Intelligent Computer Applications Research Articles

Intelligent Computing: The Latest Advances, Challenges, and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence, and internet of things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human–computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: Intelligent computing is not only intelligence oriented but also intelligence driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy, and an abundance of innovations in the theories, systems, and applications of intelligent computing is expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.

Preface

International Conference on Applications of Intelligent Computing in Engineering and Science (AICES)-2022 12th - 13th February 2022National Institute of Technology (NIT) Raipur is proud to host the IOP International Conference on Applications of Intelligent Computing in Engineering and Science (AICES)-2022 from 12th to 13th February 2022 in virtual mode. It was jointly organised by the Department of Applied Geology, Department of Information Technology (IT) and Department of Biomedical Engineering of NIT Raipur.Intelligent computing is one of the emerging fields that can add new and better application in a wide range of sectors like healthcare, service sector, and processing industry to name some. The conference showcases advancements that will enable the researchers to develop improved approaches for solving science and engineering problems.The conference aims to provide a forum for researchers, practitioners and professionals from the industry and the academia to share their research trends, findings, and results in the multidisciplinary field of intelligent computing. Various applications of intelligent computing in earth and environment science, biomedical engineering, and information technology was covered. Topics like geospatial analysis, weather and climate forecasting, biomedical imaging and signal processing, internet of things, machine learning and deep learning applications, biomedical instrumentation, medical imaging physics etc. are in the scope of conference. The Conference highlights diverse perspectives to interested geologists, meteorologists, biomedical engineers, software engineers and computer professionals, medical professionals, biotechnologists, pharmaceutical experts, sustainability researchers and also provided a platform for potential knowledge exchange on recent trends, theories and practices in the field of intelligent computing.List of Chief Patron, Patron(s), Chairman, Secretary, Technical Advisory Committee, Technical Program Chair (Internal), Management Chair, Session Chair/Reviewer, Editors are available in the pdf

Applications of intelligent computing in vehicular networks

Purpose This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood. Design/methodology/approach In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles. Findings In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system. Originality/value This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.

Design of an expert system for the prediction and comprehensive diagnosis of CNC machining fault

Unlike the earlier research works which only focused on fault diagnosis, in this paper a smart fault preventive step is taken in avoiding some category of failures which may be predicted at the initial stage of CNC operation before a fault is diagnosed. Besides this, the domain of fault diagnosis has also been expanded by incorporating human error which was not part of the earlier system. This is the first phase of the study and a comprehensive fault diagnosis expert system algorithm for CNC machine is going to be designed, which can later on be transformed into a knowledge-based intelligent computer applications program. This will be accomplished by consulting experts on various aspects of CNC operations.

Mining Coherent Topics With Pre-Learned Interest Knowledge in Twitter

Discovering semantic coherent topics from the large amount of user-generated content (UGC) in social media would facilitate many downstream applications of intelligent computing. Topic models, as one of the most powerful algorithms, have been widely used to discover the latent semantic patterns in text collections. However, one key weakness of topic models is that they need documents with certain length to provide reliable statistics for generating coherent topics. In Twitter, the users’ tweets are mostly short and noisy. Observations of word co-occurrences are incomprehensible for topic models. To deal with this problem, previous work tried to incorporate prior knowledge to obtain better results. However, this strategy is not practical for the fast evolving UGC in Twitter. In this paper, we first cluster the users according to the retweet network, and the users’ interests are mined as the prior knowledge. Such data are then applied to improve the performance of topic learning. The potential cause for the effectiveness of this approach is that users in the same community usually share similar interests, which will result in less noisy sub-data sets. Our algorithm pre-learns two types of interest knowledge from the data set: the interest-word-sets and a tweet-interest preference matrix . Furthermore, a dedicated background model is introduced to judge whether a word is drawn from the background noise. Experiments on two real life twitter data sets show that our model achieves significant improvements over state-of-the-art baselines.

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain.

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.

The chemical information ontology: provenance and disambiguation for chemical data on the biological semantic web.

Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).

