B-Scopus
Permanent URI for this communityhttps://dspace.psgrkcw.com/handle/123456789/3730
Browse
65 results
Search Results
Item EFFICIENT PREDICTION OF PHISHING WEBSITES USING SUPERVISED LEARNING ALGORITHMS (Conference Paper)(Elsevier B.V, 2012-12-09) Lakshmi V, Santhana; Vijaya, M SPhishing is one of the luring techniques used by phishing artist in the intention of exploiting the personal details of unsuspected users. Phishing website is a mock website that looks similar in appearance but different in destination. The unsuspected users post their data thinking that these websites come from trusted financial institutions. Several antiphishing techniques emerge continuously but phishers come with new technique by breaking all the antiphishing mechanisms. Hence there is a need for efficient mechanism for the prediction of phishing website. This paper employs Machine-learning technique for modelling the prediction task and supervised learning algorithms namely Multi layer perceptron, Decision tree induction and Naïve bayes classification are used for exploring the results. It has been observed that the decision tree classifier predicts the phishing website more accurately when comparing to other learning algorithms.Item AIR QUALITY INDEX PREDICTION MODEL USING TEMPORAL FUSION TRANSFORMER(International Journal of Intelligent Systems and Applications in Engineering, 2024-02-02) Santhana Lakshmi, V; Vijaya, M SAir pollution’ emerges as a substantial universal concern with far-reaching consequences for people health, affecting numerous persons worldwide. Its adverse effects encompass various respiratory and cardiovascular issues. The Air Quality Index (AQI) serves as a numeric gauge for evaluating air quality, furnishing details about pollutant levels like particulate matter, ammonia, carbon monoxide, NO2, ozone and SO2. The anticipation of AQI proves instrumental in empowering individuals and communities to undertake precautionary measures against the detrimental impacts of air pollution. Leveraging deep learning for AQI prediction becomes imperative. Positioned within machine learning, deep learning employs artificial neural networks as a potent tool to address complex challenges. This study employs an attention-based Arcane Neural Web, specifically the TFT, for constructing the estimating model. The model's efficacy is then juxtaposed with other deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Fenced Repeated UnitItem 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 SIC50 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.Item AN INTELLIGENT DEEP LEARNING BASED AQI PREDICTION MODEL WITH POOLED FEATURES(Journal of Theoretical and Applied Information Technology, 2024-01-15) Lakshmi Santhana, V; Vijaya, M SAirborne pollution poses a significant threat to public health, leading to detrimental health effects. Despite global economicgrowth, ensuring access to clean air has become increasingly challenging worldwide. The contamination of air occurs as dust particlesand smoke, released by vehicles and industries, suspend into the atmosphere, exacerbating the challenge of providing clean air forpeople. Hence, it is imperative to predict the Air Quality Index (AQI) to safeguard the lives of people, especially considering the severe health effects caused by the inhalation of small particles. This paper outlines a deep learning methodology for constructing Air Quality Index (AQI) prediction models. The models utilize hourly meteorological data and pollutant information, aiming to fulfill the critical requirement for precise assessments of air quality. The aim of this paper is to formulate predictive models for AQI in Thiruvananthapuram, Kerala, employing deep learning algorithms, thereby addressing the escalating challenge of air pollution in theregion. Deep neural network architectures, such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM), and Gated Recurrent Unit (GRU), are implemented to construct the prediction model. When compared to other algorithms,GRU demonstrated promising outcomes. The findings of this research contribute not only to the advancement of AQI prediction models but also highlight the practical significance of employing deep learning techniques for accurate and timely air quality assessments. The outcomes have practical implications for public health and environmental management, providing a basis for informed decision-making in mitigating the adverse effects of air pollution.Item SUPPORT VECTOR MACHINE BASED EPILEPSY PREDICTION USING TEXTURAL FEATURES OF MRI(Elsevier Ltd, 2010) Sujitha, V; Sivagami, P; Vijaya, M SEpilepsy is a disorder of the central nervous system, specifically the brain. It is a neurological malfunction affecting about 1% of the population and is the third most common neurological disorder following rheumatic heart disease and Alzheimer’s disease, but it imposes higher costs on society. Magnetic Resonance Imaging (MRI) is one of the most common diagnostic tests used for patients for epilepsy prediction. Shortage of radiologists and the large volume of MRI scan images that need to be analyzed may lead to labor intensive, expensive and inaccurate prediction. Hence there is a need to generate an efficient prediction model for making a correct diagnosis of epilepsy and accurate prediction of its type. This paper describes the modeling of epilepsy prediction using Support Vector Machines (SVM), a machine learning algorithm. The prediction model has been generated by training