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.
