Machine Learning based Respiratory Sound Analysis for Disease Detection (Pulmo Sense)
Respiratory illnesses like COPD and asthma offer global health implications. Early detection is vital yet problematic due to limited diagnostic technologies and manual assessment by healthcare professionals. This project addresses the need for a machine learning based system to detect respiratory problems through analyzing respiratory sounds.
An ensemble model utilizing feature CNNs was created, including audio preprocessing approaches such as noise handling and segmentation to preprocess breathing sound data for analysis. Each feature CNN consisted of eight layers. Features like Mel-Frequency Cepstral Coefficients (MFCC), Chroma, and Mel-Spectrogram were retrieved to facilitate disease classification. The model was subjected to hyperparameter optimization and cross-validation to enhance its performance.
Preliminary testing shows promising results, with the model achieving an accuracy of 86% and sensitivity of 88% on a diverse dataset of lung sounds. These results indicate the model’s potential in supporting early disease detection, even outside clinical settings.
OptiBooking: Hotel Booking Cancellation Prediction and Overbooking Recommendation
OptiBooking is a predictive decision-support system developed to address the challenges of hotel booking cancellations and ineffective overbooking strategies within the Sri Lankan hospitality industry. The solution is specifically designed for small to medium-sized hotels, which form the majority of Sri Lanka’s hotels and are most vulnerable to fluctuations in guest reservations. The system provides a web interface that allows front desk staff, managers, and super users to perform key tasks such as adding new bookings manually or uploading bulk bookings via CSV files, viewing and managing reservations, monitoring cancellation likelihoods, and implementing overbooking strategies. Different user roles are defined to provide access privileges to maintain security and usability across staff levels. The backend of the system is built using Python, where core booking operations, cancellation probability predictions, and automated customer email notification features are handled. A trained machine learning model predicts the likelihood of cancellation based on booking characteristics, supporting proactive operational decision-making. Persistent booking data, room data, and prediction results are stored in a MySQL database. The OptiBooking project not only improves operational efficiency by reducing last-minute cancellations and increasing occupancy rates but also promotes sustainable digital transformation in the hospitality sector. Designed with modularity, accessibility, and future scalability in mind, OptiBooking lays a strong foundation for real-world adoption and continued enhancement.
Floro-X
The floriculture industry faces a major issue due to the extremely perishable characteristics of its products, covering inventory uncertainty due to demand variability across consumers and seasonality. Seasonal fluctuations have the impact of leading to enormous wastage of flowers, monetary loss, and inefficiency in handling stocks. The traditional method of inventory control, which is still in use in small and medium-scale florist enterprises lacks the tools, technologies and prediction capabilities required to take necessary measures to address such issues. As a solution to these constraints, this project introduces Floro-X, a forecasting-based inventory management system for the floriculture industry.
Floro-X is a web-based software solution that offers a demand prediction solution to florist managers in order to align inventory levels with real-world consumer demand. Using Facebook Prophet, a well-tested time series forecasting algorithm, the machine learning model is trained from historical sales trends and seasonal patterns for five types of flowers. The prediction algorithm delivers forecast accuracies above 85% for almost all flowers enabling users to make better inventory and sales planning decisions as to avoid overstocking or running out of stock. System features a user-friendly interface accessible via desktop, with fundamental modules of stock management, demand planning, and user profile handling. Florist managers are able to update the inventory details, place future demand forecast orders, and examine previously calculated outcomes, all from a single, user-friendly console.
While the initial version of Floro-X runs with fixed data sets which was used for training, future enhancements will involve the integration with Point-of-Sale (POS) systems to enable real-time tracking of sales and retraining of the model based on new sales data in an automated process, inclusion of real-time weather as a forecasting variable, and expanding prediction capability to include more varieties of flowers. Lastly, Floro-X addresses a major issue in the flower supply chain by introducing digital transformation to a traditionally manual and labour-intensive industry, helping florists make smarter, on-time, data driven decisions. At its core, the adoption of Floro-X facilitates a more profitable, robust, and sustainable floral system, a platform for future innovation and growth within the floriculture sector across the globe.
