Papers by Chithra Selvaraj
International Journal of Green Energy, Jun 13, 2021
ABSTRACT Wind is one of the most important natural resources from which we can generate power thr... more ABSTRACT Wind is one of the most important natural resources from which we can generate power through wind turbines, Presently the wind turbines may be small or large but the output power generated is used for various purposes. There may be a possibility of wear and tear of the turbines which may lead to physical damage. There are no proper mechanisms available for monitoring the turbines from remote locations through wireless mode. The implementation of live remote monitoring and intelligent condition monitoring techniques reduces the downtime and would increase the lifetime of the turbines. This paper proposes a novel smart and proactive maintenance system that would aid in diagnosing the major faults with the wind turbines and a prediction analysis tool that would forecast the generation status of the small wind turbines. A Sensor based IoT system (SBIS) will help to monitor the significant parameters of the wind turbines which determine its working conditions like wind speed, vibration, temperature, and output power. A combined approach of Machine Learning techniques for SBIS have been used for proactive maintenance of small wind turbines. The diagnosis part of the system takes input from the sensors and uses a cloud platform for predictive analysis. The machine learning algorithms like Linear Regression (LR), Support Vector Machine (SVM), Optimized Artificial Neural Network (OANN), and XG Boost (XGB) are applied and the results are summarized for prediction. The results of the algorithms are compared for accuracy of the sensed data and it is observed that the OANN algorithm is better performing for proactive maintenance and power prediction of the small wind turbines.
International Journal of Intelligent Systems Technologies and Applications, 2018
Feature selection methods are deployed in machine-learning algorithms for reducing the redundancy... more Feature selection methods are deployed in machine-learning algorithms for reducing the redundancy in the dataset and to increase the clarity in the system models without loss of much information. The objective of this paper is to investigate the performance of feature selection methods when they are exposed to different datasets and different classification algorithms. In this paper, we have investigated standard parameters such as accuracy, precision and recall over two feature selection algorithms namely Chi-Square feature selection and Boruta feature selection algorithms. Observations of the experiments conducted using R studio resulted around 5-6% increased performance in above said parameters when they were exposed to Boruta feature selection algorithm. The experiment was done on two different datasets with different set of features and we have used the following five standard classification algorithms-Naive Bayes, decision tree, support vector machines (SVM), random forest and gradient boosting.
2023 International Conference on Networking and Communications (ICNWC)
International Journal of Green Energy, 2021
ABSTRACT Wind is one of the most important natural resources from which we can generate power thr... more ABSTRACT Wind is one of the most important natural resources from which we can generate power through wind turbines, Presently the wind turbines may be small or large but the output power generated is used for various purposes. There may be a possibility of wear and tear of the turbines which may lead to physical damage. There are no proper mechanisms available for monitoring the turbines from remote locations through wireless mode. The implementation of live remote monitoring and intelligent condition monitoring techniques reduces the downtime and would increase the lifetime of the turbines. This paper proposes a novel smart and proactive maintenance system that would aid in diagnosing the major faults with the wind turbines and a prediction analysis tool that would forecast the generation status of the small wind turbines. A Sensor based IoT system (SBIS) will help to monitor the significant parameters of the wind turbines which determine its working conditions like wind speed, vibration, temperature, and output power. A combined approach of Machine Learning techniques for SBIS have been used for proactive maintenance of small wind turbines. The diagnosis part of the system takes input from the sensors and uses a cloud platform for predictive analysis. The machine learning algorithms like Linear Regression (LR), Support Vector Machine (SVM), Optimized Artificial Neural Network (OANN), and XG Boost (XGB) are applied and the results are summarized for prediction. The results of the algorithms are compared for accuracy of the sensed data and it is observed that the OANN algorithm is better performing for proactive maintenance and power prediction of the small wind turbines.
Advances in Intelligent Systems and Computing, 2017
A large scale Wireless Sensor Network (WSN) or Mobile Ad hoc Network is to be definitely integrat... more A large scale Wireless Sensor Network (WSN) or Mobile Ad hoc Network is to be definitely integrated into Internet as a backbone of Cyber Physical System (CPS), it is indispensable to believe that Cyber physical systems are free from security challenges, such as the detection of malicious attacks. A trust based model is attributed as an important door to defend a large distributed sensor networks in CPS. Trust is perceived as a critical tool to detect malicious node attacks in distributed computing and communication entities, detection of unreliable entities, and uphold decision-making process of various protocols. In this paper, Trust is invoked between participating nodes to improve the performance of Cyber physical systems by improving the degree of cooperation among them. The proposed schemes are also used to establish reliable path in packet forwarding and route finding. The realism, robustness and effectiveness of the proposed model is validated through a broad set of simulations.
