Artificial Intelligence in Computer-Aided Auditing Techniques and Technologies (CAATTs) and an Application Proposal for Auditors

2021 ◽  
pp. 361-384
Author(s):  
Tamer Aksoy ◽  
Burcu Gurol
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals Application of artificial intelligence in pancreaticobiliary diseases

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.

Use of the designer's experience in systems of computer-aided design and artificial intelligence

2021 ◽  
pp. 89-95
Author(s):  
А.И. Гайкович ◽  
С.И. Лукин ◽  
О.Я. Тимофеев
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Sets ◽  
Computer Aided Design ◽  
Design Tool ◽  
Design System ◽  
Design Problems ◽  
General Arrangement ◽  
Computer Aided ◽  
A Chain ◽  
Aided Design

Процесс создания проекта судна или корабля рассматривается как преобразование информации, содержащейся в техническом задании на проектирование, нормативных документах и знаниях проектанта, в информацию, объем которой позволяет реализовать проект. Проектирование может быть представлено как поиск решения в пространстве задач. Построение цепочки последовательно решаемых задач составляет методику проектирования. Проектные задачи могут быть разбиты на две группы. Первая группа ‒ это полностью формализуемые задачи, для решения которых есть известные алгоритмы. Например, построение теоретического чертежа по известным главным размерениям и коэффициентам формы. Ко второй группе задач можно отнести трудно формализуемые или неформализуемые задачи. Например, к задачам этого типа можно отнести разработку общего расположения корабля. Важнейшим инструментом проектирования современного корабля или судна является система ав­томатизированного проектирования (САПР). Решение САПР задач первой группы не представляет проблемы. Введение в состав САПР задач второй группы подразумевает разработку специального ма­тематического аппарата, базой для которого, которым является искусственный интеллект, использующий теорию нечетких множеств. Однако, настройка искусственных нейронных сетей, создание шкал для функций принадлежности элементов нечетких множеств и функций предпочтений лица принимающего решения, требует участие человека. Таким образом, указанные элементы искусственного интеллекта фиксируют качества проек­танта как специалиста и создают его виртуальный портрет. The process of design a project of a ship is considered as the transformation of information contained in the design specification, regulatory documents and the designer's knowledge into information, the volume of which allows the project to be implemented. Designing can be represented as a search for a solution in the space of problems. The construction of a chain of sequentially solved tasks constitutes the design methodology. Design problems can be divided into two groups. The first group is completely formalizable tasks, for the solution of which there are known algorithms. For example, the construction of ship's surface by known main dimensions and shape coefficients. Tasks of the second group may in­clude those which are difficult to formalize or non-formalizable. For example, tasks of this type can include develop­ment of general arrangement of a ship. The most important design tool of a modern ship or vessel is a computer-aided design system (CAD). The solu­tion of CAD problems of the first group is not a problem. Introduction of tasks of the second group into CAD implies development of a special mathematical apparatus, the basis for which is artificial intelligence, which uses the theory of fuzzy sets. However, the adjustment of artificial neural networks, the creation of scales for membership functions of fuzzy sets elements and functions of preferences of decision maker, requires human participation. Thus, the above elements of artificial intelligence fix the qualities of the designer as a specialist and create his virtual portrait.

Computer-Aided Characterisation Of Colorectal Polyps Using Artificial Intelligence

2021 ◽  
Author(s):  
R Kader ◽  
P Brandao ◽  
O Ahmad ◽  
M Hussein ◽  
S Islam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Polyps ◽  
Computer Aided

scholarly journals Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

2019 ◽  
Vol 4 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Omer F Ahmad ◽  
Antonio S Soares ◽  
Evangelos Mazomenos ◽  
Patrick Brandao ◽  
Roser Vega ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Diagnosis ◽  
Current Evidence ◽  
Future Directions ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

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