Pozi Milow

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.

Ethno-medicinal Plants Used by the Temuan Villagers in Kampung Jeram Kedah, Negeri Sembilan, Malaysia

Abstract This report is based on information obtained through general conversation with elderly villagers of Kampung Jeram Kedah. A total of 56 species of medicinal plants with various uses was recorded. The plants are used to treat many types of ailments ranging from simple ones such as joint aches and pains to serious ailments such as diabetes, malaria and tumors. The most frequently used plant part in term of percentage of total number of species was the root (51.8%). This was followed by stem (17.9%), leaf (16.1%), whole plant (5.4%), root and leaf (3.6%), fruit (1.8%), inflorescence (1.8%), and rhizome (1.8%). Knowledge and usage of medicinal plants is decreasing due to various factors such as modern medicines are easily available, the younger generation are less interested in folk medicine, changes in habitat causing certain medicinal plants to be unavailable or less available.

Traditional Medicinal Plants Used by the Temuan Villagers in Kampung Tering, Negeri Sembilan, Malaysia

Abstract The authors report a total of 35 species of medicinal plants used by the villagers in Tering village. 20 species (57%) were native plants while 15 species (43%) were cultivated plants. The plants were used to treat various kinds of ailments and other health problems normally faced by these people. The common mode of administration was oral (54.3%) followed by external use (37.1%). Decoction was the more common method of preparing herbal medicine (48.6%) followed by pounded or mashed (25.7%).The plants were used to treat many types of ailments ranging from simple ones such as joint aches and pains to serious ailments such as bone fractures, hypertension and tumors. Traditional knowledge and usage of medicinal plants is decreasing due to various factors such as modern medicines are easily available, the younger generation are less interested in folk medicine, changes in habitat causing certain medicinal plants to be unavailable or less available.

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.

Traditional Medicinal Plants Used by the Kensiu Tribe of Lubuk Ulu Legong, Kedah, Malaysia

Abstract This study is based on information obtained through interviews with respondents, observations, collection and identification of medicinal plants in Kampung Orang Asli Lubuk Ulu Legong Baling, Kedah. A total of 39 species from 35 families of medicinal plants used for treating various ailments were recorded. 10.2% of the species were used to treat more than one ailment. The common mode of administration was oral (69.2%) followed by external use (30.8%).The common part of plant used is the root followed by leaves, stem, fruit, whole plant and tuber. Decoction (69.2%) is a common method of preparing herbal medicine followed by pounded (15.4%), mashed (7.7%), burned (2.6%), shredded and incantation (3%). 59% of the species were obtained from the wild, 28.2% were planted and 12.8% species of the species were both wild and planted.

Traditional Knowledge and Usage of Medicinal Plants among the Semai Orang Asli at Kampung Batu 16, Tapah, Perak, Malaysia

Abstract A study was carried out on the traditional knowledge and usage of medicinal plants among the Semai at a village in the state Perak, Malaysia. Information was obtained from talking with adults guided by a predetermined set of questions, and also by observing and participating in their activities during each visit using the method of ethno-botanical enquiry. A total of 37 species was recorded of which most of the species are native. Most species are herbs, followed by trees, climbers and shrubs. Plant parts most commonly used are leaves, roots, flowers, sap, stems. More species are used as external medicine than internal medicine. Many species of plants are used in rituals for healing and protection followed by herbal medicines for restoring and protecting post partum mothers.

Preliminary Survey on Plants in Home Gardens in Pahang, Malaysia

Abstract This preliminary survey was carried out on home gardens in Pahang, Malaysia in order to gain better insights into the trends in plant utilization and species diversity among households in the state. The survey was carried out through observation of the home gardens and semi-structured interviews with their owners. A total of 93 species of plants were encountered in thirteen home gardens. Most of the species were food plants with Cocos nuciferaand Mangifera indicabeing the most common among the home gardens. The presence of medicinal plant Eurycoma longifoliain one the home gardens indicates that the species is being domesticated.

Traditional Knowledge of a Practitioner in Medicinal Plants of Masjid Ijok Village, Perak, Malaysia

Abstract This report is based on information gathered through semi-structured interviews of several villagers who have knowledge about traditional uses of medicinal plants in a rural Malay village in Perak, Malaysia. A total of 50 plant species belonging to 36 families with medicinal uses were recorded. Herbs constitute 38 percent of the plant species used. This was followed by shrubs (30%) and trees (28 %). Leaves are the most common plant part used in preparing herbal medicine. The most common method of preparation is decoction (54%), followed by poultice (24%) and infusion (22%). More plants are used in gastro-intestinal problems than others.

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.