Browsing Faculty of Applied Science by Subject "Machine learning"

Browsing Faculty of Applied Science by Subject "Machine learning"

Sort by: Order: Results:

  • AI Based Student Absenteeism Prediction with Personalized Recommendations: A Study in Batticaloa Manmunai West Zone 
    Mathilojana, S.; Mithushika, M.; Shandru, M. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Student absenteeism is a growing issue, especially in rural and under-resourced areas like the Manmunai West Educational Zone in Bat ticaloa, Sri Lanka. Poor attendance has a strong impact on students’ academic performance ...
  • THE APPROACHES OF PERSONALIZED ADVERTISEMENTS IN TELEVISION USING MACHINE LEARNING 
    Anuruddha, I.D.; Suthaharan, S. S. (Faculty of Applied Science, University of Vavuniya, 2023-10-25)
    In recent years, personalized television advertising that uses machine learning methods to boost interaction with viewers and the success of advertisers have received a lot of attention. In this investigation, we have ...
  • Assessing Heart Disease Risk in Patients with Anemia through Predictive Modeling 
    Thulashika, R.; Elango, R. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Cardiovascular diseases are one of the leading causes of death worldwide and anemia is a common condition marked by low hemoglobin levels. Despite its prevalence anemia is often overlooked even though it plays a significant ...
  • Cardiovascular Diseases Risk in People with Mental Illnesses 
    Indika, P.G.N.S.; Dias, G.K.A. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)
    Cardiovascular diseases (CVD), including coronary heart disease, stroke, and peripheral arterial disease, are major global health concerns. Mental health disorders, such as depression, anxiety, and Post-traumatic stress ...
  • Cinnamon Price Prediction and Grade Identification: A Comprehensive Study in the Galle District 
    Helitha, E.D.K.; Pathulpana, D.G.S.; Nishadhi, R.P.B.; Ranasooriya, U.E.; Ekanayake, E.M.U.W.J.B. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    The cinnamon industry in Sri Lanka, centered in the Galle District, one of the most important economic pillars, represents over 85% of the world supply of Ceylon cinnamon. However, the industry faces challenges from ...
  • Comparative Study of Machine Learning Models for Non-Communicable Disease Prediction 
    Mahendran, L.; Kuhaneshwaran, B.; Rasathurai, V.; Karunaharan, L.; Abishethvarman, V.; Prasanth, S.; Kumara, B.T.G.S. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Cardiovascular and other non-communicable diseases (NCDs), especially diabetes and cancers, are some of the critical global health problems that are resulting in a high rate of morbidity and mortality. The prevalence of ...
  • A Comprehensive Study of Hate Speech Detection in Child Abuse-Related YouTube Comments: Insights from Traditional and Deep Learning Approaches 
    Sweshthika, S.; Yasotha, R. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)
    The widespread use of social media has significantly contributed to the proliferation of user comments by posing serious threats to individuals and communities. Due to the emotionally charged and nuanced nature of the ...
  • EMOTION DETECTION FROM TEXT BASED SENTENCES USING MACHINE LEARNING 
    Jayasinghe, H.; Thirukumaran, S. (Faculty of Applied Science, University of Vavuniya, 2023-10-25)
    In the realm of digital communication, extracting emotions from text assumes a pivotal role in deciphering human sentiment. This research delves into the significance of discerning sentiments from textual content, leveraging ...
  • Extracting knowledge from Noise Information 
    Chowuraya, T.; Kirushanth, S.; Kabaso, B. (Academic Conference International (ACI), 2018-11-12)
    Information is an important part of creating knowledge. We live at a time when so much information is created. Unfortunately, much of the information is redundant. There is huge amount of online information in the form of ...
  • Identifying Novel Diagnostic Biomarkers in Bladder Cancer 
    Fathima Nufla, M.N. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Bladder cancer remains a significant clinical challenge due to limitations in current diagnos tic methods, which are invasive, costly, or lack sensitivity particularly for early-stage detection. This study aimed to ...
  • Machine Learning Algorithms to Improve Early Diagnosis of Thyroid Disease: A Sri Lankan-Based Study 
    Senani, W.M.U.; Thirukumaran, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)
    Thyroid diseases are a major health, well-being, and health care problem across the world. This study proposes a new thyroid diagnosis system using efficient feature selection techniques and machine learning methods for ...
  • Machine Learning and Deep Learning Techniques for Betel Leaf Disease Detection in Imbalanced Datasets 
    Rajapaksha, R.A.N.M.; Edwin Linosh, N. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    The cultivation of betel leaf (Piper betle) is an important supplementary livelihood for many farmers in Sri Lanka’s wet zone, particularly in the districts of Gampaha, Kalutara, and Colombo. However, the productivity ...
  • Machine Learning Approaches for Early Diagnosis of Liver Diseases using Sri Lankan Patient Data 
    Balasubramaniyam, K.; Tuvensha, J.; Thusyanthan, V.; Thayalini, M. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    The liver is one of the most vital organs in the human body, responsible for essential func tions such as detoxification, metabolism, protein synthesis, and regulation of biochemical processes nec essary for survival. ...
  • Machine Learning Approaches to Discover Novel Biomarkers in Lung Cancer 
    Mithushika, M. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Lung cancer is one of the leading causes of cancer related deaths worldwide. For early diagnosis and better treatment, finding reliable biomarkers is essential. In lung cancer patients, certain genes identified as ...
  • Multiclass classification approach to identify the stage of autism spectrum disorder 
    Kayanan, M.; Wijekoon, P. (Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka, 09-01-20)
    Autism Spectrum Disorder (ASD) is known as a neurodevelopmental disorder that affects communication, social interaction and behavioural skills. ASD affects the children at the age of two years old and continues lifelong. ...
  • A Review on Integrating IoT Devices and Machine Learning for Disease Recognition and Management of Respiratory System 
    Wickramarathne, U.C.; Jayawickrama, M.G.; Tissera, S.S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)
    The convergence of the Internet of Things (IoT) and Machine Learning (ML) has brought significant advancements in healthcare, particularly in the detection and management of respiratory diseases. This comprehensive review ...
  • SENTIMENT ANALYSIS FOR SRI LANKAN NEWS PAPER HEADLINES USING MACHINE LEARNING AND DEEP LEARNING METHODS 
    Pirakasini, M. (Faculty of Applied Science, University of Vavuniya, 2023-10-25)
    Newspaper headlines have emerged as a ubiquitous form of communication in our modern society. These succinct news snippets hold the power to shape public perception and influence opinions on a wide range of topics. However, ...
  • SPAM EMAIL CLASSIFICATION AND IDENTIFICATION 
    Keerththana, K.; Charles, E.Y.A. (Faculty of Applied Science, University of Vavuniya, 2023-10-25)
    Email has become one of the most wide spread ways of communication in today’s society. Email spam, commonly known as junk email, spam mail, or simply spam, refers to unsolicited messages sent in large quantities through ...
  • Static Tamil Sign Language Recognition using Mediapipe Hand Landmark Detection and Machine Learning 
    Perera, G.V.D.P.P.; Jeyamugan, T.; Vijayakanthan, G. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)
    Sign language is essential for communication among the deaf and hard-ofhearing communities, addressing the needs of over 430 million people globally, including 34 million children. By 2050, this number is expected to surpass ...
  • Tamil Music Playlists with Gradient Boost Classifiers 
    Adittan, T. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    This study develops a music recommendation system based on user preferences, emphasizing genre classification and playlist generation. The classification task is addressed using a self-created, bal anced dataset comprising ...

Search


Browse

My Account