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Journal articles on the topic 'Distributed Artificial Intelligence {Computing Methodologies}'

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Published: 4 June 2021

Last updated: 3 February 2022

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Bhargavi, K., B. Sathish Babu, and Jeremy Pitt. "Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based." Journal of Intelligent Systems 30, no. 1 (2020): 40–58. http://dx.doi.org/10.1515/jisys-2019-0084.

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Abstract Cloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques.

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Hirota, Toshio Fukudand Kaoru. "Message from Editors-in-Chief." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (1997): 0. http://dx.doi.org/10.20965/jaciii.1997.p0000.

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We are very pleased and honored to have an opportunity to publish a new journal the "International Journal of Advanced Computational Intelligence" (JACI). The JACI is a new, bimonthly journal covering the field of computer science. This journal focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and evolutionary computations, in order to assist in fostering the application of intelligent systems to industry. This new field is called computational intelligence or soft computing. It has already been studied by many researchers, but no single, integrated journal exists anywhere in the world. This new journal gives readers the state of art of the theory and application of Advanced Computational Intelligence. The Topics include, but are not limited to: Fuzzy Logic, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Network Systems, Multimedia, the Human Interface, Biologically-Inspired Evolutionary Systems, Artificial Life, Chaos, Fractal, Wavelet Analysis, Scientific Applications and Industrial Applications. The journal, JACI, is supported by many researchers and scientific organizations, e.g., the International Fuzzy Systems Association (IFSA), the Japan Society of Fuzzy Theory and Systems (SOFT), the Brazilian Society of Automatics (SBA) and The Society of Instrument and Control Engineers (SICE), and we are currently negotiating with the John von Neumann Computer Society (in Hungary). Our policy is to have world-wide communication with many societies and researchers in this field. We would appreciate it if those organizations and people who have an interest in co-sponsorship or have proposals for special issues in this journal, as well as paper submissions, could contact us. Finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its president, Mr. K. Hayashi, and to the editor, Mr. Y. Inoue, for their efforts in publishing this new journal. Lotti A. Zadeh The publication of the International Journal of Advanced Computational Intelligence (JACI) is an important milestone in the advancement of our understanding of how intelligent systems can be conceived, designed, built, and deployed. When one first hears of computational intelligence, a question that naturally arises is: What is the difference, if any, between computational intelligence (CI) and artificial intelligence (AI)? As one who has witnessed the births of both AI and CI, I should like to suggest an answer. As a branch of science and technology, artificial intelligence was born about four decades ago. From the outset, AI was based on classical logic and symbol manipulation. Numerical computations were not welcomed and probabilistic techniques were proscribed. Mainstream AI continued to evolve in this spirit, with symbol manipulation still occupying the center of the stage, but not to the degree that it did in the past. Today, probabilistic techniques and neurocomputing are not unwelcome, but the focus is on distributed intelligence, agents, man-machine interfaces, and networking. With the passage of time, it became increasing clear that symbol manipulation is quite limited in its ability to serve as a foundation for the design of intelligent systems, especially in the realms of robotics, computer vision, motion planning, speech recognition, handwriting recognition, fault diagnosis, planning, and related fields. The inability of mainstream AI to live up to expectations in these application areas has led in the mid-eighties to feelings of disenchantment and widespread questioning of the effectiveness of AI's armamentarium. It was at this point that the name computational intelligence was employed by Professor Nick Cercone of Simon Fraser University in British Columbia to start a new journal named Computational Intelligence -a journal that was, and still is, intended to provide a broader conceptual framework for the conception and design of intelligent systems than was provided by mainstream AI. Another important development took place. The concept of soft computing (SC) was introduced in 1990-91 to describe an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic (or evolutionary) computing (GC), and probabilistic computing (PC). In essence, soft computing differs from traditional hard computing in that it is tolerant of imprecision, uncertainty and partial truth. The basic guiding principle of SC is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. More recently, the concept of computational intelligence had reemerged with a meaning that is substantially different from that which it had in the past. More specifically, in its new sense, CI, like AI, is concerned with the conception, design, and deployment of intelligent systems. However, unlike mainstream AI, CI methodology is based not on predicate logic and symbol manipulation but on the methodologies of soft computing and, more particularly, on fuzzy logic, neurocomputing, genetic(evolutionary) computing, and probabilistic computing. In this sense, computational intelligence and soft computing are closely linked but not identical. In basic ways, the importance of computational intelligence derives in large measure from the effectiveness of the techniques of fuzzy logic, neurocomputing, genetic (evolutionary) computing, and probabilistic computing in the conception and design of information/intelligent systems, as defined in the statements of the aims and scope of the new journal of Advanced Computational Intelligence. There is one important aspect of both computational intelligence and soft computing that should be stressed. The methodologies which lie at the center of CI and SC, namely, FL, NC, genetic (evolutionary) computing, and PC are for the most part complementary and synergistic, rather than competitive. Thus, in many applications, the effectiveness of FL, NC, GC, and PC can be enhanced by employing them in combination, rather than in isolation. Intelligent systems in which FL, NC, GC, and PC are used in combination are frequently referred to as hybrid intelligent systems. Such systems are likely to become the norm in the not distant future. The ubiquity of hybrid intelligent systems is likely to have a profound impact on the ways in which information/intelligent systems are conceived, designed, built, and interacted with. At this juncture, the most visible hybrid intelligent systems are so-called neurofuzzy systems, which are for the most part fuzzy-rule-based systems in which neural network techniques are employed for system identification, rule induction, and tuning. The concept of neurofuzzy systems was originated by Japanese scientists and engineers in the late eighties, and in recent years has found a wide variety of applications, especially in the realms of industrial control, consumer products, and financial engineering. Today, we are beginning to see a widening of the range of applications of computational intelligence centered on the use of neurofuzzy, fuzzy-genetic, neurogenetic, neurochaotic and neuro-fuzzy-genetic systems. The editors-in-chief of Advanced Computational Intelligence, Professors Fukuda and Hirota, have played and are continuing to play majors roles both nationally and internationally in the development of fuzzy logic, soft computing, and computational intelligence. They deserve our thanks and congratulations for conceiving the International Journal of Advanced Computational Intelligence and making it a reality. International in both spirit and practice, JACI is certain to make a major contribution in the years ahead to the advancement of the science and technology of man-made information/intelligence systems -- systems that are at the center of the information revolution, which is having a profound impact on the ways in which we live, communicate, and interact with the real world. Lotfi A. Zadeh Berkeley, CA, July 24, 1997

