Scopus ID: 85079283298
Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering, (2019), pp. 51-56
Conference Paper | Published: November 9, 2019
Abstract
In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.
Scopus ID: 85084763596
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-4
Conference Paper | Published: November 1, 2019
Abstract
Cost estimation of building demolition, like any engineering project, requires ample amount of time and experience to accomplish since it involves calculations of complex relationships between its influencing factors. Since artificial neural networks (ANNs) are known to be effective in the cost-forecasting domain with complex parameters involve, the study aims to develop an ANN that can predict building demolition cost in Quezon City. One-hundred demolition projects from the Department of Building Official in Quezon City were gathered, evaluated and divided randomly into two sets: 90% for training, validation and internal testing and 10% for external application. Nine demolition cost-influencing factors were identified, namely: building condition, materials and classification, number of floors, total floor area, site accessibility, location, demolition methods used and debris removal options. The training was applied with feedforward backpropagation algorithm. The resulting architecture for the selected ANN model consists of 12 hidden nodes. The model tested and was successful in predicting demolition cost in Quezon City with an average accuracy rating of 90.21%.
Scopus ID: 85084759812
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Conference Paper | Published: November 1, 2019
Abstract
Pulsars enabled astronomers to study neutron stars and verify general relativity under intense gravitational field conditions. However, finding pulsars is not as easy as it seems because most of them have weak pulses that get drowned in the background noise and hence do not get detected. This paper presents a novel way of classifying radio emission patterns collected from a radio telescope whether it is from a pulsar or not through machine learning and genetic algorithm. The dataset was acquired from the High Time Resolution Universe (HTRU) survey two which contains eight numerical features and one target variable describing the pulse profile. Synthetic Minority Oversampling Technique (SMOTE) was applied to the dataset to fix the imbalance between classes. A genetic algorithm library was used to automatically select the best feature preprocessing method, feature selection/reduction technique, machine learning model inside the scikit-learn library, and hyperparameter settings. The genetic algorithm suggested using a single stack and multiple stack classifiers for different sets of features. The optimum level of hyperparameters was also given with the help of the same algorithm. The selected pipelines consistently reported a score of more than 99% in all the evaluation metrics used.
Scopus ID: 85084749145
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019)
Conference Paper | Published: November 1, 2019
Abstract
The Internet of Things (IoT) and Blockchain are increasingly growing focus on research. Blockchain is like the Internet when we first knew of it. This new technology could revolutionize how we do everything. Seriously, everything. Think how the Internet changed our daily lives today. And just like that, we don't really have to understand the technology behind it and be knowledge of its importance. But what makes this innovation revolution a bigger deal than Bitcoin and possibly even the Internet itself, are the exponential opportunities the concept provides. However, the potential to integrated Blockchain to Internet of Things (IoT) has constraint due to the required high computational power. IoT comprises of numerous platforms which have limited performance. Usually, these platforms cannot handle intensive procedures and often has scalability issues. In this paper, we give an overview of consensus algorithms and the necessity of this method to blockchain technology. Moreover, we focused on three of the utmost common consensus algorithms used in blockchain technology and explore their potential adaptation in an IoT framework with respect to its requirements.
Scopus ID: 85084746978
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Conference Paper | Published: November 1, 2019
Abstract
Shrimp farming is a highly profitable business in the aquaculture industry. The farming profitability can be achieved by the implementation of better management practices in conjunction with optimal shrimp feed management and growth monitoring. Manual measurement for shrimp growth on a large population is a tedious and difficult task. Underfeeding results to lower growth rate, and overfeeding results to environmental pollution. Automated, continuous, and non-invasive methods therefore such as computer vision are being increasingly employed. However, existing researches of vision-based measurement of growth parameters are not yet incorporated to shrimp feed management. This paper presented an Artificial Neural Network-based decision support system of classifying feed type whether starter, grower or finisher using area, length and weight derived from image processing techniques. The neural network was trained using scaled conjugate gradient back propagation. The decision support system exhibited promising results in feed type classification.
Scopus ID: 85084754766
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Diana Rose T. Rivera, Alvin B. Culaba, ... Wei-Hsin Chen
Conference Paper | Published: November 1, 2019
Abstract
Torrefaction is a thermochemical process for upgrading raw biomass into a more energy-dense fuel. However, the production of torrefied microalgae biochar may include environmental impact as it consumes raw materials and energy. In this study, a life cycle assessment study was conducted to understand and assess the corresponding global warming potential associated with the production of torrefied microalgae biomass, using a cradle-to-gate scope. Using different scale models of torrefied microalgae biomass production, this study identifies the contribution of the torrefaction process to the overall environmental impact. Using the experimental data, the study was able to analyze the impact of the torrefaction process on biomass thermal degradation using the torrefaction severity factor. The inclusion of the torrefaction severity factor shows that there was a strong relationship on the resulted global warming potential. It revealed that the influence of the torrefaction temperature was higher as compared to the torrefaction duration.Result of the study shows that the torrefaction process had a minimal contribution of 1-20% to the resulted overall environmental impacts. The overall impact shows that up-scaling production can result in a negative carbon dioxide emission.
