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Work-In-Progress: Feasibility of anonymous grading for reducing performance discrepancies across student demographics

Work-In-Progress: Feasibility of anonymous grading for reduc...

引用

2023 ASEE Annual Conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Raikar, Neha Banerjee, Nilanjan Department of Chemical Biochemical and Environmental Engineering University of Maryland Baltimore County United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States

The authors would like to highlight that this work focuses on anonymous grading for in-person exams and quizzes. Anonymous grading through the LMS like Blackboard is difficult unless there is a requirement to scan and upload the exams. Using our system, we hope to lower the barrier for instructors to employ anonymous grading. Grading bias, whether conscious or unconscious, can occur across various demographics like ethnicity, gender, sexual orientation, or even based on GPA. We have currently tested anonymous grading on the entire class due to small class sizes. But in the future, we will test our system using an A/B split, where we randomly assign the students with similar demographics to the control and the test group. This will enable us to compare performances on the same exam with or without anonymous grading. The authors are applying for IRB approval for conducting the surveys and focus groups. © American Society for engineering Education, 2023.

关键词： Grading

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Flamingo Diet and Health Detection Based on Colour Classification 11

Flamingo Diet and Health Detection Based on Colour Classific...

引用

11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Halwani, Said Elbatanouny, Hagar Turky, Ayad Khan, Wasiq Tawfik, Hissam Hussain, Abir University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Department of Computer Science Sharjah United Arab Emirates Liverpool John Moores University School of Computer Science and Mathematics Liverpool United Kingdom

ISBN: (纸本)9798350367300

Flamingos are known for their vibrant pink and reddish hues, which are not merely aesthetic but indicative of their overall health and diet. These colors are derived from carotenoid pigments in their food sources, making coloration a vital marker for monitoring their well-being and environmental conditions. This study introduces a two-stage classification methodology designed to safeguard flamingo populations by leveraging deep learning techniques. Convolutional Neural Networks (CNNs) are used for both shape and color detection, ensuring accurate identification of flamingos and insights into their health status. Simulation results demonstrated the CNNs model's effectiveness, making it a valuable resource for wildlife conservation efforts aimed at preserving flamingo habitats. The first stage employs digital classification filters to distinguish flamingo images from other species, achieving an accuracy of 97.52%, while the second stage refines these detections through color analysis with an accuracy of 86.27%. This approach promises to mark a significant advancement in wildlife conservation, offering reliable methods for assessing and managing flamingo populations in their natural environments. © 2024 IEEE.

关键词： Convolutional neural networks

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An Abstractive Text Summarization Using Decoder Attention with Pointer Network 1

An Abstractive Text Summarization Using Decoder Attention wi...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Manohar, M. Sunil, G. Al-Attabi, Kassem Mohammed, Ibraheem Hatem Buvaneswari, P.R. Department of Computer Science and Engineering Bangalore India School of Computer Science & Artificial Intelligence Sr University Warangal India The Islamic University Najaf Iraq Al Rafidain University College Department of Computer Communications Engineering Bagdad Iraq School of Electrical and Electronic Engineering Vit Bhopal University Sehhore India

ISBN: (纸本)9798350316469

Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods. © 2023 IEEE.

关键词： Abstractive text summarization Decoder Attention Deep learning Pointer network Recall Oriented Understudy for Gisting Evaluation

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Design and Surface Flashover Test of 10 kV SiC Power Module for DC Shipboard Application

Design and Surface Flashover Test of 10 kV SiC Power Module ...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Li, Xiaoling Liu, Yang Schwartz, Skyler Guo, Ning Karimi, Ebrahim Mantooth, H. Alan Graber, Lukas Cuzner, Robert University of Arkansas Department of Electrical Engineering Fayetteville United States Georgia Institute of Technology School of Electrical & Computer Engineering Atlanta United States University of Wisconsin-Milwaukee College of Engineering and Applied Science Milwaukee United States

ISBN: (纸本)9798350316445

Future all-electric ships will likely use a Medium Voltage DC (MVDC) power distribution system, which has several advantages over MVAC, such as higher power density and higher reliability. As key components for power distribution systems, 10 kV power modules need to meet the insulation requirements for various fault conditions. Grounding faults can cause extremely high electric stress between the module terminals and baseplate hence resulting in surface flashover breakdown. This paper aimed at the insulation challenges of 10 kV power module for the MVDC shipboard application. The maximum voltage stresses of four typical grounding schemes are compared at single line-to-ground faults. The surface flashover tolerance of a 10 kV SiC power module is verified under steady DC voltage and the worst-case transient fault overvoltage. The experimental results can provide a reference for the MV power module insulation design and the module baseplate grounding schemes. © 2023 IEEE.

