Recent advancements in computer vision, notably in 6D pose estimation with methods like DeepLabv3 and Dense Fusion, show promising results. However, a substantial research gap exists in addressing the limitations of these methods in occluded scenarios, which in need further research to enhance their effectiveness and adaptability in situations involving hidden or partially obscured objects. This study introduces a novel approach employing 6D object pose estimation and pose correction by integrating DeepLabV3 and modified dense fusion. The study exhibits enhanced accuracy in pose estimation and correction for occluded 3D object model x-ray source. Evaluation metrics, including intersection-over-union and mean average, demonstrate high accuracy percentages for detecting the body (98.91%), handle (96.57%), and aperture (89.54%). The mean IoU for each part of the 3D model portable X-ray source ranges from 65.69% to 76.42%. Pose estimation accuracy, assessed through the Average Distance Difference (ADD) metric, indicates superior performance for static pose estimation closer to the camera. Dynamic pose estimation exhibits higher average ADD metrics in scenes with total occlusion. The robustness metric reveals lower lost tracking counts in scenes without occlusion, emphasizing the algorithm's challenges in fully occluded scenarios.

The state of object detection has quickly progressed due to the rapid development of hardware and deep learning models for the said task. The continuous development of the latter allowed its application in real-time. That is, from two-stage to one-stage detectors, which greatly reduced the processing time; and from anchor-based to anchor-free detectors, which allowed a more efficient and flexible model implementation. The purpose of this study is to explore the various YOLO versions available in Ultralytics to detect large office objects for obstacle avoidance. More specifically, this study observed how the versions, 3u, 5mu, 8 and 6m, from which the first three are anchor-free, perform over the dataset. Based from the results, it was found that there were no significant differences between the detectors, except for the anchor-based v6m, which has the worst scores. The four models, v8n, v8s, v8m, and v5mu, were, however, chosen as the candidate models due to their processing speed, ranging from 66 to 75 FPS on the NVIDIA GeForce RTX 3060 GPU. The said models were also subjected to an optimization process in terms of the confidence and IoU threshold values to find the pair that could provide the best metric scores. Results have provided candidate models for the study’s detection task for obstacle avoidance in real-time.

Printed Circuit Boards (PCBs) are vital in modern manufacturing, acting as essential electronic device components and contributing to various technologies' seamless functioning. Due to their significant impact on device performance, any notable flaws in PCBs can lead to operational issues and potential safety hazards. This emphasizes the need for effective defect detection and quality control measures in this crucial electronic component. This research aims to tackle the challenges associated with identifying defects in PCBs by employing deep learning methods. Defects are categorized into functional and cosmetic types, with sub-datasets covering common issues such as missing holes, mouse bites, open circuits, short circuits, spur issues, and spurious copper. Recognizing the variations in PCB designs and the limited availability of defect samples, the project utilizes the You Only Look Once (YOLO) model, which is widely used in object detection. The objective is to train the model to accurately classify defects within the PCB dataset, integrating TensorRT to improve inference program time. The proposed YOLOv8s-TensorRT model had a mean average precision 50 (mAP50) score of 98.5%, which is only 0.1 % lower than the YOLOv8s. Despite that, the integration of TensorRT reduced inference time by 0.9ms and lowered the model size by about 39.3%, both of which are significant in real-time object detection.

In this study, we present a novel Federated Byzantine Agreement (FBA)-inspired consensus framework for swarm robotics, integrating blockchain technology to enhance security and efficiency. The proposed system leverages decentralized, scalable consensus without requiring global agreement, ensuring robustness against Byzantine attacks. We conducted extensive simulations using the ARGoS simulator and e-puck robots to validate the framework. Results indicate that the FBA approach significantly outperforms frameworks without blockchain and base blockchain configurations, demonstrating superior resilience and reliability in consensus maintenance. This framework is particularly applicable to real-world pi-puck and e-puck robots, offering a promising solution for secure and efficient swarm operations. The significant implications of this study include enhanced robustness of swarm robotics against cyber-attacks and improved consensus reliability, which are critical for practical deployments in complex and potentially adversarial environments.

