Dulani Meedeniya

• PhD
• Lecturer at University of Moratuwa

Glaucoma is a leading cause of blindness, affecting millions of people worldwide. It is a chronic eye condition that damages the optic nerve and, if left untreated, can lead to vision loss and a decreased quality of life. Therefore, there is a need to explore practical and reliable mechanisms for glaucoma identification. This study systematically r...

Brain tumour segmentation is critical in medical image analysis, facilitating diagnosis and treatment planning in neurosurgery. Brain tumour segmentation with supervised learning shows robust results in medical imaging; however, it requires a sufficient amount of annotated data for effective learning. It is important to detect boundaries of tumour...

Cervical cancer remains an important global health challenge among women. Early and accurate identification of abnormal cervical cells is crucial for effective treatment and improved survival rates. This paper addresses the development of a novel weakly supervised segmentation framework that combines binary classification, Explainable Artificial In...

Dynamic workflow scheduling in cloud environments is a challenging task due to task dependencies, fluctuating workloads, resource variability, and the need to balance makespan and energy consumption. This study presents a novel scheduling framework that integrates Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) using the Proxima...

Accurate segmentation of the ventricular structures and myocardium from Cardiac Magnetic Resonance (CMR) images is essential to diagnose and manage cardiovascular diseases. This study systematically evaluates the performance of five U-Net variants in cardiac MRI segmentation using the Automated Cardiac Diagnosis Challenge (ACDC) dataset and a hybri...

Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation. This study addresses elephant sound classification utilizing raw audio processing. Our focus lies o...

The gradual development of smart city, healthcare, and health data collection has paved the path for federated learning (FL)-based heart disease detection. In 2021, cardiovascular diseases claimed about twenty million deaths and accounted for more than a third of all the mortality in the world. For most preventable heart diseases, a proactive appro...

Continuous Authentication (CA) using behavioral biometrics is a type of biometric identification that recognizes individuals based on their unique behavioral characteristics. Many behavioral biometrics can be captured through multiple sensors, each providing multichannel time-series data. Utilizing this multichannel data effectively can enhance the...

Melanoma, a highly prevalent and lethal form of skin cancer, has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the early identification of melanoma. Despite their high performance, relying solely...

Integrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for deliveri...

The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop d...

Identifying autism spectrum disorder (ASD) symptoms accurately is a challenging task. The traditional subjective diagnostic process of ASD relies on time‐consuming behavioural and psychological observations. In this study, we introduce an ensemble learning‐based classification model using an open‐access database focusing on functional magnetic reso...

Integrating Artificial Intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analyzing intricate patterns within audio data. This paper presents a two-phase approach for enhanced diagnosis of respiratory diseases. In the initial binary classification phase, lung sounds are identified as healthy or a...

Deep Learning (DL) and Internet of Things (IoT) based applications have indeed become integral to many smart applications. Designing such solutions with high performance and low resource consumption is challenging. We present a Hardware-aware Neural Architecture Search (HW-NAS) process that automates the design of lightweight Convolutional Neural N...

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their "Black-Box" nature raises transparency concerns in medical diagnostics. This paper introduces a s...

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like Gra...

Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be dep...

Melanoma is a highly prevalent and lethal form of skin cancer, which has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the identification of diseases using medical imaging. The paper introduces a...

Glaucoma is a progressive eye condition that causes irreversible vision loss due to damage to the optic nerve. Recent developments in deep learning and the accessibility of computing resources have provided tool support for automated glaucoma diagnosis. Despite deep learning’s advances in disease diagnosis using medical images, generic convolutiona...

Detecting respiratory diseases is of utmost importance, considering that respiratory ailments represent one of the most prevalent categories of diseases globally. The initial stage of lung disease detection involves auscultation conducted by specialists, relying significantly on their expertise. Therefore, automating the auscultation process for th...

Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensor data has practical applications in healthcare and assisted living environments. However, its use in real-world scenarios has been limited due to the lack of comprehensive IMU-based HAR datasets covering various activities. Zero-shot HAR (ZS-HAR) can overcome these data li...

Continuous Authentication (CA) using behavioural biometrics is a type of biometric identification that recognizes individuals based on their unique behavioural characteristics, like their typing style. However, the existing systems using keystroke or touch stroke data have limited accuracy and reliability. To improve this, smartphones’ Inertial Mea...