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Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 3 (1998): 69–71. http://dx.doi.org/10.20965/jaciii.1998.p0069.

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Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).

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Eduardo, Luis, and Castillo Hern. "On distributed artificial intelligence." Knowledge Engineering Review 3, no. 1 (1988): 21–57. http://dx.doi.org/10.1017/s0269888900004367.

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AbstractDistributed Artificial Intelligence has been loosely defined in terms of computation by distributed, intelligent agents. Although a variety of projects employing widely ranging methodologies have been reported, work in the field has matured enough to reveal some consensus about its main characteristics and principles. A number of prominent projects are described in detail, and two general frameworks, theSystem conceptual modeland theagent conceptual model, are used to compare the different approaches. The paper concludes by reviewing approaches to formalizing some of the more critical capabilities required by multi-agent interaction.

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Corchado, Juan M., Li Weigang, Javier Bajo, Fei Wu, and Tian-cheng Li. "Special issue on distributed computing and artificial intelligence." Frontiers of Information Technology & Electronic Engineering 17, no. 4 (2016): 281–82. http://dx.doi.org/10.1631/fitee.dcai2015.

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Bajo, Javier, and Juan M. Corchado. "Neural Systems in Distributed Computing and Artificial Intelligence." Neurocomputing 231 (March 2017): 1–2. http://dx.doi.org/10.1016/j.neucom.2016.08.096.

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Bajo, Javier, and Juan M. Corchado. "Neural networks in distributed computing and artificial intelligence." Neurocomputing 272 (January 2018): 1–2. http://dx.doi.org/10.1016/j.neucom.2017.06.022.

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Dzitac, Ioan, and Boldur E. Bărbat. "Artificial Intelligence + Distributed Systems = Agents." International Journal of Computers Communications & Control 4, no. 1 (2009): 17. http://dx.doi.org/10.15837/ijccc.2009.1.2410.

