Sorayya Malek

A preliminary study on automated freshwater algae recognition and classification system

BackgroundFreshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.ResultsThe development of the automated freshwater algae detection system involved image preprocessing, segmentation, feature extraction and classification by using Artificial neural networks (ANN). Image preprocessing was used to improve contrast and remove noise. Image segmentation using canny edge detection algorithm was then carried out on binary image to detect the algae and its boundaries. Feature extraction process was applied to extract specific feature parameters from algae image to obtain some shape and texture features of selected algae such as shape, area, perimeter, minor and major axes, and finally Fourier spectrum with principal component analysis (PCA) was applied to extract some of algae feature texture. Artificial neural network (ANN) is used to classify algae images based on the extracted features. Feed-forward multilayer perceptron network was initialized with back propagation error algorithm, and trained with extracted database features of algae image samples. Systems accuracy rate was obtained by comparing the results between the manual and automated classifying methods. The developed system was able to identify 93 images of selected freshwater algae genera from a total of 100 tested images which yielded accuracy rate of 93%.ConclusionsThis study demonstrated application of automated algae recognition of five genera of freshwater algae. The result indicated that MLP is sufficient, and can be used for classification of freshwater algae. However for future studies, application of support vector machine (SVM) and radial basis function (RBF) should be considered for better classifying as the number of algae species studied increases.

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

BackgroundThis study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.ResultsSame data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.ConclusionsOverall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.

Prediction of Population Dynamics of Bacillariophyta in the Tropical Putrajaya Lake and Wetlands (Malaysia) by a Recurrent Artificial Neural Networks

Phytoplankton becomes a concern to the society when it forms a dense growth at water surface known as algae bloom. This paper discusses feasibility of applying recurrent artificial neural network to predict occurrence of selected phytoplankton population the Bacillariophyta population in Putrajaya Lake and Wetlands for one month ahead prediction. The data used are monthly data collected from August 2001 until May 2006. Network performance is measured based on the root mean square error value (RMSE). Input selection is carried out by means of correlation analysis, sensitivity analysis and unsupervised neural network SOM. Better results are achieved for simpler network where variables are selected using method stated above. Thus the capability of neural network model as a predictive tool for tropical lake cannot be disregarded at all.

Challenges of Digital Note Taking

There are world efforts to make technology act with education field for better learning achievements. Technology tries to replace the traditional learning environments, media, and activities into digital age. However, slow progress has been achieved to transfer the note taking activities into digital era. In this study, we explored current note taking tools which developed to bridge the gap between paper-based and technology-based notes. We tried to identify key specific problems and challenges that prevent note taking from existing in the digital age. This study is providing extensive investigation with systematic analysis about the impacts of current note taking tools in learning to identify constrains and limitations of typical note taking systems. Unfortunately, we agreed with similar previous studies that current tools are still inadequate and inefficient to be used for replacing the traditional note taking due to several issues. We found that developing a successful note taking applications is challenges because of four main issues, complexity, technology learning dilemma, integrity, and inefficiency issues. This study discusses the main implications to shape the future of digital notes.

Analysis of Algal Growth Using Kohonen Self Organizing Feature Map (SOM) and its Prediction Using Rule Based Expert System

Phytoplankton becomes a concern to the environment when it forms dense growth at the water surface, known as algal bloom. However, studies on mechanism of algal bloom are not straight forward mainly caused by uncertainty and complexity of alga ecosystems. This paper describes the analysis of limnological time-series of Putrajaya Lake and wetlands to determine the growth of alga based on Kohonen self organizing feature maps (SOM). It specifically concentrates on the total Bacillariophyta species due to formation of largest algal composition in the Lake Putrajaya. An expert system was then developed based on the rules extracted from the SOM to model and predict the algal growth. The effectiveness of this system was tested on an actual tropical lake data which yields an acceptable high level of accuracy

Random Forest and Self Organizing Maps Application for Analysis of Pediatric Fracture Healing Time of the Lower Limb

Abstract In this study, we examined the lower limb fracture healing time in children using random forest (RF) and Self Organizing feature Maps (SOM) methods. The study sample was obtained from the pediatric orthopedic unit in University Malaya Medical Centre. Radiographs of long bones of lower limb fractures involving the femur, tibia and fibula from children ages 0–12 years, with ages recorded from the date and time of initial injury. Inputs assessment extracted from radiographic images included the following features: type of fracture, angulation of the fracture, contact area percentage of the fracture, age, gender, bone type, type of fracture, and number of bone involved. RF is initially used to rank the most important variables that effecting bone healing time. Then, SOM was applied for analysis of the relationship between the selected variables with fracture healing time. Due to the limitation of available dataset, leave one out technique was applied to enhance the reliability of RF. Results showed that age and contact area percentage of fracture were identified as the most important variables in explaining the fracture healing time. RF and SOM applications have not been reported in the field of pediatric orthopedics. We concluded that the combination of RF and SOM techniques can be used to assist in the analysis of pediatric fracture healing time efficiently.

