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Start date: 2020Subject: search.filters.subject.Prediction

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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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    A NEW HYBRID ADAPTIVE OPTIMIZATION ALGORITHM BASED WAVELET NEURAL NETWORK FOR SEVERITY LEVEL PREDICTION FOR LUNG CANCER DATASET (Article)
    (Intelligent Network and Systems Society, 2024) Divya, T; Gripsy J, Viji
    This study proposes three contributions focused on lung cancer detection and severity level identification. The absence of non-invasive technologies for predicting lung cancer necessitates faster, more efficient, and more accurate classification procedures due to the absence of non-invasive technologies for predicting lung cancer. Creating an automated and intelligent prediction system is crucial for identifying phases and predicting the possibility of a recurrence. The objective is to create an automated detection system for identifying lung cancer using an optimizationfocused deep learning model. We develop an adaptive multi-swarm PSO and combine it with the firefly algorithm to determine the ideal weight values for the Wavelet Neural Network (WNN) model. We use the HAPSO-FFA WNN method to explore problems with multiple optimal solutions. This study evaluated two lung cancer datasets, and the proposed HAPSO-FFA-WNN model achieved 97.58% accuracy for dataset 1 and 98.54% accuracy for dataset 2. Furthermore, the proposed model achieved better precision, recall, and MCC performance metrics.
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    A NEW HYBRID ADAPTIVE OPTIMIZATION ALGORITHM BASED WAVELET NEURAL NETWORK FOR SEVERITY LEVEL PREDICTION FOR LUNG CANCER DATASET
    (Intelligent Network and Systems Society, 2024) Divya, T; Gripsy J, Viji
    This study proposes three contributions focused on lung cancer detection and severity level identification. The absence of non-invasive technologies for predicting lung cancer necessitates faster, more efficient, and more accurate classification procedures due to the absence of non-invasive technologies for predicting lung cancer. Creating an automated and intelligent prediction system is crucial for identifying phases and predicting the possibility of a recurrence. The objective is to create an automated detection system for identifying lung cancer using an optimizationfocused deep learning model. We develop an adaptive multi-swarm PSO and combine it with the firefly algorithm to determine the ideal weight values for the Wavelet Neural Network (WNN) model. We use the HAPSO-FFA-WNN method to explore problems with multiple optimal solutions. This study evaluated two lung cancer datasets, and the proposed HAPSO-FFA-WNN model achieved 97.58% accuracy for dataset 1 and 98.54% accuracy for dataset 2. Furthermore, the proposed model achieved better precision, recall, and MCC performance metrics.
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    GRAPH CONVOLUTIONAL NEURAL NETWORK FOR IC50 PREDICTION MODEL USING AMYOTROPHIC LATERAL SCLEROSIS TARGETS
    (Springer Link, 2024-02-25) Devipriya, S; Vijaya, M.S
    IC50 prediction for neurodegenerative disorders like amyotrophic lateral sclerosis is crucial in biomedical studies. Traditional machine learning models use molecular descriptors for IC50 prediction where most of the descriptors created by different tools are irrelevant and undefined. Hence, graph convolutional neural network, a deep learning algorithm is used in this paper for building more accurate IC50 prediction model based on the structural properties of drug molecules in graph format. The work is implemented with 32 protein targets of amyotrophic lateral sclerosis disorder. IC50 prediction is made by collecting canonical SMILES and their corresponding IC50 values of 2100 drugs from the ChEMBL databases. Featurization and conversion of SMILES to molecular graphs are done by the Deepchem library. The library is used for dataset creation and model building. The results show that the proposed GCNN model with their fine-tuned hyperparameters achieves a prediction rate of 73%.
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    GRAPH CONVOLUTIONAL NEURAL NETWORK FOR IC50 PREDICTION MODEL WITH DRUG SMILES GRAPHS AND GENE EXPRESSIONS OF AMYOTROPHIC LATERAL SCLEROSIS
    (Journal of Theoretical and Applied Information Technology, 2024-01) Devipriya, S; Vijaya, M S
    IC50 prediction for neurodegenerative disorders like Amyotrophic Lateral Sclerosis is crucial in biomedical studies. Traditional machine learning models that use molecular descriptors and gene expression for building IC50 prediction models produce less accuracy and also most of the descriptors created by different tools are irrelevant and undefined. In this paper, a Graph Convolutional Neural Network, a deep learning algorithm, is employed for constructing a more precise IC50 prediction model. The model leverages the structural properties of drug molecules represented in graph format, and incorporates gene expression data as global features. So, the model is able to learn drug-gene interactions better. The drug-gene interactivity is learned by the model without drug-induced gene expressions as it is not found for most of the diseases. The work is implemented with well-known and most relevant 80 drugs related to ALS based on the pIC50 values of 32 protein targets of ALS disorder. The Canonical Smiles graph and their corresponding IC50 values of 80 drugs have been derived from the ChEMBL databases. Based on information from the Repurposing Hub in the Depmap database gene expression data for drug-related genes connected with ALS-related conditions is collected. The predictive results show that the proposed GCNN model with fine-tuned hyperparameters achieves MAE of 0.18, RMSE of 0.16 and R2 Score of 0.90.
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    TEMPORAL FUSION TRANSFORMER: A DEEP LEARNING APPROACH FOR MODELING AND FORECASTING RIVER WATER QUALITY INDEX
    (International Journal of Intelligent Systems and Applications in Engineering, 2023-07-23) Jitha P, Nair; Vijaya, M S
    Water quality is a major factor when it comes to human and environmental health. The WQI is a key performance indicator for water management effectiveness. Water quality changes over time due to several seasonal attributes and physiochemical properties. Asthe seasons change at each site, the weather records are transformed into time series data, and the values of the physiochemical parameters shift accordingly. This paper introduces a novel temporal fusion transformer architecture for modelling and forecasting river water quality index. The WQI prediction model for the Bhavani River utilizes the temporal fusion transformer to incorporate temporal features fromvarious scales of time series data obtained from monitoring stations.The performance results of the study are compared with other existing prediction models and demonstrated the effectiveness of the temporal fusion transformer approach for modelling and forecasting river water quality.
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    GATED RECURRENT NEURAL NETWORK FOR AUTISM SPECTRUM DISORDER GENE PREDICTION
    (International Journal on Emerging Technologies, 2020) Pream Sudha, V; Vijaya, M S
    Autism Spectrum Disorder (ASD) is the fastest-growing complex disorder and the genetic ground of this comprehensive developmental disability is very difficult to research. Autism diagnosis for an average child is not done till the age of four, though it can be given at the age of 18 months to two years. Hence a computational model that enables the early diagnosis and personalized treatment is the need of the hour. In this research work, a deep learning based approach is proposed for Autism Spectrum Disorder (ASD) gene prediction. There are various contributors for Autism including genes, mutations, chromosomal settings influence of the environment, prenatal influences, family factors and problems during birth. Recurrent Neural Network (RNN) based Gated Recurrent Units (GRU) are implemented to develop a model that predicts ASD genes, mutations and gene susceptibility. GRUs with their internal memory capability are valuable to store and filter information using the update and reset gates. Also GRU offers a powerful tool to handle sequence data. The model is trained using three simulated datasets with features representing genes, mutations and gene susceptibility to ASD. Besides, the proposed approach is compared to original RNN and Long Short Term Memory Units (LSTM) for ASD prediction. The experimental results confirm that the proposed approach is promising with 82.5% accuracy and hence GRU RNN is found to be effective for ASD gene prediction.
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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.