Elmer Dadios

De La Salle University, Manufacturing Engineering and Management, Faculty Member

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Elmer was the chair of the De La Salle University Department of Manufacturing Engineering and Management (2019-2021). He is a Professorial Chairholder of Thomas J. Lee Chair in Manufacturing Engineering Management and Victor T. Lu Chair in Production Management. He is a consultant on software and hardware development in the area of robotics and intelligent systems applications. He is the founder and Chairman of the Board of Neuronmek Inc., and Intelligent Systems Innovation Corporation. A multi-awarded educator and scholar, Elmer has earned over 20 recognitions and distinctions from various international and national scientific award-giving bodies and professional organizations. Among the awards, he garnered include The 2019 top 100 scientists listed in Asian Scientist Magazine

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Andrej Dujella

University of Zagreb

Hemin Koyi

Uppsala University

Jana Javornik

University of East London

Graham Martin

University of Leicester

Gwen Robbins Schug

University of North Carolina at Greensboro

Gabriel Gutierrez-Alonso

University of Salamanca

John Sutton

Macquarie University

Eros Carvalho

Universidade Federal do Rio Grande do Sul

Kevin Arbuckle

Swansea University

Jesper Hoffmeyer

University of Copenhagen

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Papers by Elmer Dadios

Observation-Centric with Appearance Metric for Computer Vision-Based Vehicle Counting

Journal of Advances in Information Technology, Dec 31, 2022

Tracking objects in video sequences is a key step for applications involving computer vision like... more Tracking objects in video sequences is a key step for applications involving computer vision like traffic monitoring and security systems. Occlusion is a frequent problem in object tracking which can result in the tracker losing track of the occluded object or misidentifying it with the occluding object. Moreover, the limited memory and computing power of traffic analysis systems presents a scaling problem, especially in object tracking applications. This paper aims to improve object tracking performance by minimizing data association errors in low frame rate tracking applications. Reducing frame rates alleviates memory and computing power limitations, and utilizing a tracker that can handle occlusion can address occlusion-related issues in object tracking. The proposed tracking method, Mask-OCSORT, uses the observation-tracking method with cosine similarity, intersection-over-union, and velocity consistency metrics for the association problem. The paper analyzes the effect of using bounding box and mask predictions of deep learning models in generating tracks. This study uses evaluation metrics like HOTA, MOTA, and IDF1 to assess the proposed tracking method, and employs evaluation metrics such as precision, recall, and F-score to assess the counting based on generated IDs from the tracking method. The study applied the Mask-OCSORT tracking for vehicle counting application and achieved an F-score of 87.18% at 5 frames per second (fps), and 75% at 1 fps.

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Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correc... more This study proposes the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to correct the latitude and longitude of Global Positioning System (GPS) used in locating towed vehicle system for underground imaging. The input used was the collected data from a developed Real-time Kinematic Global Positioning System sensor integrated with Inertial Measurement Unit. Different ANFIS models were developed and evaluated. For latitude correction, ANFIS model with hybrid optimization trained at 300 epochs was chosen, whereas for longitude correction, ANFIS model with hybrid optimization trained at 100 epochs was selected. Both models achieved the lowest Mean Squared Error (MSE), the highest Coefficient of Determination (R 2), and lowest Mean Absolute Error (MAE). Moreover, selected best ANFIS models were compared to Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) models, but the results showed that the ANFIS models have superior performances. The selected ANFIS models were verified by testing on the collected actual dataset and the visualized map demonstrated that the corrected GPS latitude and longitude have significantly reduced error, indicating that the fuzzy system with neural network capabilities is a cost-effective and convenient method for error reduction in vehicle localization making it applicable to be integrated for capacitive resistivity underground imaging systems.

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Coverage Path Planning on Multi-Depot, Fuel Constraint UAV Missions for Smart Farm Monitoring

2018 IEEE Region Ten Symposium (Tensymp)

UAVs used in monitoring crop fields are flying higher than 6 meters and capture telemetric data t... more UAVs used in monitoring crop fields are flying higher than 6 meters and capture telemetric data that provides information on the general condition of the plants in the field. But, in order to obtain specific information on the actual conditions of the plants based on individual morphological aspects, lower altitude monitoring, at most 3 meters, is required. Low-altitude missions cover less than high-altitude and requires UAVs to fly longer to cover more area. In this paper, an approach for multi-depot, fuel constrained coverage path planning is presented. First, target coverage is segmented into smaller regions based on the number of available charging depots. Then, each region is further decomposed into multitude of cells with area equivalent to the camera FOV when UAV is flying at 3 meters above the field. All possible routes are generated and fed into evolutionary optimization in aim to identify the optimal path considering the fuel constraints and availability of recharging depots. The optimization yields a significant improvement in obtaining the route that will provide the minimum distance that the UAV should traverse to cover the entire Area-of-Interest. This approach proved to be useful for crop field monitoring using UAVs.

