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.

Engineering students face challenges of on-time successful degree completion. Universities incorporate academic advising as a solution to these challenges. Decision support systems enhance the effectivity of academic advising. Combined with machine learning, it can predict future student performance providing useful information. Compared to common ‘black box’ models, linguistic rules provide better interpretation and insight discovery. However, existing models often use positive predictors of academic excellence, with limited consideration on factors of negative effect. This work, therefore, generates linguistic rules for academic advising based on three predictors using rough set theory (RST) and then compared with artificial neural network (ANN) for benchmarking. Forty-eight samples of mechanical engineering students taking up machine design were considered. RST attained accuracy of 72.92% while ANN attained 66.66%. The model generated 13 linguistic rules, having reflected unrealized insights. The findings from this study may be utilized by academic advisers for pattern recognition, in identifying ‘at-risk’ students.

Decision-making in choosing a degree in engineering, especially for women, involves trade-offs between numerous conflicting objectives. To increase the number of female students enrolling in the Mechanical Engineering Department of Far Eastern University, a survey was conducted among the 30 female engineering students of the Mechanical Engineering Department of Far Eastern University, Institute of Technology. In this study, the objective is to determine the factor that affects female students in choosing engineering field. A multi-criteria decision analytical process was done using a pairwise comparison matrix wherein the goal is to rank the n alternatives. The results of the study revealed that 39.48% of their decision is based on their own choice. It is followed by the influence of the economy with 19%, while the influence of family, available opportunity and the impact of the latest trend have close relative weights, with value within 10% to 12%. Lastly, peer pressure had the least influence with relative weights of 7%. Since personal choice is based on individual's skills, it is recommended that universities should invest in various activities and workshops during their promotional campaign. This will help incoming college students to familiarized and be confident to include engineering in their degree of choice.

Hearing is one of the most basic human senses. Research shows that 97 percent of the total population of elderly people suffer from hearing loss. This project is designed to address problems that the existing hearing aid users face throughout their use of the product which includes prices, high maintenance, sensitivity to noise, limited functional range and low-quality sound produced. This project aims to provide a hearing aid with noise filtration using Noise Reduction (NR) algorithm. NR algorithm which uses Multi-channel Wiener Filter is implemented to reduce noise and improve speech intelligibility. The proponents create a program for the Intel compute stick using NR algorithm to function as the filter and for the Raspberry Pi as the user interface of the device. The signal gathered by the microphone was processed by the Intel compute stick then outputted through the earphone audio output. The system is tested through simulation in terms of filtering the noise.

The firearms manufacturing industry has investigated replacing firearms frames with lightweight materials, such as polymer, in recent years. This kind of innovation in the manufacturing industry can help the users be more comfortable and reduce possible injuries. Using additive manufacturing or 3D printing, prototyping the firearms' frame made of polymer is possible. Additive manufacturing uses a 2-dimensional layer fabrication approach to form almost any 3-dimensional object. However, there are no studies published yet regarding the performance of the firearms frames made of polymer. In addition, supporting studies regarding the capability to withstand the pressure and stress from the blowback and recoil process should also be done. To solve the gap, this study investigates the stress and deformation of a 3D-printed pistol frame made of polymer using Finite Element Analysis (FEA). A comparison between steel and polymer frame materials is also analyzed to show its performance. The analysis results revealed that changing the frame's material to polymer will decrease the weight by 51.73%. The resulting stress is slightly comparable for both materials but with an increase in the displacement due to recoil. The study is expected to provide valuable theoretical references for the design. Employing better structure, other curves, and increasing the thickness are recommended to remove the high concentration of stress on some model areas.

The Learning Management System is an innovative tool to facilitate online learning using technology. It monitors students’ learning progress and actions. As most academic institutions are already shifted from the traditional learning to online and blended learning approaches, analysis of students’ learning behaviors is empirical to design necessary and suited academic intervention programs. With this, the researchers aimed to identify significant attributes affecting student academic performance in an online education environment. The knowledge discovery in databases (KDD) was used to provide step by step process in extracting and evaluating the predictive and cluster models which aim to classify students who will have academic learning difficulty based on sets of parameters and constraints. The study reveals that students with low engagement in online learning are those with problems in terms of their academic performance. Therefore, the study reaffirmed that there is a strong relationship between student behaviors in LMS and academic achievement.

The sudden transition to emergency remote education (ERE) caused by the pandemic has been a highly complex undertaking for teachers and students alike. For developing countries, such a disruption only aggravates the pre-existing global education crisis and influences the sector in unprecedented ways. Thus, we explored how teachers and students from higher education in a developing country adapt to ERE during the pandemic. Specifically, we attempted to identify the common challenges faced by teachers and students and their coping strategies to handle pandemic-induced stress. To this end, we conducted a comparative cross-sectional study from October to November 2021 with 78 teachers and 94 students from a higher education institution in Manila, Philippines. Our results show that while self-regulation is the greatest challenge among students, it is the conduciveness of the home environment for teachers. Interestingly, although teachers and students have varying concerns, both groups rely on acceptance, humor, and positive reframing as their coping strategies. By painting a holistic picture of the challenges and coping strategies of both teachers and students, education policymakers and administrators can make an informed decision on how to best continue ERE and prepare in advance for the resumption of school in the new normal.

Recent studies have acknowledged social media as a valuable pedagogical tool for connecting the formal and informal learning gap. However, as a new platform, the literature is sparse on the potential of TikTok as a knowledge source. In this study, we explored programming TikTok videos in the #LearnProgramming webpage in terms of content (programming languages and topics) and characteristics (video styles and types). Although TikTok is principally an entertainment destination, our results show that the platform likewise has informative videos. The 349 videos that we examined received a total of 10,046,000 views, 10,523 comments, 932,871 likes, and 35,095 shares, implying extremely high levels of user engagement. Tiktokers showing tips and tricks are the most recurring content type. From a macro perspective, we noticed that TikTokers do not follow the ethos of the platform (e.g., dancing) when producing educational content. This deviation demonstrates the intent of TikTokers to educate than to entertain. Although it is too early to conclude that TikTok can operate as a nanolearning platform, we discovered a substantial amount of content for and engagement from programming learners. Our results lay a potent foundation for devising actionable scholastic implications, policies, and recommendations concerning TikTok consumption. Future works and research prospects were also discussed to propel the social media and nanolearning literature forward.

With the aggravation of the Russia-Ukraine conflict and the rising involvement of foreign powers, it has become more substantial to identify whether an endorsement or condemnation of war efforts is the universal message. This goal is empowered by the clear literature on the vital linkage between public opinion and international relations. Thus, we investigated the sentiments and emotions of the international community on the Russian invasion of Ukraine. A total of 27,894 tweets posted within the first day in the #UkraineRussia hashtag were analyzed. Results show that "war", "people", "world", "putin", and "peace" were some of the most frequently occurring words in the tweets. There were more negative sentiments than positive sentiments, and sadness was the most salient emotion. To date, this study is the first to examine the Russo-Ukrainian War and one of the few sentiment and emotion analyses for exploring Twitter data in the context of modern war.

Automated medical image analysis driven by artificial intelligence can revolutionize modern healthcare in producing swift and precise diagnostics. Due to doctors’ varying breadths of training and expertise, traditional leukemia screening methods frequently involve considerable subjectivity. Using a 3-stage transfer learning approach and stacks of convolutional neural networks, we constructed an efficient pathway for automatic leukemia identification and classification through various phases. Experimental findings disclosed that our pipeline powered by InceptionResNetV2 architecture decisively affects the accuracy with 99.60% (normal vs. leukemia) and 94.67% (normal to L3). Moreover, it reduces error rates by 1.65% and 6.05%, respectively. A consistent result via the T-test confirms our proposed framework robustness with a significant positive difference of 4.71% over the standard transfer learning mechanism (p-value = 0.0001 & t = 0.85310). This research could aid and support oncologists in early yet reliable prognoses of acute lymphoblastic leukemia types.