Search Results - centre learning algorithm

Artificial intelligent integrated into sun-tracking system to enhance the accuracy, reliability and long-term performance in solar energy harnessing by Tan, Jun You

“…The proposed AI algorithm integrates two deep learning models which are object detection algorithm and reinforcement learning. …”
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A Mininet emulation study for SDN fat tree data center sleep mode routing algorithms by Fawzi S., Din N.M.

“…In this work meta heuristic algorithm is incorporated at the SDN central controller in a fat tree-based data centre for bandwidth usage monitoring, sleep decisions and path selection using Mininet emulation. …”

Machine learning classifications of multiple organ failures in a malaysian intensive care unit by Norliyana, Nor Hisham Shah, Normy Norfiza, Abdul Razak, Athirah, Abdul Razak, Asma’, Abu-Samah, Fatanah, M. Suhaimi, Ummu Kulthum, Jamaludin

“…This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. Ninety-eight ICU patients’ data were obtained retrospectively from Universiti Malaya Medical Centre for analysis. …”
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Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit by Shah N.N.H., Razak N.N.A., Razak A.A., Abu-Samah A., Suhaimi F.M., Jamaluddin U.

“…This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. …”

Detecting emotions and depression through voice by Gunawan, Teddy Surya

“…A deep learning algorithm can detect emotion, including depression, using a voice signal. …”
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An adaptive opposition-based learning selection: The case for jaya algorithm by Nasser, Abdullah B., Kamal Z., Zamli, Hujainah, Fadhl, Ghanem, Waheed Ali H. M., Saad, Abdul-Malik H. Y., Mohammed Alduais, Nayef Abdulwahab

“…Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. …”
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Kodepoly: an engaging approach to blended futuristic learning in coding by Abd Rahman, Norsyafrina, Azhar Amanullah, Ayn Nur Azhana

“…By combining the strategic elements of Monopoly with a curriculum comprised of coding challenges, debugging exercises, and algorithmic puzzles, Kodepoly aims to render the learning process both enjoyable and substantial in content. …”
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Assessing the outcome of Competencies Level from Knowledge-Based Project by Sungsinlert, Anyamanee

“…Attheend, it will generate the outcome which said to be the Success Rate of the Learning process. Therefore, we shall now have the alternative way in order to provide the more accuracy assessment of the success rate , in Percentage, ofthe learning process at thelearning centre which we have chosen Halliburton Excellence Centre to be our client. …”
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Deep learning metaphor detection with emotion-cognition association by Razali, Md Saifullah, Abdul Halin, Alfian, Chow, Yang-Wai, Mohd Norowi, Noris, Doraisamy, Shyamala

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Sequential Learning Methods on RBF with Novel Approach of Minimal Weight Update by Asirvadam , Vijanth Sagayan, McLoone, Sean

“…This paper investigates sequential learning method with new form of weight update applied on a decomposed form of training algorithms using Radial Basis Function (RBF) network. …”
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DNA enhancer prediction using machine learning techniques with novel feature representation by Fong, Pui Kwan

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Bio-signal identification using simple growing RBF-network (OLACA) by Asirvadam , Vijanth Sagayan, McLoone, Sean, Palaniappan, R

“…An enhanced online adaptive centre allocation algorithms (or resource allocation network (RAN)) using simple/stochastic back-propagation method with minimal weight update variant are developed for direct-link radial basis function (DRBF) networks. …”
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Allah’s wisdom in creating the universe as a source of inspiration in artificial intelligence by Mahmod Attar Bashi, Zainab Senan, Maspo, Nur-Adib, Hassan Abdalla Hashim, Aisha, Senan, Shayma

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Kernerlized Correlation Filters Parameters Optimization For Enhanced Visual Tracking by Ong, Chor Keat

“…In this research, the tracking is proposed by using the overlap ratio (OR) and centre location error (CLE). In our case, our target is to obtain a better accuracy, which is higher overlap ratio and lower centre location error than the result from the algorithms available in public. …”
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Architecture for latency reduction in healthcare internet-of-things using reinforcement learning and fuzzy based fog computing by Shukla, S., Hassan, M.F., Jung, L.T., Awang, A.

“…This hybrid approach integrates healthcare IoT devices with the cloud and uses fog services with Fuzzy Reinforcement Learning Data Packet Allocation (FRLDPA) algorithm. …”
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Architecture for latency reduction in healthcare internet-of-things using reinforcement learning and fuzzy based fog computing by Shukla, S., Hassan, M.F., Jung, L.T., Awang, A.

“…This hybrid approach integrates healthcare IoT devices with the cloud and uses fog services with Fuzzy Reinforcement Learning Data Packet Allocation (FRLDPA) algorithm. …”
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Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

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Driver Behaviour Monitoring And Alert System By Using Machine Learning by Nur Arina Hazwani Samsun Zaini, Fauzun Abdullah Asuhaimi, Liyana Ramli, Khairul Nabilah Zainul Ariffin

A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. by Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Ali Abbas, Kassim

“…Moreover, this controller's robustness to output disturbances has also been tested and the results clearly indicated the remarkable performance of this controller and its learning algorithm. In addition, the result of comparing the performance of this controller with a genetically tuned classical PID controller has shown the superiority of the PID-like ANFIS controller.…”
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Predictive analytics for the sentiment of malaysian place of interest using machine learning models by Qiryn Adriana, Kharul Zaman

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