Factors affecting the learning achievement of undergraduate students through electronic-based instructional delivery of selected industrial marketing topics

E-learning has become an important part of education agenda around the world. Though it may be known by different names such as web-based education, distant education, cyber education, networked learning, it typically uses the internet to create an educational experience for students. This study primarily determines the effectiveness of on-line learning delivery of selected topics in the subject Industrial Marketing (INDUMAR). This study compared on ground vs. e-based learning to determine if there were any difference in learning achievement of students. Several factors that influence student learning via e-learning have been also studied including their gender and learning styles, and the time spent learning on-line and off-line . De La Salle students enrolled in INDUMAR are the research subjects. The methods of data collection included questionnaires, and student marks

ECG print-out features extraction using spatial-oriented image processing techniques

© 2018 Universiti Teknikal Malaysia Melaka. All rights reserved. Analyzing cardiovascular activity of patients using ECG clinical paper printouts requires prior knowledge and practice. This research used spatial-oriented image processing methods for analyzing ECG readings by retrieving only the essential features, and not all ECG data, to assist physicians in diagnosis. Different values such as Atrial (rate/min) and Ventricular (rate/min), QRS interval (sec), QT interval (sec), QTc (sec), and PR interval (sec) were successfully extracted with indication as to whether the values are within the accepted normal values, given the patient’s gender and age. Performance of the system was tested based on accuracy, RMSE and normalized RMSE. The methodology achieved average accuracy as high as 95.424 % while the PR interval feature extraction achieved a relatively low average accuracy of 87.196%

Hybrid tree-fuzzy-rough set decision support for determining plant growth using vision-based descriptors

© 2019 IEEE. Correct identification of the growth stage of crops contributes largely to the proper allocation and control of environmental factors for optimized harvestable products. Machine vision approaches for lettuce growth stage prediction has issues such as feature extraction, feature selection and dimensionality reduction for optimum classification accuracy, and robust framework for the prediction system. This paper presented a methodology of classifying lettuce growth stage using a Hybrid Decision Tree-Fuzzy- Rough Set. Vision features are extracted and subjected to dimensionality reduction using Decision Tree. The reduced inputs are used to design the Mamdani Fuzzy Inference system. Rough Set Theory is then applied to the Fuzzy Logic model to simplify the rules. Experimental results show a high performance in determining the growth stage of test lettuce images

Color space analysis using KNN for lettuce crop stages identification in smart farm setup

Advancing technologies are being done in improvement and enhancement of the smart farming all over the world. The growth of the plants is being monitored through the vision system and image processing is done to identify their growth stages. This is important since the amount of light, temperature and water varies at each stage. One of the challenges in the image processing is the selection of the color space that will be appropriate for a particular setup. In this study, K-nearest neighboring is used in the image segmentation for the RGB, HSV, CIELab, and YCbCr color spaces. The specificity and sensitivity of each color spaces were computed and compared. Based on the result obtained, CIELab color space is the best color space to be used in the identification of the growth stage of the lettuce. © 2018 IEEE

Vision-based canopy area measurements

Canopy area measurement is one of the important crop growth factors that is considered for the crop yield. This has been widely used parameter all over the world. Yet, the development of a system that can automatically computes the canopy area of the crop is still a challenge. In this study, a vision-based system is proposed. The system captures the image of the crop and process this through image processing algorithm. The extracted feature of the image is the pixel count. This pixel count determines the canopy area of the crop. A mathematical model was developed for the approximation of the canopy area. © 2018 IEEE