Conference Papers
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Item Text document analysis using map-reduce framework(Springer Verlag service@springer.de, 2018) Kanimozhi, K.V.; Prabhavathy, P.; Venkatesan, M.Due to the advance Internet and increasing globalization, the electronics forms of information grow in a rapid manner. Extracting the useful hidden information from those multiple documents is a recent challenge. Hence, efficient and automated clustering algorithm which is effective in identifying topics plays the main role in information retrieval. In this paper, the analysis regarding the large unstructured text document corpus using our proposed map-reduce algorithm has been performed, and the results show the advantage of the proposed method by detecting clusters of document features within less computation time and provides premier solution for increasing the precision rate of retrieval in information extraction. © 2018, Springer Nature Singapore Pte Ltd.Item Graph based Unsupervised Learning Methods for Edge and Node Anomaly Detection in Social Network(Institute of Electrical and Electronics Engineers Inc., 2019) Venkatesan, M.; Prabhavathy, P.In the last decade online social networks analysis has become an interesting area of research for researchers, to study and analyze the activities of users using which the user interaction pattern can be identified and capture any anomalies within an user community. Detecting such users can help in identifying malicious individuals such as automated bots, fake accounts, spammers, sexual predators, and fraudsters. An anomaly (outliers, deviant patterns, exceptions, abnormal data points, malicious user) is an important task in social network analysis. The major hurdle in social networks anomaly detection is to identify irregular patterns in data that behaves significantly different from regular patterns. The focus of this paper is to propose graph based unsupervised machine learning methods for edge anomaly and node anomaly detection in social network data. © 2019 IEEE.Item Unsupervised learning method for mineral identification from hyperspectral data(Springer, 2021) Prabhavathy, P.; Tripathy, B.K.; Venkatesan, M.Hyperspectral imagery is one of the research area in the field of Remote sensing. Hyperspectral sensors record reflectance (also called spectra signature) of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of Remote sensing of Hyperspectral data. EO-1 hyperion dataset is unlabeled data. Various types of clustering algorithms are proposed to identify minerals. In this work principal component analysis is used to reduced it’s dimension by reducing bands. Hard-clustering and soft-clustering algorithms are applied on given data to classify the minerals into classes. K-means is hard type of clustering which classify only non-overlapping cluster however, PFCM is soft type of clustering which allow a data points to belongs more than one cluster. Further, results are compared using cluster validity index using DBI value. Both clustering algorithms are experiments on original HSI image and reduced bands. Result shows that PFCM is perform better than K-means for the both type of images. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.Item Deep Learning-Based Prediction, Classification, Clustering Models for Time Series Analysis: A Systematic Review(Springer Science and Business Media Deutschland GmbH, 2022) Naik, N.N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.Analysis of time series is a prominent issue in the field of data analysis. With large amount of existing data in time series, multiple algorithms for analyzing time series data are being proposed. A variety of deep learning models are being designed to enhance the diversity of datasets related to time series across different fields. In comparison with the existing methods, only few have incorporated deep neural networks to perform this task. In most of the cases, deep neural networks are being applied for image data but it can also be used for sequential data such as text and audio. Here, we throw light on the recent advancements in hybrid deep learning models which consist of combination of various frameworks of deep neural networks with statistical models that have led to an improvement in time series analysis. Deep learning models are categorized into discriminative, and generative models provide an insight into the data based on the perception of conditional or joint probability. In this paper, we have surveyed newly devised algorithms and limitations of prediction, classification and clustering for time series analysis which describes how the temporal information can be merged into the analysis of the time series data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Is Data Science and Blockchain a Perfect Match?