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A Unified Conceptual Hybrid Framework for the Automated Assessment of Short Answers

International Journal of Artificial Intelligence in Education 2025年 1-52页

作者： Ramnarain-Seetohul, Vidasha Rosunally, Yasmine Bassoo, Vandana Dept. of Information and Communication Technologies University of Mauritius Reduit Mauritius Dept. of Computer Science and Creative Technology University of the West of England London UK Dept. of Electrical and Electronic Engineering University Of Mauritius Reduit Mauritius

Various automated essay scoring (AES) methods have been proposed over the past five decades, but the application of AES in the educational field has gained popularity since the COVID-19 pandemic, as most educational institutions have shifted to online teaching modes. Consequently, the assessment of student knowledge has become a major challenge. Researchers are focusing on new state-of-the-art techniques to devise a more performant AES to facilitate online grading tasks. However, few studies have analyzed the common features of existing AES. There are no general guiding principles for the implementation and improvement of AES. This work aims to address the research gap by proposing a unified conceptual hybrid framework for AES, adapted for short answers and inspired by an in-dept. analysis of existing AES based on short answers. The unified framework consists mainly of the most frequently used components in existing AES except that a new important module namely the data augmentation module has been identified and added to the framework and also two existing modules have been modified. Following the proposed unified framework, the different essential components in an AES can be easily understood, and the implementation and improvement of AES can be achieved effortlessly using a hybrid approach. Furthermore, experiments have been carried out to validate the framework’s performance. During the experimentation, single, ensemble, and hybrid models were compared with and without the data augmentation technique respectively. Results confirmed that the data augmentation module did help in improving results for all the models. Notably, in the hybrid models, results demonstrated an average increase of 40.75% in QWK values and a mean reduction of 18% in RMSE values. Additionally, ensemble and hybrid models outperformed single models in terms of performance.

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Frame Selection Strategies for Real-Time Structure-from-Motion from an Aerial Platform

Frame Selection Strategies for Real-Time Structure-from-Moti...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Buck, Andrew R. Akers, Jack D. Anderson, Derek T. Keller, James M. Camaioni, Raub Deardorff, Matthew Luke, Robert H. University of Missouri Dept. of Electrical Engineering and Computer Science ColumbiaMO United States US Army DEVCOM C5ISR Center Fort BelvoirVA United States

ISBN: (纸本)9798350359527

Determining dept. from a single camera in motion is a challenging problem that has numerous applications, including autonomous navigation of an unmanned aerial vehicle (UAV). Using traditional computer vision techniques such as structure-from-motion (SfM), a dept. estimate can be generated using two image pairs from the video stream. The choice of image pairs directly impacts the quality of reconstruction, which is based largely on the camera extrinsics and image features. In this article, we discuss frame selection algorithms to select appropriate image pairs to process for dept. estimation. Frames are stored in a rolling buffer, and several measures are computed on potential frame pairs based on camera extrinsics. We use a customized SfM algorithm, Epidept., which has been designed for handling sequences of aerial imagery with embedded GPS and camera pose metadata. We demonstrate our technique on a simulated dataset created using Unreal Engine and AirSim. © 2023 IEEE.

关键词： Cameras

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction 14

Vader protocol based sentiment analysis technique using LSTM...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ticku, Amrita Tripathy, Niva Mishra, Pankaj Kumar Sinha, Anurag Jadon, Shivangi Raj, Aditya Kulshrestha, Harsh Rai, Vipin Shamim, Rejuwan Bharti Vidyapeeth's College of Engineering India Driems Autonomous Engineering College Cuttack India Rungta College of Engg. & Technology R1 Department of Electronics and Telecommunication Engg. CG Bhilai India Ignou School of Computing and Information Science New Delhi India Galgotias University Dept. of Computer Science and Engineering Noida India Galgotias University Department of Computer Science and Engineering Noida India Maharishi University of Information Technology Dept. of Computer Science and Engineering with Data Science Noida India

ISBN: (纸本)9798350335095

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& P 500 dataset reveal that it outperforms traditional stock prediction approaches. © 2023 IEEE.