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Agrivita : Journal of Agricultural Science, Feb 1, 2021

Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. ... more Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.

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SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence

Journal of Advanced Computational Intelligence and Intelligent Informatics

Land surveying has been one of the core operations in performing underground imaging. It is known... more Land surveying has been one of the core operations in performing underground imaging. It is known that dynamic and continuous resistivity readings were employed through this technique using the array of capacitive electrodes being towed with a light vehicle. However, the main challenge in doing subsurface surveying is the change in speed of the system when there are inevitable obstacles and sloping road surfaces. To address it, this study will develop prediction models using different computational intelligence such as multigene symbolic regression genetic programming (MSRGP), regression-based decision tree (RTree), and feed forward neural network (FFNN) that will result in a smart speed controller system that can maintain the constant speed of the towed subterranean system. The best performing prediction model will be considered as the SpeedX. The expected output is a correction factor that will signal the speed controller in slow down or inclined plane road environment to maintain...

MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter

Journal of Advanced Computational Intelligence and Intelligent Informatics

Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to v... more Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are fun...

Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)

Fault detection and monitoring system in photovoltaic (PV) energy management system is important ... more Fault detection and monitoring system in photovoltaic (PV) energy management system is important in achieving its optimal performance. An effective diagnostic system involves correct analysis of electrical parameters of a PV array on a given weather condition. In the study, mean-shift clustering was applied for pre-classification and anomaly detection of time-series data of electrical parameters from grid-tied inverter, and solar-irradiance. Classification and anomaly detection applied is based in ensemble learning, where its base learners are based from multilayer perceptron. A stacking ensemble is used in classification of energy production profile while bagging ensemble is used detecting anomalous trend in time-series data. A stacking ensemble got a highest accuracy value of 94% compared to single classifiers which have accuracy value of 85.25%, 84.14%, and 63.4%, respectively. The bagging ensemble autoencoders have the lowest mean squared error during model reconstruction compared to single autoencoder. It has a fair performance in classifying anomaly points from normal datapoints, having an AUC value of 0.795 and F1-score of 0.71, given that the hyperparameter is 0.5. Overall, ensemble learners improve the performance in classification and detection tasks.

Feature Driven Development of a Smartphone Based Vision-Aware mHealth Framework

Journal of Engineering and Applied Sciences, 2015

Mobile health (mHealth) applications attempts to capitalize on the ubiquity and exponential growt... more Mobile health (mHealth) applications attempts to capitalize on the ubiquity and exponential growth of mobile technologies for the benefit of public health, leading to a growing research interest in devising frameworks for addressing specific or general mHealth challenges. In this context, the primary goal of this paper is to present a novel smartphone-based development framework for prototyping vision-aware native mHealth applications developed using Feature Driven Development (FDD) methodology. Then, we describe a prototype mHealth educational application, ’Dibdib** Advocacy App’, for breast cancer awareness, utilizing the proposed vision-aware mHealth framework in Android platform. The results illustrate that FDD is a viable option in mHealth application development.

Neuro-Fuzzy based Safe Landing Control System for UAVs

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021

The number of aerial drone users continue to increase due to its availability, usage, and depreci... more The number of aerial drone users continue to increase due to its availability, usage, and depreciation. The low cost of drones results in low-quality components that are prone to damage. One of the most common problems of drones is the landing system, where most drones crash due to uncontrolled maneuvering of the drone. In this study, Adaptive Neuro-Fuzzy inference Systems (ANFIS) using MATLAB was developed to perform a safe landing system on low-cost drones where the Gaussian Bell Membership function was used due to a low training error of 0.0015693.

Automated Tungsten Inert Gas Welding Process using Fuzzy Logic Operation

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021

Industrial welding processes involve significant human inputs that consequently include a deviati... more Industrial welding processes involve significant human inputs that consequently include a deviation in the accuracy of the system and risk of human errors. Automation for welding processes lessens the possibility of obtaining human welding errors and improves workplace safety due to less human interaction. The study utilizes fuzzy logic operation to control the welding process by setting categorical inputs and outputs based on the function applied to the fuzzy logic. The input parameters and output values will adjust the welding device accordingly for the duration of the process.