Forest ecosystems are of paramount importance to the sustainable existence of life on earth. Unique natural and artificial phenomena pose severe threats to the perseverance of such ecosystems. With the advancement of artificial intelligence technologies, the effectiveness of implementing forest monitoring systems based on acoustic surveillance has...

Continuous Authentication (CA) using behavioural biometrics is a type of biometric identification that recognizes individuals based on their unique behavioural characteristics, like their typing style. However, the existing systems that use keystroke or touch stroke data have limited accuracy and reliability. To improve this, smartphones' Inertial...

Internet of Things (IoT) applications typically collect and analyse personal data that is categorised as sensitive or special category of personal data. These data are subject to a higher degree of protection under data privacy laws. Regardless of legal requirements to support privacy practices, such as in Privacy by Design (PbD) schemes, these pra...

“iLearn” is a mobile learning application built to promote online teaching and learning for primary education. iLearn provides competency-based adaptivity with a recommender algorithm, learning style-based adaptivity, and interactive learning for primary students. It provides a customizable interactive video lesson authoring tool for teachers. This...

Machine learning has opened a new era of effective and easy ways of data analysis in biological research. The use of high throughput data analysis tools and libraries facilitated cancer research among many other benefits. Breast cancer is one of the main reasons for mortality among women. Apart from traditional molecular detection, it is also neces...

Today we use smartphones for banking, shopping, and monitoring our health. These applications store sensitive data in the smartphone, making reliable authentication a crucial element in mobile devices. However, current mobile authentication systems such as pin codes, passwords, pattern locks, fingerprints, and face IDs have security vulnerabilities...

Melanoma is a fatal skin cancer with a high prevalence worldwide. The likelihood that a melanoma patient would recover considerably increases with early detection. At present deep learning (DL) approaches are becoming popular in assisting early melanoma identification. Although DL techniques provide high performance, the utilization of an image cla...

Given the rapid increase of respiratory illnesses in the recent times, the demand for medical report writing for chest X-Rays (CXR) has significantly increased. In practice, a specialized medical expert has to go through an X-Ray image to compile the accompanying report, which is tedious, not scalable and potentially prone to human error. Therefore...

The study of environmental sound classification (ESC) has become popular over the years due to the intricate nature of environmental sounds and the evolution of deep learning (DL) techniques. Forest ESC is one use case of ESC, which has been widely experimented with recently to identify illegal activities inside a forest. However, at present, there...

With the advancement of robust deep learning techniques, a significant number of applications pertaining to biomedical research and clinical practice can be noticed within the computer vision domain. Radiologists use Chest X-Ray (CXR) images, prominent among medical imaging, to diagnose and treat diseases. Proper anatomical segmentation of CXR imag...

Glaucoma is a prevalent cause of blindness worldwide. If not treated promptly, it can cause vision and quality of life to deteriorate. According to statistics, glaucoma affects approximately 65 million individuals globally. Fundus image segmentation depends on the optic disc (OD) and optic cup (OC). This paper proposes a computational model to segm...

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With th...

Tumour-Analyser is a web application that classifies a brain tumour into three classes, namely, lower-grade astrocytoma (A), oligodendroglioma (O), glioblastoma & diffuse astrocytic glioma (G). We use a magnetic resonance imaging (MRI) sequence and a whole slide imaging (WSI) that are classified using DenseNet and ResNet, respectively. The tool int...

Respiratory diseases have been known to be a main cause of death worldwide. Pneumonia and Covid-19 are two of the dominant diseases. Several deep learning based studies are available in the literature that classifies infection conditions in chest X-ray images. In addition, image segmentation has been also applied to obtain promising results in deep...

Glaucoma is a fatal and worldwide ocular disease that can result in irreversible blindness to the optic nerve fibers of the eye. After cataracts, glaucoma is a main reason for blindness. Optic Disc (OD) and Optic Cup (OC) are important for fundus image segmentation. This study proposes attention U-Net models with three Convolutional Neural Networks...

Alzheimer's Disease (AD) is a progressive neurological disease commonly found in adults over 65 years. Significant growth is estimated for this neurological disorder where diagnosis should be handled effectively and efficiently. Therefore, early detection and medication are crucial in the progression of AD. This study focuses on developing a deep-l...

Human gaze estimation plays a major role in many applications in human–computer interaction and computer vision by identifying the users’ point-of-interest. Revolutionary developments of deep learning have captured significant attention in gaze estimation literature. Gaze estimation techniques have progressed from single-user constrained environmen...

