Artificial intelligence based methods for hot spot prediction

Well-defined terminology and scope are essential in formal standardization work. In the broad domain of Information and Communications Technology (ICT) the necessity is even more so due to proliferation and appropriation of terms from other fields and public discourse – the term ‘smart’ is a classic example; as is ‘deep learning’. In reviewing the emerging impact of Artificial Intelligence (AI) on the field of Information Technology for Learning, Education, and Training (ITLET), this paper highlights several questions that might assist in developing scope statements of new work items.While learners and teachers are very much foregrounded in past and present standardization efforts in ITLET, little attention has been placed until recently on whether these learners and teachers are necessarily human. Now that AI is a hot spot of innovation it is receiving considerable attention from standardization bodies such as ISO/IEC, IEEE and pan-European initiatives such as the Next Generation Internet. Thus, terminology such as ‘blended learning’ necessarily now spans not just humans in a mix of online and offline learning, but also mixed reality and AI paradigms, developed to assist human learners in environments such as Adaptive Instructional Systems (AIS) that extend the scope and design of a learning experience where a symbiosis is formed between humans and AI. Although the fields of LET and AI may utilize similar terms, the language of AI is mathematics and terms can mean different things in each field. Nonetheless, in ‘symbiotic learning’ contexts where an AIS at times replaces a human teacher, a symbiosis between the human learner and the AIS occurs in such a way where both can exist as teacher and learner. While human ethics and values are preeminent in this new symbiosis, a shift towards a new ‘intelligence nexus’ is signalled where ethics and values can also apply to AI in learning, education, and training (LET) contexts. In making sense of the scope of standardization efforts in the context of LET based AI, issues for the human-computer interface become more complex than simply appropriating terminology such as ‘smart’ in the next era of standardization. Framed by ITLET perspectives, this paper focuses on detailing the implications for standardization and key questions arising from developments in Artificial Intelligence. At a high level, we need to ask: do the scopes of current LET related Standards Committees still apply and if not, what scope changes are needed?

Download Full-text

Soft Multimedia Assisted New Energy Productive Landscape Design Based on Environmental Analysis and Edge-Driven Artificial Intelligence

10.21203/rs.3.rs-400855/v1 ◽

2021 ◽

Author(s):

Bin Ma ◽

Yu Dong ◽

Hongxiu Liu ◽

Zixu Cao

Keyword(s):

Artificial Intelligence ◽

Environmental Analysis ◽

Landscape Architecture ◽

Urban Landscape ◽

Hot Spot ◽

Energy Landscape ◽

Landscape Design ◽

Impact Factors ◽

Multimedia Technology ◽

New Energy

Abstract New energy landscape architecture is a new energy building integrated with aesthetics and art design, which can meet people's dual needs of low energy consumption and architectural aesthetics. In recent years, the development speed of new energy landscape architecture is increasing, and the design and research of new energy landscape architecture has become a hot spot. In the era of media and information, the thinking mode, aesthetic concept and living space demand of the public and landscape architects are changing. The new values, aesthetics, technology and design concepts will always stimulate and promote the urban landscape design to constantly enrich itself. Therefore, it is a new exploration direction to shape new urban landscape design through multimedia technology. Multimedia technology uses computer to process text, graphics, images, sound, animation, video and other information to establish logical relationship and human-computer interaction. The virtual reality technology in multimedia integrates the latest development achievements of computer graphics, multimedia, artificial intelligence, multi-sensor, network, parallel processing and other technologies. Multimedia technology makes the color of the landscape form the most visual impact factors, it is easy to leave a deep impression on people, to achieve the visual effect of the landscape. Therefore, this paper studies the multimedia assisted landscape design of new energy production based on environmental analysis and artificial intelligence.

Download Full-text

Research on quantitative analysis method of street space quality evaluation, Whuan City centre

Proceedings of the 56th ISOCARP World Planning Congress ◽

10.47472/oxms9596 ◽

2020 ◽

Author(s):

Huihui Yan ◽

◽

Runzhi Huang ◽

Yunming Cheng ◽

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Evaluation System ◽

Information Technologies ◽

Quality Evaluation ◽

Hot Spot ◽

Central City ◽

Current Status ◽

City Centre ◽

Street Space

ith the continuous development of technical means, information technologies such as big data and artificial intelligence have gradually become one of the core technical means of planning and design. Applying AI and big data to evaluate street space has also become one hot spot in recent years. However, there are few studies on the street space quality of Wuhan based on new technology, and especially there is almost no evaluation system that combines planning technology and information technology. This study employs big data, traditional planning data and current status survey data, combined with artificial intelligence, ArcGIS spatial analysis and spatial syntax and other analytical techniques, to propose a comprehensive system for evaluating street space quality. This paper selects an area in the central city of Wuhan for the case study on the quality evaluation system, and accordingly provides an analytic idea for the planning and construction of streets, so as to guide the implementation of street-related projects and planning.

