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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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Explainable Hybrid Model for Presaging Tumor Stage Classification with Survival Survey

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SN computer Science 2025年第4期6卷 1-24页

作者： Manna, Sweta Mistry, Sujoy Zhao, Zhongming Dahal, Keshav Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia W.B Kolkata 741249 India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX United States School of Engineering and Computing University of the West of Scotland Paisley PA1 2BE United Kingdom

Tumor stage classification with a survival survey during detection time is the most crucial part of cancer treatment. The survivability period is directly associated with the early detection of the stage. This study developed a hybrid model to accurately classify the tumor (T) stages with the survivability analysis. The study aims to provide an explainable Artificial Intelligence framework (such as SHAP, and SHAPASH), through which the trustability and interpretability of the proposed model can increase. Along with the XAI, this hybrid approach uses two statistical models, ANOVA and LASSO with the standard TNM (tumor, node, metastasis) method for the T-stage classification. Afterward, the survivability analysis for each T stage is shown by the Kaplan–Meier (KM) method, proportion hazard ratio (Cox). Where KM is used to represent the survivability of individual tumor stages in months and the Cox regression shows the risk factors of the events. Initially, the proposed model was examined on a full dataset that contained malignant and benign data for evaluating the performance of the model, therefore stage classification for T1, T2, and T3 performs for malignant data. The K-fold and repeated-stratified K-fold cross-validation are used to measure the accuracy of the whole dataset for lung and breast cancer. For lung cancer, maximum accuracy was attained by MLP–97.64 in K-fold cross-validation. In breast cancer maximum accuracy achieved by GPC-99.57 in repeated-stratified K-fold cross validation. The results of both datasets are compared with the state-of-the-art models. The study contributed by representing the significance of individual features for tumor staging using the advanced approach of XAI, the SHAPASH interface. The accuracy of the T1, T2, and T3 stages based on malignant data was demonstrated with various classifier models with cross-validation. Finally, it is expected that executing that approach will improve patient outcomes. By using, advanced artificial in

关键词： ANOVA Breast cancer Deep learning Explainable artificial intelligence F-score Gaussian process classification LASSO Lung cancer Machine learning Random-Forest Shapely additive explanation XGBoost

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Detection of Yoga Poses Using CNN and LSTM Models 3rd

Detection of Yoga Poses Using CNN and LSTM Models

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3rd International Conference on Computing, Communication and Learning, CoCoLe 2024

作者： Rukmini, Bheemanapalli Sushmini, Chaganti Sai Biswal, Saubhagya Ranjan Computer Science Engineering School of Computing and Informatics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India

ISBN: (纸本)9783031790409

Yoga is a holistic practice that encompasses physical, mental, and spiritual well-being. Central to yoga are its postures, or "asanas," which are designed to promote flexibility, strength, and balance in the body. Each yoga posture holds its own significance, targeting specific muscle groups and energy centers. Proper alignment in these postures is crucial not only for reaping physical benefits but also for cultivating mindfulness and concentration. In this work, we presented an approach to detect the yoga poses using CNN and LSTM methods. For each model, the data loading process involves reading images, resizing them, and organizing them into appropriate formats for training and testing. The CNN model comprises several convolutional and pooling layers, while the LSTM model includes LSTM layers for temporal sequence processing. The combined CNN-LSTM model emerged as a robust solution, leveraging the strengths of both architectures to achieve a comprehensive understanding of yoga poses. Its performance was evaluated on a test dataset, revealing insights into its accuracy and loss metrics. Notably, the CNN model exhibited an accuracy of 85.53%, surpassing other models studied in the research. This highlights the efficacy of the CNN approach in recognizing spatial features inherent in yoga postures, contributing significantly to the overall success of the model in identifying and categorizing yoga stances from input images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolution

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Increasing Reliability in Hybrid RF-Optical Networks: A Cross-Band Modulation Design

Increasing Reliability in Hybrid RF-Optical Networks: A Cros...

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IEEE Conference on Wireless Communications and Networking

作者： Thrassos K. Oikonomou Sotiris A. Tegos Panagiotis D. Diamantoulakis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper presents a novel hybrid cross-band radio frequency (RF)-optical system that utilizes coherent RF detection with $M$ -QAM modulation and optical intensity modulation and direct detection (IM/DD) to fully utilize the diversity gains of cross-band architectures. By performing joint signal processing in the RF and optical domains, the proposed system achieves significant improvements in reliability, spectral efficiency, and data rates compared to conventional approaches. A key feature of the system is an optimized linear mapping between the RF and optical signals, which ensures seamless signal integration and improved performance. To evaluate the system, we derive the theoretical mutual information (MI) and a tight closed-form approximation for the symbol error probability (SEP) of the proposed modulation scheme. Numerical results validate the theoretical analysis and show that the proposed scheme significantly outperforms state-of-the-art cross-band modulations in terms of MI and SEP. The proposed scheme, with its low-complexity design and superior performance, is a promising candidate for next-generation wireless networks.

关键词： Radio frequency Error probability Spectral efficiency Wireless networks Symbols Reliability theory Optical receivers Adaptive optics Optical signal processing Mutual information

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Simultaneous Communications and Sensing with Hybrid Reconfigurable Intelligent Surfaces

Simultaneous Communications and Sensing with Hybrid Reconfig...

