Browsing by Author "Alosius, J"

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GLIB: Ameliorated English Skills Development with Artificial Intelligence
(IEEE, 2020-10) Srikanthan, P; Nizar, R; Ravikumar, A; Lalitharan, K; Harshanath, S. M. B; Alosius, J
In language learning, most of the learners can learn the theory and memorize the sound of a language. However, the ability to speak and learn a language properly requires good practice, experience and good learning strategies. This research is an approach to innovate and improve the language learning strategies along with practices that help to improve the language skills for non-native learners and children who are in the early stage of learning. In this research, the English language will be used to experiment with our solution as the source due to its demand and familiarity in the world. This research is focused on four main skills such as conversation skills, pronunciation skills, listening skills and grammatical skills. This research is done by analyzing the difficulties in each of the skills mentioned above and also discusses and provides details about the solution implemented to improve learning English in an efficient way. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The application of this research is delivered by using a cross-platform application called GLIB. The name GLIB is inspired by a library in C language which represents fluency. This mobile application provides facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.
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Historical Places & Monuments Identification System
(IEEE, 2020-11-16) Godewithana, N; Jayasena, K; Nagarawaththa, C; Croos, P; Harshanath, B; Alosius, J
Sri Lanka, which is known as “the pearl of the Indian ocean” provides great survival and civilization history dating back to the 3rd century. Most of the archaeological sites are attracted by not only Sri Lankans but also by tourists. When searching for the information about the archaeological sites, there are lack of trusted information sources and smart online platforms. Even though some information is available, no convenient and efficient ways to retrieve them. When trusted information is provided in a user-friendly manner, the value will be added to the Sri Lankan economy. Since the world is driving towards the "E-Era", everything is involved with Information Technology. The proposed system contributes to solve the above problems with Artificial Intelligence & Machine Learning concepts. The system is assisted using four major components namely, image identification, community platform, conversational bot, and image visualization. The image Identification component identifies the archaeological sites using image processing techniques. The community platform gathers trusted information from archaeologists and deep learning techniques are used to deliver that content to the users. The artificial intelligence conversational bot is established to communicate and retrieve available information in a convenient manner. The image visualization component is used to provide reality visualization on archaeological sites using the augmented reality techniques. The techniques and the algorithms are evaluated to deliver better performance with brilliant user experience.
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Learning Assistant to Acquire the Fundamental Language Skills for Non-Native Learners using AI
(IEEE, 2020-12-11) Srikanthan, P; Nizar, R; Ravikumar, A; Lalitharan, K; Harshanath, S. M. B; Alosius, J
The ability to speak and learn a language properly requires good practice, experience and good learning strategies but the existing solutions do not provide proper guidance to learn a language with instant feedback. This research is an approach to devise an improved language learning assistant with practices that will help to improve the fundamental language skills for non-native learners and children who are in the early stage of their education. The four main skills focused on this application will be conversation, pronunciation, listening and grammatical skills. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The solution of this research is delivered by using a cross-platform application called GLIB which facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.
Embargo
Product Recommendation System for Supermarket
(IEEE, 2020-12-14) Satheesan, P; Haddela, P. S; Alosius, J
Customers who seek the services at supermarkets are subjected to inconsistencies & ambiguities over choosing their desired products from a wide range of products with the closest quality. Meanwhile, supermarkets find it very difficult to satiate the customers' demand. Therefore, proposing a method to analyze the customers' need plays an important role in attracting new and regular customers. The purpose of this study is to formulate a product recommendation system which analyze customers' needs and thus recommend the best products. This system recommends products to the regular customers and to the new customers as well. New customers mean obviously the customers with no purchasing history at the supermarket in question. The system referred to recommends the products to the new customers using up two methods. One method recommends the most popular products while the other method solely focuses on the product description for recommendation. The system recommends the products to the regular customers using up user-based collaborative filtering, item based collaborative filtering and association rule mining. It recommends products to regular customers based on purchasing history and priority ratings given by other users who bought the products. Initially, the recommendation algorithm finds a set of customers who purchased and rated the products that overlap with the user who purchased and rated the products. The algorithm aggregates products from the customers with similar preference and eliminates the products the user has already purchased or rated. The proposed methodology improves the shopping experience of customers by recommending accurately and efficiently the products that are personalized to the need of the customers.