Browsing by Author "Wijendra, D"

Now showing 1 - 20 of 21

Open Access
Advance Technology for Kids to Improve Knowledge and Skills using Motion Gesture Recognition – Leap Mania
(SLIIT, 2014-12-16) Nandasiri, K. G. M. P; Nawarathna, N. H. C. E. M; Mohamad, M. M. R; Herath, H. M. C. K; Kasthuriarachchi, K. T. S; Wijendra, D
Leap mania is a gesture controlled e-leaning system which targets the nursery level kids to improve their knowledge and skills in a pleasurable learning environment. Game-based learning is becoming popular in the academic discussion of Learning Technologies. However, even though the educational potential of games has been thoroughly discussed in modern days, teaching to small kids became difficult due to the short attention spans of them. In addition to traditional methods of learning and teaching, such as reading books and newspapers, a huge variety of online educational resources are available to provide an atmosphere of fun and interactive designs to keep children engaged. However, there is no proper e-learning game tools with gesture control mechanism found among the tools and computer based applications for kids. This research focuses on building an enthusiastic and pleasurable learning environment to enhance the knowledge and skills of kids by implementing a game-based learning application using leap motion controller.
Embargo
An AI based Chatbot to Self-Learn and Self-Assess Performance in Ordinary Level Chemistry
(IEEE, 2020-12-10) Mahroof, A; Gamage, V; Rajendran, K; Rajkumar, S; Rajapaksha, S, K; Wijendra, D
Education is one of the fast-growing fields in the global perspective. Advancement of technology can be used in this sector to provide an effective and a valuable education system. In general, the students are more attracted to displays rather than the textbooks. In Sri Lanka, there is an inadequacy of resources and teachers cannot provide one on one attention to the students. Sri Lanka is not equipped with any platform to self-learn or self-evaluate their performance using an application either. Fortunately, “Edubot” acts as a solution for the stated research gap by providing a self-learning and self-evaluating AI based chatbot platform for Ordinary Level students in Chemistry domain. The self-learning component will provide the students a classroom environment by providing interactive tutorials. Explanatory responses would be given by Edubot by capturing doubts raised by the students and the self-evaluating component will provide an exam-based environment in which the Edubot auto generates the question and answers. The research finding shows that each component has an accuracy of more than 70 percent and helps to achieve the main goal of increasing the resources available to the ordinary level students in the Chemistry domain. This would then lead to an increase in the pass rate of the chemistry subject in the G.C.E Ordinary Level exam.
Embargo
AI Powered Integrated Code Repository Analyzer for Efficient Developer Workflow
(Institute of Electrical and Electronics Engineers Inc., 2025) Akalanka, I; Silva, S.D; Ganeshalingam, M; Abeykoon, A; Wijendra, D; Krishara, J
Transitioning between new and legacy codebases in diverse project environments poses significant challenges for developers, especially with traditional Knowledge Transfer (KT) methods, which are often resource intensive and prone to obsolescence. These limitations hinder the Software Development Life Cycle (SDLC), particularly in fast-paced industrial settings. This research introduces an AI-driven automation solution that leverages large language models (LLMs) and advanced artificial intelligence technologies to address critical gaps in technical knowledge transfer, with a focus on modern software frameworks. The proposed system reduces development costs, improves team performance, and accelerates adaptation to complex codebases. Key features include a documentation generation tool that cuts manual effort by up to 90%, with an average generation time of 6.8 minutes. Additionally, a virtual knowledge transfer assistant enhances onboarding efficiency, potentially reducing senior developer involvement by 50-60%. The system also includes an automated diagram generator that achieves 97% validation accuracy and a code smell detection tool with 71% accuracy, resulting in better code quality assessments. These findings demonstrate the effectiveness of AI-driven automation in improving developer productivity, streamlining onboarding processes, and optimizing software development workflow
Embargo
Ai-Based Urine Microscopy Image Analysis for Predicting Urinary Tract and Renal Diseases
(Institute of Electrical and Electronics Engineers, 2026-05-29) Senanayake, K; Panagoda, P; Dharmapriya, S; Chathurya, R; Wijendra, D; De Silva, H; Jayawardana, D
The manual microscopic examination of urine is a crucial step in diagnosis of urinary tract and renal diseases. However, the process is time-consuming and operator dependent. Most of the existing automated urinalysis techniques only consider the microscopic examination of individual components or the black-box-based prediction models. There is a lack of a comprehensive framework that incorporates the microscopic examination of the microscopic components with the clinical diagnostic logic. In this regard, the present work proposes an artificial intelligence-based urinalysis system for the microscopic examination of the components in the urine sample to generate diagnostic outcomes. In the proposed system, the microscopic components like white blood cells, red blood cells, bacteria, yeast, crystals, and casts are detected and analyzed to generate the diagnostic outcomes for the diagnosis of urinary tract infection, kidney stone risk, hematuria causes, and casts-related renal diseases. In the proposed system, efficient lightweight models ensure precision and effectiveness in identifying various biological entities. White blood cells are detected with a mAP@0.5 score of 0.96, yeast with over 0.94, and crystals with more than 0.91, yielding a classification accuracy of 99.41% for crystals. The system detects microscopic elements like casts with a mAP@0.5 score of 0.80. The system also incorporates auxiliary clinical data to enhance diagnostic results for various diseases.
