Homegardens are defined as less complex agroforests which look like and function as natural forest ecosystems but are integrated into agricultural management systems located around houses. Study on the factor affecting the diversity of plant resource in homegardens is paramount important to improve productivity and sustainability. Previous studies related to homegradens analysis are conducted using ordination techniques (e.g. Principal Component Analysis, Correspondence Analysis). In this study, we introduced the application of Self-Organizing Map (SOM) a type of Artificial neural networks (ANNs) to analyze the effects of socioeconomic variable and homegardens characteristic toward diversity of plant resource and to investigate the spatial configuration occurring within homegardens. The inter-relationships among the socioeconomic variable and homegardens characteristic were extracted and interpreted using the pattern analysis visualized in component planes. Sequential agglomerative hierarchical non-overlapping (SAHN) clustering technique was also used to verify results obtained from SOM by using the unweighted pair grouping method with arithmetic-mean (UPGMA). Ten homegardens were identified from SOM U-Matrix and each of the homegardens was investigated for their horizontal and vertical profile. Inspection of SOM indicates that the region with high d-values for size of homegardens coincides with those of food, ornamental, and medicinal plants. Region of high d-value in Shannon index coincides with region of high d-value in Evenness index. Region of low d-value in income coincides with high d-values in both the Shannon and Evenness indices. Region of high d-value in age of household also coincides with high d-values in both the Shannon and Evenness indices. Combination of SOM, SAHN and spatial analysis techniques has a potential to analyst and monitor not only the factors affecting homegardens biodiversity but also their development and improvement which to our best knowledge has yet been reported in literature.
Abstract. Ramli MP, Malek S, Milow P, Aziz NJ. 2021. Traditional knowledge of medicinal plants in the Kampung Orang Asli Donglai Baru, Hulu Langat, Malaysia. Biodiversitas 22: 1304-1309. Documentation on traditional knowledge of medicinal plants is important before it is completely exhausted by the loss of natural habitats surrounding it and the passing away of older generations. In this study, an ethnobotanical survey for the medicinal plants in the Kampung Orang Asli Donglai Baru, Hulu Langat, Selangor, Malaysia was carried out. A semi-structured interview was prepared to record the medicinal uses of the local medicinal plants in the study area. The information such as the Orang Asli local name, parts used for medication, methods of preparation, and type of ailments were all collected. The total numbers of recorded species in the study sites were 39 species belonging to 22 families. The families Zingiberaceae, Marantaceae, Leguminosae, Vitaceae, Lamiaceae, Melastomataceae, and Araceae have recorded the highest species of medicinal plants. The most frequently utilized plant parts were the leaves (25%) followed by roots (20%), whole plants (10%), fruit (5%) and flowers (2.5%). Gastrointestinal problems including stomach ache, diarrhea, indigestion and bloating were among the most frequent ailments treated with the medical plants. This study revealed that many medicinal plants are still broadly used by the community for treating various diseases in ailments. Further investigation needs to be carried out to explore the potential of these plants in scientific usage.
Temporal patterns in ecological data can be visualized and communicated effectively through graphical means. The aim of this study was to develop a data prediction and visualization system based on historical data and thematic map technology to visualize forecast temporal ecological changes. The visualization system consists of prediction and data visualization modules. The prediction module is developed using a hybrid evolutionary algorithm (HEA) to classify and predict noisy ecological data. The visualization module is developed using Dotnet Framework 2.0 to implement thematic cartography for volume visualization. The visualization system is evaluated by its capability in representing the output data on a map, and by predicting the abundance of Chlorophyta based on other water quality parameters. Rules for predicting Chlorophyta abundance had a success rate of almost 90%. The integration of computational data mining using HEA and visualization using thematic maps promises practical solutions and better techniques for forecasting temporal ecological changes, especially when data sets have complex relationships without clear distinction between various variables.
Ficus is one of the largest genera in plant kingdom reaching to about 1000 species worldwide. While taxonomic keys are available for identifying most species of Ficus, it is very difficult and time consuming for interpretation by a nonprofessional thus requires highly trained taxonomists. The purpose of the current study is to develop an efficient baseline automated system, using image processing with pattern recognition approach, to identify three species of Ficus, which have similar leaf morphology. Leaf images from three different Ficus species namely F. benjamina, F. pellucidopunctata and F. sumatrana were selected. A total of 54 leaf image samples were used in this study. Three main steps that are image pre-processing, feature extraction and recognition were carried out to develop the proposed system. Artificial neural network (ANN) and support vector machine (SVM) were the implemented recognition models. Evaluation results showed the ability of the proposed system to recognize leaf images with an accuracy of 83.3%. However, the ANN model performed slightly better using the AUC evaluation criteria. The system developed in the current study is able to classify the selected Ficus species with acceptable accuracy.
Abstract Background Thrombolysis in Myocardial infarction (TIMI) is used in predicting the mortality rate of the acute coronary syndrome (ACS) patients. TIMI was developed based on the Western cohort with limited data on the Asian cohort. There are separate TIMI scores for STEMI and NSTEMI. Deep learning (DL) and machine learning (ML) algorithms such as support vector machine (SVM) in population-specific dataset resulted in a higher area under the curve (AUC) to TIMI. The limitation of DL is selected features by the algorithm is unknown compared to ML algorithms. Purpose To construct a single in-hospital mortality risk scoring system that combines SVM feature importance and the DL algorithm in ASIAN patients with ACS that is applicable for both STEMI and NSTEMI patients. To investigate DL performance constructed using predictors selected from SVM feature extraction and DL using complete features and compare with TIMI risk score for STEMI and NSTEMI patients. Methods We constructed four algorithms: i) DL and SVM algorithm with feature selected from SVM variable importance, ii) DL and SVM algorithm without feature selection. SVM feature importance with the backward elimination method is used to select and rank important variables. We used registry data from the National Cardiovascular Disease Database of 13190 patient's data. Fifty-four parameters including demographics, cardiovascular risk, medications and clinical variables were considered. AUC was used as the performance evaluation metric. All algorithms were validated using validation dataset and compared to the conventional TIMI for STEMI and NSTEMI. Results Validation results in Figure 1 are by STEMI and NTEMI patients. Both DL algorithms outperformed ML and TIMI score on validation data. Similar performance is observed for DL and SVM algorithms using all predictors (54 predictors) with DL and SVM algorithm using selected predictors (14 predictors). Predictors selected by the SVM feature selection are: age, heart rate, Killip class, fasting blood glucose, ST-elevation, CABG, cardiac catheterization, angina episode, HDLC, LDC, other lipid-lowering agents, statin, anti-arrhythmic agent, oralhypogly. CABG and pharmacotherapy drugs as selected predictors improve mortality prediction compared to TIMI score. In DL, 25.87% of STEMI patients and 19.71% of NSTEMI patients are estimated as high risk (risk probabilities of >50%). TIMI underestimated the risk of mortality of high-risk patients (≥5 risk scores) with 13.08% from STEMI patients and 4.65% from NSTEMI patients (Figure 2). Conclusions In the ASIAN multi-ethnicity population, patients with ACS can be better classified using one single algorithm compared to the conventional method like TIMI which requires two different scores. Combining ML feature selection with DL allows the identification of distinct factors related to in-hospital mortality of ACS patients in a unique ASIAN population for better mortality prediction. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 Figure 1. Performance resultsFigure 2. Analysis on the validation set
Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model was performed against Thrombolysis in Myocardial Infarction (TIMI) using an additional dataset of 102 patients. The Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post-ACS. The performance of MLmodels using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with TIMI using an additional dataset resulted in the best ML model outperforming the TIMI score (AUC = 0.75 vs. AUC = 0.60). The findings of this study will provide a basis for developing an online ML-based population-specific risk scoring calculator.
Freshwater 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. The 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. System's 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%. This 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.
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