Aquaponics systems allow simultaneous growth between vegetables and fish with the use of aquatic products. This process is due to the presence of beneficial microbial communities such as nitrifying bacteria. Numerous water quality problems can be addressed by improving the conversion efficiency of biological filtration systems. Maintaining parameters including increased ammonia and nitrite concentration, organic matter accumulation, and decreased levels of dissolved oxygen are imperative to maximize system productivity. The pilot-scale model of the aquaponics system features two independent systems: the control (with biofilter), and the experimental (without biofilter). The media in the biofilter is as follows: an aeration system (for oxygen supply), and a centralized sensory chamber (to facilitate automatic monitoring of pH, temperature, dissolved oxygen, and turbidity). This study assessed the performance of an aquaponic setup with a biofiltration system in terms of reducing the concentration of dissolved solid particles in the water. The correlations of ammonia, nitrate, and nitrate concentrations with turbidity were explored. The turbidity was monitored using a turbidity sensor. To test ammonia, nitrite, and nitrate, solutions were dropped into water samples, whose developed color was compared to a color chart. The system must operate in conditions that cater to the collective growth and development of the fish, plants, and nitrifying bacteria. The plant crop utilized in this study is mint (Mentha spicata), while the fish used was the Red Tilapia (Oreochroomis aureus x Oreochromis mossambicus). Ammonia levels were found to be substantially connected with nitrate levels, and both parameters were shown to be inversely correlated with turbidity. Turbidity in water may be caused by active microorganisms or algae that may metabolize ammonia and nitrate.

Public transportation is the key economic driver of a country. The true measure of a country's progress level is scaled on the number of people using the public transportation rather than of people riding private cars. In the Philippines, Western Visayas region (Region VI) is one of the regions which needs extensive support in public transport data organization. Due to the complexity of a public transport network, handling of big data becomes a bottleneck for transport planners. Addressing this problem will help them move forward to more important tasks such as improving transport service for passengers. In this study, a framework was designed in modeling public transportation data. TigerGraph database was utilized to preconnect data and to allow acquisition of geospatial intelligence on route while Django-python was used as the web framework for the geographic insight web application. With the framework and software solution developed, the study intended to make data organization scalable, visualize data relationships, and preconnect data. Preconnecting data in public transport such as terminals, PUV stops, and facilities in conjunction with massive parallel processing (MPP) function, speeds up data analysis. This also enables expanded capability of a system to return answers to queries which need deeper analysis.

Chlorophyll content is an imperative indicator of lettuce (Lactuca Sativa) health status. Through computational intelligence, this paper bestowed a noninvasive, accurate, cost-effective ensemble of machine learning algorithms for chlorophyll-b concentration prediction. A total of 107 images of loose-leaf lettuce var. Altima from an aquaponic farm situated in Rizal province in the Philippines was utilized. By employing CIELab color space, the leaf canopies were segmented and extracted with 18-feature predictors. The regression tree ranked and selected 10 selected significant leaf features (spectral: R, G, S, a*, b*, Cr; morphological: canopy area; textural: contrast, correlation, and homogeneity). A fitness function that optimized the recurrent neural network architecture was constructed using GPTIPSv2 which is a symbolic multigene regression (SMGR) tool. This convergence function was the main element in developing a genetic algorithm (GA)-optimized recurrent neural network model considered as the PIGMENTnet. It provides the optimal quantity of neurons in each of the three hidden layers in neural network architecture. A 75-100-10 conglomeration of neurons in each layer was recommended. The RMSE (0.1486), R2 (0.9998), and MAE (0.0751) results of PIGMENTnet surpassed the unoptimized RNN. Based on these findings, it implies that the developed PIGMENTnet is an effective Chl-b concentration predictor as it provided highly accurate and sensitive results than the sole RNN model.

Invasive genetic and chemical-based laboratory techniques are very limited for in situ and in vivo applications especially in classifying leaf species in the wild. Out of 41 recorded Salvia species, 25 are endemic to the Central Asian region. In this study, a non-destructive model for profiling and identifying Salvia species (SalviaNet) was developed by employing computer vision allied with feature-based machine learning. The image set is composed of 25 Salvia species collected over Uzbekistan and other territories (Locus classicus) and photo-scanned to capture the totality of the leaf surface. CIELab thresholding was employed to fully segment the leaf pixels. Classification tree (CTree) was used to select the most significant spectro-textural-morphological leaf signatures resulting in only 11 attributes. These leaf signatures were profiled using the distance method with a distance power of 2. Hybrid CTree and linear discriminant analysis (CTree-LDA or SalviaNet) outperformed other computational models in classifying Salvia species based on the accuracy (90.7%) and sensitivity (90.7%). Based on profiling, leaf's red reflectance, compactness, and shape factor 2 are the strong determinants in discriminating Salvia species. Overall, the developed SalviaNet is proven reliable for on-site application and will essentially help the field of plant taxonomy.

