DIGITAL IMAGE IDENTIFICATION OF PLANKTON USING REGIONPROPS AND BAGGING DECISION TREE ALGORITHM

Peranan plankton sangat penting bagi kehidupan organisme disekitarnya, sehingga penelitian prihal plankton sangatlah dibutuhkan karena kaitannya dengan kelangsungan kehidupan mahluk hidup lainnya.              Kendala yang sering didapatkan dalam hal penelitian plankton khususnya dalam hal pengidentifikasian plankton yaitu tidak efisiennya dalam aspek waktu dan organisme ini memiliki ukuran rata-rata yang sangat kecil. Dalam hal ini diperlukan alternatif yang lebih baik dalam pengidentifikasian jenis plankton ini dengan cara pemrosesan gambar pada citra plankton secara digital atau biasa disebut dengan istilah “Digital Image Processing”. Penelitian ini bertujuan untuk melakukan pengolahan citra digital plankton sebanyak 144 citra yang yang dibagi menjadi 75% sebagai data pelatihan dan 25% sebagai data pengujian, dan citra tersebut didapatkan dari riset pada yayasan Kanopi Indonesia. Dalam prosesnya citra ini dianalisa bentuk menggunakan fungsi Regionprops sehingga didapatkan fitur pembeda dari masing-masing jenis plankton. Setelah citra terekstraksi fitur nya selanjutnya dilakukan pengolahan data dengan mengklasifikasikan setiap jenis plankton tersebut. Untuk menghasilkan sebuah klasifikasi data yang lebih baik, dalam penelitian ini menggunakan algoritma Bagging Decision Tree dalam pengolahan data nya dan menghasilkan akurasi sebesar 92.59%. Algoritma Bagging Decision Tree ini cukup baik dan mudah untuk di implemntasikan kedalam sebuah program identifikasi jenis plankton, terbukti dengan pengujian pada data citra pengujian menghasilkan 33 citra teridentifikasi dengan benar dari total pengujian sebanyak 36 citra

Introducing artificial data generation in active learning for land use/land cover classification

Fonseca, J., Douzas, G., & Bacao, F. (2021). Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers over three AL-specific performance metrics in seven benchmark datasets. We show that this modification of the AL framework significantly reduces cost and time requirements for a successful AL implementation in all of the datasets used in the experiment.publishersversionpublishe

Investigating marine particle distributions and processes using in situ optical imaging in the Gulf of Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2015The Gulf of Alaska is a seasonally productive ecosystem surrounded by glaciated coastal mountains with high precipitation. With a combination of high biological production, inputs of suspended sediments from glacial runoff, and contrasting nutrient regimes in offshore and shelf environments, there is a great need to study particle cycling in this region. I measured the concentrations and size distributions of large marine particles (0.06-27 mm) during four cruises in 2014 and 2015 using the Underwater Vision Profiler (UVP). The UVP produces high resolution depth profiles of particle concentrations and size distributions throughout the water column, while generating individual images of objects >500 μm including marine snow particles and mesozooplankton. The objectives of this study were to 1) describe spatial variability in particle concentrations and size distributions, and 2) use that variability to identify driving processes. I hypothesized that UVP particle concentrations and size distributions would follow patterns in chlorophyll a concentrations. Results did not support this hypothesis. Instead, a major contrast between shelf and offshore particle concentrations and sizes was observed. Total concentrations of particles increased with proximity to glacial and fluvial inputs. Over the shelf, particle concentrations on the order of 1000-10,000/L were 1-2 orders of magnitude greater than offshore concentrations on the order of 100/L. Driving processes over the shelf included terrigenous inputs from land, resuspension of bottom sediments, and advective transport of those inputs along and across the shelf. Offshore, biological processes were drivers of spatial variability in particle concentration and size. High quantities of terrigenous sediments could have implications for enhanced particle flux due to ballasting effects and for offshore transport of particulate phase iron to the central iron-limited gyre. The dominance of resuspended material in shelf processes will inform the location of future studies of the biological pump in the coastal Gulf of Alaska. This work highlights the importance of continental margins in global biogeochemical processes

Customer retention

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018The aim of this study is to model the probability of a customer to attrite/defect from a bank where, for example, the bank is not their preferred/primary bank for salary deposits. The termination of deposit inflow serves as the outcome parameter and the random forest modelling technique was used to predict the outcome, in which new data sources (transactional data) were explored to add predictive power. The conventional logistic regression modelling technique was used to benchmark the random forest’s results. It was found that the random forest model slightly overfit during the training process and loses predictive power during validation and out of training period data. The random forest model, however, remains predictive and performs better than logistic regression at a cut-off probability of 20%.MT 201