the support vector machine with descriptive features derived from MRI data of 350 patients and observed that the SVM based model with a Radial Basis Function (RBF) kernel produces 93.87% of prediction accuracy.Item CLASSIFICATION OF SEED COTTON YIELD BASED ON THE GROWTH STAGES OF COTTON CROP USING MACHINE LEARNING TECHNIQUES(IEEE Xplore, 2010-07-29) Jamuna, K S; Karpagavalli, S; Vijaya, M S; Revathi, P; Gokilavani, S; Madhiya, ECotton, popularly known as "White Gold" has been an important commercial crop of national significance due to the immense influence of its rural economy. Cotton seed is an important and critical link in the chain of agricultural activities extending farmer industry linkage. Cotton yield is associated with high quality seed as the seed contains in itself the blue print for the agrarian prosperity in incipient form. Transfer of technology to identify the quality of seeds is gaining importance. Hence this work employs machine learning approach to classify the quality of seeds based on the different growth stages of the cotton crop. Machine learning techniques - Naïve Bayes Classifier, Decision Tree Classifier and Multilayer Perceptron were applied for training the model. Features are extracted from a set of 900 records of different categories to facilitate training and implementation. The performance of the model was evaluated using 10 -fold cross validation. The results obtained show that Decision Tree Classifier and Multilayer Perceptron provides the same accuracy in classifying the seed cotton yield. The time taken to build the model is higher in Multilayer Perceptron as compared to the Decision Tree Classifier.Item ELECTROENCEPHALOGRAM WAVE SIGNAL ANALYSIS AND EPILEPTIC SEIZURE PREDICTION USING SUPERVISED CLASSIFICATION APPROACH(ACM Digital Library, 2010-09-16) Devi S. T, Pavithra; Vijaya, M SThe transient and unexpected electrical disturbances of the brain results in an acute disease called Epileptic seizures. A significant way for identifying and analyzing epileptic seizure activity in human is by using electroencephalogram (EEG) signal. Manually reviewing and analyzing lengthy data of EEG recordings, for detection and classification of electro graphical patterns at present requires trained personnel and time consuming. Hence, there is a need for an efficient automated system based on pattern classification for analysis and classification of seizure-related EEG signals to assist the expert in the diagnosis. This paper presents the modeling of epileptic seizure prediction as binary classification problem and provides a suitable solution by implementing supervised classification algorithms, namely Decision table, Naive Baye's Tree, k-NN and support vector machine. The classification models are trained using the EEG data sets and the prediction accuracy of the classifier has been evaluated using 10-fold cross validation. It has been observed that the model produce about 86% of prediction accuracy in predicting the presence of epileptic seizure in human brain.Item REVOLUTIONIZING FOETAL CARDIAC ANOMALY DIAGNOSIS: UNLEASHING THE POWER OF DEEP LEARNING ON FOETALECHO IMAGES(Journal of Theoretical and Applied Information Technology, 2023) Divya, M O; Vijaya, M SThe use of Artificial Intelligence (AI) has amplified in various fields, with remarkable results in medicine in recent times. Despite the potential of AI in the medical field, there are still many unexplored areas due to data unavailability. One such area is cardiac foetal anomaly diagnosis, which is poorly diagnosed globally with a rate of only 50%. The complexity of the task requires a high level of expertise to understand minute hints and conduct thorough exams for accurate image captures. In this research, the FoetalEcho_V01 dataset was used for foetal cardiac anomaly diagnosis, consisting of pre-classified ultrasound images representing 15 different anomalies and a class representing normal heart images. The deep learning models which are efficient in producing potential classifiers for ultra sound scan images are identified. The models are CNN, AlexNet, VGG16 and ResNet50. The best performing deep learning models were used to produce classifiers, and their performance was evaluated. The results showed that the deep learning models performed well on the FoetalEcho_V01 dataset images for diagnosing structural cardiac anomalies in the foetus, with consistent performance as demonstrated by the calculated standard deviation. The results obtained from the research for the FetalEcho_V05 dataset are as follows. The CNN model achieved a precision of 0.94, recall of 0.89, accuracy of 0.90, and F1 score of 0.91. Comparatively, the AlexNet model demonstrated a precision of 0.92, recall of 0.87, accuracy of 0.89, and F1 score of 0.89. The VGG16 model exhibited precision of 0.91, recall of 0.85, accuracy of 0.87, and F1 score of 0.88. Lastly, the ResNet50 model displayed a precision of 0.93, recall of 0.90, accuracy of 0.93, and F1 score of 0.93. Among these models, the CNN model emerged as the best classifier for the FetalEcho_V05 dataset, with its superior performance in terms of precision, recall, accuracy, and F1 score.Item 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 SWater 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.Item ARTIFICIAL INTELLIGENT MODELS FOR AUTOMATIC DIAGNOSIS OF FOETAL CARDIAC ANOMALIES: A META-ANALYSIS(Springer Link, 2023-01-01) Divya, M O; Vijaya, M SThe 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.