Browsing by Author "Manish Kumar Pandey"

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A note on boundaries in atlas maps
(Geological Society of India, 2014) K.N. Prudhvi Raju; Manish Kumar Pandey; Shraban Sarkar
[No abstract available]
A novel storage architecture for facilitating efficient analytics of health informatics big data in cloud
(Institute of Electrical and Electronics Engineers Inc., 2017) Manish Kumar Pandey; Karthikeyan Subbiah
Analytics of health big data are very crucial for providing cost effective quality health care. Over recent years, the analytics on healthcare big data has evolved into a challenging task for getting insights into a very large data set for improving the health services. This enormous amount of data, which is being generated incessantly over a long period of time, has put a great deal of stress on the write performance as well as on scalability. Moreover, there is a requirement of efficient storage and meaningful processing of these data which is an another challenging issue. The traditional relational databases, which were used in the storage of health data, are now unable to handle due to its massive and varied nature. Besides, these databases have some inherent weakness in terms of scalability, storing varied data format, etc. So there is a necessity for a new kind of data storage management system. This paper proposes a new big data storage architecture consisting of application cluster and a storage cluster to facilitate read/write/update speedup as well as data optimization. The application cluster is used to provide efficient storage and retrieval functions from the users. The storage services will be provided through the storage cluster. © 2016 IEEE.
A probe into performance analysis of real-time forecasting of endemic infectious diseases using machine learning and deep learning algorithms
(Springer Science and Business Media Deutschland GmbH, 2021) Manish Kumar Pandey; Prashant K. Srivastava
The current work aims at probing the performance of real-time forecasting of endemic infectious diseases by means of machine learning and deep learning techniques. An LSTM-based time series forecasting framework and machine learning-based framework are proposed for forecasting the endemic infectious diseases in real time. With recent outbreaks of Ebola, Zika, cholera, and COVID 2019, a question is being raised on our alertness as well as preparedness toward controlling the spread of these pandemics. Accurate and reliable prediction occurrences of these diseases are compulsory for the health personals to enable timely response in handling these outbreaks. The diversities of the communities make it more complex along with the humongous data generated due to the convergence of SMAC technologies. The data generated due to this complex network is nonlinear and non-stationary. Processing of this data requires an effort from a multidimensional perspective. The current work proposed the utilization of machine learning and deep learning-based long short-term memory (LSTM) techniques for the assessment of time series forecasting of casualties in case of cholera outbreak that happened recently in Yemen. The feasibility of these two techniques is probed using performance evaluation metrics. The core objective of using these two techniques is in considering nonlinear and non-stationary behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
An Econometric Time Series Forecasting Framework for Web Services Recommendation
(Elsevier B.V., 2020) Vijendra Pratap Singh; Manish Kumar Pandey; Pangambam Sendash Singh; Subbiah Karthikeyan
The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, various econometric models are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) value is used as an evaluation metric and their value for the prediction of Response time is found to be 1.7392e+04 and 1.7416e+04 respectively with the ARMA model. The AIC and BIC value for throughput (TP) is found to be 1.5249e+04 and 1.5334e+04 respectively with the ARIMA. The value of variance is 13.2437 and 6.8131 for RT and TP respectively which is also the lowest among other models with t-statistic greater than the p-value. Thus, the experimental results show that the ARMA and ARIMA model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time and throughput respectively. © 2020 The Authors. Published by Elsevier B.V.
An empirical mode decomposition (EMD) enabled long sort term memory (LSTM) based time series forecasting framework for web services recommendation
(IOS Press BV, 2019) Vijendra Pratap Singh; Manish Kumar Pandey; Pangambam Sendash Singh; Subbiah Karthikeyan
The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, empirical mode decomposition (EMD) enabled deep learning model long short term memory (LSTM) is used for the prediction of these time aware QoS parameters and the results are compared with the previous approaches. The experimental results show that the EMD-LSTM based Time Series Forecasting Framework is performing better. The RMSE, MAE and MAPE are used as an evaluation metric and their value for the prediction of Response time (RT) is found to be 0.085661, 0.049031 and 1.46208 respectively. The RMSE, MAE and MAPE are used as an evaluation metric and their value for the prediction of throughput (TP) is found to be 0.043878, 0.030688 and 1.485613 respectively. Thus, the experimental results show that the EMD-LSTM model of Time Series Forecasting for Web Services Recommendation Framework is performing better as compared to previous methods. © 2019 The authors and IOS Press. All rights reserved.
