Other Conference and Symposium Proceedings

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"Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation
(Institute of Electrical and Electronics Engineers Inc., 2025) Pramod, I; Arachchi, A.M; Rashen, C; Chinthaka, G; Pandithage, D; Gamage, N
The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields.
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Context-Aware Behavior-Driven Pipeline Generation
(Institute of Electrical and Electronics Engineers Inc., 2025) Gunathilaka, P; Senadheera, D; Perara, S; Gunawardana, C; Thelijjagoda, S; Krishara, J
Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization
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Dynamic Bandwidth Allocation in Enterprise Network Architecture: A Real-Time Optimization Approach
(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramasinghe T.M.L.D; Costa M.M.R.S; Dissanayake S.C.W.; Abayakoon A.M.W.Y.; Lokuliyana, S; Gamage, N
Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization.
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UrbanGreen - E-Waste Detection and Analysis using YOLOv5
(Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
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An Adaptive E-Learning Platform for Individuals with Down Syndrome
(Institute of Electrical and Electronics Engineers Inc., 2025) Sandaruwan U.V.S.; Dias A.H.J.S.S; Shamindi H.M.H; Priyawansha N.G.D.; Chandrasiri L.H.S.S; Attanayaka B.
Children with Down Syndrome (DS) encounter varying degrees of learning disabilities within the traditional education framework, requiring personalized interventions. This paper presents Blooming Minds, an adaptive, Machine Learning (ML) driven e-learning platform designed to support the development of cognitive, linguistic, and motor skills in children with DS. Built on the VARK (Visual, Auditory, Reading/Writing, Kinematic) theory, the platform provides personalized activities using real-time feedback mechanisms. The system includes nine interactive modules that cover the above VARK theory. It uses ML algorithms, including Support Vector Machine (SVM) and Random Forest (RF) for screening, Convolutional Neural Networks (CNN) for handwriting and speech analysis, Long Short-Term Memory (LSTM) for sequence prediction, and Reinforcement Learning (RL) for adaptive difficulties. Handwritten letters and voice samples from children with DS, both domestic and international, were specifically considered as inputs for this research. Progress tracking dashboards provide visual insights for educators, parents, and caregivers, improving support and adaptability. The system achieved 91.26% accuracy in letter recognition and 88% in speech classification. This e-learning platform has been recognized as an effective solution in Sri Lanka, allowing for further correlations and investigations to assess the knowledge capacity and ability to express that knowledge in children with DS
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Personalized Adaptive System for Enhancing University Student Performance in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Dissanayake, N; Samarakoon, C; Wickramasinghe, D; Pathirana, M; Gamage N.D.U; Attanayaka, B
The growing need for personalized learning strategies has driven the development of data-driven solutions to meet the diverse needs of Sri Lankan university students. A key challenge lies in identifying optimal learning paths that align with individual capabilities, learning styles, and engagement behaviors to improve academic performance. While previous research has explored generalized learning models, these often fail to adapt to the specific demands of individual learners. Traditional strategies lack personalization, resulting in inconsistent learning progress. To address this gap, the research introduces an assistive, data-driven approach that leverages Self-Organizing Maps (SOMs), Adaptive Learning (AL), Content-Based Filtering, Graph Neural Networks (GNNs), and Social Network Analysis (SNA) to create optimized, personalized learning strategies. Clustering algorithms and predictive analysis were used to segment learners and deliver tailored interventions based on their behavior. The proposed system integrates advanced machine learning techniques to enhance student engagement and improve overall academic outcomes through personalized pathways.
