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Ramachandran Manikandan

Bio: Ramachandran Manikandan is an academic researcher from Shanmugha Arts, Science, Technology & Research Academy. The author has contributed to research in topics: Computer science & Very-large-scale integration. The author has an hindex of 13, co-authored 67 publications receiving 379 citations.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed approach achieves better false positive rate, accuracy of prediction, and reduced delay in comparison to the conventional techniques.

103 citations

Journal ArticleDOI
TL;DR: A prototype to classify stroke that combines text mining tools and machine learning algorithms, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes.
Abstract: This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.

80 citations

Journal ArticleDOI
TL;DR: Hashed Needham Schroeder's Cost Optimized Deep Machine Learning (HNS-CODML) method for secure Industrial IoT data transmissions via cloud environment has been proposed by indicating the necessity of providing Industrial IoT security using machine learning technique.

56 citations

Journal ArticleDOI
TL;DR: The performance of Secure Data is approved through simulations in terms of energy cost, computation time, etc., of the proposed algorithms and the outcomes demonstrate that Secure Data can be efficient while applying for ensuring security chances in IoT-based healthcare systems.
Abstract: The ever-growing advancement in communication innovation of modern smart objects carries with it a new era of improvement for the Internet of Things (IoT) based networks. The health care system is the best approach to store the patient’s health data online with high privacy. Ensuring the privacy and confidentiality of patient information in the cloud is of utmost importance; here, the enhanced security model of healthcare data gives rise to trust. For the secure communication, the healthcare data sensed by the IoT sensor network is encrypted by Lightweight SIMON block cipher. For improving the privacy of healthcare data among individuals, we implemented the share generation model. Then, share creation model, i.e., Chinese Remainder Theorem (CRT) is developed to generate the copy of every ciphertext based on the selected number of users and the data is shared among the optimal number of users. The selection of the users in IoHT is made by the metaheuristic algorithm called Hybrid Teaching and Learning Based Optimization (HTLBO). Then, we present healthcare service providers for giving the full scope of medical services to people enrolled in IoHT. The performance of Secure Data is approved through simulations in terms of energy cost, computation time, etc., of the proposed algorithms and the outcomes demonstrate that Secure Data can be efficient while applying for ensuring security chances in IoT-based healthcare systems.

47 citations

Journal ArticleDOI
01 Jan 2020
TL;DR: This work proposes a novel decision-making framework that combines the strength of both interval-valued fuzzy set and IFS that is more effective in handling vagueness with a simple formulation setup.
Abstract: Personnel selection is a challenging problem for any organization. The success of a project is determined by the human resources that handle the project. To make better personnel selections, researchers have adopted multi-criteria decision-making (MCDM) approaches. Among these, fuzzy-based MCDM methods are most frequently used, as they handle vagueness and imprecision better. Intuitionistic fuzzy set (IFS) is a popular MCDM context which provides degree of membership and non-membership for preference elicitation. In this work, we propose a novel decision-making framework that consists of two stages. In the first stage, a new extension to the popular VIKOR method is presented under IFS context. The positive and negative ideal solutions are determined, and VIKOR parameters are calculated using transformation procedure. The proposed method combines the strength of both interval-valued fuzzy set and IFS that is more effective in handling vagueness with a simple formulation setup. In the second stage, a personnel selection problem is used to validate the proposed framework. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.

42 citations


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Journal ArticleDOI
TL;DR: This is Applied Cryptography Protocols Algorithms And Source Code In C Applied Cryptographic Protocols algorithms and Source Code in C By Schneier Bruce Author Nov 01 1995 the best ebook that you can get right now online.

207 citations

Journal ArticleDOI
TL;DR: This paper aims to identify, compare systematically, and classify existing investigations taxonomically in the Healthcare IoT (HIoT) systems by reviewing 146 articles between 2015 and 2020, and presents a comprehensive taxonomy in the HIoT.

161 citations

Journal ArticleDOI
TL;DR: A three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) is proposed to address the heterogeneity issue while keeping the privacy information of IoT devices, and can be widely applied to smart cities.

135 citations

Journal ArticleDOI
01 Sep 2016
TL;DR: A hybridization of two techniques, Tolerance Rough Set and Firefly Algorithm are used to select the imperative features of brain tumor to show the effectiveness of the proposed technique as well as improvements over the existing supervised feature selection algorithms.
Abstract: Brain tumor is one of the most harmful diseases, and has affected majority of people including children in the world. The probability of survival can be enhanced if the tumor is detected at its premature stage. The intention of feature selection approach is to select a small subset of features which minimizes redundancy and maximizes relevance to the target such as the class labels in classification. Thus, the machine learning model receives a brief organization with high predictive accuracy using the selected prominent features. Therefore, currently, feature selection plays a significant role in machine learning and knowledge discovery. A novel hybrid supervised feature selection algorithm, called TRSFFQR (Tolerance Rough Set Firefly based Quick Reduct), is developed and applied for MRI brain images. The hybrid intelligent system aims to exploit the benefits of the basic models and at the same time, moderate their limitations. Different categories of features are extracted from the segmented MRI images, i.e., shape, intensity and texture based features. The features extracted from brain tumor Images are real values. Hence Tolerance Rough set is applied in this work. In this study, a hybridization of two techniques, Tolerance Rough Set (TRS) and Firefly Algorithm (FA) are used to select the imperative features of brain tumor. Performance of TRSFFQR is compared with Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), Supervised Tolerance Rough Set-PSO based Relative Reduct (STRSPSO-RR) and Supervised Tolerance Rough Set-PSO based Quick Reduct (STRSPSO-QR).The experimental result shows the effectiveness of the proposed technique as well as improvements over the existing supervised feature selection algorithms.

128 citations