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European Conference on Antennas and Propagation, EuCAP

作者： Ioannis Gavras George C. Alexandropoulos Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Hybrid Reconfigurable Intelligent Surfaces (HRISs) constitute a new paradigm of truly smart metasurfaces with the additional features of signal reception and processing, which have been primarily considered for channel estimation and self-reconfiguration. In this paper, leveraging the simultaneous tunable reflection and signal absorption functionality of HRIS elements, we present a novel framework for the joint design of transmit beamforming and the HRIS parameters with the goal to maximize downlink communications, while simultaneously illuminating an area of interest for guaranteed localization coverage performance. Our simulation results verify the effectiveness of the proposed scheme and showcase the interplay of the various system parameters on the achievable Integrated Sensing and Communications (ISAC) performance.

关键词： Location awareness Simulation Human-robot interaction Europe Reconfigurable intelligent surfaces Integrated sensing and communication Metasurfaces Reflection Reliability Terahertz communications

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Brain Magnetic Resonance Imaging Segmentation System in MS with Deep Neural Networks 18th

Brain Magnetic Resonance Imaging Segmentation System in MS w...

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18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024

作者： Adamides, Giorgos Panayides, Andreas Loizou, Christos P. Nicolaou, Andria Pantzaris, Marios Pattichis, Constantinos Department of Computer Science University of Cyprus Nicosia Cyprus 3AE HEALTH LTD Nicosia Cyprus CYENS CoE Nicosia Cyprus Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus School of Molecular Medicine The Cyprus Institute of Neurology and Genetics University of Cyprus Nicosia Cyprus

ISBN: (纸本)9783031855719

Multiple Sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Nerves are covered by a layer called myelin, which is responsible for protecting and maintaining their functionality. In the case of MS, the myelin becomes damaged, resulting in the nerves functioning unpredictably. MS is a disease characterized by relapses, which can cause permanent and irreversible damage to the patient's mobility, vision, and sensations, unless appropriate medical treatment is administered in time. This highlights the importance of the early detection of the presence or differentiation of MS using computer assisted diagnosis (CAD) systems. This study proposes an automatic segmentation approach of MS damaged regions using deep neural networks that can be integrated with any 3rd party CAD system. The data utilized comprised of a total of 1838 T2-type Magnetic Resonance Imaging (MRI) images collected from a cohort of 38 patients who underwent imaging at two distinct time points. Each MRI image was accompanied by meta-data, detailing the specific locations of the observed damage attributed to MS. The experimental setup investigated and optimized a comprehensive combination of different data preprocessing and augmentation steps, using several variations of the U-Net convolutional neural network (CNN), like U-Net++, Attention U-Net, ResUNet-a and TransUNet. The proposed methods achieved a segmentation accuracy of 0.70 based on the Dice Similarity Coefficient (DSC), a performance comparable to 2D and 3D state-of-the-art approaches in literature. A software framework encapsulating the automated model was also developed to facilitate the clinical practice workflow and underpin adoption by medical practitioners. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Image segmentation

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity

A Review on Leveraging Language Agnostic Automated Text Data...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sarith Wijesundera Suresh Michael Peiris Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages.

关键词： Sensitivity Explainable AI Reviews Semantics Transfer learning Data augmentation Search problems Natural language processing Multilingual Optimization

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Understanding User Attention and Engagement in Digital Marketing: An Eye-Tracking Analysis of Native Advertisements 24

Understanding User Attention and Engagement in Digital Marke...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Theodora Kremantala Stefanos Balaskas Maria Rigou Computer Engineering and Informatics Department University of Patras Patras Greece Department of Management Science and Technology University of Patras Patras Greece

ISBN: (纸本)9798400713170

This study investigates the effectiveness of various types of native ads in terms of capturing users' attention and engagement using eye-tracking data to analyze user interactions. To this end, 47 participants compared four types of native ads with two traditional banner ads embedded within a website designed specifically for the needs of the study. Results confirm that native ads, and particularly content integrated ones like recommendation widgets and native ads in-feed, attract higher levels of attention compared to traditional banner ads. These ads maintain high deception rates, with users mostly failing to recognize them, a fact that raises questions about users' trust and annoyance. The variation that creates the highest click-through rate is the promoted listing, while typical banner advertisements attracted limited engagement suffering from banner blindness. This study highlights the delicate balance between effective ad placement and ethical transparency by advertisers, emphasizing the need for clear labelling to maintain user trust. These findings present useful insights for marketers and interface designers, with guidance on creating effective yet usable ad designs in digital spaces.

关键词： dark design patterns

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Knowledge Augmented Significant Language Model-Based Chatbot for Explainable Diabetes Mellitus Prediction

Knowledge Augmented Significant Language Model-Based Chatbot...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mazen Elfayoumi Mohamed AbouElazm Omar Mohamed Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user's understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT- 3.5, and Gemma2. This approach augments the chatbot's responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures.

关键词： Technological innovation Accuracy Large language models Computational modeling Retrieval augmented generation Medical services Predictive models Chatbots Diabetes Medical diagnostic imaging

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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