Abstract: Water is essential for life and fresh water is a finite
resource that is becoming scarce day by day even though it is
recycled by hydrological cycle. The fresh water reserves are being
polluted due to expanding irrigation, industries, urban population and
its development. Contaminated water leads to several health
problems. With the increasing demand of fresh water, solar
distillation is an alternate solution which uses solar energy to
evaporate water and then to condense it, thereby collecting distilled
water within or outside the same system to use it as potable water.
The structure that houses the process is known as a 'solar still'. In this
paper, ‘Modified double slope solar still (MDSSS)’ & 'Modified
double slope basin type multiwick solar still (MDSBMSS)' have been
designed to convert saline, brackish water into drinking water. In this
work two different modified solar stills are fabricated to study the
performance of these solar stills. For modification of solar stills,
Fibre Reinforced Plastic (FRP) and Acrylic sheets are used. The
experiments in MDSBMSS and MDSSS was carried on 10
September 2015 & 5 November 2015 respectively. Performances of
the stills were investigated. The amount of distillate has been found
3624 Ml/day in MDSBMSS on 10 September 2015 and 2400 Ml/day
in MDSSS on 5 November 2015.
Abstract: Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the combination of the few mentioned methods are used. In this study, symptom-disease relationships are presented by a framework which is modeled with a formal concept analysis and theory, as diseases, objects and attributes of symptoms. After a concept lattice is formed, Bayes theorem can be used to determine the relationships between attributes and objects. A discernibility relation that forms the base of the rough sets can be applied to attribute data sets in order to reduce attributes and decrease the complexity of computation.
Abstract: In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).