Search Results - (( intelligence based m algorithm ) OR ( intelligence based toward algorithm ))

Power system stabilization based on artificial intelligent techniques: a review by Hassan, L.H., Moghavvemi, M., Mohamed, H.A.F.

“…These techniques are Artificial Neural Network (ANN), fuzzy logic, hybrid artificial intelligent, expert systems, and optimization techniques base AI such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS) algorithm, etc. …”
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Intelligent energy allocation strategy for PHEV charging station using gravitational search algorithm by Rahman, Imran, Vasant, Pandian, Mahinder Singh, Balbir Singh, Abdullah-Al-Wadud, M.

“…Most of these works require formulation of mathematical models which extensively use computational intelligence-based optimization techniques to solve many technical problems. …”
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Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects by Lipu M.S.H., Miah M.S., Hannan M.A., Hussain A., Sarker M.R., Ayob A., Saad M.H.M., Mahmud M.S.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles by Rahman, I., Vasant, P.M., Mahinder Singh, B.S., Abdullah-Al-Wadud, M.

“…Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. …”
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Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks by Abdolrasol M.G.M., Hannan M.A., Suhail Hussain S.M., Ustun T.S., Sarker M.R., Ker P.J.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles by Rahman, Imran, Vasant, Pandian, Mahinder Singh, Balbir Singh, Abdullah-Al-Wadud, M.

“…Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. …”
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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.

“…In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the inâ��depth integration of Metocean data interoperability for intelligent operations and automation using an MLâ��based ap-proach, is presented. …”
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Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future by Abu S.M., Hannan M.A., Mansor M., Ker P.J., Yaw Long C.

“…This research study findings highlights the essential role of PSO in elevating sustainability and maximizing resource utilization within microgrid-based hybrid energy systems, establishing a pathway towards a greener and more sustainable energy future…”

A Focal load balancer based algorithm for task assignment in cloud environment by Mohammed, Mostafa Abdulghfoor, Hasan, Raed Abdulkareem, Ahmed, Munef Abdullah, Tapus, Nicolae, Shnan, Marwan Ali, Khaleel, M. K., Ali, Ahmed H.

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Software agent as an effective tool for managing the Internet of thing data complexity by Mustafa, M.B., Yusoof, M.A.M.

“…In the agent-based approach to middleware, the agent’s property such as autonomous, mobile and intelligent are suitable as e-assistance tool for big data management. …”
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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.

“…In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the inâ��depth integration of Metocean data interoperability for intelligent operations and automation using an MLâ��based ap-proach, is presented. …”
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A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

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A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

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Artificial Intelligent Based Damping Controller Optimization for the Multi-Machine Power System: A Review by Hannan M.A., Islam N.N., Mohamed A., Lipu M.S.H., Ker P.J., Rashid M.M., Shareef H.

Digital Transformation Using Industry 4.0 and Artificial Intelligence by Keerthika M., Pragadeesh M., Santhiya M., Jebamalar G.B., Venu H.

“…Explainable artificial intelligence (XAI) conducts studies and creates tools, techniques, and algorithm designs that produce information and judgments made by AI-based systems that are comprehensible to humans. …”

Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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Computational comparison of major proposed methods for graph partitioning problem by Rais, H.M., Abed, S.A., Watada, J.

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A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines by Tahan, M., Muhammad, M., Abdul Karim, Z.A.

“…In recent years, a new discipline, known as artificial intelligence-based methods, has emerged, which allows the behavior of the system to be studied using operational data. …”
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A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier by Azad, Saiful, Amin Salem, Saleh Bllagdham, Mahmud, Mufti, Kaiser, M. Shamim, Miah, Md Saef Ullah

“…To ensure security, the threats can largely be tackled by employing a Trust Management Model (TMM) which will critically evaluate the behavior or activity pattern of the nodes and block the untrusted ones. Towards securing these frameworks through an intelligent TMM, this work proposes a machine learning based Behavioral Trust Model (BTM), where an improved Frequent Pattern Growth (iFP-Growth) algorithm is proposed and applied to extract behavioral signatures of various trust classes. …”
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User-centric learning for multiple access selections by Dzulkifly S., Hashim W., Ismail A.F., Dohler M.