Data, Structure, and Information in Artificial Intelligence

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15549

Special Issue Editor


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Guest Editor
Ageno School of Business, Golden Gate University, San Francisco, CA 94105, USA
Interests: cloud computing; artificial intelligence; computing models; distributed computing; information technologies; cognitive computing models
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Special Issue Information

Dear Colleagues,

We live in a time when information has become visible, and its importance has become highly consequential. Timely information, and its veracity, can have a profound impact on the choice between life or death. Life is founded on information processes. All intelligence, natural or artificial, depends on information access, processing, and production. A basic feature of information processing is its dependence on structures. Physical, chemical, and biological systems use matter and energy to form a variety of structures for information processing, as well as other purposes.

Recent advances in our understanding of information processing structures are shaping the theory and practice of how we interact with each other and our environment. As many lectures in the recent is4si summit 2021 (IS4SI 2021) and the Conference – Theoretical and Foundational Problems (TFP) in Information Studies (tfpis.com) pointed out, structures play a very important role in how both living beings and digital systems convert information into knowledge and use it to develop cognitive capabilities. Our understanding of the following structures play key roles in these advances:

  1. The genome and its ability to create autopoietic systems through information coding and managing of physical and chemical processes in nature;
  2. The digital computing structures and their ability to model, monitor, and manage information processing and communication between us and our environment;
  3. New mathematics of named sets, knowledge structures, cognizing agents, and structural machines, which provide a theoretical framework of unified information processing mechanisms going beyond symbolic computing and neural networks.

These advances allow us to expand our understanding of the evolution of sentient, resilient and intelligent systems in nature, technology, and society. They also enable the designing of a new class of digital automata with autopoietic behavior to enhance our current information processing systems with a higher degree of sentience, resilience, and intelligence at scale.

This Special Issue aims to bring together contributions from multiple disciplines dealing with information processing structures and their evolution, with the purpose to understand how intelligence is evolving in nature, as well as to design and build a new class of artificially intelligent machines that blend with human intelligence while providing privacy, security, local autonomy, and global ethics based on globally federated regulatory mechanisms.

We solicit high-quality papers that discuss the nature of information processing structures and their evolution to design next-generation digital automata that blend human and artificial consciousness and cultural mechanisms. This can include, but is not limited to, the topics listed below.

Prof. Dr. Rao Mikkilineni
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 submissions that pass pre-check are 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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • Global information theory
  • Artificial intelligence
  • Self-managing automata
  • Autopoietic machines
  • Information processing
  • Information systems
  • Big data
  • Data analysis
  • Structures and categories
  • Information science
  • Machine learning for testing
  • Machine learning for diagnostics
  • Machine learning
  • Deep learning
  • Robotics
  • Impact on internet of things
  • Impact on information technologies

Published Papers (4 papers)

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Research

19 pages, 5202 KiB  
Article
Iris Liveness Detection Using Multiple Deep Convolution Networks
by Smita Khade, Shilpa Gite and Biswajeet Pradhan
Big Data Cogn. Comput. 2022, 6(2), 67; https://doi.org/10.3390/bdcc6020067 - 15 Jun 2022
Cited by 8 | Viewed by 3357
Abstract
In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained [...] Read more.
In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants. Full article
(This article belongs to the Special Issue Data, Structure, and Information in Artificial Intelligence)
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21 pages, 312 KiB  
Article
Operations with Nested Named Sets as a Tool for Artificial Intelligence
by Mark Burgin
Big Data Cogn. Comput. 2022, 6(2), 37; https://doi.org/10.3390/bdcc6020037 - 1 Apr 2022
Viewed by 2473
Abstract
Knowledge and data representations are important for artificial intelligence (AI), as well as for intelligence in general. Intelligent functioning presupposes efficient operation with knowledge and data representations in particular. At the same time, it has been demonstrated that named sets, which are also [...] Read more.
Knowledge and data representations are important for artificial intelligence (AI), as well as for intelligence in general. Intelligent functioning presupposes efficient operation with knowledge and data representations in particular. At the same time, it has been demonstrated that named sets, which are also called fundamental triads, instantiate the most fundamental structure in general and for knowledge and data representations in particular. In this context, named sets allow for effective mathematical portrayal of the key phenomenon, called nesting. Nesting plays a weighty role in a variety of fields, such as mathematics and computer science. Computing tools of AI include nested levels of parentheses in arithmetical expressions; different types of recursion; nesting of several levels of subroutines; nesting in recursive calls; multilevel nesting in information hiding; a variety of nested data structures, such as records, objects, and classes; and nested blocks of imperative source code, such as nested repeat-until clauses, while clauses, if clauses, etc. In this paper, different operations with nested named sets are constructed and their properties obtained, reflecting different attributes of nesting. An AI system receives information in the form of data and knowledge and processing information, performs operations with these data and knowledge. Thus, such a system needs various operations for these processes. Operations constructed in this paper perform processing of data and knowledge in the form of nested named sets. Knowing properties of these operations can help to optimize the processing of data and knowledge in AI systems. Full article
(This article belongs to the Special Issue Data, Structure, and Information in Artificial Intelligence)
22 pages, 628 KiB  
Article
Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis
by Jogeswar Tripathy, Rasmita Dash, Binod Kumar Pattanayak, Sambit Kumar Mishra, Tapas Kumar Mishra and Deepak Puthal
Big Data Cogn. Comput. 2022, 6(1), 24; https://doi.org/10.3390/bdcc6010024 - 23 Feb 2022
Cited by 13 | Viewed by 3478
Abstract
In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few [...] Read more.
In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all. Full article
(This article belongs to the Special Issue Data, Structure, and Information in Artificial Intelligence)
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15 pages, 1426 KiB  
Article
Infusing Autopoietic and Cognitive Behaviors into Digital Automata to Improve Their Sentience, Resilience, and Intelligence
by Rao Mikkilineni
Big Data Cogn. Comput. 2022, 6(1), 7; https://doi.org/10.3390/bdcc6010007 - 10 Jan 2022
Cited by 5 | Viewed by 4251
Abstract
All living beings use autopoiesis and cognition to manage their “life” processes from birth through death. Autopoiesis enables them to use the specification in their genomes to instantiate themselves using matter and energy transformations. They reproduce, replicate, and manage their stability. Cognition allows [...] Read more.
All living beings use autopoiesis and cognition to manage their “life” processes from birth through death. Autopoiesis enables them to use the specification in their genomes to instantiate themselves using matter and energy transformations. They reproduce, replicate, and manage their stability. Cognition allows them to process information into knowledge and use it to manage its interactions between various constituent parts within the system and its interaction with the environment. Currently, various attempts are underway to make modern computers mimic the resilience and intelligence of living beings using symbolic and sub-symbolic computing. We discuss here the limitations of classical computer science for implementing autopoietic and cognitive behaviors in digital machines. We propose a new architecture applying the general theory of information (GTI) and pave the path to make digital automata mimic living organisms by exhibiting autopoiesis and cognitive behaviors. The new science, based on GTI, asserts that information is a fundamental constituent of the physical world and that living beings convert information into knowledge using physical structures that use matter and energy. Our proposal uses the tools derived from GTI to provide a common knowledge representation from existing symbolic and sub-symbolic computing structures to implement autopoiesis and cognitive behaviors. Full article
(This article belongs to the Special Issue Data, Structure, and Information in Artificial Intelligence)
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