Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Video tamper detection techniques based on DCT-SVD and multi-level SVD(Institute of Electrical and Electronics Engineers Inc., 2016) Dabhade, A.V.; Bhople, Y.J.; Chandrasekaran, K.; Bhattacharya, S.The videos are widely used nowadays for different purposes. But due to easily available software tools, it has become very easy to modify the videos. The videos sent from one end to other can be tampered maliciously in between. The frames in the video can be edited or the sequence of the frames can be altered or even some frames can be deleted, any such malicious alteration is possible. Thus it is very necessary to verify integrity of video data to ensure trustworthiness of the information content. In many cases, such as surveillance, medical, forensic investigations it is necessary to consider authenticity of the video. If there is any tampering in the video, it must be detected. Therefore, there is need to do some work for developing such tamper detection system so that information in a video can be verified. In this paper we propose mechanisms for detecting any such tampering in a video. © 2015 IEEE.Item Performance evaluation of dimensionality reduction techniques on high dimensional data(Institute of Electrical and Electronics Engineers Inc., 2019) Vikram, M.; Pavan, R.; Dineshbhai, N.D.; Mohan, B.R.With a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency. ©2019 IEEE.Item A Hybrid Approach to Predict Ratings for Book Recommendation System Using Machine Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2024) Roy, T.; Shetty D, P.A recommender system is a tool that suggests products or services to users based on their preferences and past behavior, enhancing user satisfaction and engagement. Accurate rating prediction is crucial as it directly impacts the system's ability to provide relevant and personalized recommendations, thereby improving the overall user experience. In this study, we introduce an innovative approach to recommendation systems by proposing an Weighted Hybrid Model that combines an Adaptive K-Nearest Neighbors (AKNN) algorithm and Singular Value Decomposition (SVD). The AKNN algorithm dynamically adjusts the number of neighbors based on user rating density, providing a tailored neighborhood size for each user. By incorporating a hybrid similarity measure that combines cosine similarity, Pearson correlation, and Variance Mean Difference (VMD), our AKNN algorithm effectively captures the multifaceted nature of user-item relationships. We further enhance our recommendation model by combining AKNN with SVD through optimized weighting, creating a Weighted Hybrid Model. This model balances the contributions of the AKNN and SVD components, leveraging the strengths of both approaches to minimize prediction errors. Our evaluation results demonstrate that the Weighted Hybrid Model outperforms several algorithms, including standalone KNN with Z-Score, Item-wise Variance-Mean based Recommender System (IVMRS), KNN with RJAC DUB, and Pearson Baseline with Weighted KNN. The Weighted Hybrid Model achieved the lowest Root Mean Squared Error (RMSE) of 1.54491 and Mean Absolute Error (MAE) of 1.17839, indicating superior predictive accuracy. © 2024 IEEE.