Other Conference and Symposium Proceedings

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A BI Approach for Student Engagement and Retention along with Cognitive Load Analysis for Educator
(979-833153098-3, 2025) Algewatta, M. N; Manathunga, K
This research presents a systematic approach to monitoring student engagement, retention, and cognitive load within higher education by integrating Business Intelligence (BI) tools with cognitive load analysis. The proposed framework utilizes a diverse range of data sources -including attendance, academic performance, mental health indicators, demographic variables, and student feedback to generate real-time insights into student behavior patterns. The BI system identified critical trends, such as irregular attendance, declining academic performance, and the influence of demographic factors, enabling educators to identify at-risk students and intervene proactively. Additionally, cognitive load analysis was employed to evaluate the mental demands of course content, categorizing learning objectives in alignment with Bloom's Taxonomy. This allowed for the identification of content that could potentially overwhelm students, facilitating adjustments in instructional complexity. The integration of BI insights with cognitive load data provided a holistic approach that not only enhanced the monitoring of student engagement but also supported the tailoring of instructional content to optimize learning without inducing cognitive overload. The findings suggest that combining BI tools with cognitive load metrics offers a robust framework for both improving student retention and assisting educators in creating a balanced, engaging, and supportive learning environment. This study contributes a practical model for institutions seeking to leverage data-driven insights to promote student success and address the dynamic challenges of modern higher education.
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A Dual-Branch CNN and Metadata Analysis Approach for Robust Image Tampering Detection
(Institute of Electrical and Electronics Engineers Inc., 2025) Zakey, A; Bawantha, D; Shehara, D; Hasara, N; Abeywardena, K.Y; Fernando, H
Image tampering has become a widespread issue due to the availability of advanced tools such as Photoshop, GIMP, and AI-powered technologies like Generative Adversarial Networks (GANs). These advancements have made it easier to create deceptive images, undermining their reliability and fueling misinformation. To address this growing problem, we propose a hybrid approach for image forgery detection, combining deep learning with traditional forensic techniques. Our study integrates a dual-branch Convolutional Neural Network (CNN) with handcrafted features derived from Error Level Analysis (ELA), noise residuals from the Spatial Rich Model, and metadata analysis to enhance detection capabilities. Metadata analysis plays a crucial role in identifying inconsistencies in image properties such as timestamps, geotags, and camera details, which often accompany tampered images. The CASIA dataset, a publicly available benchmark for tampered images, was used to train and evaluate the proposed model. After 30 epochs of training, the hybrid method achieved an accuracy of 95%, demonstrating its effectiveness in distinguishing between authentic and tampered images. This research highlights the advantages of combining deep learning models with traditional feature extraction methods and metadata analysis, offering a robust solution for detecting manipulated images. Our findings contribute to advancing image forensics by improving detection accuracy, even in cases involving sophisticated tampering methods driven by AI.
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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'AAYU', Paralyze Ease Home Suite and Mobility Companion
(IEEE Computer Society, 2025) Tharushi N.K.; Ranaweera D.G.K.T.T.; Munasinghe A.S.; Wijesekara P.N; Gamage N.D.U; Pandithage D
Ensuring the safety and well-being of paralyzed individuals remains a critical challenge, particularly in resource-limited settings. Limited access to assistive technology and real-time monitoring increases health risks and dependency. This paper presents AAYU (Assistive Automation for Your Upliftment) Paralyze Ease Home Suite and Mobility Companion, an intelligent system integrating home automation and wearable technology to enhance patient safety, communication, and autonomy. AAYU addresses four key challenges: (1) optimizing home environments through automated adjustments based on vital signs, (2) enabling nonverbal communication via a voice-to-text smart device, (3) detecting falls with a real-time positioning belt, and (4) preventing deep vein thrombosis (DVT) using a sensor-equipped monitoring belt. An initial evaluation demonstrates AAYU's potential to improve the quality of life for paralyzed individuals through proactive and adaptive support.
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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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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
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AI-Driven Vehicle Valuation and Market Trend Analysis for Sri Lanka's Automotive Sector
(Institute of Electrical and Electronics Engineers Inc., 2025) De Silva K.P.N.T.; Shehan H.A.; Jayawardhane A.S; Premarathne A.P.S.; Krishara, J; Wijendra, D.R
The automotive sector in Sri Lanka faces challenges in vehicle valuation accuracy and market trend analysis due to fluctuating prices, varying vehicle conditions, and environmental concerns. This paper presents an AI-driven vehicle valuation system integrating machine learning models for automated vehicle identification, damage detection, market trend analysis, and environmental sustainability assessments. Using deep learning techniques such as Convolutional Neural Networks (CNNs) and time-series models like Long Short-Term Memory (LSTM), the system delivers accurate valuation and market trend insights. Experimental results demonstrate 9 2% accuracy in damage classification and a mean absolute error (MAE) of 5.3% in repair cost estimation, supporting informed decision-making. This research bridges gaps in valuation transparency and sustainability in emerging automotive markets.
