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AG- Learn: Adaptive Gamification Approach for Interactive Learning Systems

SN computer science 2025年第2期6卷 1-32页

作者： Bennani, Souha Maalel, Ahmed Ghezala, Henda Ben Daouahi, Achref RIADI Laboratory National School of Computer Sciences University of Manouba Manouba 2010 Tunisia Higher Institute of Applied Science and Technology University of Sousse Sousse 4003 Tunisia Envast Technopôle de Sousse Cité Hammam Maarouf Route Ceinture de Sahloul Sousse 4000 Tunisia

The learner and the learning content are the key factors that contribute to the success of any learning system. In this regard, scientific research is focused on these two factors to improve learner engagement with the learning environment. Each learner seeks an environment appropriate to their needs, which provides personalized and adaptive content to ensure a more successful and useful learning experience for them. To deliver this content, we identify the learner’s intentions, needs and preferences. Existing research is emerging towards the adaptation of learning systems and shows that modeling the learner profile is an important process for achieving this adaptation. We have found that it is necessary to focus on the learner and focus on their dynamic profile to improve their engagement with the learning environment. In this work, we integrate the concept of adaptation into the learning systems, but also to integrate the concept of gamification, based on the dynamic learner profile, to offer an environment not only adequate for academic needs but also a fun and entertaining environment. It is therefore this context that we propose an adaptive gamification approach called “AG-Learn” (Adaptive Gamification in Learning) by focusing on several learner characteristics, using machine learning techniques and recommender systems. We have combined the two concepts “adaptive learning” and “gamification” to provide gamified learning adapted to the learner profile with the objective of motivating and engaging them more and improving the quality of education and learning outcomes. We tested the effectiveness of our contributions in a gamified learning environment called “Class Quiz”. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive gamification Adaptive learning Artificial intelligence E-learning Learner profile Recommendation system

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GyanSagAR 1.0: An AR Tool for K-12 Educational Assistance

GyanSagAR 1.0: An AR Tool for K-12 Educational Assistance

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Taneja, Shweta Sharma, Nidhi Bhatt, Arshita Gupta, Khushboo Department of Computer Science Bhagwan Parshuram Institute of Technology GGSIPU Delhi India Department of Applied Science VIPS Technical Campus Shalimar Bagh Delhi India

ISBN: (纸本)9789811948305

With the slowly shifting dynamics of education from simple textbook recitals to practical experiments, video lectures, and interactive classrooms, Education and learning domain is slowly adopting the miracles of technology to make this process exciting and enjoyable for students at all levels. While these solutions may have been the best alternative to the old school teaching and learning method, the COVID-19 pandemic brought a colossal change to this system. Keeping this in mind, we have applied the concept of Augmented reality in education and developed a tool- GyanSagAR 1.0 for K-12 educational assistance. Our tool currently supports common STEM subjects such as physics, chemistry, mathematics, and biology which will help students to visualize various concepts. The user interface is designed keeping accessibility and ease of usage in mind. It has many advantages- it's a complete learning package that will enrich the user experience and will promote open-ended learning in students. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dingzhou Zhu Guoheng Huang Xiaochen Yuan Xuhang Chen Guo Zhong Chi-Man Pun Jie Deng School of Computer Science and Technology Guangdong University of Technology Faculty of Applied Sciences Macao Polytechnic University School of Computer Science and Engineering Huizhou University Department of Computer and Information Science University of Macau School of Information Science and Technology Guangdong University of Foreign Studies The First Affiliated Hospital of Sun Yat-Sen University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual Information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model’s performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies.

关键词： computer vision Convolution Image color analysis Quaternions Frequency-domain analysis Biological systems Reflection Real-time systems Medical diagnosis Medical diagnostic imaging

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Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions

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Procedia computer science 2024年 235卷 2-11页

作者： Akshya J Neelamegam G C. Sureshkumar Nithya V Seifedine Kadry SRM Institute of Science and Technology Department of Computational Intelligence Chennai 603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore 641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore 641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore 641032 India Noroff University College Department of Applied Data Science Kristiansand 4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R 2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications.

关键词： Unmanned Aerial Vehicles (UAVs) Trajectory Generation Deep Neural Networks (DNNs) Adam Optimization Area Coverage Missions

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Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques

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Environmental science and Pollution Research 2025年第1期32卷 234-247页

作者： Thangarasu, Thenmozhi Aldehim, Ghadah Alruwais, Nuha Kandasamy, Anguraj Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Salem India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia P.O.Box 22459 Riyadh11495 Saudi Arabia Department of Electronics & Communication Engineering Sona College of Technology Tamil Nadu Salem India

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for wastewater treatment plants using a combination of machine learning, remote sensing, and GIS-based multicriteria decision analysis (MCDA). Several datasets were analysed, with the analytical hierarchy process (AHP) assigning weights to factors such as slope (18.51) and elevation (31.39), which were found to be crucial in site selection. The study classified the suitability of sites into five categories, with the western region being the most favourable due to its low elevation (147 to 200 m), gentle slopes (0–3%), and substantial land availability (approximately 309.00 sq. km). Overall, the site suitability analysis revealed that 14.48% (110.2 sq. km) of the area falls within "Very High Favourable Zones," while 11.21% (85.3 sq. km) is categorized as "High Favourable Zones." Moderate and low favourable zones account for 9.63% (73.3 sq. km) and 24.04% (182.9 sq. km), respectively. The remaining 40.57% (308.7 sq. km) is considered "Very Low Favourable Zones." These results can guide urban planning decisions, highlighting the importance of factors such as land availability, population growth, wastewater volume, and flood vulnerability. Integrating GIS technology with decision-making processes enhances the strategic placement of urban utilities, ensuring long-term sustainability for Coimbatore South's wastewater management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Site selection

