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IEEE International Conference on Image Processing

作者： Ammar Ahmed Ali Shariq Imran Mohib Ullah Zenun Kastrati Sher Muhammad Daudpota Department of Computer Science (IDI) Intelligent Systems and Analytics (ISA) Research Group Norwegian University of Science & Technology (NTNU) Gjøvik Norway Department of Informatics Linnaeus University Växjö Sweden Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The exploration of automated wrist fracture recognition has gained considerable research attention in recent years. In practical medical scenarios, physicians and surgeons may lack the specialized expertise required for accurate X-ray interpretation, highlighting the need for machine vision to enhance diagnostic accuracy. However, conventional recognition techniques face challenges in discerning subtle differences in X-rays when classifying wrist pathologies, as many of these pathologies, such as fractures, can be small and hard to distinguish. This study tackles wrist pathology recognition as a fine-grained visual recognition (FGVR) problem, utilizing a limited, custom-curated dataset that mirrors real-world medical constraints, relying solely on image-level annotations. We introduce a specialized FGVR-based ensemble approach to identify discriminative regions within X rays. We employ an Explainable AI (XAI) technique called Grad-CAM to pinpoint these regions. Our ensemble approach outperformed many conventional SOTA and FGVR techniques, underscoring the effectiveness of our strategy in enhancing accuracy in wrist pathology recognition.

关键词： Wrist Pathology Visualization Image recognition Accuracy Explainable AI Face recognition

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Fuzzy Risk Model for Research and Development department in Clinical Laboratory

Fuzzy Risk Model for Research and Development Department in ...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Nilo Legowo Wahyu Sarjdono Eddy Susanto Ditdit Nugeraha Utama Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Information security risk is of utmost importance and a crucial concern, particularly within a clinical laboratory responsible for managing sensitive public health information. Various endeavors have been undertaken by institutions to tackle this pressing challenge effectively. This research seeks to develop a computer-based decision model for assessing information security risks. The model is scientifically constructed using the fuzzy logic method as its core approach and designed through an object-oriented approach. Impressively, the model successfully simulates 31 risk scenarios with an accuracy rate of 93.55%.

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RoboPick: ArUco-Driven Task Prioritized Manipulation on Mobile Robotic systems

RoboPick: ArUco-Driven Task Prioritized Manipulation on Mobi...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Mir Mohibullah Sazid Syma Afsha Rupom Razzaque Kamruddin Nur EMJM in Intelligent Field Robotic Systems (IFRoS) Universitat de Girona Girona Spain School of Computer Science and Engineering Queen Mary University of London Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This paper presents RoboPick, an advanced Vehicle-Manipulator System (VMS) designed to perform efficient and precise object manipulation tasks. The system integrates a TurtleBot 2 mobile base with a 4-DOF Swift Pro robotic arm, enabling the robot to autonomously navigate and interact with its environment. The system is equipped with an Intel RealSense D435i camera and ArUco markers for accurate object localization and positioning. The control system uses a task-priority algorithm to ensure that the robot performs pick-and-place operations efficiently, while adhering to safety constraints and maintaining high precision throughout. Behavior trees are implemented to manage the sequence of operations, ensuring that the robot executes tasks smoothly and effectively. The system's capabilities are validated through extensive simulation tests, demonstrating its high reliability and consistent efficiency in completing tasks. The robot's performance in terms of task completion time and success rate highlights its robustness in handling complex manipulation routines across multiple trials. This research emphasizes the importance of combining robust control architectures with precise localization techniques to enhance the performance of autonomous mobile manipulators in real-world object manipulation applications.

关键词： Location awareness Robust control Navigation Operating systems Robot vision systems Kinematics Manipulators Control systems Robustness Safety

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Aspect-based Sentiment Analysis for Improving Online Learning program Based on Student Feedback 6

Aspect-based Sentiment Analysis for Improving Online Learnin...

