Palm Oil Fresh Fruit Bunch Ripeness Grading Recognition Using Convolutional Neural Network

This research investigates the application of Convolutional Neural Network (CNN) for palm oil Fresh Fruit Bunch (FFB) ripeness grading recognition. CNN has become the state-of-the-art technique in computer vision especially in object recognition where the recognition accuracy is very impressive. Even though there is no need for feature extraction in CNN, it requires a large amount of training data. To overcome this limitation, utilising the pre-trained CNN model with transfer learning provides the solution. Thus, this research compares CNN, pre-trained CNN model and hand-crafted feature and classifier approach for palm oil Fresh Fruit Bunch (FFB) ripeness grading recognition. The hand-crafted features are colour moments feature, Fast Retina Keypoint (FREAK) binary feature, and Histogram of Oriented Gradient (HOG) texture feature with Support Vector Machine (SVM) classifier. Images of palm oil FFB with four different levels of ripeness have been acquired, and the results indicate that with a small number of sample data, pre-trained CNN model, AlexNet, outperforms CNN and the hand-crafted feature and classifier approach

Manifestation of Self-Directed Learning by Adult Students in A Post-Graduate Distance Education Program

The purpose of this research is to understand the manifestation of self-directed learning by a group of adult students studying in a post-graduate distance education program. The research is conducted by examining four research questions: (1) What characteristics of participants as learners are manifested that predispose them to be self-directed?; (2) What are the motivational factors that predispose learners to be self-directed?; (3) What are the self-directed learning activities carried out by learners in the distance education program?; and (4) what are the contextual factors that influence the manifestation of self-directed learning? The study employs the qualitative methodology and the data are collected through in-depth interviews with participants. Informal observations, examination of available documents and interviews with spouses and colleagues are carried out to verify information given by participants. The adult students were purposefully selected to participate in this study. They included seven adult students studying on a part-time basis in the Master of Science in Human Resource Development program, through distance education at a local university. Interviews have been conducted to obtain their perception, experiences, activities related to self-directed learning in a post-graduate distance education program. The main source of data is from the semi-structured interviews that were taped, transcribed and analyzed. The interviews with the participants lasted for one and a half to two hours. The study reveals seven characteristics as manifested by the adult students that predispose them to be self-directed, five motivational factors that explained their self-directedness, and two contextual factors that influence the manifestation of self-directed learning. Apart from these findings, one distinct aspect has emerged from the study. It was the transformation of adult students into lifelong self-directed learners as a result of the distance education process that they have gone through

Image Retrieval Based on Texton Frequency-Inverse Image Frequency

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method

Automatic plant recognition using convolutional neural network on malaysian medicinal herbs: the value of data augmentation

Herbs are an important nutritional source for humans since they provide a variety of nutrients. Indigenous people have employed herbs, in particular, as traditional medicines since ancient times. Malaysia has hundreds of plant species; herb detection may be difficult due to the variety of herb species and their shape and color similarities. Furthermore, there is a scarcity of support datasets for detecting these plants. The main objective of this paper is to investigate the performance of convolutional neural network (CNN) on Malaysian medicinal herbs datasets, real data and augmented data. Malaysian medical herbs data were obtained from Taman Herba Pulau Pinang, Malaysia, and ten kinds of native herbs were chosen. Both datasets were evaluated using the CNN model developed throughout the research. Overall, herbs real data obtained an average accuracy of 75%, whereas herbs augmented data achieved an average accuracy of 88%. Based on these findings, herbs augmented data surpassed herbs actual data in terms of accuracy after undergoing the augmentation technique

Text localisation for roman words from shop signage / Nurbaity Sabri ... [et al.]

