Content based image retrieval is an active research issue that had been famous from 1990s till present. The main target of CBIR is to get accurate results with lower computational time. This paper discusses on the comparative method used in color histogram based on two major methods used frequently in CBIR which are; normal color histogram using GLCM, and color histogram using KMeans. A set of 9960 images are used to test the accuracy and the precision of each methods. Using Euclidean distance, similarity between queried image and the candidate images are calculated. Experiment results shows that color histogram with K-Means method had high accuracy and precise compared to GLCM. Future work will be made to add more features that are famous in CBIR which are texture, color, and shape features in order to get better results.
Subsequently, there exist various kinds of screening tools for learning disabilities but most of these screening tools only restricted to static binary output, less attractive, stressful, boring, and time consuming which lead to incomplete activities and unfulfilled objectives. In addition, most of them only targeted on dyslexia, dyscalculia and autism. This preliminary study aims to identify current automated screening tools tailoring for learning disabilities domain. It is guided by several important steps starting from the selection from multiple digital databases (information sources), categorization (study selection), comparison (search and data selection) and summarization of appropriate literature reviews, leading towards a more thorough analysis. Findings indicate that there are various kinds of screening tools available in the market with such different techniques and methods, majorly are interactive and attractive multimedia approaches and artificial intelligence approaches. Thus, the findings are beneficial in the enhancement of future works towards screening and diagnosis in learning disabilities.
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