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检索条件"机构=Faculty of Computing and Information Technology"

共 7488 条记录，以下是191-200 订阅

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Development and Integration of an AI-Driven Adapter & Grade Prediction Model for Mathematical Question Classification and Assessment which Enhance Student Profiling 16

Development and Integration of an AI-Driven Adapter & Grade ...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Nishamali, M.K.C.P. Jayakody, Anuradha Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331505264

The transformative growth of AI can be seen in almost every sector. AI can be a useful application for the educational domain as well. Education should be prioritized because it's the root of every other field. This research aims to combine IT to develop mathematics subjects by leveraging AI in practice mainly introducing Capabilities of Open AI. The primary object is to create Open AI API through a specially created PHP adapter to classify mathematical questions into six main themes Sets and Probability, Algebra, Numbers, Geometry, Measurements, and Statistics. This automated AI-driven classification system helps to create online assessments within the blink of an eye. The Integration of Open AI API with a PHP-based framework makes a bridge between AI capabilities and education needs. This framework is the ideal solution for manual and traditional school assessments. This AI- driven mathematics classification system is designed to optimize the assessment process by providing additional objectives such as leveraging automated student grading feedback while predicting final grades so teachers and students can see the result instantly. Additionally, answers are automatically created after the assessment showing solving steps which facilitate students to identify the corrections as well. © 2024 IEEE.

关键词： Forecasting

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Artificial Intelligence (AI) in Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) Experiences: Enhancing Immersion and Interaction for User Experiences

Artificial Intelligence (AI) in Augmented Reality (AR), Virt...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Asra, Sahabdeen Aysha Wickramarathne, Jagath Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350350821

The utilisation of Artificial Intelligence (AI) generated material is one of the most fascinating advancements in the rapidly growing fields of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Two examples of how AI-generated material is revolutionising how we interact with AR, VR and MR are video games and training simulations. In this essay, we'll examine the intriguing potential of AI-generated content and how it's being used to the development of hybrid real-world/virtual experiences. Using this strategy, we acquired the information from primary and secondary sources. We surveyed AR, VR, and MR users to compile the data for the primary source. Then, utilising published papers as a secondary source, information was gathered. By elucidating the concept of context immersion, this research can lay the foundation for the advancement of information regarding immersive AR, VR, and MR contexts. We are able to offer recommendations for overcoming the weak parts and strengthening the good ones based on the questionnaire survey findings. © 2024 IEEE.

关键词： Virtual environments

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BANANA BUDDY: Mobile Based Solution to Empower Sri Lankan Banana Farmers to Make Optimal Decisions and Win the War Against Plant Diseases and Pest Attacks 15

BANANA BUDDY: Mobile Based Solution to Empower Sri Lankan Ba...

引用

15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Rathnayake, R.M.S.A. Samuel, P.J. Krishara, Jenny Rajendran, Karthiga Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798350324433

Banana farming in Sri Lanka faces multifaceted challenges, including diseases, pests, and uncertainties in yield. This research introduces 'Banana Buddy,' a comprehensive solution comprising two key components. The first employs deep learning and image processing to create a mobile application for accurate diagnosis of banana diseases and pests. Achieving a remarkable accuracy of 99.1% for disease identification and 100% for pest attack detection, this tool provides timely, bilingual diagnoses in Sinhala and English, thereby enhancing disease management and crop health. The second component focuses on predicting suitable cultivation areas and recommending optimal banana varieties based on climate conditions. Leveraging advanced methodologies such as Random Forest and XGBoost algorithms, alongside the Haversine formula, the system identifies optimal locations and suggests banana varieties, achieving superior accuracy. The XGBoost and Random Forest classifiers outperformed other algorithms in predicting suitable cultivation areas. Integrating these components into a user-friendly mobile app addresses the challenges faced by Sri Lankan banana farmers. By harnessing technology and data-driven insights, this initiative empowers farmers to make informed decisions, manage diseases, and optimize cultivation practices. The exceptional accuracy values in disease and pest identification, coupled with the superior performance of machine learning models, underscore the practicality and effectiveness of the approach. Looking ahead, future work includes expanding disease identification capabilities and incorporating additional banana varieties. Furthermore, introducing parameters like humidity and wind speed will enhance the system's accuracy. In conclusion, this research represents a significant advancement in banana farming practices, providing a valuable tool for farmers to navigate challenges and cultivate bananas sustainably. The findings hold promise for broader applications

关键词： Fruits

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Smart Monitoring System for Optimization of Tea Plantation

Smart Monitoring System for Optimization of Tea Plantation

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Weerasinghe, W.P.S.W. Goonatilleke, M.D.K.M. Prasadi, W.V.C. Chandrasiri, Sanjeevi Jayathilake, K.D.M.I. Mahaadikara, Hansika Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798350319200