A Primary Study on Application of Artificial Neural Network in Classification of Pediatric Fracture Healing Time of the Lower Limb

In this study we examined the lower limb fracture in children and classified the healing time using supervised and unsupervised artificial neural network (ANN). Radiographs of long bones from 2009 to 2011 of lower limb fractures involving the femur, tibia and fibula from children ages 0 to 13 years, with ages recorded from the date and time of initial injury was obtained from the pediatric orthopedic unit in University Malaya Medical Centre. ANNs was developed using the following input: type of fracture, angulation of the fracture, displacement of the fracture, contact area of the fracture and age. Fracture healing time was classified into two classes that is less than 12 weeks which represent normal healing time in lower limb fractures and more than 12 weeks which could indicate a delayed union. This research was designed to evaluate the classification accuracy of two ANN methods (SOM, and MLP) on pediatric fracture healing. Standard feed-forward, back-propagation neural network with three layers was used in this study. The less sensitive variables were eliminated using the backward elimination method, and the ANN network was retrained again with minimum variables. Accuracy rate, area under the curve (AUC), and root mean square errors (RMSE) are the main criteria used to evaluate the ANN model results. We found that the best ANN model results was obtained when all input variables were used with overall accuracy percentage of 80%, with RMSE value of 0.34, and AUC value of 0.8. We concluded here that the ANN model in this study can be used to classify pediatric fracture healing time, however extra efforts are required to adapt the ANN model well by using its full potential features to improve the ANN performance especially in the pediatric orthopedic application.

Applying artificial neural network theory to exploring diatom abundance at tropical Putrajaya Lake, Malaysia

This article explores the relationship between diatom abundance and water quality variables in tropical Putrajaya Lake based on limnological data collected from 2001 to 2006, using supervised and unsupervised artificial neural networks (ANNs). Recurrent artificial neural network (RANN) was used for the supervised ANNs and Kohonen Self Organizing Feature Maps (SOMs) for the unsupervised ANNs. The RANN was developed for the prediction of diatom abundance using variables selected by sensitivity analysis (water temperature, pH, dissolved oxygen, and turbidity). The RANN model performance was measured using root mean squared error (19.0 cell/mL) and the r-value (0.7). SOM was used in this study for classification and clustering of diatom abundance in relation to selected water quality variables and was validated using a sensitivity curve of diatom abundance over the selected variable range generated from RANN. SOM has been employed in this study for pattern discovery of diatom abundance at Putrajaya Lake. The extracted patterns of diatom abundance in terms of propositional IF…else rules were tested and yielded an accuracy rate of 87%.

A Comparison between Neural Network Based and Fuzzy Logic Models for Chlorophll-a Estimation

This paper describes the application of two novel computational methods such as fuzzy logic and supervised artificial neural network (ANN) to model algal biomass in tropical Putrajaya Lake and Wetlands (Malaysia). Limnological time series data collected from 2001 until 2004 was utilized using input parameters such as water temperature, pH, secchi depth, dissolved oxygen, ammoniacal nitrogen and nitrate nitrogen. Performance measure for the models developed was in terms of root mean square error (RMSE). Both models developed gave similar result with models developed using fuzzy logic approach performed slightly better compared to feed-forward artificial neural network model.

Analysis of selected algal growth (Pyrrophyta) in tropical lake using Kohonen self organizing feature map (SOM) and its prediction using rule based system

In this paper we describe the feasibility of applying Kohonen self organizing feature maps (SOM) and rule based system to determine the growth of selected algal division, Pyrrophyta using limnological time-series data of tropical Putrajaya Lake and Wetlands (Malaysia). A rule based model was developed based on the rules extracted from the SOM to model and predict Pyrrophyta growth. Input parameters are selected based on correlation analysis. Input parameters selected are temperature, pH and Biochemical Oxygen Demand (BOD). The effectiveness of this system was tested on an actual tropical lake data that is Putrajaya Lake and Wetlands which yields an acceptable high level of accuracy.