Journal articles: 'Artificial intelligent techniques'

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Hashmi, Abdul Wahab, Harlal Singh Mali, Anoj Meena, Irshad Ahamad Khilji, Mohammad Farukh Hashmi, and Siti Nadiah binti Mohd Saffe. "Artificial intelligence techniques for implementation of intelligent machining." Materials Today: Proceedings 56 (2022): 1947–55. http://dx.doi.org/10.1016/j.matpr.2021.11.277.

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Saleem, Nada, Baydaa Khaleel, and Shahbaa Khaleel. "Artificial Intelligent Techniques with Watermarking." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 2 (July 1, 2009): 229–66. http://dx.doi.org/10.33899/csmj.2009.163810.

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Pappas, Marios, and Athanasios Drigas. "Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics." International Journal of Engineering Pedagogy (iJEP) 6, no. 4 (November 24, 2016): 12. http://dx.doi.org/10.3991/ijep.v6i4.6063.

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Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.

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Ibrahim, Laheeb, and Ibrahim Saleh. "Face Recognition using Artificial Intelligent Techniques." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 2 (July 1, 2009): 211–27. http://dx.doi.org/10.33899/csmj.2009.163809.

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Sundhararajan, Mahalingam, Xiao-Zhi Gao, and Hamed Vahdat Nejad. "Artificial intelligent techniques and its applications." Journal of Intelligent & Fuzzy Systems 34, no. 2 (February 27, 2018): 755–60. http://dx.doi.org/10.3233/jifs-169369.

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Long, Teng, Zhangbing Zhou, Gerhard Hancke, Yang Bai, and Qi Gao. "A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization." Journal of Sensor and Actuator Networks 11, no. 3 (August 29, 2022): 50. http://dx.doi.org/10.3390/jsan11030050.

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Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.

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Kumar, Avinash, Abhishek Kumar, and Arun Prasad Burnwal. "CORRELATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES WITH SOFT COMPUTING IN VARIOUS AREAS." International Journal of Students' Research in Technology & Management 5, no. 4 (November 27, 2017): 58–65. http://dx.doi.org/10.18510/ijsrtm.2017.548.

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Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas.

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Thanh, Cong Truong, and Ivan Zelinka. "A Survey on Artificial Intelligence in Malware as Next-Generation Threats." MENDEL 25, no. 2 (December 20, 2019): 27–34. http://dx.doi.org/10.13164/mendel.2019.2.027.

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Recent developments in Artificial intelligence (AI) have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and will probably try to use it in their activities. This survey aims at providing an overview on the way artificial intelligence can be used to power a malicious program that is: intelligent evasion techniques, autonomous malware, AI against itself, and applying bio-inspired computation and swarm intelligence.

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Khaleel, Shahbaa, Baydaa Khaleel, and Alaa khaleel. "Image Compression Based on Artificial Intelligent Techniques." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 3 (September 1, 2009): 75–109. http://dx.doi.org/10.33899/csmj.2009.163839.

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Roy, Sanjiban Sekhar, and V. Madhu Viswanatham. "Classifying Spam Emails Using Artificial Intelligent Techniques." International Journal of Engineering Research in Africa 22 (February 2016): 152–61. http://dx.doi.org/10.4028/www.scientific.net/jera.22.152.

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Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

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Benlahbib, Boualam, Farid Bouchafaa, Saad Mekhilef, and Noureddine Bouarroudj. "Wind Farm Management using Artificial Intelligent Techniques." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1133. http://dx.doi.org/10.11591/ijece.v7i3.pp1133-1144.

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This paper presents a comparative study between genetic algorithm and particle swarm optimization methods to determine the optimal proportional–integral (PI) controller parameters for a wind farm management algorithm. This study primarily aims to develop a rapid and stable system by tuning the PI controller, thus providing excellent monitoring for a wind farm system. The wind farm management system supervises the active and reactive power of the wind farm by sending references to each wind generator. This management system ensures that all wind generators achieve their required references. Furthermore, the entire management is included in the normal controlling power set points of the wind farm as designed by a central control system. The performance management of this study is tested through MATLAB/Simulink simulation results for the wind farm based on three doublyfed induction generators

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Filander-Caratar, Jesús, Andrés Mauricio-Valencia, Gladys Caicedo-Delgado, and Cristian Chamorro. "Evaluation of artificial intelligence techniques used in the diagnosis of failures in power plants." Respuestas 25, no. 2 (May 1, 2020): 177–89. http://dx.doi.org/10.22463/0122820x.2966.

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This article presents an evaluation about the research related to the development of computational tools based on artificial intelligence techniques, which focus on the detection and diagnosis of faults in the different processes associated with a power generation plant such as: hydroelectric, thermoelectric and nuclear power plants. Initially, the main techniques of artificial intelligence that allow the construction of intelligent systems in the area of fault diagnosis is described in a general way, techniques such as: fuzzy logic, neural networks, knowledge-based systems and hybrid techniques Subsequently A summary of the research based on each of these techniques is presented. Subsequently, the different articles found for each of the techniques are presented in tables, illustrating the year of publication and the description of the research carried out. The result of this work is the comparison and evaluation of each technique focused on the diagnosis of failures in power plants. The novelty of this work is that it presents an extensive bibliography of the applications of the different intelligent techniques in solving the problem of detection and diagnosis of failure in power plants

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Wang, Ruishu, Jiannan Li, Wanbing Shi, and Xin Li. "Application of Artificial Intelligence Techniques in Operating Mode of Professors’ Academic Governance in American Research Universities." Wireless Communications and Mobile Computing 2021 (November 18, 2021): 1–7. http://dx.doi.org/10.1155/2021/3415125.

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Artificial intelligence technology is an important transformative force for teaching innovation in the intelligent era. It is being widely used in American school teaching, including the design of intelligent tutoring systems to achieve precise problem solving, the machine learning technology to ensure personalized activity design, the creation of intelligent virtual reality to promote classroom teaching contextualization, and the development of intelligent evaluation systems to ensure the scientific evaluation of capabilities. In the process of advancing the teaching and application of artificial intelligence technology, the United States has built a linkage mechanism of federal leadership, university follow-up, and social collaboration and implemented the smart technology in school teaching and professors’ academic governance. This paper is aimed at studying the professors’ academic governance of American research universities by Internet data mining, historical analysis method, documentary method, survey method, and other methods. Professors’ academic governance is a vital part of the modern university system that causes the institutional reform of the internal governance structure of modern universities. The United States is a powerful country in higher education, and professors in American research universities have always participated in university academic governance for centuries. By studying the definition, history, and development and mode of operation of professors’ academic governance in American research universities, the results indicate a clear division of power and responsibility between the professors and administrators based on an artificial intelligence decision system in American research universities. Also, there is a good communication platform based on artificial intelligence environment for professors to discuss their opinions on academic affairs. Third, professors exercise academic power under the guarantee of diversified guaranteed systems based on the artificial intelligence evaluation system and the ideology of mutual respect based on the artificial intelligence management and service system. Studying the application of artificial intelligence techniques in operating mode and enlightenment of professors’ academic governance in an American research university is of great significance to promote the construction of other modern universities’ professors’ academic governance system.

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Hsieh, Nan-Chen, Lun-Ping Hung, Chun-Che Shih, Huan-Chao Keh, and Chien-Hui Chan. "Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques." Journal of Medical Systems 36, no. 3 (December 24, 2010): 1809–20. http://dx.doi.org/10.1007/s10916-010-9640-7.

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Barrett, Steven J. "Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems." Genetic Programming and Evolvable Machines 7, no. 3 (July 14, 2006): 283–84. http://dx.doi.org/10.1007/s10710-006-7003-4.

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Lie, Luo. "Heuristic Artificial Intelligent Algorithm for Genetic Algorithm." Key Engineering Materials 439-440 (June 2010): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.516.

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

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C. Dalwai, Kalpana. "AN OVERVIEW OF SWARM INTELLIGENCE IN ARTIFICIAL INTELLIGENT SYSTEMS." International Journal of Advanced Research 9, no. 08 (August 31, 2021): 673–75. http://dx.doi.org/10.21474/ijar01/13314.

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Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. The concept of a swarm suggests multiplicity, stochasticity, randomness, and messiness. Advancement of technology has led to problems that are complex and more challenging.Swarm intelligence techniques were mostly developed for solving optimization problems.

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Seng, Kah Phooi, Li Minn Ang, and Ericmoore Ngharamike. "Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks." International Journal of Distributed Sensor Networks 18, no. 3 (March 2022): 155014772110628. http://dx.doi.org/10.1177/15501477211062835.

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The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things.

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Belowska-Bień, Kinga, and Bartosz Bień. "Application of artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases." Aktualności Neurologiczne 21, no. 3 (December 20, 2021): 163–72. http://dx.doi.org/10.15557/an.2021.0021.

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The first two decades of the 21st century have seen great advances in artificial intelligence and machine learning. The techniques have found their way into everyday life, for example in smartphones, search engines, digital customer assistants, motion control systems, and biomedical devices. The aims of this paper are to outline the possibilities for using artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases, and to discuss selected applications of these techniques based on the most recent published reports. First, contemporary definitions of artificial intelligence and machine learning are presented. This is followed by a review of the most important techniques for intelligent data processing: search methods, mathematical logic, probabilistic methods, classifiers, and artificial neural networks (including deep and convolutional networks). Areas of application of these techniques in medicine are identified, including disease diagnosis and support of treatment as well as monitoring and prediction of changes in health status. The role of artificial intelligence and machine learning in neuroscience is presented, together with examples of diagnostic applications based on anatomical, morphological and functional brain connectivity data. Sample applications of intelligent techniques in supporting the treatment (including surgical management) of nervous system diseases are also described. Ambient smart devices monitoring the health status of patients with chronic neurological conditions are discussed, and selected projects based on smart techniques to support early detection of symptoms of neurodegenerative disorders are described. The conclusions highlight the potential of the techniques, as well as the challenges and risks associated with them. A possible synergy between intelligent systems and actions taken by medical staff is outlined as a way to improve the safety and quality of life of patients with acute and chronic neurological diseases.

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Ismail, Firas Basim, Deshvin Singh, and Mohammad Shakir Nasif. "ADOPTION OF INTELLIGENT COMPUTATIONAL TECHNIQUES FOR STEAM BOILERS TUBE LEAK TRIP." Malaysian Journal of Computer Science 33, no. 2 (April 24, 2020): 133–51. http://dx.doi.org/10.22452/mjcs.vol33no2.4.

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Frequent boiler tube trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. Several methodologies for the fault diagnosis in a plant have been developed. However these methodologies are difficult to be implemented. In this study, two artificial intelligent monitoring systems specialized in boiler trips have been proposed. The first intelligent monitoring system represents the use of pure artificial neural network system whereas the second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. In the first system using pure artificial neural network, the trip was predicted 5 minutes before the actual trip occurrence. The hybrid intelligent system was able to optimize the selection of the most influencing variables successfully and predict the trip 2 minutes before the actual trip. The first intelligent system performed better than the second one based on the prediction time. The proposed artificial intelligent system could be adopted on-line as a reliable controller of the thermal power plant boiler.

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Felfernig, Alexander, and Franz Wotawa. "Intelligent engineering techniques for knowledge bases." AI Communications 26, no. 1 (2013): 1–2. http://dx.doi.org/10.3233/aic-2012-0541.

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Faceli, Katti, André C. P. L. F. de Carvalho, and Solange O. Rezende. "Combining Intelligent Techniques for Sensor Fusion." Applied Intelligence 20, no. 3 (May 2004): 199–213. http://dx.doi.org/10.1023/b:apin.0000021413.05467.20.

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Gkeka, Eugenia, Eleni Agorastou, and Athanasios Drigas. "Artificial Techniques for Language Disorders." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 7, no. 4 (December 10, 2019): 68. http://dx.doi.org/10.3991/ijes.v7i4.11845.

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<p class="0abstract">This review focuses on artificial techniques which include the artificial intelligent techniques and applications, the robot technology and the serious games supporting the procedure of learning and teaching of language disorders and deficits as well the evolution of speech. Especially, the written language, the oral language and the phoneme pronunciations, the communication and consequently the social interaction are benefited and are assisted by these achievements of technology.</p>

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Cabrera, Juan A. "Advances in Intelligent Vehicle Control." Sensors 22, no. 22 (November 9, 2022): 8622. http://dx.doi.org/10.3390/s22228622.

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Advanced intelligent vehicle control systems have evolved in the last few decades thanks to the use of artificial-intelligence-based techniques, the appearance of new sensors, and the development of technology necessary for their implementation [...]

