Data scarcity introduces variabilities that compromise forecasting accuracy and reliability. In energy systems, precise load forecasting is vital for grid optimization, resource management, and outage prevention, supporting operational and economic stability. However, most models perform poorly under short time series, non-stationary patterns, and abrupt demand shifts. This paper employs Gramian Angular Field (GAF) to encode load data as images and develops a hybrid Autoencoder–Stacked LSTM network. The Autoencoder extracts latent temporal features from load snapshots, while the Stacked LSTM forecasts day-ahead hourly demand. A ten-year hourly demand dataset from the Philippine grid is aggregated, with missing values zero-filled and outliers preserved. The Autoencoder compresses snapshots into a latent space for feature representation, and transfer learning enables LSTM models trained on data-rich sub-grids to forecast in datascarce regions. Performance, evaluated via MAE, MSE, MAPE, and RMSE, remains consistent across sub-grids. ANOVA results (F0.94) confirm no significant performance differences, validating the model's robustness and generalizability. Future work focuses on integrating attention, multimodal data, and real-time deployment to enhance forecasting accuracy and grid adaptability.

The carbon emissions from public utility vehicles (PUVs) in the Philippines are projected to contribute up to 80% of vehicle kilometers traveled and become a major source of emissions by 2035 without intervention. Recognizing the stochastic and condition-dependent nature of vehicular emissions, the research aims to identify and prioritize the factors influencing C O2emissions using advanced analytical techniques. Emissions data were clustered using K-Means to identify distinct operational states, while Principal Component Analysis (PCA) reduced dimensionality and revealed key influencing factors. The Fuzzy Analytic Hierarchy Process (FAHP) was then applied to prioritize these factors, considering environmental, technical, and economic implications. Results showed a positive relationship between road slope and C O2emissions$(r=0.3111)$and an opposite relationship with respect to speed$(r=-0.3078)$, while acceleration had a minor positive effect (r=0.1330). FAHP assigned the highest weight to CO2emissions (0.3589), followed by slope (0.3121) and speed (0.2972). Cluster analysis highlighted Cluster 0 as the most emission-intensive operational state, with an average C O2level of 31394.25 g/km, moderate speed, and uphill road conditions. The integration of PCA and FAHP revealed that C O2emissions and slope together accounted for over 67% of the emission profile importance. These insights inform the development of emission control strategies, eco-driving guidelines, and data-driven transport policies.

There is restricted access to reliable data from distribution utilities due to privacy concerns, and inadequate infrastructure results in frequent outages and hinders analysis of power reliability issues in regional areas in the Philippines. The paper evaluated power distribution reliability by calculating the Customer Average Interruption Duration Index (CAIDI) using data from a real-time intelligent monitoring system across multiple sites served by local utilities. The intelligent monitoring system utilized an ETL model to collect and manage data via cloud infrastructure and integrate AI models to detect anomalies in the system. VECO and CEBECO I exceeded DOE CAIDI limits with values of 185 and 140 minutes, respectively, while CEBECO II, III, and MECO demonstrated strong reliability with zero interruptions in key areas. To address these gaps, the paper recommends deploying reclosers, advanced outage management systems, and integrating distributed energy resources to reduce outage durations by up to$\mathbf{6 0} \boldsymbol{\backslash} \boldsymbol{\%}$and enhance grid resilience.

Reliable and adaptive wireless communication is critical for Maritime Autonomous Vehicles (MAVs) operating in hybrid land-sea environments. This study proposes an intelligent framework that integrates crowdsourced cellular geodata with fuzzy logic inference to optimize mobile network selection and radio technology mapping across maritime zones. Using publicly available OpenCellID data, k-means clustering was applied to stratify cell tower distributions and determine radio-type availability in key land and offshore locations. Then a fuzzy inference system (FIS) was developed to assess the preference of the mobile network operator and predict the optimal radio technologies, GSM, UMTS, or LTE, based on signal strength, tower proximity, sample density, and location priority. Visualization techniques, including geospatial graphs, surface response maps, and signal overlays, were used to validate the FIS outputs. The results show that Smart (MNC 3) is the top choice of operators due to its strong coverage of LTE at ground stations and reliable UMTS access in maritime areas. This radio mapping is an important reference point for creating adaptable data routing strategies, which are crucial to maintaining efficient low-power communications with MAVs that can handle delays. This approach contributes to a scalable, data-driven methodology to enhance wireless communication reliability in maritime Internet of Things (IoT) systems.

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.

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.

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.

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.