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    Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation
    (IEEE, 2021-12-06) Kooragama, K.G.C.M; Jayashanka, L. R. W. D; Munasinghe, J. A; Jayawardana, K. W; Tissera, M; Jayasingha, T. B
    Every English speaker wishes to expertise his/her public speaking skills sharply. However, it is extremely difficult and requires a significant amount of practice and experience on an individual basis. This paper introduces a novel online tool “Speech Master” to practice and improve public English speech delivering skills in a professional manner. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the user's speech in terms of content, grammatical accuracy, grammatical richness, facial expressions, and flow. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the tool. “Speech Master” achieves an average accuracy of more than 80% and produces a better overall result. This novel tool benefits English speakers all over the world by meeting the demand for a simple and easy-to-use solution for improving or practicing English speech delivery skills; enhancing oratory skills, boosting confidence, and delivering well-articulated speeches.
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    Voice-based Road Navigation System Using Natural Language Processing (NLP)
    (IEEE, 2018-12-21) Withanage, P; Liyanage, T; Deeyakaduwe, N; Dias, E; Thelijjagoda, S
    In a highly technological era, voice-based navigation systems play a major role in bridging the gap between man and machine. To overcome the difficulty in understanding the user's voice commands and natural language simulations, process the path with the user's turn by turn directions with the mention of key entities such as street names, landmarks, points of interest, connections and path mapping in an interactive interface, we propose a user-centric roadmap navigation mobile application called “Direct Me”. To generate the user's preferred path, the system will first convert audio streams to text through ASR using the Pocket Sphinx library, followed by Natural Language Processing (NLP) by taking advantage of Stanford CoreNLP Framework to retrieve navigation-related information and handle the path in the map using the Google Map API at the user's request. This system is used to provide an effective approach to translating natural language commands into a format that can be fully understood by machine and will benefit in the development of human-machine-oriented interface.
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    E-Agrigo
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kartheepan, T.; SirigajanK, B.; Subangan, K.; Mohammed Azzam, M.A.; Bandara, P.; Mahaadikara, M.M.D.J.T.H.
    To feed this population, food production should be increased by at least 70%. Developing nations have a vast potential to increase the amount of food produced by doubling the current production. However, the traditional methods of farming are making agriculture unviable and inefficient. The increasing food production needs to be met by double the current level of farming. The conventional of farming is making industry uncompetitive and inefficient. This paper aims to analyze the various factors that affect the implementation of autonomous machinery in agriculture. The development of autonomous machinery for agriculture has emerged as vital step towards achieving this goal. Now a day’s farmers are planning their cultivation by finding proper weather and geographical condition on their own experience, but they are failing to cultivate profitable crop and unaware of the diseases that will affect their crops, sometimes these diseases may affect their whole crops and let the farmers to sink in zero profit. Despite these issues plays a major role, there are some other problems also have an impact like, lack of irrigation plans and question of how and where to sell their cultivated crops. By considering these major threats we have planned to propose a solution to some of the selected issues. This can be achieved by applying machine learning algorithm, Image processing and IOT systems. By using our platform farmers will get a chance to plan their yield in a profitable way by using our optimized weather and geographical data.