An LSTM Based Time Series Forecasting Framework for Web Services Recommendation
(Instituto Politecnico Nacional, 2020) Vijendra Pratap Singh; Manish Kumar Pandey; Pangambam Sendash Singh; Subbiah Karthikeyan
The convergence of Social Mobility Analytics and Cloud (SMAC) technologies gives rise to an unforeseen aggrandization of the web services on the internet. The resilience and payment-based approach of the cloud makes it an obvious choice for the deployment of web services-based applications. Out of available web services, to gratify the similar functionalities, the choice of the web service based on the personalized quality of service (QoS) parameters plays an important role in determining the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the non-functional performance of web services correlated behavior with the invocation time. This happens because service status usually changes over time. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and becomes a challenging research issue. In this study, LSTM based deep learning models were used for the prediction of these time aware QoS parameters and the results are compared with the previous approaches. The experimental results show that the LSTM based Time Series Forecasting Framework is performing better. The RMSE, MAE, and MAPE are used as an evaluation metric and their value for the prediction of Response time (RT) is found to be 0.030269, 0.02382 and 0.59773 respectively with adaptive moment estimation as the training option and is found to be 0.66988, 0.66465 and 27.9934 respectively with root mean square propagation as the training option. The RMSE, MAE, and MAPE value for the prediction of throughput (TP) is found to be 0.77787, 0.4792 and 1.61 respectively with adaptive moment estimation as the training option and is found to be 0.2.7087, 1.4076and 7.1559 respectively with root mean square propagation as the training option respectively. Thus, the experimental results show that the LSTM model of Time Series Forecasting for Web Services Recommendation Framework is performing better as compared to previous methods. © 2020 Instituto Politecnico Nacional. All rights reserved.
Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators
(Taylor and Francis Ltd., 2022) Ramandeep Kaur M. Malhi; Manish Kumar Pandey; Akash Anand; Prashant K. Srivastava; George P. Petropoulos; Prachi Singh; G. Sandhya Kiran; B.K. Bhattarcharya
Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about foliar traits. The current study investigated the performance of three novel Band Selection (BS) algorithms (i.e. the Chi-squared-statistics based attribute evaluator (CSS), the Recursive elimination of features-based attribute evaluator (REF) and the Correlation-based attribute subset evaluator (CBS)) in identifying the spectral bands of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) from visible and Near Infrared (NIR) regions that are sensitive to variation in Chlorophyll Content (CC). Identified bands were employed to formulate Hyperspectral Indices (HIs) by incorporating combinations of Blue, Green, Red, and NIR regions. CC models were built by establishing a linear fit between ground CC and HIs. For all the three BS algorithms, optimum bands varied for visible and NIR regions. REF-HI (NIR,R), REF-HI(NIR,R + G), CSS-HI(NIR,R) and CSS-HI(NIR,R + G) had the best correlation with CC. HI(NIR,R) is identified as the best HI and REF the best BS algorithm for retrieving CC. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Comparative study on machine learning techniques in predicting the QoS-values for web-services recommendations
(Institute of Electrical and Electronics Engineers Inc., 2015) Sunil Kumar; Manish Kumar Pandey; Abhigyan Nath; Karthikeyan Subbiah; Manoj Kumar Singh
This is an era of Internet computing and computing as a service on the internet is called cloud computing. Mainly three services like SaaS (applications), PaaS, and IaaS are being accessed through internet on demand, pay as per usage basis. Quality of Service (QoS) is the main issue in internet based computing for service providers and user-dependent as well as user-independent QoS parameters. In the current work we compared different machine learning algorithms for predicting the response time and throughput QoS values using past usage data. Bagging and support vector machines are found to be better performing prediction methods in comparison with other learning algorithms. © 2015 IEEE.
