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A. Anitha

Bio: A. Anitha is an academic researcher from VIT University. The author has contributed to research in topics: Rough set & Computer science. The author has an hindex of 6, co-authored 14 publications receiving 168 citations.

Papers
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Journal ArticleDOI
TL;DR: The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA and it is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1%.
Abstract: Information and technology revolution has brought a radical change in the way data are collected. The data collected is of no use unless some useful information is derived from it. Therefore, it is essential to think of some predictive analysis for analyzing data and to get meaningful information. Much research has been carried out in the direction of predictive data analysis starting from statistical techniques to intelligent computing techniques and further to hybridize computing techniques. The prime objective of this paper is to make a comparative analysis between statistical, rough computing, and hybridized techniques. The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA. It is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1% as compared to rough computing techniques.

66 citations

Journal ArticleDOI
A. Anitha1
01 Nov 2017
TL;DR: A system which will notify the corporations to empty the bin on time and put a sensor on top of the garbage bin which will detect the total level of garbage inside it according to the total size of the bin.
Abstract: Nowadays certain actions are taken to improve the level of cleanliness in the country. People are getting more active in doing all the things possible to clean their surroundings. Various movements are also started by the government to increase cleanliness. We will try to build a system which will notify the corporations to empty the bin on time. In this system, we will put a sensor on top of the garbage bin which will detect the total level of garbage inside it according to the total size of the bin. When the garbage will reach the maximum level, a notification will be sent to the corporation's office, then the employees can take further actions to empty the bin. This system will help in cleaning the city in a better way. By using this system people do not have to check all the systems manually but they will get a notification when the bin will get filled.

48 citations

Journal ArticleDOI
A. Anitha1
01 Nov 2017
TL;DR: Home security is a very useful application of IoT and the system will inform the owner about any unauthorized entry or whenever the door is opened by sending a notification to the user.
Abstract: IoT refers to the infrastructure of connected physical devices which is growing at a rapid rate as huge number of devices and objects are getting associated to the Internet. Home security is a very useful application of IoT and we are using it to create an inexpensive security system for homes as well as industrial use. The system will inform the owner about any unauthorized entry or whenever the door is opened by sending a notification to the user. After the user gets the notification, he can take the necessary actions. The security system will use a microcontroller known as Arduino Uno to interface between the components, a magnetic Reed sensor to monitor the status, a buzzer for sounding the alarm, and a WiFi module, ESP8266 to connect and communicate using the Internet. The main advantages of such a system includes the ease of setting up, lower costs and low maintenance.

48 citations

Journal ArticleDOI
TL;DR: An effort has been made to process the uncertainties by hybridizing rough set on fuzzy approximation space and neural network by analyzing agriculture data of Vellore District of Tamil Nadu, India and achieving 93% of classification accuracy in validation.
Abstract: In Indian economy, agriculture is the prime vocation that avails in the overall development of the country. Tamil Nadu occupies approximately 7% of the nation's population, with 3% of water resources and 4% of land resources at the country level. The crop suitability prediction is of prime importance to enhance the nutritional security to the developing country. Based on several crops grown in a particular place, and the availability of natural resources, one can identify the suitability of crops that can be grown in a particular place. To this end, many mathematical tools were developed, but they failed to include processing of uncertainties present in the accumulated data. Therefore, in this paper an effort has been made to process the uncertainties by hybridizing rough set on fuzzy approximation space and neural network. The rough set on fuzzy approximation space identifies the almost indiscernibility among the natural resources and helps in minimizing the computational procedure on employing data reduction techniques, whereas neural network helps in prediction process. The proposed model is analysed on agriculture data of Vellore District of Tamil Nadu, India, and achieved 93% of classification accuracy in validation. The model is compared with an earlier model and achieved 8% more accuracy while predicting unseen associations.

36 citations

Journal ArticleDOI
TL;DR: Two processes, pre-process and post-process, are used to predict the decisions for the missing associations in the attribute values, where rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for theMissing associations.
Abstract: Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.

19 citations


Cited by
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Journal ArticleDOI
TL;DR: The collection and decomposition of waste in the smart way so that benefit from the waste is maximized and the actual waste is minimized efficiently is discussed.

80 citations

Journal ArticleDOI
TL;DR: The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA and it is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1%.
Abstract: Information and technology revolution has brought a radical change in the way data are collected. The data collected is of no use unless some useful information is derived from it. Therefore, it is essential to think of some predictive analysis for analyzing data and to get meaningful information. Much research has been carried out in the direction of predictive data analysis starting from statistical techniques to intelligent computing techniques and further to hybridize computing techniques. The prime objective of this paper is to make a comparative analysis between statistical, rough computing, and hybridized techniques. The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA. It is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1% as compared to rough computing techniques.

66 citations

DOI
18 Nov 2020
TL;DR: A QR CODE-based Online Attendance System with REST architecture was developed which resulted in web and mobile-based applications, so that students can make attendance more practical and efficient with their gadgets, besides that lecturers can monitor data and student attendance graph on a web-based application.
Abstract: The attendance system found in an institution or university uses an id card, finger print or using the manual method. This method still has many shortcomings such as signature forgery, loss of ID cards, queue up which can waste a lot of time. In this study, a QR CODE-based Online Attendance System with REST architecture was developed which resulted inweb and mobile-based applications, so that students can make attendance more practical and efficient with their gadgets, besides that lecturers can monitor data and student attendance graph on a web-based application. The attendance System was developed using the REST architecture because the architecture is language and platformagnostic, so it can be used by many programming languages and many platforms, and the REST architecture has a design and philosophy closer to the web, using the HTTP protocol.

48 citations

Journal ArticleDOI
TL;DR: A new random key encoding method is recommended which is used in the proposed work (PSORS-FS) to convert classical PSO algorithm in discrete domain and it reduces the issues related to maximum velocity of particles as well as sigmoid function which is related with binary PSO.
Abstract: The set of permissions required by any Android app during installation time is considered as the feature set which are used in permission based detection of Android malwares. Those high dimensional feature set should be reduced to minimize computational overhead by choosing an optimal sub set of features. In recent times, selection of meaningful attributes is an inevitable step for mining of large dimensional data and the application of heuristic feature selection algorithms are the main research directions in this field. “Quality of classification” measure is inspired by rough set theory and can be combined with bio inspired heuristic search techniques (Particle swarm optimization, Genetic Algorithm etc.) in selecting optimal or near optimal subsets of features. In this work, a feature selection technique based on rough set and improvised particle swarm optimization (PSO) algorithm is proposed for selection of features in the permission based detection of Android malwares. The main contribution of this work is to recommend a new random key encoding method which is used in the proposed work (PSORS-FS) to convert classical PSO algorithm in discrete domain. It also reduces the issues related to maximum velocity of particles as well as sigmoid function which is related with binary PSO. PSORS-FS ensures diversity in the search process and it also reduces the tendency of premature convergence. Datasets of UCI, KEEL machine learning repository and two Android permission datasets have been used to evaluate the performance of the proposed method. Better classification performance has been yielded by proposed method over conventional filters and wrapper methods for most of the machine learning classifiers.

46 citations

Journal ArticleDOI
01 Aug 2020-Energies
TL;DR: A smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment is presented and improved accuracy is provided by utilizing machine learning as compared to existing solutions based on simple approaches.
Abstract: Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

43 citations