关键词： 10 kV power module MVDC shipboard power system surface flashover

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Generating CGAN-based Synthetic Dermoscopy Images and Classifying with Deep Lightweight CNN 4

Generating CGAN-based Synthetic Dermoscopy Images and Classi...

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4th International Conference on Computing, Networking, Telecommunications and engineering sciences Applications, CoNTESA 2023

作者： Sultana, Suriya Yasmin, Israt Patwary, Muhammed J. A. Bhuiyan, Mohammad Arif Sobhan Salam, Iftekhar Miraz, Mahdi H. International Islamic University Department of Computer Science and Engineering Chittagong Bangladesh Xiamen University Malaysia School of Electrical and Electronic Engineering Sepang Malaysia Sepang Malaysia

ISBN: (纸本)9798350382754

Effective dermatological diagnosis and treatments significantly depend on the accurate categorisation of the skin lesions. It is difficult to classify skin lesions using automated diagnostic techniques due to class-imbalance datasets and the absence of labelled data. The applications of various deep learning methods have demonstrated superior performance in medical diagnosis in the recent past. Unfortunately, training these models requires a substantial quantity of labelled instances. A novel approach, combining Deep Learning convolutional neural networks (CNNs) as well as conditional generative adversarial networks (CGANs), for classifying skin lesion images, has been presented in this study. CGAN is an enhanced version of the GAN, in which the generator takes input images with specific class labels. Applying conditions for minority classes can help to equalise the dataset by increasing the sample size for those minority classes. In this research, CGANs were used to produce synthetic images of minority classes for a skin lesion dataset named PAD UFES-20. The images generated from CGANS were added then to the original dataset to train a lightweight deep-learning CNN network named MobileNetv2. The analysis of the results shows an improvement of 6% average accuracy and 5% recall value, when the synthetic data augmentation was added to the original dataset. This synthetic data augmentation approach can also widely be used in various medical classification applications to reduce the imbalance problems, resulting in enhanced diagnosis. © 2023 IEEE.

关键词： Image classification

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INTERPRETABLE AUTOMATED LEARNING: EVOLVING FUZZY NETWORKS TO EXPLICIT APPLICATIONS OF AI

INTERPRETABLE AUTOMATED LEARNING: EVOLVING FUZZY NETWORKS TO...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Rathi, Vikas Vijayalakshmi, V.J. Mahesh, Bhandari Ninoria, Shalini Gupta, Ankur Kumar Premananthan, G. Department of Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Electrical And Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Vishwakarma Institute of Information Technology Pune India CCSIT TMU India School of Computer Science and Applications IIMT University Meerut India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore6410 India

ISBN: (纸本)9798350355338

One of the biggest developments in intelligent machines has been the development of evolving fuzzification. They are flexible system designs created using evolving methods. Fluid simulation now has excellent capabilities to use in a variety of data scientific situations as a result of this combination. The goal with this addition is to create a policy document that provides a thorough study of the evolving hazy ecosystems field of study. In order to comprehend the present setting of this subject and its importance, queries are asked and answered with some of this goal in mind. It will be made clear why evolving neural nets are significant from an explicable perspective, even before they started, how much they are utilized for, and where scholars should devote themselves in this field going forward. People must really be crucial to the developing field of data-driven schooling. © 2024 IEEE.

