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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Zhultynska, Anhelina Prystavka, Pylyp National Aviation University Faculty of Computer Science and Technology Department of Applied Mathematics Kyiv03058 Ukraine

ISBN: (纸本)9798331534141

This article presents a new approach to detecting anomalies in data obtained from unmanned aerial vehicles using spline models. The relevance of the study is driven by the need for fast and accurate identification of anomalies in real time, which is critical for military intelligence and civilian monitoring. The proposed model is based on the use of momentary characteristics, such as average illumination intensity and standard deviation, which allows for effective data parameterization and detection of anomalous observations. The advantage of the spline model is its ability to adapt to complex and heterogeneous image textures, which significantly reduces the risk of missing critical anomalies. The model also demonstrates high robustness to variations in the data, making it a reliable tool for analyzing images in different lighting and landscape conditions. Experiments using forest, road, and car textures have shown that the spline model is able to accurately identify anomalies, which increases the efficiency of analysis and allows for a quick response to detected changes. © 2024 IEEE.

关键词： Splines

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Comparative Analysis of Detector-Tracker Architecture for Object Tracking Based on SBC for UAV 7

Comparative Analysis of Detector-Tracker Architecture for Ob...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Prystavka, Pylyp Shevchenko, Andrii Rokitianska, Irina National Aviation University Faculty of Computer Science and Technology Department of Applied Mathematics Kyiv03058 Ukraine

ISBN: (纸本)9798331534141

This paper investigates the tracking technology of moving objects from a UAV camera (or streaming video) for systems with limited computational resources, such as modern SBCs. A detector-tracker architecture is proposed, combining the advantages of object detection methods and tracking algorithms. A comparative analysis of the performance and accuracy of various detector and tracker combinations on modern SBCs is conducted. The results demonstrate the high efficiency of the proposed approach for object-tracking tasks under limited computational resources, which has practical significance for UAV-based systems. © 2024 IEEE.

关键词： Object tracking

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A new model for efficiency estimation and evaluation: DEA-RA-inverted DEA model

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International Journal of applied Decision sciences 2024年第6期17卷 733-748页

作者： Zhang, Bingjiang Su, Shilan Gong, Qizhou School of Applied Science Beijing Information Science and Technology University Beijing100096 China School of Mathematics and Computer Science Shaanxi University of Technology Shaanxi Hanzhong723001 China School of Journalism and Communication Beijing Normal University Beijing100875 China

Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises. © 2024 Inderscience Enterprises Ltd.

关键词： Data envelopment analysis

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CMTJ:Simulation package for analysis of multilayer spintronic devices

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npj Computational Materials 2023年第1期9卷 1804-1813页

作者： Jakub Mojsiejuk Sławomir Ziętek Krzysztof Grochot Witold Skowroński Tomasz Stobiecki Institute of Electronics AGH University of Science and TechnologyAl.Mickiewicza 30Kraków 30-059Poland Faculty of Physics and Applied Computer Science AGH University of Science and TechnologyAl.Mickiewicza 30Kraków 30-059Poland

We present CMTJ—a simulation package for large-scale macrospin analysis of multilayer spintronics *** from conventional simulations,such as magnetoresistance and magnetisation hysteresis loops,CMTJ implements a mathematical model of dynamic experimental techniques commonly used for spintronics devices characterisation,for instance:spin diode ferromagnetic resonance,pulse-induced microwave magnetometry,or harmonic Hall voltage *** find that macrospin simulations offer a satisfactory level of agreement,demonstrated by a variety of *** a unified simulation package,CMTJ aims to accelerate wide-range parameter search in the process of optimising spintronics devices.

