Stroke has been one of the leading causes of death. Recognizing its warning signs early can lessen its severity and maximize the effectiveness of its prevention. A highly effective data-driven predictive algorithm is needed for this, which can be done through various machine learning-based techniques. In this paper, Linear Support Vector Machines (linear-SVM), Radial Support Vector Machines (rbf-SVM), Logistic Regression, Decision Tree, Gaussian Naive Bayes, and Random Forests were used for stroke prediction. Among these models, linear-SVM, rbf-SVM, and Logistic Regression yielded the highest accuracy value of 95%. Cross-validation and boosting through ensembling were also conducted to further enhance the overall confidence of model prediction.

This study presents numerical simulations and analysis of rain power Pelton wheel turbine for a household application. The material used and compared in the simulation are ABS AND A390 Aluminum Alloy. An average rainfall data was used in the modeling simulation with different head height and number of buckets. Torque, RPM and Shaft Power were simulated using SolidWorks. Based on the simulation the 22 & 24 number of buckets at a height of 3 meter have a close result with the two dissimilar materials. In this research a rain power Pelton wheel turbine with different materials, varying height and number of buckets was simulated and analyzed.

The quest for alternative sustainable energy sources has led to innovative solutions, such as harnessing energy from rainwater. This study aims to evaluate the performance of a 3D printed pelton wheel turbine for a household rain gutter application with a height of 3m and 5m. The design and simulation were done via SolidWorks with varying number of buckets: 15, 18, and 20, evaluating the shaft power and rpm. Initial design parameters were calculated and simulated. The actual prototype and test were done. The results show an overall turbine efficiency of 71 percent and was able to produce an 8W power output. In this research, a 3D printed pelton turbine for a household application during rainy season was designed, simulated, fabricated, tested, and analyzed.

This paper presents the use of machine learning in predicting the symbiotic insights such as coefficient and carrying capacity of a given modular mobile robot configuration doing a cooperative load pushing task. The independent parameters were load and the number of modules. The actual experiment results from a DFRobot Maqueen mobile robot pushing a range of loads were used as dataset in the study. Various regression models were run as an ML algorithm to assess symbiotic relationship of a given configuration whether harmful, beneficial or has no effect and to yield the total distance that can be traversed given a set of input parameters. The researchers used python in running the ML model on Google Colab Notebook. Results show that Gradient Boosting performed best in the prediction of distance carrying capacity with 95.87% and Extra Trees for symbiotic coefficient with 93.75% accuracy which took about only 5 seconds training time on a T4 GPU device. The researchers were able to develop a custom trained ML regressor model that can immediately return a symbiotic insight on a cooperative pushing modular mobile robots.

Counting high volumes of product and packages is prone to human error and is time consuming especially on a fast-moving conveyor system. To address this issue, automated counting systems are typically operated, the performance of which is dependent on the reliability of instruments used such as the sensors and scanners. Technological advancements in machine vision are progressing exponentially, making it increasingly accessible, cheap, and dependable for solving and executing real-time image related problems. In this paper, a vision-based high speed counting system is proposed. YOLOv8 was utilized as the machine learning model and is executed via Roboflow. Soap and small parcel items were focused on as a dataset. The generated mean average precision (mAP), precision, and recall for combined items is 98.6%, 96.8%, 96.3% with an inference and prediction time of 1:3 and 1:1 ratio respectively.

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.

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.

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.