Authors - Valinho Antonio, Eric Umuhoza, Pierre Bakunzibake, Moise Busogi Abstract - Accurate crop discrimination is vital for effective agricultural planning and sustainability management, especially in regions like Sub-Saharan Africa (SSA), where small-scale farming predominates and ground data is scarce. Conducting field surveys in SSA is challenging due to labor and cost constraints, as well as logistical and political barriers. This paper proposes a framework design of cost-effective satellite-based machine learning for crop type classification in crop growth with limited reference data. So, we have identified the important satellite timeseries features and the machine learning model architecture to be used to timely and accurately identify crops in a small and intercropped farms. This study therefore has great role on agricultural data collection at large scale which is one of the ways to accomplish food security advocated by the sustainable development goal two, zero hunger.
Authors - Asini Silva, Thushani Weerasinghe Abstract - The rapidly evolving IT job market presents a significant challenge with the growing gap between academic curricula and industry needs. Our study addressed this misalignment with a data-driven approach. The study employed a constructive research approach to develop a user-friendly platform that extracts real-time data from LinkedIn job postings and creates a dataset of IT jobs and their top-demanding skills. The authors utilized tools such as Selenium and BeautifulSoup to extract job titles and descriptions, identifying the most sought-after skills within the IT industry. Testing and evaluation were conducted weekly for several months, resulting in a dataset consisting of more than 50 IT job positions, each listing 20 in-demand skills. This comprehensive dataset not only captures but also retains records over time, allowing for an in-depth examination of skills associated with historical postings. The analysis uncovers notable discrepancies between the skills highlighted in academic programs and those required by employers, revealing a critical barrier to graduate employability. Hence, this research provides valuable insights for educational institutions, policymakers, and job seekers, equipping them to align programs with industry expectations while empowering students to make informed decisions regarding skill development. Furthermore, the methodology utilized in this study is scalable. It can be applied to any region in the world, offering a robust framework for aligning education with the evolving demands of the job market.
Authors - Jose Luis Chavez Torres, Tyrone Alexander Guarderas Cabrera, Camila Nickole Fernandez Morocho, KunYong Zhang Abstract - This article presents a comparative structural analysis of two housing typologies: one composed of reinforced concrete and the other of steel frame structures. The structural design was carried out using the force-based design method (DBF), considering the seismic and geotechnical parameters of the study area, obtained through SPT tests and bibliographic sources. The fundamental period of both structures was determined, and the corresponding response spectrum was generated, enabling the structural design and subsequent load descent ac-cording to the NEC-2015 combinations. In the foundation stage, three viable alternatives were proposed: isolated footing, combined footing, and mat foundation. Using the specialized software SAFE 2016, the performance of each option was evaluated based on soil pressure, punching shear resistance, and settlement behavior. Finally, a cost-benefit analysis was performed, considering concrete volumes and steel quantities, to select the most technically and economically suitable structural and foundation system for the study area.
Authors - Taufik Iqbal Ramdhani, Riri Fitri Sari Abstract - Real-time and accurate road infrastructure monitoring is a major challenge in urban areas. Traditional methods, such as manual inspections by municipal staff or vehicular surveys using costly technologies like LiDAR or laser scanners, are prohibitively expensive, geographically constrained, and deployed infrequently. To address this, crowdsourcing has emerged as an effective approach for expanding both the coverage and frequency of infrastructure monitoring. Building on this concept, CrowPotChain introduces a novel platform that combines AI-driven pothole detection with secure blockchain-based report submission, ensuring tamper-proof and reliable crowdsourced data collection. The framework utilizes the YOLOv11s-seg model for semantic segmentation, combining convolutional neural networks (CNN) with transformer-based elements, which provides impressive detection metrics (Precision: 0.889, Recall: 0.894, mAP@0.5: 0.944). Every verified report includes geolocation, date/time, and pothole size, securely embedded in a Proof-of-Work (PoW) blockchain for verifiability and immutability. To examine the system's performance, a benchmark was performed on four setups: no AI & no blockchain, AI only, blockchain only, and AI + blockchain, using batches of transactions from 10 to 100. The findings show that the no AI & no blockchain deployment provides the most rapid per-transaction time (approximately 0.010s), followed by AI only (0.059s to 0.135s), blockchain only (0.075s to 0.162s), and AI + blockchain (0.145s to 0.696s). Although blockchain does incur substantial overhead, its combination with AI can still serve response needs for civic infrastructure crowdsource reporting. Future development will add gamification, NFT, and IPFS to enhance participation, encourage reporting, and provide scalable decentralized storage.
Authors - Jiwan N. Dehankar, Virendra K. Sharma Abstract - The advanced incorporation of Machine Learning (ML) in blockchain systems present special challenges related to security, scalability, and adversarial robustness. The traditional consensus protocols and aggregation techniques suffer from high latencies, susceptibility to Byzantine node attacks, and inefficiencies in communicating gradients that cripple real-time federated learning on the blockchain networks. On the other hand, existing solutions like PoW (Proof-of-Work) and centralized aggregation do not adapt dynamically to ML workloads and remain vulnerable to adversarial attacks, thus putting the model's integrity into jeopardy and causing grave computational overhead. To mitigate these issues, we present Blockchain-Federated Secure Learning Network (BFSL-Net), an infrastructural framework with a dual purpose of enhancing security and efficacy while providing scalability to blockchain-based ML systems. BFSL-Net is comprised of (1) the Multi-Tiered Hierarchical Consensus Framework (MHC-BCML), (2) Adaptive Byzantine-Resilient Aggregation (ABRA), (3) Secure Adversarial Gradient Masking (SAGM-MLBC) and (4) Hierarchical Graph Neural Network-Based Threat Intelligence (HGNN-TI). BFSL-Net, which brings all the above-mentioned methods into a unified system of real-time threat resistance federated learning. Proposed model shows an adversarial threat mitigation success of 99.6 %, marked 2.8 times improvement in efficiency of ML processing, and a 4.5-fold reduction in blockchain computational overhead, thereby promising secure, scalable ML production environments in the blockchain space.
Authors - Natalya Aleynikova, Anna Loskutova, Mikhail Matveev, Elena Sviridova Abstract - The paper solves the problem of controlling the states of a system that may exhibit prolonged transient processes. As an example, the learning process of students is considered. Control is carried out based on predicting the dynamics of student academic performance. Academic performance – one of the key indicators of learning quality – is typically measured on graded scales and represented, for instance, by time series of student grades (scores). Instead of traditional time series analysis of grades, the present paper proposes transitioning to a space of fuzzy states (categories): “Fail”, “Satisfactory”, “Good”, and “Excellent”. The dynamics of these fuzzy categories are described using a discrete-time Markov chain model with fuzzy states, analyzing not the current but the limiting (steady-state) distributions of a student’s states. The paper presents a recurrent algorithm for the transition from the space of numerical grades to the space of fuzzy states, constructing the stochastic matrix of the Markov chain. The properties of the stochastic matrix are investigated to determine the existence and uniqueness of the limiting state distributions. Additionally, an approach is proposed for identifying change points – the moments when a shift in a student’s performance trend occurs.
Authors - Jose Luis Chavez Torres, Camila Nickole Fernandez Morocho, Tyrone Alexander Guarderas Cabrera, Ulbio Fernando Mendoza Hidalgo, KunYong Zhang Abstract - This article analyzes a detailed lithological characterization of the Punzara area, located in Loja, Ecuador. Through a systematic field survey and spatial analysis through a geological map at a scale of 1:2500, six different lithological units were identified and delimited. These include heterogeneous alluvial deposits, clayey deposits characterized by a remarkable and anomalous presence of organic roots at depths between 1.0 and 1.5 meters, a massive sandy silt unit with outcrops up to 3 meters thick, a conglomerate with spheroidal clasts of 1 to 4 cm in a silt-sandy matrix, another conglomerate with intercalated lenses of sand and clay, and shale outcrops with intercalations of gray silts with thicknesses of up to 30 meters. The spatial distribution of these units, clearly visualized through the geological map, provided fundamental geological information for the understanding of the local geological framework and lays the foundations for evaluating their potential influence on slope stability in the area.
Authors - Gusti Anisa Wulandari, Dewi Susita, Mohammad Sofwan Effendi Abstract - This study investigates the mediating role of Innovative Work Behaviour (IWB) in the relationship between Knowledge Sharing (KS), Work Engagement (WE), and Job Performance (JP) among state civil apparatus (ASN) at the Directorate of Road Transport, Ministry of Transportation of the Republic of Indonesia. Using a mixed-methods approach, the research employs Structural Equation Modeling (SEM) with AMOS to analyze survey data from 203 respondents, followed by qualitative exploration to contextualize the statistical findings. Results show that KS and WE significantly influence IWB, and although they also directly impact JP, IWB fully mediates these effects. Descriptive analysis reveals gender-based differences in IWB, with female respondents showing higher scores than males, while males reported slightly higher scores in KS, WE, and JP. These findings highlight the strategic importance of fostering IWB to optimize job performance. The study recommends organizational focus on strengthening IWB through targeted interventions, and suggests future research include gender and educational background as potential moderating variables, supported by qualitative methods to deepen understanding.
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.