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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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An Explainable Deep Learning Framework for Coconut Disease Detection Using MobileNetV2, Super-Resolution, and Grad-CAM++
(Institute of Electrical and Electronics Engineers Inc., 2025) Balasooriya R.C.; Adithya E.L.A.Y; Gunarathne M.M.S.U; Silva T.C.D; Lokuliyana, S; Wijesiri, P
Coconut production is a significant industry in Sri Lanka's economy and food security. However, it is constantly under threat from diseases such as Grey Leaf Spot and pests such as Coconut Mites (Aceria guerreronis). Detection must be early, but it is difficult, especially in field conditions where image quality is low and symptoms are not visually distinguishable. This paper proposes a two-stage deep learning solution to enhance and automate disease and pest recognition with a lightweight and mobile system. The system combines Real-ESRGAN based image super-resolution to restore visual detail in poor-quality mobile images and MobileNetV2-based classification, a lightweight convolutional neural network. The model recognizes grey leaf spot with over 97% accuracy and greatly enhanced mite recognition performance when combined with super-resolution preprocessing. In the interest of transparency and trust for users, the Grad-CAM++ and LIME interpretation techniques are utilized, and visual explanations of the predictions are presented. A mobile application was created with React Native and integrated with a Flask-based backend to enable real-time image enhancement and classification to facilitate practical deployment. Smartphone-captured field-level photos were preprocessed and categorized into healthy, diseased, and non-coconut samples. Farmers can use the proposed system in real time because it maintains good accuracy while being computationally efficient. This framework provides a scalable method for intelligent and sustainable agriculture.
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"articulearn": An Integrative, AI-Driven Speech Therapy System for Children With Speech Disorders
(Institute of Electrical and Electronics Engineers Inc., 2025) Ranasinghe, K; Zoysa, S.P.D; Annasiwatta, S; Fernando, P; Thelijjagoda, S; Weerathunga, I
"ArticuLearn", a personalized speech therapy system for children with speech sound disorders that integrates advanced machine learning techniques and interactive digital tools to provide targeted intervention across four key domains: phonological disorder detection, fluency disorder identification and intervention, therapy for childhood apraxia of speech, and personalized speech activity filtering for articulation disorders. By leveraging dedicated LSTM-based classifiers and feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs), this approach automatically identifies specific error types, including phoneme substitutions, omissions, and vowel mispronunciations. In addition, a hierarchical deep learning framework employing attention mechanisms and dynamic time warping is applied to quantify motor planning deficits associated with childhood apraxia of speech, while a reinforcement learning agent adapts therapy prompts based on individual performance. Data were collected from eight children per disorder category along with a normative sample of twenty typically developing children, providing a basis for personalized intervention and progress monitoring. ArticuLearn is designed to complement traditional therapy methods by offering an accessible, scalable solution that supports remote intervention and enhances clinical decision-making. Pilot evaluations suggest that the system can facilitate targeted speech exercises, improve self-monitoring, and foster adaptive learning in young users. This research underscores the potential of combining AI-driven analysis with interactive therapy to transform speech rehabilitation, particularly in resource-limited settings where access to specialized care is challenging.
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Autonomous Water Quality Monitoring: Integrating UWB Ad-Hoc Networks, Sensor Calibration, and Kubernetes Cloud Architecture
(IEEE Computer Society, 2025) Tharindu, K; Abeysinghe, M; Karunarathne, S; Dilshan, K; Primal, D; Jayakody, A
Water quality monitoring plays a critical role in ensuring environmental sustainability and public health. Traditional methods, while accurate, are time-consuming and lack the ability to provide real-time insights. This study proposes a secure, scalable IoT-based solution utilizing autonomous sensor-equipped boats designed to measure pH, turbidity, and temperature in aquatic environments. The boats navigate predefined grid coordinates generated through a Python-based script and communicate data using UWB in a decentralized ad hoc network operating under the AODV routing protocol. Preprocessed sensor data is transmitted to a base station and securely forwarded to a Kubernetes-based cloud infrastructure for real-time processing and visualization. Communication between the base station and cloud services is secured using HTTPS/TLS encryption. Experimental trials confirm reliable navigation, high sensor accuracy, low latency, and robust security. The system remains cloud-agnostic and is compatible with a range of open-source Kubernetes distributions, enabling deployment flexibility across various environments. This research demonstrates an effective, autonomous approach to real-time water quality monitoring, advancing scalable and sustainable environmental management
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Blockchain-Based Custody Evidence Management System for Healthcare Forensics
(Institute of Electrical and Electronics Engineers Inc., 2025) Jayasinghe R.D.D.L.K; Sasanka M.W.K.L; Athukorala D.A.S.M; Sandeepani M.A.D; Jayakody, A; Senarathna, A
