Machine Learning Algorithms in Big data Analytics

Karade, Divya; Bhargavi, P.; Jyothi, S.

doi:10.26438/ijcse/v6i1.6370

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…Deep learning is an area of machine learning that relies on algorithms influenced by the function and structure of the brain, referred to as Artificial Neural Networks [7]. Unlike most classical learning techniques, which rely on shallow-structured learning architectures, deep learning primarily employs supervised and/or unsupervised algorithms in deep architectures to automatically learn hierarchical representations [11].…”

Section: Deep Learningmentioning

confidence: 99%

“…Unlike most classical learning techniques, which rely on shallow-structured learning architectures, deep learning primarily employs supervised and/or unsupervised algorithms in deep architectures to automatically learn hierarchical representations [11]. Deep learning has the property of improving model performance by expanding the depth or representational capacity of the model by training it with additional examples [7]. It is more appropriate to use the architectures and algorithms of deep learning to work with the variety and volume of big data analytics [12].…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Machine Learning for Big Data Processing

Almutiri¹,

Alhabeeb²,

Alhumud³

et al. 2022

Journal on Big Data

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Today's world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives. New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms. Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it. This study conducts a literature review on the application of machine learning techniques in big data processing. It provides a general overview of machine learning algorithms and techniques, a brief introduction to big data, and a discussion of related works that have used machine learning techniques in a variety of sectors to process big amounts of data. The study also discusses the challenges and issues associated with the usage of machine learning for big data.

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Section: Deep Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

A Survey of Machine Learning for Big Data Processing

Almutiri¹,

Alhabeeb²,

Alhumud³

et al. 2022

Journal on Big Data

View full text Add to dashboard Cite

show abstract

“…Rathor and Gyanchandani [41] and Divya, Bhargavi and Jyothi [42] present a comparative analysis of ML algorithms to better reconcile Big Data challenges based on optimized performance for time and the precision obtained in the prediction.…”

Section: Challenges Of Machine Learning In Big Datamentioning

confidence: 99%

Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture

Cravero

Sepúlveda

2021

Electronics

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The data generated in modern agricultural operations are provided by diverse elements, which allow a better understanding of the dynamic conditions of the crop, soil and climate, which indicates that these processes will be increasingly data-driven. Big Data and Machine Learning (ML) have emerged as high-performance computing technologies to create new opportunities to unravel, quantify and understand agricultural processes through data. However, there are many challenges to achieve the integration of these technologies. It implies making some adaptations to ML for using it with Big Data. These adaptations must consider the increasing volume of data, its variety and the transmission speed issues. This paper provides information on the use of Big Data and ML for agriculture, identifying challenges, adaptations and the design of architectures for these systems. We conducted a Systematic Literature Review (SLR), which allowed us to analyze 34 real cases applied in agriculture. This review may be of interest to computer or data scientists and electronic or software engineers. The results show that manipulating large volumes of data is no longer a challenge due to Cloud technologies. There are still challenges regarding (1) processing speed due to little control of the data in its different stages, raw, semi-processed and processed data (value data); (2) information visualization systems, which support technical data little understood by farmers.

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“…User Interface merupakan bagian yang sangat penting dalam hal untuk meningkatkan kegunaan aplikasi karena merupakan media interaksi manusia dan komputer (Divya et al, 2018). Berikut merupakan tampilan rancangan user interface halaman utama sistem informasi kearsipan pengelolaan surat.…”

Section: Perancangan User Interfaceunclassified

Evaluasi Perancangan Sistem Informasi Pengelolaan Pengarsipan Surat Berbasis Web

Fathullah¹,

Ambarwati²

2020

Jur.Ilm.Kom.Bis

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Dalam sebuah organisasi, penggunaan teknologi informasi diantaranya sistem pengarsipan surat masuk surat keluar sangat penting untuk menunjang perkembangan organisasi dari sisi kegiatan operasional, salah satu organisasi yang membutuhkan sistem infromasi pengarsipan surat yaitu Lembaga BS di Kabupaten Sidoarjo. Penyimpanan arsip secara fisik dapat menimbulkan kerusakan, kendala dalam proses pencarian dan memerlukan tempat penyimpanan yang besar. Pada penelitian ini akan dibuat suatu perancangan sistem informasi pengersipan surat menggunakan metode berorientasi objek dan pemodelan Unified Modeling Language (UML) dan evaluasi menggunakan metode Traceability yang dapat memberikan kemudahan dalam lembaga. Hasil dalam penelitian ini yaitu, proses bisnis model yang ada saat ini dan yang diusulkan, analisis persyaratan yang mencakup 19 fitur, 15 persyaratan fungsional, 4 persyaratan non-fungsional, delapan aktor, dan sepuluh use case. Hasil rancangan sistem terdiri dari analisis use case diagram, activity diagram, rancangan sequence diagram dan rancangan user interface. Dengan adanya perancangan sistem informasi pengarsipan ini diharapkan kedepannya dapat membantu urusan pendataan surat masuk dan surat keluar serta mempermudah proses pendisposisian surat yang ada di Lembaga BS.

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Machine Learning Algorithms in Big data Analytics

Cited by 19 publications

References 8 publications

A Survey of Machine Learning for Big Data Processing

A Survey of Machine Learning for Big Data Processing

Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture

Evaluasi Perancangan Sistem Informasi Pengelolaan Pengarsipan Surat Berbasis Web

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