Text localisation determinesthe location of the text in an image. This process is performed prior to text recognition. Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF). A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Resultsindicate that HOG with quadratic kernel function localises text for shop signage better than SURF

Comparative assessment of self-sampling device and gynecologist sampling for cytology and HPV DNA detection in rural and low resource setting: Malaysian experience

Purpose: This study was conducted to assess the agreement and differences between cervical self-sampling with a Kato device (KSSD) and gynecologist sampling for Pap cytology and human papillomavirus DNA (HPV DNA) detection. Materials and methods: Women underwent self-sampling followed by gynecologist sampling during screening at two primary health clinics. Pap cytology of cervical specimens was evaluated for specimen adequacy, presence of endocervical cells or transformation zone cells and cytological interpretation for cells abnormalities. Cervical specimens were also extracted and tested for HPV DNA detection. Positive HPV smears underwent gene sequencing and HPV genotyping by referring to the online NCBI gene bank. Results were compared between samplings by Kappa agreement and McNemar test. Results: For Pap specimen adequacy, KSSD showed 100% agreement with gynecologist sampling but had only 32.3% agreement for presence of endocervical cells. Both sampling showed 100% agreement with only 1 case detected HSIL favouring CIN2 for cytology result. HPV DNA detection showed 86.2%agreement (K=0.64, 95% CI 0.524-0.756, p=0.001) between samplings. KSSD and gynaecologist sampling identified high risk HPV in 17.3% and 23.9% respectively (p= 0.014). Conclusion: The self-sampling using Kato device can serve as a tool in Pap cytology and HPV DNA detection in low resource settings in Malaysia. Self-sampling devices such as KSSD can be used as an alternative technique to gynaecologist sampling for cervical cancer screening among rural populations in Malaysia

Mean Field Bias Correction to Radar QPE as Input to Flood Modeling for Malaysian River Basins

The occurrence of unprecedented flood events has increased in Malaysia recently. To mitigate the impact of the disaster, the National Flood Forecasting and Warning System (NaFFWS) has endeavored to improve the system so as to produce more accurate and reliable early warning to the public. The paper describes the use of radar composites from the radar network in Peninsular Malaysia to produce quantitative precipitation estimates (QPE) as input to the NaFFWS flood model. The processing of the raw radar data and the conversion of rain rate are described. The comparison between radar QPE and gauge rainfall shows that radar QPE underestimates the gauge rainfall, and the results are better at the western parts of Peninsular Malaysia compared to the eastern parts of Peninsular Malaysia. The comparison between Marshall Palmer (MP) and Rosenfeld (RF) conversion equations shows that there is not much difference in performance between the two equations. Both underestimate the rainfall, although RF estimates higher radar QPE for high rainfall intensity. The underestimated radar QPE is improved by calibration process via the Mean Field Bias (MFB) correction technique. The study introduced zoning into smaller regions for the MFB factors derivation.  Results indicated that the radar QPE is much improved after the calibration process. Simulation of flood event in December 2021 for the case study of Langat River basin indicates the improvement of correlation coefficient from 0.67 to 0.99 after the calibration process via MFB for smaller zones

Mean Field Bias Correction to Radar QPE as Input to Flood Modeling for Malaysian River Basins

The occurrence of unprecedented flood events has increased in Malaysia recently. To mitigate the impact of the disaster, the National Flood Forecasting and Warning System (NaFFWS) has endeavored to improve the system so as to produce more accurate and reliable early warning to the public. The paper describes the use of radar composites from the radar network in Peninsular Malaysia to produce quantitative precipitation estimates (QPE) as input to the NaFFWS flood model. The processing of the raw radar data and the conversion of rain rate are described. The comparison between radar QPE and gauge rainfall shows that radar QPE underestimates the gauge rainfall, and the results are better at the western parts of Peninsular Malaysia compared to the eastern parts of Peninsular Malaysia. The comparison between Marshall Palmer (MP) and Rosenfeld (RF) conversion equations shows that there is not much difference in performance between the two equations. Both underestimate the rainfall, although RF estimates higher radar QPE for high rainfall intensity. The underestimated radar QPE is improved by calibration process via the Mean Field Bias (MFB) correction technique. The study introduced zoning into smaller regions for the MFB factors derivation.  Results indicated that the radar QPE is much improved after the calibration process. Simulation of flood event in December 2021 for the case study of Langat River basin indicates the improvement of correlation coefficient from 0.67 to 0.99 after the calibration process via MFB for smaller zones