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The connection with Wirth’s book goes beyond the title, albeit confining the area to modern Artificial Intelligence (AI). Whereas thirty years ago, to devise effective programs, it became necessary to enhance the classical algorithmic framework with approaches applied to limited and focused subdomains, in the context of broad-band technology and semantic web, applications - running in open, heterogeneous, dynamic and uncertain environments-current paradigms are not enough, because of the shift from programs to processes. Beside the structure as position paper, to give more weight to some basic assertions, results of recent research are abridged and commented upon in line with new paradigms. Among the conclusions: a) Nondeterministic software is unavoidable; its development entails not just new design principles but new computing paradigms. b) Agent-oriented systems, to be effectual, should merge conventional agent design with approaches employed in advanced distributed systems (where parallelism is intrinsic to the problem, not just a mean to speed up).

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Bajo, Javier, and Juan M. Corchado. "Special issue on distributed computing and artificial intelligence systems." Neurocomputing 172 (January 2016): 382–84. http://dx.doi.org/10.1016/j.neucom.2015.05.114.

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Kathidjiotis, Yiannis, Kostas Kolomvatsos, and Christos Anagnostopoulos. "Predictive intelligence of reliable analytics in distributed computing environments." Applied Intelligence 50, no. 10 (2020): 3219–38. http://dx.doi.org/10.1007/s10489-020-01712-5.

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Abstract Lack of knowledge in the underlying data distribution in distributed large-scale data can be an obstacle when issuing analytics & predictive modelling queries. Analysts find themselves having a hard time finding analytics/exploration queries that satisfy their needs. In this paper, we study how exploration query results can be predicted in order to avoid the execution of ‘bad’/non-informative queries that waste network, storage, financial resources, and time in a distributed computing environment. The proposed methodology involves clustering of a training set of exploration queries along with the cardinality of the results (score) they retrieved and then using query-centroid representatives to proceed with predictions. After the training phase, we propose a novel refinement process to increase the reliability of predicting the score of new unseen queries based on the refined query representatives. Comprehensive experimentation with real datasets shows that more reliable predictions are acquired after the proposed refinement method, which increases the reliability of the closest centroid and improves predictability under the right circumstances.

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Campbell, Jonathan G. "Soft computing — Fuzzy logic, neural networks, and distributed artificial intelligence." Neurocomputing 12, no. 4 (1996): 367–69. http://dx.doi.org/10.1016/0925-2312(96)83759-3.

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Dunn, S., S. Peucker, and J. Perry. "Genetic Algorithm Optimisation of Mathematical Models Using Distributed Computing." Applied Intelligence 23, no. 1 (2005): 21–32. http://dx.doi.org/10.1007/s10489-005-2369-1.

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Wang, Hao, Yan Yang, Xiaobo Zhang, and Bo Peng. "Parallel multi-view concept clustering in distributed computing." Neural Computing and Applications 32, no. 10 (2019): 5621–31. http://dx.doi.org/10.1007/s00521-019-04243-4.

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RYU, TAE W. "A COMMON CHARACTERISTIC KNOWLEDGE DISCOVERY SYSTEM IN DISTRIBUTED COMPUTING ENVIRONMENT." International Journal on Artificial Intelligence Tools 14, no. 03 (2005): 425–43. http://dx.doi.org/10.1142/s0218213005002181.

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This paper describes an automated query discovery system for retrieving common characteristic knowledge from a database in a distributed computing environment. The paper particularly centers on the problem of discovering the common characteristics that are shared by a set of objects in a database. This type of commonalities can be useful in finding a typical profile for the given object set or outstanding features for a group of objects in a database. In our approach, commonalities within a set of objects are described by database queries that compute the given set of objects. We use the genetic programming as a major search engine to discover such queries. The paper discusses the architecture and the techniques used in our system, and presents some experimental results to evaluate the system. In addition, for the performance improvement, we built a distributed computing environment for our system with clustered computers using the Common Object Request Broker Architecture (CORBA). The paper briefly discusses our clustered computer architecture, the implementation of distributed computing environment, and shows the overall performance improvement.

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Ramsay, A. "Distributed versus parallel computing." Artificial Intelligence Review 1, no. 1 (1986): 11–25. http://dx.doi.org/10.1007/bf01988525.

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BONISSONE, PIERO, and KAI GOEBEL. "Soft Computing for diagnostics in equipment service." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 4 (2001): 267–79. http://dx.doi.org/10.1017/s0890060401154028.

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We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.

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De la Prieta, Fernando, and Juan M. Corchado Rodríguez. "Neural networks and learning systems in distributed computing and artificial intelligence." Neurocomputing 423 (January 2021): 668–69. http://dx.doi.org/10.1016/j.neucom.2020.05.001.

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Chen, Jianguo, and Ahmad Salah. "Editorial introduction: special issue on advances in parallel and distributed computing for neural computing." Neural Computing and Applications 32, no. 10 (2020): 5531–33. http://dx.doi.org/10.1007/s00521-020-04887-7.

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Wang, Yingxu, George Baciu, Yiyu Yao, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

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Debauche, Olivier, Meryem Elmoulat, Saïd Mahmoudi, et al. "Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (2021): 11–17. http://dx.doi.org/10.5383/juspn.15.02.002.

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Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

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Bordetsky, Alexander B. "Reasoning on infeasibility in distributed collaborative computing environment." Annals of Mathematics and Artificial Intelligence 17, no. 1 (1996): 155–76. http://dx.doi.org/10.1007/bf02284629.

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Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, et al. "Silicon photonics for artificial intelligence applications." Photoniques, no. 104 (September 2020): 40–44. http://dx.doi.org/10.1051/photon/202010440.

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Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

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Pei, Songwne, Junjie Wu, Tao Li, Yong Chen, and Stephane Zuckerman. "IEEE Access Special Section Editorial: Artificial Intelligence in Parallel and Distributed Computing." IEEE Access 9 (2021): 9535–38. http://dx.doi.org/10.1109/access.2020.3048598.

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Yang, Bo, Jiming Liu, and Dayou Liu. "An autonomy-oriented computing approach to community mining in distributed and dynamic networks." Autonomous Agents and Multi-Agent Systems 20, no. 2 (2009): 123–57. http://dx.doi.org/10.1007/s10458-009-9080-2.

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Manikanthan, S. V., T. Padmapriya, Azham Hussain, and E. Thamizharasi. "Artificial Intelligence Techniques for Enhancing Smartphone Application Development on Mobile Computing." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 17 (2020): 4. http://dx.doi.org/10.3991/ijim.v14i17.16569.

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<p>Nowadays, Artificial Intelligence is being integrated into the modern innovations, including mobile, Electronic gadgets and as well as our daily lives. The smartphones are becoming a crucial and indistinguishable part of modern life. Whether that be in terms of speech, prototype, efficiency, features, quality and so forth, together all system requirements are provided in one machine. Researchers and innovations analysts are making advances in mobile computing with the excellent technologies. While Artificial Intelligence as a commercial product has been directly accessible. In this, both corporations and violent offenders take benefit of emerging technologies and advances. Cyber-security specialists and authorities have predicted there have been high possibilities of cyber-attacks. There's really, besides that, a need to improve quite advanced and powerful data security processes and software to protect all fraudulent activities and threats. The objective of this study is to introduce latest developments of implementing Methodologies to mobile computing, to prove how such techniques could become an efficient resource for data security and protocols, and to provide scope for future research.</p>

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Ciric, Ivan, Zarko Cojbasic, Vlastimir Nikolic, Predrag Zivkovic, and Mladen Tomic. "Air quality estimation by computational intelligence methodologies." Thermal Science 16, suppl. 2 (2012): 493–504. http://dx.doi.org/10.2298/tsci120503186c.

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The subject of this study is to compare different computational intelligence methodologies based on artificial neural networks used for forecasting an air quality parameter - the emission of CO2, in the city of Nis. Firstly, inputs of the CO2 emission estimator are analyzed and their measurement is explained. It is known that the traffic is the single largest emitter of CO2 in Europe. Therefore, a proper treatment of this component of pollution is very important for precise estimation of emission levels. With this in mind, measurements of traffic frequency and CO2 concentration were carried out at critical intersections in the city, as well as the monitoring of a vehicle direction at the crossroad. Finally, based on experimental data, different soft computing estimators were developed, such as feed forward neural network, recurrent neural network, and hybrid neuro-fuzzy estimator of CO2 emission levels. Test data for some characteristic cases presented at the end of the paper shows good agreement of developed estimator outputs with experimental data. Presented results are a true indicator of the implemented method usability.

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Deepak, BBVL, G. Bala Murali, MVA Raju Bahubalendruni, and BB Biswal. "Assembly sequence planning using soft computing methods: A review." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 233, no. 3 (2018): 653–83. http://dx.doi.org/10.1177/0954408918764459.

