Authors - Ronald C. Barriga, Dan Jeward Rubis, Arlene A. San Pablo, Rochelle Joy S. Tagle, Helinor Y. Medina Abstract - This study investigates the influence of AI-enabled self-service food kiosks on customer satisfaction and their intention to use such technology in quick service restaurants. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the research examines key factors such as convenience, efficiency, security, and enjoyment that mediate customer perceptions. Findings indicate that positive experiences with AI kiosks significantly enhance customer satisfaction, suggesting that strategic implementation of AI technologies can lead to improved operational efficiency and customer engagement in the food service industry.
Authors - Michael Savariapitchai, Priyanka S. Dhore Abstract - This study investigates the levels of burnout among healthcare professionals, its impact on job performance, and its relationship with organizational sustainability. The research employs a descriptive and correlational design, utilizing a sample of 101 healthcare workers from various roles and organizations. A structured questionnaire with Likert-scale questions was used to collect data, which was then analyzed using descriptive and inferential statistics, including correlation analysis. The findings reveal moderate levels of burnout across dimensions such as emotional exhaustion, physical fatigue, and motivation. Burnout significantly affects job performance, including quality of patient care, concentration, efficiency, and job satisfaction. Furthermore, burnout is perceived to contribute to organizational challenges, including employee turnover and decreased organizational sustainability. Although there is some recognition of burnout’s impact, respondents indicated that organizational support systems are inadequate in addressing these issues. The study underscores the need for comprehensive burnout prevention strategies, including improved workload management, employee well-being programs, and enhanced support systems to promote both individual and organizational health. Recommendations include enhancing leadership initiatives, fostering work-life balance, and strengthening mental health support within healthcare organizations.
Authors - Moksha Patel, Anuradha Desai, Happy Patel Abstract - Despite advancements in health care, cervical cancer is still one of the most prevalent causes of death amongst women globally. This highlights the importance of early diagnosis, which significantly improves the chances of positive treatment outcomes. Traditionally, the Pap sample screening has been used; however, its interpretation is human-dependent, which causes variability and prospective delays in patient treatment. To address these issues, this study offers a model called CerviScan, which utilizes Convolutional Neural Net-works, specifically InceptionV3, to automatically multiclass cervical cell image classification. The SIPaKMeD dataset, containing 5 different types of cervical cell images, served as the training dataset for our model. Through transfer learning and extensive augmentation, we sought to improve generalization capability. As a result, CerviScan was able to surpass a classification threshold of 96.8%, exhibiting remarkable accuracy and recall for all cell type categories. Concerning this medical imaging problem, InceptionV3, along with fine-tuning techniques, has deeply enhanced classification performance owing to its deep feature extraction capabilities. This system is highly accurate, cost-effective, and efficient, which offers remarkable assistance in eliminating repetitive tasks for pathologists and thereby accelerating the diagnosis process. Adapting deep learning methodologies to enhance the early detection of cervical cancer, as discussed in CerviScan, demonstrates the potential it has to improve cervix cancer detection and healthcare equality on a global scale.
Authors - Bhargav Dhengre, Reena Satpute Abstract - Lightning Web Components (LWC) is a new, ambient framework for creating dynamic and responsive user interfaces for the Salesforce ecosystem. LWC is built on web standards and uses these standards to enhance user experience (UX) through improved performance, customizability, accessibility, and integration. This report outlines the areas in which LWC enhances UX through faster rendering (through Virtual DOM, lazy loading), reusable and themeable components, and ARIA support/semantic HTML for accessibility. In addition to accessibility, LWC is built for seamless integration with Salesforce data and third-party libraries, as well as other developer-friendly tools that speed up application development while being consistent and scalable. Use cases for LWC have already shown tremendous improvements in existing applications, enhanced e-commerce platforms, enhanced load speeds, packaging checkout information to streamline checkout, and responsiveness for all mobile devices. The modularity or components in LWC can help decrease the effort to maintain components while increasing engagement, session creation, and conversion rates. The future of LWC has great potential in terms of AI integration, more and more component libraries, and added compatibility with cross-platform applications in the Salesforce ecosystem. LWC is likely to become a key technology for Salesforce development. Organizations that adopt LWC are likely to provide users with a fast and intuitive application to satisfy users and help organizations operate more efficiently in their business operations. For that reason, Salesforce is and will continue to be an elite leader in the development of enterprise CRM technologies.
Authors - Rochma Sudiati, Ayatulloh Michael Musyaffi, Eka Ary Wibawa, Dinda Rahma Tiara, Putri Haryani, Ellis Annisa, Hera Khairunnisa, Eka Septariana Puspa, Surya Anugrah Abstract - Digital transformation in tax administration, particularly through the implementation of the Core Tax Administration System (CTAS), has brought significant changes in the way taxes are managed and reported in Indonesia. This study points to assess the viability of using CTAS in learning tax collection for bookkeeping understudies. The most center of this study is to analyze students' state of mind towards advanced system-based learning innovation and survey their level of fulfillment after utilizing CTAS. The inquire about strategy utilized is pre-experimental with a one gather pre-test and post-test plan, where understudies take after a recreation of utilizing CTAS to report charges carefully. Information were collected through multiple-choice questions and a Likert scale-based survey to degree students' understanding, attitude, and fulfillment with the utilize of the framework. The comes about appeared that in spite of the fact that there was a slight decrease in positive attitude towards advanced learning, students' fulfillment level expanded altogether after taking part within the recreation utilizing CTAS. This consider concludes that although innovation encompasses a positive impact on understudy learning fulfillment, its impact on learning demeanor isn't exceptionally noteworthy. Therefore, there's a require for change in learning strategies to extend students engagement with charge innovation, as well as guaranteeing that advanced learning can be more effective in making strides students' understanding and aptitudes in tax assessment. This inquire gives understanding into the significance of innovation integration in taxation instruction to form graduates who are ready to confront challenges in an progressively digitized world of work.
Authors - Harsh Chauhan, Henrijs Kalkis Abstract - To analyze the process of internal competition within organization’s structure, the concentration is on employee’s perspective of internal social comparison followed by intra-organizational competition developing between subordinates. The organization’s perspective of internal competition is subjected to intra-organizational evolution for developing competitiveness in business operations. For evaluation of internal competition in organization a comprehensive literature review has been developed in 2 parts. Part 1: “Intra-organizational competition”: Intra-organizational competition is the resultant of Internal social comparison. Not all employees are competition ready. Employees benchmark their performance and contribution against each other. They compare remuneration and work behavior. Part 2: “Intra-organizational evolution”: Based on ‘Variation’ associated with decentralization of command, ‘Selection’ in terms of allocating scares resources and ‘Retention’ depending on managerial ability for implementing organizational strategy for exhibiting internal competition. Scientific database such as Google Scholar, Emerald Insight, EBSCO host and Science Direct has been researched. 37 research papers includes internal social comparison and intra-organizational competition, similarly 23 research papers are selected for intra-organizational evolution. Methodology of selecting papers is based on PRISMA 2020. The literature review emphasize on treating co-workers as competitors, however organizations management focuses on increasing productivity by developing internal competition. A conceptual framework of internal competition has been developed. The level of competition and control between employees and business management derives the span and scale of internal competition within the organization’s boundaries.
Authors - Jose Luis Chavez Torres, KunYong Zhang, Camila Nickole Fernandez Morocho, Tyrone Alexander Guarderas Cabrera Abstract - This study investigates the ability of vetiver grass (Chrysopogon zizanioides) to improve the geomechanical characteristics of fine soils in the province of Loja, Ecuador. Given the problem of erosion and slope instability in the region, the effect of the vetiver root system on soil shear resistance was experimentally evaluated. Undisturbed soil samples were extracted, both with the presence of vetiver and control (without vetiver), at depths between 0.80 and 1.5 meters. Laboratory tests included physical characterization of the soil and geomechanical tests such as the undrained consolidated triaxial test (CU) and the direct shear test (CD). The results obtained demonstrate a notable increase in the apparent cohesion and shear resistance of the samples with vetiver roots compared to the control samples. The presence of vetiver markedly enhanced the soil's shear resistance capacity, reflected in a considerably greater apparent cohesion in the reinforced samples. These findings validate vetiver grass as an effective and sustainable bioengineering solution for slope stabilization and mitigation of erosive processes in soils with characteristics similar to those studied.
Authors - Sunil Sangve, Prathmesh Kalaskar, Yash Kathoke, Tejas Joshi, Amartya Khandare, Anish Kadu Abstract - Elderly individuals and those with chronic illnesses often struggle to stick to their medication schedules, especially when they do not have someone around to help. To address this challenge, this paper introduces an IoT-based medicine dispenser designed to automatically dispense medication at specific times of the day—morning, noon, and night. The system is built using an Arduino microcontroller, servo motors, LEDs, an LCD display, and a buzzer to provide clear visual and audio reminders. The design of the medicine dispenser facilitates easy management of medication regimens. A basic cardboard container holds three compartments designed for medication at different times of the day. The servo motor operates the dispensing system to deliver medications at the correct times. Through a app users and caregivers can effortlessly manage medication schedules which enhances medication adherence and leads to better health results.
Authors - Happy Patel, Anuradha Desai, Moksha Patel Abstract - Breast cancer can be considered one of the fatal disorders distinguished by unusual and uncontrolled development of breast cells, which requires early and precise detection in order to be effectively treated. Ultrasound image is frequently used for breast cancer screening as it is widely available and non- invasive. Diagnostic difficulties may arise from the subjective and variable manual interpretation of ultrasound pictures. In this work, we suggest a Deep Learning Model utilizing EfficientNetB4 for automated classification of ultrasound images of breast cancer. There are 647 images in the collection that have been categorized as either benign or malignant. Binary masking, histogram equalization, and grayscale conversion are some of the steps to improve feature extraction. The proposed model outperforms traditional CNN architectures achieving an accuracy of 90.24% on the testing dataset after being trained with a transfer learning approach. The model indicates exceptional sensitivity in identifying malignant cases, decreased the rate of false-negative outcomes, and increased diagnosis accuracy. To ensure efficient model training, accurate iterative improvement, and consistent performance improvement, learning rate scheduling and check pointing are used. The experimental results demonstrate how well EfficientNetB4 performs feature extraction and classification, making it a potentially useful tool to help radiologists diagnose breast cancer. By providing a better approach for identifying and categorizing breast cancer, this research promotes the use of deep learning in the field of medical imaging.
Authors - Prince Kelvin Owusu, Philomina Pomaah Ofori, Moses Aggor Ofori, Dzordzoe Koffie-Ocloo, Gibson Afriyie Owusu, Martins Larweh Nuertey Abstract - The Internet of Everything (IoE) extends traditional IoT by integrating people, processes, data, and things into a highly dynamic and context-sensitive ecosystem. This convergence introduces complex security, privacy, and trust challenges that cannot be effectively addressed using static or device-centric models. In this paper, we propose CAT-M, a novel Context-Aware Trust Management framework that integrates dynamic trust evaluation, semantic policy enforcement, and context-sensitive privacy controls to secure heterogeneous IoE environments. CAT-M leverages fuzzy logic, semantic translation, and lightweight cryptography to ensure scalable, interoperable, and human-centric security. Through simulation in a smart healthcare scenario, we demonstrate the framework’s effectiveness in reducing privacy leakage, improving trust accuracy, and enabling real-time access control. The results highlight CATM’s potential as a unified approach to building secure and trustworthy IoE systems, while paving the way for future enhancements through intelligent trust prediction and cross-domain interoperability.
