Authors: Sirajudeen, Mohamed | Anitha, R.
Article Type: Research Article
Abstract: Manually verifying the authenticity of the physical documents (personal identity card, certificates, passports, legal documents) increases the administrative overhead and takes a lot of time. Later image processing techniques were used. But most of the image processing based forgery document detection methods are less accurate. To improve the accuracy, this paper proposes an automatic document verification model using Convolutional Neural Networks (CNN). Furthermore, we use Optical Character Recognition (OCR) and Linear Binary Pattern (LBP) to extract the textual information and regional edges from the documents. Later, Oriented fast and Rotated Brief (ORB) is used to extract the images from the …scanned documents. To train the CNN, MIDV-500 dataset of 256 Azerbaijani passport images, each with the size of 1040*744 pixels is taken. The proposed CNN model uses sliding window operations layers to evaluate the authenticity. The proposed model analyzes both the textual authenticity and image (seal, stamp and hologram) authenticity of the scanned document. The experimental analysis is carried out on the TensorFlow using python programming language. The results derived from the proposed CNN based forgery detection model is compared with existing models and the results are promising to implement on the real time applications Show more
Keywords: Document verification, convolution neural network, forgery document detection, cognitive document processing
DOI: 10.3233/JIFS-189128
Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8057-8068, 2020
Authors: Muthunagai, S.U. | Anitha, R.
Article Type: Research Article
Abstract: As a result of the advancements in Industry 4.0, the amount of data collected within industries are continuously expanding to achieve an innovative environment within the industry by maximizing asset usage. Meanwhile, the redundancy rate is increasing in cloud storage, which has an impact on data storage and analysis. To lower the rate of redundancy, the proposed system comprises a Time series-based deduplication technique. In the Time series-based deduplication technique, the Adaptive Multi-Pattern Boyer Moore Horspool (AM-BMH) algorithm, and Merkle tree were used to produce time-series data. Another significant challenge is that the geographically distributed cloud system has encountered that …the data placement methodology with high-priced transportation costs for data transmission. To overcome this issue, an optimal data placement strategy using Modified Distribution is proposed. Thus the proposed Time Series-based Deduplication and Optimal Data Placement Strategy (TDOPS) is found to be effective when compared with the existing system. The various parameters like space reduction, efficient retrieval, data transportation costs, and data transmission time are taken into the account in the cloud environment for an evaluation. The proposed scheme saves 98 percent of storage space, 55 percent computation overhead, and improves 60% of cloud storage efficacy. Show more
Keywords: Data placement, deduplication, modified distribution, Merkle tree, time series analysis
DOI: 10.3233/JIFS-212568
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1583-1597, 2022
Authors: Anitha, R. | Bapu, B.R. Tapas
Article Type: Research Article
Abstract: In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is …proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods. Show more
Keywords: Wireless sensor network, secured routing protocol, malicious node attacks, Deep Dropout extreme machine learning, Improved Alpha-Guided Grey Wolf, Crypto Hash Signature token, blockchain technology
DOI: 10.3233/JIFS-212455
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7525-7543, 2022
Authors: Senthamizh Selvi, S. | Anitha, R.
Article Type: Research Article
Abstract: In India, most of the Science and Technology resources available are in English. Developing an Automatic Language Translation Engine from English (source language) to Tamil (target language) is very essential for the people who need to get technical resources in their native language. The challenges in designing such engines using Natural Language Processing (NLP) tools include Lexical, Structural, and Syntax level ambiguity. To solve these challenges, the development of a Part-Of-Speech (POS) tagger is essential. The Verb-Framed languages like Tamil, Japanese, and many languages in Romance, Semitic, and Mayan languages families have high morphological richness but lack either a large …volume of annotated corpora or manually constructed linguistic resources for building POS tagger. Moreover, the Tamil Language has a low resource, high word sense ambiguity, and word-free order form giving rise to challenges in designing Tamil POS taggers. In this paper, we postulate a Hybrid POS tagger algorithm for Tamil Language using Cross-Lingual Transformation Learning Techniques. It is a novel Mining-based algorithm (MT), which finds equivalent words of Tamil in English on less volume of English-Tamil bilingual unannotated parallel corpus. To enhance the performance of MT, we developed Tamil language-specific auxiliary algorithms such as Keyword-based tagging algorithm (KT) and Verb pattern-based tagging algorithm (VT). We also developed a Unique pair occurrence-tagging algorithm (UT) to find the one-time occurrence of Tamil-English pair words. Our experiments show that by improving Context-based Bilingual Corpus to Bilingual parallel corpus and after leaving one-time occurrence words, the proposed Hybrid POS tagger can predict 81.15% words, with 73.51% accuracy and 90.50% precision. Evaluations prove our algorithms can generate language resources, which can improve the performance of NLP tasks in Tamil. Show more
Keywords: Natural language processing, part-of-speech tagger, sandhi, bilingual parallel corpus, cross-lingual transformation learning
DOI: 10.3233/JIFS-221278
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 8329-8348, 2022