关键词： Forecasting

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Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Developing xApps for Rogue Base Station Detection in SDR-Ena...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Huang, Jun-Hong Cheng, Shin-Ming Kaliski, Rafael Hung, Cheng-Feng National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Sun Yat-sen University Dept. of Electrical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665494274

In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E- Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision 'recall' and F1 score. © 2023 IEEE.

关键词： Base stations

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A Lightweight-CNN Model for Efficient Lung Cancer Detection and Grad-CAM Visualization

A Lightweight-CNN Model for Efficient Lung Cancer Detection ...

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Information and Communication Technology for Sustainable Development (ICICT4SD), International Conference on

作者： Tasmim Sagor Chandro Bakchy Hasibul Islam Peyal Md. Injamul Islam Golam Kibria Yeamin Shafiun Miraz Md. Nazmul Abdal Dept. of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering Uttara University Dhaka Bangladesh Dept. of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

The lungs' abnormal cell growth leads to the development of lung cancer. Early cancer identification could make treatment easier, potentially saving millions of lives annually. This study's main goal is to more rapidly and effectively classify various types of lung cancer by employing a lightweight, computationally efficient convolutional neural network (CNN) model to categorize three different types of lung cancer. With an outstanding validation accuracy of 99.48%, the suggested model surpasses the achievements of previous works. The 15,000 CT scan images in our dataset include three different forms of lung cancer. The proposed model performs exceptionally well, as evidenced by its astounding precision, recall, and F1-score, all above 99%, and by its flawless Area Under Curve (AUC) score of 100%. The proposed model has fewer parameters than the existing transfer learning models. Gradient Weighted Class Activation Mapping (Grad-CAM) was used to create class activation maps, which were then used to create a heatmap to display the classification zone.

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An approach of energy self-sufficiency at sports facilities

An approach of energy self-sufficiency at sports facilities

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International Conference on Electronics, engineering Physics and Earth science 2022, EEPES 2022

作者： Karapidakis, E. Apostolakis, S. Vidakis, N. Electrical and Computer Engineering Dept. Hellenic Mediterranean University Heraklion Greece Mechanical Engineering Dept. Hellenic Mediterranean University Heraklion Greece

Sports facilities are special cases of large-scale building complexes that demand large amounts of energy and power. This is due to the specific requirements of the thermal energy and the different kind of several activities that need considerable high load demands in relatively short time. Energy self-sufficiency of a building complex is defined as the ability to fulfil its own energy needs. This paper evaluates the energy consumption of the Olympic Athletic Center of Athens and proposes ways to support and enhance its operation, introducing the self-sufficiency concept, where load demand will be served by energy sources from the internal grid of the sport center, targeting to the highest possible autonomous operation. To achieve the minimum imports from the utility grid, the paper proposes the operation of a CHP unit and the installation of photovoltaics (PV), with the support of battery storage system and the existing gensets of the facility. © 2022 Published under licence by IOP Publishing Ltd.

关键词： Sports

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On the Performance of Terahertz Communications for Vehicular Wireless Networks

On the Performance of Terahertz Communications for Vehicular...

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2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

作者： Satche, Solomon Rawat, Danda B. Howard University Dept of Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (数字)9781665426718

ISBN: (纸本)9781665426718

Terahertz band appears as a solution to the spectrum scarcity for wireless communications. But, it presents beam pointing error, multipath interference, and atmospheric hurdle challenges. In this paper, we analyse the impacts of these constraints on the communication link in the THz band based on the mobile speeds of an end users in vehicles. We use a generic approach to assess the performance in terms of link failure probability. To that end we use the Mellin transform theorem approach to derive the joint pdf of the channel coefficient h and derive their joint probability density function that would be useful to assess the performance of the mobile vehicular communication link. The performance as a function of speeds is analyzed for pointing error, joint pointing and multipath error, and joint pointing, multi-path, and atmospheric error. The simulated results show severe and rapid degradation at higher speeds of the THz link when the SNIR is below a certain threshold. We then infer the performance with Doppler effects. © 2022 IEEE.

