Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Logistic regression

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    Prevention of webshell attack using machine learning techniques
    (Grenze Scientific Society, 2021) Satish, Y.C.; Naik, P.M.; Rudra, B.
    Webshell is a web vulnerability and a security threat to any user or a server that can be accessed by attackers to control our system. And also, they may use our system as a command control device to attack other systems. It is difficult to monitor and identify such threats because attackers always tried to attack in different methods and new technologies. However, we can detect the webshell with Machine Learning Techniques with better accuracy; all we need is more number of samples. With this project, we presented a PHP based webshell detecting model. We used different ML algorithms: Logistic Regression(LR), Random Forest(RF), Support Vector Machine(SVM) and K-Nearest Neighbour(KNN). Addition to this PHP file's standard statistical features, we also added an opcode sequence from the PHP files, consisting of the TF-IDF Vector and the Hash Vector. Depending upon these features, we trained with different machine learning models(SVM, RF, LR, KNN). In these models, we got better results with Random Forest having an accuracy of 96.45\% with a false-positive rate of 3.5\%, which is good results compared to several popular detection techniques. © Grenze Scientific Society, 2021.
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    Enhanced Medicare Fraud Detection Using Graph Convolutional Networks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Rakesh, M.; Shetty D, P.
    This paper explores the applications of Graph neural networks (GNN) for enhancing Medicare fraud detection. Graph convolutional network (GCN) is a type of graph neural network. Governments and insurance companies are continuously adapting new technologies to detect and prevent fraud activities and trying to minimize financial losses and improve services because every year they lose billions of dollars due to Medicare fraud. Machine learning algorithms fail to analyze the graph data structure but Graph neural networks are good at analyzing the complex relational data and they directly integrate with the learning process. Machine learning algorithms are facing scalability and generalization across diverse graphs. GNN works on graph data structure, using unique IDs as nodes in a graph, with edges illustrating their relationships. Graph Neural Networks is used to improve the accuracy and efficiency of fraud detection by learning the complex relational information obtained from providers, beneficiaries, and physicians. We created a graph database based on the healthcare provider dataset. In this graph database, two types of heterogeneous nodes are there that are beneficiary and medicare provider nodes. The connection between the beneficiary and medicare providers is a power edge and the connection between providers is a shared-physician edge. We developed a fraud detection model using both machine learning and graph neural networks. Our Graph convolutional Network (GCN) model performed well compared to the basic machine learning (Logistic regression) model. The complex relationships between provider and beneficiary, provider and physician helped to detect medicare fraud using our model. © 2024 IEEE.
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    Accuracy Comparison of Logistic Regression and Decision Tree Prediction Models Using Machine Learning Technique
    (Springer Science and Business Media Deutschland GmbH, 2025) Tantri, B.R.; Bhat, S.
    With the advancements in data science and machine learning, it has become beneficial for scientists, technologists, social scientists, and businessmen to adopt the latest developments in machine learning into their domains to make important decisions about their problems of interest. The biggest advantage of machine learning algorithms in such fields is their prediction capability. Statistical tools in powerful machine-learning languages like R have led to simpler solutions to more complex problems. Various models are in use in the process of making decisions and predictions. The most commonly used model in many situations is the regression model. Herein, it is intended to use the logistic regression model and the decision tree model in the prediction of binary categorical variables. R programming is used in the development of these prediction models. It is intended to compare the accuracy of the two models by using the confusion matrices. Two different datasets have been used for the prediction using these models and their comparisons. It has been observed that prediction using a decision tree model has a better accuracy as compared to that of a logistic regression model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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    Autism Spectrum Disorder Detection Using Machine Learning Techniques
    (Springer Science and Business Media Deutschland GmbH, 2025) Shetty, S.; Shetty, S.; Saranya, P.
    A developmental disease called autism spectrum disorder (ASD) greatly reduces a patient's capacity for social interaction and communication in everyday situations. Using various machine learning strategies, necessitating (KNN)-K Nearest Neighbors, (LR)-Logistic Regression, (DT)-Decision Tree Classifier, (RF)-Random Forest Classifier, and (SVM)-Support Vector Machine. Using data taken from potential ASD patients’ medical records, a strong machine learning-driven strategy for autism detection is established. The ASD dataset, which is accessible to the public, is used to assess the suggested methods. There are 800 cases and 22 distinct attributes in the ASD screening dataset. The framework involves data collection, data visualization, data preprocessing, and implementation of machine learning model. A machine learning-based ASD detection system using logistic regression aims to determine if an individual has ASD or not in accordance with relevant features and behavioral patterns. Testing results are evaluated based on the performance metrics and the proposed system utilizes Logistic Regression which yields 0.85 accuracy, 0.78 precision, 0.76 recall, and 0.77 F1 score following comparison with the other models of machine learning. The suggested framework for ASD detection greatly streamlines and expedites the process of diagnosing ASDs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.