Journal Information
AI
https://www.mdpi.com/journal/ai
Publisher:
MDPI
ISSN:
2673-2688
Viewed:
970
Tracked:
0

Call For Papers
Aims

AI (ISSN 2673-2688) is an international and interdisciplinary scholarly open access journal on artificial intelligence. It publishes original research articles, reviews, communications, that offer substantial new insight into any field of study that involves artificial intelligence (AI), including machine and deep learning, knowledge reasoning and discovery, automated planning and scheduling, natural language processing and recognition, computer vision, robotics, and artificial general intelligence. There is no restriction on the length of the papers. Our aim is to encourage scientists and engineers to publish their experimental and theoretical research in as much detail as possible. Full experimental details and/or study methods must be provided for research articles so that the results can be reproduced.

Scope

    AI Theory
    AI Technique
    AI Application
    Intelligent machine/agent
    Knowledge representation
    Knowledge reasoning
    Machine learning
    Deep learning
    Computer vision
    Planning
    Robotics
    Natural language processing
    Artificial neural networks
    Evolutionary computing
    Probabilistic computing
    Genetic algorithms
    Fuzzy logic
    Expert systems

Other Keywords: reinforcement learning; data science; machine perception; machine recognition; automated reasoning and inference; case-based reasoning; reasoning under uncertainty; knowledge engineering; data mining; knowledge discovery; speech recognition; multi-agent planning; multi-agent systems; automated planning and scheduling; information retrieval; text mining; question answering; machine translation; smart computer programs; AI software development; heuristic search; human interfaces; intelligent robotics; motion and manipulation; affective computing; robotic systems; neuromorphic computing; human–machine interaction; multi-sensor; ethical machines; artificial consciousness; robot rights; AI logistics; automotive AI; data mining; AI medical diagnosis; AI healthcare; big data; AI military; AI testing; etc.
Last updated by Dou Sun in 2020-05-07
Special Issues
Special Issue on Recognition of Human Emotions Using Machine Learning and Deep Learning Algorithms
Submission Date: 2020-09-30

Dear Colleagues, Emotion is a psycho–physiological response triggered by conscious and/or unconscious stimuli. Emotion cannot be explained by scientific principles such as rational thought, logical arguments, testable hypotheses, and repeatable experiments. Emotions play a crucial role in human communication and can be expressed by multidimensional cues, such as vocabulary, intonation of voice, facial expressions, and gestures. The recognition of emotions in the affective computing scenario may lead to understanding human cognitive processes, such as attention, memory, and decision making. For instance, (i) modeling emotional feelings and (ii) considering their behavioral implication (i.e., stress-related implications) are useful in preventing emotions from having a negative effect on the workplace. Accordingly, the decision-making process should discard emotion whenever possible: Both positive and negative emotions can distort the validity of a decision. Machine learning and deep learning techniques have already been applied to consistently recognize human emotion using physiological data, facial expression, body gestures, speech, and text. However, several challenges are still present. The learning model should be robust against high dimensional and heterogeneous data, unbalanced classes, and time ambiguity. For instance, modeling and predicting the emotional state over time is not a trivial problem, because continuous data labeling is costly and not always feasible. This is a crucial issue in real-world applications, where the labeling of the features is sparse and eventually describes only the most prominent emotional events. This Special Issue on “Recognition of Human Emotions Using Machine Learning and Deep Learning Algorithms” calls for manuscripts proposing new machine learning and deep learning methods, approaches, and applications able to face the challenges related to human motion recognition. Manuscripts focused on interpretable models which also provide explanations as to why and how the learning model achieved a prediction are particularly welcome. Dr. Luca Romeo Dr. Sara Moccia Guest Editors Manuscript Submission Information Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI. Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions. Keywords Emotion recognition Affective computing Machine learning Deep learning Stress recognition
Last updated by Dou Sun in 2020-05-07
Special Issue on Artificial Intelligence on Business Intelligence
Submission Date: 2020-10-30