Data mining today is much slower than before because of the advancement of computing and information systems. Relevant recommendation based on customers' preferences and needs in e-commerce gets more complicated. In the recent pandemic, people are reluctant to go out and has engaged more on internet to get their daily food and services. This phenomenon exacerbated the existing recommendation system, as the data has grown up drastically. In this study, the author recommends a relevant image quality based on the quality queries of the clothes and footwear dataset by observing their highest similarity score. Fashion MNIST images used were existing dataset for clothes and footwear. The testing on image reconstruction using training and validation approaches has shown an accurate result by showing only 0.01 loss in the dataset. Using 11 classes of the image queries, the system image has been identically reconstructed according to the queries supplied. With this result, businesses will have an implementation alternative to a faster and more efficient data mining method. Hence, this alternative will boost the speed of many recommendation systems in the e-commerce platforms and will create a better customer experience.

Excess nitrate concentration leads to excessive algal growth that reduces dissolved oxygen for aquatic animals. A significant strategy to preserve the water quality of aquatic systems is through nitrate level assessment. However, use of nitrate sensors and existing laboratory approach is costly and requires a huge effort. This study investigated the application of computational intelligence for measurement of nitrate concentration in a tilapia fishpond at Rizal province, Philippines, based on physico-limnological parameters such as temperature, electrical conductivity, and pH level. Artificial neural network (ANN) algorithms including feed-forward (FNN) and recurrent (RNN) neural networks were developed and optimized using genetic algorithm (GA) to improve their predicting performances. Genetic programming (GP), through GPTIPSv2 tool, was configured to generate a fitness function. This function is the principal component of GA optimization to produce optimal number of hidden neurons for ANN architecture that resulted in 2 neurons for GA-FNN and combination of 92, 31, and 11 neurons for each hidden layer using the GA-RNN model. Based on evaluation results, all models provided acceptable results with error and predictive accuracy values approaching 0 and 1, respectively. However, the GA-FNN model outperformed other models with 3.26 RMSE, 2.23 MAE, and 0.97 R2values which proved to be the most effective and suitable model for the indirect measurement of nitrate concentration.

Fish feeding management is one of the most crucial considerations in aquaculture production. The traditional feeding method such as table-based and scheduled automated feeding schemes are inaccurate. In contrast, the automated on-demand feeding system has reduced the inaccuracies of the older feeding schemes. However, existing on-demand systems have limited accessibility because their monitoring systems are only stored by their local devices. This paper proposes an on-demand fish feeding system with online and real-time monitoring using the Internet of Things (IoT) and an accelerometer to sense the fish' demand by hitting it. An overhead surveillance camera was installed on the fish tank to automatically record and monitor the fish feeding activity on the first day of the implementation. Two groups of fish were used for the observation—the adults and pre-growth Nile tilapia (Oreochromis niloticus). Results have shown that the on-demand feeding system is highly effective on 21 pre-growth fish with an average weight of 88 grams and a standard deviation (SD) of ± 39 grams. Additionally, the feed intake ratio (FIR) of the pre-growth fish was$1.35\pm 0.69$grams, i.e., 73% to 86% lower than the recommended table-based feeding scheme. Thus, more efficient.

Food shortage is a serious problem facing the world and is prevalent in urban areas. The scarcity of food is mainly caused by crop failure. Environmental factors offered by the rural areas determine the condition of crops to be produced. This scenario pomps, the explication of urban farming. However, urban farming requires all-out monitoring and control. This study specifically solves the predicament of identifying the developmental growth of plants from seed leaf to amend the techniques of plant science and cultivation management. With a view to this, the paper shows coupled color-based superpixels and multifold watershed transformation in segmenting the lettuce image from the background. To fathom it out, a comparative analysis of three unsupervised machine learning algorithms: Self Organizing Map (SOM), Hierarchical, and K - means algorithms were conducted. These were done by modeling each algorithm from the features extracted from morphological computations of the lettuce images raised in a smart aquaponics setup. Each of the models was optimized to increase cross and hold-out validations. The results showed that K – means algorithm having the parameters of algorithm = ‘auto’, copyx= ‘True’, init = ‘K- means++’, maxiter = ‘1000’, nclusters = ‘3’, ninit = ‘15’, n_jobs = ‘1’, precompute_distance = ‘auto’, random_state = ‘10’, tol = ‘0.000001’, verbose = ‘1’, leaf_size = ‘10’ was the most effective model for the given dataset, yielding a high precision and recall unsupervised clustering percentage of 91%.

Six Sigma is an engineering systematic methodology modeled within the DMAIC course (Define, Measure, Analyze, Improve and Control). It is used for improving high risk areas that can affect the profitability of the organization. This paper aims to prove the flexibility of Six Sigma by increasing the efficiency rate of internal audit team in an existing real-estate company by targeting standard man-days or further reducing it to less than the set timeline. The efficiency rate of the audit team is 41.67% with average man-days of 26.9. Timeliness of audit report has been an integral contributor of on- time resolution of non-conformities and appropriate mitigation of high risks incurred on those findings. Through cause-effect analysis guided by Affinity method probable causes were listed down and for the perceived causes with no measurable data further investigation and walkthrough was done using GEMBA walk. Data were collected and analyzed to validate these causes. Hypothesis tests and control charts like ANOVA test, Ishikawa Diagram and Boxplot were used through the use of Engineering simulation programs, to analyze the gathered data and results revealed the root causes of the problem such as manual extraction of audit evidence, difficulty searching and retrieving records, variation on auditors’ performance, and unawareness of risk management procedures among auditees. Upon implementation of improvement solutions, the average man-days reduced from 26.9 days to 23.8 days translating to an efficiency rate of 80%.

Segmentation is a challenging task for the complex and low-quality underwater images, as this is prerequisite to advanced tasks in fish monitoring such as fish detection and classification. A demand exists for underwater image segmentation algorithms that can robustly segment fish from its background. A competitive approach is the integration of states-of-the-art image segmentation algorithms: SLIC superpixels merging by KAZE Keypoints clustering and Fast Marching Method (FMM) to a single framework. The combination of these established methods offers robustness towards underwater images of different visual qualities. First, a locally acquired underwater image is represented as superpixels. Then, the KAZE features of an underwater image is extracted. Such features are utilized by the k-means clustering to group superpixels which contains fish pixels into a region. Lastly, the merged region is further segmented with Fast Marching Method and corresponding morphological processes. The study presents the viability of the integration of different image segmentation techniques for localized application. The number of superpixels, KAZE Keypoint score threshold and FMM threshold are identified to affect the performance of the proposed algorithm. Qualitative observations and quantitative measures validate the robustness of this generated algorithm to address this difficult and persistent task.

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.

Batteries are very important in many different applications. In the solar energy system, the batteries are used as power storage when solar energy is not available especially during night time. Batteries need to be maintained and closely monitor their condition. Battery management systems are normally used for this application but many of them are not monitoring the battery's temperature. This study will use a fuzzy logic-controlled system to manage the operation of the battery. This system will maintain the operation of the battery in the allowed operating temperature to prevent it from damaged caused by excessive internal temperature.

Visual inspection of plant health status and disease severity may yield subjective assessments due to error-prone sphere of colors and textures as affected by angular photosynthetic light source and the complexity of chlorosis. Quantification of damages on leaves due to destructive diseases is paramount for plant and pathogen interactions. To address this challenge, the proposed solution is the integration of computer vision and computational intelligence for tomato Septoria leaf spot necrotic and chlorotic region computational assessment. Dataset contains healthy and diseased tomato leaves that were captured individually. Non-vegetation pixels removal was done using CIELab color space. RGB color components and five Haralick texture features were extracted from the segmented leaf. Hybrid neighborhood component analysis and ReliefF algorithm were employed to select the important predictors resulting to RGB-entropy vector. A new tomato leaf disease index (tomLDI) optimized using artificial bee colony (ABC) was developed by normalizing visible red reflectance, and introducing red-green and red-blue reflectance ratios to enhance Septoria leaf spots pixels and reducing sensitivity to healthy green pixels. KNN bested classification tree, linear discriminant analysis and Naïve Bayes in detecting Septoria leaf disease with accuracy of 97.46%. Deep transfer image regression was tested using raw infected leaf images and the tomLDI transformed colored channels through MobileNetV2, ResNet101 and InceptionV3. Using tomLDI channel, MobileNetV2 and ResNet101 bested other networks in estimating leaf diseased region percentage and number of Septoria spots with R2values of 0.9930 and 0.9484 respectively. tomLDI channel proved to be more accurate than using raw images for regression.

Lightness signal and color reflectance constitute the reflected luminance spectra from camera captured image to camera lenses. The intensity of lightness and visible RGB signals deviates as the camera distance to object varies. The presence of uneven distribution of photosynthetic light causes angular light effect of shadowing on the focal object and light emitting objects placed on the visually noisy background added a challenge in materializing an efficient greenness index for crop phenotyping. The proposed method in this study compensates excessive relative brightness on the image by introducing lightness rectification coefficient and employing genetic algorithm to derive a novel visible tetrahedron greenness index (gvTeGI) based on normalized green waveband. Hybrid neighborhood component analysis and Pearson's correlation coefficient approach for feature selection resulted to retaining photosynthetic canopy area, and correlation and homogeneity texture features as highly important descriptors for biophysical signatures considered in this study which are lettuce fresh weight, height and number of spanning leaves. The selection, crossover and mutation rates used to optimize the genetic algorithm model are 0.2, 0.8 and 0.01 respectively. Indoor and outdoor aquaponic system was deployed for 6-week full crop life cycle cultivation. Regression machine learning models were used to estimate biophysical signatures from extracted gvTeGI channels. Optimized Gaussian processing regression model bested regression support vector machine and regression tree in estimating fresh weight, height and number of spanning leaves with R2values of 0.7939, 0.7662 and 0.7446. The proposed gvTeGI proved to be more accurate than previously published greenness index for the estimation of biophysical signatures of lettuce using consumer-grade RGB camera.

Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.

With machine learning-based innovations becoming a trend, practical resolutions of its implementation to large-scale data and computing problems must be able to cope up as well. Currently, Graphic Processing Units (GPUs) are being chosen over other available physical devices due to its powerful computing capability and easier handling. Several cloud service providers also made it possible for these to be accessible online allowing higher serviceability and lower cost upfront for businesses. With this said, the proponent would implement a common machine learning-based application, automated annotation through Mask RCNN Object Detection Model in CVAT, using AWS instance. The key purpose is to showcase the viability of deploying data and computing intensive system on the cloud.

Soil nutrients and season have direct impact on the growth and yield of a crop. Deficiency on the nutrient level of the soil may result to plant disease while applying excessive amount of soil fertilizer on the other hand, may also cause negative results to the development of the crop. Nutrients on the soil also changes as the season changes from wet season to dry season. This study aims to develop a fuzzy logic-based program that will provide an appropriate amount of fertilizer to soil. The parameters such as season, nitrogen, phosphorus and potassium level are the inputs used on the fuzzy logic system. The researchers proposed four kinds of fertilizer to use in this paper such as Complete, Urea, Solophos and Muriate of Potash. Combination and amount of these fertilizers will be based on the input parameters and fuzzy rules. These soil fertilizer recommendations can be used for rice in an inbred light soil.

Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.

This study centers on the design and development of a microcontroller based hardware interface that connects the serial camera, the processor, the WiFi module, and the LCD screen and identification software for corn plant diseases through web-server using image processing and artificial neural network. This is done by capturing and displaying the image of the leaf inside the box and transmits it to the web server as an input image; process, analyze and interpret the data through image processing. The result of the processed image will be sent to the displaying microcontroller based hardware interface through the web-server and display the Pest Management Recommendations.

In the Philippines, the number of earthquakes occurring has risen to an alarming rate. 'The Big One' is one of the biggest expected catastrophes that is undoubtedly going to occur in the next decade as said by various experts. Buildings that were able to withstand the upcoming earthquakes, are to be inspected by engineers without knowing if the safety of the building is compromised. Thus, there is a need for a system that can inspect the cracks on the wall for faster and safer inspection. The objective of this study is to develop a crack detecting system capable of analyzing physical characteristics of cracks and mapping the surface of the wall. The model to be used for classifying and determining what cracks are, was trained with the use of Faster R-CNN machine learning architecture. Trained using the SDNET2018 combined with actual data generated by the proponents, the resulting system can detect cracks with an accuracy of 90% and classify the cracks according to the shape. The system also calculates its physical properties and has a recommender system that provides remarks on what necessary actions can be done.

