scispace - formally typeset
Author

S. Sankar Ganesh

Bio: S. Sankar Ganesh is an academic researcher from VIT University. The author has contributed to research in topic(s): Air quality index & Gradient descent. The author has an hindex of 6, co-authored 20 publication(s) receiving 85 citation(s). Previous affiliations of S. Sankar Ganesh include CMR Institute of Technology & Lovely Professional University.

...read more

Papers
More filters

Journal ArticleDOI
TL;DR: Several ensemble models of individual neural network predictors and individual regression predictors have been presented for the final forecast of the PM2.5 concentration.

...read more

Abstract: Inhaling particulate matter such as PM2.5 can have a hazardous impact on the human health. In order to predict the PM2.5 concentration, Artificial Neural Networks trained with conjugate gradient descent such as Multi Layer Perceptron (MLP), cascade forward neural network, Elman neural network, Radial Basis Function (RBF) neural network and Non-linear Autoregressive model with exogenous input (NARX) along with regression models such as Multiple Linear Regression (MLR) consisting of batch gradient descent, stochastic gradient descent, mini-batch gradient descent and conjugate gradient descent algorithms and Support Vector Regression (SVR) were implemented. In these models, the concentration of PM2.5 was the dependent variable and the data related to concentrations of PM2.5, SO2, O3 and meteorological data including average Maximum Temperature (MAX T), daily wind speed (WS) for the years 2010–2016 in Houston and New York were the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors have been presented.

...read more

15 citations


Journal ArticleDOI
Abstract: The wideband antennas have presented in this paper. The proposed antennas have a tunable, able to provide the multiband applications; which also offered the various modern wireless communication systems. It consists of two different shapes of (EF and ΨU) conductive strips; stack on Epoxy based composite substrate in the array pattern. It has offered the resonant of 4.87 GHz and 5.42 GHz, with Fractional Bandwidth (FBW) of 24.64% and 23.33% respectively. These proposed antennas have a compact dimensional of 18.64x17.92x1 mm2. Hence, these prototypes have validated with respective wideband frequencies of the measurement analysis. These proposed antennas have effectively offered the Ultra Wide-Band (UWB) bands of operation, which includes the Industry, Scientific and Medical applications.

...read more

15 citations


Journal ArticleDOI
Abstract: The 3x4 array pattern of Jerusalem cross shaped microstrip patch antenna have designed, fabricated and validated in this paper. It consists of 1 mm thickness of substrate mounted on metal strips which is one side of conductive strip of 31.25 × 22.9 mm2 and another side of conductive strip of 37.25x 28.9 mm2 for ground plane. The fabricated design has provided the return loss of 27.106 dB at 3.884 GHz and Fractional Bandwidth (FBW) of 23.68%. Therefore, these fabrication results have validated with vector network analyser.

...read more

15 citations


Journal ArticleDOI
TL;DR: To forecast the air quality index (AQI), artificial neural networks trained with conjugate gradient descent (CGD) along with regression models such as multiple linear regression (MLR) and support vector regression (SVR) are implemented.

...read more

Abstract: Abstract Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO2, CO, O3, PM2.5, SO2, and PM10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.

...read more

9 citations


Proceedings ArticleDOI
01 Apr 2017-
TL;DR: Different regression models to forecast air quality index (AQI) in particular areas of interest are presented and support vector regression (SVR) exhibited high performance in terms of investigated measures of quality.

...read more

Abstract: It is always important to monitor the quality of air that we inhale to protect ourselves from the respiratory diseases. In this paper, we present different regression models to forecast air quality index (AQI) in particular areas of interest. Support vector regression (SVR) and linear models like multiple linear regression consisting of gradient descent, stochastic gradient descent, mini-batch gradient descent were implemented. In these models, the air quality index (AQI) is dependent on pollutant concentrations of NO 2 , CO, O 3 , PM 2.5 , PM 10 and SO 2 . Among these models, support vector regression (SVR) exhibited high performance in terms of investigated measures of quality.

...read more

8 citations


Cited by
More filters

Journal Article
TL;DR: Der DES basiert auf einer von Horst Feistel bei IBM entwickelten Blockchiffre („Lucipher“) with einer Schlüssellänge von 128 bit zum Sicherheitsrisiko, und zuletzt konnte 1998 mit einem von der „Electronic Frontier Foundation“ (EFF) entwickkelten Spezialmaschine mit 1.800 parallel arbeit