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GNSS Satellite Occultation Detection applied to Radio Occultation

GNSS Satellite Occultation Detection Applied to Radio Occult...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2023

作者： Abouelouafa, Safae Latif, Adnane Reboul, Serge Information Technology and Modeling Team National School of Applied Sciences Cadi Ayyad University Marrakesh Morocco Computer Science Signal and Image Laboratory of the Côte d’Opale Littoral Côte d’Opale University Calais France

ISBN: (纸本)9783031542879

In the midst of growing worries about the energy, increasing demand for improved weather forecasting, and climate monitoring, the word is working to shift to sustainable energy sources and reduce the impacts of climate change. Simultaneously, the accuracy of weather forecasts and climate monitoring have become more crucial not only for energy efficiency but also for the environment preservation. The Global Navigation Satellite System (GNSS) Radio Occultation (RO) technique stands as well-suitable technique that provides measurements for observing the Earth’s atmosphere, as it is characterized with global coverage and high resolution. The concept of the occultation sounding uses a receiver placed on Low Earth Orbit (LEO) satellite to measure the Global Navigation Satellite System (GNSS) satellite’s observables, such as the atmospheric contribution of the signal along the limb in terms of a phase delay. These observables serve as essential insights that allow us to understand the atmosphere’s behavior. However, the GNSS receivers have limited onboard storage capacity. Therefore, it is important to precisely predict when the occultation event between the satellites occurs in a given day, so that the receiver can activate and record exclusively during the event. This study focuses first on evaluating the satellite’s visibility, then predicting the occultation events moments between LEO and GNSS satellites, and extracting the bending of the GNSS signal within the Earth’s atmosphere at these determined occultation events moments. The bending angles retrieved, as a result, empower us determining precise atmospheric profiles, ultimately enhancing our contribution to improved weather forecasting and climate monitoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Visibility

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Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations

arXiv

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arXiv 2025年

作者： Prusik, Michal Frej, Bartosz Przewozniczek, Michal W. Faculty of Pure and Applied Mathematics Wroclaw University of Science and Technology Wroclaw Poland Departament of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland

Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL is important for practice. In this work, we analytically estimate the size of a population sufficient for obtaining a perfect decomposition in case of concatenations of certain unitation-based functions. The experimental study confirms the accuracy of the proposed estimate. Finally, using the proposed estimate, we identify those problem types that may be considered hard for SLL-using optimizers. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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Predicting Software Reusability Using Classification Techniques

Predicting Software Reusability Using Classification Techniq...

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International Conference on Information and Communication Systems (ICICS)

作者： Neda’a Alsalman Raed Shatnawi Ammar Jalamneh Omar AlZoubi Computer Science Jordan University of Science and Technology Irbid Jordan Software Engineering Department Jordan University of Science and Technology College of Arts & Science Applied Science University Bahrain

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

This study predicts software reusability at the class level using machine learning and a dataset of 65 Java applications. The reuse rates were calculated using cohesion, coupling, complexity, inheritance, documentation, and size metrics. The data was segmented by project size (all, small, medium, and big). Several classifiers were tested once the labels were changed to multi-class labels (low: 0, moderate: 1, high: 2). On large projects, the voting model was 93% accurate. The results demonstrate how well machine learning predicts software reusability and provide recommendations for increasing software quality measures.

关键词： Q-factor Machine learning Software quality Predictive models Complexity theory Maintenance Software measurement

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Data Preparation for Deep Learning Based Code Smell Detection: A Systematic Literature Review

SSRN

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SSRN 2024年

作者： Zhang, Fengji Zhang, Zexian Keung, Jacky Wai Tang, Xiangru Yang, Zhen Yu, Xiao Hu, Wenhua Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Engineering & Applied Science Yale University New Haven United States School of Computer Science and Technology Shandong University Tsingtao China

Code Smell Detection (CSD) plays a crucial role in improving software quality and maintainability. And Deep Learning (DL) techniques have emerged as a promising approach for CSD due to their superior performance. However, the effectiveness of DL-based CSD methods heavily relies on the quality of the training data. Despite its importance, little attention has been paid to analyzing the data preparation *** systematic literature review analyzes the data preparation techniques used in DL-based CSD methods. We identify 36 relevant papers published by December 2023 and provide a thorough analysis of the critical considerations in constructing CSD datasets, including data requirements, collection, labeling, and cleaning. We also summarize seven primary challenges and corresponding solutions in the ***, we offer actionable recommendations for preparing and accessing high-quality CSD data, emphasizing the importance of data diversity, standardization, and accessibility. This survey provides valuable insights for researchers and practitioners to harness the full potential of DL techniques in CSD. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

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Intelligent Automation & Soft Computing 2023年第11期38卷 139-151页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road *** a result,reckless driving behaviour can cause congestion and *** vision and multimodal sensors have been used to study driving behaviour categorization to lessen this *** research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s *** the other hand,driving a car is a complicated action that requires a wide range of body *** this work,we proposed a ResNet-SE model,an efﬁcient deep learning classiﬁer for driving activity clas-siﬁcation based on signal data obtained in real-world trafﬁc conditions using smart ***-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning *** data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this *** performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its *** to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.

关键词： Smart glasses human activity recognition deep learning wearable sensors driving activity

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