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The implementation of artificial intelligence techniques is increasing rapidly in recent years to solve numerous engineering problems. Assembly sequence planning is one of the prominent complex combinatorial problem draw attention of industrial engineers to economize the overall manufacturing cost by minimizing the assembly time and energy. Due to large search space and multiple assembly predicate criteria, researchers are motivated towards efficient utilization of AI techniques to address the problem. Literature review on various artificial intelligence techniques for obtaining the optimal assembly sequence planning are analyzed and the limitations of the existed methodologies are discussed in detail. This review provides an outlook for the researchers on various artificial intell1igent techniques which will be useful to carry out research for obtaining the optimum assembly sequence planning while qualifying various assembly predicate criteria.

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Rudall, Brian H. "Distributed computing initiatives." Kybernetes 34, no. 6 (2005): 757–65. http://dx.doi.org/10.1108/03684920510595463.

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STUCKENSCHMIDT, HEINER. "Foreword: ontologies for distributed systems." Knowledge Engineering Review 18, no. 3 (2003): 193–95. http://dx.doi.org/10.1017/s0269888904000013.

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The benefits of using ontologies have been recognised in many areas such as knowledge and content management, electronic commerce and recently the emerging field of the Semantic Web. These new applications can be seen as a great success of research in ontologies. On the other hand, moving into real application comes with new challenges that need to be addressed on a principled level rather than for specific applications. This special issue will be devoted to less well-explored topics that have come into focus recently as a response to the new problems we face when trying to use ontologies in heterogeneous distributed environments. These environments include the use of ontologies in peer-to-peer and pervasive computing systems.

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PERTSELAKIS, MINAS, CHRISTOS FERLES, KOSTAS TSIOLIS, and ANDREAS STAFYLOPATIS. "WIRELESS DISTRIBUTED IMPLEMENTATION OF A FUZZY NEURAL CLASSIFICATION SYSTEM." International Journal on Artificial Intelligence Tools 14, no. 04 (2005): 661–82. http://dx.doi.org/10.1142/s0218213005002314.

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Recent years have seen a surge of interest in the field of pervasive context-aware computing. In this framework we propose a novel real implementation of an adaptive self-configurable system, applied within the scope of wireless ad-hoc networks. WiDFuNC is an integrated system that consists of an intelligent unit implemented on a real PDA, a number of sensors and a remote server device to form an efficient prototype system that can be applied in various domains. This implementation of WiDFuNC focuses on pure classification problems with satisfactory experimental results, presenting great adaptability and context-awareness.

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Huang, De-Shuang. "Special issue on advanced intelligent computing theories and methodologies." Neurocomputing 137 (August 2014): 1–2. http://dx.doi.org/10.1016/j.neucom.2014.02.009.

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Maturana, F. P., R. J. Staron, and K. H. Hall. "Methodologies and Tools for Intelligent Agents in Distributed Control." IEEE Intelligent Systems 20, no. 1 (2005): 42–49. http://dx.doi.org/10.1109/mis.2005.13.

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Lv, Peng Liang, and Guo Shun Chen. "Distributed Remote Monitoring System Key Technology Research on Agent." Advanced Materials Research 860-863 (December 2013): 2774–82. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2774.

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Recent years, as computer science and the development of high-tech communications networks, distributed artificial intelligence field of artificial intelligence research as an important branch, more and more industry attention. One of the Agent and Multi-Agent technology research became a hot topic of distributed artificial intelligence research. Research on Multi-Agent technology is mainly a group of autonomous distributed open Agent in a dynamic environment, through interaction, cooperation, competition, negotiation and other acts perform complex control or task solving. Because of its better reflect human social intelligence, more suitable for an open, dynamic social environment, thus causing researchers in various fields of interest, and is widely used in scientific computing, computer network e-commerce, business management and traffic control, etc

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Lim, JongBeom, Joon-Min Gil, and HeonChang Yu. "A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments." Symmetry 10, no. 1 (2018): 30. http://dx.doi.org/10.3390/sym10010030.

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TSITSOULIS, ATHANASIOS, and NIKOLAOS BOURBAKIS. "A FIRST STAGE COMPARATIVE SURVEY ON HUMAN ACTIVITY RECOGNITION METHODOLOGIES." International Journal on Artificial Intelligence Tools 22, no. 06 (2013): 1350030. http://dx.doi.org/10.1142/s0218213013500309.

