THE EFFECT OF USING THE PROJECT BASED LEARNING MODEL ON PROCESS SKILLS AND SCIENCE LITERATION SKILLS

This research is motivated by the learning process carried out in class V of SD Negeri 8 Metro Timur, SD Negeri 1 Metro Barat, SD Negeri 1 Metro Utara dan SD Negeri 5 Metro Pusat. The implementation of the learning process carried out by teachers who generally still use conventional learning methods and models that are only fixated on books and more teacher-centered, students listen to explanations from the teacher, take notes, memorize information and work on problems. practice questions rather than doing practical activities through experiments or experiments. As a result, the learning process becomes less attractive and seems monotonous because students tend to be passive and less participatory in learning activities. Teachers lack innovation in the use of active and innovative methods or learning models that are more student centered. That is, learning that provides more opportunities for students to construct independently the process of understanding material through a more active learning process through search activities and problem solving. In response to this, researchers conduct learning using a project based learning learning model with the aim of improving students' science process skills and scientific literacy skills. The purpose of this study was to describe the influence of the PjBL model on process skills and scientific literacy skills. The type of this study is quasi-experimental research using the design of nonequivalent groups pre test-post test from Fraenkel and Wallen. The design of nonequivalent groups pre test - post test was started by setting the experimental group and the control group, then doing the pre test, followed by giving treatment to the two classes and ending with a post test. The subjects in this study were fifth grade elementary school students in Metro City. Data collection techniques in the form of tests. The research instrument is a spatial literacy test sheet. Data were analyzed using normality test and Mann Whitney test. The results of this study indicate that the PjBL model can improve science process skills and higher scientific literacy skills compared to groups of students who get conventional learning. Keywords: skills of science literacy, skills of science process, elementary students

Microalgae production in fresh market wastewater and its utilization as a protein substitute in formulated fish feed for oreochromis spp.

Rapid growing of human population has led to increasing demand of aquaculture production. Oreochromis niloticus or known as tilapia is one of the most globally cultured freshwater fish due to its great adaptation towards extreme environment. Besides, farming of tilapia not only involves small scales farming for local consumption but also larger scales for international market which contributes to a foreign currency earning. Extensive use of fishmeal as feed for fish and for other animals indirectly caused an increasing depletion of the natural resource and may consequently cause economic and environmental unstable. Microalgae biomass seems to be a promising feedstock in aquaculture industry. It can be used for many purposes such as live food for fish larvae and dried microalgae to substitute protein material in fish feed. The microalgae replacement in fish feed formulation as protein alternative seem potentially beneficial for long term aqua-business sustainability. The present chapter discussed the potential of microalgae as an alternative nutrition in fish feed formulations, specifically Tilapia

Fine-Grained Product Class Recognition for Assisted Shopping

Assistive solutions for a better shopping experience can improve the quality of life of people, in particular also of visually impaired shoppers. We present a system that visually recognizes the fine-grained product classes of items on a shopping list, in shelves images taken with a smartphone in a grocery store. Our system consists of three components: (a) We automatically recognize useful text on product packaging, e.g., product name and brand, and build a mapping of words to product classes based on the large-scale GroceryProducts dataset. When the user populates the shopping list, we automatically infer the product class of each entered word. (b) We perform fine-grained product class recognition when the user is facing a shelf. We discover discriminative patches on product packaging to differentiate between visually similar product classes and to increase the robustness against continuous changes in product design. (c) We continuously improve the recognition accuracy through active learning. Our experiments show the robustness of the proposed method against cross-domain challenges, and the scalability to an increasing number of products with minimal re-training.Comment: Accepted at ICCV Workshop on Assistive Computer Vision and Robotics (ICCV-ACVR) 201

Entropy-based active learning for object recognition

Most methods for learning object categories require large amounts of labeled training data. However, obtaining such data can be a difficult and time-consuming endeavor. We have developed a novel, entropy-based ldquoactive learningrdquo approach which makes significant progress towards this problem. The main idea is to sequentially acquire labeled data by presenting an oracle (the user) with unlabeled images that will be particularly informative when labeled. Active learning adaptively prioritizes the order in which the training examples are acquired, which, as shown by our experiments, can significantly reduce the overall number of training examples required to reach near-optimal performance. At first glance this may seem counter-intuitive: how can the algorithm know whether a group of unlabeled images will be informative, when, by definition, there is no label directly associated with any of the images? Our approach is based on choosing an image to label that maximizes the expected amount of information we gain about the set of unlabeled images. The technique is demonstrated in several contexts, including improving the efficiency of Web image-search queries and open-world visual learning by an autonomous agent. Experiments on a large set of 140 visual object categories taken directly from text-based Web image searches show that our technique can provide large improvements (up to 10 x reduction in the number of training examples needed) over baseline techniques

Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl