The artificial intelligence approach to pattern recognition—a perspective and an overview

The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach. The conventional approach comprises two methodologies—statistical and structural. The paper reviews salient issues in the application of conventional techniques for extraction of information. The systems that use the artificial intelligence approach are characterized with respect to three key properties. The basic differences between the approaches and the computational aspects are reviewed. Current trends in the use of the AI approach are indicated. Some key ideas in current literature are reviewed.     

Human activity recognition in artificial intelligence framework: a narrative review

Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future.

     

An artificial intelligence approach to speech recognition and understanding

This paper presents the I.R.S.T. Automatic Speech Recognition and Understanding (A.S.R.U.) Research Program for continuously spoken Italian without previous knowledge of the identity of the speaker. The acoustic analysis is performed in time domain and works in real-time. Acoustic ambiguities are overcome by using various levels of contextual information (orthophonic, syntactic, semantic) to formulate hypotheses to be verified by means of an hypothesize and test paradigm. The architecture is an analysis by synthesis loop.     

Consciousness,intentionality and intelligence: some foundational issues for artificial intelligence

Three fundamental questions concerning minds are presented. These are about consciousness, intentionality and intelligence. After we present the fundamental framework that has shaped both the philosophy of mind and the Artificial Intelligence research in the last forty years or so regarding the last two questions, we turn to consciousness, whose study still seems evasive to both communities. After briefly illustrating why and how phenomenal consciousness is puzzling, a theoretical diagnosis of the problem is proposed and a framework is presented, within which further research would yield a solution. The diagnosis is that the puzzle stems from a peculiar dual epistemic access to phenomenal aspects (qualia) of our conscious experiences. An account of concept formation is presented such that both the phenomenal concepts (like the concepts RED and SWEET) and the introspective concepts (like the concepts EXPERIENCING RED and TASTING SWEET) are acquired from a firstperson perspective as opposed to the third-person one (the standard concept formation strategy about objective features). We explain the first-person perspective in information-theoretic and computational terms: Nature (the Art whereby God hath made and governes the World) is by the Art of man, as in many other things, so in this also imitated, that it can make an Artificial Animal. For seeing life is but a motion of Limbs, the beginning whereof is in some principall part within; why may we not say, that all Automata (Engines that move themselves by springs and wheels as doth a watch) have an artificiall life? For what is the Heart, but a Spring; and the Nerves but so many Strings; and the Joynts, but so many Wheeles, giving motion to the whole Body, such as was intended by the Artificer? Art goes yet further, imitating that Rationall and most excellent worke of Nature, Man. (Hobbes 1651, p. 81) So declared Thomas Hobbes in 1651 in the Introduction to his well-known work, Leviathan, published one year after Réne Descartes' death. Descartes was also interested in mechanical explanations of bodily processes and organic life. In fact, on the basis of his neuroanatomical and physiological studies, as well as philosophical arguments, Descartes had already argued that human and animal bodies could be mechanically understood as complicated and intricately designed machines (Descartes 1664). What differentiated Descartes from Hobbes lay in his belief that human beings, unlike non-human animals, were not merely bodies; they were unions of material bodies and immaterial souls. The immaterial soul was necessary for Descartes to explain the peculiar capacities and activities of the human mind. As such, materialist mechanical explanations could never be sufficient to account for the whole human being.     

Advanced issues in artificial intelligence and pattern recognition for intelligent surveillance system in smart home environment

During the last decades, many researchers in image processing and AI community have been focused on developing image and video analysis and understanding. However, despite their extensive efforts on these, there are still several significant challenges such as robust object segmentation and tracking, motion feature extraction, context modeling, and machine learning algorithms. Therefore, more advanced related issues should be taken into account. We have selected nine research papers whose topics are strongly related to the intelligent surveillance system in smart home environment.     

The race for an artificial general intelligence: implications for public policy

AI & SOCIETY - An arms race for an artificial general intelligence (AGI) would be detrimental for and even pose an existential threat to humanity if it results in an unfriendly AGI. In this...     

