The data were analyzed by using descriptive statistics interpreted in 4 rating scales, they are: very good, good, fair and poor. The instruments used are observation sheets and analytic rubrics. Data on digital literacy Skills is obtained through performance observation and assessment of structured task reports during the learning process.
The sample of the research is 71 students majoring in physics education from Universitas Negeri Padang (UNP) and Universitas Islam Negeri Imam Bonjol (UIN IB). The subject of this research is early semester students who took a basic physics course. This research is an alternative solution to the students’ Skills and awareness to utilize and integrate technology in learning physics. This research aims to find out the impact of implementing a physics learning based on Project, Technology and Active (PROTECTIVE) learning model in building three digital literacy skills namely information literacy, media literacy, and ICT literacy. Project, Technology And Active (PROTECTIVE) Learning Model To Develop Digital Literacy Skills In The 21st Century This study would help to laboratory users to identify medically important bacteria in an easy way. Prediction accuracy of this model was 97.11% to distinguish medically important bacteria. This model was trained by back propagation process by reducing Sum Squared Error(SSE) through Stochastic Gradient Descent(SGD) technique. The INN model has been designed with two layers of fully connected neurons, where the first layer neurons has taken input as the features of bacteria and produced input for hidden neurons and in the second layer the output from hidden neurons provided as input of decision neurons and the output of decision neurons was the expected result. A mathematical model of new generation artificial neural network called Intelligent Neural Network (INN) has been proposed, which would solve that problem and would make the decision like a human. Our objective is to design a neural network which will have the intelligence by which it can generate most prominent decision.
The conventional ANN model is being used in some financial sectors for prediction and analysis of financial data, but it would not make an outcome due to less applicable data. ANN was efficiently used for decision making on labeled and unlabeled data but problem was that it was always generated as a result though the short input data. The work focused on reliable outcome from next generation artificial neural network (ANN). Intelligent Neural Network For Bacteria Classification: An Innovation In Artificial Neural NetworkĪnanda Khamaru, Sunil Karforma, Soumendranath Chatterjee, Ishita Saha Raktima Bandyopadhyay
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