Analysis of Dimensionality Reduction Techniques on Big Data

Reddy, G. Thippa; Maddikunta, Praveen Kumar Reddy; Lakshmanna, Kuruva; Kaluri, Rajesh; Rajput, Dharmendra Singh; Srivastava, Gautam; Baker, Thar

doi:10.1109/access.2020.2980942

Cited by 506 publications

(245 citation statements)

References 36 publications

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“…Training the vast data with variations in the dimensionality reduces the effectiveness of the machine learning model. The authors in [34] implement a secondary principal component analysis (PCA) algorithm [35] to decrease the data dimensions. This algorithm is applied to manage the ML techniques to increase the stability of the grid systems.…”

Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches. INDEX TERMS Multidirectional long short-term memory (MLSTM), machine learning (ML), smart grid (SG), cyber physical systems (CPS).

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Section: Literature Surveymentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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“…This approach yielded high accuracy. Some more research work on the neural network and knowledge reductions are mentioned in ref [29][30][31][32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

et al. 2020

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The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

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“…The system architecture is implemented with a Support Vector Machine. To reduce the irrelevant attributes and keep only important attributes Raddy et al [39] propose a method that uses two important features selection methods Linear Discriminant Analysis and Principal Component Analysis. Four machine learning algorithms Naive Bayes, Random Forest, Decision Tree Induction, and Support Vector Machine classifiers are used by the authors.…”

Section: Related Workmentioning

confidence: 99%

Attributes Reduction in Big Data

Albattah

Khan

2020

Applied Sciences

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Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.

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Analysis of Dimensionality Reduction Techniques on Big Data

Cited by 506 publications

References 36 publications

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

Attributes Reduction in Big Data

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