High Availability of Datacenter in Karachi, Pakistan

The datacenter is the core infrastructure of today's world. Every data center should have many resources and applications that are running for several decades or even more. Many failures happen in the physical datacenter in on-premises environments. In this research paper evaluating the datacenter by having the dataset provided by Premier Systems (Pvt.) Ltd. This dataset is having all the failures and datacenter related issues from Jan-2016 to Dec-2019 in Karachi, Pakistan. This research performed the Linear Regression via the Microsoft Azure Machine Learning Studio for the machine learning model. It would allow us to know which fault will more and which is not for the concern requirement. This experiment would have the Feature Engineering feature in Microsoft Azure Machine Learning Studio, which will automatically apply the filters required. After knowing which the central issue are related to any physical data center in Karachi. This research allows to handle the required precaution in datacenters of Karachi, Pakistan.

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Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202874 ◽

2021 ◽

pp. 1-13

Author(s):

C S Pavan Kumar ◽

L D Dhinesh Babu

Keyword(s):

Machine Learning ◽

Social Networks ◽

Social Media ◽

Sentiment Analysis ◽

Membership Function ◽

Online Social Networks ◽

Learning Model ◽

Feature Engineering ◽

Machine Learning Model ◽

Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

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Developing an efficient feature engineering and machine learning model for detecting IoT-Botnet cyber attacks

IEEE Access ◽

10.1109/access.2021.3092054 ◽

2021 ◽

pp. 1-1

Author(s):

Mrutyunjaya Panda ◽

Abd Allah A. Mousa ◽

Aboul Ella Hassanien

Keyword(s):

Machine Learning ◽

Learning Model ◽

Cyber Attacks ◽

Feature Engineering ◽

Machine Learning Model

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Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model

HKIE Transactions ◽

10.33430/v28n1thie-2020-0005 ◽

2021 ◽

Vol 28 (1) ◽

pp. 22-30

Author(s):

Hon Fung Chow

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Hybrid Model ◽

Regression Models ◽

Demand Forecasting ◽

Learning Model ◽

Support Vector ◽

Linear Regression Models ◽

Short Term ◽

Machine Learning Model

This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.

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Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip

10.1115/gt2021-58914 ◽

2021 ◽

Author(s):

Enzo Losi ◽

Mauro Venturini ◽

Lucrezia Manservigi ◽

Giuseppe Fabio Ceschini ◽

Giovanni Bechini ◽

...

Keyword(s):

Machine Learning ◽

Gas Turbine ◽

Peak Load ◽

Learning Model ◽

Remaining Useful Life ◽

Data Selection ◽

Feature Engineering ◽

Homogeneous Groups ◽

Machine Learning Model ◽

Definition Of

Abstract A gas turbine trip is an unplanned shutdown, of which the consequences are business interruption and a reduction of equipment remaining useful life. Therefore, detection and identification of symptoms of trips would allow predicting its occurrence, thus avoiding damages and costs. The development of machine learning models able to predict gas turbine trip requires the definition of a set of target data and a procedure of feature engineering that improves machine learning generalization and effectiveness. This paper presents a methodology that focuses on the steps that precede the development of a machine learning model, i.e., data selection and feature engineering, which are the key for a successful predictive model. Data selection is performed by partitioning units into homogeneous groups according to different criteria, e.g., type, region of installation, and operation. A subsequent matching algorithm is applied to rotational speed data of multiple gas turbine units to identify start-ups and shutdowns so that the considered units can be partitioned according to their operation, i.e., base load or peak load. Feature engineering aims at creating features that improve machine learning model accuracy and reliability. First, the Discrete Fourier Transform is used to identify and remove from the time series the seasonal components, i.e., patterns that repeat with a given periodicity. Then, new features are created based on gas turbine domain knowledge in order to capture the complex interactions among system variables and trip occurrence. The outcomes of this paper are the definition of a set of target examples and the identification of a meaningful set of features suitable to develop a machine learning model aimed at predicting gas turbine trip.

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