Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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Characterization of nuclear materials signatures using statistical analysis processing in conjunction with quantitative morphology: a preliminary study

Journal of Radioanalytical and Nuclear Chemistry ◽

10.1007/s10967-021-07640-z ◽

2021 ◽

Author(s):

Ashton D. Lesiak ◽

Floyd E. Stanley ◽

Lav Tandon

Keyword(s):

Statistical Analysis ◽

Nuclear Materials ◽

Quantitative Morphology ◽

Preliminary Study

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Geotechnical characterization of Norwegian peat: database

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/710/1/012016 ◽

2021 ◽

Vol 710 (1) ◽

pp. 012016

Author(s):

P Paniagua ◽

M Long ◽

J-S L’Heureux

Keyword(s):

Geotechnical Characterization

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Application of artificial intelligence in pancreaticobiliary diseases

Therapeutic Advances in Gastrointestinal Endoscopy ◽

10.1177/2631774521993059 ◽

2021 ◽

Vol 14 ◽

pp. 263177452199305

Author(s):

Hemant Goyal ◽

Rupinder Mann ◽

Zainab Gandhi ◽

Abhilash Perisetti ◽

Zhongheng Zhang ◽

...

Keyword(s):

Artificial Intelligence ◽

Response To Treatment ◽

Endoscopic Retrograde ◽

Artificial Intelligence Techniques ◽

Computer Aided ◽

Rapid Pace ◽

Cystic Pancreatic Lesions ◽

Aided Diagnosis

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

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Development and validation of an artificial intelligence platform for characterization of the exergy-emission-stability profiles of the PPCI-RCCI regimes in a diesel-methanol operation under varying injection phasing strategies: A Gene Expression Programming approach

Fuel ◽

10.1016/j.fuel.2021.120864 ◽

2021 ◽

Vol 299 ◽

pp. 120864

Author(s):

Dipankar Kakati ◽

Sumit Roy ◽

Rahul Banerjee

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

Gene Expression Programming ◽

Programming Approach ◽

Emission Stability ◽

Development And Validation

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Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

Journal of Clinical Medicine ◽

10.3390/jcm9103313 ◽

2020 ◽

Vol 9 (10) ◽

pp. 3313 ◽

Cited By ~ 2

Author(s):

Hemant Goyal ◽

Rupinder Mann ◽

Zainab Gandhi ◽

Abhilash Perisetti ◽

Aman Ali ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Cancer Screening ◽

Colorectal Cancer Screening ◽

Narrow Band Imaging ◽

Screening Methods ◽

Polyp Detection ◽

Mortality And Morbidity ◽

Magnifying Chromoendoscopy

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

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