Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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Author: search.filters.author.Fernando, H

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    Intelligent Detection of Corporate Targeted Phishing Emails - A Hybrid Approach Combining Deep Learning Models with Domain Anomaly Detection
    (Institute of Electrical and Electronics Engineers Inc., 2025) Seethawaka, R; Chathurya N.E.G; Chandrasiri D.K.W.G.G.T; Kavithma K.A.S; Fernando, H; Wijesooriya, A
    This paper introduces a system designed to detect corporate-targeted phishing emails by combining two key strategies: advanced email content analysis and domain similarity analysis. The system first examines the text of emails using a hybrid deep learning model that merges modern language understanding techniques with sequential pattern recognition, achieving high accuracy in identifying phishing intent. Two models were tested - a standalone Bi-LSTM sequential model and a hybrid version(BERT - Bi-LSTM) with the hybrid model proving superior, scoring an F1 score of 0.97 compared to 0.93 for the standalone model. Second, the system verifies sender domains to detect spoofing attempts, such as subtle typos, homograph attacks or TLD/subdomain spoofing. This domain check reduces reliance on text analysis alone, helping analysts prioritize threats more effectively. Tested against a mix of legitimate and malicious domains, the domain module achieved near-perfect accuracy, minimizing false alarms. By integrating these approaches, the system addresses a critical gap in existing methods, which often focus on only one aspect of phishing (e.g., email content or URL features). This dual strategy ensures a more comprehensive defense, particularly against sophisticated attacks that use convincing language paired with fake domains. The combined model not only improves detection accuracy but also supports security teams by providing clear, actionable insights, making it practical for real-world corporate environments.
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    Precision Agriculture with Centralized IoT-Enabled Greenhouse Management for Sustainable Vanilla Production
    (Institute of Electrical and Electronics Engineers Inc., 2025) Karunathilaka M.M.D.N; Samaraweera H.M.C.D; Balachandra B.A.D.K.M; Thenabandu W.S.D; Silva, S; Fernando, H
    After saffron, vanilla is the second most significant spice in terms of economic impact worldwide. The vanilla business faces challenges from pests, illnesses, and environmental variables, especially fungal diseases like fusarium wilt and unfavorable climatic circumstances that can significantly reduce productivity and lower bean quality. This study offers a clever remedy that helps all parties involved by identifying and categorizing plant illnesses, predicting vanilla bean growth and quality, vanilla bean market value analysis and future prediction and build cost prediction and improve operational efficiency. Stakeholders can also obtain forecasts for the quality and growth of vanilla beans in the future. Deep learning algorithms are used in the suggested solution to track the location of diseased areas, diagnose and classify plant diseases in real-time, and apply pesticides or growth-regulating chemicals selectively. For sustainable vanilla production, machine learning algorithms are used to forecast yields, advise ideal greenhouse conditions, and recommend the best vanilla beans. In precision agriculture, the types, applications, and monitoring of IoT devices and sensors are also discussed. Data analysis and management, disease and pest control, fertilization and irrigation management, and environmental monitoring are a few examples. The suggested method produced high accuracy rates in identifying illnesses, evaluating bean quality, estimating yields, and optimizing greenhouse conditions in controlled studies using data from vanilla farms and greenhouses. This technology could assist the vanilla business, producers, and sustainable agricultural practices. It could also boost productivity and production of vanilla, decrease yield loss, and maintain constant bean quality with the help of our suggesting vanilla greenhouse application.
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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.
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    Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications
    (Springer Nature Link, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, C
    Au nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.