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Merat, P.Eng, Soorena, and Dr Wahab Almuhtadi, P.Eng. "STANDARD ARPU CALCULATION IMPROVEMENT USING ARTIFICIAL INTELLIGENT TECHNIQUES." International Journal on Smart Sensing and Intelligent Systems 8, no. 4 (2015): 1917–34. http://dx.doi.org/10.21307/ijssis-2017-836.

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Oloduowo, Ameen, Akande Babalola, and Aruleba Daniel. "Solving Network Routing Problem Using Artificial Intelligent Techniques." British Journal of Mathematics & Computer Science 17, no. 3 (January 10, 2016): 1–9. http://dx.doi.org/10.9734/bjmcs/2016/27033.

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Rashidinejad, M., H. Farahmand, M. Fotuhi-Firuzabad, and A. A. Gharaveisi. "ATC enhancement using TCSC via artificial intelligent techniques." Electric Power Systems Research 78, no. 1 (January 2008): 11–20. http://dx.doi.org/10.1016/j.epsr.2006.12.005.

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Narsimha, B., Ch V. Raghavendran, Pannangi Rajyalakshmi, G. Kasi Reddy, M. Bhargavi, and P. Naresh. "Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application." International Journal of Electrical and Electronics Research 10, no. 2 (June 30, 2022): 87–92. http://dx.doi.org/10.37391/ijeer.100206.

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Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.

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Yang, Jinyu, and Bo Zhang. "Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines." Applied Sciences 9, no. 10 (May 20, 2019): 2078. http://dx.doi.org/10.3390/app9102078.

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This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR) and summarizes the status quo of applying artificial intelligence (AI) techniques. We first analyze the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu, and the curriculum. Then, we transform the relationship model into the perception-planning-action model to explore what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting a human-robot teaching-learning process and AI-assisted educational techniques, which illustrates the design guidelines and future research perspectives in intelligent tutoring robots.

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Meiliana, Anna, Nurrani Mustika Dewi, and Andi Wijaya. "Artificial Intelligent in Healthcare." Indonesian Biomedical Journal 11, no. 2 (August 1, 2019): 125–35. http://dx.doi.org/10.18585/inabj.v11i2.844.

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BACKGROUND: Giant transformations are going on currently in health care, and the greatest force behind this phenomenon is data.CONTENT: Big data has arrived into medicine field, lead to potential enhancement in accountability, quality, efficiency, and innovation. Most updated, artificial intelligence (AI) and machine-learning (ML) techniques rapidly developed, bring forth the big data analysis into more useful applications, from resource allocation to complex disease diagnosis. To realize this, a very large set of health-care data is needed for algorithms training and evaluation, including patients’ treatment data, patients respond to treatment, and personal patient information, such as genetic data, family history, health behavior, and vital signs.SUMMARY: Precision Health involving preventive, predictive, personalized and precise. The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes, thus the patient–doctor bond is expected to be reformed and not be eroded.KEYWORDS: artificial intelligence, machine learning, deep learning, electronic health records, big data

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Sharma, Saurabh, Vijay Kumar Gahlawat, Kumar Rahul, Rahul S. Mor, and Mohit Malik. "Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics." Logistics 5, no. 4 (September 27, 2021): 66. http://dx.doi.org/10.3390/logistics5040066.

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The agri-food sector is an endless source of expansion for nourishing a vast population, but there is a considerable need to develop high-standard procedures through intelligent and innovative technologies, such as artificial intelligence (AI) and big data. This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. Logistics, supply chain, marketing, and production patterns are covered along with food sub-sector applications for artificial intelligence techniques. It is found that utilization of AI techniques and the intelligent optimization algorithm also leads to significant process and production management. Thus, digital technologies are a boon for the food industry, where AI and big data have enabled us to achieve optimum results in realtime.

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Surianarayanan, Chellammal, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, and Chaminda Hewage. "A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)." Sensors 23, no. 3 (January 22, 2023): 1279. http://dx.doi.org/10.3390/s23031279.

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Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through ondevice data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

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SU, D., and M. WAKELAM. "Evolutionary optimization within an intelligent hybrid system for design integration." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 5 (November 1999): 351–63. http://dx.doi.org/10.1017/s0890060499135054.

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An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail.

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Chen, Cai. "Investigation into the Development of Intelligent Financial Management Systems Based on Artificial Intelligence." Advances in Economics and Management Research 1, no. 3 (February 7, 2023): 429. http://dx.doi.org/10.56028/aemr.3.1.429.

