Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
IoT and Sustainable Smart Cities
The Internet of Things (IoT) is an emerging technology and provides connectivity with the physical world using the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. However, it is observed that transmission system in constraint oriented network is still a burning research issue in smart cities. Also, there is an existence of a lot of malicious machines that can damage sustainable services of smart cities and compromised the connected devices. Thus, proposing an efficient solution using a 5G system is a demanding task for a smart environment that efficiently utilizes the communication resources and securing the data over insecure routes.
  • 515
  • 27 Aug 2021
Topic Review
Artificial Intelligence Marketing for Customer-Relationships
Artificial intelligence marketing (AIM), which is an interdisciplinary research topic, is a disruptive technology that enables machines to automate the process of collecting and processing a massive amount of data and information to create knowledge related to marketing mix. This capability is essential to manifest personalization at scale, which has been impossible through human effort alone. This paper synthesizes the literature and develops an AIM framework to create a quantum leap in customer relationship enhancement, including customer trust, satisfaction, commitment, engagement, and loyalty.
  • 515
  • 29 Sep 2021
Topic Review
Webby Award
A Webby Award is an award for excellence on the Internet presented annually by The International Academy of Digital Arts and Sciences, a judging body composed of over two thousand industry experts and technology innovators. Categories include websites, advertising and media, online film and video, mobile sites and apps, and social. Two winners are selected in each category, one by members of The International Academy of Digital Arts and Sciences, and one by the public who cast their votes during Webby People’s Voice voting. Each winner presents a five-word acceptance speech, a trademark of the annual awards show. Hailed as the "Internet’s highest honor," the award is one of the oldest Internet-oriented awards, and is associated with the phrase "The Oscars of the Internet."
  • 515
  • 18 Oct 2022
Topic Review
Petr–Douglas–Neumann Theorem
In geometry, the Petr–Douglas–Neumann theorem (or the PDN-theorem) is a result concerning arbitrary planar polygons. The theorem asserts that a certain procedure when applied to an arbitrary polygon always yields a regular polygon having the same number of sides as the initial polygon. The theorem was first published by Karel Petr (1868–1950) of Prague in 1908. The theorem was independently rediscovered by Jesse Douglas (1897–1965) in 1940 and also by B H Neumann (1909–2002) in 1941. The naming of the theorem as Petr–Douglas–Neumann theorem, or as the PDN-theorem for short, is due to Stephen B Gray. This theorem has also been called Douglas's theorem, the Douglas–Neumann theorem, the Napoleon–Douglas–Neumann theorem and Petr's theorem. The PDN-theorem is a generalisation of the Napoleon's theorem which is concerned about arbitrary triangles and of the van Aubel's theorem which is related to arbitrary quadrilaterals.
  • 515
  • 28 Nov 2022
Topic Review
Deep Learning in Optical Coherence Tomography Angiography
Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies.
  • 515
  • 13 Feb 2023
Topic Review
Computer-Assisted Tissue Image Analysis in Minimally Invasive Surgery
Computer-assisted tissue image analysis (CATIA) enables an optical biopsy of human tissue during minimally invasive surgery and endoscopy. Thus far, it has been implemented in gastrointestinal, endometrial, and dermatologic examinations that use computational analysis and image texture feature systems.
  • 514
  • 21 Dec 2021
Topic Review
Coronavirus
Coronaviruses are indeed a huge family of viruses that are found both in humans and animals. Seven different types have been identified, including the ones that caused COVID-19 and the SARS and MERS illnesses.
  • 514
  • 09 Nov 2022
Topic Review
Classic Empire
Empire (or Classic Empire) is a 1977 turn-based wargame with simple rules. The game was conceived by Walter Bright starting in 1971, based on various war movies and board games, notably Battle of Britain and Risk. The game was ported to many platforms in the 1970s and 80s. Several commercial versions were also released, often adding basic graphics to the originally text-based user interface. The basic gameplay is strongly reminiscent of several later games, notably Civilization.
  • 514
  • 11 Nov 2022
Topic Review
Designing for Hybrid Intelligence
A taxonomy and survey of crowd-machine interaction is proposed. Specifically, this summary aims to provide a glimpse into the unique characteristics of artificial intelligence (AI)-powered crowdsourcing by characterizing its uses, limitations, and prospects when seen from a socio-technical perspective grounded on hybrid machine-crowd interaction. To this end, a scoping review of the existing literature was performed  in order to frame the relevant aspects of this particular form of hybrid intelligence in light of the progress reported in prior research when considering human-algorithmic arrangements at a massive scale. From understanding the role of crowd-AI ethicality to the analysis of the spatio-temporal characteristics of crowd activity and the behavioral traces left by crowd workers as a way of improving performance outcomes and user experience (UX) design.
  • 514
  • 01 Mar 2023
Topic Review
Forecasting Pollution in Urban Area
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions.  Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
  • 513
  • 16 Jul 2021
  • Page
  • of
  • 366
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
ScholarVision Creations
Feedback