scholarly journals Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4727
Author(s):  
Maysam Abbod ◽  
Jiann-Shing Shieh
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Big Data ◽  
Deep Learning ◽  
Health Monitoring ◽  
Intelligent Systems ◽  
Measurement Techniques ◽  
Mechanical Systems ◽  
Data Sets ◽  
Special Issue

Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

scholarly journals Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”

2021 ◽  
Vol 13 (15) ◽  
pp. 2883
Author(s):  
Gwanggil Jeon
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Deep Learning ◽  
Special Issue ◽  
The Earth

Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...]

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.

scholarly journals Editorial: Special Issue “Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems”

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6383
Author(s):  
Sigfredo Fuentes ◽  
Eden Jane Tongson
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Urban Agriculture ◽  
Environmental Hazards ◽  
Special Issue ◽  
Hazards Assessment ◽  
Editorial Special Issue

Artificial intelligence (AI), together with robotics, sensors, sensor networks, internet of things (IoT) and machine/deep learning modeling, has reached the forefront towards the goal of increased efficiency in a multitude of application and purpose [...]

Call for Special Issue Papers: Big Data Analytics and Intelligent Systems for Cybersecurity

Big Data ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 146-147
Author(s):  
Ahmed A. Abd El-Latif ◽  
Lo'ai Tawalbeh ◽  
Yassine Maleh ◽  
Gokay Saldamli
Keyword(s):  
Big Data ◽  
Intelligent Systems ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Special Issue

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

scholarly journals Artificial Intelligence and Health in Nepal

2020 ◽  
Vol 10 (3) ◽  
pp. 915-918
Author(s):  
Alexander Van Teijlingen ◽  
Tell Tuttle ◽  
Hamid Bouchachia ◽  
Brijesh Sathian ◽  
Edwin Van Teijlingen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
South Asian ◽  
Low Income ◽  
Short Communication ◽  
Data Sets ◽  
Low Income Country ◽  
Local Resources ◽  
Self Driving Cars ◽  
Key Terms

The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago.  There has been a technological revolution of big data and Artificial Intelligence (AI).  Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI.  This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data.  This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal’s health and education systems to track such developments and apply them locally.  Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions.  This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI. 

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