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Digital Pathways in Smart Tourism: A Systematic Literature Review on Key Drivers of Travelers' Decision-Making
(Institute of Electrical and Electronics Engineers Inc., 2025) Karunaratna, D; Senevirathne, C; Fernando, K; Peksha, P; Jayasinghe, P; Samarakkody, T
This study systematically investigates how digital components including information reliability, accessibility, cost efficiency, virtual reality and mobile adoption influence travelers' preferences in smart tourism. To do this, the study utilizes the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework to aggregate evidence from across the globe - showing both regional technology disparities, and a variety of different methodological approaches. In developed countries, technology integration is quite strong while developing regions face constraints of finance and infrastructure. These tech-centric factors are important to traveler decision-making, as indicated by the review; however, greater coverage of understudied regions is needed. The resulting insights provide the basis for targeted and holistic solutions to improve tourism services for the benefit of policymakers, industry practitioners and academics.
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IoT-Based Smart Hydroponics for Automated Nutrient, Climate, Irrigation, and Health Monitoring
(Institute of Electrical and Electronics Engineers Inc., 2025) Ashik M.A.M; Bogahawatta C.A; Perera M.R.D; Dassanayake D.R.I.P; Jayakody, A; Gamage, N
This study presents HydroNutraLeaf as a selfgoverning hydroponic tower system built with Internet of Things technology to automate the critical aspects of hydroculture farming by uniting water supply management with environmental control and watering systems and plant health monitoring capabilities. The system unites multiple essential components to operate as one unit. The system incorporates an automatic plant disease detection system through real-time image acquisition which uses Convolutional Neural Network (CNN) algorithms and cloud-based warning protocols for classification purposes. An automated system comprising Raspberry Pi actuators, NPK sensors, and machine learning functions delivers nutrients at proper stages during plant growth. A reinforcement learning system directs the management of climate factors including temperature and humidity together with Light Emitting Diode (LED) spectrums to achieve superior yield production and product quality. The system includes a self-operated irrigation system with Electrical Conductivity (EC), potential of hydrogen (pH) regulation features which utilizes SVM-based prediction methods in combination with real-time monitoring to achieve optimum root environment conditions. Users can access a dashboard in Grafana to monitor and control the system by using cloud platforms which include Firebase and AWS. The experimental findings reveal water consumption decreased by 30% along with improved nutritional efficiency reaching 25% and enhanced crop yield reaching 15% with better health performance. The sustainable farming operations and commercial greenhouse implementation benefit from HydroNutraLeaf solution which operates through a scalable model based on data analysis and requires minimal human intervention.
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Determining Factors Related to Artificial Intelligence Adoption Among Sri Lankan ICT Service Providers
(Institute of Electrical and Electronics Engineers Inc., 2025) Nizar, Z; Lakshan, J; Wijayarathne, C; Jayasuriya, N; Oshani, W; Rathnapriya, S
This study investigates the factors influencing Artificial Intelligence (AI) adoption among ICT service providers in Sri Lanka, a developing economy where AI integration remains in its early stages. Using the Technology-Organization-Environment (TOE) framework, six key determinants were examined, relative advantage, data quality, top management commitment, employee adaptability, competitive pressure, and external support. Data was collected via a structured questionnaire distributed to 146 ICT service providers, with the sample size determined using G*Power software for statistical robustness. Ordered probit regression was employed to analyze the data, as it offers precise insights for ordinal variables. The findings identify DQ and RA as significant drivers of AI adoption, underscoring the critical role of high-quality data and the operational benefits of AI technologies. However, TMC exhibited a negative impact, highlighting barriers in leadership awareness and alignment with AI strategies. Although CP, ES, and EA were not statistically significant, they demonstrated potential as mediators or moderators in specific contexts. This study bridges a critical gap by providing localized insights into AI adoption challenges and opportunities in Sri Lanka. It emphasizes the importance of data management, leadership commitment, and strategic alignment, offering actionable recommendations for policymakers and industry leaders to enhance competitiveness and digital transformation in the global economy.
