Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
4 results
Search Results
Item Machine learning approach to manage adaptive push notifications for improving user experience(Association for Computing Machinery, 2020) Madhusoodanan, A.; Anand Kumar, M.; Fraser, K.; Yousuf, B.In this modern connected world mobile phone users receive a lot of notifications. Many of the notifications are useful but several cause unwanted distractions and stress. Managing notifications is a challenging task with the large influx of notifications users receive on a daily basis. This paper proposes a machine learning approach for notification management based upon the context of the user and his/her interactions with the mobile device. Since the proposed idea is to generate personalised notifications there is no ground truth data hence performance metrics such as accuracy cannot be used. The proposed solution measures the diversity score, the click through rate score and the enticement score. © 2020 ACM.Item Predictive Intelligent System Development for Disease Classification in Diagnostic Applications(Springer Science and Business Media Deutschland GmbH, 2024) Shrivathsa, T.V.; Rao, S.S.; Karanth, P.N.; Adiga, K.; Mahabala, M.; Dakappa, P.H.; Prasad, K.With ever increasing explosion in information domain and demand for highest accuracy in medical diagnosis, the existence of a reliable, accurate prediction system is the need of the hour. In this work, an effective prediction system has been developed for accurate classification of undifferentiated ailments using a unique approach. Prediction of undifferentiated diseases at an early stage always helps in better diagnosis. Illnesses like tuberculosis, non-tubercular bacterial infection, dengue fever, non-infectious diseases have regular manifestation of fever. In present work, the uniqueness lies in the use of only temperature data of the patient being referred in predicting the nature of fever, with highest degree of accuracy, instead of several self-defined parameters over limited interval of time. The system has been developed based on artificial intelligent technique, and optimization has been achieved by assessing the performance of different classifiers available. Using prediction model with classifiers, decision can take over comparative results between different classifier algorithms. A result of predictive system defines the combination of good classifier and system developed. Accuracy score and other salient parameters describe the complete picture of the system. Predictive model development in this work proved to be one of the best assistant tools to a doctor to take call over the disease crucial period. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item An Approach for Integrating Behavioral Analytics and Machine Learning for Enhanced Cybersecurity(Institute of Electrical and Electronics Engineers Inc., 2024) Shivappa, P.K.; Shetty D, P.Data breaches and cyber threats have evolved into increasingly complex and stealthy forms. Conventional anomaly detection based on rules is ineffective in identifying numerous contemporary attacks. Hence, User Behavior Analysis is performed on the network traffic flow data to comprehend, model, and forecast users' actions. Nevertheless, the diversity of the methods makes their understanding exceedingly complex. Therefore, domain experts use machine learning (ML) to accomplish their goals. Thus, this paper aims to suggest an innovative architecture that can detect anomalies in the network traffic flow by analyzing user behavior. The two different sets of data are used for two-class and four-class classification. Both the data are pre-processed for duplicates, missing values, and performing encoding techniques. The correlation analysis is performed to understand the user's behavior before training the ML models. The four different ML algorithms, like Logistic regression LR, KNN, DT, and RF algorithms are applied to the pre-processed datasets. The Random Forest algorithm outperforms by achieving 100% accuracy on two- and four-class classification. The described behavioral modeling approach updates cyber threat detection to match the needs of the modern, ever-changing threat landscape. © 2024 IEEE.Item 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.