De La Salle University

About: De La Salle University is a education organization based out in Manila, Philippines. It is known for research contribution in the topics: Population & Computer science. The organization has 2951 authors who have published 4374 publications receiving 49567 citations. The organization is also known as: Pamantasang De La Salle.

The Dark Side of Electoralism: Opinion Polls and Voting in the 2016 Philippine Presidential Election

Abstract: Despite the limits of elections as a mechanism to secure accountability and ensure substantive representation, the 2016 elections drew the highest turnout across elections held since the political transition in 1986, a clear indication of electoralism. The high turnout may be a result of a relatively tightly contested race, with each of the main contenders appealing to constituencies that they symbolically represent. Nonetheless, the 2016 Presidential elections remained personality-oriented, media driven and political clan dominated. The eventual winner, Rodrigo Roa Duterte, garnered the presidency given a combination of factors: the clarity of his campaign message – focused on curbing a single problem (criminality, in general, and the illegal drug trade, in particular) that he elevated as the most serious concern that the next president should address; significant support from a geographic area (Mindanao) and associated ethno-linguistic groups (i.e., Bisaya); and, serious questions of character and competence raised against his opponents (i.e. Binay, Poe and Roxas).

Diseased Surface Assessment of Maize Cercospora Leaf Spot Using Hybrid Gaussian Quantum-Behaved Particle Swarm and Recurrent Neural Network

Abstract: Cercospora zeae-maydis (CERCZM) is a destructive fungus that is strengthened by hot tropical weather and high humidity such as in the Philippines, resulting to recurrent adverse impacts of having maize Cercospora leas spot disease, and quantification of leaf damage is essential for plant phenotyping in understanding pathogen interaction. Visual detection of this disease often results in subjective classification. To address this challenge, the integration of computer vision and computational intelligence is employed in detecting healthy and damaged corn leaves and predicting the surface damage percentage due to maize Cercospora leaf spot. Dataset with 583 images contains matured healthy and diseased corn leaves that were grown outdoor and individually captured by a digital camera. Graph-cut segmentation through lazysnapping segmented the vegetation pixels and CIELab thresholding segmented healthy and diseased regions. Spectro-textural-morphological leaf signatures were extracted and selected using combined neighborhood component analysis and ReliefF resulting in R, H, *a, Cb, Cr, entropy, and whole leaf area. MobileNetV2 exhibited the best performance in classifying maize leaf health status. Gaussian quantum-behaved particle swarm optimized recurrent neural network (GQPSO-RNN) bested other feature-based machine learning and deep transfer image networks in predicting maize Cercospora leaf spot surface damage percentage with R2 of 0.949, RMSE of 6.290, and inference time of 3 seconds. This developed seamless MobileNetV2-GQPSO-RNN model provides reliable disease detection and quantitative assessment on the maize leaf surface in on-field phenotyping.

Energy cost optimization in irrigation system of smart farm by using genetic algorithm

Abstract: The challenge of increasing crop yield to suffice the food demand is being addressed by using modern technology in agriculture or by implementing a Smart Farm Irrigation plays a very important role in Smart Farm but also requires optimized operating conditions to maintain sufficient water supply while minimizing energy consumption In this study, operation of water pumps in the irrigation system limited by the amount of available energy from the solar power station, are simulated and optimized to minimize the energy cost Genetic algorithm is performed several times under different optimization parameters to obtain the most optimal solutions Operating conditions are handled as penalty functions for the objective functions The performance of the algorithm in solving multi-objective optimization problem is evaluated and presented

What Kinds of Value Motives Guide People in Their Moral Attitudes? The Role of Personal and Prescriptive Values at the Culture Level and Individual Level

Abstract: Opinions about moralized issues are arguably one of the most difficult issues in interpersonal dialogues given that they can result in intolerance and prejudicial behavior toward those with divergent moral beliefs. Recent research has shown that moral attitudes vary not only depending on the individual’s characteristics but also as a function of culture. Individuals from individualistic-oriented cultures exhibit more lenient judgments toward moralized issues than those from collectivistic-oriented cultures. What is unclear to date is what kinds of cultural value motives underlie these attitudes—Are they driven only by intrinsic value motives (personal values) or also by extrinsic value motives (prescriptive values in the form of societal expectations about what should be valued)? The cultural press to conform is arguably stronger if moral attitudes are predicted by the latter. Participants from eight countries (N = 1,456) responded to a questionnaire containing a modified version of the Schwartz Value Sur...