Embargo
AloeGreen: Smart IOT-Based System for Aloe Vera
(Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Megasooriya, E; Rajapaksha, H; Rajapaksha, H; Bandara, A; Krishara, J; Wijendra, D
Agriculture plays a vital role in food security, yet Aloe vera cultivation remains vulnerable to environmental variability, nutrient imbalance, disease occurrence, and unstable market conditions. This study presented AloeGreen, a crop-specific AI-IoT smart agriculture framework designed to support Aloe vera cultivation through integrated sensing, forecasting, and decision-support modules. The system combined real-time IoT-based field monitoring with machine learning models for yield prediction, environmental forecasting, disease detection, fertilizer recommendation, and price forecasting. A key contribution of the study was a forecast-informed yield prediction strategy in which short-term environmental forecasts were incorporated into the yield estimation pipeline to support future-aware decision-making. In addition, domain-specific agronomic features, including water stress and heat stress indices, were introduced to better represent Aloe vera growth conditions. For the yield prediction module, the cleaned hourly cultivation dataset contained 1,048,330 observations after removing missing critical fields and duplicates. Experimental results showed that XGBoost achieved the best yield prediction performance with an RMSE of $\mathbf{1 0. 0 2}$ and an $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{0. 8 9 2}$, while the environmental forecasting module achieved strong performance for temperature and humidity prediction, although rainfall prediction remained comparatively weaker. The disease detection module achieved balanced classification performance of approximately 77% accuracy, and Random Forest performed best in both price forecasting and fertilizer recommendation tasks. Overall, the findings showed that integrating IoT sensing with intelligent analytics in a unified Aloe vera cultivation platform can improve decision support, reduce uncertainty, and contribute to more sustainable smart agriculture practices.
Embargo
An Integrated Deep Learning Framework for Early Detection of Vision Disorders
(Institute of Electrical and Electronics Engineers Inc., 2025) Jayathilaka, S; Balaruban, D; Kumanayake, I; Elladeniya, A; Wijendra, D; Krishara, J; De Silva, M
Vision impairment due to retinal diseases like Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), Glaucoma, and Retinal Vein Occlusion (RVO) poses a significant health challenge in Sri Lanka, where these conditions are leading causes of blindness. This research presents a novel multi-disease prediction system leveraging advanced deep learning techniques for early detection of DR, AMD, Glaucoma, and RVO. The study utilized publicly available datasets, including retinal fundus images from repositories such as RFMiD, IDRiD, APTOS validated by medical professionals to ensure diagnostic reliability. These images were preprocessed and augmented to train robust convolutional neural network (CNN) models tailored to each disease. The predictive models were developed and optimized using hybrid architectures, integrating attention mechanisms and feature fusion for enhanced performance. This approach achieved high accuracies 93% for DR, 92% for AMD, 94% for Glaucoma, and 94% for RVO demonstrating robustness and consistency across diverse retinal conditions. To validate real-world applicability, the models underwent further testing in clinical settings using a Sri Lankan dataset, reflecting local disease prevalence and imaging conditions. By combining validated public data with clinical testing, this scalable system supports ophthalmologists in early diagnosis, reducing diagnostic delays and improving patient outcomes. This work offers a reliable, innovative solution to mitigate the burden of blindness in Sri Lanka and beyond.
