Human curiosity and the pursuit of areas of interest are fundamental drivers of exploration and skill development. One avenue for understanding vocational interests is the Reading-Free Vocational Interest Inventory Third Edition (RFVII-3), particularly relevant for individuals, including those with mental disabilities. The RFVII-3 assesses vocational interests and aligns individuals with specific vocational clusters. However, its traditional face-to-face, one-on-one administration, and manual scoring present significant challenges, consuming approximately 1.5 hours. This paper presents a novel approach to streamline the RFVII-3 assessment process by developing a digital prototype. This device captures and scores RFVII-3 item images selected by examinees, instantly aligning them with relevant vocational clusters. The result is generated in less than five seconds after completing the last item, effectively expediting vocational interest assessment. This innovative technology promises to enhance the efficiency and accessibility of vocational interest assessment, particularly benefiting individuals with intellectual disabilities.

Automated chess is an emerging challenge from the field of robotics to human interaction. Customarily, chess playing robots use video camera for detecting the state of the board and robotic arm to manipulate the pieces, which makes it either expensive or too fragile to move. In this study, an automated chess board called “Chessbot” was built by combining a multitude of technologies and techniques such as voice command recognition, x and y plotting and recognition of chess pieces in a Cartesian plane, artificial intelligence algorithm, and Android mobile application. To properly execute chess moves either from a human player or computer via voice commands or a mobile game interface, the microcontroller provides motors the current and landing squares of chess pieces on the board. For the evaluation, several matches were simulated to perform various testing procedures such as voice command recognition, move log accuracy, display and user input acceptance accuracy, self-arrangement, and chess move reliability. Upon testing, Chessbot performs all the necessary tasks efficiently and accurately, which indicates the possibility of using this automated device for chess games at a hobbylevel or professional matches.

When it comes to learning, there are beneficial effects of incorporating gestures in learning mathematics especially within the early ages of life. At first, we learn Math by counting and incorporating our fingers as a guide, as well as writing numbers along on a piece of paper. These gestures are known to be examples of hands-on learning experience for children. Aside from counting, they learn basic mathematical operations as well.Teaching children mathematics is not an easy task. The child’s attentiveness plays a huge factor in the learning process. The average attention span of the child from 2 to 10 years of age is 20 to 30 minutes only. Making the learning interactive can help in maximizing the learning experience of the child within the timespan. Thus, we aim to develop a compact device that is interactive with children when it comes to learning basic mathematical operations such as addition, subtraction, and multiplication.This device has software that uses computer-vision and image detection as a way of teaching children math in an interactive way. The child has two different options of answering, by using hand gestures or premade printed numbers. The child is asked five random basic mathematical questions within a time limit. It has three levels of difficulty which the child must pass in order to progress each level. Furthermore, the software also has audio feedback as a way of letting the child know the correct answer. With these features, children are engaged in an interactive learning opportunity in mathematics.

When it comes to learning, there are beneficial effects of incorporating gestures in learning mathematics especially within the early ages of life. At first, we learn Math by counting and incorporating our fingers as a guide, as well as writing numbers along on a piece of paper. These gestures are known to be examples of hands-on learning experience for children. Aside from counting, they learn basic mathematical operations as well.Teaching children mathematics is not an easy task. The child’s attentiveness plays a huge factor in the learning process. The average attention span of the child from 2 to 10 years of age is 20 to 30 minutes only. Making the learning interactive can help in maximizing the learning experience of the child within the timespan. Thus, we aim to develop a compact device that is interactive with children when it comes to learning basic mathematical operations such as addition, subtraction, and multiplication.This device has software that uses computer-vision and image detection as a way of teaching children math in an interactive way. The child has two different options of answering, by using hand gestures or premade printed numbers. The child is asked five random basic mathematical questions within a time limit. It has three levels of difficulty which the child must pass in order to progress each level. Furthermore, the software also has audio feedback as a way of letting the child know the correct answer. With these features, children are engaged in an interactive learning opportunity in mathematics.

Nowadays, road accidents have become a major concern. The drowsiness of drivers owing to overfatigue or tiredness, driving while intoxicated, or driving too quickly is some of the primary causes of this. Drowsy driving contributes to or increases the number of traffic accidents each year. The study presented a technique for detecting driver drowsiness in response to this issue. The sleep states of the drivers in the driving environment were detected using a deep learning approach. To assess if the eyes of particular constant face images of drivers are closed, a convolutional neural network (CNN) model has been developed. The suggested model has a wide range of possible applications, including human-computer interface design, facial expression detection, and determining driver tiredness and drowsiness. The YOLOv3 algorithm, as well as additional tools like Pascal VOC and LabelImg, were used to build this approach, which collects and trains a driver dataset that feels drowsy. The study's total detection accuracy was 100%, with detection per frame accuracy ranging from 49% to 89%.