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Exploring the opportunities and challenges of ChatGPT in academia

Discover Education 2024年第1期3卷 1-14页

作者： Islam, Iyolita Islam, Muhammad Nazrul Department of Computer Science and Engineering Bangladesh University of Professionals (BUP) Dhaka 1216 Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka 1216 Bangladesh

The Artificial Intelligence (AI) natural language model ChatGPT (Chat Generative Pre-trained Transformer), often referred to as ChatGPT-4, has a wide range of possible uses in the fields of research, business, academia, health, and similar fields. This language model can accomplish a number of academic jobs that were previously completed by people, taking a significant amount of time and effort. The purpose of the article is to investigate ChatGPT’s potential opportunities and challenges in academia. To attain this objective, a review of relevant literature and online resources (news, articles, etc.) was carried out. The noticing-collecting-thinking approach was adopted to explore and categorize all observed concerns. The outcome of this study reveals that research, education, personal skill development, and social aspects constitute the four broad perspectives that articulate the opportunities and constraints of ChatGPT in academia. For instance, from the education perspective, ChatGPT can help students have a personalized learning experience. On the other hand, it might provide false information as well as lack the ability to generate responses on its own because those responses depend on training datasets, which may contain errors. Similarly, from the point of view of the personal skill development, this model may impair a person’s capacity for critical thought and content production;while providing reading and writing practice sessions and relevant content, it can improve a person’s language proficiency. © The Author(s) 2024.

关键词： ChatGPT, GPT-3, GPT-4 academia, Challenges, Opportunities, Conceptual analysis, Artificial Intelligence, Education, Writing

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV's Against DDoS Attacks 18

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 167页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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A Quantum Approach for Text based Steganography 15

A Quantum Approach for Text based Steganography

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Naik, Darsana A. Pallavi, N. Keerthi, P. Rai, Abhinav Kumar Jha, Adarsh Kumar Bindal, Avaneesh Chauhan, Shubhi Singh M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Impact Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331300579

In or daily lives, maintaining the security of sensitive information has proven to be very difficult. communications sent frequently through a communication channel like the Internet may catch the attention of crackers and hackers, leading to possible attempts to decrypt and reveal the odd communications. With the development of digital communication, the Internet, and cloud-based storage, there is an increasing need for novel and effective methods for protecting the privacy and security of digital data. Steganogra-phy and cryptography are often used together as a solid privacy protection strategy. However, numerous studies on steganography have been established by various academics. Here, a text steganography method has been developed using quantum circuits and XOR-based symmetric cryptography. In this case, the quantum technique also includes boosting security. using a quantum gate truth table to map the logic allows for the embedding of cov-ert and hidden messages. Since text messages are generally regarded as being safe and not requiring further investigation, this technique offers an excel-lent data protection system. Steganography is an art form that can be used to transmit data securely through any form of open communication. © Grenze Scientific Society, 2024.

关键词： Quantum electronics

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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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Maxillary Sinus Disease Detection and Analysis Approaches in Deep Learning: Survey 1

Maxillary Sinus Disease Detection and Analysis Approaches in...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Vimala, R. Preethi, D.M.D. Government College of Engineering Department of Computer Science and Engineering Bodinayakkanur Theni India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350372748

The intend of this literature survey is to lessen the problems faced by dentists in the field of maxillary sinus diagnosis in image processing and to serve as a valuable reference to the literature related application. The odontogenic diseases may be diagnosed with atypical symptoms or it might mimic other conditions. This can create difficulty to disembark an accurate diagnosis. Sinusitis or temporomandibular joint disorder possibly is a symptom that resemble an odontogenic infection. Some odontogenic diseases may have overlapping symptoms, making it difficult to differentiate between them when based solely on clinical presentation. For instance, both a periapical abscess and a periodontal abscess can cause localized pain, swelling, and sensitivity. Diagnosing maxillary sinus issues through digital imaging, such as panoramic dental Xray, Cone Beam Computed Tomography (CBCT) and Computed Tomography (CT) scans, can be challenging due to the complex anatomy and the potential for overlapping structures. Radiologists utilize assorted computerized methods for maxillary sinus disease detection. CT scan analysis uses algorithms for segmentation and feature extraction, aiding machine learning algorithms in pattern recognition. CBCT provides detailed three-dimensional images, enabling comprehensive assessments of maxillary sinus anatomy and pathology. MRI utilizes signal intensity variations and texture analysis to identify potential diseases. Moreover, the integration of ultrasound, analysis of endoscopic video, and reporting of automated systems utilizing techniques of deep learning such as Convolutional Neural Networks and Recurrent Neural Networks, enhances precise detection by combining information from various imaging modalities. Interpreting dental radiographs can be complex, and certain conditions may not be clearly visible or may appear differently on different imaging modalities. It requires expertise and experience to accurately interpret radiographic findings and

关键词： Diseases

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Pioneering Excellence in Semiconductor Manufacturing via XGBoost with BAT Optimization for Cutting-Edge Predictive Maintenance

Pioneering Excellence in Semiconductor Manufacturing via XGB...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Dwivedi, Abhishek Khan, Nikhat Raza IES University Department of Computer Science & Engineering Bhopal India IES College of Technology Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350374100

This study aims to revolutionize predictive maintenance in semiconductor manufacturing by using the SECOM dataset. The main objective is to create a precise and flexible predictive model by combining the XGBoost algorithm with BAT optimization techniques. The methodology involves intricate data preprocessing, incorporating data cleaning, handling multicollinearity, and using SMOTE-EEN to address the imbalanced SECOM dataset. The model is built using the robust XGBoost algorithm, and hyperparameter tuning is performed using BAT optimization techniques. The research results show great potential, with the model achieving a high accuracy of 0.99 and a strong F1 score of 0.95. The results confirm the model's effectiveness in predicting equipment failures in semiconductor manufacturing processes. The combination of XGBoost and BAT optimization techniques highlights the robustness and flexibility in dealing with the complex nature of the SECOM dataset. This study represents a significant advancement in predictive maintenance for semiconductor manufacturing. The integrated model provides a cutting-edge fault prediction solution, minimizing downtime and improving production efficiency. © 2024 IEEE.

关键词： Data handling

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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