Faculty of Computing

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Subject: search.filters.subject.optimization

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Now showing 1 - 5 of 5
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    PublicationEmbargo
    AgroPro: Optimizer for Traditional Agricultural System in Sri Lanka
    (IEEE, 2022-12-09) De Silva, D.I.; Suriyawansa, G.M. T. K. D. S.; Senevirathna, M.R. U. M. T.; Balasuriya, I.D. I.; Deshapriya, A. G. S. P.; Gadiarachchi, G. A. D. K. M.
    Today, in many countries around the world, big data analysis and machine learning methods are used for industrial development. However, such techniques are rarely used in Sri Lankan agricultural industry. The success of agriculture depends heavily on the selection of the right crop. Choosing the right crop depends primarily on predicting future yields. Machine learning methods can be used very successfully to make future predictions about crop yields. Crop prediction mainly depends on the soil, geography, and climate of the growing location. Hence historical data with agricultural facts such as temperature, humidity, pH, and rainfall are used to predict yield as parameters in the machine learning function. Sri Lanka uses a traditional approach to distribute fertilizers among farmers. Not having an organized way to distribute fertilizers to the needed areas leads to many abnormalities along the way. As a result, the country is facing economic losses and resource wastage. Having an optimized distribution network is the key to overcoming those abnormalities. This research assesses the efficiency of the fertilizer distribution system and consists of time-series predictions on fertilizer usage to gain future value. The aim is to identify performance gaps in distribution management that lead to delayed fertilizer distribution affecting agricultural productivity.
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    PublicationOpen Access
    Sustainable manufacturing: application of optimization to textile manufacturing plants
    (Global Journals, 2020-10-21) Liyanage, I; Nuwanga, S; Anjana, R; Rankothge, W; Gamage, N
    The main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands.In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant.
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    PublicationOpen Access
    Three Layer Super Learner Ensemble with Hyperparameter optimization to improve the performance of Machine Learning model
    (Faculty of Technology, USJ, 2021-03-13) Kasthuriarachchi, K. T. S; Liyanage, S. R
    A combination of different machine learning models to form a super learner can definitely lead to improved predictions in any domain. The super learner ensemble discussed in this study collates several machine learning models and proposes to enhance the performance by considering the final meta- model accuracy and the prediction duration. An algorithm is proposed to rate the machine learning models derived by combining the base classifiers voted with different weights. The proposed algorithm is named as Log Loss Weighted Super Learner Model (LLWSL). Based on the voted weight, the optimal model is selected and the machine learning method derived is identified. The meta- learner of the super learner uses them by tuning their hyperparameters. The execution time and the model accuracies were evaluated using two separate datasets inside LMSSLIITD extracted from the educational industry by executing the LLWSL algorithm. According to the outcome of the evaluation process, it has been noticed that there exists a significant improvement in the proposed algorithm LLWSL for use in machine learning tasks for the achievement of better performances.
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    PublicationEmbargo
    IGOE IoT framework for waste collection optimization
    (IEEE, 2017-01-27) Lokuliyana, S; Jayakody, A; Rupasinghe, L; Kandawala, S
    Waste management has become a major issue in all the part of the world and tends to grow day by day. Mismanagement in waste has become one of the key environmental and health issue. With the increase of population, especially in the urban areas waste collection, categorization, and disposal has become a major hazard for the government authorities. An IoT based waste collection framework is proposed to automate the solid waste identification, localization and collection process. The authors are involved in the identifying key impact factors in the waste collection process and provide systematic and automated solution to optimize the process to achieve higher efficiency. A layered architecture is introduced to handle the waste collection process and an optimization algorithm is derived for the existing business process based on the proposed evaluation criteria. The final outcome is a complete framework which compromises the Inputs, Outputs, Guide and Enables. The main objective is to implement an optimized automated waste collection system with the use of a vast sensor network capable of gathering waste data and by implementing an optimization algorithm in waste collection.
  • Thumbnail Image
    PublicationEmbargo
    IGOE IoT framework for waste collection optimization
    (IEEE, 2017-01-27) Lokuliyana, S; Jayakody, J. A. D. C. A.; Rupasinghe, L; Kandawala, S
    Waste management has become a major issue in all the part of the world and tends to grow day by day. Mismanagement in waste has become one of the key environmental and health issue. With the increase of population, especially in the urban areas waste collection, categorization, and disposal has become a major hazard for the government authorities. An IoT based waste collection framework is proposed to automate the solid waste identification, localization and collection process. The authors are involved in the identifying key impact factors in the waste collection process and provide systematic and automated solution to optimize the process to achieve higher efficiency. A layered architecture is introduced to handle the waste collection process and an optimization algorithm is derived for the existing business process based on the proposed evaluation criteria. The final outcome is a complete framework which compromises the Inputs, Outputs, Guide and Enables. The main objective is to implement an optimized automated waste collection system with the use of a vast sensor network capable of gathering waste data and by implementing an optimization algorithm in waste collection.