Open Access
Automated Customer Care Service System for Finance Companies
(NCTM, 2014-12-16) Warnapura, A. K; Rajapaksha, D. S; Ranawaka, H. P; Fernando, P. S. S. J; Kasthuriarachchi, K. T. S; Wijendra, D
In general, to obtain information about a product one should visit the company or contact the company via a phone call or some sort of a communication type, for example E-mail. Even so under normal circumstances the customer will receive the necessary information sent by a human being. There can be many disadvantages in this method. At the onset if a particular customer gives a phone call to the company the customer will have to wait for a considerable time. This is obvious because due to lack of human resources and phone lines there may be a question of customers waiting to get connected to the company line. On the other hand if a customer sends an email, the reply for the email will take time because the particular email should be perused by another human being at company in order to reply. These are few disadvantages apart from human errors that can happen. Ultimately as a result of above detrimental facts a faithful customer could get unsatisfied and lose confidence on a particular company. However, in the system that we are going to introduce, a particular customer can get any type of information in real time by the Aid of the Artificial Intelligence in the form of text/voice or E-mails. The advantages over the other method are that the customers will not have to wait for a reply, there are no space for human error and more importantly the company can use their human resources in other activities while the system takes care of the Customer care unit at least partially. Further, this system will be help to people who needs the immediate customer care assistance and will be able to get help by their own without involved human agent in another party for their assistance
Embargo
Automated Sinhala Voice Assistant to Manage Tasks using Natural Language Processing - ශ්‍රී Voice
(IEEE, 2022-12-26) Senarathne, K.H.I.R.; Nirash, J.M.I.; Herath, H.M.C.P.; Bandara, V.D.; Wijendra, D; Krishara, J
Voice assistants are programs on digital devices that listen and respond to verbal commands. In this dynamic world, users can use these voice assistants to manage daily tasks, plan their day, get answers to problems, and for entertainment purpose. Most of existing voice assistant applications functioned using the English language. Since Sinhala is the native language in Sri Lanka, it is not recognized internationally as well as within Sri Lanka for technical applications. As Sri Lankans are more inclined to use the Sinhala language, it is expected to develop this for the benefit of all the Sri Lankans despite their age and to use their native language through a technical application. Furthermore, the lack of English knowledge will lead to the decline of Information technology literacy. This project expects to take the Sinhala language forward to a standard where it is recognized locally and internationally. Thus, building a mobile application that supports Sinhala voice commands will solve the above-mentioned issues. It will facilitate the individuals to do daily activities efficiently and effortlessly within a less time. Machine Learning and Natural Language Processing are the man technologies used in this project. A computer vision-based algorithm from face detection technology is trained. This application is capable of answering questions and following the instructions for daily tasks and also can be used for entertainment. Apart from the mentioned specialty, it also supports and responds to the Sinhala language that will be shown off on a display.
Embargo
CINNOVA: Advancing Sustainable Cinnamon Farming through AI and Collaborative Solutions
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Bandara D.; Fernando M.K.K.L; Senadeera N.A.D.N; De Silva R.C.T; Wijendra, D; Krishara, J
Cinnamon is one of the most economically significant export crops in Sri Lanka. However, its cultivation is challenged by plant diseases, nutrient deficiencies, and inefficient harvesting practices, which reduce yield quality and productivity. Traditional methods for identifying plant health issues are time-consuming and require expert evaluation, often inaccessible to rural farmers. To address these limitations, this study introduces an AI-driven intelligent monitoring system for sustainable cinnamon cultivation, a mobile-based solution specifically designed to enhance cinnamon farming practices. The system leverages Artificial Intelligence (AI), Deep Learning (DL), and Image Processing techniques to support real-time plant health diagnostics. It integrates multiple AI-powered components for early detection of bark diseases such as Rough Bark Disease (RBD) and Canker Disease (CD) using a contrastive learning-based model, severity prediction of leaf diseases, including Leaf Gall and Leaf Blight, using the YOLO model, identification of nutrient deficiencies, particularly Magnesium and Potassium, through transfer learning and prediction of cinnamon bark maturity and quality grades utilizing spatial attention mechanisms based on diameter and color. Each model is optimized for mobile deployment to provide real-time feedback and enable efficient decision-making. This AI-driven approach enhances disease management and improves yield quality while promoting sustainable, data-driven cinnamon cultivation in Sri Lanka.