Sri Lanka is a global hub for tea production worldwide, and the tea industry has enormous economic and cultural significance to the country. However, the tea industry, though, is confronted with numerous challenges, including unpredictable weather conditions, improper fertilizer application, pest and disease outbreaks, and suboptimal cultivation practices. These challenges are leading to lower yield and more losses. With the increasing unpredictability of weather patterns, it is challenging to determine the precise fertilizer application schedule and dosage. In terms of pest and diseases, effect of the Shot-hole borer, Tea Blister Blight and Stem and Branch Canker are more destructive to the tea plant. Therefore, accurate weather prediction and the early detection of those infections are crucial tasks in tea cultivation. This study presents a smart monitoring system for Sri Lankan tea crop that combines IoT, image processing, audio processing, and DL technologies to address such problems. The Smart Monitoring System gives growers a full toolkit by providing real-time weather insights, fertilizer delivery depending on weather conditions, early insect detection, disease identification, and damage quantification. According to performance evaluations, the proposed method's accuracy results for weather prediction, fertilizer application and identifications of disease, pest, deficiency, and degree of diseased conditions are in the range of 80-100%. © 2023 IEEE.

关键词： Audio Processing Deep Learning Disease Identification Fertilizer application Image Processing IOT Pest Detection Tea Cultivation Weather Prediction

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ITConnect: Real-time Personalized Job Posting Platform for IT Professionals 8

ITConnect: Real-time Personalized Job Posting Platform for I...

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8th International Conference on information technology Research, ICITR 2023

作者： Karunanayake, K.R.D.S.K. Chandrawansa, K.T.M. Gunatilleke, L.C.T. Menuranga, K.Y.D.B. Gawesha, Asiri Manathunga, Kalpani Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798350359503

This study offers an innovative job platform created especially for IT professionals in Sri Lanka to address pressing issues in the IT job market. The platform focuses on four main challenges: identifying fake job postings using NLP techniques, providing customized job recommendations based on skill assessments, enhancing the candidate application process through facial analysis, and evaluating resumes based on experience and job levels. The DistilBERT text analysis fake job detection model achieves a remarkable overall accuracy of 98%. Our system combines skill assessments with content-based filtering to deliver useful job recommendations. The stress level estimator and Siamese models are used to face verification while assessing the candidate's stress level. The resume enhancement system powered by PyPDF2 and a deep neural network, boasts an impressive 86.6% accuracy in evaluating job candidates' experience. It also streamlines resume creation with a ReactJS generator. © 2023 IEEE.

关键词： Natural language processing systems

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Advancing Canine Health and Care: A Multifaceted Approach using Machine Learning 5

Advancing Canine Health and Care: A Multifaceted Approach us...

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5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Wimukthi, Yasith Kottegoda, Hashen Andaraweera, Dilshan Palihena, Pabasara Fernando, Harinda Kasthurirathnae, Darshana Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798350337525

This research paper proposes a comprehensive approach to enhance the well-being of dogs through a range of innovative technologies. Firstly, we develop an automated system for dog breed and age identification using a Convolutional Neural Network (CNN) and a transfer learning model. This system aims to provide an efficient and reliable solution for dog owners and new adopters who are interested in discovering more about their canine companions. Secondly, we propose the development of a system that uses Reinforcement Learning to generate personalized meal plans based on a variety of factors such as the dog's breed, age, weight, health status, and emotional state. The system aims to provide dog owners with a reliable and effective tool for generating personalized meal plans that will enhance their pets' overall health and well-being. Thirdly, we present a dog disease recognition application that utilizes an artificial neural network (ANN) for identifying dog diseases based on their symptoms. Lastly, we introduce a real-time remote dog monitoring system using loT devices with edge computing to detect aggressive and anxious sounds. Our system provides an accurate classification of dog sounds related to aggression and anxiety, which can help dog owners detect and respond to potential issues early on. This research aims to provide dog owners and veterinarians with a range of technologies that can help them better understand and care for their furry friends. © 2023 IEEE.

关键词： Convolutional neural networks

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Smart System for Freshwater Pisciculture(Ornamental Fish Farming) 14

Smart System for Freshwater Pisciculture(Ornamental Fish Far...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Udeshika, W.G.A. Jathunarachchi, R.T. Nawagamuwa, N.G.K. Sangeeth, L.S. Weerasinghe, Lokesha Prasadhini Dassanayake, Gaya Thamali Sri Lanka Insitute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798350335095

In recent times, there has been a significant surge in the advancement of information analysis technology specifically designed for underwater environments. This remarkable progress has brought about numerous advantages for the fields of aquatic resource exploration and aquaculture. Aquaculture is the breeding and harvesting of aquatic animals and plants. The rearing and breeding of fish under controlled conditions is called pisciculture. In this research, we present an automated system to improve technical facilities in Ornamental fish farming. We focus on Fish body length and weight prediction and Water quality measuring, Fish Behavioral Patterns Identification, Predicting the harvest and analyzing data, and can know whether the fish in the tank are alive or not using Machine Learning (ML) and Image Processing techniques. And this project invents a smart device, that contains (IoT) devices and sensors to capture images (by a camera), to obtain data on Ammonia (NH3), Nitrate (NO2-), Nitrite (NO3-), Temperature, Oxygen level (O2) and pH level in the water and we use food pallet to hold and release food according to the conditions. The distinguished data will upload to a cloud system and the web application will display the expected outputs. We use Convolutional Neural Networks (CNN) to analyze the water quality and, to identify different behavioral patterns of fish and identity whether the fish is live or not. Length and weight prediction identified behavioral patterns, identified live or dead fish, harvest prediction and other details also display through the known web application. © 2023 IEEE.