Enhancing Personalized, Contextual, and Temporal Recommendations Using Multi-Task Learning: A MovieLens Case Study
Traditional recommender systems struggle to adapt to users’ changing preferences due to their reliance on single-task learning. This research proposes a multi-task learning (MTL) approach that jointly addresses personalized rating prediction, contextual tag recommendation, and temporal preference modeling. By using shared embeddings and task-specific branches, the model better captures dynamic user behavior and context, improving recommendation accuracy and relevance.
FED-Ensemble: A Federated Learning Ensemble Architecture with Generative Models for Poisoning Attack Mitigation
Federated Learning (FL) endorses promising avenues for organizations, enabling collaborative model training across a distributed client network, exempting clients from relaying raw data, thus preserving privacy and lowering communication overhead. Despite these benefits, the decentralized nature of FL systems remains highly susceptible to adversarial threats, particularly Poisoning Attacks (PA) from the network’s edge. Countermeasures against PA are often tailored to specific client behaviors, yet the global model’s integrity remains at risk due to evolving malicious strategies.
The author aims to bridge this gap by proposing a feature representation learner using a novel unsupervised detection framework. It leverages an Ensemble of Deep Generative Models (EDGMs) to assign suspicious scores to model updates, eliminating irregularities and aggregating only legitimate contributions. The objective is to deflect anomalies through continuous representation learning and provide a practical solution grounded in theoretical models.
The system underwent preliminary testing under limited experimental configurations, in line with available resources. Results showed promising detection accuracy—around 90% on MNIST and 70–80% on CIFAR-10—while mitigating poisoning even when 20–40% of clients were compromised. Future work includes improving client data diversity, boosting detection efficiency, and enhancing system scalability.
StackTraceXAI
An explainable hybrid fraud detection system combining a Variational Autoencoder (VAE) and XGBoost.
The VAE learns the underlying patterns of non-fraudulent ERP financial transactions and generates reconstruction errors and latent features, which are then used to enhance the accuracy of an XGBoost classifier. The system integrates SHAP and LIME based explainable AI to provide transparent and interpretable fraud predictions.
AIRA – Generalized detection of AI-generated images leveraging Explainable AI
Artificially generated images have rapidly risen in both quality and quantity, largely driven by their popularity across domains due to their ease of access and use. This poses threats of misinformation and lack of trust in artificially generated images that pass as real, particularly in news and social media. Current research focuses on the development of methods to enhance detection, improving generalization, or on the addition of interpretability factors using explainable artificial intelligence (XAI). This project addresses the need for a detection system that allows for efficient prediction of “AI-generated” images across a variety of image generators and of various content types over “Real” images, whilst providing insights and explanations for the prediction.
In this research, a novel approach is proposed for the detection of AI-generated images while integrating Explainable Artificial Intelligence techniques to facilitate interpretability. The proposed solution implementation was developed using a Convolutional Neural Network (CNN) classifier built on top of the DenseNet121 architecture, which leveraged pre-trained weights from ImageNet, allowing for efficient feature extraction. The model was trained on a curated dataset consisting of images from five AI-image generators, which included Midjourney, Stable diffusion, Dall-E, ProGAN, and other images from State-of-The-Art image generators.
The proposed solution was evaluated using a range of data science metrics, including the F1 score, precision, accuracy and recall on an unseen portion of the dataset. The initial results prove promising, achieving on average an accuracy of 93% while maintaining an F1-score of 93% and loss of 0.22.
MMAD : Multi-model Adversarial Defense for Medical Images
Adversarial attacks on medical imaging pose a critical threat to AI-guided diagnosis, as these invisible perturbations can cause catastrophic misclassifications. This research presents the Multi-model Adversarial Defense (MMAD) system, introducing novel algorithmic innovations for medical imaging protection, specifically targeting Magnetic Resonance Imaging (MRI).