Embargo
Enhancing Conversational AI Model Performance and Explainability for Sinhala-English Bilingual Speakers
(IEEE, 2022-12-09) Dissanayake, I; Hameed, S; Sakalasooriya, A; Jayasinghe, D; Abeywardhana, L; Wijendra, D
Natural language processing has become essential to modern conversational tools and dialogue engines, including Chatbots. However, applying natural language processing to low-resource languages is challenging due to their lack of digital presence. Sinhala is the native language of approximately nineteen million people in Sri Lanka and is one of many low-resource languages. Moreover, the increase in using code-switching: alternating two or more languages within the same conversation, and code-mixing: the practice of representing words of a language using characters of another language, has become another major issue when processing natural languages. Apart from natural language processing, the explainability of opaque machine learning models utilized in chatbots has become another prominent concern. None of the existing modern chatbot development platforms supports explainability and relies on a performance score such as accuracy or f1-score. This paper proposes a no-code chatbot development platform with a series of built-in novel natural language processing, model evaluation, and explainability tools to tackle the problems of processing Sinhala-English code-switching and code-mixing natural language data and model evaluation in modern chatbot development platforms.
Embargo
Face Skin Disease Detection and Community based Doctor Recommendation System
(IEEE, 2022-12-09) Udara, M.A.A.; Wimalki Dilshani, D.G.; Mahalekam, M.S.W.; Wickramaarachchi, V.Y.; Krishara, J; Wijendra, D
In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.
Embargo
FITGEN - AI Smart Fitness Companion: An Integrated Multi-Modal Approach to Personalized Health and Fitness Management
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Gunawardena S.N.A; Jayamali P.L.W; Methsandi K.A.N.; Gamage R.P.D.D; Wijendra, D; Krishara, J
This research presents FITGEN, a comprehensive AI-powered fitness companion system that integrates four core modules: AI Gym Trainer with Gamification, Medical Guidance System, AI Nutritionist, and Social Bridge for inclusive fitness engagement. Unlike existing solutions that address singular aspects of fitness technology, FITGEN provides a holistic approach combining real-time pose detection, chronic disease management, personalized nutrition planning, and accessibility features for special abled youth. The system utilizes Flutter framework with Firebase backend, implementing Google ML Kit for pose estimation, TensorFlow models for nutrition recommendations, and ESP32-based IoT sensors for vital sign monitoring. Comprehensive evaluation demonstrates 93.5% pose detection accuracy, 96% nutrition recommendation accuracy, 73% user retention after 4 weeks, and significant improvements in accessibility compliance. The integrated approach addresses critical gaps in existing fitness technologies while maintaining high user engagement and safety standards for diverse user populations including those with chronic conditions and developmental disabilities.
Embargo
Gamifying Coding Education for Beginners: Empowering Learners with HTML, CSS and JavaScript
(Institute of Electrical and Electronics Engineers Inc., 2025) Chandrasekara, S; Hewavitharana, D; Weerasinghe, M; Gayasri, B; Wijendra, D; De Silva, D
Traditional coding education often fails to engage and motivate beginners due to its lack of interactivity and personalized learning experiences. This paper presents a gamified learning platform designed to teach Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript (JS) to beginners. The platform incorporates interactive lessons, AI (Artificial Intelligence)-powered coding assistance, and advanced gamification mechanics to enhance learner motivation, engagement, and success. Furthermore, key features include performance-based recommendation engines, virtual coding environments with real-time feedback, and a collaborative platform for peer interactions. The integration of AI provides personalized feedback and adaptive learning paths, while gamified elements such as badges, points, and leaderboards foster competitive and enjoyable experiences. Preliminary findings demonstrate a 40% increase in student engagement metrics and a 35% improvement in coding competency compared to traditional methods. This research lays the groundwork for future expansion to additional programming languages and broader educational applications, with potential implications for transforming computer science education on a scale.
