Platforms for big data analytics: Trend towards hybrid era

Londhe, Alka; Rao, P. Venkateswara

doi:10.1109/icecds.2017.8390056

Cited by 24 publications

(6 citation statements)

References 10 publications

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“…Archenaa and Anita [6] conducted a survey on the applications of BDA in healthcare and the government. Londhe and Rao [7] presented various software frameworks available for BDA and discussed some widely used data mining algorithms. Grady et al [8] illustrated the implications of an agile process for the cleansing, transformation, and analytics of data in BDA.…”

Section: Related Work a Big Data Analyticsmentioning

confidence: 99%

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

et al. 2019

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Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-theart data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process. INDEX TERMS Big data analytics (BDA), data mining, data visualization, neural network, time series forecasting.

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Section: Related Work a Big Data Analyticsmentioning

confidence: 99%

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

et al. 2019

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“…The conventional classifier-based sentiment prediction is not capable to handle these large datasets. So, for an effective and efficient sentiment prediction, hybrid models on hybrid platforms need to be used [16]. In this paper an efficient deep learning-based hybrid model for sentiment prediction is proposed.…”

Section: Proposed Methodologymentioning

confidence: 99%

Incremental Learning based Optimized Sentiment Classification using Hybrid Two-Stage LSTM-SVM Classifier

Londhe,

Rao

2022

IJACSA

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Sentiment analysis is a subtopic of Natural Language Processing (NLP) techniques that involves extracting emotions from unprocessed text. This is commonly used on customer review posts to automatically determine if user / customer sentiments are negative or positive. But quality of these analysis is completely dependent on its quantity of raw data. The conventional classifier-based sentiment prediction is not capable to handle these large datasets. Hence, for an efficient and effective sentiment prediction, deep learning approach is used. The proposed system consists of three main phases, such as 1) Data collection and pre-processing, 2) Count vectorizer and dimensionality reduction is used for feature extraction, 3) Hybrid classifier LSTM-SVM using incremental learning. Initially the input raw data is gathered from the e-commerce sites for product reviews and collected raw is given to pre-processing, which do tokenization, stop word removal, lemmatization for each review text. After pre-processing, features like keywords, length, and word count are extracted and given to feature extraction stage. Then a hybrid classifier using two-stage LSTM and SVM is developed for training the sentimental classes by passing new features and classes for incremental learning. The proposed system is developed using python and it is compared with the state-of-the-art classification techniques. The performance of the proposed system is compared based on performance metrics such as accuracy, precision, recall, sensitivity, specificity etc. The proposed model performed an accuracy of 92% which is better compared to the state-of-the-art existing techniques.

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“…As a consequence, the collected data is large volumes of data and it is also in the unstructured data formats. The collected data is stored into different databases [9,10,17].…”

Section: Crime Data Analysismentioning

confidence: 99%

Crime Data Analysis, Visualization and Prediction Using LSTM

Manengadan¹,

Nandanan²,

Subash³

2021

IJDSA

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Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process.

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Platforms for big data analytics: Trend towards hybrid era

Cited by 24 publications

References 10 publications

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Incremental Learning based Optimized Sentiment Classification using Hybrid Two-Stage LSTM-SVM Classifier

Crime Data Analysis, Visualization and Prediction Using LSTM

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