Authors - Fhatuwani Mapande, Tranos Zuva, Kayode Oyetade Abstract - This paper presents a comprehensive and integrative review of key user perception models in the context of technology adoption. It critically examines five influential frameworks like Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DOI), Innovation Adoption Lifecycle Model, and Technology Readiness Model (TRM) to uncover the multidimensional factors shaping user attitudes and behaviors toward emerging technologies. Through a structured comparative analysis, the study explores core constructs such as perceived usefulness, ease of use, social influence, user readiness, and psychological traits including optimism and discomfort. The novelty of this work lies in its synthesis of diverse theoretical perspectives, offering a holistic view that bridges cognitive, emotional, and sociocultural dimensions of technology adoption. The findings underscore the significance of integrated, user-centered approaches and highlight the role of contextual and sector-specific variables in influencing adoption outcomes. Practical recommendations are provided for researchers, developers, educators, and policymakers to design inclusive and adaptive strategies that enhance technology acceptance and sustained engagement. This study contributes to advancing theoretical understanding and guiding practical interventions in the evolving landscape of digital transformation.
Authors - Staphord Bengesi, Hoda El-Sayed, Md Kamruzzaman Sarker Abstract - Alzheimer's and Parkinson's diseases are two progressive neurodegenerative disorders that primarily affect senior citizens worldwide, and currently, there is no cure. In recent years, the number of diagnosed cases has been increasing. Since both diseases have an impact on the brain, MRI images are used as a crucial diagnostic tool. With advancements in AI, machine learning models are showing great promise in diagnosing and classifying MRI images. To explore this potential, we developed and tested five transformer models, such as ViT, Swin, DeiT, MedT, and Swin-ViT, using a Kaggle dataset containing MRI images from individuals with Alzheimer’s, Parkinson’s, and healthy controls. The models were evaluated on both a balanced dataset of over 2,900 samples and an unbalanced dataset of more than 7,000 samples. Our findings revealed that models trained on the unbalanced dataset outperformed those trained on the balanced dataset, highlighting the advantage of larger datasets in enhancing model performance.
Authors - Zuzana Trabalkova, Martin Stevik, Kamil Zelenak, Jakub Dandar, Zdenek Straka, Daniel Kvak, Karolina Kvakova, Petra Ovesna Abstract - The growing demand for chest radiography in healthcare, combined with radiologist shortages and increasing workloads, underscores the need for innovative diagnostic support tools. This crossover study evaluates the effect of commercially available deep learning-based automatic detection software (DLAD) on radiologists’ diagnostic performance in chest X-ray (CXR) interpretation. Five radiologists independently assessed a dataset of 540 anonymized CXRs, both independently and with DLAD assistance, in two phases separated by a 30-day washout period. DLAD assistance significantly improved diagnostic performance, with overall sensitivity (Se) increased from 0.762 (95% CI: 0.705–0.811) to 0.911 (0.870–0.941, p < 0.001), while specificity (Sp) remained unchanged at 0.850 (0.805–0.887, p = 0.331). The positive predictive value (PPV ) slightly improved from 0.810 (0.755–0.856) to 0.836 (0.788–0.876, p = 0.331), and the negative predictive value (NPV ) increased from 0.810 (0.763–0.850) to 0.941 (0.882–0.947, p < 0.001). These improvements were consistent across radiologists, with notable reductions in false-negative rates. The findings emphasize DLAD’s potential to standardize diagnostic accuracy, enhance sensitivity, and support radiologists in chest X-ray interpretation. These results highlight the clinical value of AI-assisted workflows in improving detection rates while maintaining specificity.
Authors - Prince Kelvin Owusu, Caleb Annan, Ruhiya Abubakar, Moses Aggor, Emelia Sarpong, Gibson Afriyie Owusu, Jefferson Oduro Asiamah, Martins Larweh Nuertey Abstract - This study explores the market feasibility, challenges, and prospects of integrating mini-grids in island communities on Ghana’s Volta Lake, utilizing an exploratory sequential mixed-method design. Purposive and census sampling techniques were employed for qualitative and quantitative research, respectively, with 65 participants. Thematic analysis was applied to qualitative data from semi-structured interviews, while the Analytic Hierarchy Process (AHP) assessed Likert scale-based questionnaire responses. Identifying 21 challenges categorized into economic, political, technical, environmental, and social classes, economic challenges ranked highest (38.64%), with access to nance as the most significant challenge (12.03%). Despite a viable market, the study highlights a potential decrease in donor funding for mini-grid development in Ghana. Significantly, it concludes that policy unsuitability has cascading effects, necessitating a modification in the approach to enhance minigrid development in Ghana, emphasizing policy, tari scheme, and business model adjustments for holistic improvement.
Authors - Prince Kelvin Owusu, Philimina Pomaah Ofori, Moses Aggor, Gibson Afriyie Owusu, Jefferson Oduro Asiamah, Martins Larweh Nuertey, Joseph Djossou Akwetey, Joel Nana Sarfo Konadu Abstract - The integration of immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) is revolutionizing global marketing and advertising strategies, yet their application within Ghana’s communication landscape remains underexplored. This study investigates the pioneering role of AR and VR in transforming marketing practices in Ghana, with a focus on how these technologies influence consumer engagement, brand perception, and strategic communication. Employing a mixed-methods research design, the study combines qualitative interviews with 15 marketing professionals across major urban centers and quantitative survey data from 250 consumers who have interacted with immersive advertisements in retail, real estate, and tourism sectors. The findings reveal a rising trend in experimental AR/VR adoption among Ghanaian firms, driven by a desire to differentiate brands and deepen customer interaction. However, results also indicate significant barriers, including high implementation costs, limited technological infrastructure, and a lack of skilled personnel. Consumer responses demonstrated high engagement and positive emotional reactions to AR/VR content, particularly among younger demographics, though accessibility concerns persist. The study concludes that while AR and VR offer transformative potential for Ghana’s marketing sector, their long-term success depends on strategic investment in digital infrastructure, public-private partnerships, and targeted capacity-building programs. It recommends that policymakers support immersive technology adoption through subsidies and training initiatives, while marketers should focus on culturally relevant, mobile-optimized AR/VR campaigns to maximize reach and effectiveness. This research contributes to the growing discourse on digital innovation in emerging economies and provides a roadmap for integrating immersive technologies into Ghana’s evolving communication ecosystem.
Authors - Fawzy Alsharif, Irem Yildirim Abstract - Microwave imaging is a promising non-invasive technique for early stage cancer detection, leveraging its sensitivity to variations in the dielectric properties of biological tissues. In this work, a compact ultra-wideband (UWB) antenna specifically designed for lung cancer imaging is presented and analyzed through electromagnetic simulations. The antenna is fabricated on a Rogers RT5880 substrate (εr = 2.2, thickness = 1.65 mm) with overall dimensions of 23 × 21 × 1.58 mm³ and is impedance-matched to a 50 Ω feedline. Performance evaluations using CST and HFSS reveal operation across three frequency bands centered at 3.08 GHz, 6.04 GHz, and 9.54 GHz. The antenna achieves a peak gain of 4.52 dBi and a maximum radiation efficiency of 86% at the highest frequency. It offers a wide operational bandwidth from 2.58 GHz to 11.67 GHz. A realistic lung phantom modeled in CST demonstrates the antenna’s effectiveness in detecting signal changes caused by dielectric contrast in tissues, highlighting its potential for accurate and non-invasive lung cancer diagnosis.
Authors - Manideep Pendyala, Udit Goel, Jim Samuel, Pal Patel, Janki Kanakia, Alexander Pelaez, Neel Savalia, Tanya Khanna Abstract - Emojis have become an integral part of modern digital communication. Despite their widespread use, most sentiment analysis methods and models disregard emojis during preprocessing, leading to the loss of vital emotional cues. This paper introduces a curated dataset of sentence pairs, with and without emojis, each annotated across three sentiment categories, to assess the impact of emoji inclusion on sentiment classification. We evaluate emoji-inclusive and emoji-exclusive strategies against our human-determined gold standard, using a range of approaches, including the traditional lexicon-dictionary based methods, and also artificial intelligence (AI) methods including pre-trained machine learning (ML) based classifiers, and large language models (LLMs). Results show that retaining emojis significantly enhances the performance of all the LLMs we tested, with models such as Qwen, Deepseek, Bert and Mistral achieving accuracy improvements of over 25%, over an emoji-exclusive strategy. These findings highlight that emojis carry meaningful semantic and affective signals. We emphasize the limitations of current approaches to emoji handling, where emojis are often ignored or treated as irrelevant noise. Instead, we advocate for more thoughtful methods that recognize emojis as meaningful components of communication and incorporate them as valuable sources of information.
Authors - Diego Ricardo Salazar-Armijos, Hector Mauricio Revelo-Herrera, Holger Alfredo Zapata-Mayorga, Paul Diaz-Zuniga, Aida Noemy Bedon-Bedon, Nelson Fernando Vinueza-Escobar Abstract - This research, conducted within the framework of the project "Dropout in Higher Education – Early Warning Model with Emerging Technologies at the University of the Armed Forces ESPE", analyzed the factors influencing the dropout of Information Technology students at the Santo Domingo campus between 2017 and 2023. Socioeconomic and academic variables were considered, based on enrollment data in accordance with the regulations of higher education in Ecuador. Logistic regression and decision tree algorithms were applied due to their classification capabilities and statistical relevance. Additionally, an ANOVA-based comparison was performed. The study concluded that academic performance is the main factor associated with student dropout.
