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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

SN computer science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

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computers, Materials & Continua 2022年第7期72卷 333-348页

作者： Anwer Mustafa Hilal Jaber SAlzahrani Ibrahim Abunadi Nadhem Nemri Fahd NAl-Wesabi Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen

In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be *** technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber *** blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs *** this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G *** proposed PPBDLIIoT technique aims at identifying the existence of intrusions in ***,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the ***,CGO technique is applied to fine tune the hyperparameters in BiGRNN *** algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN ***,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT *** performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various *** experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

关键词： 6G networks industrial iot blockchain security intrusion detection artificial intelligence

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A comparative analysis of MQTT and IoT application protocols 2

A comparative analysis of MQTT and IoT application protocols

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Al Enany, Marwa O Harb, Hany M. Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Al-Azhar University Faculty of Engineering Systems and Computers Department Cairo Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Choosing the appropriate IoT protocol for a certain application was and still a critical issue for IoT applications, as protocols vary in behavior in different applications and under different network status. IoT application protocols have a recent focus of attention especially those protocols for the low-powered IoT applications and constrained devices. A brief survey of IoT layers and applied protocols for each layer and their applications were presented, besides, a detailed survey of IoT protocols with a presentation of the main researches that have been done to evaluate their performance. As a use case for this comparative study, the MQTT protocol was chosen because of its simplicity and its publish /subscribe model to be evaluated and compared against other application protocols. © 2021 IEEE.

关键词： Surveys

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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European Conference on Power Electronics and Applications

作者： Svenja Zöller Claudia Cabre-Piqueras Boris Schilder Alber Filba-Martinez Lukas Acker Faculty of Computer Science and Engineering Frankfurt University of Applied Science Frankfurt Germany Power Systems Dept. Catalonia Institute for Energy Research (IREC) Barcelona Spain

ISBN: (数字)9798350364446

ISBN: (纸本)9798350364453

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module.

关键词： Couplings Accuracy Europe Switches Power electronics Digital twins Power system reliability Reliability Thermal stresses Stress

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Exploring Auditory Hand Guidance for Eyes-free 3D Path Tracing

Exploring Auditory Hand Guidance for Eyes-free 3D Path Traci...

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Abe, Yuki Hara, Kotaro Sakamoto, Daisuke Ono, Tetsuo Human-Computer Interaction Lab. Hokkaido University Hokkaido Sapporo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Division of Computer Science and Information Technology Hokkaido University Hokkaido Sapporo Japan Faculty of Engineering Kyoto Tachibana University Kyoto Japan

ISBN: (纸本)9798400713958

Guiding a user’s hand along a 3D path can help individuals avoid obstacles and manipulate everyday items with eyes-free. While prior work focused on haptic approaches using robots, auditory approaches for 3D path guidance remain underexplored. We prototyped and assessed two auditory hand guidance techniques for 3D path tracing: (1) VERBAL repeating spoken directional prompts;(2) Follow-Your-Finger (FYF) using the user’s index finger in moving hand as an embodied reference and sonifying whether the user should follow the index finger direction. In a controlled pilot study with 12 sighted participants under an eyes-free condition, VERBAL yielded about 1.5 times faster performance than FYF, while FYF achieved an error rate of less than half VERBAL’s. We thus recommend using VERBAL for wider paths with fewer obstacles and FYF for narrower paths with higher collision risks. We also discuss the research direction for applying these findings to real-world tasks. © 2025 Copyright held by the owner/author(s).

关键词： auditory display eyes-free interaction spatial hand guidance

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Improved Ingredients-based Recipe Recommendation Software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and Information systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking

arXiv

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arXiv 2024年

作者： Martins, Inês Matos, João Gonçalves, Tiago Celi, Leo A. Wong, An-Kwok Ian Cardoso, Jaime S. Faculty of Engineering University of Porto Portugal Institute for Systems and Computer Engineering Technology and Science Portugal Duke University United States Massachusetts Institute of Technology United States

Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors;for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable testbed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 hours, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of ≥ 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P © 2024, CC BY-NC-ND.

关键词： Mirrors

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Hybrid Machine Learning Approach of "KNN – LSTM" for detection of Brain Tumour in MR images 15

Hybrid Machine Learning Approach of "KNN – LSTM" for detect...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uganya, G. Gunale, Anuja Govindrao Kulkarni, Shruthi V. Rajalakshmi, D. Valsaraj, Vani Srividhya, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Gujarat Vataria India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Karnataka Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Application Faculty of Humanities and Sciences SRM Institute Of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350370249

Now a days, more existing techniques are available to found tumours on brain from MR images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of "K-Nearest Neighbour – Long Short-Term Memory" ("KNN-LSTM") with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the MR images from huge dataset with usage of machine learning approach of "KNN-LSTM". Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. Due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model "KNN-LSTM" for categorizing the tumours with predicting through MR images. MRI brain imaging collection is used for our experimentation. The "KNN" algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes. © 2024 IEEE.

关键词： Image segmentation

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Defect Detection for Printed Circuit Board Assembly Using Deep Learning 8

Defect Detection for Printed Circuit Board Assembly Using De...

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8th International Conference on Control science and systems engineering, ICCSSE 2022

作者： Ren, Jing Gabbar, Hossam A. Huang, Xishi Saberironaghi, Alireza Ontario Tech University Dept. of Electrical and Computer Engineering OshawaON Canada Faculty of Energy Systems and Nuclear Science Engineering and Applied Science Ontario Tech University OshawaON Canada Rs Opto Tech Ltd Jiangsu Suzhou China

ISBN: (纸本)9781665484138

In many industrial applications, the number of defect samples is often insufficient for defect detection using conventional deep learning techniques. Also, the frequent change of PCBA board types on the product line introduces new defect types and adds a layer of challenge to the detection task considered in this paper. We propose a deep learning algorithm that targets learning patterns from various defect types with unbalanced training samples in the PCBA manufacturing product lines. A novel batch sampling method is proposed for the deep learning method for PCBA defect detection. We have validated the proposed algorithm using normal and defective images. The results show that the proposed deep learning method can accurately identify defects in PBCA images and achieve an overall accuracy of 98%. This deep learning technique can also be extended to detect other surface-level defects. © 2022 IEEE.

关键词： Defects

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Contrast enhancement of optical videoscope images

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Iran Journal of computer science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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