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Start date: 2020Subject: search.filters.subject.Machine learning

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    LEVERAGING PRETRAINED TRANSFORMERS FOR ENHANCED AIR QUALITY INDEX PREDICTION MODEL (Article PDF)
    (Institute of Advanced Engineering and Science, 2025) Velusamy, Santhana Lakshmi; Madhaya Shanmugam, Vijaya
    Air pollution mitigation is essential to ensure sustainable development, as it directly affects climate change, economic productivity, and social well-being. Despite the availability of numerous prediction techniques, machine learning (ML) remains the optimal solution for forecasting air pollution. Constructing a prediction model for a region with limited data poses a challenge. This study presents a novel technique that combines temporal fusion transformer (TFT) with transfer learning to create an inventive air quality index (AQI) prediction model, effectively utilizing temporal insights and prior knowledge. The TFT is an advanced deep neural architecture engineered to enhance time series forecasting through the fusion of sequence modelling and global temporal patterns. By fusing TFT with transfer learning, the research pioneers a fresh approach to AQI prediction for region with data scarcity issue, capitalizing on cross-domain knowledge transfer. Utilizing meteorological and pollutant data from the Cochin region, a hybrid AQI prediction model is constructed through TFT and transfer learning. Employing a preexisting TFT model trained on Trivandrum data, transfer learning technique is utilized to adapt the model for predicting AQI in the Cochin region. The study demonstrates that integrating TFT with transfer learning yields superior accuracy compared to an exclusive TFT-based approach.
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    ARTIFICIAL INTELLIGENT MODELS FOR AUTOMATIC DIAGNOSIS OF FOETAL CARDIAC ANOMALIES: A META-ANALYSIS
    (Springer Link, 2023-01-01) Divya, M O; Vijaya, M S
    The foetal anomaly scanning is one of the most challenging areas where accuracy of diagnosis much fluctuating with respect to the expertise of the radiologist and the mental equilibrium of the radiologist at the time of scanning. Amongst the various anomalies, foetal heart anomaly diagnosis expects precise and sensitive intellectual presence since perilous congenital heart diseases are one of the common causes resulting in the major population of infant mortality or into permanent natal faults. The accuracy of manual diagnosis of foetal cardiac abnormalities from the ultrasound scan images vary based on the human expertise and the presence of mind. Therefore, the scope of computer-assisted judgement can produce accurate diagnosis irrespective of the operator’s profile. Numerous researches are going on to explore the scope of computer-assisted judgement of abnormalities using ultrasound imaging technique (USIT), specifically using machine learning and deep learning models. This work exploits the opportunities of computer-assisted diagnosis in foetal cardiac anomaly diagnosis as this is one of the most sensitive areas where appropriate diagnosis can save a life and a wrong diagnosis may lose a life unnecessarily.
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    MALWARE FAMILY CLASSIFICATION MODEL USING USER DEFINED FEATURES AND REPRESENTATION LEARNING
    (Springer Link, 2020-11-20) Gayathri, T; Vijaya, M S
    Malware is very dangerous for system and network user. Malware identification is essential tasks in effective detecting and preventing the computer system from being infected, protecting it from potential information loss and system compromise. Commonly, there are 25 malware families exists. Traditional malware detection and anti-virus systems fail to classify the new variants of unknown malware into their corresponding families. With development of malicious code engineering, it is possible to understand the malware variants and their features for new malware samples which carry variability and polymorphism. The detection methods can hardly detect such variants but it is significant in the cyber security field to analyze and detect large-scale malware samples more efficiently. Hence it is proposed to develop an accurate malware family classification model contemporary deep learning technique. In this paper, malware family recognition is formulated as multi classification task and appropriate solution is obtained using representation learning based on binary array of malware executable files. Six families of malware have been considered here for building the models. The feature dataset with 690 instances is applied to deep neural network to build the classifier. The experimental results, based on a dataset of 6 classes of malware families and 690 malware files trained model provides an accuracy of over 86.8% in discriminating from malware families. The techniques provide better results for classifying malware into families.
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    MEASURING WEB CONTENT CREDIBILITY USING PREDICTIVE MODELS
    (Springer Link, 2020-01-30) Manjula, R; Vijaya, M S
    Web content credibility is a measure of believable and trustworthy of the web content that is perceived. Content can turn out to be unreliable if it is not up-to-date and it is not measured for quality or accuracy and therefore, web content credibility is important for the individuals to access the content or information. The analysis of content credibility is an important and challenging task as the content credibility is expressed on essential factors. This paper focus on building predictive models to discover and evaluate credibility of a web page content through machine learning technique. A corpus of 300 web page contents have been developed and the factors like Readability, Freshness, Duplicate Content are defined and captured to model the credibility of web content. Two different labeling such as binary labeling and numeric labeling are used for defining credibility. In case of binary labeling, the high and low credibility of web content are represented by 1 and 0, respectively, whereas in case of numeric labeling five-point scale rating is used to mark the content credibility. Accordingly, two independent datasets have been developed. Different regression algorithms such as Linear Regression, Logistic Regression, Support Vector Regression (SVR) are employed for building the predictive models. Various experiments have been carried out using two different datasets and the performance analysis shows that the Logistic Regression model outperforms well when compared to other prediction algorithms.
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    A REVIEW ON CLASSIFICATION AND RETRIEVAL OF BIOMEDICAL IMAGES USING ARTIFICIAL INTELLIGENCE
    (Springer Link, 2021-08-12) Greeshma, K V; Viji Gripsy, J
    Image retrieval and classification are the most prominent area of research in computer vision. Nowadays, bounteous medical images are generated through different types of medical imaging modalities in healthcare systems. It is often very difficult for researchers and doctors to access manage and retrieve images easily. The efficient and effective analysis and usage of heterogeneous biomedical images growing rapidly are a tedious task. Content-based image retrieval (CBIR) is one of the most widely used methods for automatic retrieval of images and widely used in medical images. Abundant research articles are published in different domain of applications related to CBIR and classification. The aim of this study is to provide a road map for researchers by exploring the various approaches, techniques, and algorithms used for medical image retrieval and classification. Feature extraction is the main subject for improving the performance of image classification and retrieval. Bag of visual words techniques and deep convolutional neural networks are widely used in content-based medical image retrieval (CBMIR). The state-of-the-art methods presented in this review are well suited to classify and retrieve multimodal medical images for different body organs. The methods include preprocessing of images, feature extraction, classification, and retrieval steps to develop an efficient biomedical image retrieval system. This chapter briefly reviews the various techniques used for biomedical images, and different methods adopted in classification and retrieval are focused.