C-calculus: An elementary approach to some problems in pattern recognition

This work utilizes the concept of “Composite set” (C-set) and of the related C-calculus to study some standard problems of pattern analysis and general processing of signals. After some basic definitions and notations about composite sets are briefly stipulated, it is shown how a family of C-sets can be associated with a digitized picture. Each element in the family conveys partial information about the picture itself, yet it is possible to combine the various contributions from each C-set in such a way as to completely retrieve the image. Conditions that guarantee such “convergence” are theoretically investigated: the cases of nonconvergence are also proved to be of some interest.

C-calculus is concretely applied to the extraction of significant regions in a digitized picture, of contours, etc. An application to texture discrimination and analysis is also outlined.     

An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market

We present a knowledge discovery-based framework that is capable of discovering, analyzing and exploiting new intraday price patterns in forex markets, beyond the well-known chart formations of technical analysis. We present a novel pattern recognition algorithm for Pattern Matching, that we successfully used to construct more than 16,000 new intraday price patterns. After processing and analysis, we extracted 3518 chart formations that are capable of predicting the short-term direction of prices. In our experiments, we used forex time series from 8 paired-currencies in various time frames. The system computes the probabilities of events such as “within next 5 periods, price will increase more than 20 pips”. Results show that the system is capable of finding patterns whose output signals (tested on unseen data) have predictive accuracy which varies between 60 and 85% depending on the type of pattern. We test the usefulness of the discovered patterns, via implementation of an expert system using a straightforward strategy based on the direction and the accuracy of the pattern predictions. We compare our method against three standard trading techniques plus a “random trader,” and we also test against the results presented in two recently published studies. Our framework performs very well against all systems we directly compare , and also, against all other published results.     

A system for pattern recognition and pattern summarization in multi-band satellite images

A new system (C.T.R.F.’s LSGENSYS—Linguistic Summary Generation System) that has been developed for pattern recognition and summarization of patterns in multiband (RGB) satellite images is described in this paper. The system design is described in some detail. The system has been tested successfully with SPOT MS and LANDSAT images. It extracts, analyzes, and summarizes patterns such as land, island, water body, river, fire, and urban settlements from these images. The results are presented by allowing the system to automatically classify and interpret these images. Some elements of supervised classification are also introduced, and a comparison is made between the results in each case. The text was submitted by the author in English. Hema Nair. Date of Birth: November 21, 1965. Education: Hema Nair received her Bachelors Degree in Electrical Engineering from Government Engineering College, University of Calicut, Kerala, India, in 1986. She received her Masters Degree in Electrical Engineering from National University of Singapore in 1993. Ms. Nair received her Masters Degree in Computer Science from Clark Atlanta University, Atlanta, United States, in 1996. Membership: A member of IEEE (USA) and ACM (USA) since 1997. A member of the Institution of Engineers (India) since 1988. Awards: 1. Ms. Nair’s Masters Degree research in the United States was funded by a US Army Grant. 2. One of Ms. Nair’s publications was cited with the Abstract in NASA’s Scientific and Technical Information Program Reports of 2006. Work Experience: 1. Ms. Nair was employed as Senior Technical Associate II at AT and T, New Jersey, United States, between 1996 and 2000. Her work included research and leading AT&T Projects as Project Leader. 2. She also served as Faculty in Apple Information Technology, Ltd, Bangalore, India, between 1987 and 1990. 3. Ms. Nair worked on contract as a lecturer in Multimedia University, Malaysia, between 2001 and 2005. 4. Since 2005, she has been working as a Researcher at C.T.R.F., a research and education foundation in India. Research Interests: Ms. Nair’s research interests include Image Analysis, Pattern Recognition, Databases, Artificial Intelligence, and Data Mining. Publications: Ms. Nair has published several papers internationally. These include 7 International Conference Papers and 4 International Journals. Reviewer for LASTED International Conference 2004.     