Download Full-text

Big Data- and Artificial Intelligence-Based Hot-Spot Analysis of COVID-19: Gauteng, South Africa, as a case study

SSRN Electronic Journal ◽

10.2139/ssrn.3803878 ◽

2021 ◽

Author(s):

Benjamin Lieberman ◽

Roy Gusinow ◽

Ali Asgary ◽

Nicola Luigi Bragazzi ◽

Nalomotse Choma ◽

...

Keyword(s):

Artificial Intelligence ◽

South Africa ◽

Big Data ◽

Hot Spot ◽

Hot Spot Analysis ◽

Spot Analysis

Download Full-text

Sistema experto para la definición de umbrales adaptivos en procedimientos de mantenimiento en transmisión eléctrica

Revista de Investigaciones en Energía Medio Ambiente y Tecnología RIEMAT ISSN 2588-0721 ◽

10.33936/riemat.v2i1.941 ◽

2017 ◽

Vol 2 (1) ◽

pp. 25

Author(s):

Yolanda Llosas ◽

Italo Navarrete ◽

Ney Balderramo ◽

Gabriel Pico ◽

Julio Cesar Guamán Segarra

Keyword(s):

Artificial Intelligence ◽

Expert System ◽

Hot Spot ◽

System Failures ◽

Electrical Systems ◽

Connection System ◽

Adaptive Thresholds ◽

Machine Interface ◽

Index Terms ◽

Selection Of

In the work the design of a connectionist expert system is realized, which uses tools of the artificial intelligence that allow the decision making for the establishment of adaptive thresholds as classifiers of the type of maintenance to be used. For the selection of a maintenance in electrical systems in general, parameters and conditions are taken into account that facilitate an adequate selection of the maintenance to be applied, for that reason the incidences of a failure, are limited in selection ranges of a maintenance for each incidence ; From 0 - 0.49 preventive maintenance is considered, taking into account the technology of the material to be used, the experience acquired by the personnel in charge of maintenance and considering the presence of a passive hot spot in all the electrical installations in full operation; Of 0.5 -0.69 is considered a predictive maintenance, with presence of an active hot spot, in this maintenance an additional range of 0.7 - 0.79 is considered considering a proactive maintenance due to the possibility of some failure, Of 0.8 - 1.0 is considered a corrective maintenance, for the equipment change. It builds the system starting from the acquisition of the data for the construction of the database, the machine inference expert system based on the operators' expertise and the human machine interface in a comfortable, friendly and reliable manner. The results are displayed on the computer screen, and the connection system database is available for other applications. Index Terms— artificial intelligence, expert system, failures, maintenance, classifiers.

Download Full-text

Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis

Frontiers in Medicine ◽

10.3389/fmed.2021.629080 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jiang Kailin ◽

Jiang Xiaotao ◽

Pan Jinglin ◽

Wen Yi ◽

Huang Yuanchen ◽

...

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

Early Gastric Cancer ◽

Publication Bias ◽

Hot Spot ◽

Diagnostic Value ◽

Future Perspective ◽

Current Evidence ◽

Cochrane Library ◽

Funnel Plot

Background & Aims: Gastric cancer is the common malignancies from cancer worldwide. Endoscopy is currently the most effective method to detect early gastric cancer (EGC). However, endoscopy is not infallible and EGC can be missed during endoscopy. Artificial intelligence (AI)-assisted endoscopic diagnosis is a recent hot spot of research. We aimed to quantify the diagnostic value of AI-assisted endoscopy in diagnosing EGC.Method: The PubMed, MEDLINE, Embase and the Cochrane Library Databases were searched for articles on AI-assisted endoscopy application in EGC diagnosis. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated, and the endoscopists' diagnostic value was evaluated for comparison. The subgroup was set according to endoscopy modality, and number of training images. A funnel plot was delineated to estimate the publication bias.Result: 16 studies were included in this study. We indicated that the application of AI in endoscopic detection of EGC achieved an AUC of 0.96 (95% CI, 0.94–0.97), a sensitivity of 86% (95% CI, 77–92%), and a specificity of 93% (95% CI, 89–96%). In AI-assisted EGC depth diagnosis, the AUC was 0.82(95% CI, 0.78–0.85), and the pooled sensitivity and specificity was 0.72(95% CI, 0.58–0.82) and 0.79(95% CI, 0.56–0.92). The funnel plot showed no publication bias.Conclusion: The AI applications for EGC diagnosis seemed to be more accurate than the endoscopists. AI assisted EGC diagnosis was more accurate than experts. More prospective studies are needed to make AI-aided EGC diagnosis universal in clinical practice.

Download Full-text

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

Sustainability ◽

10.3390/su132212560 ◽

2021 ◽

Vol 13 (22) ◽

pp. 12560

Author(s):

Sheikh Kamran Abid ◽

Noralfishah Sulaiman ◽

Shiau Wei Chan ◽

Umber Nazir ◽

Muhammad Abid ◽

...

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Disaster Management ◽

Impact Analysis ◽

Situational Awareness ◽

Hot Spot ◽

Management Approach ◽

Intelligence Analysis ◽

Critical Question ◽

Gis And Remote Sensing

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

Download Full-text