Crafting a good digital experience for children can be difficult; game designers have to consider children's cognitive and motor skill limitations, understand their target audience, create something entertaining and educational at the same time. This should also comply with national and international jurisdiction, and time appeal to parents as well. In this paper the researchers used of an educational game called Tahderiyyah Literacy App (TLA) with standard general framework which designers and developers created for children ages four to six years old. This platform has been the testing ground for the edutainment game in the field of user experience. The purpose of the paper is to observe the interactions, experience and adaptation of kindergarten pupil (learners) on a particular on the TLA based on game's user experience. TLA was design using their mother tongue which can be played on mobile devices. Focusing only to learners from Muslim Community. While the term User Experience encompasses both physical and digital experiences, the main focus for the framework is purely for digital services wherein only touch based devices are evaluated in greater detail. On the assessment process, students were observed and interviewed about the game and what they learned about numbers and letters from game they played. This process was done to help the kindergarten answers their questionnaires. Every learner that act as respondents were guided to answer the answer the questionnaires given to them. Results of using TLA to analyze an HCI factors in learning environment are described in the later part of this paper. Implications for using TLA as a design for pedagogical tool are also discussed.

In this study, the Smart University came out of the concept of smart cities by applying the principles of smart cities to the operation of university. The Smart University is a vision where the University, as a platform, provides foundational context data to deliver the university of the future. To deal with this reality, the researcher developed a framework for transforming a traditional university into a Smart University, which is more efficient, effective, and has more involvement from both students and teachers, all working together to accomplish the shared goal of improved learning. To achieve this objective, the researchers propose to develop a Technology Management Framework that will address issues and challenges to be considered in establishing and adopting a Smart University in the Philippines. The study will also measure the level of preparedness on Smart Universities among the students, teachers, university administrators, and IT consultant and practitioners that serve in different government agencies. Finally, the paper will also address adaptability of universities and to measure the Level of Smartness of Universities in Metro Manila. This study adopts a mixed-method approach that combines qualitative and quantitative research approaches. The authors also used the sequential exploratory design method. The researchers applied the Nominal Group Technique to acquire knowledge from the group of experts for the qualitative portion of the study. Cronbach's alpha is used by the authors for the designed survey instruments that are taken from the Nominal group. Cronbach's alpha is a tool used to evaluate the internal consistency, or reliability, of a collection of scale or test items. The researchers used the Friedman non-parametric hypothesis test for the Level of Smartness ranking. The outcome of the survey instrument uncovers the strength and weaknesses based on the Level of Smartness every university participant. The authors concluded that the developed Technology Management Framework for Smart University System in the Philippines is aligned with the literature and the available systems and applications. These standards are clustered in the three pillars and five criteria, which will guide the academic institutions on establishing Smart University. The three pillars are Technology Infrastructure, Educational Pedagogy, and Government Collaboration together with the five major criteria (Learning Environment, Educational Technology, Academic System, Governance, Health Services) and the application of a roadmap will support the success rate of Smart University in the Philippines. This leads to increasing the efficiency and responsiveness of the academic institution, holistic educational system, dynamic & safer academic community, and exceptional student experience.

Wireless multipath clustering is an important area in channel modeling, and an accurate channel model can lead to a reliable wireless environment. Finding the best technique in clustering wireless multipath is still challenging due to the radio channels’ time-variant characteristics. Several clustering techniques have been developed that offer an improved performance but only consider one or two parameters of the multipath components. This study improved the K-PowerMeans technique by incorporating weights or loads based on the principal component analysis and utilizing the Minkowski distance metric to replace the Euclidean distance. K-PowerMeans is one of the several methods in clustering wireless propagation multipaths and has been widely studied. This improved clustering technique was applied to the indoor datasets generated from the COST 2100 channel Model and considered the multipath components’ angular domains and their delay. The Jaccard index was used to determine the new method’s accuracy performance. The results showed a significant improvement in the clustering of the developed algorithm than the standard K-PowerMeans. 

The association between nutrition and health has been repeatedly established by the field of nutrition science and evidence-based practices. Nevertheless, inadequate nutrition is still prevalent among Filipino households. As a response to this public health issue, a nutrition system called Virtual Dietitian (VD) was conceived. Through a mixed-methods approach, VD was beta tested via a user study and System Usability Scale (SUS) by six information technology experts and six registered dietitians. Participants performed the standardized tasks with a mean of 85% completion rate and 106.2 seconds, and graded SUS with a mean score of 83.4 (excellent). Albeit the prototype successfully exhibited the potential of VD as a nutrition system, qualitative feedback from experts revealed some modifications that are needed to accomplish before going to the next phase of the study. Healthcare professionals delivered their feedback on the correctness of processes and meal plan generation while the information technology experts pointed out the deficiencies of VD from the technical perspective (e.g., web standards, layout and design, functionality, navigation, usability). With this beta evaluation, an overview of the true experience gained by end users while using VD was determined without the trouble of undergoing the whole project lifecycle. Feedback from experts, which will be used in the next phase, were beneficial to ensure that the final version of VD will be correct, useful, and valid.

Torrefaction is a thermochemical process used to convert the biomass into solid fuel. In this study, torrefaction increased the raw microalgal biomass’ energy content from 20.22 MJ⋅kg−1 to 27.93 MJ⋅kg−1. To determine if more energy is produced than energy consumption from torrefaction, this study identified the energy balance of torrefied microalgal biomass production based on a life cycle approach. The energy analysis showed that, among all processes, torrefaction had the least amount of energy demand. The experimental setup, defined as scenario A, revealed that the principal source of energy demand, about 85%, was consumed on the microalgal growth using a photobioreactor system. A sensitivity analysis was also performed to determine the varying energy demand for torrefied microalgal biomass production. The different types of cultivation methods and various production scales were considered in scenarios B to D. Scenario D, which represented the commercial production-scale, the energy demand drastically decreased by 59.46% as compared to the experimental setup (scenario A). The open-pond cultivation system resulted in the least energy requirement, regardless of the production scale (scenarios B and C) among all the given scenarios. Unlike scenarios A and D, scenarios B and C identified the drying process to consume a high amount of energy. All the scenarios have shown an energy demand deficit. Therefore, efforts to decrease the energy demand on the upstream processes are needed to make the torrefied microalgal biomass a viable alternative energy source.

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%.

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National Institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image.

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it’s still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation.

Computer programming education is often delivered using individual learning strategies leaving group learning techniques as an under-researched pedagogy. This pose a research gap since novice programmers tend to form their own group discussions after lecture meetings and laboratory activities, and often rely on peers when a topic or activity is difficult. Thus, this study intends to evaluate the impact of cooperative learning using jigsaw technique when teaching computer programming to novice programmers. A quasi-experimental research using a nonequivalent control group pretest-posttest design was adopted to examine the impact of jigsaw teaching strategy. After a 14-week programming course, pre- and post-test results revealed a significant increase in terms of attitude and self-efficacy, and the experimental group demonstrated significantly higher scores than in the control group. Therefore, it was concluded that cooperative learning using Jigsaw technique is a valid and effective teaching strategy when handling novice programmers in an introductory programming course.

People are anxious about the potential dangers of scuba diving and like in all sports, there are dangers involved in it. Typically, people think sharks and shark attacks are the dangers of scuba diving, as sharks are one of the ocean's biggest predators, and the great white shark, in particular, is one of the primary threats to divers. The study proposes a deep learning approach to shark detection for underwater diving surveillance. A large collection of great white sharks' datasets underwater is used by the system for training as sharks are hard to differentiate from other sharks like animals in an underwater environment. A YOLOv3 algorithm that uses convolutional neural networks for object detection, multiscale prediction, and bounding box prediction through the use of logistic regression is used in the study. And with this approach, the testing of the shark detection system generates a good result.