Search Results - (( development drive learning algorithm ) OR ( java implication based algorithm ))

Machine learning: tasks, modern day applications and challenges by Aljuaid, Lamyaa Zaed, Koh, Tieng Wei, Sharif, Khaironi Yatim

“…Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. …”
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A review of object detection in traffic scenes based on deep learning by Zhao, Ruixin, Tang, SaiHong, Supeni, Eris Elianddy, Abdul Rahim, Sharafiz, Fan, Luxin

“…Finally, it concludes the development trends of object detection algorithms in traffic scenarios, providing research directions for intelligent transportation and autonomous driving.…”
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Localization for autonomous car using deep learning by Lee, Woon Shin

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Self-driving car with Artificial Intelligence (AI) technology / Asrul Amin Mohd Isa ... [et al.] by Mohd Isa, Asrul Amin, Hasnan, Aimuni Aina, Hashim, Zuyyin, Othman, Jamal

“…It is based on the use of sensors, actuators, sophisticated algorithms, machine learning systems, and robust processors. …”
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A Quantum Lightning Search Algorithm-Based Fuzzy Speed Controller for Induction Motor Drive by Hannan M.A., Ali J.A., Hussain A., Hasim F.H., Amirulddin U.A.U., Uddin M.N., Blaabjerg F.

Driving school student management system by Tengku Diyana, Tengku Ibrahim

“…There are only a few driving schools are operating around Pahang,but the total of the driving school cannot manage the number of customers who want to learn driving that are increasing each year.Most of the school company does not apply any computerized system to manage their business properly.All of the process of registering new student is done in traditional way which is using paper forms. …”
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Recognising Malaysia traffic sign with rain drop disturbance using deep learning by Pang, Wan Chee

“…Other than the driver's carelessness, weather such as rain might challenge the visibility and recognition of the traffic signs. Hence, we develop a road sign recognition system based on a deep learning algorithm that can identify the Malaysia traffic sign with raindrop disturbance. …”
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Smart object detection using deep learning algorithm and jetson nano for blind people by Tan, Mei Yan, Anis Farihan, Mat Raffei, Mohd Arfian, Ismail

“…Visual impairment may cause people difficulties with normal daily activities such as driving, reading, socializing and walking. Therefore, this project develops a smart object detection using deep learning algorithm and jetson nano to improve object detection for blind people. …”
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DEVELOPMENT OF DRIVER DROWSINESS DETECTION ALGORITHM by YVONNE, PHUA YEE WUN

“…To contribute to the existing works of driver drowsiness detection systems, this project aims to develop a more useful drowsiness detection algorithm with low complexity and high performance using Python 3.10.1 software. …”
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Quantum-Behaved Lightning Search Algorithm to Improve Indirect Field-Oriented Fuzzy-PI Control for im Drive by Hannan M.A., Ali J.A., Mohamed A., Amirulddin U.A.U., Tan N.M.L., Uddin M.N.

Distracted driver detection with deep convolution neural networks by Basubeit, Omar Gumaan Saleh

“…This project aims to develop a python algorithm to detect the distracted activities while driving. …”

Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Saad M.H., Ayob A., Uddin M.N.

Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Rahman, Md. Arafatur, Alenezi, Ali H.

“…Then, we consider the problem of chain collision avoidance as a Markov Decision Process problem in order to propose a reinforcement learning-based decision-making strategy and analyse the safety efficiency of existing methods in driving security. …”
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Deep reinforcement learning based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Rahman, Md. Arafatur, Alenezi, Ali H.

“…Then, we consider the problem of chain collision avoidance as a Markov Decision Process problem in order to propose a reinforcement learning-based decision-making strategy and analyse the safety efficiency of existing methods in driving security. …”
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Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Ansari S., Rahman S.A., Saad M.H.M., Muttaqi K.M.

“…This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. …”

Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm by Hannan M.A., Lipu M.S.H., Hussain A., Saad M.H., Ayob A.

Efficiency optimization of variable speed induction motor drive using online backpropagation by Mohamad Yatim, Abdul Halim, Utomo, W. M.

“…In order to achieve a robust BPEOC from variation of motor parameters, an online learning algorithm is employed. Simulation of the BPEOC and laboratory experimental set up has been developed using TMS320C60 digital signal processor. …”
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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An acquisition and a modulated recognition system for driver profiling in Malaysia / Ward Ahmed Alaulddin Al-Hussein by Ward Ahmed Alaulddin , Al-Hussein

“…The collected data were then utilized to establish credible driver profiles based on criteria developed in consultation with traffic experts. Following that, three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), were modulated to classify the recorded driving data according to the established profiles. …”
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Real Time Traffic Sign Recognition using Deep Learning by Sarmindaran Naidu a/l Siva Kumar