Knowledge, Volume 4, Issue 4 (December 2024) – 8 articles

Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. Conventional CRMD detection and treatment methods are time-consuming, expensive, and ineffective. To address these challenges, we propose a real-time deep-learning model that provides disease detection and pesticide dosage recommendations. In the model development process, we collected 5000 maize leaf images experimentally, with permission from Haramaya University, and increased the size of the dataset to 8000 through augmentation. We applied image preprocessing techniques such as image equalization, noise removal, and enhancement to improve model performance. Additionally, during training, we utilized batch normalization, dropout, and early stopping to reduce overfitting, improve accuracy, and improve execution time. The optimal model recognizes CRMD and classifies it according to scientifically established severity levels. For pesticide recommendations, the model was integrated with the Gradio interface, which provides real-time recommendations based on the detected disease type and severity. We used a convolutional neural network (CNN), specifically the ResNet50 model, for this purpose. To evaluate its performance, ResNet50 was compared with other state-of-the-art algorithms, including VGG19, VGG16, and AlexNet, using similar parameters. ResNet50 outperformed the other CNN models in terms of accuracy, precision, recall, and F-score, achieving over 97% accuracy in CRMD classification—surpassing the other algorithms by more than 2.5% in both experimental and existing datasets. The agricultural experts verified the accuracy of the recommendation system across different stages of the disease, and the system demonstrated 100% accuracy. Additionally, ResNet50 exhibited lower time complexity during model development. This study demonstrates the potential of ResNet50 models for improving maize disease management. Full article

Education 4.0 arises to provide citizens with the technical/digital competencies and cognitive/interpersonal skills demanded by Industry 4.0. New technologies drive this change, though time-independent learning remains a challenge, because students might face a lack of support, advice and surveillance when teachers are unavailable. This study proposes complementing presential lessons with online learning driven by ChatGPT, applied as an educational tool able to mentor K-12 students learning science at home. First, ChatGPT’s performance in the field of K-12 science is evaluated, scoring A (9.3/10 in 2023, and 9.7/10 in 2024) and providing detailed, analytic, meaningful, and human-like answers. Then, an empirical interventional study is performed to assess the impact of using ChatGPT as a virtual mentor on real K-12 students. After the intervention, the grades of students in the experimental group improved by 30%, and 70% of students stated a positive perception of the AI, suggesting a positive impact of the proposed educational approach. After discussion, the study concludes ChatGPT might be a useful educational tool able to provide K-12 students learning science with the functional and social/emotional support they might require, democratizing a higher level of knowledge acquisition and promoting students’ autonomy, security and self-efficacy. The results probe ChatGPT’s remarkable capacity (and immense potential) to assist teachers in their mentoring tasks, laying the foundations of virtual mentoring and paving the way for future research aimed at extending the study to other areas and levels, obtaining a more realistic view of AI’s impact on education. Full article

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy is above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a Zener diode, biased on the avalanche region. Results showed that, observing some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits. Results showed an average of 2.8 dB in the signal-to-noise ratio for square and triangular waveforms. Full article

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid cancers, is the most prevalent type of thyroid malignancy. Accurate prediction of DTC is crucial for improving patient outcomes. Machine learning (ML) offers a promising approach to analyze risk factors and predict cancer recurrence. In this study, we aimed to develop predictive models to identify patients at an elevated risk of DTC recurrence based on 16 risk factors. We developed six ML models and applied them to a DTC dataset. We evaluated the ML models using Synthetic Minority Over-Sampling Technique (SMOTE) and with hyperparameter tuning. We measured the models’ performance using precision, recall, F1 score, and accuracy. Results showed that Random Forest consistently outperformed the other investigated models (KNN, SVM, Decision Tree, AdaBoost, and XGBoost) across all scenarios, demonstrating high accuracy and balanced precision and recall. The application of SMOTE improved model performance, and hyperparameter tuning enhanced overall model effectiveness. Full article

Student performance prediction is a critical research challenge in the field of educational data mining. To address this issue, various machine learning methods have been employed with significant success, including instance-based algorithms, decision trees, neural networks, and ensemble methods, among others. In this study, we introduce an innovative approach that leverages the Regularized Greedy Forest (RGF) algorithm within an active learning framework to enhance student performance prediction. Active learning is a powerful paradigm that utilizes both labeled and unlabeled data, while RGF serves as an effective decision forest learning algorithm acting as the base learner. This synergy aims to improve the predictive performance of the model while minimizing the labeling effort, making the approach both efficient and scalable. Moreover, applying the active learning framework for predicting student performance focuses on the early and accurate identification of students at risk of failure. This enables targeted interventions and personalized learning strategies to support low-performing students and improve their outcomes. The experimental results demonstrate the potential of our proposed approach as it outperforms well-established supervised methods using a limited pool of labeled examples, achieving an accuracy of 81.60%. Full article

Knowledge comes in various forms: scientific, artistic, legal, and many others. For most non-computer scientists, it is far easier to express their knowledge in text than in programming code. The dynamic decision tree system is a system for supporting the authoring of expertise in text form and navigation via an interface that limits the cognitive load on the reader. Specifically, as the reader answers questions, relevant tree nodes appear and irrelevant ones disappear. Searching by a keyword can help to navigate the tree. Database calls bring in information from external datasets. Links bring in other decision trees as well as websites. This paper describes the reader interface, the authoring interface, the related state-of-the-art work, the implementation, and case studies. Full article

In higher education institutions, there is a strong interaction between research and teaching activities. This paper presents a case study on the research–teaching nexus based on an analysis of academic results related to the course “Instrumentation and Data Analyses in Marine Sciences” within the Marine Sciences and Technologies Bachelor’s Degree at the Universitat Politècnica de Catalunya (UPC), taught at the Vilanova i la Geltrú campus (Barcelona, Spain). The start of this degree in the academic year 2018–2019 allowed the assignment of technological subjects in the degree to a research group with extensive experience in the research and development of marine technologies. The first section of this paper aims to provide a justification for establishing the Marine Sciences and Technologies Bachelor’s Degree. It highlights the necessity of this program and delves into the suitability of the profiles of the professors responsible for teaching marine technology subjects. Their entrepreneurial research trajectory and their competence in electronic instrumentation are strong arguments for their appropriateness. The next section of the paper explores a detailed analysis of academic results based on surveys and student performance indices. Through a thorough examination of these data, this case study demonstrates, within the context of all UPC degrees, that assigning a research group made up of experienced professors and researchers in the field who are accustomed to working as a team produces superior academic results compared to assignments to professors who do not work as a team. Teamwork presents specific skills necessary for operating the infrastructures and equipment associated with an experimental degree. Full article

In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a model region in Germany, our results demonstrate an 18% improvement in the prediction interval width. While traditional Bayesian CNN models may yield broader prediction intervals to adequately capture uncertainties, the 2DCNN-QD method prioritizes quality-driven interval optimization, resulting in narrower prediction intervals without sacrificing coverage probability. Notably, this approach is nonparametric, allowing it to be effectively utilized across a range of real-world scenarios. Full article