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6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Phuket83120 Thailand

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

This paper presents an empiric results of aspectbased sentiment analysis in education to extract and classify opinions, sentiments, evaluations, attitudes, and emotions from newly graduates of an online learning program. As part of continuous education monitoring system, the sentiment analysis process produces valuable input to leverage service quality of online learning program. In this study, the aspect-based sentiment analysis is implemented to analyze a set of feedbacks from 162 newly graduate from Binus Online program majoring in Accounting, Management, Information System, and computer science. The important qualitative results of this study are confirmation that the main benefits of online learning from student perspective are mainly: the knowledge they gained from the program, learning guidance, reliable student team to work on thesis, quality of education support system, and learning happiness. © 2022 IEEE.

关键词： Sentiment analysis

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Nge-BatikVR: Innovative Batik Learning and Marketing Through Virtual Reality Serious Games with Immersive Hands-On Experience

Nge-BatikVR: Innovative Batik Learning and Marketing Through...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Dimas Ramdhan Galih Dea Pratama Michael Francis Wijaya Muhammad Edo Syahputra Wihendro Computer Science Department Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

Batik is a cultural heritage of Indonesia, recognized by WHO as an Intangible Cultural Heritage. Batik is dyed by skilled craftsmen who make patterns with dots and lines on the fabric from melted wax. The process is complicated, so few people can experience all the steps in crafting Batik. From these problems, immersive learning media are needed so everyone can learn and gain experience in Batik crafting from start to finish. In this study, we will present Nge-BatikVR, a serious game application that introduces Batik through Virtual Reality and offers an immersive experience of learning Batik from various regions with interactive hands-on feature aimed at people to better understand and learn Batik. Nge-BatikVR offers four main features called Sinau (Learn), Kuis (Quiz), Nge-Batik (Simulation) and Toko (Shopping), with the main purpose to present interactive and engaging media useful for introducing and learning Batik with a fully immersive experience.

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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OCET: One-Dimensional Convolution Embedding Transformer for Stock Trend Prediction 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yang, Peng Fu, Lang Zhang, Jian Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Shenzhen Securities Information Co. Ltd. Shenzhen China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Due to the strong data fitting ability of deep learning, the use of deep learning for quantitative trading has gradually sprung up in recent years. As a classical problem of quantitative trading, Stock Trend Prediction (STP) mainly predicts the movement of stock price in the future through the historical price information to better guide quantitative trading. In recent years, some deep learning work has made great progress in STP by effectively grasping long-term timing information. However, as a kind of real-time series data, short-term timing information is also very important, because stock trading is high-frequency and price fluctuates violently. And with the popularity of Transformer, there is a lack of an effective combination of feature extraction and Transformer in STP tasks. To make better use of short term information, we propose One-dimensional Convolution Embedding (OCE). Simultaneously, we introduce effective feature extraction with Transformer into STP problem to extract feature information and capture long-term timing information. By combining OCE and Transformer organically, we propose a noval STP prediction model, One-dimensional Convolution Embedding Transformer (OCET), to capture long-term and short-term time series information. Finally, OCET achieves a highest accuracy up to 0.927 in public benchmark FI-2010 When reasoning speed is twice that of SOTA models and a highest accuracy of 0.426 in HKGSAS-2020. Empirical results on these two datasets show that our OCET is significantly superior than other algorithms in STP tasks. Code are available at https://***/langgege-cqu/OCET. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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HS-Gen: Human-Like Handwriting Synthetic Generation—A Preliminary Investigation 27th

HS-Gen: Human-Like Handwriting Synthetic Generation—A Preli...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Bensalah, Asma Parziale, Antonio Coccaro, Rosanna Marcelli, Angelo Fornés, Alicia Lladós, Josep Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Science Department Universitat Autònoma de Barcelona Barcelona Spain DIEM University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy AI3S Unit CINI National Laboratory of Artificial Intelligence and Intelligent Systems University of Salerno SA Fisciano Italy