The technological capabilities of computers in today's time continues to improve in ways that seemed impossible before. It is common knowledge that most people use computers to make everyday lives easier. Therefore, it is vital to bridge the gap between humans and computers to provide more suitable aid to the user. One way to do this is to use emotion recognition as a tool to make the computer understand and analyze how it can help its user on a much deeper level. This paper proposes a way to use both face and speech emotion recognition as a basis for selecting an appropriate music that can improve or relieve one's emotion or stress. To accomplish this, Support Vector Machine with different kernels are used to create the models for validation and testing on both the face and speech emotion recognition. The final integrated system yielded an accuracy rate of 78.5%.

The Aspergillus genus is deemed relevant for distinction and classification in the field of food, agriculture and medicine. As there are harmful and useful ones, it adds to the necessity of correct classification. Categorization of this conidial fungi is usually done through manual microscopical procedures which apparently has a degree of subjectiveness. In order to classify Aspergillus samples faster and more accurately, technology, specifically image processing and machine learning are incorporated in this study. Pre-trained deep learning models are employed in classifying 9 kinds of Aspergillus. The methodology is generally comprised of preprocessing, deep-learning (training) and performance evaluation. Performance evaluation pertains to the validation accuracy and running times of the system after training through visual display of graphs and tabulation of acquired data. This study achieved a 93.3333% testing accuracy proving that the transferred knowledge is accurate, compatible and reliable.

Monitoring the dynamics of fish behavior is impactful both in the research for fisheries and aquaculture production. One of the most explored approaches to monitor the fish is tracking-by-detection along with computer vision. Presently, there are several challenges in this field, including underwater environment conditions and fish movement complexity. This study presents an initial investigation towards tracking the fish by predicting the trajectory 2D coordinates of fish from the sequential sampled frames in underwater videos. Here, the authors explored the Genetic Algorithm based on natural evolution selection and the Long Short-Term Memory (LSTM) algorithm. Results have shown tolerable trajectory prediction inaccuracies using the GA and LSTM. Specifically, it obtained the Mean Absolute Percentage Error at 2.8% to 30.5% and 3.33% to 17.74% for GA and LSTM, respectively. These results have allowed the authors and researchers to extend its study towards tracking the fish using these approaches.

The tuning process for a magnetic levitation to control the object's gap from the electromagnet is laborious and demands immense effort to obtain an adaptive PID compensator. Hence, this study has schemed an unexplored adaptive feedforward compensator for a 1-DOF maglev system using equation search based on a symbolic regression through an evolutionary algorithm. Results have shown an exceptional accuracy with an r2of 0.9997, almost zero root mean square error (RMSE) and mean absolute error (MAE). The approach has paved the way for an adaptive nonlinear system requiring a highly accurate model with a baseline dataset containing few modifiable parameters.

This study presents the design and development of a nutrient film technique hydroponics system for lettuce. Hydroponics is a method of cultivating crops with the use of water with nutrient solutions as medium. This nutrient film technique hydroponics system was built as an alternative to traditional farming that requires a lot of space. This system can produce a good number of crops without consuming large land area. The system also features monitoring of the key parameters needed for by the crop to survive. A fuzzy logic control will also be used to maintain the level of the parameters. Data from the sensors for measuring electrical conductivity, pH, and as well as the water level of the mixing tank will be the input of the fuzzy logic and will control the pumps of fresh water and nutrient concentrate reservoir, and the drain of the mixing tank. The optimum values for electrical conductivity, pH, water flow rate, and temperature were all based on the existing studies that also cultivate lettuce as their primary crop.

The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson's correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.

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.

This research proposes a method in order to classify vehicles in a highly congested roads , a robust technique for vehicle classification with low computational power must be used. So, a proposed solution is to embed an AKAZE feature matching extraction which is ran in an artificial neural network will be used. AKAZE was used because it is faster than SIFT. The features extracted from the AKAZE algorithm will be grouped according to the type of vehicle where it was used and be placed to an Artificial Neural Network (ANN) for the training of the network itself. The results yielded good for real-time Vehicle Classification.