Scopus ID: 85084756999
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Kyle Jericho Grecia, Antoine Albert Luce, ... Ivan Henderson Gue
Conference Paper | Published: November 1, 2019
Abstract
Climate change has drastically affected our production patterns, negatively distressing the yearly agricultural produce. A core process in the industry is the drying of biomass. Drying increases the value and extends the shelf life of the agricultural products. However, modern drying technologies are still reliant on fossil fuels. Solar-based drying technologies are needed to counteract the fossil fuel dependence. Other than reduction of fuel consumption, solar dryers are easily adaptable to rural communities with heavy reliance on the drying process. Alternative designs have been proposed to improve the performance of the solar dryers, notably integrating thermal energy storage (TES) systems. A limiting factor, however, is that the performance is constrained to the heating capacity of the TES. Previous study has examined the integration of TES with air recirculation, indicating an improved performance. Further evaluation of the dryer with another biomass is needed to illustrate the adaptability of the hybrid feature. This study, therefore, evaluates the performance of solar drying with TES and air recirculation for mango drying. Comparisons were made with other design combinations as a benchmark. Results reveal that the hybrid solar dryer can reduce the drying time from 7.17 hours to 5.32 hours.
Scopus ID: 85084754357
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Conference Paper | Published: November 1, 2019
Abstract
Primary and backup relays pairs are protection schemes for power systems which are set in conjunction to one another to ensure that the protection system operates by limiting an abnormality within its zone of protection. Breakpoints are the starting points of all assumptions and calculations done in protection systems. Previous methods of determining breakpoints favor linear graph theory and expert theory system rather than machine learning. In this study, an adaptive neuro-fuzzy inference (ANFIS) approach is used to determine the breakpoint set for directional overcurrent relays of a given 3-bus network. The two most influential input variables from 15 inputs affecting breakpoint set are determined by Exhaustive Search. The reduced inputs are then used to design the Sugeno type ANFIS. Experimental results show promising results in terms of Root Mean Square Error.
Scopus ID: 85084765618
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-4
Conference Paper | Published: November 1, 2019
Abstract
The use of reinforced concrete as a structural material originates from two different components; Reinforcing steel, known as a highly-tensile material, combined with concrete, a highly-compressive material, act together to resist different types of loadings. However, this composite action is dependent upon the transfer of loads between the steel and concrete known as the bond and is concluded to be on a form of continuous stress along the juncture of steel and concrete. This study focused on producing a bond stress model as a function of concrete compressive strength, embedment length and bar diameter, using single pullout test, with the help of multiple regression analysis by using Microsoft Excel. For the compressive strength, 40 MPa, 50MPa and 60MPa were used, with 50mm, 75mm and 100mm embedment length and bar diameters of 12mm, 16mm and 20mm, producing a total of 27 specimens. After subjecting each specimen to pull out test, data were tabulated and analyzed using Multiple Regression producing a model with 97.81% correlation coefficient. The model was then subjected to parametric testing and it was concluded that increasing the compressive strength of concrete would result to an increase in bond stress; however, increasing the embedment length and bar diameter would result to a decrease in bond stress.
Scopus ID: 85084747218
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Conference Paper | Published: November 1, 2019
Abstract
The electroencephalogram (EEG) signals being a recording of the electrical activity of the brain provides valuable information in the analysis of its function and disorder. Epilepsy is a brain disorder characterized by uncontrolled excessive activity. Repeated abnormal disturbance or seizure causes epilepsy. Hence, EEG signals act as a diagnostic tool for epilepsy. An approach based on tree-based pipeline optimization tool (TPOT) is presented for classification of EEG signals as either seizure or normal activity in the brain. A binarized dataset with sampled signal levels and the corresponding class is subjected to a genetic approach for acquiring an optimized predictive model. In TPOT, the tedious process involved in machine learning being repeatedly performed until arriving at the best solution is automated using genetic algorithm, i.e., evaluate-select-crossover-mutate is repeated to tune the pipeline. In the settings used in this paper, there are 90 pipeline configurations for evaluation for which around 450 models are fitted and evaluated against the training data in one grid search. The best pipeline is the one with the highest cross-validation score in the run at 95.94%. The test accuracy is at 95.27% which is just a little lower than the cross-validation score. The predictive model consists of pre-processing steps Maximum Absolute Scaler and Function Transformer which is utilized by a Gaussian Naïve Bayes classifier. The system is trained and tested for epileptic seizure detection using raw EEG signals. The optimized features and predictor obtained via TPOT resulted to a high-performance accuracy for epileptic seizure detection.