(Springer Science and Business Media Deutschland GmbH, 2023) Swathi, P.; Venkatesan, M.; Prabhavathy, P.Blockchain technology can help with IoT, digital transaction records, and supply chain management. Enterprises over the world are focusing more on the innovative work of blockchain technology. Blockchain innovation permits the end-client to do exchanges with each other without the association of the middle man. Any benefits from cash to motion pictures can be moved, put away, executed, oversaw, and traded with no intermediaries. Data science, as we know it, is the science to extract valuable insights and information from both structured and unstructured data to solve real-world problems. With a lot of advantages and challenges, blockchain and data science can end up being an amazing blend to oversee information amount with quality productively. In this paper, we are investigating the new aspect of mixing data science and blockchain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Attention-Based Bitemporal Image Deep Feature-Level Change Detection for High Resolution Imagery(Springer Science and Business Media Deutschland GmbH, 2023) Naik, N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.To understand the intricacy of changes on the surface of the land, change detection is an important field in the area of remote sensing. Bitemporal remote sensing images are resourceful information to perform the analysis related to classification and change detection. Most of the architectures proposed for improving the performance of change detection in high resolution images pose a challenge due to composite texture features and finer image details. In this paper, we propose a change detection approach for bitemporal images using supervised learning. Firstly, extraction of the features is performed using a pretrained neural network. Then, the extracted features are provided to a (DSDEN) deep supervised-based difference evaluation network. Then, channel and spatial-based attention components are incorporated for fusing the difference image features with the deep features of raw images for the reconstruction of the final change map. The experimental evaluation on public “LEVIR-CD†dataset demonstrates the effectiveness and superiority over traditional methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Ontology for Contextual Fake News Assessment Based on Text and Images(Institute of Electrical and Electronics Engineers Inc., 2024) Chandrasekaran, K.; Kandasamy, A.; Venkatesan, M.; Prabhavathy, P.; Gokuldhev, M.; Aishwarya, C.The spread of false news on social networks is a major challenge in the digital age across various sectors, encompassing technology, politics, public health, and finance. This paper introduces an ontology-based method that combines text and image analysis to evaluate the accuracy of news stories in the context of social media. We investigate the role of social engineering tactics in crafting and dispersing fake news and advocate for a comprehensive multi-contextual perspective that covers content, source, social media, psychological, and impact aspects. Using OWL (Web Ontology Language), we present an ontology framework for assessing fake news, providing a structured approach to analyze text, visuals, audio, audience behavior, source credibility, and news propagation patterns. This framework serves as a foundation for advanced detection systems, contributing to the fight against digital misinformation. © 2024 IEEE.Item Detecting Fake News: A Comparative Evaluation of Machine Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2024) Aishwarya, C.; Venkatesan, M.; Prabhavathy, P.; Shetty, A.S.Fake news is a significant and well-acknowledged problem in contemporary society due to its rapid spread via social media and various online networking platforms, thereby making it difficult to determine the validity of information. In this study, we examine literature for this issue, prevalent datasets like LIAR, Politifact, and COVID-19, as well as classical machine learning and deep learning models such as SVM, BiLSTM, and CNN- BiGRU for fake news detection, and analyze their effectiveness and scope of application for fake news detection. © 2024 IEEE.Item Grapevine SDLC Model for Real-Time Fake News Classification(Springer Science and Business Media Deutschland GmbH, 2025) Aishwarya, C.; Shekokar, T.P.; Naga Mukesh, K.; Venkatesan, M.; Prabhavathy, P.In an era of rapid information distribution, the presence of fake news presents enormous difficulties to society, influencing public opinion and decision-making on a global scale. To address this issue, a reliable and efficient system capable of detecting and classifying fake news in real time must be developed. This project proposes the design and implementation of a specialized Software Development Life Cycle (SDLC) model, called the Grapevine SDLC, specifically designed for developing a real-time fake news classifier using Large Language Models (LLMs) and Apache Kafka. The Grapevine SDLC takes a methodical, iterative approach, starting with a thorough requirements analysis that identifies both system capabilities and limitations. During the design and development phase, the system architecture is crafted with a focus on scalability and real-time processing, integrating LLMs for highly accurate content analysis and categorization. Kafka’s distributed messaging platform ensures seamless and efficient data streaming, enabling the system to handle large volumes of data in real time. Further, the model includes continuous monitoring and feedback loops to improve detection accuracy and adapt to evolving fake news patterns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.