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Development of heuristic task scheduling algorithm in cloud computing
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2
Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter
Published 2019“…In recent times, with the rapid development of cloud computing has affected to energy consumption which gives negative impact towards the environment through production of carbon dioxide. …”
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3
Completion time driven hyper-heuristic approach for optimisation of scientific workflow scheduling in cloud environment / Ehab Nabiel Mohammad
Published 2018“…The second stage (i.e. approach development stage) is the development of the proposed CTDHH approach, which includes two main parts, the cost optimisation model of SWFS and the dynamic hyper-heuristic algorithm. …”
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4
Hardware development of autonomous mobile robot based on actuating lidar
Published 2022“…As opposed to a point cloud generated from high-end LiDAR sensors where many algorithms have been developed for object detection, sparse LiDAR point clouds still possess large room for improvement. …”
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Detection of mature and immature oil palm from image Sentinel-2 using Google Earth Engine (GEE) / Nurul Ain Nabilah Sharuddin
Published 2022“…Recent developments in big data and cloud computing enable quick mapping on a wide scale. …”
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Study of machine learning in computer vision using Raspberry Pi
Published 2024text::Final Year Project -
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Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms
Published 2018“…Mapping protected areas (PAs) is essential in understanding the disturbance that can affect biodiversity and conservation management. Land use-land cover (LULC) maps can be used as a decision making tool by policy makers to ensure sustainable development and understanding of the effect of human activities within and around PAs. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The locations of landslides were detected accurately by employing two Machine learning classifiers, namely, SVM and RF, decision rule and hierarchal rules sets were developed by applying decision tree (DT) algorithm to provide improved landslide inventory. …”
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models
Published 2019“…In FFB and PO models, the data used 15 variables, namely: percentage of mature area and percentage of immature area, rainfall, rainy days, humidity, radiation, temperature, surface wind speed, evaporation and cloud cover, O3, CO, NO2, SO2, and PM10. The study used input energy data from 8 variables for developing energy models. …”
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Proposed conceptual Iot-based patient monitoring sensor for predicting and controlling dengue
Published 2023“…The growing development of wearable Internet of Things (IoT), cloud computing, analytical approaches provide better alternative for dengue prediction and control. …”
Article -
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Monitoring cathodic protection system for underground pipeline
Published 2013“…The world we live in today is greatly affected by corrosion issues and therefore multiple methods of continuous monitoring of a structure can help investigate the corrosion process. …”
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