Search Results - intelligence path ((((modeling algorithm) OR (learning algorithm))) OR (matching algorithm))

Optimisation of Environmental Risk Assessment Architecture using Artificial Intelligence Techniques by Salem S. M. Khalifa

“…Fuzzy arithmetic operations on fuzzy numbers and artificial neural networks with a back-propagation learning algorithm were used to represent the structure of the neuro-fuzzy risk assessment model, whereas genetic algorithms were used to develop the safe path selection model. …”

Design of Modeling Elements of Luoshan Shadow Puppets Creative Goods Based on Deep Learning by Taotao, Xu, Musdi, Shanat, Brendan Chan, Kah Le, Wei, Zhang

“…In this paper, based on the design of modeling elements of mountain shadow puppets, the characteristics of visual elements of shadow puppets creative goods are analyzed, and an intelligent design algorithm of shadow puppets creative goods based on deep learning (DL) is innovatively proposed. …”
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Proposal for ontology based approach to fuzzy student model design by Muhammad Sani, Salisu, Mohd Aris, Teh Noranis

“…In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student's learning behaviour with the aim of improving the learning path and increase the system's adaptability. …”
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Advances in materials informatics: A review by Sivan, Dawn, Kumar, K. Satheesh, Aziman, Abdullah, Raj, Veena, Izan Izwan, Misnon, Ramakrishna, Seeram, Jose, Rajan

“…The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. …”
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Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: a review by Mohammed Musta, Njiti, Osman, Noor Diyana, Mansor, Mohd Syahir, Rabaiee, Nor Ain, Abdul Aziz, Mohd Zahri

“…However, challenges in implementation and machine learning model reliability require further exploration. …”
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A Novel Map-matching Algorithm to Improve Vehicle Tracking System Accuracy by Dewandaru, Agung, Md Said, Abas, Matori, A. N.

“…The curve-to-curve matching algorithms measure the similarity between the track and possible road path. …”
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Industry 5.0 and Education 5.0: Transforming Vocational Education through Intelligent Technology by Zhang, Hongli, Leong, Wai Yie

“…By analyzing the research gaps in personalized learning paths, emotion-driven learning, crossdisciplinary integration, and long-term learning behavior analysis, the paper proposes four improved algorithms: the adaptive learning path generation algorithm, the emotion-driven personalized learning algorithm, the cross-disciplinary knowledge graph algorithm, and the long-term learning behavior prediction algorithm. …”
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Ontology based approach to Fuzzy student model design by Muhammad Sani, Salisu, Mohd Aris, Teh Noranis

“…In this paper we intend to employ the use of Fuzzy logic and Ontology techniques to model the student’s learning behaviour with the aim of improving the learning path and increase the system’s adaptability. …”
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Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction by Ayman Mohammed Shaher Yafouz, Mr.

“…The hybrid technique has been developed by using deep learning algorithms with the structure of multiple layers (with several neurons) of CNN and LSTM. …”

A novel map-matching algorithm to improve vehicle tracking system accuracy by A.M., Said, A.N., Matori, A., Dewandaru

“…Point-to-curve matching algorithm is not suitable to the problem since it ignores any historical data and often gave unstable, jumping results. …”
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A conceptual Malaysian private healthcare university-of-the-future business model: staying relevant in the digital and post-covid era by Saiid, Muhammad, Bah, Ahmadou Pathe, Ahmad Dahlan, Abdul Rahman

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A Map-matching Algorithm to Improve Vehicle Tracking Systems Accuracy by Dewandaru, Agung

“…The results showed that the proposed algorithm is able to improve the map-matching accuracy as compared to the point-to-curve algorithm. …”
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Development of a motion planning and obstacle avoidance algorithm using adaptive neuro fuzzy inference system for mobile robot navigation by Muslim, Farah Kamil Abid

“…The designed multilayer decision-based fuzzy logic model effectively solves the path planning queries in crowded and complex situations without any failure. …”
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A Map-matching Algorithm to Improve Vehicle Tracking Systems Accuracy by Agung Dewandaru, Agung

“…The results showed that the proposed algoritlimi is able to improve the neap-matching accuracy as compared to the point-to-curve algorithm. …”
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Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning by Yan, Suqing, Su, Yalan, Luo, Xiaonan, Sun, Anqing, Ji, Yuanfa, Kamarul Hawari, Ghazali

“…Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.…”
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Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review by Danyaro, K.U., Hussain, H.H., Abdullahi, M., Liew, M.S., Shawn, L.E., Abubakar, M.Y.

“…A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. …”
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Adaptive route optimization for mobile robot navigation using evolutionary algorithm by Kit Guan Lim, Guan Lim, Yoong Hean Lee, Hean Lee, Min Keng Tan, Keng Tan, Hou, Pin Yoong, Tienlei, Wang, Tze, Kenneth Kin Teo

“…For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. …”
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A Novel Path Prediction Strategy for Tracking Intelligent Travelers by Motlagh, Omid Reza Esmaeili

“…A novel decision support system (DSS) is devised with the ability of learning decisional as well as kinematical behaviors of intelligent travelers. …”
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Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms by Yafouz A., Ahmed A.N., Zaini N., Sherif M., Sefelnasr A., El-Shafie A.

Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review by Danyaro, K.U., Hussain, H.H., Abdullahi, M., Liew, M.S., Shawn, L.E., Abubakar, M.Y.

“…A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. …”
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