关键词： Aquaculture

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Vision GNN Based Phishing Website Detection

Vision GNN Based Phishing Website Detection

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Lindamulage, J.M. MandiraPabasari, L. Yapa, S.P.J. Perera, I.S.S. Krishara, Jenny Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9798350319200

Phishing, which is a common cyber threat, exploits human vulnerabilities by impersonating legitimate entities to deceive users into sharing sensitive information. Traditional methods of detecting phishing websites primarily rely on textual features and heuristics, often failing to capture the evolving sophistication of phishing tactics. This study introduces an innovative approach for identifying phishing websites through an image of the website utilizing VisionGNN architecture which is a Graph Neural Networks (GNN) based approach. By using the VisualPhish dataset, consisting 1195 pages across 155 websites, our approach focuses on significantly enhancing accuracy in phishing website detection using RGB images of the websites. Through training with image augmentations and the application of optimization techniques such as AdamW and cosine decay for learning rate adaptation, a good result in predictive capabilities are attained. The outcomes demonstrate the model's efficacy in accurately classifying both benign and phishing websites. Impressively, our VIsionGNN-based approach with its pyramidsmall model architecture out of its four combinations achieves 97% accuracy on the VisualPhish data set. With our approach being the first time VisionGNN architecture has been utilized in phishing website detection, our study spotlights the potential of pioneering Graph Neural Network-based approaches in strengthening cyber security against the escalating threat of phishing websites. Furthermore, this study lays the groundwork for future research areas aimed at utilizing VisionGNN-based approaches for more real-world applications. © 2023 IEEE.

关键词： Graph Neural Network(GNN) Phishing VisionGNN

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Reinforcement Learning Approach for Dialogue Management to Aid Child-Robot Interaction in ASD Screening 6

Reinforcement Learning Approach for Dialogue Management to A...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Abayakoon, Hasith Pulasinghe, Koliya Kasthurirathna, Dharshana Perera, Jeewaka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9781665491259

This paper investigates how Q-learning, State Action Reward State Action (SARSA), and Deep Q Networks (DQN) algorithms in Reinforcement Learning (RL) are used for dialogue management in a child-robot interaction system. Based on observations of the child's behavior and response to questions, the system determines how to structure the next question to maintain the child's engagement in a dialogue that is developed based on Autism Diagnostic Observation Schedule (ADOS). To accomplish this, a virtual environment is created to simulate interactions between the child and the robot, allowing for the development of an initial model that can be refined and tested in the real world. This paper presents how robust a DQN is to withstand an increasing variety of questions in dialogues. Further, it has been found SARSA is the fastest converging algorithm to learn the optimal policy for such interactions. © 2023 IEEE.

关键词： Reinforcement learning

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E-Gait: Enhanced Efficient Graph Convolution Network for Gait Recognition 17

E-Gait: Enhanced Efficient Graph Convolution Network for Gai...

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17th International Conference on Machine Vision, ICMV 2024

作者： Naz, Neelma Rasheed, Saim Ali, Sara Sajid, Hasan National University of Sciences & Technology Islamabad Pakistan Faculty of Computing & Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781510688278

Gait recognition is a promising biometric technology because it can recognize individuals based on their walking patterns, even from a distance. Traditional methods used for gait recognition have relied on extracting gait features from a person's silhouette sequence. While silhouette-based methods have been effective, they come with several limitations. Silhouette images often lack detailed spatial information or might include irrelevant visual information unrelated to gait, which can affect identification accuracy and introduce system vulnerabilities. In contrast, model-based methods, specifically those leveraging advances in human pose estimation, have gained traction recently. In this paper, we propose E-Gait, which combines individual's pose information with an Efficient Graph Convolutional Network (GCN). The proposed method uses separable convolution layers based architecture to extract effective gait features. We also use a spatiotemporal joint attention mechanism that directs the model to focus on the most significant joints, thereby learning efficient spatiotemporal patterns. Extensive experimentation on a popular gait dataset Casia-B demonstrates significant performance improvements of 5.11% and 1.05% in terms of average rank-1 accuracy over state-of-the-art (SOTA) pose-based methods. Additionally, we provide visualizations of activated joints to illustrate the efficacy of our proposed model in deriving gait specific features. © 2025 SPIE.

关键词： Gait analysis

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