...read more

Abstract: Im Jahre 1977 wurde der „Data Encryption Algorithm“ (DEA) vom „National Bureau of Standards“ (NBS, später „National Institute of Standards and Technology“ – NIST) zum amerikanischen Verschlüsselungsstandard für Bundesbehörden erklärt [NBS_77]. 1981 folgte die Verabschiedung der DEA-Spezifikation als ANSI-Standard „DES“ [ANSI_81]. Die Empfehlung des DES als StandardVerschlüsselungsverfahren wurde auf fünf Jahre befristet und 1983, 1988 und 1993 um jeweils weitere fünf Jahre verlängert. Derzeit liegt eine Neufassung des NISTStandards vor [NIST_99], in dem der DES für weitere fünf Jahre übergangsweise zugelassen sein soll, aber die Verwendung von Triple-DES empfohlen wird: eine dreifache Anwendung des DES mit drei verschiedenen Schlüsseln (effektive Schlüssellänge: 168 bit) [NIST_99]. Der DES basiert auf einer von Horst Feistel bei IBM entwickelten Blockchiffre („Lucipher“) mit einer Schlüssellänge von 128 bit. Da die amerikanische „National Security Agency“ (NSA) dafür gesorgt hatte, daß der DES eine Schlüssellänge von lediglich 64 bit besitzt, von denen nur 56 bit relevant sind, und spezielle Substitutionsboxen (den „kryptographischen Kern“ des Verfahrens) erhielt, deren Konstruktionskriterien von der NSA nicht veröffentlicht wurden, war das Verfahren von Beginn an umstritten. Kritiker nahmen an, daß es eine geheime „Trapdoor“ in dem Verfahren gäbe, die der NSA eine OnlineEntschlüsselung auch ohne Kenntnis des Schlüssels erlauben würde. Zwar ließ sich dieser Verdacht nicht erhärten, aber sowohl die Zunahme von Rechenleistung als auch die Parallelisierung von Suchalgorithmen machen heute eine Schlüssellänge von 56 bit zum Sicherheitsrisiko. Zuletzt konnte 1998 mit einer von der „Electronic Frontier Foundation“ (EFF) entwickelten Spezialmaschine mit 1.800 parallel arbeitenden, eigens entwickelten Krypto-Prozessoren ein DES-Schlüssel in einer Rekordzeit von 2,5 Tagen gefunden werden. Um einen Nachfolger für den DES zu finden, kündigte das NIST am 2. Januar 1997 die Suche nach einem „Advanced Encryption Standard“ (AES) an. Ziel dieser Initiative ist, in enger Kooperation mit Forschung und Industrie ein symmetrisches Verschlüsselungsverfahren zu finden, das geeignet ist, bis weit ins 21. Jahrhundert hinein amerikanische Behördendaten wirkungsvoll zu verschlüsseln. Dazu wurde am 12. September 1997 ein offizieller „Call for Algorithm“ ausgeschrieben. An die vorzuschlagenden symmetrischen Verschlüsselungsalgorithmen wurden die folgenden Anforderungen gestellt: nicht-klassifiziert und veröffentlicht, weltweit lizenzfrei verfügbar, effizient implementierbar in Hardund Software, Blockchiffren mit einer Blocklänge von 128 bit sowie Schlüssellängen von 128, 192 und 256 bit unterstützt. Auf der ersten „AES Candidate Conference“ (AES1) veröffentlichte das NIST am 20. August 1998 eine Liste von 15 vorgeschlagenen Algorithmen und forderte die Fachöffentlichkeit zu deren Analyse auf. Die Ergebnisse wurden auf der zweiten „AES Candidate Conference“ (22.-23. März 1999 in Rom, AES2) vorgestellt und unter internationalen Kryptologen diskutiert. Die Kommentierungsphase endete am 15. April 1999. Auf der Basis der eingegangenen Kommentare und Analysen wählte das NIST fünf Kandidaten aus, die es am 9. August 1999 öffentlich bekanntmachte: MARS (IBM) RC6 (RSA Lab.) Rijndael (Daemen, Rijmen) Serpent (Anderson, Biham, Knudsen) Twofish (Schneier, Kelsey, Whiting, Wagner, Hall, Ferguson).

...read more

488 citations


Journal ArticleDOI
Yue-Shan Chang1, Hsin-Ta Chiao2, Satheesh Abimannan3, Yo-Ping Huang4  +2 moreInstitutions (4)
TL;DR: An Aggregated LSTM (Long Short-Term Memory) model (ALSTM) based on the L STM deep learning method is proposed that can effectively improve the accuracy of prediction of air pollution.

