Samantha Thelijjagoda

This study identifies the determinants of cloud computing adoption and its effect on the performance of Sri Lankan small and medium-sized enterprises (SMEs). The Technology-Organization-Environment (TOE) framework, Technology Acceptance Model (TAM), and individual context were used to derive the study variables. This quantitative cross-sectional study adopted items from previous validated studies. Google Form was employed to collect data, and 418 responses were received from Sri Lankan SMEs. Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS 4 and Artificial Neural Network (ANN) analysis via IBM SPSS 29 were used for data analysis. Based on the results, all hypotheses are confirmed except for one, and SME performance is significantly affected by cloud computing adoption. This study adds to the existing empirical evidence on cloud computing adoption by introducing an all-inclusive model that integrates the TOE, TAM, and individual factors. This demonstrates the effectiveness of the PLS-SEM/ANN hybrid methodology in analysing the determinants of cloud computing adoption. The significance of top management as a factor is highlighted by providing training and education to employees. Managers can benefit from this result by improving cloud computing adoption among SMEs in Sri Lanka. This is the first study of its kind in Sri Lanka, integrating the TOE, TAM, and individual variables and using a hybrid methodology combining PLS-SEM and ANN.

E-government projects which utilize Information and Communication Technology (ICT) to enhance public service delivery and improve governmental efficiency, hold significant promise. However, the success rate of these initiatives, particularly in developing countries such as Sri Lanka, remains alarmingly low. A key factor contributing to this high failure rate is the inadequacy of the Request for Proposal (RFP) process, which often results in budget overruns and project delays due to incomplete or inaccurate requirements. This research explores the pivotal role of RFPs in the successful implementation of e-government projects. Using a mixed-method approach, the study incorporates three case studies, and a Likert scale questionnaire prepared based on case study outcomes. The data for quantitative analysis was collected from questionnaires and quantitative data analysis was done using SPSS software. The findings from data highlight the critical importance of a well-crafted RFP in mitigating budgetary issues and ensuring precise requirement specifications. The study culminates in the development of ten practical guidelines aimed at enhancing the RFP preparation process, thereby increasing the likelihood of success for future e-government projects. Adopting these guidelines will enable government organizations to prepare more effective RFPs, contributing significantly to the overall success of their projects. While the guidelines are broadly applicable, slight adjustments may be necessary depending on the specific nature of the project. These guidelines offer valuable insights for both industry professionals and policymakers in the e-government sector.

This systematic research paper explores the impact of Industry 4.0 technology, specifically the Internet of Things (IoT), on Warehouse Management Performance (WMP) in the worldwide logistics sector. IoT serves as a pivotal component in the paradigm shift towards Industry 4.0, facilitating the seamless storage, packaging, and distribution of commodities. By harnessing interconnected devices and data-driven insights, warehouses have undergone significant transformations since the inception of IoT technology. This study reviews state-of-the-art literature on the influence of IoT on WMP, emphasizing the vital role warehouse operations play in ensuring the smooth functioning of the supply chain management process. Warehouse operations encompass aspects ranging from planning and layout to receiving, order picking, shipping, and distribution. To optimize warehouse operations, we conduct a comprehensive literature assessment using Scopus as our primary database. The findings from this systematic review indicate that the integration of IoT technologies such as RFID, QR codes, scanners, and Warehouse Management Systems (WMS) can substantially enhance warehouse management performance. These technologies provide real-time data, predictive maintenance, and improved inventory accuracy, resulting in increased operational efficiency, reduced operational expenses, and improved customer satisfaction. Additionally, the IoT framework combines big data, blockchain, cloud computing, and wireless sensor networks to tackle key challenges related to data storage, retrieval, and utilization. This integration significantly improves the effectiveness of the warehouse management process and facilitates data-driven decision-making. Furthermore, IoT's communication capabilities enhance efficiency and reduce costs in warehouse operations. In conclusion, this systematic review underscores the transformative potential of IoT technology in the global logistics sector. It highlights the importance of technological integration, data-driven decision-making, and smart packaging solutions. Recognizing the disruptive effects of IoT on the logistics industry, it emphasizes the need for companies to adopt and leverage IoT technologies to remain competitive and agile in the evolving global supply chain landscape. Keywords: Warehouse Management, IoT, Warehouse Management Performance, Logistics, Supply Chain Management and Warehouse.

Over the years brain drain has given risen to a lasting imprint on the economy of Sri Lanka, which has resulted variouse consequenses. Currently educated professionals from various sectors are migrating to developed countries at an increasing rate. This study aims to provide a comprehensive understanding on the factors influence skill migration in Sri Lanka. This study utilizes a comprehensive systematic review of past literature over the period of 22 years (2000-2022). The findings of this study demonstrate that migration of skilled professionals has been increased up to 2022 and how social, economic, and political factors affected migration. Some identified examples for economic factors that influence skill migration are better working opportunites, higher wages and higher living standards. Some identified social factors are political violence and better facilities like health and educational services. Increasing income tax rate and loss of liberty are some recgonized political factors that affect skill migration. Some of the studies have argued that there are positive concequences of skill migration in Sri Lanka, while some other studies have brought up arguments that negative impacts of skill migration take over the positive impacts. Keywords: Brain drain, Economic Impact and Migration.