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Dynamic Resource Allocation and Management in SDN for Multi-Service Network
(Institute of Electrical and Electronics Engineers Inc., 2025) Fernando U.S.K; Pieris M.H.N; Abhayathunge H.I; Athapattu A.R.B.L; Dharmakeerthi, U; Senarathne, A
The increase in network traffic due to technological advancements has led to considerable network congestion, complicating manual network management. Software Defined Networking (SDN) addresses these limitations by decoupling the control plane from the data plane, thereby facilitating centralized and programmable network management. This study introduces a novel dynamic resource allocation method utilizing the Ryu controller and Mininet to optimize traffic flow by identifying congestion-free channels based on critical network attributes such as link bandwidth utilization, latency, and packet loss. The proposed method comprises four principal components: traffic categorization, a dynamic queuing system, an advanced status collection module, and a path selection module. The Ryu controller's dynamic packet direction based on priority levels ensures efficient resource utilization and improved Quality of Service (QoS). Simulated traffic scenarios demonstrate the efficacy of the proposed algorithm, hence highlighting its ability to enhance network performance beyond traditional static routing methods.
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Predictive Policing with Neural Networks: A Big Data Approach to Crime Forecasting in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Nauzad, H; Dayawansa, D; Dias, N.Y; Haddela, P.S; Ratnayake, S
The surge in crime rates, particularly in urban regions, has underscored the importance of predictive policing within law enforcement strategies. This research introduces a neural network-based crime prediction model, specifically tailored to address the complexities of Sri Lanka's crime landscape. By combining big data analytics with advanced machine learning methods - including ensemble models such as Random Forest and Gradient Boosting, alongside Artificial Neural Networks (ANNs) - our study presents a robust framework to forecast crime incidents, locations, and time spans. While neural networks excel in predictive accuracy, their "black-box"nature can hinder practical applications in critical fields like law enforcement. To address this, our model integrates Explainable AI (XAI), making the decision-making process of the system transparent and interpretable for end-users. XAI helps break down complex neural network predictions, ensuring trust and clarity in the model's insights. With a prediction accuracy rate of 85%, this approach demonstrates substantial potential to improve crime prevention efforts and optimize resource allocation. Our research not only highlights the predictive strengths of neural networks but also showcases the essential role of interpretability for deploying these models effectively in real-world policing.
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TOWARDS A CIRCULAR ECONOMY: EVALUATING EFFECTIVE IMPLEMENTATION STRATEGIES FOR RECYCLING AND REUSE PROGRAMMES IN SRI LANKA’S CONSTRUCTION INDUSTRY
(Ceylon Institute of Builders, 2025) Lakshan S.D.V.; Wijekoon W.M.C.L.K; Buddhini P.H.Y
Construction waste accounts for a significant portion of the total waste generated in Sri Lanka. To promote a Circular Economy (CE) in the construction industry, it is essential to adopt building procedures that minimize waste, increase the use of recycled materials in new construction, and create markets for recycled and reused materials. However, the lack of modern recycling facilities and technologies hampers the effective processing and reuse of construction materials. Additionally, there is limited market demand for recycled construction materials, primarily due to concerns about product quality and the absence of standardized products. In order to promote sustainability and reduce the industry's reliance on new materials, it is crucial to implement recycling and reuse programs. Therefore, this research aims to evaluate the effective implementation strategies for recycling and reuse programs in Sri Lanka's construction industry. The literature review highlighted these existing programs and identified the challenges and opportunities for implementing recycling and reuse initiatives in Sri Lanka's construction industry. Additionally, semi-structured interviews were conducted with 9 experts, and a questionnaire survey was administered to 48 participants to gather data. The interviews revealed strategies to address the challenges, while thematic analysis was employed to analyze the interview data, and the Relative Importance Index (RII) method was used to evaluate the effectiveness of the identified strategies. A total of 9 strategies were identified for integrating recycling and reuse programs into Sri Lanka's construction industry. The findings of this study may enable to enhance the sustainability in the construction sector by minimizing waste and promoting sustainability goals.
