scholarly journals The Use of Artificial Intelligence in Assessing Affective States in Livestock

2021 ◽  
Vol 8 ◽  
Author(s):  
Suresh Neethirajan
Keyword(s):  
Artificial Intelligence ◽  
Affective Computing ◽  
Multidisciplinary Approach ◽  
Farm Animals ◽  
Affective States ◽  
Indirect Measures ◽  
Detection Of Stress ◽  
Production Animals ◽  
Self Learning ◽  
Labor Consuming

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

scholarly journals A Multidisciplinary Artificial Intelligence Model of an Affective Robot

10.5772/45662 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Hooman Aghaebrahimi Samani ◽  
Elham Saadatian
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Network ◽  
Multidisciplinary Approach ◽  
Affective State ◽  
Dynamic Bayesian Network ◽  
Endocrine System ◽  
Affective States ◽  
Intelligence System ◽  
Human Love ◽  
High Level

A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA), based on the psychology of love, Artificial Endocrine System (AES), based on the physiology of love, and Affective State Transition (AST), based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN). The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

scholarly journals How will artificial intelligence and bioinformatics change our understanding of IgA in the next decade?

2021 ◽  
Author(s):  
Roman David Bülow ◽  
Daniel Dimitrov ◽  
Peter Boor ◽  
Julio Saez-Rodriguez
Keyword(s):  
Artificial Intelligence ◽  
Multidisciplinary Approach ◽  
Immunoglobulin A ◽  
Improve Patient Care ◽  
Integrated Analysis ◽  
Clinical Expertise ◽  
End Stage ◽  
The Complement System ◽  
Improve Patient ◽  
Generation Sequencing

AbstractIgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

scholarly journals Artificial Intelligence in Biomedicine: A Legal Insight

BioTech ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 15
Author(s):  
Takis Vidalis
Keyword(s):  
Artificial Intelligence ◽  
Personal Data ◽  
Health Data ◽  
The Self ◽  
Data Handling ◽  
Direct Control ◽  
Eu Legislation ◽  
Attending Physicians ◽  
Self Learning ◽  
The Eu

The involvement of artificial intelligence in biomedicine promises better support for decision-making both in conventional and research medical practice. Yet two important issues emerge in relation to personal data handling, and the influence of AI on patient/doctor relationships. The development of AI algorithms presupposes extensive processing of big data in biobanks, for which procedures of compliance with data protection need to be ensured. This article addresses this problem in the framework of the EU legislation (GDPR) and explains the legal prerequisites pertinent to various categories of health data. Furthermore, the self-learning systems of AI may affect the fulfillment of medical duties, particularly if the attending physicians rely on unsupervised applications operating beyond their direct control. The article argues that the patient informed consent prerequisite plays a key role here, not only in conventional medical acts but also in clinical research procedures.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

ARTIFICIAL INTELLIGENCE AND ITS STARRING ROLE IN DENTISTRY: A PERSPECTIVE QUESTIONNAIRE STUDY WITH REVIEW OF LITERATURE.

2021 ◽  
pp. 44-47
Author(s):  
V. Shakuntala Soujanya. V ◽  
N.Abhishek Reddy ◽  
K. Kranthi ◽  
Vinuthna Vinuthna ◽  
Prabhakar Rao
Keyword(s):  
Artificial Intelligence ◽  
Oral Cancer ◽  
Multidisciplinary Approach ◽  
Geriatric Patients ◽  
Special Care ◽  
Questionnaire Study ◽  
Review Of Literature ◽  
Advanced Technologies ◽  
Severe Infections ◽  
Dental Diseases

As there is increased preponderance and prevalence of varied diseases affecting huge population including dental diseases like severe infections secondary to pulpal and periodontal pathologies, Maxillary pathologies, Oral cancer, Osteoporosis, esthetical issues like Malocclusion, etc. which in turn should be given special care when it comes to geriatric patients and people suffering with various comorbidities where sometimes it demands for advanced technologies especially in terms of multidisciplinary approach, Articial intelligence has become a boon to dentistry making their work more simpler and accurate. This article is one of its own kind of rare questionnaire study which focus on knowing knowledge, awareness and perception of dentists of northern telangana population regarding Articial intelligence.

