Sustainable grazing management requires balancing livestock productivity with ecosystem preservation, yet existing monitoring systems integrate heterogeneous data from IoT sensors, satellite imagery, and field surveys without a unified semantic layer, limiting holistic decision support. This paper proposes ONTOGRAZING, an ontology-based monitoring architecture for sustainable grazing management. Using the Uschold and King ontology engineering framework, domain knowledge was collected through surveys involving 23 livestock farmers and 4 agro-pastoral institutions in Cameroon, complemented by a systematic literature review. Seven core concepts and fourteen semantic relationships were modeled in OWL using Protégé. A five-module monitoring architecture composed of Query Reformulator, Data Integrator, Source Monitoring, Alert, and Storage modules was designed around the ontology. ONTOGRAZING was evaluated using the HermiT 1.4.3.456 reasoner and SPARQL queries. The ontology contains 47 classes, 14 object properties, and 9 data properties, and passed all consistency checks. Comparative analysis demonstrates that ONTOGRAZING is the first ontology to jointly cover forage management, dietary preferences, pasture composition, ecological–economic trade-offs, and land-use regulations. These results highlight the potential of ontology-based integration to improve interoperability and semantic decision support in agro-pastoral systems, while future work will focus on full prototype implementation and integration with real-world IoT platforms and agricultural databa.

One of the effects of the rapid adoption of the cashless policy in Nigeria and the introduction of new naira notes is operational difficulties among financial institutions, which have led to a significant increase in ATM card theft and fraud among clients. Absence of real-time analysis of access points, combined with the intermittent and simultaneous quality of fraudulent dealings, are two major factors that make conventional fraud detection systems fail regularly. Towards reducing ATM fraud, this paper will present a high-performance, intelligent based, AI-based model to integrate three factors of biometric authentication, spending pattern analysis, and password verification into a three-factor model. Results of experiments based on real banking data prove that the proposed solution is superior to traditional models in terms of accuracy, precision, recall, and F1-score. The model uses an optimized Bi -Directional Long Short-Term Memory (BiLSTM) network to analyze historical ATM transaction records and identify behavioral abnormalities that could point to fraud. A Cuttlefish Optimization (MCFA) algorithm that is based on mapping is used to fine-tune the parameters, thus improving the reliability and accuracy of the classification. Biometric verification combined with behavioral modeling using AI stands out as a scalable and dependable framework of minimizing ATM card fraud and instilling confidence within the banking industry.

This paper introduces a novel approach an AI-powered Multi-Agent System (MAS) for dynamically optimizing support to enhance real-time travel reservation-side customer experience. It has an architecture with specialized agents working together under a centralized agent manager, including natural language processing, booking, optimization, and context-aware modules. The system proposes to address common constraints encountered in traditional travel platforms: delayed response to user queries, ambiguity treated poorly, and adaptation to user preferences not incorporated. Through simulated environments and realistic use cases, the MAS enables complex travel requests to be dealt with, availability to be changed dynamically, and user satisfaction to be enhanced. The modular architecture design allows easy integration into larger smart tourism infrastructures. This study thus pushes the frontier further by merging AI, multi-agent collaboration, and user-centered design in a time-sensitive application world. Future directions include adaptive learning agents, multilingual interaction capabilities, and broadening the domain applications to hotel management and intelligent itinerary planning.

This research implements an intelligent, bilingual pregnancy health monitoring system for expectant mothers. A significant problem commonly experienced by expectant mothers in rural areas in Nigeria is the unavailability of a decent antenatal system and a shortage of experienced medical personnel and equipment. The proposed system comprises IoT sensors, including Electrocardiogram (ECG), body temperature, and heart rate sensors, connected to an ESP32 microcontroller for data acquisition and transmission. A predictive system built using Random Forest and Support Vector Machine (SVM) classifiers categorises pregnancy risk into low, medium, and high. A Flask-based web application for real-time data visualization and diagnosis was developed to display the collected data and visually represent the risk level diagnosis. The performances of the predictive models, Random Forest and Support Vector Machine (SVM), were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved an accuracy surpassing SVMs by a margin of 5.28%. Random Forest and SVM precision were then compared and there was an improvement of 6.49%. 
In addition, Random Forest had a higher recall than SVM by 6.58%, and also had a performance increase of 6.49% on F1-score as compared to SVM. The comparative analysis shows that the Random Forest model works better than SVM in all the main measures. In this project, the Random Forest model was better than the SVM because it uses ensemble learning to manage the non-linear relationship, imbalance data and noise better to achieve superior accuracy, recall, and the F1 Scores. It was also more reliable in categorizing risks in pregnancy, as it was interpretable, which was also strong and guaranteed the timely and suitable intervention of health care.

Alzheimer disease is a chronic neurodegenerative disorder and the primary cause of dementia among the population, which has a huge burden to the patients, their caregivers and the health care system. Timely intervention is necessary to reduce disease progression, facilitate timely intervention and improve the quality of life. But the traditional forms of diagnostic are frequently costly and non-available especially in resource-deficient environments. The research paper proposes an interpretable and cost-efficient machine-learning model that can be used to identify the presence of Alzheimer disease at its early stages based on clinical and demographic metrics based on the Open Access Series of Imaging Studies cross-sectional dataset, which contains 436 participants. The data consists of seven numeric and two categorical variables, whereas the Clinical Dementia Rating was changed into two categories namely demented and non-demented. An extensive preprocessing pipeline was used, which entailed missing value imputation, categorical encoding and elimination of irrelevant variables, as well as class balancing with the Synthetic Minority Oversampling Technique. A number of machine learning models were tested, which comprise Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. The results show that the highest accuracy of 92% was attained using the model implemented by the ensemble and the tree, with the most accuracy being returned by the Random Forest and the ensemble model. Random Forest, too, had a sensitivity of 95%, whereas Gradient Boosting and Extreme Gradient Boosting had the highest area under the receiver operating characteristic curve of 98%. The models were implemented as a lightweight web application on the Flask framework, which can make real-time predictions and color coded. The system illustrates the possibility of combining interpretable machine learning with web technologies to make it possible to conduct easy and effective early screening of Alzheimer disease under resource-limited healthcare conditions.

The fields of augmented engineering are confronted with formidable obstacles because of the absence of chances for self-paced learning, the wide coverage of undergraduate curricula, uneven academic content standards, and shortages in teacher knowledge. This study suggests a thorough strategy to overcome these drawbacks. In order to enhance current course offerings through bridge or add-on courses, we want to integrate the NPTEL Swayam and NIELIT platforms as additional resources of Self-Paced Learning. This plan will improve students’ knowledge acquisition, give them various learning options, and promote a continuous learning culture reinforced by certification processes. The project intends to solve issues with skill development, student engagement, and standardized academic material by incorporating various online platforms as supplemental or add-on courses which are used for Curriculum Enhancement. To test the efficacy of this strategy, a pilot deployment encompassing course selection, curriculum integration, and student enrollment was carried out. Positive student results in terms of knowledge acquisition and skill enhancement are indicated by preliminary studies. Nonetheless, issues with workload management and technical difficulties were noted.

The proliferation of massive IoT networks has created an environment where distributed AI can be achieved. At the same time, it introduces serious privacy and security challenges. Federated learning (FL) allows training local models on IoT devices and aggregating them without sharing data, but still suffers from problems such as gradient inference attack, Byzantine model poisoning attack and the failure in single point of failure centralized aggregation point. In this paper, we propose QFL-BC, a framework combining Quantum Key Distribution (QKD) and a permissioned blockchain to holistically tackle the problem. Using the BB84 protocol with decoy states, QKD generates a One-Time Pad key to encrypt the model update and achieve information-theoretic security with provable security against a quantum attacker. The central aggregator is replaced by the permissioned blockchain with a smart contract, which ensures an immutable audit trail and distributes the orchestration of FL training decent rally, as well as imposes a penalty on malicious participants by automatic reputation score maintenance. The experiments with MNIST and CIFAR-10 on 100 IoT clients under Non-IID conditions show QFL-BC obtains an accuracy of 96.8% against 41.5% for classic FL under 10% poisoning attack (133% relative improvement). We have tested its robustness across adversary percentages of 10%-40% with accuracy above 87.3% and measured scalability up to 500 clients, showing good degradation, communications overhead of 5.84 MB per round, which is only 12.3% higher than the classic FL and analysed latency and energy to evaluate its feasibility on resource-constrained IoT devices.

This study presents RiceVision, a cross-platform software system for real-time rice variety identification using deep learning–based image analysis. Unlike prior work that primarily focuses on classification accuracy, RiceVision emphasizes reproducibility, deployment, and usability in real-world agricultural environments. The system integrates a web-based platform and an offline-capable Android application within a unified architecture, ensuring consistent preprocessing and inference across platforms. Deep learning models are deployed using TensorFlow and TensorFlow Lite to support both online and on-device inference. The proposed hybrid framework combines convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures using a stacked ensemble strategy. Experimental evaluation on a 62-class rice variety dataset demonstrated strong classification performance, where the stacked ensemble achieved an average 5-fold validation accuracy of 98.64%, outperforming individual VGG16 (90.64%) and ViT-B/16 (91.28%) models. The system further demonstrated stable convergence behavior and low inter-fold variance, indicating robust generalization capability. A centralized model management mechanism enables version control and seamless updates across deployment platforms. Detailed model configurations, validation results, and explainability analyses are provided in the Supplementary Material. RiceVision highlights the potential of deployable AI systems for practical decision support in digital agriculture.