Embargo
Grade 6 Coding Education through Interactive Learning in Sri Lanka
(Institute of Electrical and Electronics Engineers, 2025-12-08) Nallaperuma, S; Perera, A; Nanayakkara, N; Hansaja, D; Krishara, J; Wijendra, D
This study proposes an AI-powered Adaptive Learning Management System (LMS) to enhance coding education for school students. The system integrates six AI/ML models and FaceNet-based facial authentication to deliver adaptive learning pathways, real-time feedback, and personalized assessments. It begins with an initial benchmarking assessment across seven categories, after which the Exam Difficulty and Question Count Model and a fine-tuned GPT-2 generator collaboratively create customized multiple-choice questions based on each learner's capability. Learning is supported through skill prediction, dynamic difficulty monitoring, and emotional state detection to ensure both cognitive and emotional engagement. Students scoring above 60% advance directly to coding curricula, while others receive personalized scaffolding through multimodal learning-style-based modules. Teachers benefit from AI-powered analytics tools that offer continuous feedback, weak-topic detection, and pedagogical guidance to enhance teaching effectiveness. By combining authentication, adaptive assessment, emotional intelligence, and teacher support, this study presents a comprehensive framework to democratize coding education and foster resilience, motivation, and mastery among students aged 10-12.
Embargo
Hybrid Deep Learning Approach Using YOLO and Spatial Transformers for Kinesthetic Math Education in Low-Resource Settings
(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Sellapperuma M.S; Wasana, K.H. I; Dhananjana, B.K. T; Anuruddhika, D. L. N; Krishara, J; Wijendra, D
This research presents a hybrid deep-learning system to enhance kinesthetic math education for Grade 4 and 5 students in low-resource Sri Lankan classrooms, addressing addition, subtraction, and time-telling skills in Sinhala. Integrating fine-tuned You Only Look Once version 11 (YOLOv11) for real-time abacus recognition and pre-trained Spatial Transformer Network (STN) with Residual Network 50 (ResNet50) for analog clock analysis, the system bridges physical manipulatives with digital feedback. Optimized for low-cost hardware via post-training quantization, it reduces model sizes by up to 56.6% and inference times by 45.9%, enabling deployment on standard PCs. The methodology employs YOLOv11 to detect abacus beads (mean Average Precision, mAP50-95: 0.898) and STN + ResNet50 to correct clock perspectives (95.2% accuracy on SynClock), delivering immediate Sinhala feedback through a culturally adapted interface. The system was evaluated on an Intel Core i5 PC with 8GB RAM and achieved sub-second inference (YOLOv11: 170.1 ms, STN + ResNet50: 32 ms post-quantization) while retaining accuracy. A pilot study with 20 students showed a 25% improvement in arithmetic scores and 30% in time-telling accuracy, with 85% reporting higher engagement. These findings demonstrate the system's efficacy in boosting numeracy and interaction in resource-scarce settings, aligning with the Visual, Aural, Read/Write, Kinesthetic (VARK) framework. Despite challenges like hardware constraints and real-world variability, this scalable, offline-capable solution offers a novel approach to educational technology, with potential for broader deployment in Sri Lanka and similar contexts, addressing gaps in artificial intelligence-driven kinesthetic learning tools. ©
Embargo
A Mobile Based Garbage Collection System
(IEEE, 2022-12-29) Wijendra, D; De Silva, D. I.; Gunawardhena, N. M.; Wijayarathna, S. M.; Aluthwaththage, J. H.
