Proposed big data architecture for facial recognition using machine learning

Asaithambi, Suriya Priya R.; Venkatraman, Sitalakshmi; Venkatraman, Ramanathan

doi:10.3934/electreng.2021005

Cited by 10 publications

(8 citation statements)

References 47 publications

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“…Some studies have explored cross-domain areas such as between technologies and management of people requirements [35,36]. However, most studies published on smart cities remain to be 'thing-based' rather than people-based, where the emphasis of the study predominantly revolves more around infrastructure, engineering, Internet, data, technology, etc., than about the people who live in it [11,12] as claimed in the recent studies.…”

Section: Management-people/businessmentioning

confidence: 82%

“…Insights obtained from the information collected are utilised to manage multiple resources, assets and services more efficiently, thereby improving the operations enabling corporate sustainability [2] (considering sustainability's four pillars affecting environmental, social, human and economic spheres) and service quality across the entire city [3]. Some recent reviews carried out in this context [4] covering the technical standards [5], future of smart cities [6], technologies and sustainability [7,8], Big Data architecture [9], self-driving vehicles [10] smart home automation [11], facial recognition [12] and applications of artificial intelligence and machine learning [13] to name a very few of the studies. The objective of this review is to reorient the perception of 'smart city' from the idea of machines and things, and towards people, which should be at the heart of these things.…”

Section: Introductionmentioning

confidence: 99%

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Creating Smart Cities: A Review for Holistic Approach

Rozario

Venkatraman²,

Marimuthu

et al. 2021

ASI

Self Cite

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With the rapid proliferation of Internet of Things (IoT) into urban people’s everyday walk of life, the functions of smart cities are fast approaching to be embedded in every step of people’s life. Despite the concept of smart cities founded in the late 1990s, there has been limited growth until recent popularity due to the advancements of IoTs. However, there are many challenges, predominantly people-centric, that require attention for the realisation of smart cities and expected real-life success. In this paper, we intend to investigate the state-of-the-art focus of smart cities from three angles: infrastructure engineering, information technology and people-centric management. We adopt a mixed-methods analysis of currently published literature on the topic of smart cities. Our study attempts to draw attention to the need for developing smart cities with a holistic approach involving multiple perspectives rather than a siloed emphasis on technology alone. We highlight that the fields of specialisations such as information technology and infrastructure engineering in contributing to smart cities need a cross-domain holistic approach of managing people-centric service requirements for improving consumer satisfaction and sustainability.

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Section: Management-people/businessmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Creating Smart Cities: A Review for Holistic Approach

Rozario

Venkatraman²,

Marimuthu

et al. 2021

ASI

Self Cite

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“…In [ 32 ], the authors propose a new big data architecture that uses image and text data to improve the sentiment analysis task. The architecture is designed based on the three big data principles - Volume, Velocity, and Variety.…”

Section: Related Workmentioning

confidence: 99%

Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches

2023

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Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people’s hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people’s sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people’s sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people’s reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people’s feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.

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“…Testing procedure and data were analyzed using QuEChERS with GC-MS. Organochlorine pesticide residues are hazardous to humans and animals, causing immediate and long-term consequences such as immune system and reproductive damage, mutation, and carcinogenic effects. In [12], a big data architecture enabling preprocessing, as well as categorization of image and text analytics was presented using two significant processes: the big data (BD) workflow and the machine learning (ML) workflow. e retrieved tweets from a user may contain geolocation information in some of them, which, if not handled appropriately, might result in an error since various pages of tweet entries would have varied numbers of fields.…”

Section: Related Workmentioning

confidence: 99%

“…We are running Centos 7.3. e latest edition of Hadoop includes 2.6.5. Table 2 (14,12), 13)/dist (20,24) Table 3: F-score (%) of proposed and existing techniques.…”

Section: Performance Analysismentioning

confidence: 99%

Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

Zhang¹

2022

Mobile Information Systems

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From standard software to crucial applications, face recognition (FR) is always at the core of many unique advances over the past 20 years. Big data is a rapidly growing collection of data. It is important in many applications such as medical, academic, and industries, and it refers to vast data sources that are hard to analyze, store, and interpret for subsequent procedures. Hadoop is an open-source large data processing platform that stores and analyses data in scalable computer clusters. FR technology is built on Hadoop processing to boost the effectiveness of recognition using MapReduce computing. This paper presents a novel adaptive fine-tuned AdaBoost (AFTA) algorithm to enhance the FR in the Hadoop processing. Collected face data sets are employed in preprocessing stage to normalize and enhance the superiority through the median filter (MF) and contour-based image enhancement (CIE), correspondingly. To handle vast quantities of the data, boosted k-means clustering (BKC) approach is used over the Hadoop servers for the MapReduce process. The binary partition tree (BPT) approach is employed in the segmentation stage to split the data into many subgroups. To lessen the dimension of the data, we use the Gabor filter. To select the consistent features, threshold-based binary particle swarm optimization (T-BPSO) is applied. Then, our proposed technique is utilized in the FR process. Finally, the performance metrics of the proposed technique are examined and compared with existing techniques to accomplish our research with the greatest effectiveness. For accuracy, precision, recall, and F-score, we attained the proposed (AFTA) values of 99.67, 97.13, 95.21, and 94.11, respectively. The outcomes are depicted in graphical representation by employing the MATLAB tool.

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Proposed big data architecture for facial recognition using machine learning

Cited by 10 publications

References 47 publications

Creating Smart Cities: A Review for Holistic Approach

Creating Smart Cities: A Review for Holistic Approach

Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches

Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

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