Effect of nitrate presoaking of okra (Abelmoschus esculentus L.) seeds on growth and nitrate assimilation of seedlings
(2003) Bandana Bose; Manish Kumar Pandey
Percent germination, radicle length, change in fresh weight, absolute water content and water uptake of okra (Abelmoschus esculentus L) seeds were higher with soaking of the seeds in different nitrate containing salts like Mg (NO3)2, Ca (NO3)2 and KNO 3 during germination. Seed dry weight decreased with the increasing time of germination and that was more pronounced in nitrate treatments. The higher level of electrical conductivity (EC) and nitrate content were noticed in nitrate treated seeds. Nitrate reductase activity of the cotyledons of 24 h nitrate soaked seeds was more as compared to distilled water treated seeds. However, among nitrate salts, Mg (NO3)2 showed higher response for most of the parameters.
Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques
(Springer Science and Business Media B.V., 2022) Pangambam Sendash Singh; Vijendra Pratap Singh; Manish Kumar Pandey; Subbiah Karthikeyan
In the era of climate change, monitoring and effective retrieval of soil, water bodies, vegetation parameters etc. are of utmost importance which is successfully being executed using remote sensing from last few decades. The advancement of technologies has enabled us to reach effective decision making through these sensors. The advantage of acquiring multitemporal spatially continuous data sometimes turns into a disadvantage due to class imbalance where minority class instances are often misclassified by most of the classifiers. The current work explored the solution to handle this problem by resampling the datasets before the application of classification algorithms by proposing a new computationally efficient class wise resampling technique which is based on SMOTE and centroid-based clustering. The experiment was conducted on two benchmarked publicly available hyperspectral datasets. The output of the current work shows the superiority of the current work over past studies based on the performance evaluation metrics, accuracy, precision, recall and kappa values. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.
Future pathway for research and emerging applications in GPS/GNSS
(Elsevier, 2021) Manish Kumar Pandey; Prashant K. Srivastava; George P. Petropoulos
Satellite navigation system has an unparalleled advantage in navigational technologies due to its high-precision delivery of location in terms of position, time, and velocity on any object or person. It had found its application in the areas ranging from transportation, geodesic, communication, disaster prevention to its handling to security, etc., to name a few. This chapter provides an overview of the satellite navigation system and the associated vulnerabilities to explore the future pathway for research and emerging applications. This chapter begins with a brief introduction of the satellite navigation systems and briefly describes the constellations of the Global Navigation Satellite System (GNSS). The evolution of the GNSS is discussed along with its market emergence and convergence followed by a detailed discussion on the challenges and vulnerabilities of the GNSS. These challenges and vulnerabilities provide a pathway for future research and help the researchers in developing emerging applications. © 2021 Elsevier Inc. All rights reserved.
Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology
(Nature Research, 2021) Ram Kumar Singh; Martin Drews; Manuel De la Sen; Prashant Kumar Srivastava; Bambang H. Trisasongko; Manoj Kumar; Manish Kumar Pandey; Akash Anand; S.S. Singh; A.K. Pandey; Manmohan Dobriyal; Meenu Rani; Pavan Kumar
The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections. © 2021, The Author(s).
Improved Carpooling Experience through Improved GPS Trajectory Classification Using Machine Learning Algorithms
(MDPI, 2022) Manish Kumar Pandey; Anu Saini; Karthikeyan Subbiah; Nalini Chintalapudi; Gopi Battineni
Globally, smart cities, infrastructure, and transportation have led to a rise in vehicle numbers, resulting in an increasing number of problems. This includes problems such as air pollution, noise pollution, high energy consumption, and people’s health. A viable solution to these problems is carpooling, which involves sharing vehicles between people going to the same location. As carpooling solutions become more popular, they need to be implemented efficiently. Data analytics can help people make informed decisions when selecting a ride (Car or Bus). We applied machine learning algorithms to select the desired ride (Car or Bus) and used feature ranking algorithms to identify the foremost traits for selecting the desired ride. Based on the performance evaluation metric, 11 classifiers were used for the experiment. In terms of selecting the desired ride, Random Forest performs best. Using ten-fold cross-validation, we obtained a sensitivity of 87.4%, a specificity of 73.7%, an accuracy of 81.0%, a sensitivity of 90.8%, a specificity of 77.6%, and an accuracy of 84.7% using leave-one-out cross-validation. To identify the most favorable characteristics of the Ride (Car or Bus), the recursive elimination of features algorithm was applied. By identifying the factors contributing to users’ experience, the service providers will be able to rectify those factors to increase business. It has been determined that the weather can make or break the user experience. This model will be used to quantify and map intrinsic and extrinsic sentiments of the people and their interactions with locality, socio-economic conditions, climate, and environment. © 2022 by the authors.