人工智能范式的革命与通用智能理论的创生

人工智能的研究取得了不少可喜的进展,也面临着许多严峻的挑战.为了应对这些挑战,学术界提出了各种各样的研究思路.笔者相信,每种思路都有其合理之处,都有可能获得一定的成效.不过,根据笔者的理解,人工智能面临的最深刻最严峻的挑战,是学科和时代的大转变所带来的大阵痛:人工智能范式的张冠李戴.因此,必须对人工智能的范式实施"正冠...     

A recursive framework for expression recognition: from web images to deep models to game dataset

In this paper, we propose a recursive framework to recognize facial expressions from images in real scenes. Unlike traditional approaches that typically focus on developing and refining algorithms for improving recognition performance on an existing dataset, we integrate three important components in a recursive manner: facial dataset generation, facial expression recognition model building, and interactive interfaces for testing and new data collection. To start with, we first create candid images for facial expression (CIFE) dataset. We then apply a convolutional neural network (CNN) to CIFE and build a CNN model for web image expression classification. In order to increase the expression recognition accuracy, we also fine-tune the CNN model and thus obtain a better CNN facial expression recognition model. Based on the fine-tuned CNN model, we design a facial expression game engine and collect a new and more balanced dataset, GaMo. The images of this dataset are collected from the different expressions our game users make when playing the game. Finally, we run yet another recursive step—a self-evaluation of the quality of the data labeling and propose a self-cleansing mechanism for improve the quality of the data. We evaluate the GaMo and CIFE datasets and show that our recursive framework can help build a better facial expression model for dealing with real scene facial expression tasks.     

An application of artificial intelligence to object-orientedperformance design for real-time systems

The paper describes an application of artificial intelligence technology to the implementation of a rapid prototyping method in object-oriented performance design (OOPD) for real-time systems. OOPD consists of two prototyping phases for real-time systems. Each of these phases consists of three steps: prototype construction, prototype execution, and prototype evaluation. We present artificial intelligence based methods and tools to be applied to the individual steps. In the prototype construction step, a rapid construction mechanism using reusable software components is implemented based on planning. In the prototype execution step, a hybrid inference mechanism is used to execute the constructed prototype described in declarative knowledge representation. MENDEL, which is a Prolog based concurrent object-oriented language, can be used as a prototype construction tool and a prototype execution tool. In the prototype evaluation step, an expert system which is based on qualitative reasoning is implemented to detect and diagnose bottlenecks and generate an improvement plan for them     

An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization

In view of the shortcomings such as slow search speed, low optimization precision and premature convergence of artificial hummingbird algorithm, an enhanced artificial hummingbird algorithm based on golden sine factor named DGSAHA is proposed. Firstly, chaos mapping is used to generate the initial candidate solution to increase the diversity of the population, which lays the foundation for the global search. Then, perturb the individuals by means of the differential variation between individuals on the group, thereby enhancing the diversity of the population, preserving the excellent individuals, eliminating the inferior individuals, and guiding the search process to approach the global optimal solution, avoiding the phenomenon of premature convergence. Finally, the golden sine factor were introduced in the guided foraging stage is conducive to the full exploration of the global optimal solution, reducing the search space for individuals to approach the optimal solution. And, it facilitates the balance between “exploration” and “exploitation” of algorithm. Thereby, the accuracy and speed of the DGSAHA can be improved to a certain extent. 25 classic functions, the CEC2014 and CEC2019 benchmark functions were tested, and several representative meta-heuristic algorithms and its improved algorithm are compared for evaluate the validity of DGSAHA. Meanwhile, the dimensional scalability of the variable-dimensional test function is discussed. The results of non-parametric statistical analysis and performance index show that DGSAHA in this paper has better comprehensive optimization performance, significantly improves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. Finally, the performance of DGSAHA and the practicability of truss structure are answered by three engineering examples of plane and space truss topology optimization problem. This optimization problem considers not only the static constraints such as stress, displacement and buckling, but also the dynamic constraints of frequency and motion stability. In order to avoid singularity and unnecessary analysis, the stiffness, mass and load matrices are reconstructed in finite element analysis. A lighter truss structure than the existing solution is obtained. The validity, extensibility and practicability of the algorithm are further illustrated.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.