scholarly journals Detection of sensorimotor contingencies in infants before the age of one year: a comprehensive review

2019 ◽  
Author(s):  
Lisa Jacquey ◽  
Jacqueline Fagard ◽  
Rana Esseily ◽  
J. Kevin O'Regan
Keyword(s):  
Artificial Intelligence ◽  
Sensory Feedback ◽  
Recent Literature ◽  
Developmental Robotics ◽  
Comprehensive Review ◽  
Contingency Detection ◽  
Major Interest ◽  
Sensorimotor Contingencies ◽  
One Year ◽  
Sensorimotor Contingency

In order to benefit from the exploration of their body and their physical and social environment, infants need to detect sensorimotor contingencies linking their actions to sensory feedback. This ability, which seems to be present in babies from birth and even in utero, has been widely used by researchers in their study of early development. However, a careful look at the literature, particularly recent literature, suggests that babies may not be uniformly sensitive to all sensorimotor contingencies. This literature review examines in detail the mechanism of sensorimotor contingency detection in infants before the age of one year. Four aspects of sensorimotor contingency detection are considered: characteristics of action and feedback, contingency parameters, exposure conditions, and inter-individual differences. For each topic we highlight what favours and what hinders the detection of sensorimotor contingencies in infants. Our review also demonstrates the limitations of our knowledge about sensorimotor contingency detection. We advocate the importance of making progress in this field at a time when sensorimotor contingency detection is of major interest in developmental robotics and artificial intelligence.

Detection of sensorimotor contingencies in infants before the age of 1 year: A comprehensive review.

2020 ◽  
Vol 56 (7) ◽  
pp. 1233-1251
Author(s):  
Lisa Jacquey ◽  
Jacqueline Fagard ◽  
Rana Esseily ◽  
J. Kevin O'Regan
Keyword(s):  
Comprehensive Review ◽  
Sensorimotor Contingencies

scholarly journals Applications of Artificial Intelligence in COVID-19 Pandemic: A Comprehensive Review

2021 ◽  
pp. 115695
Author(s):  
Muzammil Khan ◽  
Muhammad Taqi Mehran ◽  
Zeeshan Ul Haq ◽  
Zahid Ullah ◽  
Salman Raza Naqvi
Keyword(s):  
Artificial Intelligence ◽  
Comprehensive Review

scholarly journals COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

2021 ◽  
Author(s):  
Jawad Rasheed ◽  
Akhtar Jamil ◽  
Alaa Ali Hameed ◽  
Fadi Al-Turjman ◽  
Ahmad Rasheed
Keyword(s):  
Artificial Intelligence ◽  
Comprehensive Review

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

Artificial intelligence research in agriculture: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amit Sood ◽  
Rajendra Kumar Sharma ◽  
Amit Kumar Bhardwaj
Keyword(s):  
Artificial Intelligence ◽  
Peer Review ◽  
Diffusion Of Innovation ◽  
Comprehensive Review ◽  
Content Type ◽  
Design Elements ◽  
Extant Literature ◽  
Use Of Technology ◽  
Challenges And Opportunities ◽  
Market Factors

PurposeThe purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.Design/methodology/approachThe authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.FindingsFour factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.Research limitations/implicationsThe proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.Practical implicationsPractitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.Social implicationsThis paper brings new developed methods and practices in agriculture for betterment of society.Originality/valueThis paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

A Comprehensive Review of Recent Advances in the Estimation of Natural Gas Compressibility Factor

2021 ◽  
Author(s):  
Oluwasegun Cornelious Omobolanle ◽  
Oluwatoyin Olakunle Akinsete
Keyword(s):  
Artificial Intelligence ◽  
Equation Of State ◽  
Compressibility Factor ◽  
Operating Conditions ◽  
Measurement Equation ◽  
Comprehensive Review ◽  
Holistic Understanding ◽  
Deviation Factor ◽  
Gas Compressibility ◽  
Limited Accuracy

Abstract Accurate prediction of gas compressibility factor is essential for the evaluation of gas reserves, custody transfer and design of surface equipment. Gas compressibility factor (Z) also known as gas deviation factor can be evaluated by experimental measurement, equation of state and empirical correlation. However, these methods have been known to be expensive, complex and of limited accuracy owing to the varying operating conditions and the presence of non-hydrocarbon components in the gas stream. Recently, newer correlations with extensive application over wider range of operating conditions and crude mixtures have been developed. Also, artificial intelligence is now being deployed in the evaluation of gas compressibility factor. There is therefore a need for a holistic understanding of gas compressibility factor vis-a-vis the cause-effect relations of deviation. This paper presents a critical review of current understanding and recent efforts in the estimation of gas deviation factor.

scholarly journals Intermittent Fasting and Fat Mass: What Is the Clinical Magnitude?

Obesities ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Heitor O. Santos
Keyword(s):  
Mass Loss ◽  
Fat Mass ◽  
Clinical Studies ◽  
Recent Literature ◽  
Energy Restriction ◽  
Intermittent Fasting ◽  
Reasonable Approach ◽  
Treatment Of Obesity ◽  
One Year ◽  
Fat Mass Loss

Clinical studies addressing the benefits of intermittent fasting (IF) diets have evoked interest in the treatment of obesity. Herein, the overall effects of IF regimens on fat-mass loss are explained in a brief review through a recent literature update. To date, human studies show a reduction in fat mass from 0.7 to 11.3 kg after IF regimens, in which the duration of interventions ranges from two weeks to one year. In light of this, IF regimens can be considered a reasonable approach to weight (fat mass) loss. However, the benefits of IF regimens occur thanks to energy restriction and cannot hence be considered the best dietary protocol compared to conventional diets.

scholarly journals Ethical issues in computational pathology

2021 ◽  
pp. medethics-2020-107024
Author(s):  
Tom Sorell ◽  
Nasir Rajpoot ◽  
Clare Verrill
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Ethical Issues ◽  
Recent Literature ◽  
Personal Data ◽  
Ethical Problems ◽  
Data Ethics ◽  
Slide Image ◽  
Data Protection Law ◽  
Computational Pathology

This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from (1) the tension between artificial intelligence (AI) research—with its hunger for more and more data—and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; (2) the fact that computational pathology lends itself to kinds of data fusion that go against data ethics norms and some norms of biobanking; (3) the fact that AI methods are esoteric and produce results that are sometimes unexplainable (the so-called ‘black box’problem) and (4) the fact that computational pathology is particularly dependent on scanning technology manufacturers with interests of their own in profit-making from data collection. We shall suggest that most of these issues are resolvable.

scholarly journals The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Gulshan Kumar ◽  
Krishan Kumar
Keyword(s):  
Artificial Intelligence ◽  
Intrusion Detection ◽  
Supervised Learning ◽  
Ensemble Learning ◽  
Classification Performance ◽  
Intrusion Detection Systems ◽  
Future Directions ◽  
Comprehensive Review ◽  
Detection Systems ◽  
Combination Methods

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

2011 ◽  
pp. 1017-1029
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Statistical Analysis ◽  
Medical Records ◽  
Clinical Decision Making ◽  
Ct Scanning ◽  
Clinical Decision ◽  
Radiology Department ◽  
Self Organizing Map ◽  
One Year

There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of medical records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

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