Local Binary Ensemble based Self-training for Semi-supervised Classification of Hyperspectral Remote Sensing Images
(Instituto Politecnico Nacional, 2020) Pangambam Sendash Singh; Vijendra Pratap Singh; Manish Kumar Pandey; Subbiah Karthikeyan
Supervised classification of hyperspectral remote sensing images is still challenging due to the scarcity of enough labelled samples. Semi-supervised methods have been adopted to handle this issue. Self-training is a popular semi-supervised technique which is widely used for training a classifier with limited labelled data and a large quantity of unlabeled data. However, traditional self-training approaches often give poor classification results in high dimensional data. In the current work, a novel efficient self-training approach for handling the deficiency of labelled samples for semi-supervised classification of hyperspectral remote sensing images is proposed. The proposed method first trains an ensemble of locally specialized supervised binary classifiers independently by using the dimensionally reduced spectral feature vectors of few available labelled samples. The trained local binary classifiers are then used to extend the labelled set by iterative addition of highly informative unlabeled samples to it by exploiting both the spectral and spatial information of the hyperspectral image. The classifiers are then retrained with the extended dataset in a batchwise manner and the procedure is repeated until adequate quantity of labelled samples are generated. Finally, a supervised multiclass classifier is trained on the extended dataset to produce the final classification map. Experimental results on two benchmark hyperspectral image datasets prove the effectiveness of the proposed method over supervised and traditional self-training based semi-supervised pixelwise classification approach in terms of different classification measures. © 2020 Instituto Politecnico Nacional. All rights reserved.
Missing QoS-values predictions using neural networks for cloud computing environments
(Institute of Electrical and Electronics Engineers Inc., 2016) Sunil Kumar; Manish Kumar Pandey; Abhigyan Nath; Karthikeyan Subbiah
Cloud computing environment is influenced by user-dependent quality of service (QoS) parameters in evaluating the performance of Web services apart from others factors. Among the performance QoS parameters, mainly response-time and throughput could be modulated to provide very efficient services for cloud users. As per user's requirement, the service provider's recommendation of appropriate Web services to the end-users with proper QoS satisfaction is one of the critical issues. This can be recommended to end-users in the Service Level Agreement (SLA) under Web Service Modeling Ontology (WSMO) of WS-Policy. Generally, the matrix of collected QoS parameter values is sparse and the accurate prediction of the missing QoS values is important for the recommendation of appropriate web services to the end users. To address this issue, we worked out an artificial neural network model for the prediction of missing QoS-values using past QoS performance parameter data. In this current work, the performances of different learning algorithms of ANN are analyzed for enhanced prediction of QoS performance values. The ANN model with Bayesian-Regularization is found to be better performing when compared to other learning algorithms. © 2015 IEEE.
Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation
(Elsevier B.V., 2020) Vijendra Pratap Singh; Manish Kumar Pandey; Pangambam Sendash Singh; Subbiah Karthikeyan
The convergence of Social Mobility Analytics and Cloud (SMAC) technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of the non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turns out to be a challenging research issue. In the current work, various neural network models like Levenberg Marquardt (LM), Bayesian-Regularization (BR) and Scaled-Conjugate-Gradient (SCG) are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Pearson's Correlation Coefficient is used as an evaluation metric and their value for the prediction of Response time is found to be 0.84985 and for throughput (TP) is found to be 0.99082 with the Levenberg Marquardt algorithm. Thus, the experimental results show that the with the Levenberg Marquardt model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time as well as throughput. © 2020 The Authors. Published by Elsevier B.V.
Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor
(Springer Science and Business Media B.V., 2024) Pavan Kumar; Aishwarya; Prashant Kumar Srivastava; Manish Kumar Pandey; Akash Anand; Jayanta Kumar Biswas; Martin Drews; Manmohan Dobriyal; Ram Kumar Singh; Manuel De la Sen; Sati Shankar Singh; Ajai Kumar Pandey; Manoj Kumar; Meenu Rani
There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.
One-class Classifier Ensemble based Enhanced Semisupervised Classification of Hyperspectral Remote Sensing Images
(Institute of Electrical and Electronics Engineers Inc., 2020) Pangambam Sendash Singh; Vijendra Pratap Singh; Manish Kumar Pandey; Subbiah Karthikeyan
The scarcity of labelled training data as well as uneven class distribution among the limitedly available labelled data have posed a critical issue in supervised hyperspectral remote sensing image classification. Semisupervised methods can be an easy solution to this critical problem. However, traditional self-training based semi-supervised approaches often give poor classification results in high dimensional multiclass classification problems. This paper proposes a novel efficient one-class classifier ensemble based self-training approach for semisupervised classification of hyperspectral remote sensing images with limited labelled data. The proposed method initially trains an ensemble of locally specialized one-class classifiers independently by using the dimensionally reduced spectral feature vectors of the available labelled samples. The trained one-class classifiers are then used to extend the labelled set by iterative addition of high quality unlabelled samples to it through the exploitation of both spectral and spatial information. The classifiers are then retrained with the extended dataset in a batchwise fashion. The procedure is repeated until an adequate quantity of labelled samples are generated. Finally, a supervised multiclass classifier is trained on the extended dataset for the final image classification purpose. Experimental results on two benchmark hyperspectral images verify the effectiveness of the proposed method over supervised and traditional self-training based semisupervised pixelwise classification in terms of different classification measures. © 2020 IEEE.
Optimal balancing & efficient feature ranking approach to minimize credit risk
(Elsevier Ltd, 2021) Manish Kumar Pandey; Mamta Mittal; Karthikeyan Subbiah
The banking industries are struggling with massive growth in the Non-Performing Assets (NPAs) that is raising the concerns of the financial institutions across the world. For gaining sustainable competitive advantages: detection, prediction, and prevention of credit Risks are becoming the foremost priorities for the banks. This data is vast, highly unstructured and imbalanced; thus, optimal balancing and efficient feature ranking are required, to predict the Credit Risk customers using Machine Learning techniques. Further, feature ranking algorithms are applied to identify the most vital characteristics of triggering the Credit Risk. The experiments have been conducted on credit Risk data set from a German bank, downloaded from the standard data repository of the UCI. Random Forest at optimal balancing ratio of 1:1.1335 has been found to be the best performing with a sensitivity of 81.6%, specificity value of 85.3%, the accuracy of 83.4%, MCC of 0.669 and AUC of 0.914. © 2021
Performance Analysis of Ensemble Supervised Machine Learning Algorithms for Missing Value Imputation
(Institute of Electrical and Electronics Engineers Inc., 2016) Sunil Kumar; Manish Kumar Pandey; Abhigyan Nath; Karthikeyan Subbiah
In this era of cloud computing, web services based solutions are gaining popularity. The applications running on distributed environment seek new parameters for them to perform efficiently to satisfy end user's requirements. Finding these parameters for increasing efficiency has become a talk of researchers now days. Non functional performance of a web service is described through User dependent QoS properties. These QoS parameters are generally described in WS-Policy in Service Level Agreement (SLA). Usually in web service QoS datasets, web service QoS values are missing, which makes missing value imputations an important job while working with cloud web services. In the current work we compared the prediction accuracy of two groups of supervised machine learning ensembles based Meta learners: bagging and additive regression (boosting) with a fusion of the seven base learners in both. Random forest is found to be better performing in both Meta learners: bagging and boosting than other learning algorithms. © 2016 IEEE.