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Artificial intelligence technology is primarily used to create and investigate methods of simulating human brain thinking. It is a new science and technology that extends current science and technology. In a theoretical sense, AI technology is a branch of computer science. We can create an intelligent machine similar to how humans think about problems by researching AI technology. The innovative financial management system has been gradually integrated into artificial intelligence technology. Artificial intelligence has endowed the intelligent financial management system with multi-agent systems, pattern recognition, expert techniques, and other vital technologies. Based on the influence of AI on financial information technology and financial management functions, this paper explores a new financial management model in the era of AI. It constructs an intelligent financial management system based on AI. It will assist businesses in utilizing intelligent technological resources to undertake forward-looking assessments, prevent financial risks, enhance the efficiency of financial management, and achieve tremendous company success.

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S, Iwin Thanakumar Joseph. "SURVEY OF DATA MINING ALGORITHM’S FOR INTELLIGENT COMPUTING SYSTEM." Journal of Trends in Computer Science and Smart Technology 01, no. 01 (September 8, 2019): 14–23. http://dx.doi.org/10.36548/jtcsst.2019.1.002.

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The Intelligent computing system, described to be a collection of the connected device working in mutual understanding to attain a particular purpose, is an incorporation of artificial intelligence and the computational intelligence, and are employed in variety of applications. The paper presents the survey on the data mining algorithms and the techniques that could be employed with the intelligent computing system, presenting a basic conception of the data mining along with the prominent algorithms of the data mining and the classification of its techniques, further the survey concludes with the challenges included in the overview of the survey done along with the future enhancement in the research that analyses the data mining techniques in the intelligent computing applications.

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Agrawal, Vikas, Christopher Archibald, Mehul Bhatt, Hung Bui, Diane J. Cook, Juan Cortés, Christopher Geib, et al. "The AAAI-13 Conference Workshops." AI Magazine 34, no. 4 (September 9, 2013): 108–15. http://dx.doi.org/10.1609/aimag.v34i4.2511.

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The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14‚Äì15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).

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Gong, Tao, Jia Jia Zhou, and Lei Qi. "Intelligent Techniques in Teaching Science of Artificial Immune System." Applied Mechanics and Materials 48-49 (February 2011): 637–40. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.637.

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Building on three theoretical paradigms (student model, ICAI model, and multi-dimension education immune agent), some intelligent techniques are proposed and designed to teach Fuzzy Mathematics and Science of Artificial Immune System in a web-based way. The goal of the teaching methodology is a new learning, which is interactive, sharing, open, cooperative, and autonomous. The great difference between traditional approaches for teaching such knowledge and the new approach in this paper is the centre of teaching. The traditional teaching is centered with teachers but the new teaching is centered with students. The teaching system for Fuzzy Mathematics and Science of Artificial Immune System is a virtual classroom based on the web, and the two courses are designed as web-based courses. Moreover, for Science of Artificial Immune System, the web-based course system is a typical artificial immune system in fact, and students can learn more real knowledge from the web-based course immune system.

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Magdy, Mona, Salama Abu-Zaid, and Mahmoud A. Elwany. "Artificial intelligent techniques based on direct torque control of induction machines." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (December 1, 2021): 2070. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2070-2082.

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The direct torque control (DTC) system, which is based on induction machine drive is a developed and simple control method. It allows high dynamic performance with very simple hysteresis control scheme; However, its disadvantages are high current, torque, and flux ripple. In this paper, DTC of induction machine drive has been improved by using the applications of artificial intelligence (AI) approaches to reduce the current, torque, and flux ripples and also get better performance of the machines. At the conclusion of this study, the outcomes of traditional DTC and artificial intelligent methods are compared. By the program MATLAB/SIMULINK, the modeling and simulation results of the DTC system for induction machine (IM) have been proposed.

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Xu, Yang, Wenliang Qian, Na Li, and Hui Li. "Typical advances of artificial intelligence in civil engineering." Advances in Structural Engineering 25, no. 16 (November 3, 2022): 3405–24. http://dx.doi.org/10.1177/13694332221127340.

