The MobileNet model was used by applying transfer learning on the 7 skin diseases to create a skin disease classification system on Android application. The proponents gathered a total of 3,406 images and it is considered as imbalanced dataset because of the unequal number of images on its classes. Using different sampling method and preprocessing of input data was explored to further improved the accuracy of the MobileNet. Using under-sampling method and the default preprocessing of input data achieved an 84.28% accuracy. While, using imbalanced dataset and default preprocessing of input data achieved a 93.6% accuracy. Then, researchers explored oversampling the dataset and the model attained a 91.8% accuracy. Lastly, by using oversampling technique and data augmentation on preprocessing the input data provide a 94.4% accuracy and this model was deployed on the developed Android application.

This study employs digital image processing as a tool to reduce the complexity in tongue analysis. Through visual inspection, classification of tongue coat and body color is entirely based on the verdict of the medical personnel thus not providing an absolute truth towards chronic and acute irregularities the patient is suffering from. A firmware which can extract the natural color of the tongue and yield an accurate list of symptoms that might lead to a conclusion of anomalous working on an individual's internal organ was developed. Its system framework comprises of: (1) image acquisition (2) image segmentation; (3) body and coat segmentation; (4) color classification and; (5) neural network. Moreover, the proposed device is an open source technology developed using Python programming language and Linux operating system on a Raspberry Pi 2 B+. The experiment revealed that the proposed method produced significant results with an accuracy of 93.33%.

Thorough and precise estrus detection plays a crucial role in the fertility of dairy cows. Farmers commonly used direct visual monitoring in recognizing estrus signs which demands time and effort and causes misinterpretations. The primary sign of estrus is the standing heat, where the dairy cows stand to be mounted by other cows for a few seconds. Through the years, researchers developed various detection methods, yet most of these methods involve contact and invasive approaches that affect the estrus behaviors of cows. So, the proponents developed a non-invasive and non-contact estrus detection system using image processing to detect standing heat behaviors. Through the TensorFlow Object Detection API, the proponents trained two custom neural network models capable of visualizing bounding boxes of the predicted cow objects on image frames. The proponents also developed an object overlapping algorithm that utilizes the bounding box corners to detect estrus activities. Based on the conducted tests, an estrus event occurs when the centroids of the detected objects measure a distance of less than 360px and have two interior angles with another fixed point of less than 25° and greater than 65° for Y and X axes, respectively. If the conditions are met, the program will save the image frame and will declare an estrus activity. Otherwise, it will restart its estrus detection and counting. The system observed 17 cows, a carabao, and a bull through the cameras installed atop of a cowshed, and detects the estrus events with an efficiency of 50%.

<span lang="EN-US">This study presents a system that combines a conventional refrigerator, microcontrollers and a smart phone to create an inventory monitoring that can monitor the stocks inside the refrigerator wirelessly by accessing an Android application. The developed refrigerator uses a sensor network system that is installed in a respective compartment inside the refrigerator. Each sensor will transmit data to the microcontrollers, such as Arduino Yun and Arduino Uno, which are interconnected by the I2C communications. All data and images will be processed to provide the user an Internet of Things application through the cloud-based website Temboo. Temboo will have access to send data to the Dropbox. A smartphone is connected to the Dropbox where all the data and images are stored. The user can monitor the stocks or contents of the refrigerator wirelessly using an Android Application.</span>

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