An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
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Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
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Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
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Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
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Review on Data Securing Techniques for Internet of Medical Things
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Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
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Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
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A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
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An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
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Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
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Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
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Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
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Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 4 | Issue - 2 | june 2022
Published
25 July, 2022
The detection of video piracy has improved and emerged as a popular issue in the field of digital video copyright protection because a sequence of videos often comprises a huge amount of data. The major difficulty in achieving efficient and simple video copy detection is to identify compressed and exclusionary video characteristics. To do this, we describe a video copy detection strategy that created the properties for a spatial-temporal domain. The first step is to separate each video sequence into the individual video frame, and then extract the boundaries of each video frame by using PCA SIFT and Hessian- Laplace. Next, for each video frame, we have to implement SVM and KNN features in the spatial and temporal domains to measure their performance matrices in the feature extraction. Finally, the global features found in the Video copy detection are accomplished uniquely and efficiently. Experiments arranged a commonly used VCDB 2014 video dataset, showing that result. The proposed approach is based on various copy detection algorithms and shows various features in terms of both accuracy and efficiency.
KeywordsFeature extraction video copy detection copyright protection video security SVM KNN and video plagiarism detection
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