关键词： Wireless networks

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All patched up: Effective integration of real and synthetic features into a single image for object detection

All patched up: Effective integration of real and synthetic ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Dale, Ashley S. Christopher, Lauren Reindl, William Sanchez, Edwin Brunes, Sam Bickel, Will Martin, Jasmine William, Albert Indiana University Purdue University Indianapolis Dept. of Electrical and Computer Engineering IndianapolisIN46202 United States Indiana University Purdue University Indianapolis Dept. of Computer and Information Science IndianapolisIN46202 United States Indiana University Purdue University Indianapolis Dept. of Human-Centered Computing Media Arts and Sciences Indiana IndianapolisIN46202 United States

ISBN: (纸本)9798350359527

Synthetic data and data augmentation are commonly used to supplement a small set of real images and create a dataset with diverse features, improving the robustness of a computer vision model. However, there is no consensus on how to best leverage synthetic data for object detection tasks in a real environment. Our work answers the following questions: First, is it more important for synthetic data to have realistic backgrounds or realistic instances? Second, is pixel saliency a valid metric for explaining the relative importance of real and synthetic instances? We explore these questions by placing synthetic object instances in real environments and vice versa to create synthetically blended data, rather than a dataset with distinct real and synthetic images. Our results show that when there is good overlap between the feature domains of the real and synthetic images, there is no benefit to augmenting the training data with patches. However, given a domain gap between real and synthetic data for a given class, model performance improved ≈ 60%, with greater gains observed when increasing the number of images with real backgrounds. These results show that the relationship of synthetic to real features should be considered when constructing a dataset for domain transfer tasks. © 2023 IEEE.

关键词： Object detection

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Autonomous Drone Landing on a Moving Naval Base using Vision-Based Robot Control

Autonomous Drone Landing on a Moving Naval Base using Vision...

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2023 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Subamanian, Senthil Pon Vignesh Subramanian, Suvetha Meenaa Muthiah, Padmapriya Shajahan, Jalaluddin Mohd Ansari Saveetha University Simats School of Engineering Dept. of Elec. and Comm. Engg. Chennai India City University of New York City College of New York Dept. of Computer Science New York United States Monroe College King Graduate School Dept. of Computer Science New York United States City University of New York City College of New York Dept. of Electrical Engineering New York United States

ISBN: (纸本)9798350322972

This paper proposes a novel technique of an Autonomous Drone landing on top of a moving Naval Vessel Base using Vision-Based Robot Control. To achieve an effective landing a camera is fixed under the drone which captures the images and detects the movement of the landing base. The landing base on top of the vessel is specially designed with a nested tag which is scanned by the fixed camera on the drone from a high altitude. This nested tag is a fiducial marker that is used by the imaging system of the drone as a point of reference in the Naval Vessel Base for precise landing. The controller is designed in the image coordinate frame captured by the imaging system on the drone with the help of the Jacobian Matrix. Whether the vessel remains stationary or moving, it always trembles, especially, when a vessel experiences bad weather at sea, it will be quite challenging for the drone to land on the trembling Naval Vessel Base. These unexpected movements cause the landing tag to disorient from the horizontal view of the captured frames, which makes it difficult to obtain the expected mid-point of the tag. To overcome this complication the translational and rotational motions of the drone are decoupled from the camera using the Virtual Camera Method where the mid-point of the tag is set as the feature point which helps to obtain the expected position during the horizontal plane disorientation of the tag image captured by the camera from the drone. Additionally, Kalman Filter is employed in the imaging system to reduce the observation noise so that precise estimation of the feature point can be achieved. © 2023 IEEE.

关键词： Cameras

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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