is characterized by cognitive changes without impairment of activities of daily living and insufficient to represent dementia. MCI is an important risk state for dementia. Neuropsychiatric symptoms may be present in MCI. METHODS We executed a PubMed search for articles on the neuropsychiatric manifesta...

This work categorizes and reviews behavioral biometrics with the inclusion of future-oriented techniques. A general introduction to this field is given alongside the benefits of this non-intrusive approach. It presents the examination and analysis of the current research in the field and the different types of behavior-centric features. Accuracy rates for verifying users with different behavior...

This paper looks at the present standing of ecommerce in Saudi Arabia, as well as the challenges and strengths of Business to Customers (B2C) electronic commerce. Many studies have been conducted around the world in order to gain a better understanding of the demands, needs and effectiveness of online commerce. A study was undertaken to review the literature identifying the factors influencing ...

Vision based bin picking is increasingly more di cult as the complexity of target objects increases We propose an e cient solution where complex objects are su ciently represented by simple features cues thus invariance to object complexity is established The re gion extraction algorithm utilized in our approach is capable of providing the focus of attention to the simple cues as a trigger towa...

We present preliminary work on automatically scoring constructed responses elicited as part of a certification test designed to measure the effectiveness of the test-taker as a K-12 music teacher. This content scoring differs from most previous work in that the responses are relatively long and are written by an adult population of generally proficient English writers. We obtain reasonably good...

We show that people’s travel destinations are predictable based on simple features of their home and destination. Using geotagged Twitter data from over 200,000 people in the U.S., with a median of 10 visits per user, we use machine learning to classify whether or not a person will visit a given location. We find that travel distance is the most important predictive feature. Ignoring distance, ...