The proposed solution features a groundbreaking hybrid classifier integrating Vision Transformer, Convolutional Neural Network, and Spiking Neural Network through a novel attention-based fusion algorithm: α = Softmax(W_attn · [f_vit, f_cnn, f_snn]), where fused features f_fused = Σ α_i · f_i dynamically weight each model’s contribution. This architecture incorporates auxiliary classifiers with weighted focal loss, achieving remarkable benchmarking results on MNIST dataset: 98% accuracy for FGSM, 96% for BIM, 90.4% for PGD, and 100% for clean images at epsilon=0.05.
The two-phase purification framework introduces an innovative multi-branch output strategy: Purified Image = Main + 0.3 × Detail + 0.15 × Edge, utilizing a U-Net generator with dual attention mechanisms (channel+spatial) and self-attention at the bottleneck. Phase-two refinement employs a PatchGAN discriminator with spectral normalization, achieving peak PSNR of 34.61dB and SSIM of 0.9551.
Unlike existing solutions, MMAD eliminates the accuracy-security trade-off while providing generalized defense against multiple white-box attacks (FGSM, BIM, PGD) at low perturbation intensities (ε=0.01-0.05). The medical-first algorithmic design preserves anatomical structures and diagnostic features, establishing new benchmarks for adversarial defense in healthcare AI systems, ultimately enhancing patient safety and diagnostic reliability.
Microeconomic Level Household Income Sufficiency Predictor Using a Novel Hybrid Deep Learning Approach with XAI
This project presents a comprehensive analysis of the microeconomic-level household income sufficiency indicator through the application of advanced machine learning techniques. The core objective is to develop an intelligent system capable of accurately predicting household expenditures and assessing income sufficiency at a microeconomic scale. To achieve this, a N-tiered modeling approach is employed.
The primary model is a novel hybrid deep learning architecture designed specifically for predicting household expenditures. This model integrates the strengths of both a convolutional neural network, a multi-layer perceptron, and XGBoost, thereby enhancing the accuracy and robustness of expenditure predictions.
Complementing this, a secondary model is implemented to predict the household income sufficiency indicator. This model not only processes household input data but also integrates Explainable Artificial Intelligence (XAI) techniques. The inclusion of XAI enhances the transparency, interpretability, explainability, and trustworthiness of the model’s predictions, enabling stakeholders to understand the reasoning behind the sufficiency assessments. Such interpretability and explainability are essential for householders who require clear and actionable insights for effective decision-making.
Together, the dual-model framework provides a practical and scalable solution for understanding and addressing income adequacy at the household level, significantly contributing to socio-economic planning and efforts to reduce household income insufficiency through data-driven intelligence and explainable outcomes.
A Novel Approach of Airbag anticipation detection before a crash incident
An Intelligent Airbag Deployment System has been developed to address the suboptimal performance of current airbag systems, particularly in budget-friendly vehicles, which often suffer from unexpected deployments or failures to deploy during critical incidents. These issues typically arise from a reliance on predefined crash algorithms and sensor data that may not adequately capture complex real-world collision dynamics, thereby compromising passenger safety.
This research introduces a novel approach that leverages machine learning and computer vision to overcome these limitations. The system integrates the YOLOv8 object detection model with a Logistic Regression classifier to anticipate accidents in real-time by analyzing live dashcam footage. Key visual data features are extracted, preprocessed, and used to train the model to predict the necessity of airbag deployment. The model’s performance was fine-tuned through hyperparameter optimization and assessed using standard metrics like accuracy, precision, recall, and F1-score.
Testing in both simulated and real-world environments demonstrated significant improvements in deployment precision and a reduction in false activations compared to conventional systems. Initial evaluations showed the system’s capability to differentiate between crash and non-crash events, achieving an accuracy of 60.8%, a precision of 57.1%, and an F1 score of 26.1% in predicting correct airbag deployment events. This system aims to enhance passenger safety and mitigate economic burdens associated with unnecessary deployments