Dear Colleagues, The explosion of data and computational power has been a key determinant in the development of Artificial Intelligence (AI), including machine learning and especially deep learning in recent years. AI methods have become increasingly popular as a methodological tool to understand complex data and offer intelligent processing to help people to save time and effort. Artificial Intelligence on Business Intelligence are difficult fields and bring great benefits. Many issues, such as customer lifetime value modeling, churn customer modeling, dynamic pricing, customer segmentation, recommendation systems, stock price prediction, etc., have been proposed and researched to help the community gain more benefits. This Special Issue aims at collecting high-quality papers on recent advances and reviews that address the challenge of Artificial Intelligence on Business Intelligence. Topics of interest include but are not limited to: Machine learning theory and applications; Deep learning and applications; Class imbalanced problem; Data mining approaches; Knowledge-based systems; Expert system. Dr. Tuong Le Guest Editor Manuscript Submission Information Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI. Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions. Keywords machine learning neural networks deep learning class imbalanced problem data mining
Last updated by Dou Sun in 2020-05-07
Special Issue on Artificial Intelligence in Agriculture
Submission Date: 2020-12-31

Dear Colleagues, Over the last decades, several emerging technologies and techniques have been developed for precision agriculture application. Artificial neural networks and deep learning algorithms are increasingly used in remote sensing and machine vision applications. Deep convolutional neural networks (CNNs) are the most widely used deep learning approach for image recognition. These methods have achieved dramatic improvements in many domains, and have attracted considerable interest from both academic and industrial communities. This Special Issue focuses on the recent advances in artificial intelligence applications in agriculture and natural resources. For this purpose, we invite researchers to contribute original research papers in the areas of machine and computer vision, Internet of things, big data analytics, automation and robotics, machine learning, deep and transfer learning, reinforcement learning, logistics and optimization, and so on. Potential Topics include, but are not limited to, the following: Remote sensing UAV applications in agriculture Machine and computer vision Automatic tools for disease and pest detection High-throughput phenotyping tools Yield prediction techniques Big data analytics Precision agriculture Digital and smart agriculture and machinery Decision support systems, crop modeling, and optimization Agroclimatology. Dr. Yiannis Ampatzidis Dr. Spyros Fountas Dr. Wonsuk (Daniel) Lee Dr. Panos Pardalos Guest Editors Manuscript Submission Information Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI. Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Last updated by Dou Sun in 2020-05-07
Special Issue on Artificial Intelligence and Machine Learning for Intelligent Sensing and Signal Processing in Smart-X Technologies
Submission Date: 2021-03-29

The pervasive use of sensors, portable or worn by the user and incorporated in the surrounding environment, generates increasingly large and diversified data flows, big sensor data, which are ill suited to be processed using only traditional signal processing techniques. The application of machine learning in the signal and image processing area has proven very useful in addressing this growing complexity. The systematic use of machine learning and artificial intelligence, with particular focus on the emerging areas of autoML (automatic machine learning) and deep learning (deep artificial neural networks), is receiving much attention in industry and academia for modeling, design, and development of smart technological solutions. In this context, intelligent sensing and advanced signal processing techniques, well suited to treat a large amount of multi-sensor and multi-channel data, generated at a constant rate by the ever-growing number of permanently connected smart devices (according to the Internet of Things paradigm), are the main focus of this Special Issue, aiming at the same time to highlight their great impact in different smart-x sectors, such as smart home, smart building, smart city, smart healthcare, smart transportation, and smart industry, just to name a few. The purpose of this Special Issue is to reflect the most recent advances, present representative applications, and define future research directions related to the application of AI for intelligent sensing and advanced signal processing in smart-x technology, through machine learning, deep learning, computational intelligence, cognitive computing, and other emerging areas of AI. Prospective authors are invited to submit original and high-quality papers that are related, but not limited, to one or more of the following topics: Machine learning techniques for multi-sensor time series analysis, classification, clustering, and forecasting in smart-x applications; Pattern recognition and predictions using multi-sensor time series data; Multi-sensor fusion strategies and their application in smart-x areas; Synthetic long-term multi-sensor time series simulation and generation; Change point detection in multi-sensor time series data; Intelligent sensing-based decision support system for diagnosis, maintenance planning, and operation scheduling in smart-x applications; Machine learning-based critical event detection, diagnosis, and prediction using multi-sensor time series signals; Long-term multi-sensor time series signal processing. Dr. Giovanni Diraco Guest Editor Manuscript Submission Information Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI. Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions. Keywords multi-sensor time series abnormal event detection and prediction anomaly detection and prediction time series classification time series clustering long-term time series forecasting change point detection decision support systems decision-making techniques synthetic multi-sensor time series long-term multi-sensor time series signal processing predictive analytics predictive maintenance hazard detection/prediction machining monitoring structural health monitoring personal health monitoring
Last updated by Dou Sun in 2020-05-07
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