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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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EXISTENCE AND APPROXIMATE CONTROLLABILITY FOR A CLASS OF FRACTIONAL ORDER HEMIVARIATIONAL INEQUALITIES

arXiv

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arXiv 2024年

作者： Gupta, Garima Dabas, Jaydev Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India

This paper discusses the approximate controllability of a fractional differential control problem driven by a nonlinear hemivariational inequality in a Hilbert space. First, we prove the existence of a mild solution for a fractional control inclusion problem which is equivalent to a hemivariational inequality by using the nonsmooth analysis and fixed point technique. Further, we established sufficient conditions for the approximate controllability of our inclusion problem by taking corresponding linear system is approximately controllable. The existence and controllability results obtained for the inclusion problem are valid for considered nonlinear hemivariational problem. Finally, we provide an example to illustrate the efficiency of the developed results. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllability

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THE EXISTENCE AND CONTROLLABILITY OF NONAUTONOMOUS SYSTEM INFLUENCED BY IMPULSES ON BOTH STATE AND CONTROL.

arXiv

引用

arXiv 2024年

作者： Gupta, Garima Dabas, Jaydev Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India

This paper examines impulsive controls related to nonautonomous impulsive integro-differential equations in Hilbert space, highlighting their significance. We establish the existence of the mild solution by using fixed point approach and present conditions for approximate controllability using impulsive resolvent operators and the adjoint problem, supported by an illustrative example. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllability

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Optimizing Phishing URL Detection: A Comparative Analysis of Feature Counts and Training Times 5

Optimizing Phishing URL Detection: A Comparative Analysis of...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Alajlouni, Moayyad Tashtoush, Yahya Dossari, Saud Alhajaj Al. Darwish, Omar Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University Department of Electrical Engineering Wadi Addwasir Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic web sites to obtain sensitive information such as passwords and bank statements. One common technique used in these attacks is malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. This paper presents a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the required features and the training time by using fewer features after applying XAI techniques. Our technique only uses statistical features extracted from n-grams and the n-gram itself, without requiring any high-level features. We applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features and 87 features used in the original experiment and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by optimizing feature selection and analyzing the impact on training times. Our findings show the importance of using minimal statistical features to identify malicious URLs effectively and efficiently. © 2024 IEEE.

关键词： Phishing

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

引用

Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data 2

Customer Churn Prediction in the Telecom Sector with Machine...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

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Pandemic state’s impact on agriculture

Pandemic state’s impact on agriculture

引用

International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350313604

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models. © 2024 IEEE.

关键词： Decision making

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Design of Intelligent Home Care System based on Internet of Things technology 2

Design of Intelligent Home Care System based on Internet of ...

引用

2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Wang, Liangliang Jia, Lianqin Liu, Bingzhan College of Cloud Computing Industry Shandong Institute of Commerce and Technology Jinan China Institute of Commerce and Technology Department of Applied Electronics Shandong Jinan China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

In recent years, China's population is aging seriously, and the elderly population is increasing year by year. The pension issue is a major social issue related to the national economy and people's livelihood. At present, influenced by traditional Chinese concepts, about 70% of the elderly are willing to take care of themselves at home. During the epidemic, the elderly at home were unable to meet their daily needs, unable to monitor their own health status, and unable to adjust their mental state due to physical aging, absence of children, and other reasons. The AIOT-based home care system proposed in this paper uses AIOT technology to monitor the living environment and health status of the elderly at home in real time through various sensors and intelligent electronic devices, and transmits early warning information to children or communities. Human-machine dialogue realizes spiritual companionship and health promotion, realizes online shopping, medical treatment, entertainment and learning through intelligent APP application software, and effectively collects and processes information in combination with the intelligent cloud platform of the Internet of Things to realize health file management. © 2022 IEEE.

关键词： Application programs

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Empirical Analysis of Machine Learning Methods for Predicting Student Performance

Empirical Analysis of Machine Learning Methods for Predictin...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Prashanth Kumar Bolisetty Satyanarayana Bora Department of Information Technology College of Computing and Information Sciences University of Technology And Applied Sciences Shinas Sultanate of Oman Preparatory Studies Center University of Technology and Applied Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Performance prediction for students is relatively a new topic of research that aims to apply quantitative and qualitative approaches to evaluate student performance. This paper reviews existing approaches in predicting student performance across various learning environments. It explores fundamental statistical models, Machine Learning (ML) algorithms, and Deep Learning (DL) models, highlighting their pertinent characteristics and suitability for analyzing academic, behavioral and demographic data. The paper focuses on analyzing the existing methodologies for student performance prediction. The datasets used consist of student grades, attendance records, socioeconomic status, and interaction rates, along with an assessment of their efficiency in feature selection and data preprocessing. Lastly, the explainability and interpretability of the proposed predictive models are explored to enable reliability for educational stakeholders on these predictions for timely interventions. This survey concludes with a discussion of future scopes, focusing on enhancing prediction accuracy, ensuring fairness in models, and integrating multiple data modalities into performance analysis.

关键词： Wireless communication Surveys Analytical models Reviews Predictive models Prediction algorithms Data models Stakeholders Reliability Socioeconomics

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