Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (dl) and ...
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Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (dl) and machine learning-based approach is used in this study for passive image forgery detection. A dl algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.
For effective measurement and collection of soil moisture information an electronic system has been designed and developed. Since soil water holding capacity varies depending on the soil structure so a sensor probe is...
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For effective measurement and collection of soil moisture information an electronic system has been designed and developed. Since soil water holding capacity varies depending on the soil structure so a sensor probe is designed with a sensing system for efficient detection of moisture in the soil. The capacitive sensor probe is designed to detect a change of capacitance response from 15 to 1200pF. According to this change of capacitance response, a series RLC network is designed to find out an optimum frequency for efficient sensing of moisture. The designed sensor probe is calibrated for three different types of soil (silt/sandy/clay) using Thermogravimetric (TG) Analysis. To process the sensing data, DataLogger (dl) algorithm has been developed and programmed in an ARM controller for monitoring and storing of data on server using IoT (Internet of Things). For the designed sensing circuit, an optimum frequency of 218 kHz is found out from the resonance curve. At this frequency, the response of electronic interfacing circuit is observed in terms of capacitance and voltage. Data obtained from linear analysis (R square: similar to 0.99, Pearson's r: similar to 0.99) shows that the designed sensor system responds appreciably to the moisture change.
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