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The development of vision-based human activity recognition and analysis systems has been a matter of great interest to both the research community and practitioners during the last 20 years. Traditional methods that require a human operator watching raw video streams are nowadays deemed as at least ineffective and expensive. New, smart solutions in automatic surveillance and monitoring have emerged, propelled by significant technological advances in the fields of image processing, artificial intelligence, electronics and optics, embedded computing and networking, molding the future of several applications that can benefit from them, like security and healthcare. The main motivation behind it is to exploit the highly informative visual data captured by cameras and perform high-level inference in an automatic, ubiquitous and unobtrusive manner, so as to aid human operators, or even replace them. This survey attempts to comprehensively review the current research and development on vision-based human activity recognition. Synopses from various methodologies are presented in an effort to garner the advantages and shortcomings of the most recent state-of-the-art technologies. Also a first-level self-evaluation of methodologies is also proposed, which incorporates a set of significant features that best describe the most important aspects of each methodology in terms of operation, performance and others and weighted by their importance. The purpose of this study is to serve as a reference for further research and evaluation to raise thoughts and discussions for future improvements of each methodology towards maturity and usefulness.

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Wang, Yingxu, and Victor J. Wiebe. "Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks." International Journal of Cognitive Informatics and Natural Intelligence 8, no. 3 (2014): 29–44. http://dx.doi.org/10.4018/ijcini.2014070103.

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Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

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Jackson, Gretchen, and Jianying Hu. "Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications." Yearbook of Medical Informatics 28, no. 01 (2019): 052–54. http://dx.doi.org/10.1055/s-0039-1677925.

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Objective: To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018. Methods: Ovid MEDLINE® and Web of Science® databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH®) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook. Results: Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies. Conclusions: Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.

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Bonatti, Piero, Eugenio Oliveira, Jordi Sabater-Mir, Carles Sierra, and Francesca Toni. "On the integration of trust with negotiation, argumentation and semantics." Knowledge Engineering Review 29, no. 1 (2013): 31–50. http://dx.doi.org/10.1017/s0269888913000064.

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AbstractAgreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing.

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Bai, Junjie, Kan Luo, Jun Peng, et al. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 4 (2017): 80–92. http://dx.doi.org/10.4018/ijcini.2017100105.

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Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

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Lin, Sen, Jianxin Huang, Wenzhou Chen, et al. "Intelligent warehouse monitoring based on distributed system and edge computing." International Journal of Intelligent Robotics and Applications 5, no. 2 (2021): 130–42. http://dx.doi.org/10.1007/s41315-021-00173-4.

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AbstractThis paper mainly focuses on the volume calculation of materials in the warehouse where sand and gravel materials are stored and monitored whether materials are lacking in real-time. Specifically, we proposed the sandpile model and the point cloud projection obtained from the LiDAR sensors to calculate the material volume. We use distributed edge computing modules to build a centralized system and transmit data remotely through a high-power wireless network, which solves sensor placement and data transmission in a complex warehouse environment. Our centralized system can also reduce worker participation in a harsh factorial environment. Furthermore, the point cloud data of the warehouse is colored to visualize the actual factorial environment. Our centralized system has been deployed in the real factorial environment and got a good performance.

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Kuang, Hairong, Lubomir F. Bic, and Michael B. Dillencourt. "PODC: Paradigm-oriented distributed computing." Journal of Parallel and Distributed Computing 65, no. 4 (2005): 506–18. http://dx.doi.org/10.1016/j.jpdc.2004.11.009.

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Wong, Kenneth F., and Mark Franklin. "Checkpointing in Distributed Computing Systems." Journal of Parallel and Distributed Computing 35, no. 1 (1996): 67–75. http://dx.doi.org/10.1006/jpdc.1996.0069.

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Johnson, Bonnie, and William A. Treadway. "Artificial Intelligence — An Enabler of Naval Tactical Decision Superiority." AI Magazine 40, no. 1 (2019): 63–78. http://dx.doi.org/10.1609/aimag.v40i1.2852.