Glaucoma is a fatal and worldwide ocular disease that can result in irreversible blindness to the optic nerve fibers of the eye. After cataracts, glaucoma is a main reason for blindness. Optic Disc (OD) and Optic Cup (OC) are important for fundus image segmentation. This study proposes attention U-Net models with three Convolutional Neural Networks...

The concept of gaze object estimation predicts a bounding box that a person looks steadily. It is a applicable and contemporary technique in the retail industry. However, the existing datasets for gaze object prediction in retail is limited to controlled environments and do not consider retail product category area segmentation annotations. This pa...

Human gaze estimation is a widely used technique to observe human behavior. The rapid adaptation of deep learning techniques in gaze estimation has evolved human gaze estimation to many application domains. The retail industry is one domain with challenging unconstrained environmental conditions such as eye occlusion and personal calibration. This...

Respiratory diseases have been a main reason for death in many countries worldwide. This study considers Pneumonia which is a common lung infection condition and COVID-19 which was declared a pandemic in 2020. Since both diseases can lead to life-threatening conditions, detecting these conditions at an early stage is crucial to properly treat the p...

Glaucoma is a fatal, worldwide disease that can cause blindness after cataracts for people over 40-60 years. Statistics on glaucoma have shown that around 65 million people worldwide affect by glaucoma, and it is the second major reason for vision impairment after cataract. This study uses three different Convolutional Neural Networks (CNNs) archit...

A brain tumor is a potentially fatal growth of cells in the central nervous system that can be categorized as benign or malignant. Advancements in deep learning in the recent past and the availability of high computational power have been influencing the automation of diagnosing brain tumors. DenseNet and U-Net are considered state of the art deep...

The popularity of m-learning has created endless possibilities for improving education. Mobile based educational applications assist to enhance knowledge, provide personalized learning experience, support interactivity and accessibility to different learning content. Among many different features in m-learning applications, competency-based adaptiv...

The field of neuroimage classification using deep neural networks (DNN) is a fast-moving area. With the absence of sufficient neuroimaging data, transfer learning based DNN plays a significant contribution in image classifications. The associated optimizers support generating promising results using these deep learning models. This study explores t...

Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also considered to be a role of Artif...

The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a pr...

Geospatial analytics is a promising method of spatial data processing and analysis. This study presents a deep learning-based geospatial analytics model to classify the satellite images and geographical information system (GIS) data to estimate the agricultural land area under paddy cultivation. The fine-tuned predictive model is validated against...

Smart agriculture has been attracting greater attention from the agricultural research community to enhance current practices through the incorporation of data engineering techniques. This chapter presents an approach to classify the stand age and land utilization of rubber plantation using deep learning techniques in conjunction with remote sensin...

At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in...

Diseases in the respiratory system affect many people worldwide and can lead to life-threatening conditions. Pneumonia is an acute infection of the lungs and Coronavirus is a recently emerged respiratory disease that has been recorded in many deaths around the world and announced as a pandemic in early 2020. It is crucial to detect these conditions...

Online education has inevitably become the trend for education supporting distance learning. However, due to the transformation from traditional teaching to online mode, effective teaching has become a challenge. This paper proposes an m-learning toolkit compose of a video-based learning content authoring framework that allows the creation of inter...

At present, DevOps environments are getting popular in software organizations due to better collaboration and software productivity over traditional software process models. Software artefacts in DevOps environments are vulnerable to frequent changes at any phase of the software development life cycle that create a continuous integration continuous...

Recent research has produced efficient algorithms based on deep learning for text-based analytics. Such architectures could be readily applied to text-based social media content analysis. The deep learning techniques, which require comparatively fewer resources for language modeling, can be effectively used to process social media content data that...

At present, a significant demand has emerged for online educational tools that can be used in replacement for classroom education. Due to the ease of access, the preference of many users is focused on m-learning applications. This paper presents an architectural framework for an interactive mobile learning toolkit. This study explores different sof...

Autism spectrum disorder (ASD) is one of the most common neurodevelopment disorders which severely affects the patients in performing their day-today activities and social interactions. Early and accurate diagnosis can help to decide the right therapeutic adaptations for the patients to lead an almost normal life. The present practices of diagnosis...

Data privacy deals with the sensitive information of individuals and has become a major topic in modern society. Although the practicability of mobile apps has become an essential part of the routines of many people, consumers are increasingly concerned about their data privacy. Most of the time, these privacy policies that are used to collect and...

