Deep Learning in Big Data and Internet of Things

2019 ◽  
pp. 70-81
Author(s):  
Dimpal Tomar ◽  
Pradeep Tomar ◽  
Gurjit Kaur
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Internet Of Things

scholarly journals Artificial Intelligence Sebagai Penggerak Industri 4.0 dan Tantangannya Bagi Sektor Pemerintah dan Swasta

2019 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Reza Yogaswara
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Competitive Advantage ◽  
Smart City ◽  
Chief Information Officer ◽  
Deep Learning Neural Network

Artificial Intelligence (AI) atau kecerdasan buatan menjadi penggerak revolusi industri 4.0 yang menjanjikan banyak kemudahan bagi sektor pemerintah maupun industri. Internet of Things (IoT) dan big data contohnya dimana AI dapat diimplementasikan, teknologi yang telah banyak diadopsi di era industri 4.0 ini mampu menghubungkan setiap perangkat, seseorang dapat mengotomatisasi semua perangkat tanpa harus berada di lokasi, lebih dari itu, saat ini telah banyak mesin yang dapat menginterprestasi suatu kondisi atau kejadian tertentu dengan bantuan AI, sebagaimana telah kamera cerdas pendeteksi kepadatan volume kendaraan di jalan raya menggunakan teknologi Deep Learning Neural Network, yang telah diimplementasikan pada beberapa Pemerintah Daerah Kabupaten dan Kota dalam mendukung program Smart City yang telah dicanangkan. Pada sektor industri, banyak juga dari mereka yang telah mengotomatisasi mesin produksi dan manufaktur menggunakan robot dan Artificial Intelligence, sehingga Industri 4.0 akan meningkatkan daya saing melalui perangkat cerdas, setiap entitas yang mampu menguasai teknologi ini disitulah keunggulan kompetitifnya (competitive advantage). Namun ditengah perkembangan industri 4.0 yang cukup masif pemerintah harus bergerak cepat dalam mengadopsi platform ini, jika tidak, mereka akan menurunkan efisiensi proses bisnis untuk menjaga stabilitas layanan publik. Oleh sebab itu diperlukan keilmuan dan pemahaman yang benar bagi pemerintah dalam menghadapai era Industri 4.0, dimana Chief Information Officer (CIO) dapat mengambil peranan penting dalam memberikan dukungan yang didasari atas keilmuan mereka terkait tren teknologi industri 4.0, khususnya AI yang telah banyak diadopsi di berbagai sektor.

Artificial Intelligence and Machine Learning Algorithms

2020 ◽  
pp. 188-219
Author(s):  
Amit Kumar Tyagi ◽  
Poonam Chahal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things

2017 ◽  
Vol 3 (4) ◽  
pp. 392-404 ◽  
Author(s):  
Mohammad-Parsa Hosseini ◽  
Dario Pompili ◽  
Kost Elisevich ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Seizure Prediction

scholarly journals WearableDL: Wearable Internet-of-Things and Deep Learning for Big Data Analytics—Concept, Literature, and Future

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Aras R. Dargazany ◽  
Paolo Stegagno ◽  
Kunal Mankodiya
Keyword(s):  
Nervous System ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Background Information ◽  
Wearable Technologies ◽  
The Brain

This work introduces Wearable deep learning (WearableDL) that is a unifying conceptual architecture inspired by the human nervous system, offering the convergence of deep learning (DL), Internet-of-things (IoT), and wearable technologies (WT) as follows: (1) the brain, the core of the central nervous system, represents deep learning for cloud computing and big data processing. (2) The spinal cord (a part of CNS connected to the brain) represents Internet-of-things for fog computing and big data flow/transfer. (3) Peripheral sensory and motor nerves (components of the peripheral nervous system (PNS)) represent wearable technologies as edge devices for big data collection. In recent times, wearable IoT devices have enabled the streaming of big data from smart wearables (e.g., smartphones, smartwatches, smart clothings, and personalized gadgets) to the cloud servers. Now, the ultimate challenges are (1) how to analyze the collected wearable big data without any background information and also without any labels representing the underlying activity; and (2) how to recognize the spatial/temporal patterns in this unstructured big data for helping end-users in decision making process, e.g., medical diagnosis, rehabilitation efficiency, and/or sports performance. Deep learning (DL) has recently gained popularity due to its ability to (1) scale to the big data size (scalability); (2) learn the feature engineering by itself (no manual feature extraction or hand-crafted features) in an end-to-end fashion; and (3) offer accuracy or precision in learning raw unlabeled/labeled (unsupervised/supervised) data. In order to understand the current state-of-the-art, we systematically reviewed over 100 similar and recently published scientific works on the development of DL approaches for wearable and person-centered technologies. The review supports and strengthens the proposed bioinspired architecture of WearableDL. This article eventually develops an outlook and provides insightful suggestions for WearableDL and its application in the field of big data analytics.

Advanced Deep Learning Applications in Big Data Analytics

2021 ◽  
pp. 1-28
Author(s):  
Venkatesan Manian ◽  
Vadivel P.
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Infrastructure Development ◽  
Learning Approaches ◽  
The Internet Of Things

This chapter analyzes the Internet of Things (IoT), its history, and its tools in brief. This chapter also explores the contribution of IoT towards the recent development in infrastructure development of nations represented as smart world. This chapter also discuss the contribution of IoT towards big data analytics era. This chapter also briefly introduce the smart bio world and how it is made possible with the internet of things. This chapter also introduces the machine learning approaches and also discusses the contribution of Internet of Thing for this machine learning. This chapter also briefly introduces some tools used for IoT developments.

Sign in / Sign up

Export Citation Format

Share Document