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Artificial intelligence (AI) provides advanced mathematical frameworks and algorithms for further innovation and vitality of classical civil engineering (CE). Plenty of complex, time-consuming, and laborious workloads of design, construction, and inspection can be enhanced and upgraded by emerging AI techniques. In addition, many unsolved issues and unknown laws in the field of CE can be addressed and discovered by physical machine learning via merging the data paradigm with physical laws. Intelligent science and technology in CE profoundly promote the current level of informatization, digitalization, autonomation, and intellectualization. To this end, this paper provides a systematic review and summarizes the state-of-the-art progress of AI in CE for the entire life cycle of civil structures and infrastructure, including intelligent architectural design, intelligent structural health diagnosis, intelligent disaster prevention and reduction. A series of examples for intelligent architectural art shape design, structural topology optimization, computer-vision-based structural damage recognition, correlation-pattern-based structural condition assessment, machine-learning-enhanced reliability analysis, vision-based earthquake disaster evaluation, and dense displacement monitoring of structures under wind and earthquake, are given. Finally, the prospects of intelligent science and technology in future CE are discussed.

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Ojekudo, Okardi, Biobele, and Nathaniel Akofure. "ANALYSIS OF MODERN TECHNIQUES FOR SOFTWARE OPTIMIZATION." International Journal of Computer Science and Mobile Computing 10, no. 7 (July 30, 2021): 46–55. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.007.

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Traditional Methods of optimization have failed to meet up the rapid changing world in the demand of high quality and accuracy in solution delivery. Optimization literally means looking for the best possible or most desired solution to a problem. Optimization techniques are basically classified into three groups, namely; the Traditional Method, Artificial Intelligent Method, and Hybrid Artificial Intelligent technique. In this paper, an attempt is made to review literatures on different modern optimization techniques for application in various disciplines. A general review was made on some of the modern optimization methods such as Genetic Algorithm, Ant colony method, Honey Bee optimization method, and Simulated Annealing optimization.

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Menyhárt, József. "Artificial Intelligence Possibilities in Vehicle Industry." International Journal of Engineering and Management Sciences 4, no. 4 (December 12, 2019): 148–54. http://dx.doi.org/10.21791/ijems.2019.4.16.

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There have been several attempts during the last decades to extend the ranges of application of artificial intelligence. The aim of the development for AI is to replace human intelligence and experience. The ultimate aim for machines and vehicles is to run much more efficiently and with higher reliability than ever before. The Artificial Techniques (AI) used a wide range of expert systems to optimize problems. Hybrid intelligent management systems have become increasingly influential in artificial intelligence during the last decades. As a result, maintenance and fleet management systems have undergone significant development. By choosing adequate maintenance or operating strategy and taking user behaviour into consideration, these systems can not only increase the reliability and efficiency of vehicles but can also result in financial savings. The paper tries to discusses the applications of AI techniques in predictive maintenance and vehicle industry.

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S. Raj, Jennifer. "A COMPREHENSIVE SURVEY ON THE COMPUTATIONAL INTELLIGENCE TECHNIQUES AND ITS APPLICATIONS." Journal of ISMAC 01, no. 03 (December 2019): 147–59. http://dx.doi.org/10.36548/jismac.2019.3.002.

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The artificial intelligence that tries to imitate the human beings by gathering a vast knowledge gained using the reasoning, planning, searching and prediction fails in certain areas that necessitate a construction of large set of rules. The AI also faces challenges due to the growing demands in the learning and the search optimization. These failures of AI paved a path for the growth of the computational tools that led to the rise of the new regimen that is the computational intelligence. The paper presents the comprehensive survey of the computational intelligent techniques and its applications as they seem to be an effective alternative for the artificial intelligence overcoming the failures and the draw backs in it.

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Awadalla, M., H. Yousef, A. Al-Shidani, and A. Al-Hinai. "Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 12 (September 23, 2016): 7263–83. http://dx.doi.org/10.24297/ijct.v15i12.4354.

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This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has beenÂ accomplished on the achieved results to validate the modelsâ€™ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4Â RMES and 97% of the test data achieved 90% accuracy.

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Benamara, Chahrazed, Kheira Gharbi, Menad Nait Amar, and Boudjema Hamada. "Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques." Arabian Journal for Science and Engineering 45, no. 2 (December 9, 2019): 1319–30. http://dx.doi.org/10.1007/s13369-019-04290-y.