As digital evidence increasingly growing in significance in healthcare forensics, safeguarding sensitive medical data's confidentiality, integrity, and limited access remains to be an important issue. Existing forensic evidence management systems are subject to data breaches and illegal access since they frequently lack significant privacy-preserving measures. In order to overcome such challenges, this research suggests a Blockchain-Based Custody Evidence Management System for Healthcare Forensics, which combines blockchain technology, machine learning, and encryption methods to improve security, privacy, and accessibility. To ensure accurate and efficient gathering of information, machine learning algorithms are used to extract handwritten and printed text from medical photographs. AES encryption ensures safe storage, while Fully Homomorphic Encryption (FHE) is used for dynamic access level control to protect gathered evidence. Identity verification is made possible via a web-based authentication system that uses Zero-Knowledge Proofs (ZKP) to protect privacy by preventing the disclosure of personal data. By preventing unintended modifications, blockchain technology is used to preserve the custody chain's integrity. Furthermore, machine learning-driven PII detection and masking methods balance the requirement for forensic investigation with privacy compliance by controlling data accessibility according to access entitlements. Based on permitted access levels, the system makes it possible to share safe evidence with law enforcement agencies, such as courts, the police, and other forensic groups. Using blockchain to guarantee data immutability, cryptographic security to restrict access, and artificial intelligence (AI) to safeguard data, this approach enhances the privacy, security, and dependability of handling forensic evidence in medical investigations
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Child's Age Range Prediction Using Sinhala Speech Recognition System
(Institute of Electrical and Electronics Engineers Inc., 2025) Kathriarachchi, A; Pulasinghe, K
This study predicts the age range of a child speaking Sinhala by analyzing voice characteristics and acoustic features. Identifying speech impairments in children aged 6 to 72 months is critical for early intervention, mainly when using a system that recognizes their native language. The developed system generates accurate insights to assist speech pathologists in diagnosing speech disorders. A Multilayer Perceptron neural network is proposed for age group prediction, leveraging Mel Frequency Cepstral Coefficients (MFCC) and pitch features to enhance recognition accuracy. The system demonstrated an overall accuracy rate of 77% in age range identification, providing a valuable tool for healthcare professionals to evaluate and monitor speech development in Sinhala-speaking children
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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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"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.
Open Access
Designing Culturally Adaptive Emotional Gestures to Enhance Child-Robot Interaction with NAO Robots in ASD Therapy
(Institute of Electrical and Electronics Engineers Inc., 2025) Manukalpa, C.S; Pulasinghe, K; Rajapakshe, S
Integrating social robots into human-robot interactions demands advancements in natural language processing, navigation, computer vision, and expressive gestures to foster meaningful interactions. However, a gap remains in designing culturally relevant and developmentally appropriate gestures, particularly in the Sri Lankan context. Autism Spectrum Disorder (ASD), a neurodevelopmental condition impacting early education, often remains underdiagnosed, exacerbating learning challenges. This study introduces a novel approach utilizing robot-child interactions for ASD screening to minimize such delays. Expressive gestures were developed for the NAO6 humanoid robot to engage Sinhala-speaking children aged 2 to 6 years, including those with ASD, in Sri Lanka. Using the NAOqi Python API and Choregraphe simulator, culturally aligned gestures expressing emotions like happiness, sadness, fear, anger, and more were designed and synchronized with voice and LED effects. Pilot studies with typical children demonstrated the significance of linguistic and cultural alignment in enhancing engagement, emotional response, and trust. By addressing cultural nuances and advancing early ASD screening, this framework holds potential for broader applications in education, therapy, and diagnosis, improving human-robot interactions globally.
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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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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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Developing Predictive Models for Future Stress Likelihood and Recovery Time Using Behavioral and Emotional Data
(Institute of Electrical and Electronics Engineers Inc., 2025) Weerasinghe W.P.D.J.N; Gunasekera H.D.P.M; Wickramasinghe B.G.W.M.C.R; Jayathunge K.A.D.T.R; Wijesiri, P; Dassanayake, T
Stress has a serious impact on mental and physical well-being, but treatments as usual are often unavailable and not effective over the long term. The AyurAura application combines imaginative Ayurvedic therapies with modern AI techniques to deliver customized stress reduction by way of Mandala art and music. This research develops two predictive models for the application. In its first model, the stress prediction probability is estimated from users' behavior in a questionnaire and the result can be used to proactively intervene. The second model forecasts time needed for recovery into a stress-free state by using the changes in daily emotional state and participation in app activities. Machine learning algorithms are used to prepare behavioral and emotional data for improved prediction performance. Trained on multi-institution datasets, both models delivered 90-95% accuracy, enabling the user to detect behavior eliciting stress and the degree needed for recovery. These results highlight the possibility of combining conventional therapeutics with contemporary tech for ongoing, affordable stress relief interventions with personalized needs in mind.
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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.