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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Hanumanthaiah, Asha Kethaganahalli
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Computer Science & IT Electrical & Electronics Engineering

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Efficient intelligent crawler for hamming distance based on prioritization of web documents Dange, Amol Subhash; Byranahalli Eraiah, Manjunath Swamy; Rao, Manju More Eshwar; Hanumanthaiah, Asha Kethaganahalli; Ganganayaka, Sunil Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1948-1958

Abstract

Search engines play a crucial role in today's Internet landscape, especially with the exponential increase in data storage. Ranking models are used in search engines to locate relevant pages and rank them in decreasing order of relevance. They are an integral component of a search engine. The offline gathering of the document is crucial for providing the user with more accurate and pertinent findings. With the web’s ongoing expansions, the number of documents that need to be crawled has grown enormously. It is crucial to wisely prioritize the documents that need to be crawled in each iteration for any academic or mid-level organization because the resources for continuous crawling are fixed. The advantages of prioritization are implemented by algorithms designed to operate with the existing crawling pipeline. To avoid becoming the bottleneck in pipeline, these algorithms must be fast and efficient. A highly efficient and intelligent web crawler has been developed, which employs the hamming distance method for prioritizing the pages to be downloaded in each iteration. This cutting-edge search engine is specifically designed to make the crawling process more streamlined and effective. When compared with other existing methods, the implemented hamming distance method achieves a high value of 99.8% accuracy.
Detection of colorization based image forgeries using convolutional autoencoder method Panchal, Soumyashree Muralidhar; Hanumanthaiah, Asha Kethaganahalli; Doddasiddavanahalli, Bindushree Channabasavaraju; Eshwar Rao, Manju More; Jayaramu, Ambika Belekere
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1114-1126

Abstract

Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H‑DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods.
Co-Authors Byranahalli Eraiah, Manjunath Swamy Dange, Amol Subhash Doddasiddavanahalli, Bindushree Channabasavaraju Eshwar Rao, Manju More Ganganayaka, Sunil Kumar Jayaramu, Ambika Belekere Panchal, Soumyashree Muralidhar Rao, Manju More Eshwar