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Artificial intelligence, as a capability enhancer, offers significant improvements to our tactical warfighting advantage. AI provides methods for fusing and analyzing data to enhance our knowledge of the tactical environment; it provides methods for generating and assessing decision options from multidimensional, complex situations; and it provides predictive analytics to identify and examine the effects of tactical courses of action. Machine learning can improve these processes in an evolutionary manner. Advanced computing techniques can handle highly heterogeneous and vast datasets and can synchronize knowledge across distributed warfare assets. This article presents concepts for applying AI to various aspects of tactical battle management and discusses their potential improvements to future warfare.

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Zhang, Zhonghua, Xifei Song, Lei Liu, Jie Yin, Yu Wang, and Dapeng Lan. "Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work." Security and Communication Networks 2021 (June 24, 2021): 1–15. http://dx.doi.org/10.1155/2021/9991535.

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Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.

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Wellman, M. P. "A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems." Journal of Artificial Intelligence Research 1 (August 1, 1993): 1–23. http://dx.doi.org/10.1613/jair.2.

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Market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralization of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, we derive the activities and resource allocations for a set of computational agents by computing the competitive equilibrium of an artificial economy. WALRAS provides basic constructs for defining computational market structures, and protocols for deriving their corresponding price equilibria. In a particular realization of this approach for a form of multicommodity flow problem, we see that careful construction of the decision process according to economic principles can lead to efficient distributed resource allocation, and that the behavior of the system can be meaningfully analyzed in economic terms.

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Amato, Christopher, Ofra Amir, Joanna Bryson, et al. "Reports of the AAAI 2016 Spring Symposium Series." AI Magazine 37, no. 4 (2017): 83–88. http://dx.doi.org/10.1609/aimag.v37i4.2691.

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The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016 at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in Socially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Non-Human Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through Social Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science.

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Horn, Nils, Fabian Gampfer, and Rüdiger Buchkremer. "Latent Dirichlet Allocation and t-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture." AI 2, no. 2 (2021): 179–94. http://dx.doi.org/10.3390/ai2020011.

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As the amount of scientific information increases steadily, it is crucial to improve fast-reading comprehension. To grasp many scientific articles in a short period, artificial intelligence becomes essential. This paper aims to apply artificial intelligence methodologies to examine broad topics such as enterprise architecture in scientific articles. Analyzing abstracts with latent dirichlet allocation or inverse document frequency appears to be more beneficial than exploring full texts. Furthermore, we demonstrate that t-distributed stochastic neighbor embedding is well suited to explore the degree of connectivity to neighboring topics, such as complexity theory. Artificial intelligence produces results that are similar to those obtained by manual reading. Our full-text study confirms enterprise architecture trends such as sustainability and modeling languages.

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Wang, Yingxu, Robert C. Berwick, Simon Haykin, et al. "Cognitive Informatics and Cognitive Computing in Year 10 and Beyond." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (2011): 1–21. http://dx.doi.org/10.4018/jcini.2011100101.

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Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers (CogC) that perceive, infer, and learn. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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Xu, Zhanyang, Wentao Liu, Jingwang Huang, Chenyi Yang, Jiawei Lu, and Haozhe Tan. "Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey." Security and Communication Networks 2020 (September 14, 2020): 1–13. http://dx.doi.org/10.1155/2020/8872586.

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With the explosive growth of data generated by the Internet of Things (IoT) devices, the traditional cloud computing model by transferring all data to the cloud for processing has gradually failed to meet the real-time requirement of IoT services due to high network latency. Edge computing (EC) as a new computing paradigm shifts the data processing from the cloud to the edge nodes (ENs), greatly improving the Quality of Service (QoS) for those IoT applications with low-latency requirements. However, compared to other endpoint devices such as smartphones or computers, distributed ENs are more vulnerable to attacks for restricted computing resources and storage. In the context that security and privacy preservation have become urgent issues for EC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges. The powerful learning ability of AI enables the system to identify malicious attacks more accurately and efficiently. Meanwhile, to a certain extent, transferring model parameters instead of raw data avoids privacy leakage. In this paper, a comprehensive survey of the contribution of AI to the IoT security in EC is presented. First, the research status and some basic definitions are introduced. Next, the IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services with AI is then presented. In the end, the open issues and challenges on the application of AI in IoT services based on EC are discussed.

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KIM, K. H. "Distributed Computing Software Building-Blocks for Ubiquitous Computing Societies." IEICE Transactions on Information and Systems E91-D, no. 9 (2008): 2233–42. http://dx.doi.org/10.1093/ietisy/e91-d.9.2233.

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