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Elkazaz, Mahmoud H., Ayman A. Hoballah, and Ahmed M. Azmy. "Operation optimization of distributed generation using artificial intelligent techniques." Ain Shams Engineering Journal 7, no. 2 (June 2016): 855–66. http://dx.doi.org/10.1016/j.asej.2016.01.008.

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Kumar, V. R., and N. Mani. "The application of artificial intelligence techniques for intelligent control of dynamical physical systems." International Journal of Adaptive Control and Signal Processing 8, no. 4 (July 1994): 379–92. http://dx.doi.org/10.1002/acs.4480080407.

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Ghozia, Ahmed, Gamal Attiya, Emad Adly, and Nawal El-Fishawy. "Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding." Computational Intelligence and Neuroscience 2020 (December 23, 2020): 1–15. http://dx.doi.org/10.1155/2020/8813089.

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Understanding video files is a challenging task. While the current video understanding techniques rely on deep learning, the obtained results suffer from a lack of real trustful meaning. Deep learning recognizes patterns from big data, leading to deep feature abstraction, not deep understanding. Deep learning tries to understand multimedia production by analyzing its content. We cannot understand the semantics of a multimedia file by analyzing its content only. Events occurring in a scene earn their meanings from the context containing them. A screaming kid could be scared of a threat or surprised by a lovely gift or just playing in the backyard. Artificial intelligence is a heterogeneous process that goes beyond learning. In this article, we discuss the heterogeneity of AI as a process that includes innate knowledge, approximations, and context awareness. We present a context-aware video understanding technique that makes the machine intelligent enough to understand the message behind the video stream. The main purpose is to understand the video stream by extracting real meaningful concepts, emotions, temporal data, and spatial data from the video context. The diffusion of heterogeneous data patterns from the video context leads to accurate decision-making about the video message and outperforms systems that rely on deep learning. Objective and subjective comparisons prove the accuracy of the concepts extracted by the proposed context-aware technique in comparison with the current deep learning video understanding techniques. Both systems are compared in terms of retrieval time, computing time, data size consumption, and complexity analysis. Comparisons show a significant efficient resource usage of the proposed context-aware system, which makes it a suitable solution for real-time scenarios. Moreover, we discuss the pros and cons of deep learning architectures.

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Cuéllar, Mariano-Florentino, and Aziz Z. Huq. "Artificially Intelligent Regulation." Daedalus 151, no. 2 (2022): 335–47. http://dx.doi.org/10.1162/daed_a_01920.

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Abstract This essay maps the potential, and risks, of artificially intelligent regulation: regulatory arrangements that use a complex computational algorithm or another artificial agent either to define a legal norm or to guide its implementation. The ubiquity of AI systems in modern organizations all but guarantees that regulators or the parties they regulate will make use of learning algorithms or novel techniques to analyze data in the process of defining, implementing, or complying with regulatory requirements. We offer an account of the possible benefits and harms of artificially intelligent regulation. Its mix of costs and rewards, we show, depend primarily on whether AI is deployed in ways aimed merely at shoring up existing hierarchies, or whether AI systems are embedded in and around legal frameworks carefully structured and evaluated to better our lives, environment, and future.

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Cuéllar, Mariano-Florentino, and Aziz Z. Huq. "Artificially Intelligent Regulation." Daedalus 151, no. 2 (2022): 335–47. http://dx.doi.org/10.1162/daed_a_01920.

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Abstract This essay maps the potential, and risks, of artificially intelligent regulation: regulatory arrangements that use a complex computational algorithm or another artificial agent either to define a legal norm or to guide its implementation. The ubiquity of AI systems in modern organizations all but guarantees that regulators or the parties they regulate will make use of learning algorithms or novel techniques to analyze data in the process of defining, implementing, or complying with regulatory requirements. We offer an account of the possible benefits and harms of artificially intelligent regulation. Its mix of costs and rewards, we show, depend primarily on whether AI is deployed in ways aimed merely at shoring up existing hierarchies, or whether AI systems are embedded in and around legal frameworks carefully structured and evaluated to better our lives, environment, and future.

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Schödel, Herbert. "Utilization of fuzzy techniques in intelligent sensors." Fuzzy Sets and Systems 63, no. 3 (May 1994): 271–92. http://dx.doi.org/10.1016/0165-0114(94)90215-1.

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Journal articles on the topic 'Artificial intelligent techniques'

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