Philippine license plates have different plate styles and character fonts making the plate character recognition challenging. This paper focuses on improving the segmentation method to recognize characters of different formats of Philippine license plates. The proposed system comprises of license plate classification, character segmentation and character recognition. License plate series was classified using color level of pixels in the image. Plate characters were segmented using 3-Class Fuzzy Clustering with Thresholding and Connected Component Analysis and were recognized using Template Matching. The system achieved an accuracy of 95% and 70% for the 2003 plate series and 2014 plate series, respectively, having tested 20 license plates from each series.

In response to addressing the issue of pipe quality checking, the researchers developed an adaptive in-pipe inspection robot that is able to detect rust as well as map the rust on the pipe network. The robot is traversed in a pipe network of horizontal, vertical, elbow, and tee type with diameters of 8, 10 and 12 inches for all. Hence, the test features the versatility, adaptability, and robustness of the robot. The leg expansion of the robot is inspired by the scissors mechanism. On the other hand, rust detection was done through a per pixel classification via image processing. To effectively map the rust, checkpoints were used as a guide of the robot. Testing of the robot were supported in both simulation and actual testing, wherein it yields a 96.45% success rate on the site. Likewise, its rust detection program proved to be successful with a high percentage accuracy of 99.18%. The localization on the other hand yielded an accuracy of 85%. Given the obtained data and results, the researchers were able to go beyond their target objective of 70%.

In this study, a fuzzy logic controller (FLC) was designed to manipulate an articulated robot gripper. An idea from a previous study was utilized to enhance the performance of the FLC using genetic algorithms by optimizing newly-introduced coefficients in the membership functions of the FLC. The proposed controller was applied on a robot gripper model in Simulink. All in all, the genetic algorithm was able to come up with optimized parameters after an average of at least eight (8) generations and the proposed controller was able to follow the reference trajectory more accurately than the simple fuzzy controller. Further research will be necessary for physical implementation and possible improvement of the utilized genetic algorithm.

Vision systems are essential in building a mobile robot that will complete a certain task like navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot controller or the operator aware what is in the environment and perform the next tasks. With the recent advancement in deep neural networks in image processing, classifying and detecting the object accurately is now possible. In this paper, Convolutional Neural Networks (CNN) is used to detect objects in the environment. Two state of the art models are compared for object detection, Single Shot Multi-Box Detector (SSD) with MobileNetV1 and a Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2. Result shows that one model is ideal for real-time application because of speed and the other can be used for more accurate object detection.

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.

Smart farming system using necessary infrastructure is an innovative technology that helps improve the quality and quantity of agricultural production in the country including tomato. Since tomato plant farming take considerations from various variables such as environment, soil, and amount of sunlight, existence of diseases cannot be avoided. The recent advances in computer vision made possible by deep learning has paved the way for camera-assisted disease diagnosis for tomato. This study developed the innovative solution that provides efficient disease detection in tomato plants. A motor-controlled image capturing box was made to capture four sides of every tomato plant to detect and recognize leaf diseases. A specific breed of tomato which is Diamante Max was used as the test subject. The system was designed to identify the diseases namely Phoma Rot, Leaf Miner, and Target Spot. Using dataset of 4,923 images of diseased and healthy tomato plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify three diseases or absence thereof. The system used Convolutional Neural Network to identify which of the tomato diseases is present on the monitored tomato plants. The F-RCNN trained anomaly detection model produced a confidence score of 80 % while the Transfer Learning disease recognition model achieves an accuracy of 95.75 %. The automated image capturing system was implemented in actual and registered a 91.67 % accuracy in the recognition of the tomato plant leaf diseases.

Several methods exists to identify the nutrient content of the soil. The most popular method is by using Soil Test Kit (STK). STK gives soil qualitative level of macronutrients and pH. Chemicals that change color upon reaction with soil samples can determine macronutrients such as nitrogen, phosphorus, and potassium. These chemicals are going to be processed based on the method given by the kit. With the use of different algorithms that is commonly used for classification, mostly, a vision system is required. In this study, the development of the vision system that will capture the image of the soil sample after conducting soil testing will be tackled together with the image processing and feature extraction. Using the extracted features as the input of the fuzzy logic gives accurate result in determining the nutrient level of the soil.

