Taboos related to food culture at the 13th–14th-century Mongols

This article reviews the characteristics of terms used by the Mongols to express taboos concerning food and examines their food-related culture based on sources from the Mongolian Empire (Plano Carpini’s Historia Mongalorum, Marco Polo’s Il milione, Chinggis Khan’s Yeke Jasag, Rashīd ad-Dīn’s Jāmiʿ al-tawārīkh, and the Yuan Dynasty’s 飮食須知 Yin shi xu zhi). The Mongols left behind fairly strict food-related taboos in the form of eating habits. To better understand Mongolia’s food-related taboo expressions, we have examined five mediaeval sources written in various languages in which diverse food taboos are recorded

Localization in Sensor Networks with Fading Channels based on Nonmetric Distrance Models

Wireless Sensor Network (WSN) applications nowadays are an emerging avenue in which sensor localization is an essential and crucial issue. Many algorithms have been proposed to estimate the coordinate of sensors in WSNs, however, the attained accuracy in real-world applications is still far from the theoretical lower bound, Crame-Rao Lower Bound (CRLB), due to the effects of fading channels. In this paper, we propose a very simple and light weight statistical model for rang-based localization schemes, especially for the most typical localization algorithms based on received signal strength (RSS) and time-of-arrival (TOA). Our proposed method infers only the order or the nomination of given distances from measurement data to avoid significant bias caused by fading channels or shadowing. In such way, it radically reduces the effects of the degradation and performs better than existing algorithms do. With simulation of fading channels and irregular noises for both the RSS-based measurement and the TOA-based measurement, we analyze and testify both the benefits and the drawbacks of the proposed models and the localization scheme

Meaningful Information Extraction from Unstructured Clinical Documents

Medical concept and entity extraction from the medical narrative unstructured documents is the crucial step in most of the contemporary health systems. For the extraction of medical concepts and entities, the Unified Medical Language System (UMLS) Metathesaurus is a big source of biomedical and health-related concepts. Recently various tools like Sophia, MetaMap and cTAKES, and many other rules-based methods and algorithm like Quick UMLS etc. have been developed which are performing a successful role in the process of medical concept extraction. The goal of this paper is to design a generic algorithm to identify a package consisting of standard concepts, their semantic types, and entity types on the basis of medical phrases and terms used in the clinical unstructured documents. The proposed algorithm implements the UMLS terminology service (UTS) and customizes to extract concepts for all the meaningful phrases and terms used in the narratives and determine their semantic and entity types in order to find exact categorization of the concepts. The proposed algorithm has produced a very useful set of results to use for labeling the biomedical data, which could in term be used for training data-driven approaches such asmachine learning

A journey from Cure to Care- Wellness management for healthy lifestyle: Diabetes management a case study

Smart ubiquitous computing has a vital role to avoid and indicate the preventable lifestyle-based chronic diseases. It is focusing to adopt a healthy lifestyle by converging science and technology in this digital world for improving health and quality of life. From the last decade, the development of wellness applications has supported personalization and self-quantification. These applications facilitate the users through activity tracking and monitoring, based on the raw sensory data to adopt healthy behavior. The challenge of behavior change is not only to indicate the issues but also provides step-by-step coaching and guidance at real time. The realization of behavior change theories through digital technology has revolutionized the lifestyle change in a systematic and measurable manner. We have proposed a methodology to understand the behavior for generating just-in-time intervention for adopting a healthy lifestyle. Wellness platform based behavior analysis is performed using unbiased life-log and questionnaire for qualitative assessment of behavior. Behavior stage wise intervention is provided to adapt behavior for enhancing the quality of life and boost the socio-economic conditions. Personalized education is provided to understand the importance of healthy behavior and motivate the users, whereas just-in-time context-based recommendations have supported the stage-wise adaptation of unhealthy behavior. These capabilities require status evaluation of the activities and an efficient way to portray the comprehensive index of lifestyle habits. The real focus is to correlate the primarily linked habits in appropriate proportion through healthy behavior index (HBI) for personalized wellness support services. The healthy behavior index and behavior change theories through smart technologies

A case-based meta-learning and reasoning framework for classifiers selection

© 2018 ACM. In machine learning area, a large number of classification algorithms are available that can be used for solving the problems of prediction and classification in different domains. These classifiers perform differently on different learning problems. For example, if one algorithm perform better on one dataset, the same algorithm may perform badly on another dataset. The reason is that each dataset has its own nature in terms of its local and global characteristics. Similarly, the number of candidate algorithms are also large in number and is therefore very hard for a machine learning practitioner to know the intrinsic behaviors of the algorithms on different kinds of datasets and are therefore unable to select a right algorithm for his problem in-hand. To overcome the issue, this study proposes an automatic classifier selection methodology. A case-based meta-learning and reasoning (CB-MLR) framework is designed and implemented to recommend appropriate classifier for mining the new dataset. The framework exploits inherit characteristics of the datasets mapped against the algorithms performance. The key contributions of CB-MLR include: (a) design of a flexible and incremental meta-learning and reasoning framework using multiview learning, and (b) implementation of the CBR methodology to accurately recommend most relevant top-3 classifiers as the suggested algorithms for the new data mining problem. The proposed framework is tested for 9 decision tree classifiers, from Weka environment, and 52 datasets from UCI repository over a case-base of 100 resolved cases. The accuracy obtained is 94% within the scope of top-3 most relevant classifiers