...read more

Abstract: During the past few years, severe air-pollution problem has garnered worldwide attention due to its effect on health and wellbeing of individuals. As a result, the analysis and prediction of air pollution has attracted a good deal of interest among researchers. The research areas include traditional machine learning, neural networks and deep learning. How to effectively and accurately predict air pollution becomes an important issue. In this paper, we propose an Aggregated LSTM (Long Short-Term Memory) model (ALSTM) based on the LSTM deep learning method. In this new model, we combine local air quality monitoring station, the station in nearby industrial areas, and the stations for external pollution sources. To improve prediction accuracy, we aggregate three LSTM models into a predictive model for early predictions based on external sources of pollution and information from nearby industrial air quality stations. We exploited the data with 17 attributes collected by Taiwan Environmental Protection Agency from 2012 to 2017 as the training data to build the ALSTM forecasting model, and we tested the model using the data collected in 2018. We conducted some experiments to compare our new ALSTM model with SVR (Support Vector Machine based Regression), GBTR (Gradient Boosted Tree Regression), LSTM, etc., in the prediction of PM2.5 for 1–8 h, and evaluated them using various assessment techniques, such as MAE, RMSE, and MAPE. The results reveal that the proposed aggregated model can effectively improve the accuracy of prediction.

...read more

39 citations


Journal ArticleDOI
Yumeng Zhang1, Li Luo1, Jianchao Yang1, Dunhu Liu2  +2 moreInstitutions (3)
TL;DR: Experimental results indicate that the proposed hybrid ARIMA-SVR approach is a promising alternative for forecasting emergency patient flow, and these findings are beneficial for efficient patient flow management and scheduling decisions optimization.

...read more

Abstract: The goal of this study is to explore and evaluate the use of a hybrid ARIMA-SVR approach to forecast daily radiology emergency patient flow. Owing to the fact that emergency patient flow is highly uncertain and dynamic, the forecasting problem is regarded as a complicated task. As the emergency patient flow may have both linear and nonlinear patterns, this paper presents a hybrid ARIMA-SVR approach, which hybridizes autoregressive integrated moving average (ARIMA) model and support vector regression (SVR) model to predict emergency patient arrivals. The proposed model is applied to 4 years of daily emergency visits data in the radiology department of a large hospital to justify the performance of the hybrid model against single models. The MAPE, RMSE and MAE of the hybrid model are 7.02%, 19.20 and 14.97, respectively. Furthermore, the hybrid model achieves better prediction performance than its competitors because it can capture the linear and nonlinear patterns simultaneously. Experimental results indicate that the proposed hybrid ARIMA-SVR approach is a promising alternative for forecasting emergency patient flow. These findings are beneficial for efficient patient flow management and scheduling decisions optimization.

...read more

14 citations


Journal ArticleDOI
Tingqing Ye1, Yuhan Liu2Institutions (2)
TL;DR: A method of parameters estimation by the principle of least squares in the multivariate uncertain regression model containing more than one response variables and assuming both explanatory variables and response variables as uncertain variables is explored.

...read more

Abstract: The multivariate regression model is a mathematical tool for estimating the relationships among some explanatory variables and some response variables. In some cases, observed data are imprecise. In order to model those imprecise data, we can employ uncertainty theory to design the uncertain regression model by regarding those data as uncertain variables. Parameters estimation is an important topic in the uncertain regression model. In this paper, we explore a method of parameters estimation by the principle of least squares in the multivariate uncertain regression model containing more than one response variables and assuming both explanatory variables and response variables as uncertain variables. Besides, when the new explanatory variables are given, we propose an approach to obtain the forecast value and the confidence interval of the response variables. At last, a numerical example of the multivariate uncertain regression model is showed.

...read more

12 citations


Journal ArticleDOI
Xu Liu1, Hibat Allah Ounifi1, Abdelouahed Gherbi1, Wubin Li2  +1 moreInstitutions (2)
TL;DR: The core idea of the proposal is when designing an ML platform, the graphics processing unit (GPU)'s high-density computing to perform model training and field programmable gate array (FPGA)’s low-latency to performs model inferencing to improve the efficiency of training and inferences significantly.

...read more

Abstract: The high-density computing requirements of machine learning (ML) is a challenging performance bottleneck. Limited by the sequential instruction execution system, traditional general purpose processors are not suitable for efficient ML. In this work, we present an ML system design methodology based on GPU and FPGA to tackle this problem. The core idea of our proposal is when designing an ML platform, we leverage the graphics processing unit (GPU)’s high-density computing to perform model training and exploit field programmable gate array (FPGA)’s low-latency to perform model inferencing. In between, we define a model converter, which enable transforming the model used by the training module to one that is used by inferencing module. We evaluated our approach through two use cases. The first is a handwritten digit recognition with convolutional neural network while the second use case is for predicting data center’s power usage effectiveness with deep neural network regression algorithm. The experimental results indicate that our solution can take advantages of GPU and FPGA’s parallel computing capacity to improve the efficiency of training and inferencing significantly. Meanwhile, the solution preserves the accuracy and the mean square error while converting the models between the different frameworks.

...read more

9 citations


Network Information
Related Authors (1)
Pachiyappan Arulmozhivarman

60 papers, 491 citations

84% related
Performance
Metrics

Author's H-index: 6

No. of papers from the Author in previous years
YearPapers
20215
20203
20192
20183
20173
20161

Top Attributes

Show by:

Author's top 5 most impactful journals