Manhattan Distance Method

Abstract: The Demite is an educational game with a horror genre using Augmented Reality technology and Speech Recognition. In this game, the player will capture and eliminate ghosts with English pronunciation. Therefore, players will not only catch and eliminate ghosts, but players will also train their English pronunciation correctly. To find out the exact and correct pronunciation in English, The Demite requires an assessment system to measure the distance in the pronunciation of English words. The assessment system requires a method to measure the distance parameters that will be used in the assessment system. Parameters to be measured are Phonetic, Syllable, and Phonetic Length. In this study focused on choosing the right method between Levenshtein Distance, Euclidian Distance, and Manhattan Distance in measuring the distance of these parameters. Based on the results of the tests performed, it was concluded that it is possible to use Levenshtein distance in measuring the distance between phonetic and after observing the test data in syllable and phonetic length measurements, the Manhattan distance method is more appropriate to measure syllable and phonetic length distances even if viewed the average measurement of the Manhattan distance and Euclidean distance is almost the same value. However, when the distance from syllable and phonetic length is far, Euclidean measurements take the midpoint of the accumulation of all parameters, whereas, The Demite assessment system requires the sum of all distances obtained from these parameters. Therefore, the Manhattan distance method is used which measures the distance of the Syllable and Phonetic parameter by sum all the distance values obtained.

Abstract: Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance The features used in this study consisted of GPA and course grades The case taken is the case with the highest similarity value If a case doesnt get a topic recommendation or is less than the trashold value of 08, a case revision will be carried out by an expert Successfully revised cases are stored in the system to be made new knowledge The test results using the Nearest Neighbor Method get an accuracy value of 9714% and Manhattan Distance Method 9429%

Abstract: Richard’s equation was approximated by finite-difference numerical scheme to model water infiltration profile in variably unsaturated soil. The published data of Philip’s semi-analytical solution was used to validate the simulated results from the numerical scheme. A discrepancy was found between the simulated and the published semi-analytical results. Morris method as a global sensitivity tool was used as an alternative to local sensitivity analysis to assess the results discrepancy. Morris method with different sampling strategies were tested, of which Manhattan distance method have resulted a better sensitivity measures and also a better scan of input space than Euclidean method. Moreover, Morris method at and Manhattan distance sampling strategy, with only 2 extra simulation runs than local sensitivity analysis, was able to produce reliable sensitivity measures ( , ). The sensitivity analysis results were cross-validated by Sobol’ variance-based method with 150,000 simulation runs. The global sensitivity tool has identified three important parameters, of which spatial discretization size was the sole reason of the discrepancy observed. In addition, a high proportion of total output variance contributed by parameters and is suggesting a greater significant digits is required to reduce its input uncertainty range.

Abstract: Objectives: The essential requirement for a successful hashing method involves two distinct stages projection and quantization. In general, the projection stage is given much importance than the quantization stage. This stage has been concentrated in this paper which has equal importance as projection stage. Methods/Analysis: The using of Manhattan Distance method has been proposed in this paper instead of the widely used Hamming Distance, since it destroys the neighbourhood structure while measuring the similarity between points in the hashcode space. Findings: The problem of destroying the neighbourhood structure that existed in Hamming Distance is overcome by Manhattan hashing. Novelty/Improvement: The outperformance of Manhattan distance compared with Hamming distance has been shown and also, this paper has made an attempt to implement them in our Biocryptosystem to show its efficiency.