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Subject: search.filters.subject.Deep Learning

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    MaizeGenie - Mobile Platform for Sustainable and Profitable Corn Cultivation
    (Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Suraweera A.G.S.S.; Pasindu J.D; Rathnayaka R.M.T.P.B; Perera P.D.P.N; Samarakoon, U; Hathurusinghe, S
    Maize is an important crop in Sri Lanka, but many farmers still face low yields and income losses due to pest and leaf disease attacks, uncertain weather, and market price changes. Farmers also have limited access to real-time, easy-to-understand decision support in Sinhala, especially in areas with weak internet coverage. This research proposes MaizeGenie, an AI-powered mobile advisory platform that supports sustainable and profitable corn cultivation. The system combines computer vision and machine learning to deliver four main services: (1) pest identification and control guidance using object detection and image classification, (2) leaf disease identification with severity-based advice, (3) yield prediction with prediction-based site-specific real time fertilizer advisory system, and (4) price forecasting and cultivation timing decision support using time-series forecasting. The application is designed for farmerfriendly use, provides Sinhala/English guidance with voice/text output, and supports operation where possible. Overall, the proposed solution aims to reduce trial-and-error farming, improve timely actions against pests and diseases, and help farmers plan inputs and selling decisions with more confidence.
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    A Lightweight YOLOv8n-Based Binary Waste Classification Model for Autonomous Garbage-Collecting Robots
    (Institute of Electrical and Electronics Engineers Inc., 2026-07-02) Perera W.B.N.; Sendanayaka H.K; Nuwanthi B.D.T.; Jayasekara R.G.S; Lokuliyana, S; Silva, S
    Efficient waste classification is critical for automation in modern waste management systems, especially in places where it is hard to use manual classification. In this work, a model for waste and non-waste classification based on the YOLOv8n framework, known for high efficiency and applicability for embedded systems, is designed. For this purpose, a customized dataset was collected using real photos under different lighting conditions, different backgrounds, and with varying objects to reflect a more realistic environment. The training process included augmentation, normalization, and hyperparameters tuning to improve the efficiency of the model. Evaluation metrics such as accuracy, precision, recall, F1 score, and confusion matrix were used. According to the experimental results, the developed model provides an accuracy of 92.5% and F1 score of 0.94 with maintaining fast inference performance for real-time applications. The designed framework aims to become a module in an autonomous waste collecting robot to validate objects before manipulating them by robots, ensuring reliable and efficient real-world system performance.
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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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    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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    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.