An integration of technology with crop yielding
prediction methodology brought a major transformation in the
production level globally. Machine learning concept has boosted
that technology in such a manner that has further optimized the
situation of farmer and agricultural industry. The combination of
different types of algorithm enhances the competency of
technological device to a level where the prediction becomes very
effective and least deviation can be expected from the agricultural
industry in the production level. The research of machine
learning states about the integration of three types of models
which is usually followed separately in programming the device.
The study has proved the intervention of Information technology
in the agricultural industry via different functions. An effective
prediction by using the ensemble algorithm makes the
agricultural industry competent enough to maintain the expected
amount of production of crop
Key informant method is an innovative technique for identifying people who are disabled in the community, by training local volunteers to act as key informants. Key informants are the local native people include teachers, village doctors, local health workers, religious leaders, community leaders, students, traditional healers, police, NGO staffs, health professionals, local journalists, village councils etc. For them, host organization organized a training to train the key informants to identify and refer the suspected disable people. The study proved key informant method as a valid method for identification of disabling children. Key informant method had a high sensitivity (average 98%) for case detection in all groups but specificity was lower (average 44%), particularly for hearing impairment. Key Informant Method can be used to collect data on types of disabilities, cause, the magnitude of impairments, severity, quantify a need for disabled people, and making access to services (including adoption, health check-up, vocational training, rehabilitation, and other facilitation training).Keywords: Bangladesh; disability; key Informant; key informant methods
Medical applications became a boon to the healthcare industry. It needs correct and fast segmentation associated with medical images for correct diagnosis. This assures high quality segmentation of medical images victimization. The Level Set Method (LSM) is a capable technique, however the quick process using correct segments remains difficult. The region based models like Active Contours, Globally Optimal Geodesic Active Contours (GOGAC) performs inadequately for intensity irregularity images. During this cardstock, we have a new tendency to propose an improved region based level set model motivated by the geodesic active contour models as well as the Mumford-Shah model. So that you can eliminate the re-initialization process of ancient level set model and removes the will need of computationally high priced re-initialization. Compared using ancient models, our model are sturdier against images using weak edge and intensity irregularity.
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