scholarly journals Deepint.net: A Rapid Deployment Platform for Smart Territories

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 236 ◽  
Author(s):  
Juan M. Corchado ◽  
Pablo Chamoso ◽  
Guillermo Hernández ◽  
Agustín San Roman Gutierrez ◽  
Alberto Rivas Camacho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Relational Databases ◽  
Smart Cities ◽  
Data Representation ◽  
Intelligent Sensors ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Decision Making Processes ◽  
Processing Techniques ◽  
New Applications

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

scholarly journals Artificial Intelligence Models and Techniques Applied to COVID-19: A Review

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2901
Author(s):  
Lilia Muñoz ◽  
Vladimir Villarreal ◽  
Mel Nielsen ◽  
Yen Caballero ◽  
Inés Sittón-Candanedo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computational Models ◽  
Population Data ◽  
Planning And Design ◽  
Predictive Algorithms ◽  
Rapid Spread ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Decision Making Processes ◽  
User Friendly

The rapid spread of SARS-CoV-2 and the consequent global COVID-19 pandemic has prompted the public administrations of different countries to establish health procedures and protocols based on information generated through predictive techniques and models, which, in turn, are based on technology such as artificial intelligence (AI) and machine learning (ML). This article presents some AI tools and computational models used to collaborate in the control and detection of COVID-19 cases. In addition, the main features of the Epidempredict project regarding COVID-19 in Panama are presented. This initiative consists of the planning and design of a digital platform, with cloud-based technology, to manage the ingestion, analysis, visualization and exportation of data regarding the evolution of COVID-19 in Panama. The methodology for the design of predictive algorithms is based on a hybrid model that combines the dynamics associated with population data of an SIR model of differential equations and extrapolation with recurrent neural networks. The technological solution developed suggests that adjustments can be made to the rules implemented in the expert processes that are considered. Furthermore, the resulting information is displayed and explored through user-friendly dashboards, contributing to more meaningful decision-making processes.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

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