This paper presents a classification algorithm for air quality index (AQI) using fuzzy logic (FL) system. AQI tells the level of cleanliness of the air and provides a corresponding health warning. In this study, two types of input pollutants are only considered which are the carbon monoxide (CO) and nitrogen dioxide (NO2). Each input is classified into six categories that include very low, low, moderate, high, very high and extremely high. Mamdani fuzzy inference system (FIS) is used to process the FL system giving an output of AQI values expressed in six categories: good, moderate, unhealthy for sensitive groups, unhealthy, very unhealthy and hazardous. Simulation is performed using MATLAB fuzzy logic toolbox, which provides effective and reliable results.

The property of the Smoothed Particle Hydrodynamics (SPH) method of being mesh free, adaptable and suitable for tracking of individual particles makes it appropriate for approximating swarm behaviors for multi-agent robotics applications. The researchers modeled each of the swarm robots as SPH particles and then subjected them to external forces to exhibit aggregation and force certain formations. The external forces subjected to the SPH particles are gravity forces and container constraints . The containers explored in the study are simple geometrical primitives: sphere and cube . Computer simulations were done to show how SPH can facilitate in forcing swarm formations with the help of bounding primitives. Algorithm benchmarking was done to show how well SPH performs. Results show that SPH performs better than the benchmark algorithm by a margin of 0.703 and 1.016 units for the two set-ups, respectively. Actual robot implementation was also done to verify the effectivity and viability of the proposed algorithm in exhibiting the aggregation behavior. After 15 seconds of system run time, the interparticle distance and motion accuracy reached 96.93% and 91.14%, respectively.

Stock market analysis has traditionally been proven to be difficult due to the large amount of noise present in the data. Different approaches have been proposed to predict stock prices including the use of computational intelligence and data mining techniques. Many of these methods operate on closing stock prices or on known technical indicators. Limited studies have shown that Japanese candlestick analysis serve as rich information sources for the market. In this paper decision trees based on the ID3 algorithm are used to derive short-term trading decisions from candlesticks. To handle the large amount of uncertainty in the data, both inputs and output classifications are fuzzified using well-defined membership functions. Testing results of the derived decision trees show significant gains compared to ideal mid and long-term trading simulations both in frictionless and realistic markets.

Describes initial experimentations done on three LPC (linear predictive coding) derived feature-based speech models: the LPC-cepstrum, the LSP (line spectral pair) and the postfilter-cepstrum (PFL). A comparative analysis of the topological structures of these models is also given. The structures are basically self-organizing feature maps which accept these models as inputs and after training, used to distinguish between isolated word utterances and speakers. A small database of 5 utterances and 4 speakers is initially used. The performance index of isolated word recognition and speaker identification for all models are calculated based on a hit-and-miss ratio and are also discussed. Experimental results reveal that the three parameters are comparable in performance. The LSP has a slight edge over the other two feature vectors in distinguishing isolated words.

Emerging techniques of intelligent or learning control seem attractive for applications in manufacturing and robotics. It is however important to understand the capabilities of such control systems. In the past the inverted pendulum has been used as a test case, however, this problem is not sufficiently testing. This research therefore concentrates on the control of the inverted pendulum with additional degrees of freedom as a testing demonstrator problem for learning control system experimentation. A flexible pole is used in place of a rigid one. The transverse displacement of the flexible pole has distributed elasticity and therefore infinite degrees of freedom. The dynamics of this new system are more complex as the system needs additional parameters to be defined due to the pole's elastic deflection. This problem also has many of the significant features associated with flexible robots with lightweight links as applied in manufacturing. Novel neural network and fuzzy control systems are presented that control such a system in real time in one of its modes of vibration. A fuzzy-genetic approach is also demonstrated that allows the creation of fuzzy control systems without the use of extensive knowledge.

This paper examines the applicability of fuzzy logic based algorithms to the control of complex and highly nonlinear systems. An online fuzzy logic controller has been developed to control a cart balancing a flexible pole under its first mode of vibration. The controller design includes 5 fuzzy logic systems (FLS) and a single rule based evaluator that centers the pole on the truck. Results of physical experiments show that the controller not only balances the flexible pole indefinitely but also brings the cart to the center of the track. The controller can also easily adapt to disturbances from the external environment (e.g. moving or shaking the track randomly, elevating the height of the track on either side, pushing the pole in any direction, preventing the pole from moving further by putting an obstacle in its path). The operation of the system can also be initialized anywhere in the track. The controller is sufficiently fast to balance the system from an initial angle of 20 degrees.