Proceedings of the International Conference on Soft Computing Systems, 2015
Advances in sensor technologies, such as Bio-sensors, along with recent developments in the embed... more Advances in sensor technologies, such as Bio-sensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of Body Area Networks (BAN). A typical BAN consists of a number of wearable and implanted sensors through wireless communication (WBAN) can be used to monitor the parameters of the human body continuously. The unstable physiological regulatory system has to report about the status of the patient to the physician. There are various issues exist associated with WBAN. One of the significant process of WBAN is the communication of the sensor nodes. A key requirement for WBAN applications is to provide a secure communication channel between the body sensors and the communication system which are prone to malicious attacks. Lack of adequate security features may not only lead to a breach of patient privacy resulting in wrong diagnosis and treatment. This paper aims for implementing a secure authentication mechanism using Elliptic Curve Digital Signature Algorithm technique (ECDSA) for securing the physiological signal from the human body during inter-sensor communications.
International Journal of Intelligent Systems Technologies and Applications, 2018
The handwritten text reader is designed to help the visually impaired listen to an audio read-bac... more The handwritten text reader is designed to help the visually impaired listen to an audio read-back of printed and handwritten scanned text. A hand-held page scanner is used to scan the text to be read. The image from the scanner is sent to the application in the paired Android phone over Bluetooth. An open source optical character recognition (OCR) engine, Tesseract is used to extract the text from the image, and this extracted text is converted to speech. Tesseract OCR engine is further trained to recognise handwritten text for a specific user. This OCR engine is trained with handwritten datasets. In addition to English, the application supports two regional languages-Hindi and Bengali.
Journal of Network and Computer Applications, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
International Journal of Intelligent Systems Technologies and Applications, 2018
Feature selection methods are deployed in machine-learning algorithms for reducing the redundancy... more Feature selection methods are deployed in machine-learning algorithms for reducing the redundancy in the dataset and to increase the clarity in the system models without loss of much information. The objective of this paper is to investigate the performance of feature selection methods when they are exposed to different datasets and different classification algorithms. In this paper, we have investigated standard parameters such as accuracy, precision and recall over two feature selection algorithms namely Chi-Square feature selection and Boruta feature selection algorithms. Observations of the experiments conducted using R studio resulted around 5-6% increased performance in above said parameters when they were exposed to Boruta feature selection algorithm. The experiment was done on two different datasets with different set of features and we have used the following five standard classification algorithms-Naive Bayes, decision tree, support vector machines (SVM), random forest and gradient boosting.
The Computer Journal, 2019
This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud ... more This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0...
Computers & Electrical Engineering, 2019
Entertainment industry has seen a phenomenal growth throughout the globe in recent times and movi... more Entertainment industry has seen a phenomenal growth throughout the globe in recent times and movie industry enjoys a crucial role in the above emergence. A movie can capture the attention of a viewer and can trigger cognitive and emotional processes in the brain. In this article we assess the emotional outcome of the viewer while they watch the movie before its actual release that is, during its preview. Traditionally FMRI was used to assess the activity of brain but proved to be non-feasible and costly so we used EEG Sensors to monitor and record the functioning of the brain of movie viewer for further analysis. The collected data through EEG sensor were analysed using deep learning framework. H 2 O package of deep learning was employed to find high and low of different brain waves mapping to the emotions depicted in the every scene of the movie. Our proposed system named BCI cinematics obtained 85% accuracy and results were validated by obtaining the feedback from the stake holders. The outcome of this work will assist the creators to understand the emotional impact of movie over a normal viewer impartially thus enable them to modify certain scenes or change sequence of scenes and so on. When deployed in real time our system prove to be a cost saver for movie makers.
