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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Yuan, Xiaochen Li, Jinlong Macao Polytechnic University Faculty of Applied Sciences Macao SAR China School of Computer Science and Engineering Macau University of Science and Technology Macao SAR China

ISBN: (纸本)9798350336313

At present, algebraic operation methods in the field of change detection still holds the dominant position. However, in the face of disturbance features, due to the characteristics of poor expansibility, the performance of algebraic operation methods varies greatly in different scenes, and cannot meet the requirements of practical application. In this paper we propose a change detection model based on Sensitivity Disparity Networks (SDNs) for performing change detection in Bi-temporal Hyper-spectral images captured by AVIRIS sensor and HYPERION sensor over time. The SNDs consist of two deep learning models, Unchanged Sensitivity Networks (USNet) and Changed Sensitivity Networks (CSNet), they have sensitivity disparity in changed and unchanged pixels, and thus to generate effective argument region. Next, we re-evaluate the change probability of argument region, and merge the change result of the argument region with that by one of the SDNs. The detected Binary Change Map (BCM) of the scheme is thus obtained. To train and evaluate the proposed schema we employ two Bi-temporal Hyper-spectral image datasets which contain challenging pseudo-changed features (PCFs) and pseudo-invariant features (PIFs) cause by various external interference factors. The proposed schema outperforms the existing state-of-the-art algorithms on tested datasets. Experimental results show that the proposed schema has good universality and adaptability. © 2022 IEEE.

关键词： Deep neural networks

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Support Vector Machine based Multi-Class Classification for Oriented Instance Selection 6

Support Vector Machine based Multi-Class Classification for ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Davuluri, Sandeep Kumar Srivastava, Deepak Aeri, Manisha Arora, Madhur Keshta, Ismail Rivera, Richard Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India CSE Graphic Era Hill University Dehradun India CSE Graphic Era Deemed to be University Dehradun India RGPV University Bhopal India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9798350398496

Support vector machine has emerged as one of the most crucial classification techniques in recent years in pattern recognition, machine learning, and data mining. The model training will become more challenging when dealing with multi-classification problems, and its training time will increase considerably as the number of samples increases. A quick training data reduction technique MOIS that works with multiple classification problems is provided to address the issues above. This method takes the cluster centre as the reference point. While removing redundant training samples, selects the decisive boundary samples to significantly reduce the training data and reduce the distribution imbalance between categories. Experimental results showthat MOIS can maintain or even improve the classification effect of support vector machines. Moreover, it can significantly improve training efficiency. For example, on the Optdigit data set, the proposed method improves the classification accuracy from 98.94% to 99.05% and, at the same time, shortens the training time to 15%;On the data set composed of the first 100 categories, when the accuracy rate is slightly improved (from 99.29% to 99.30%), the training time is significantly shortened to less than 6% of the original. In addition, MOIS itself has High operating efficiency. © 2023 IEEE.

关键词： Support vector machines

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Multiple Watermarking Based on DCT Coefficients Selection 14

Multiple Watermarking Based on DCT Coefficients Selection

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Yuan, Xiaochen Wang, Xingrun Macao Polytechnic University Faculty of Applied Sciences Macao SAR China School of Computer Science and Engineering Macau University of Science and Technology Macao SAR China

ISBN: (纸本)9798350336313

This paper proposes a multiple watermarking scheme based on Discrete Cosine Transform (DCT) coefficients selection. In the proposed method, the multiple watermarks are embedded into the low and / or middle frequency band in DCT domain of the image blocks. Firstly, the texture complexity is calculated, then based on that, two block selection methods are proposed to select the corresponding adjacent blocks. After that, embedding coefficients and reference coefficients are calculated and the multiple watermarks are embedded by adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector. In this way, the embedding efficiency is thus improved. In the experiments, cover images of different texture complexity are used, and various attacks have been simulated for evaluation of the proposed watermarking scheme. Experimental results show that the proposed multiple watermarking scheme can achieve a satisfied performance. © 2022 IEEE.

关键词： Discrete cosine transforms

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AG- Learn: Adaptive Gamification Approach for Interactive Learning Systems

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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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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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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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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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Cascade computational model for prediction impact of transient depth change on combustion parameters of certain timber species under continuous heating rate

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International Journal of Thermofluids 2024年 23卷

作者： Olimat, Abdullah N. Al-Shawabkeh, Ali F. Quran, Omar maaitah, Hussein Al-Najdawi, Nijad Al-Qa, Ziad A. Department of Mechanical Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Scientific Basic Sciences Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Science College of computer and information sciences Prince Sultan University Riyadh Saudi Arabia Department of Computer and Networks Engineering Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan

The purpose of this research is to examine the effects of depth variation and exposure duration on the combustion characterization of the Maple wood species (Acer platanoides L.) and Walnut (Juglans regia L.) according to ISO 5660–1 in case of exposing the specimens to a constant heating rate 50 kWm−2. The delivered experimental data by cone calorimeter apparatus is used to train, test, and validate the optimized cascade forward artificial neural network with topology 2–8–8–12–2. This structure is simulated to predict the target parameters: the heat release rate (HRR) and the mass burning rate (MBR). With real systems, it is possible to achieve a reasonable performance and the closest approximations for the cascade model. The experimental combustion parameters: time of flame out, average specific mass burning rate, total heat release, maximum average rate of heat emission, and fire growth index are considered to make a systematic analysis and assessment with change depth. Three regions are considered for investigation of output target curves, thermally thin (d ≤ 1.1 cm), thermally medium (1.1 cm © 2024 The Author(s)

关键词： Combustion

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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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An Integration Of Wireless Communications And Artificial Intelligence For Autonomous Vehicles 3

An Integration Of Wireless Communications And Artificial Int...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Raghuram, Kadambari Altarawni, Ibrahim Vijayalakshmi, K. Kumar, Ajay Jadeja, Abhijeetsinh Chandra, Pankaj Jntuk Department of Computer Science and Engineering Andhra Pradesh Kakinada India Aqaba University of Technology Applied Computing and Artificial Intelligent Department Aqaba Jordan Ramapuram Campus Srmist India INurture Education Solution Pvt. Ltd It Academics Delhi India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India Guru Ghasi das Viswavidyalaya Dept of Information Technology Chattisgarh Bilaspur India

ISBN: (纸本)9798350399264

The pinnacle of transportation is the development of autonomous driving, which, with the help of CAVs and related traffic management systems, can eventually lead to congestion- and accident-free driving. This vision has as of late prodded extraordinary examination interest in fields including IoV, LTE-V2X, and 5G. In any case, the huge volume of traffic information that CAVs produce makes issues for both the current organizations and the approaching 5G correspondence organizations. For outside network innovations, the VMBS fills in as both a client hub and an edge processing hub. For CAVs, it fills in as a base station and a data caching hub, melding correspondence and calculation. People offer both the VMBS-empowered handset and figuring for CAVs as well as the VMBS-helped wireless innovation for other wireless gadgets to achieve this. It is underlined and addressed that there are a number of research obstacles and open questions. Last but not least, the results of the simulation show that the planned VMBS-CCNA may significantly enhance throughput, latency, and the average amount of links. © 2023 IEEE.

关键词： Autonomous vehicles

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