نتایج جستجو برای: activity recognition
تعداد نتایج: 1363858 فیلتر نتایج به سال:
One of the major challenges in Human Activity Recognition (HAR) based on machine learning is scarcity labeled data. Indeed, collecting a sufficient amount training data to build reliable recognition problem often prohibitive. Among many solutions literature mitigate this issue, collaborative emerging as promising direction distribute annotation burden over multiple users that cooperate shared m...
abstract lexical knowledge of complex english words is an important part of language skills and crucial for fluent language use (nation, 2001). the present study, thus, was an attempt to assess the role of morphological decomposition awareness as a vocabulary learning strategy on learners’ productive and receptive recall and recognition of complex english words. so 90 sophomores (female and ma...
چکیده : مقدمه : ابتلا به آترواسکلروز در بیماران دیابتی بیشتر از افراد سالم می باشد . هموسیستئین می تواند به عنوان یکی از عوامل خطرزای آترواسکلروز مطرح با شد . آنزیم لستین کلسترول آسیل ترنسفراز نقش مهمی را در پاک سازی کلسترول آزاد در بافت ها از طریق عملکرد hdl-c بر عهده دارد .هدف از این مطالعه تعیین سطح فعالیت لستین کلسترول آسیل ترنسفراز (lcat activity ) در بیماران دیابتی نوع 2 در مقایسه با افر...
Entorhinal cortex (EC) is one of the first Entorhinal cortex (EC) is one of the first cerebral regions affected in Alzheimer’sdisease (AD). The pathology propagates to neighboring cerebral regions through a prion-likemechanism. In AD, intracellular calcium dyshomeostasis is associated with endoplasmicreticulum (ER) stress. This study was designed to examine hippocampal ER stre...
Yang’s group has been actively working in the field of activity recognition based on mobile devices. Their work includes transfer learning for activity recognition and activity recognition for indoor daily-living activity recognition via mobile devices (see http://www.cse.ust.hk/~qyang/byarea.htm#Activity_Recognition) at location, action and goal levels. Yang was an invited speaker at IJCAI Con...
Wearable technology presents a uniquely convenient and portable way to record physiological data from users, which could be used to monitor health or recreational activities. With increasing amounts of such data, it would be useful to automatically categorize a user’s activity based on this data. Our paper utilizes machine learning to classify user activity, and we compare the strengths and wea...
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the high-level context into account. Our system ...
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