Cluster Computing, 2018
Cloud computing serves as a major boost for the digital era since it handles data from a large nu... more Cloud computing serves as a major boost for the digital era since it handles data from a large number of users simultaneously. Besides the several useful characteristics, providing security to the data stored in the cloud platform is a major challenge for the service providers. Privacy preservation schemes introduced in the literature trying to enhance the privacy and utility of the data structures by modifying the database with the secret key. In this paper, an optimization scheme, Brain Storm based Whale Optimization Algorithm (BS-WOA), is introduced for identifying the secret key. The database from the data owner is modified with the optimal secret key for constructing the retrievable perturbation data for preserving the privacy and utility. The proposed BS-WOA is designed through the hybridization of Brain Storm Optimization and Whale Optimization Algorithm. Simulation of the proposed technique with the BS-WOA is done in the three standard databases, such as chess T10I4D100 K, and the retail databases. When evaluated for the key size of 256, the proposed BS-WOA achieved privacy value of 0.186 and utility value of 0.8777 for the chess database, and thus, has improved performance.
Computers & Electrical Engineering, 2018
Internet of Things [IoT] is a network that encompasses sensors, actuators and networking devices ... more Internet of Things [IoT] is a network that encompasses sensors, actuators and networking devices for the purpose of communication and control. The IoT devices are resourceconstrained which require a specialized routing protocol in order to transmit the sensed data from source to destination efficiently. The IPv6 Routing Protocol for Low power and Lossy network (RPL) is one of the widely used routing protocols in IoT networks. The performance of RPL protocol is evaluated on the three different mobility models; Manhattan Grid (MG), Gaussian Markov (GM) and Random Waypoint (RW) at different scalability levels in Contiki based Cooja simulator. The standard Quality of Service (QoS) parameters; Packet Delivery Ration (PDR), Average Power (Pavg) and Hop Count (HC) are considered for analysis. The extensive experimental analysis of RPL when exposed to different mobility model and scalability reveals that, the Manhattan Grid model provides better QoS performance by preserving the working nature of RPL optimally.
Security and Communication Networks, 2013
ABSTRACTPeer‐to‐peer (P2P) networks are distributed, decentralized, dynamic networks that are sel... more ABSTRACTPeer‐to‐peer (P2P) networks are distributed, decentralized, dynamic networks that are self‐organized and self‐managed. P2P networks have emerged over the past several years as an effective and scalable medium for sharing distributed resources. However, determining the reliability and trustworthiness of the participating peers still remains a major security challenge. Reputation‐based trust management calculates peer trust as a measure of recommendations received from other peers. Malicious peers may give wrong reputation scores and also collude with other peers to make themselves or others appear trustworthy. In this paper, we propose the use of outlier detection technique to detect false testimony as outliers. We have applied rough set theory, an efficient and intelligent mathematical tool, to detect the outliers in the trust scores. We present the detailed methodology for implementing rough set theory for P2P network and detecting outlier scores in reputation metrics given...
Computer Science Review, 2012
The objective of this paper is to present a comprehensive survey of security issues in Reputation... more The objective of this paper is to present a comprehensive survey of security issues in Reputation based Trust Management system (RTMS) also known in short as Reputation Management Systems for P2P networks. The wide adoption of P2P computing has enhanced content publishing, pervasive information collection, streaming of real-time sensed data and information sharing on an enormous global scale. At the same time, the open and anonymous nature of P2P makes it vulnerable to malicious attacks and the spread of malware. In this paper, we discuss in detail the different security attacks on P2P systems and have categorized them as network-related and peer-related attacks. RTMS helps to establish and evaluate Trust, which is the degree of belief that is established to prove that the right user is accessing the right resource. We have explained the different Trust Management schemes used in P2P networks and have compared them on the basis of trust establishment, security features, trust evaluation and weakness. We have surveyed the RTMSs currently in use and have compared them on the basis of reputation collection, aggregation, computation, storage and degree of centralization of reputation computation and management. We also present a comparison of protection provided by RTMs against the various security attacks discussed. Open research issues and challenges that have yet to be addressed in the design of current RTMs have been presented in detail. This survey can be used as a reference guide to understand Trust Management and RTMS for P2P networks and to further research in RTMSs to make them efficient, reliable and scalable to enable and promote the utilization of P2P systems for large communities and applications. c
International Journal of Computer Theory and Engineering, 2011
Peer-to-Peer Networking and Applications, 2011
Page 1. Peer profile based trust model for P2P systems using genetic algorithm Chithra Selvaraj &... more Page 1. Peer profile based trust model for P2P systems using genetic algorithm Chithra Selvaraj & Sheila Anand Received: 19 October 2010 /Accepted: 13 September 2011 © Springer Science+Business Media, LLC 2011 Abstract ...
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Papers by Chithra Selvaraj