TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

Iqbal, Rahat; Doctor, Faiyaz; More, Brian; Mahmud, Shahid; Yousuf, Usman

doi:10.1016/j.ijinfomgt.2016.05.020

Cited by 17 publications

(4 citation statements)

References 68 publications

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“…Results from 13 problems for 33 different algorithms concluded self-optimized 11 of [27]). Lately other algorithms appeared for evaluation [29][30][31].…”

Section: Machine Learning Evolving Into Deep Learning Neural Netsmentioning

confidence: 99%

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

Shafqat

Fayyaz

Khattak

et al. 2021

Neural Process Lett

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Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

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“…Results from 13 problems for 33 different algorithms concluded self-optimized 11 of [27]). Lately other algorithms appeared for evaluation [29][30][31].…”

Section: Machine Learning Evolving Into Deep Learning Neural Netsmentioning

confidence: 99%

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

Shafqat

Fayyaz

Khattak

et al. 2021

Neural Process Lett

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“…Estas técnicas se refieren a BDA y consisten en un conjunto de algoritmos, estadística avanzada y análisis aplicado. Iqbal, Doctor, More, Mahmud, & Yousuf (2016) lo definen como: "(BDA) se refiere a las técnicas utilizadas para examinar y procesar BD de modo que los patrones subyacentes ocultos se revelan, las relaciones se identifican y otros conocimientos sobre el contexto de la aplicación bajo investigación están expuestos".…”

Section: Big Dataunclassified

Towards a big data framework for analyzing social media content

Jimenez-Marquez

González‐Carrasco

López-Cuadrado

et al. 2019

International Journal of Information Management

106

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“…In various fields of application, computational intelligence technology can help us better understand and solve real-world problems [3]. Post-industrial society is the further development of the industrial society of society [4]. The key variables of post industrial society are information and knowledge, and the main sectors of the economy are the third, even fourth, and fifth industries dominated by processing and service industries, such as transportation, public welfare, trade, finance, insurance, real estate, health, scientific research, and technological development [5].…”

Section: Introductionmentioning

confidence: 99%

Concept and Application of Industrial Heritage Planning Decision in Big Data Era

Han,

Zhu,

Xie

et al. 2024

SCPE

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With the rapid development of big data technology, more and more fields are realizing the importance of big data and applying it to various decision-making processes. Industrial heritage planning decisions are no exception. This article will explore the concept and application of industrial heritage planning and decision-making in the era of big data, aiming to emphasize the importance and application value of big data in industrial heritage planning and decision-making. This article analyzes the development relationship between the construction and transformation of urban industrial utilization and the theory and practice of industrial heritage. Through theoretical and practical technical analysis of specific regions, the value utilization of industrial simulation design has been improved. Through in-depth data mining, the government found that the industrial structure of the region is dominated by traditional manufacturing, lacking support from high-tech industries, and the pollution problem of the surrounding environment is also relatively serious. In response to these issues, corresponding planning plans have been formulated, including measures to introduce high-tech industries, optimize industrial structure, and improve environmental quality. By implementing these measures, the comprehensive strength of the region has been significantly improved and the economic transformation and upgrading have been successfully achieved.

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TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

Cited by 17 publications

References 68 publications

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics

Towards a big data framework for analyzing social media content

Concept and Application of Industrial Heritage Planning Decision in Big Data Era

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