Python Based Defect Classification of Theobroma Cacao Bean using Fine-Tuned Visual Geometry Group16

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021

The study aims to classify cacao bean defects based on the captured image using vgg16. Seven clas... more The study aims to classify cacao bean defects based on the captured image using vgg16. Seven classes of cacao beans were gathered including broken, cluster, flat, germinated, good, insect and moldy. One hundred images per class were captured using an enclosed capturing box with c920 Logitech camera inside and LED as light source. Image augmentation was done to increase dataset. Transfer learning technique was implemented by utilizing the pre-trained vgg16 model architecture adding 10% Dropout after FC2 layer and using default weights of several layers through fine-tuning. Three methods of fine-tuning were conducted by freezing the convolutional blocks. Performance of the trained model using several optimizers (such as Adam, RMSprop and SGD) and loss functions (such as categorical crossentropy and mean squared error) were analysed. The effect of the no. of epochs as well as different learning rates during training was considered and checked. The metrics used in choosing the model were based on the confusion matrix. The chosen model is using vgg16 architecture with 10% dropout + adam optimizer + 0.0001 learning rate + categorical crossentropy loss function run in 20 epochs. It has 95.33% average accuracy. The model was embedded in a processor for actual testing. It has an accuracy of 97.29% based on the actual testing on prototype with 37 testing samples.

Trends in Nanotechnology in the Philippines and Laos Agricultural Industry

2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020

The nanotechnology application farming has been developed a leading IoT -agricultural controlling... more The nanotechnology application farming has been developed a leading IoT -agricultural controlling process, especially agricultural industry farming. However, Nanotechnology is no widely employed in the Philippines and Laos agricultural industry because nanotechnology is still not implemented broadly. In this study of nanotechnology in the Philippines and Laos agricultural industry is explored and analyzed, on the agricultural system to be quality and safety food, increase technical capacity inputs of agricultural less soil by using nutrients management. Provide the application of nanotechnology to analysis, the purpose of agriculture modern innovation in the growing vegetables industry sustainable, healthy life, aim nanotechnology agricultures to take off the amount of fertilization, chemical and increased yield via nutrient solution. Analysis the trend nanotechnology is improving the agricultural farming controlling the growth state lighting capacity of plants to absorb nutrients among the yield of specific published papers from 2011 to 2020 by using the mathematic linear equation y = 19.758x – 39707, this signification papers must be increase nanotechnology in agriculture industries 406.58% from 2010–2050 in term of higher R2 = 0.9974 through Microsoft Excel. Such as the predictable possibility to approach the controlling system, nanotechnology products and increase the crop of agricultural planting to sustainable in the Philippines and Laos area of nanotechnology in the improvement.

Mobile Platform Implementation of Lightweight Neural Network Model for Plant Disease Detection and Recognition

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), 2018

Due to the increasing world population, agriculture sectors from around the globe are challenged ... more Due to the increasing world population, agriculture sectors from around the globe are challenged to increase their yield per year. However, harvests suffer from defects due to plant diseases. The current methods for mitigate spreading plant diseases are entirely dependent on the detection and recognition of such. Detection and recognition systems for plant diseases often require huge database for reference and/or computationally expensive systems. In this paper, we present a computationally light neural network model for detection and recognition of plant diseases and implement it to a mobile platform. Here, a two-step training process is used: pre-training on ImageNet data set of wide variety of objects and retrained on data set of specific plant diseases. The model achieved a test accuracy of 89.0 %.

Determination of Relaxed and Hypoventilation Body Conditions using Pulse Oximetry and Temperature Measurements

2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020

This paper focuses on the study of relaxed and hypoventilation body conditions using pulse oximet... more This paper focuses on the study of relaxed and hypoventilation body conditions using pulse oximetry and temperature measurements. An Arduino-based portable pulse oximeter and temperature measurement device is developed to monitor these biomedical signals. Pulse oximetry is a non-invasive method for accurately estimating oxygen saturation (Sa02) level by reading the peripheral oxygen saturation (Sp02). A thermistor is used to measure body temperature. The Arduino microcontroller is used for signal extraction and processing. The measurements were acquired using two conditions: relaxed state and hypoventilation state. The relaxed state serves as the control group while the hypoventilation state is used to simulate the condition of hypoxemia which is a state of abnormally low level of oxygen.

