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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…The simulation is implemented with iFogSim and java programming language. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation by Lateh, Masitah bdul

“…Otherwise, learning from small sample sizes might cause prediction task produced an imprecise model. …”
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A deep reinforcement learning hybrid algorithm for the computational discovery and characterization of small proteins utilizing mycobacterium tuberculosis as a model by Ouwabunmi, Babalola AbdulHafeez

“…This study presents the development and evaluation of a novel hybrid machine learning algorithm that integrates the strengths of Random Forest and Gradient Boosting models to enhance the prediction of smORFs. …”
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OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”

Identification and control of a small-scale helicopter by Deboucha, A., Taha, Z.

“…A neural network learning algorithm is combined with the NLARX model to identify the dynamic component of the rotorcraft unmanned aerial vehicle (RUAV). …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

“…The thesis proposed a solution that employs unique characteristics of machine learning and game-theoretic framework to enable a model-free and energy-efficient small cell network. …”
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A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification by Madanan M., Gunasekaran S.S., Mahmoud M.A.

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Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

“…Cloudsim is used as Discrete Event Simulation tool and Java as coding language to evaluate LACE algorithm. …”
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Comparative performance of machine learning algorithms for cryptocurrency forecasting by Hitam, Nor Azizah, Ismail, Amelia Ritahani

“…Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. …”
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Optimizing high-density aquaculture rotifer Detection using deep learning algorithm by Alixson Polumpung, Kit Guan Lim, Min Keng Tan, Sitti Raehanah Muhamad Shaleh, Renee Ka Yin Chin, Kenneth Teo Tze Kin

“…Second, is to develop the deep learning algorithm based on YOLOv3. Third step is to training and evaluate the model using loss function. …”
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New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

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Small-scale helicopter system identification model using recurrent neural networks by Taha, Z., Deboucha, A., Dahari, M.

“…The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. …”
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Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications by Teoh, Ming Xue

“…In recent studies, we seen developers and researchers proposing solutions on deep learning algorithms like YOLO, EfficientNet, CNN, MobileNet etc. …”
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Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir by Amir, Nur Hazirah

“…It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…Second, the datasets taken for the semantic segmentation task are not balanced since certain classes are present more than the others. This biases the model performance to the most represented ones. We propose a new reinforced active learning strategy based on a deep reinforcement learning algorithm. …”
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