Search Results - (( intelligence v control algorithm ) OR ( intelligence system learning algorithm ))

A Modified Hybrid Fuzzy Controller for Real-Time Mobile Robot Navigation by Hossen J., Sayeed S., Iqbal A.K.M.P.

“…Algorithms; Controllers; Fuzzy clustering; Fuzzy systems; Intelligent control; Intelligent systems; Learning algorithms; Least squares approximations; Membership functions; Mobile robots; Navigation; Robotics; Robots; Smart sensors; and ANFIS; Apriori algorithms; Fuzzy C mean; Gradient descent algorithms; Hybrid-fuzzy controllers; Mobile Robot Navigation; Subtractive clustering; Subtractive clustering algorithms; Clustering algorithms…”

Energy management strategies, control systems, and artificial intelligence-based algorithms development for hydrogen fuel cell-powered vehicles: A review by Oladosu T.L., Pasupuleti J., Kiong T.S., Koh S.P.J., Yusaf T.

“…Therefore, this study presents the prospect of artificial intelligence-based algorithms, control systems, and energy management strategies advances on HFCEVs performance optimization. …”

An Empirical Evaluation of Artificial Intelligence Algorithm for Hand Posture Classification by Hussain, A., Hussain, S.S., Uddin, M.M., Zubair, M., Kumar, P., Umair, M.

“…In this study, exhaustive empirical research of the machine learning algorithm for hand posture classification has been established. …”
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Virtual power plant and microgrids controller for energy management based on optimization techniques by Abdolrasol M.G.M., Mohamed A., Hannan M.A.

“…Aggregates; Energy management; Forecasting; Learning algorithms; Optimization; Particle swarm optimization (PSO); Weather forecasting; Backtracking search algorithms; Distribution generation; Intelligent decisions; Micro grid; Optimization techniques; Particle swarm optimization algorithm; Virtual power plants; Virtual power plants (VPP); Energy management systems…”

MOSELM approach for Voltage Stability Indicator using phasor measurement units by Abidin I.Z., Yap K.S., Saadun N., Abdullah S.K.S., Mohd Sarmin M.K.N.

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Artificial intelligent power prediction for efficient resource management of WCDMA mobile network by Tee Y.K., Tinng S.K., Koh J., David Y.

Supervisory fuzzy learning control for underwater target tracking by Kia, Chua, Arshad, Mohd Rizal, Adom, Abd Hamid, Wilson, Philip A.

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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines by Aker, Elhadi Emhemed Alhaaj Ammar

“…The BayesNet provides the best integrated MLADR fault classifier model better at a 5 % significance level than other deployed algorithms in the intelligent supervised learning model realization. …”
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Performance of hybrid learning control with input shaping for input tracking and vibration suppression of a flexible manipulator by Md. Zain, M. Z., Tokhi, M. O., Mohamed, Z.

“…This Is Then Extended To Incorporate Iterative Learning Control With Genetic Algorithm (GA) To Optimize The Learning Parameters And A Feedforward Controller Based On Input Shaping Techniques For Control Of Vibration (Flexible Motion) Of The System. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review by Aliyu, R., Mokhtar, A.A., Hussin, H.

“…To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. …”
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Feature-based stereo vision relative positioning strategy for formation control of unmanned aerial vehicles by Yaghoobi, Yousef, Bahiki, Muhammad Rijaluddin, Syaril Azrad

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Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study by Abu-Samah A., Abdul Razak N.N., Mohamad Suhaimi F., Jamaludin U.K., Chase G.

Smart agriculture: precision farming through sensor-based crop monitoring and control system by Mohamad Hakhrani, Asyful Azhim, Abdul Hamid, Syamsul Bahrin

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A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

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Wavelet network: online sequential extreme learning machine for nonlinear dynamic systems identification by Mohammed Salih, Dhiadeen, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Raja Ahmad, Raja Mohd Kamil

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Design of intelligent control system and its application on fabricated conveyor belt grain dryer by Lutfy, Omar F.

“…The major objective of this research is to improve the performance of conveyor-belt grain dryers by designing an intelligent control system utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control the drying process. …”
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Adaptive approach in handling human inactivity in computer power management by Candrawati, Ria, Hashim, Nor Laily

“…This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption.An experiment to evaluate this model was conducted using three case studies with same activities. …”
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Obso based fractional pid for mppt-pitch control of wind turbine systems by Mehedi, I.M., Al-Saggaf, U.M., Vellingiri, M.T., Milyani, A.H., Saad, N.B., Yahaya, N.Z.B.

“…The proposed model aims to effectually extract the maximum power point (MPPT) in the low range of weather conditions and save the WT in high wind regions by the use of pitch control. The OBSO algorithm is derived from the integration of oppositional based learning (OBL) concept with the traditional BSO algorithm in order to improve the convergence rate, which is then applied to effectively choose the parameters involved in the FOPID controller. …”
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Scalable Smartification of Commercial Buildings HVAC Systems Using the Internet of Things and Machine Learning by Mahdi M.N., Bakare T.A., Ahmad A.R., Buhari A.M., Mohamed K.S.

“…The IoT paradigm has provided technologists with the ability to remotely control devices, and with the recent progress in Machine learning (ML) and Artificial Intelligence (AI), devices are trained to make smart decisions that can independently influence human to machine interactions. � 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.…”

Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…These findings demonstrate that integration of ML with GHBD significantly improved predictive capabilities, enabled real time application, reduce experimental effort, as well as improve the development of intelligent, sustainable, and scalable water treatment technology. © 2025 Elsevier B.V.…”
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