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R. Kannadasan

Bio: R. Kannadasan is an academic researcher from VIT University. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 4, co-authored 15 publications receiving 31 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper contains the survey on recent Molecular DNA big data technologies which gives the performance characteristic of each technique.

9 citations

Journal ArticleDOI
TL;DR: This paper would discuss large scale data analysis using different implementations on the above mentioned tools and after that it would give a performance analysis of these tools on the given implementation like Cap3, HEP, Cloudburst.

9 citations

Proceedings ArticleDOI
20 Apr 2018
TL;DR: This research work which initiated at an early detection of all the probable symptoms and signs which might further lead to detection of cardiac diseases using data collected from previous patients as well as data input received from the user at that particular time is initiated.
Abstract: This research work which initiated at an early detection of all the probable symptoms and signs which might further lead to detection of cardiac diseases using data collected from previous patients as well as data input received from the user at that particular time. Current scenario of health-care data used for surveillance are no longer simply a time building series of aggregate daily counts. Instead, a wealth of proposed spatial as well as temporal demographic, and symptom information is available at the data presented during the time of execution. Our proposed method incorporates all such information that is being used as a classification approach that compares recent healthcare data against data from that particular baseline distribution and hence classifies subgroups of the given data. In addition, the data sample data used is first tested against many types of classifiers and various other proposed test scores have been evaluated. Test best is further chosen to make predictions. This follows a prototype implementation using a python based data mining tool, Orange (version: 0.17.1). The database can be stored in a cloud to centralize it and make access easier.

7 citations

Book ChapterDOI
01 Jan 2019
TL;DR: A system which reduces the response time in grid system using fog computing is introduced, which makes the process extra firm but also more secure and reliable.
Abstract: The conventional grid system the world has been using ever since its advent in 1890s, but this aging infrastructure of demand driven control structure, has made in way for more robust, fast, inter-connected and a smarter system with a cutting-edge technology called the Smart Grid. Smart grid uses a bidirectional communication model between the consumer and the utility service. The sensing along the communication line is what makes the grid system smart. Linking the smart grid with fog and cloud not only makes the process extra firm but also more secure and reliable. This paper presents a mechanism to reduce the response time in the smart grid architecture so as the system is more responsive. We have introduced a system which reduces the response time in grid system using fog computing.

6 citations

Journal ArticleDOI
TL;DR: This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.
Abstract: Applications of computer and information technology are indispensable in various fields especially in the field of biology. The use of computer aided tools plays a key role in solving biological problems. The spontaneous process of molecular docking is important for finding potentially strong candidate of drug for various viruses. The binding of protein receptors with ligand molecules is essential in drug discovery process. The aim of molecular docking tools is to predict the interaction between protein and ligand. This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: An IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed, which improves the search capability using the Levy flight algorithm and achieves better accuracy than other approaches.
Abstract: The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT) The IoMT has an important role to play in reducing the mortality rate by the early detection of disease The prediction of heart disease is a key issue in the analysis of clinical dataset The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima The learning parameters are optimized utilizing MSSO to provide better results for ANFIS The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc The heart condition is identified by classifying the received sensor data using MSSO-ANFIS A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches The proposed MSSO-ANFIS prediction model obtains an accuracy of 9945 with a precision of 9654, which is higher than the other approaches

127 citations

Journal ArticleDOI
01 Apr 2020-Heliyon
TL;DR: The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.

71 citations

Journal ArticleDOI
23 Apr 2020
TL;DR: This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros.
Abstract: The cloud is the best method used for the utilization and organization of data. The cloud provides many resources for us via the internet. There are many technologies used in cloud computing systems; each one uses a different kind of protocols and methods. Many tasks can execute on different servers per second, which cannot execute on their computer. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros. Moreover, A comprehensive comparison of the tools used for the utilization of cloud computing systems is presented.

68 citations

Journal ArticleDOI
TL;DR: A novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI is proposed, which has good prediction performance and is a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.
Abstract: Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

64 citations

Journal ArticleDOI
TL;DR: Results utilizing temperature measurements indicate that the proposed Data Traffic Management based on Compression and Minimum Description Length (MDL) Techniques outperforms common methods developed especially for WSNs in reducing the amount of data transmitted and saving energy, even though the suggested system does not reach the theoretical maximum.
Abstract: The sector of agriculture facing numerous challenges for the proper utilization of its natural resources. For that reason, and to the growing risk of changing weather conditions, we must monitor the soil conditions and meteorological data locally in order to accelerate the adoption of appropriate decisions that help the culture. In the era of the Internet of Things (IoT), a solution is to deploy a Wireless Sensor Network (WSN) as a low-cost remote monitoring and management system for these kinds of features. But WSN is suffering from the motes’ limited energy supplies, which decrease the total network’s lifetime. Each mote collects periodically the tracked feature and transmitting the data to the edge Gateway (GW) for further study. This method of transmitting massive volumes of data allows the sensor node to use high energy and substantial usage of bandwidth on the network. In this research, Data Traffic Management based on Compression and Minimum Description Length (MDL) Techniques is proposed which works at the level of sensor nodes (i.e., Things level) and at the edge GW level. In the first level, a lightweight lossless compression algorithm based on Differential Encoding and Huffman techniques which is particularly beneficial for IoT nodes, that monitoring the features of the environment, especially those with limited computing and memory resources. Instead of trying to formulate innovative ad hoc algorithms, we demonstrate that, provided general awareness of the features to be monitored, classical Huffman coding can be used effectively to describe the same features that measure at various time periods and locations. In the second level, the principle of MDL with hierarchical clustering was utilized for the purpose of clustering the sets of data coming from the first level. The strategy used to minimize data sets transmitted at this level is fairly simple. Any pair of data sets that can be compressed according to the MDL principle is combined into one cluster. As a result of this strategy, the number of data sets is gradually decreasing and the process of merging similar sets into a single cluster is stopped if no more pairs of sets can be compressed. Results utilizing temperature measurements indicate that it outperforms common methods developed especially for WSNs in reducing the amount of data transmitted and saving energy, even though the suggested system does not reach the theoretical maximum.

25 citations