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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
Open Access
How E-commerce Succeeds: The Role of Information Systems in Boosting Customer Satisfaction
(Institute of Electrical and Electronics Engineers Inc., 2025) Pathirana, S.L; Pathirana, S.J; Boyagoda, G.S.B; Thalagala, S.M.K; Wisenthige, K; Aluthwala, C
The rapid growth of e-commerce has revolutionized consumer behavior, especially among Millennials and Generation Z, who increasingly rely on online platforms for their purchases. This paper shows the impact of information systems success on individual performance outcomes in e-commerce, focusing on the DeLone and McLean information system success model and its three key dimensions: system quality, information quality, and service quality. A quantitative survey method was employed to gather data from e-commerce users in the western province of Sri Lanka, which is an economically developed region where Millennials and Generation Z are highly engaged with international and local e-commerce platforms. The study uses PLS-SEM to identify that system, information, and service quality significantly increase customer satisfaction and thereby improve individual performance in e-commerce. The study will address the significant research gap in Sri Lanka, where the rapid growth of e-commerce has not been adequately studied in terms of the impact of information and individual outcomes
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Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System
(Institute of Electrical and Electronics Engineers Inc., 2025) Jayawardena, A; Pulasinghe, K; Rajapakshe, S
This paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog Management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.
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Intelligent Water Quality Monitoring and Prediction System
(Institute of Electrical and Electronics Engineers Inc., 2025) Shiraz, S; Karunasena, K; Mudelige, H; Kumarasinghe, O; Nawinna, D; Perera, J
The paper aims to develop an integrated approach to improve water treatment processes using predictive modeling and SCADA integration in order to meet the specific needs of water purification systems in Sri Lanka. The current systems utilized for this need are outdated since these systems are based on traditional technologies and do not have the means for predictions or real-time data accessibility outside the system. The proposed solution will focus on raw water quality prediction, optimization of chemical usage to bring in efficiency, sustainability, and resource management, ensuring seamless access to all the relevant data required to manage and monitor. In order to achieve this, past data provided by the Meewatura water plant in Sri Lanka, sourced from the Mahawali river, is utilized for the relevant predictions alongside of the data gathered through the SCADA system. The data is not directly accessible since the SCADA system is mainly built for monitoring, and in order to get the data, a MODBUS connection through the PLC is utilized alongside of an IOT device. In addition to the extracted data, past data that was provided by the water plant is also incorporated. The combined data set is utilized for the predictions while continuously improving itself with new data. The present study contributes to the establishment of sustainable and adaptable water treatment frameworks for a wide range of operational needs within the water plants by addressing the gaps in the existing water quality management systems and improving upon them.
Open Access
A Spatial Study on the Ecological Signatures of Landscapes in Colombo
(Springer Science and Business Media Deutschland GmbH, 2025) Subasinghe J.C; Madhushani T.M.C.I.; Gomes P.I.A
Urbanization is a governing demographic feature and a significant part of global land transformation. According to the United Nations, more than half of the world’s population lives in urban areas. If not studied and managed properly, urbanization can affect negatively its residents, and in Sri Lanka this is about 20%–30% in commercial areas and residential areas. Yet, studies related to exploring functions and status quo of different landuses are rare and rather unfound in Sri Lanka. This study the variations of temperature, humidity, soil moisture, infiltration rate, shrub cover and tree richness with different landuses namely, cemeteries, parks, residential areas and institutes have been investigated to see whether the landuses actually are the landscapes people perceive. It was found that the humidity of land plots with Institutes is significantly higher than all the other landscape types. Interestingly, it was observed that parks and cemeteries possessed high humidity levels while Institutes and Residential areas possessed a comparatively lower humidity level. The soil moisture content and infiltration rates of institutal landscape significantly differed from those of other landscape types. Shrub cover variation between Residential areas and Institutes was insignificant, while shrub cover of all the other landscape types resulted in substantial differences with a significance level of 0.00. The analysis of variation of multiple ecological factors under landscape types depicted that for all the temperatures, the shrubs cover percentage of cemeteries lies higher than the rest of the landscapes. In cemeteries, initially, the shrub cover increased with the humidity and with increments of humidity level, the shrub cover decreased. Overall sense, the Institutional areas depicted relatively adverse liveable conditions, and Cemeteries depicted most favourable conditions, interestingly it was better than Parks. This study gave insights into how these landscapes be best manged and engineering interventions needed in that regard.