Artificial intelligence: criminal and procedural aspects

2021 ◽  
pp. 75-89
Author(s):  
Igor I. Kartashov ◽  
Ivan I. Kartashov
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Human Beings ◽  
Criminal Proceedings ◽  
Legal Advice ◽  
Judicial Practice ◽  
Legal Expert ◽  
To Come ◽  
Definition Of ◽  
Self Learning

For millennia, mankind has dreamed of creating an artificial creature capable of thinking and acting “like human beings”. These dreams are gradually starting to come true. The trends in the development of modern so-ciety, taking into account the increasing level of its informatization, require the use of new technologies for information processing and assistance in de-cision-making. Expanding the boundaries of the use of artificial intelligence requires not only the establishment of ethical restrictions, but also gives rise to the need to promptly resolve legal problems, including criminal and proce-dural ones. This is primarily due to the emergence and spread of legal expert systems that predict the decision on a particular case, based on a variety of parameters. Based on a comprehensive study, we formulate a definition of artificial intelligence suitable for use in law. It is proposed to understand artificial intelligence as systems capable of interpreting the received data, making optimal decisions on their basis using self-learning (adaptation). The main directions of using artificial intelligence in criminal proceedings are: search and generalization of judicial practice; legal advice; preparation of formalized documents or statistical reports; forecasting court decisions; predictive jurisprudence. Despite the promise of using artificial intelligence, there are a number of problems associated with a low level of reliability in predicting rare events, self-excitation of the system, opacity of the algorithms and architecture used, etc.

scholarly journals Criminal Liability and Artificial Intelligence: Theoretical and Applied Aspects

2019 ◽  
Vol 13 (4) ◽  
pp. 564-574 ◽  
Author(s):  
Zarina Khisamova ◽  
Ildar Begishev
Keyword(s):  
Artificial Intelligence ◽  
Criminal Law ◽  
Legal Status ◽  
Industrial Revolution ◽  
Criminal Liability ◽  
Criminal Code ◽  
Design Development ◽  
Legal Personality ◽  
Active Involvement ◽  
Self Learning

The humanity is now at the threshold of a new era when a widening use of artificial intelligence (AI) will start a new industrial revolution. Its use inevitably leads to the problem of ethical choice, it gives rise to new legal issues that require urgent actions. The authors analyze the criminal law assessment of the actions of AI. Primarily, the still open issue of liability for the actions of AI that is capable of self-learning and makes a decision to act / not to act, which is qualified as a crime. As a result, there is a necessity to form a system of criminal law measures of counteracting crimes committed with the use of AI. It is shown that the application of AI could lead to four scenarios requiring criminal law regulation. It is stressed that there is a need for a clear, strict and effective definition of the ethical boundaries in the design, development, production, use and modification of AI. The authors argue that it should be recognized as a source of high risk. They specifically state that although the Criminal Code of the Russian Fe­deration contains norms that determine liability for cybercrimes, it does not eliminate the possibility of prosecution for infringements committed with the use of AI under the general norms of punishment for various crimes. The authors also consider it possible to establish a system to standardize and certify the activities of designing AI and putting it into operation. Meanwhile, an autonomous AI that is capable of self-learning is considerably different from other phenomena and objects, and the situation with the liability of AI which independently decides to undertake an action qualified as a crime is much more complicated. The authors analyze the resolution of the European Parliament on the possibility of granting AI legal status and discuss its key principles and meaning. They pay special attention to the issue of recognizing AI as a legal personality. It is suggested that a legal fiction should be used as a technique, when a special legal personality of AI can be perceived as an unusual legal situation that is different from reality. It is believed that such a solution can eliminate a number of existing legal limitations which prevent active involvement of AI into the legal space.

scholarly journals A review of Elizabeth Wilson's Affect and artificial intelligence

2020 ◽  
Author(s):  
Tung Manh Ho
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Affective Computing ◽  
Biographical Data ◽  
History Of

Wilson’s book is of great interest to readers of the biographical history of computer science and, more importantly, humanities scholars who would like to explore how emotions influence the works of early pioneers amongst AI theoreticians and engineers. However, I present three areas where the book can improve: engaging with affective computing, acculturation of emotion, and organization of biographical data.

scholarly journals A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

2021 ◽  
Vol 335 ◽  
pp. 04001
Author(s):  
Didar Dadebayev ◽  
Goh Wei Wei ◽  
Tan Ee Xion
Keyword(s):  
Feature Selection ◽  
Fractal Dimension ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Feature Selection Method ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Emotional States ◽  
Affective States ◽  
Emotion Classification

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

Making IoT Run

2020 ◽  
pp. 377-397
Author(s):  
Peter Schott ◽  
Torben Schaft ◽  
Stefan Thomas ◽  
Freimut Bodendorf
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Maturity Level ◽  
Smart Systems ◽  
Industrial Iot ◽  
Increasing Demand ◽  
Manufacturing Environments ◽  
Self Learning

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

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