Garbage disposal and collection is an ongoing global crisis amplified by the increasing world population, lack of funds and public awareness, and recently because of the Covid-19 pandemic. Information Technology can be utilized as a solution for the existing garbage collection methods that are old-fashioned, time-consuming, and energy-consuming due to the lack of a unified and consistent system that incorporates all the parties involved in garbage production and collection. A mobile-based garbage collection system is proposed to overcome the issues aforementioned through route and schedule optimization, AI chatbot, and optimized GPS tracking. The route and schedule optimization is achieved through vehicle routing problem with time windows(VRPTW) with synchronization and precedence that was optimized using LNS; the total travel cost went from 172 minutes to 144 minutes. The AI chatbot feature facilitates reporting garbage collection issues and complaints and enquiring about waste management tips (reduce, recycle, and reuse tips) to be used at home. The most prominent role of developing this AI chatbot is replacing the manual process of reporting garbage collection issues in Sri Lanka with an efficient and interactive way. The chatbot has waste management tips Q and A. In Optimized GPS Tracking, the user can use the map to find the nearest garbage disposal place based on the type of rubbish they generate. The truck driver can find the optimal path to the closest current garbage disposal centres and public trash bins and view the location of Homeowners on the map. The optimized path between two points is displayed based on distance, time, and fuel consumption. The main goal of the component is to show the location of garbage disposal bins and the optimal paths for truck drivers using Linear regression and the Node2vec algorithm.
Embargo
OrchiZen: Hybrid Integrated Smart Farming System for Orchid Plantations
(Institute of Electrical and Electronics Engineers Inc., 2025) Wijendra, D; Jayasinghearachchi, V; Dilshan O.A.P.; Herath H.M.K.C.B; Yapa Y.M.T.N.S; Rathnasiri K.D.M.M.
OrchiZen is a hybrid integrated smart farming system designed for orchid cultivation, leveraging Machine Learning (ML) and Internet of Things (IoT) technologies to address key horticultural challenges, including irrigation, disease treatment, choice of species, lighting, and nutrients. The OrchiZen has smart irrigation advisory, species recommendation, Ultraviolet (UV) based disease treatment, light optimization, and fertilizer advisory. The priorities are given to specific species such as Dendrobium, Vanda, and Phalaenopsis. The realities of telemonitoring, data processing, and forecasting increase organizational productivity and contribute to better environmental management. The outcomes illustrate that existing modern technologies can enhance the output and ecology of the orchid production to a significant extent, redefining the conventional technologies.
Embargo
Predictive Models for Urban Air Quality Management Using AI
(Institute of Electrical and Electronics Engineers Inc., 2026-03-19) Liyanage, D; Vithanage, N; Wijewardane, I; Fernando, N; Wijendra, D; Dassanayake, T
Air pollution threatens public health in datascarce urban areas like Sri Lanka, where sparse monitoring hinders proactive management. We propose an integrated AI framework: hybrid SARIMAX-Temporal Fusion Transformer for multi-pollutant forecasting, ensemble spatial estimation for gap-filling, CEEMDAN-Seq2Seq for 24-hour AQI risk alerting, GRU for anomaly detection, and XAI for transparency. Validated on Central Environmental Authority data (20192024), the model achieves an 81.6% decrease in the value of the RMSE metric for ozone forecasting, as well as an R2 value of 0.9077 for high-risk AQI prediction, outperforming the baseline methods by 15-81%. The framework is modular in nature, thereby providing policymakers with the ability to use real-time dashboards, thus making Sri Lanka move from reactive to proactive management.
Embargo
Software Complexity Reduction through the Process Automation in Software Development Life Cycle
(IEEE, 2021-11-29) Wijendra, D; Hewagamage, K. P
Numerous software complexity metrics have been introduced to quantify the software complexity in terms of different attributes considered in its written source code. Although the complexity determination is bounded with its source code, it should be expressed beyond its code base level, since the software is implemented as a combination of different phrases inside the Software Developments Life Cycle. The automation of the processes involved in software implementation procedure will mitigate the human effort taken during the phrases, resulting that the overall complexity of the software will also be reduced. The proposed system has the capability to demonstrate the requirement analysis, design, defects tracking, quality analysis and the complexity computation with respective to the different complexity metrics without restraining the software complexity evaluation into several quality attributes within the source code.
Embargo
System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing
(IEEE, 2022-12-09) Liyanage, M. H. S; Gajanayake, G.M.B. S; Wijewickrama, O; Fernando A, S.D.S. A; Wijendra, D; Gamage, A. I
Water covers approximately 71% of the earth’s surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource.