European Journal of Artificial Intelligence and Machine Learning

Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

2024-08-24T14:16:57-04:00

Artificial Intelligence (AI) and Machine Learning (ML) have the potential to revolutionize education, with applications ranging from personalized learning systems to teaching students about AI concepts. Beyond utilizing and integrating these technologies, it is crucial to comprehend the fundamental principles governing the field. Choosing an “attractive” area of AI suitable for students and engaging them is essential to introducing difficult Computer Science concepts. In particular, introducing these concepts in elementary and secondary (K-12) Education is not a simple task, as it involves complex algorithms and theories that could overwhelm young learners. To overcome this challenge, we can rely on nature-inspired or bio-inspired algorithms such as Swarm Intelligence (SI) family, and leverage block-based programming environments (like MIT Scratch or other Logo-like environments) to make AI concepts more accessible and intuitive for students. This article proposes the creation and implementation of simplified simulations inspired by the Artificial Fish Swarm Optimization Algorithm (AFSO)-namely how fish behave collectively in the ocean–as an educational tool for both elementary and secondary school students. The proposed educational methodology combines the integration of Constructionist Learning principles, as the “Creative Thinking Spiral” learning model, with the inquiry-based approach of the 5Es Instructional Model.

Illuminating the Future: Predictive Modelling of PV Output Using Machine Learning Techniques

2024-06-15T10:37:35-04:00

Leveraging solar energy will bring about a notable change in the fundamental production and use of power, and the parameters to achieve success in this area must be forecasted to aid steady production. This work entailed the use of advanced predictive machine learning models for optimal power output, reduced uncertainty, optimal resource planning, and a notably high degree of alignment with peak demands for energy for efficient power production from solar radiations. Models were generated by employing machine learning algorithms for data evaluation. The direct in-plane irradiance has the strongest correlation (1.00) with PV output, according to the results. Additionally, it indicated that the value of R²: 0.999567 of the Random Forest Regression was higher than all other regression models and the least Mean Squared Error (MSE) and Mean Absolute Error (MAE), 17.130680 and 2.28139, respectively. On the other hand, the Linear Regression’s Mean Squared Error (MSE), R², and Mean Absolute Error (MAE) values are, respectively, 20.645271, 0.999478, and 3.16270. Random Forest Regression is a stronger forecasting model because of its higher R² value, which also helps to explain variations in PV power output.

Assessment of the Accuracy of Various Machine Learning Algorithms for Classifying Urban Areas through Google Earth Engine: A Case Study of Kabul City, Afghanistan

2024-07-16T12:29:37-04:00

Accurate identification of urban land use and land cover (LULC) is important for successful urban planning and management. Although previous studies have explored the capabilities of machine learning (ML) algorithms for mapping urban LULC, identifying the best algorithm for extracting specific LULC classes in different time periods and locations remains a challenge. In this research, three machine learning algorithms were employed on a cloud-based system to categorize urban land use of Kabul city through satellite images from Landsat-8 and Sentinel-2 taken in 2023. The most advanced method of generating accurate and informative LULC maps from various satellite data and presenting accurate outcomes is the machine learning algorithm in Google Earth Engine (GEE). The objective of the research was to assess the precision and efficiency of various machine learning techniques, such as random forest (RF), support vector machine (SVM), and classification and regression tree (CART), in producing dependable LULC maps for urban regions by analyzing optical satellite images of sentinel and Landsat taken in 2023. The urban area was divided into five classes: built-up area, vegetation, bare-land, soil, and water bodies. The accuracy and validation of all three algorithms were evaluated. The RF classifier showed the highest overall accuracy of 93.99% and 94.42% for Landsat-8 and Sentinel-2, respectively, while SVM and CART had lower overall accuracies of 87.02%, 81.12%, and 91.52%, 87.77%, with Landsat-8 and Sentinel-2, respectively. The results of the present study revealed that in this classification and comparison, RF performed better than SVM and CART for classifying urban territory for Landsat-8 and Sentinel-2 using GEE. Furthermore, the study highlights the importance of comparing the performance of different algorithms before selecting one and suggests that using multiple methods simultaneously can lead to the most precise map.

A Hybrid Model for Detecting Intrusions on Network Logs

2024-06-13T10:28:22-04:00

The presence of malicious traffic presents a substantial risk to network systems and the integrity of confidential information. Organisations may enhance their protection against threats and mitigate the possible impact of malicious traffic on their networks by maintaining vigilance, deploying comprehensive security measures, and cultivating a cybersecurity-aware culture. The purpose of this study is to propose a theoretical framework for identifying and analysing potentially harmful network traffic within a network system. In order to identify and classify various types of malicious network traffic in a multi-class setting, we employed a dataset consisting of nine distinct categories of network system attacks. In order to optimise the performance of the model, an exploratory data analysis is conducted on the dataset. Exploratory data analysis (EDA) was employed to assess various aspects like the presence of missing values, correlation among characteristics, data imbalance, and identification of significant features. The findings derived from the exploratory data analysis indicate that the dataset exhibits an imbalance, which, if left unaddressed, may result in overfitting. The data imbalance was addressed with the implementation of the RandomOverSampling approach in Python, which involved executing random oversampling. Following the resolution of the data imbalance, a random forest classifier was employed to extract significant features from the dataset. In this study, a total of ten characteristics were extracted based on the ranking provided by the random forest model. The features that were extracted were utilised in the training process of the suggested model, which aims to identify and detect malicious activity within a network system. The findings of the model indicate a much improved level of accuracy in identifying malicious traffic within a network system, with an accuracy rate of 99.99%. Furthermore, the precision, recall, and F1-score metrics also demonstrate a consistent accuracy rate of 99.99%.

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

2024-04-10T01:35:53-04:00

Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.