关键词： Adversarial machine learning

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Cyber Security Threat Detection in Internet of Things Using Optimized Deep Learning Technique 3

Cyber Security Threat Detection in Internet of Things Using ...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Mariappan, Umasree Balakrishnan, D. Rajendran, S. Alagusundar, Nanditha Sheriff, A. Ashiq Aadith, K. Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626001 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tami lNadu Krishnankoil626126 India

ISBN: (纸本)9798350302288

Cybersecurity threat detection in the Internet of Things (IoT) refers to the process of identifying and addressing potential security risks and attacks within IoT systems and devices. It involves monitoring IoT networks and devices for abnormal behavior, detecting intrusions or unauthorized access attempts, ensuring the integrity of data, managing vulnerabilities, analyzing behavioral patterns, and staying informed about emerging threats. This ongoing process aims to maintain the security and integrity of IoT infrastructure by proactively identifying and mitigating potential security threats. Recent studies have demonstrated that deep learning algorithms are highly efficient and offer numerous advantages compared to earlier methods when it comes to conducting security assessments of IoT devices. Dual Channel Convolution Neural Network with Spider Monkey Optimization (DCCNN-SMO), a deep learning technique which visualized colored images for finding harmful intrusions in IoT network. Here, DCCNN used two channels of CNN and combined in fully connected layer for detecting intrusions in IoT network. The hyper parameters of DCCNN were fine tune by SMO which enhance the performance of DCCNN. In this paper, the convergence speed and effectively fine tuning the hyper parameters of DCCNN an Adapted SMO (ASMO) is introduced for better detection of intrusions in IoT network. The position update equation of the basic SMO algorithm is modified by incorporating a dual seek scheme. This approach, employed in the local leader (LL) phase, global leader (GL) phase, and LL decision phase, facilitates the acquisition of fresh outcomes, representing best hyper parameters of DCCNN for better detection of intrusion in IoT network. The performance of the proposed DCCNN-ASMO is demonstrated to be superior in terms of accuracy, precision, and recall, as evidenced by the experimental results. © 2023 IEEE.

关键词： Internet of things

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Application of IoT Technology in Healthcare: A Case Study of LoRa Technology

Application of IoT Technology in Healthcare: A Case Study of...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Lin, Jiun-Hung Chen, Chita Cheng, Yung-Tsung National Kaohsiung University of Science and Technology College of Electrical Engineering and Computer Science Kaohsiung Taiwan Kun Shan University Department of Electronic Engineering Tainan Taiwan National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9798350301953

In recent years, Taiwan has faced the challenge of an aging society, and long-term care has become a pressing issue. In this study, a long-term care monitoring system was developed using LoRa wireless communication technology, which is remote, low-power, and low-cost. The system automatically performs daily physiological measurements through a portable physiological sensor to reduce the workload of healthcare workers and improve the quality of care. The system automatically performs daily physiological measurements through a portable physiological sensor to reduce the workload of healthcare workers and improve the quality of care. In the future, the system can be combined with cloud servers and related application development to provide a convenient, complete, real-time and low-cost long-term care monitoring system for patients and their families, medical institutions and care providers. © 2023 IEEE.

关键词： Wireless sensor networks

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A Novel Phase Optimization Method for Sidelobe-Level Suppression of 1-Bit Reconfigurable Reflectarrays

A Novel Phase Optimization Method for Sidelobe-Level Suppres...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Hu, JianJia Chi, Pei-Ling Yang, Tao School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu Taiwan

ISBN: (纸本)9781665442282

A novel phase optimization method for sidelobe-level suppression of 1-blt reconfigurable reflectarrays (RRAs) is proposed. Simply by switching/reversing the binary phase states of unit cells, the sidelobe level suppression can be attained. Furthermore, to enhance the effectiveness without significantly increasing optimization complexity, the hybrid optimization algorithm, incorporating EM simulated and theoretically calculated results, is used to determine optimal phase distribution over reflectarray aperture. To validate the feasibility, a 15× 15 patch reflectarray, which is composed of only two physical lengths to represent two phase states, was experimentally developed and tested, where the sidelobe level is improved from 11.1 dB to 14.3 dB with small peak gain variation from 21.4 dBi to 20.8 dBi. © 2023 IEEE.

关键词： Antenna arrays

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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, Rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. Recursive Hilbert Transform (RHT) is used to extract time domain features such as mean, Root Mean Square (RMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like Deep Residual Neural Network (DRNN), Convolutional Neural Network (CNN), and Recursive Neural Network (RNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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