An Artificial Neural Network (ANN) Model for the Cell Density Measurement of Spirulina (A. platensis)

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), 2018

This paper presents a dynamic model for the cell density measurement of Spirulina platensis by us... more This paper presents a dynamic model for the cell density measurement of Spirulina platensis by using backpropagation-based Artificial Neural Network (ANN). A vision system, composed of a camera and a photodetector, is developed to measure the color features and illuminance of the algal culture, which will then serve as the training data. The input parameters are the RGB values and the lux value from the vision system. The network has three layers with structure 4 – X – 1, where the node size X of the hidden layer is varied experimentally. After several trials of training, the model with 24 nodes showed the lowest meansquared error of 0.0047813 and fastest learning time of 2 seconds. This model was validated by performing F-test on the actual dataset and the output from the model. Results show that there is no significant statistical difference between the two, and that the output from the ANN is valid.

Hybrid Tree-Fuzzy Logic for Aquaponic Lettuce Growth Stage Classification Based on Canopy Texture Descriptors

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020

Lettuce is one of the most popular crops for urban farming because it is easy to grow and it has ... more Lettuce is one of the most popular crops for urban farming because it is easy to grow and it has high nutritional value. Moreover, it is adaptable and can be combined with other food options, or it can be eaten alone without too much preparation. Predicting lettuce growth can be crucial to find the optimum maturity and harvest time. This paper proposed to use a model of a hybrid tree-fuzzy logic approach, the classification tree was used to select the most significant features from the texture features then the fuzzy inference system was utilized in predicting the lettuce growth stage classification. The hybrid system produced accurate results with low percentage error and correct classifications. Based on these results, the most accurate prediction can be observed in the head development growth stage; the harvest growth stage has a slight variance, while the vegetative stage has the most variance. Overall, the trained hybrid system is reliable in predicting and identifying lettuce growth stage classification.

Implementation of Multilayer Perceptron Neural Network on Quality Assessment of Tomato Puree in Aerobic Storage using Electronic Nose

2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2019

The adulteration of food increases the number of bacteria being develop in it that is primarily a... more The adulteration of food increases the number of bacteria being develop in it that is primarily affected from oxygen exposure and varying temperature, not suitable for its storage. In such case, food spoilage happens and leads to food poisoning. Tomato-based dishes stored in aerobic environment significantly varies its shelf-life. However, misclassification due to subjective human assumptions is the major problem on assessing the quality of food. To address this problem, a proposed solution is the development of an intelligent electronic nose (eNose) system that will discriminate the condition of tomato puree using artificial neural network (ANN) based only on ammonia and methane concentrations, and pH level. This system is composed of five sections: the development of electronic nose using Gizduino microcontroller and Mĭngan Qĭ lai (MQ) gas sensors, olfactory data acquisition, generation of smellprint, design of ANN, and the implementation of ANN for classification of tomato puree condition. This study substantially presents analysis on computational parameters of ANN. The collection data rate was set to 2 Hz for tomato puree-emitted gas samples with varying shelf life considering outdoor aerobic storage. Multilayer perceptron neural network was implemented using feedforward backpropagation algorithm. The number of hidden layers and artificial neurons were analyzed based on performance of the system computational parameters, namely, cross-entropy (CE), learning time and regression (R) coefficient. The system classifies the tomato puree sample as not spoiled, partially spoiled, and spoiled. The smellprint of each food condition was generated and the tomato puree-spoilage determinant parameters were characterized. Through 3-layer perceptron ANN with 120 and 50 artificial neurons on the first and second hidden layers respectively, an accuracy of 93.33% was yielded for tomato puree quality deterioration classification. The developed mechanism is a potential application in domotics.

Estimation of Triangular Greenness Index for Unknown PeakWavelength Sensitivity of CMOS-acquired Crop Images

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019

Notable works on the use of the triangular greenness index (TGI) to estimate vegetation fraction ... more Notable works on the use of the triangular greenness index (TGI) to estimate vegetation fraction of croplands or chlorophyll content of crops, proved that relevant metrics on crop health monitoring can be derived from images at the visible spectrum. However, the performance of the TGI-based metric in crop health monitoring greatly depends on knowledge of wavelength sensitivities of the CMOS sensors used to obtain the RGB images of the crop. This becomes a problem when generic digital cameras are used and the specifications of the CMOS sensors are not available. The proposed method in this study compensates for the lack of information on the peak wavelength sensitivities of generic CMOS sensors by performing a parametric sweep on the proportions of 670nm-, 550nm-, and 480nm-peak wavelengths to derive a TGI equation normalized by the green signal. This allows the use of any available digital cameras even without prior knowledge of the wavelength sensitivity at the visible spectrum of the installed CMOS sensors.

