|
Showing 1 - 3 of
3 matches in All Departments
IoT Based Data Analytics for the Healthcare Industry: Techniques
and Applications explores recent advances in the analysis of
healthcare industry data through IoT data analytics. The book
covers the analysis of ubiquitous data generated by the healthcare
industry, from a wide range of sources, including patients,
doctors, hospitals, and health insurance companies. The book
provides AI solutions and support for healthcare industry end-users
who need to analyze and manipulate this vast amount of data. These
solutions feature deep learning and a wide range of intelligent
methods, including simulated annealing, tabu search, genetic
algorithm, ant colony optimization, and particle swarm
optimization. The book also explores challenges, opportunities, and
future research directions, and discusses the data collection and
pre-processing stages, challenges and issues in data collection,
data handling, and data collection set-up. Healthcare industry data
or streaming data generated by ubiquitous sensors cocooned into the
IoT requires advanced analytics to transform data into information.
With advances in computing power, communications, and techniques
for data acquisition, the need for advanced data analytics is in
high demand.
A set of 44 genotypes of lentil, grown in two different cropping
systems viz., monocropping and intercropping were studied for
assessment of nature and magnitude of variability, identification
of yield attributes and genetic divergence among genotypes at the
experimental farm of the Department of Plant Breeding and Genetics,
CSK HPKV, Palampur. Sufficient variability was observed for all the
twelve characters studied in both the cropping systems except plant
height, primary branches per plant and pods per cluster in
monocropping. High PCV and GCV observed for pod cluster per plant,
pods per plant, seeds per plant and seed yield per plant in both
the systems while 100-seed weight, biological yield per plant
recorded high PCV and GCV in monocropping only. High heritability
coupled with high genetic advance was recorded for days to 50%
flowering. Correlation and path analysis studies indicated that
days to 50% flowering, days to 75% maturity, pod cluster per plant,
pods per plant, 100-seed weight, biological yield per plant, seeds
per plant and harvest index were important for selection of high
yielding and early maturing genotypes of lentil.
|
|