https://onlineresource.ucsy.edu.mm/handle/123456789/2496 2026-07-18T05:32:25Z https://onlineresource.ucsy.edu.mm/handle/123456789/2582 Vehicle Accident Detection on Highway and Communication to the Closest Rescue Service Aung, Nay Win; Thein, Thin Lai Lai The hazard information and the timely rescue performance are considered as the main elements to reduce the risk of road traffic accidents since the rate of highway accidents significantly increased. In this paper, it is aiming to detect the highway accident victims by using the data received from Sensor Fusion-Based algorithm while Ray Casting algorithm will assist the users to receive the assistance of rescue services in timely manner. With the purpose of user friendliness, these algorithms are intended to apply in the smartphones built-in high technology sensors, which are connected with the GIS, GPS and Geofence technologies. 2020-02-28T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2581 University Chatbot using Artificial Intelligence Markup Language Khin, Naing Naing; Soe, Khin Mar Chatbots are conversational systems that can do chat interactions with human automatically. It is developed to be virtual assistant, making entertainment for people, helping for answering the questions, getting driving directions, serving as human partner in smart homes etc. Most of the chatbots utilize the algorithms of artificial intelligence (AI) in order to get the required responses. In this paper, we provide the design of a University Chatbot that provides an efficient and accurate answer for any user questions about university information. This is the first University Chatbot for inquiring about school information in Myanmar Language based on Artificial Intelligence Markup Language and uses Pandorabots as the interpreter 2020-02-28T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2580 Time Delay Neural Network for Myanmar Automatic Speech Recognition Aung, Myat Aye Aye; Pa, Win Pa Time Delay Neural Network (TDNN) contains in neural network architectures. In Automatic Speech Recognition, TDNN is strong possibility in context modeling and recognizes phonemes and acoustic features, independent of position in time. There are many techniques have been applied for improving Myanmar speech processing. TDNN based acoustic model for Myanmar ASR in this paper. Myanmar language is a low resource language and no precollected data is available. A larger dataset and lexicon than our previous work are applied in this experiment. The speech corpus contains three domains: Names, Web News data and Daily conversational data. The size of the corpus is 77 Hrs and 2 Mins and 11 Secs and include 233 female speakers and 97 male speakers. The performance of TDNN for Myanmar ASR is shown by comparing with Gaussian Mixture Model (GMM) as a baseline system, Deep Neural Network (DNN) and Convolutional Neural Network (CNN). Experiments evaluation is used 2 test data: TestSet1, web news and TestSet2, recorded conversational data. The experimental results show that TDNN outperforms GMM-HMM, DNN and CNN. 2020-02-28T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2579 The Implementation of Support Vector Machines for Solving in Oil Wells Aung, Zayar; Aung, Ye Thu; Sergeevich, Mihaylov Ilya; Linn, Phyo Wai The article deals with the problem of timely forecasting and classification of problems that arise in the process of well construction remains relevant. It is necessary to create a new methodology that should help drilling personnel to make timely decisions about possible problems in the drilling process on the basis of real-time data analysis, which will increase efficiency and reduce drilling costs accordingly. 2020-02-28T00:00:00Z