关键词： multilayer analysis hysteresis

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Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Word-Based Method for Chinese Part-of-Speech via Parallel and Adversarial Network

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Chinese Journal of Electronics 2022年第2期31卷 337-344页

作者： HUANG Kaiyu CAO Jingxiang LIU Zhuang HUANG Degen School of Computer Science and Technology Dalian University of Technology School of Foreign Languages Dalian University of Technology School of Applied Finance Dongbei University of Finance and Economics

Chinese part-of-speech(POS) tagging is an essential task for Chinese downstream natural language processing tasks. The accuracy of the Chinese POS task will drop dramatically by word-based methods because of the segmentation errors and the word ***, there are several Chinese POS tagging sets with different criteria. Some of them only have a small-scale annotated corpus and are hard to train. To this end, we propose a modified word-based transformer neural network architecture. Meanwhile, we utilize an adversarial transfer learning method that splits the architecture into shared and private parts. This work directly improves the ability of the word-based model, instead of adopting a joint character-based method. Extensive experiments show that our method achieves state-of-the-art performance on all datasets, and more importantly, our method improves performance effectively for the word-based Chinese sequence labeling task.

关键词： neural nets modified word-based transformer neural network architecture adversarial transfer learning method word-based method word processing word-based Chinese sequence labeling task Chinese part-of-speech tagging learning (artificial intelligence) natural language processing Chinese downstream natural language processing tasks Chinese POS tagging sets Chinese POS task joint character-based method word sparsity adversarial network word-based model text analysis small-scale annotated corpus

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An Analysis of the Use of Machine Learning to Enhance Online Digital Learning 8

An Analysis of the Use of Machine Learning to Enhance Online...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Acharjya, Pinaki Pratim Majumdar, Jayeeta Bera, Mihir Baran Barman, Subhabrata Sinha, Jayashri Deb Das, Moumita Haldia Institute of Technology Computer Science and Engineering Haldia India Haldia Institute of Technology School of Applied Science Haldia India Haldia India

ISBN: (纸本)9798350367904

Academic institutions' interest in online learning is rising at an exponential rate due to the rapid advancement of expertise. The Learning Management System (LMS) is a key component of the online learning architecture and is also used to mine student data. This study reviews and analyses the literature on machine learning (ML) in online education and machine learning integrated learning management systems (LMS). The goal of this study is to comprehend the different efforts made to employ different ML techniques to lessen the difficulties associated with online learning environments, or online LMSs. Increasing student performance by projecting future grades is just one of several obstacles. Other issues that can be resolved using machine learning include student retention and behavior analysis for performance enhancement. The paper presents the research that different authors have done in the relevant topic. The chosen time frame for the project remains close to the Covid19 years. © 2024 IEEE.

关键词： Federated learning

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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

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Alexandria Engineering Journal 2025年 127卷 357-373页

作者： Alzughaibi, Arwa Alanazi, Adwan A. Alshahrani, Mohammed Hilali Jaghdam, Ines Hassaballa, Abaker A. Applied college Taibah University Madinah Saudi Arabia Department of Computer Science and Information College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Sciences Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Mathematics Faculty of Science Northern Border University Arar73213 Saudi Arabia

The disease of Kidney stones is a risky disease for individuals all over the world. Many people with kidney stones in the early phase do not detect it as an illness, and it harms the organ gradually. Precise analysis of kidney illness is vital, as it is a major health concern that needs accurate detection for appropriate and effective treatment. CT scans are one of the most extensively accessible imaging models, and they are employed for effective diagnosis. Deep learning (DL) techniques are gradually identified as beneficial tools for analyzing illness in the medical field. However, present techniques employing deep networks often meet low accuracy and overfitting challenges, demanding further alteration for optimum performance. This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. The presented LFFOEDL-AKSD technique mainly focuses on detecting kidney stones using CI imaging. At first, the presented LFFOEDL-AKSD technique applies the pre-processing phase, which involves image resizing for uniform CT scan dimensions and data augmentation through transformations like rotation and flipping to reduce overfitting, sobel filtering (SF) sharpens edges, and the data is separated into training, validation, and testing sets for model development. The presented LFFOEDL-AKSD technique employs the swin transformer (ST) model for the feature extraction method. Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. Finally, the presented LFFOEDL-AKSD technique implements the flying foxes optimization (FFO) approach for the hyperparameter tuning involved in the ensemble learning models. An extensive experiment is conduct

关键词： Flying foxes optimization Ensemble deep learning Kidney stone detection CI imaging Swin transformer

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