Using ontologies linked with geometric models to reason about penetrating injuries

Medical assessment of penetrating injuries is a difficult and knowledge-intensive task, and rapid determination of the extent of internal injuries is vital for triage and for determining the appropriate treatment. Physical examination and computed tomographic (CT) imaging data must be combined with detailed anatomic, physiologic, and biomechanical knowledge to assess the injured subject. We are developing a methodology to automate reasoning about penetrating injuries using canonical knowledge combined with specific subject image data. In our approach, we build a three-dimensional geometric model of a subject from segmented images. We link regions in this model to entities in two knowledge sources: (1) a comprehensive ontology of anatomy containing organ identities, adjacencies, and other information useful for anatomic reasoning and (2) an ontology of regional perfusion containing formal definitions of arterial anatomy and corresponding regions of perfusion. We created computer reasoning services ("problem solvers") that use the ontologies to evaluate the geometric model of the subject and deduce the consequences of penetrating injuries. We developed and tested our methods using data from the Visible Human. Our problem solvers can determine the organs that are injured given particular trajectories of projectiles, whether vital structures--such as a coronary artery--are injured, and they can predict the propagation of injury ensuing after vital structures are injured. We have demonstrated the capability of using ontologies with medical images to support computer reasoning about injury based on those images. Our methodology demonstrates an approach to creating intelligent computer applications that reason with image data, and it may have value in helping practitioners in the assessment of penetrating injury.

Intelligent computer applications in the energy sector: a literature review from 1990 to 2003

Intelligent computer applications (ICA) to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. This paper surveys the application of intelligent systems in the energy sector using a literature review and classification of papers from 1990 to 2003 with keyword index in order to explore how intelligent methodologies and applications have developed in this period. Based on the scope of 247 papers of intelligent energy technologies this paper surveys and classifies ICA in the energy sector using the four categories of: expert systems, neural networks, genetic algorithms, fuzzy-logic-based systems, together with their applications for different problem domains in the specific sector. The paper concludes by sharing thoughts and estimations on ICA future prospects in the energy sector.

An object-oriented knowledge-based system for automating the routing process in a manufacturing environment

The use of intelligent computer applications is an effective means to improve time management and to increase the competitiveness of any business. The developed application is designed to minimize the time it takes to meet both a company's and its customer's needs. The Blank and Steel Socket Routing System implements the first step in determining the cost of a manufactured part. The system automates the process for determining all the operations performed on the factory floor on a requested part. The complete list of operations includes information on the specific work-centers needed to manufacture the part, and the various times that the centers will be occupied. Knowing the utilized work-centers and the length of their use, the cost of manufacturing the part can be easily approximated and, therefore, can be quickly communicated back to the customer. The Black and Steel Socket Routing System is implemented with an object-oriented knowledge-based tool. Furthermore, this program is one of a multi-part programming system embedded in a Computer Integrated Manufacturing environment. The object-oriented knowledge-based system is developed via the Automated Reasoning Tool for Information Management (ART-IM) software. ART-IM provides an inference engine that utilizes the function of parallel processing. By using the RETE algorithm, an opportunistic parallel rule execution algoritm, ART-IM enables large expert-system programs that contain a large number of rules to be read and processed rather quickly and efficiently, as opposed to the traditional methods of programming with “If...then”-based structures. The knowledge-based system contains the information regarding the various characteristics of any given part design. With this information, the part routing program searches through the rules pertaining to the given design, which is based upon the dimensions of the part entered in a data file. This data file can be altered by the design engineer to facilitate a much quicker process of obtaining the various operations to be performed for various design patterns. The implementation of this knowledge-based routing system significantly reduces the amount of time it had taken to determine the operations needed to be performed for a given part design. Furthermore, this system lowers the frustration level experienced by the design engineers, as there was no further need to scan large databases in determining the routing operations. Before the use of this system, the probability of error was significantly high and, therefore, contributed to the inefficiency of the process. The automated program, therefore, increases the reliability of the process.

Expert systems: A tutorial

AbstractExpert systems are intelligent computer applications that use data, a knowledge base, and a control mechanism to solve problems of sufficient difficulty that significant human expertise is necessary for their solution. Expert systems use artificial intelligence problem‐solving and knowledge‐representation techniques to combine human expert knowledge about a problem area with human expert methods of conceptualizing and reasoning about that problem area. As a result, it is expected that such systems can reach a level of performance comparable to that of a human expert in a specialized problem area. The high‐level knowledge base and associated control mechanism of expert systems are in essence a model of the expertise of the best practitioners of the problem area in question and, hence, human users are provided with expert opinions about problems in that area. Expert systems do not pretend to give final or ultimate conclusions to displace human decision making; they are intended for consulting purposes only.