Effort estimation plays an important role in the software development process by supporting the decision-making process for the stakeholders. DevOps has become a widely used software engineering practice with the collaboration of the development and operational teams. This paper addresses the factors that affect the effort estimation strategies and...

The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of sm...

Attention deficit/hyperactivity disorder (ADHD) is a common disorder among children. ADHD often prevails into adulthood, unless proper treatments are facilitated to engage self-regulatory systems. Thus, there is a need for effective and reliable mechanisms for the early identification of ADHD. This paper presents a decision support system for the A...

Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic. Over the past decades, diverse survival analysis approaches were proposed incorporating distinct data such as imaging and...

Most of the existing techniques in handwritten character recognition are not well‐utilized for low resource languages, due to the lack of labelled data and the need for large datasets for image classification using deep neural networks. In contrast to recent advancement in deep learning‐based image classification, human cognition could quickly iden...

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention,...

Work-life balance is one of the most common workplace challenges, and it costs both organisations and individuals considerably. Employees that have a poor work-life balance are at a higher risk of burnout. Burnout manifests itself in mood swings, frustration, and a drop in work performance. Work-life balance has traditionally been defined solely in...

Software testing is a major phase in software development, and it is significant to standardize and align the testing structures and processes with business strategy. Test governance that combines software testing with development, addresses the coordination of software testing at the strategic, tactical, and operational levels. Understanding the i...

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture fe...

With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may...

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting social, communicative, and repetitive behavior. The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging, requiring highly trained clinical practitioners. The development of automated approaches to ASD classification, based on integrated psychophysi...

Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, leading for salient analysis in many aspects. Thus, genomics are been emerged into the field of prognosis analysis. In this work, w...

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture fe...

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture fe...

Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, leading for salient analysis in many aspects. Thus, genomics are been emerged into the field of prognosis analysis. In this work, w...

Parking occupancy detection systems help to identify the available parking spaces and direct vehicles efficiently to unoccupied lots by reducing time and energy. This paper presents an approach for the design and development of an end-to-end automated vehicle parking occupancy detection system. The novelty of this study lies in the methodology foll...

Next-generation sequencing has revolutionized the field of genomics by producing accurate, rapid and cost-effective genome analysis with the use of high throughput sequencing technologies. This has intensified the need for accurate and performance efficient genome assemblers to assemble a large set of short reads produced by next-generation sequenc...

During the COVID-19 pandemic, multiple aspects of human life were subjected to unprecedented changes, globally. In Sri Lanka, a developing country located in South Asia, it was possible to observe a range of events that arose due to the influence of the COVID-19 virus outbreak. Thus, the people of Sri Lanka used Social Media to voice their opinions...

The identification of the usage and coverage of the land is a major part of regional development. Crowdsourced geographic information systems provide valuable information about the land use of different regions. Although these data sources lack reliability and possess some limitations, they are useful in deriving building blocks for the usage of th...

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent psychiatric disorder with persistent patterns of inattention, hyperactivity and impulsivity behaviors among children. The perilous factor lies underneath is that often these children are commonly entangled with learning difficulties which tend to lead frustration when they reach...

The majority of currently available webpages are dynamic in nature and are changing frequently. New content gets added to webpages, and existing content gets updated or deleted. Hence, people find it useful to be alert for changes in webpages that contain information that is of value to them. In the current context, keeping track of these webpages...

DevOps practices preserve the continuous innovation in software development. The collaborative nature and stakeholder communication are keys in DevOps that lead to highly effective and quality software outcomes with customer satisfaction. The software artefacts involved in a DevOps practice must adapt to frequent changes due to continuous stakehold...

Psychophysiological chronic disorders lead to both physical disorders and emotional factors, such as anxiety and stress. This requires patients to live under medical treatments for the rest of their lives. Most of them have a genetic influence, which is observable in children at an early age. Therefore, the early identification of a disorder is imp...

Software development in DevOps practice is a widely used approach to cope with the demand for frequent artefact changes. These changes require a well-defined method to manage artefact consistency to ease the continuous integration process. This chapter proposes a traceability management approach for the artefact types in the main phases of the soft...

DevOps practices preserve the continuous innovation in software development. The collaborative nature and stakeholder communication are keys in DevOps that lead to highly effective and quality software outcomes with customer satisfaction. The software artefacts involved in a DevOps practice must adapt to frequent changes due to continuous stakehold...