ISBN: (纸本)9783031876592

The application of handwriting analysis in the health field for early detection and diagnosis is limited by a lack of data, which presents a significant challenge for the implementation of deep learning-based models. To address this issue, numerous studies have focused on generating synthetic data. Current methods for generating synthetic handwriting from offline images, including generative models and geometrical techniques, fail to account for the kinematics of handwriting movements, which is critical for achieving more human-like results. To address this limitation, we propose a novel, human-like approach for the synthetic generation of handwriting or drawing from offline images. This method creates new samples by incorporating a trajectory recovery algorithm and a human-like time law generator, as well as the extraction and manipulation of kinematic parameters. The evaluation of the proposed method from both visual and kinematic perspectives demonstrates its potential applicability across a wide range of devices and handwriting styles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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GonioPi: Towards Developing a Scalable, Versatile, Reliable and Accurate Handheld-Wearable Digital Goniometer

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International Journal of Advanced computer science and Applications 2022年第6期13卷 321-330页

作者： Garcia, Thomas Jonathan R. Gom-os, Dhong Fhel K. Center for Research in Intelligent Systems Department of Computer Science University of the Philippines Cebu Cebu Philippines

Range of Motion (ROM) Testing is an important physical examination performed in physical therapy used in assessing the ROM of a patient’s joint. The most commonly used instrument for ROM Testing is the universal goniometer. The most common cause for unreliable and inaccurate joint angle ROM measurements is measurement errors. Multiple studies have been done to mitigate measurement errors in clinical goniometry by designing and developing wearable digital goniometers using sensor technology. This study aims to design and develop a handheld-wearable digital goniometer called the GonioPi that is versatile, scalable, reliable and accurate when using the MPU-6050 IMU sensor and Raspberry Pi Pico as the main components. The results showed that the GonioPi is versatile and scalable as it is able to support multiple ROM Tests using multiple different positions on people with varying heights, weights, and BMI categories. The results also showed that the GonioPi is reliable and accurate as it was able to record joint angle ROM measurements of less than 5 degrees and 10 degrees which are the accepted standard values for reliability and accuracy, respectively © 2022. International Journal of Advanced computer science and *** Rights Reserved.

关键词： Goniometers

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PSL-CFRT: A Benchmark Multi-View Dataset for Continuous Pakistani Sign Language Fingerspelling Recognition and Translation

PSL-CFRT: A Benchmark Multi-View Dataset for Continuous Paki...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Muhammad Kamran Mubasher Baig Mian M. Awais El-Sayed M. El-Alfy Adnan Akhunzada Department of Computer Science National University of Computer and Emerging Sciences Lahore Pakistan Syed Babar Ali School of Science and Engineering Lahore University of Management Science (LUMS) Lahore Pakistan Information and Computer Science Department Computer Engineering Department IRC-Intelligent Secure Systems King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Department of Cybersecurity University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Fingerspelling, an essential component of sign language, utilizes distinct hand configurations for each letter of a written language to convey proper names, technical terms, addresses, numerical values, and other uncommon words. This intricate form of communication is challenging for the general community, creating a communication barrier between the deaf and hard-of-hearing (DHH) community and those unfamiliar with it. To bridge this gap, this paper introduces a novel multi-view dataset specifically designed for continuous fingerspelling recognition and translation in Pakistani Sign Language (PSL). This dataset is unique in its inclusion of diverse fingerspelling variations, enhancing its applicability to real-world scenarios. In addition to introducing the dataset, an object-detection-based method is proposed for PSL continuous fingerspelling recognition (PSL-CFR), marking a significant advancement in continuous PSL finger-spelling recognition technology, achieving a baseline accuracy of 63.16%.

关键词： YOLO Deep learning Sign language Technological innovation Translation Accuracy Computational modeling Streaming media Knowledge discovery Real-time systems

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