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The Influence of Generative AI on work-life balance among female software professionals in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Upeksha, S; Samarasinghe, D.T; Sanochana, M; Samarathunga, S.S; Rajamanthri, L; Samarakkody, T; Aluthwala, C
This study explores the role of generative artificial intelligence on work-life balance among female software professionals in Sri Lanka's software industry. This qualitative study explores the influence of Generative Artificial Intelligence (GenAI) tools on workload, productivity, and overall well-being to show how these technologies uniquely shape professional and personal lives within this demographic group. Data were gathered through semi-structured interviews with 15 female software professionals from various job roles, including software engineers, quality assurance engineers, system engineers, Development and Operations (DevOps) engineers, and project managers. Using thematic analysis, findings disclose that generative AI is mostly utilized for automation, communication and collaboration, creativity and innovation, and decision support, with ChatGPT being the most widely used tool. These tools will enable professionals to streamline the workload, increase efficiency, reduce overtime, and maintain healthy working conditions. The insights of this study yield important implications for employers and government organizations such as the Department of Labor, explicitly pointing out how generative AI can be instrumented to create a favorable work environment. Thus, by applying generative AI solutions, the key stakeholders of the Sri Lankan software industry can create work conditions crucial for the work-life balance of women to enhance organizational performance as well as the work-related well-being of female software professionals.
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Smart Agricultural Platform for Sri Lankan Farmers with Price Prediction, Blockchain Security, and Adaptive Interfaces
(Institute of Electrical and Electronics Engineers Inc., 2025) Kuruppu K.A.G.S.R.; Kandambige S.T; Perera W.H.T.H; Cooray N.T.L; Nawinna, D; Perera, J
Improper management of seed demand in Sri Lanka's agricultural sector can result in market imbalances, affecting farmers' decision-making and supply chain efficiency. This research introduces an integrated system for monitoring vegetable seed demand using digital technologies. The proposed system utilizes machine learning techniques to predict vegetable prices, a blockchain network for secure transactions, and a reward-based system to encourage user engagement. It also incorporates an adaptive user interface to accommodate different levels of digital literacy, ensuring accessibility for all farmers, especially senior citizens. Furthermore, the system features an AI Chatbot powered by Langchain and Pinecone, offering domain-specific responses and real-time support for farmers. The solution aims to combine advanced technology with agricultural practices to improve seed demand forecasting, promote transparency in transactions, and ensure a more efficient supply chain. This paper presents a multi-component agricultural platform that integrates predictive analytics, blockchain-secured transactions, gamified incentives, and adaptive user interfaces to support farming decision-making. The system combines machine learning for price forecasting, dynamic reward mechanisms to drive user engagement, and personalized UI/UX optimizations tailored for diverse user groups, including senior farmers. A multilingual AI-powered chatbot enhances accessibility and real-time support, enabling a robust, transparent, and inclusive digital solution for agricultural supply chain management.
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Multimodal Knowledge Graph for Domain-Specific Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2025) Mohan, K; Munasinghe, M; Bandara, L; Wijesinghe, H; Rathnayake, S; Abeywardhana, L
In the era of information abundance, transforming vast amounts of data into meaningful knowledge remains a critical challenge, especially in domains like medicine, engineering, and education, where visual and multimodal elements play a vital role. Traditional Knowledge Graphs (KGs) excel in organizing structured and textual data but struggle to incorporate multimodal information and implicit relationships, limiting their effectiveness. This paper explores the potential of Multimodal Knowledge Graphs (MMKGs) to address these limitations by integrating text, images, videos, and audio into a unified framework. We investigate how MMKGs enhance knowledge retrieval, comprehension, and interactive learning through advanced techniques, including Natural Language Processing and deep learning. Our findings demonstrate that MMKGs significantly improve knowledge retention and application in specialized fields, offering a foundation for more intuitive and effective domain-specific knowledge ecosystems.