Enhancing the independence and daily living activities of visually impaired individuals remains a significant challenge. This research proposes a novel approach that combines cutting-edge computer vision and haptic feedback technologies to address this pressing issue. This approach focuses on small environments, such as a study table, where independent mobility can significantly impact daily tasks. One notable distinction of this system lies in the real-time path planning and obstacle avoid ancecapabilities. This approach dynamically generates obstacle-free paths by continuously analyzing the user's hand movements, allowing users to reach their desired objects effortlessly. This real-time adaptation, combined with intuitive and effective communication, dramatically enhances the user's confidence during navigation. By targeting small environments, the proposed solution provides a tailored solution that optimizes the user's experience in confined spaces. This integration of state-of-the art computervision techniques and haptic feedback technologies offers a unique and holistic approach to independent mobility for visually impaired individuals.

Visual impairment affects millions of individuals worldwide, hindering their ability to interact with society. This research introduces VisionPal, a comprehensive smartphone-based application for the Visually Impaired. The system employs advanced image processing and deep learning techniques to address the challenges visually impaired individuals face in outdoor environments. The core of VisionPal is a smartphone application that integrates features such as obstacle detection, vacant seat identification, road sign recognition, and pedestrian crossing assistance. The background and literature survey delve into related works that are focused on this study. Leveraging YOLOv8, InceptionV3, Tesseract, and MobileNetV2 models, the methodology incorporates machine learning models to detect obstacles, vacant seats, crowd levels, road signs, and color lights. Researchers developed a mobile application allowing users to interact via vocal commands. Testing reveals high accuracy, and the findings depict that the YOLOv8 model precisely identifies vacant seats and crowd levels, achieving an overall accuracy of 98.74%, and obstacle identification depicts an overall accuracy of 94.38%. Further, the InceptionV3 model exhibits 97% accuracy in recognizing road signs and colors. The MobileNetV2 model achieves 98.5% accuracy in identifying traffic lights and pedestrian crossings. User feedback emphasizes the user-friendliness and effectiveness of the application in enhancing outdoor experiences for visually impaired individuals. The significance of this work is its ability to empower visually impaired individuals, enhancing their autonomy, safety, and quality of life. VisionPal addresses real-world challenges by offering a reliable and innovative solution for the visually impaired to navigate public places. The integration of state-of-the-art technologies makes VisionPal a promising tool for promoting inclusion and enabling visually impaired individuals to engage more fully in society.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that affects children. Identifying children with ADHD is a challenging task, as it requires specialized training and expertise. A novel approach to identifying children with ADHD using artificial neural networks (ANNs) and convolutional neural networks (CNNs) was proposed in this research paper. The web application “NEUROASSIST ADHD Analyzer” was developed to assist in the identification of children with attention deficit hyperactivity disorder (ADHD) using electroencephalogram (EEG) and facial expression data. The application includes three focus games, focus activities, a facial expression analyzer, a combined probability predictor, and a BOT application for the SNAP IV questionnaire. The ANN and CNN models were used to implement these components. An LR model was used to predict the overall result. The integration of these multiple models into the combined model resulted in an accuracy of 94%, with a sensitivity of 90% and a specificity of 92%. Additionally, the area under the receiver operating characteristic curve (AUC-ROC) was 0.91, indicating good discriminative power. Furthermore, a user study indicated that the application is user-friendly and effective in assisting with the diagnosis of ADHD. This research demonstrates the potential of ANNs and CNNs in identifying children with ADHD and presents a promising tool for assisting clinicians in diagnosing the disorder. Future work could focus on improving the accuracy of the models by incorporating additional data sources and features and expanding the application to a larger population.

Dyslexia is a common learning disorder that mainly causes problems with reading. Dyslexia is mainly due to the disability and difficulty with accurate word recognition, poor spelling, difficulty in recognizing or identifying the shape, numbers, colors or sounds of characters. Early classifications of dyslexia included several different forms, but recent classification include four proposed variants based on their symptoms called Surface dyslexia, Phonological dyslexia, Rapid naming dyslexia, and Visuo-spatial attention dyslexia. ‘WALIPILLA’ is a mobile based screening and refinement system to identify the risk of reading difficulties in Sinhala and English due to dyslexia. The aim of this study is to identify the primary school students in age between 8 to 10 with these types of dyslexia and provide refining activities to overcome the condition through a mobile application. This solution presents itself as a useful digital tool for parents and teachers to assess early children with reading difficulties.