Genetic Algorithm-based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020

Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has transl... more Dehazing through Dark Channel Prior (DCP), originally developed for land-based images, has translated its potential for improving the quality of underwater images. However, the DCP default parameters, which are just adapted from land-based applications, may not be applicable for underwater images. Such constraint limits the capability of this restoration algorithm to improve the quality of an underwater image; the values of these parameters must be searched for each underwater image. A proposed approach on the parameter values assignment problem is to conduct an optimized search based on Genetic Algorithm. The presentation of this proposed approach focuses on the Genetic Algorithm processes: chromosome encoding, fitness function development, and selection, mutation, and crossover, to perform an effective search of the best solution out of a pool of possible solutions. Qualitative and quantitative evaluations show that utilization of optimized combination of DCP parameters, achieves images of higher quality in comparison to the utilization of established default DCP parameters.

Fuzzy Classification Approach on Quality Deterioration Assessment of Tomato Puree in Aerobic Storage using Electronic Nose

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019

Food safety heavily deals with food spoilage that may yield food poisoning. Tomato-based dishes h... more Food safety heavily deals with food spoilage that may yield food poisoning. Tomato-based dishes have different shelf-life leading to unique acceptable standards for a person in determining the food condition, and sometimes misclassification due to confusion. To address this problem, a proposed solution is the development of an intelligent electronic nose (eNose) system that will discriminate the condition of tomato puree using fuzzy logic. This system is composed of two sections: the development of electronic nose using Gizduino microcontroller and Mĭngăn Qǐ lai (MQ) gas sensors, and the implementation of fuzzy logic system for classification of food condition. Fuzzy logic resembles human reasoning that yields definite output based on ambiguous input. The collection data rate was set to 2 Hz for tomato puree-emitted gas samples with varying shelf life considering outdoor aerobic storage. Combined Min-Max method and Mamdani inference system was used for the inference engine, and centroid method for defuzzification. The system classifies the tomato puree sample as not spoiled, partially spoiled, and spoiled. The smellprint of each food condition was generated and the tomato puree-spoilage determinant parameters were characterized. Through embedded fuzzy logic, an accuracy of 90.00 % was yielded for tomato puree quality deterioration classification. The developed mechanism is a potential application in domotics.

Observation-Centric with Appearance Metric for Computer Vision-Based Vehicle Counting

Journal of Advances in Information Technology, Dec 31, 2022

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Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive Resistivity Underground Imaging with Towed Vehicle System

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Coverage Path Planning on Multi-Depot, Fuel Constraint UAV Missions for Smart Farm Monitoring

2018 IEEE Region Ten Symposium (Tensymp)

Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Agrivita : Journal of Agricultural Science, Feb 1, 2021

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SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence

Journal of Advanced Computational Intelligence and Intelligent Informatics

MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter

Journal of Advanced Computational Intelligence and Intelligent Informatics

Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System

2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)

Feature Driven Development of a Smartphone Based Vision-Aware mHealth Framework

Journal of Engineering and Applied Sciences, 2015

Neuro-Fuzzy based Safe Landing Control System for UAVs

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021

Automated Tungsten Inert Gas Welding Process using Fuzzy Logic Operation

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021

Python Based Defect Classification of Theobroma Cacao Bean using Fine-Tuned Visual Geometry Group16

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021

Trends in Nanotechnology in the Philippines and Laos Agricultural Industry

2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020

Mobile Platform Implementation of Lightweight Neural Network Model for Plant Disease Detection and Recognition

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), 2018

Determination of Relaxed and Hypoventilation Body Conditions using Pulse Oximetry and Temperature Measurements

2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020

An Artificial Neural Network (ANN) Model for the Cell Density Measurement of Spirulina (A. platensis)

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), 2018

Hybrid Tree-Fuzzy Logic for Aquaponic Lettuce Growth Stage Classification Based on Canopy Texture Descriptors

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020

Implementation of Multilayer Perceptron Neural Network on Quality Assessment of Tomato Puree in Aerobic Storage using Electronic Nose

2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2019

Estimation of Triangular Greenness Index for Unknown PeakWavelength Sensitivity of CMOS-acquired Crop Images

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019

Genetic Algorithm-based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020

Fuzzy Classification Approach on Quality Deterioration Assessment of Tomato Puree in Aerobic Storage using Electronic Nose

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019

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