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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Sungthong, Chawanee Ruangtanusak, Saksorn Songmuang, Pokpong Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathum Thani Thailand Bedrock Analytics Ai And Robotics Ventures Applied Machine Learning Team Bangkok Thailand

ISBN: (纸本)9798350370096

This study focuses on the problem in the field of issue ticketing systems, where the need for human involvement in classifying work task in the ticket often results in insufficient classification. Our primary focus is on a problem associated with multi-label classification. A significant challenge in this study is the dependency of our dataset on low resources, which is different from the large datasets usually used in current multi-label text classification studies. Therefore, we propose a pre-training approach for our low-resource dataset. Our methodology applies several embedding techniques, including TF-IDF, Word2Vec, and BERT (WangchanBERTa and PhayaThaiBERT). The classification models, such as Logistic Regression, Support Vector Classifiers, Decision Trees, Neural Networks, and Deep Neural Networks, are experimented in this study to assess their performance in a low-resource environment. According to our experimental results, a combination of PhayaThaiBERT and Deep Neural Networks outperforms conventional methods. The combination yielded an F1 score of 0.769 on the test dataset. © 2024 IEEE.

关键词： Deep neural networks

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A Hierarchy of the Forced Retracing Computable Curves 9

A Hierarchy of the Forced Retracing Computable Curves

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9th International Conference on Computability and Complexity in Analysis, CCA 2012

作者： Abdul-Malak, David Gillespie, Megan Zheng, Xizhong Department of Computer Science and Mathematics Arcadia University GlensidePA19038 United States Departments of Mathematics and Computer Science Jiangsu University Zhenjiang212013 China

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Data-Driven Detection of Data Falsification Cyber-Attacks in Distributed Generation

Data-Driven Detection of Data Falsification Cyber-Attacks in...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Anin, Johnson Abdelsalam, Omar Nabil, Mahmoud Hu, Fei Alsharif, Ahmad University of Alabama Department of Electrical and Computer Engineering AL35487 United States Tennessee Tech University Department of Computer Science TN35405 United States University of North Carolina A&T Electrical and Computer Engineering NC27411 United States University of Alabama Department of Computer Science AL35487 United States Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt

ISBN: (纸本)9798350376715

The advancement of smart grid technology has enabled consumers to become active participants in electricity generation, particularly using renewable-based distributed energy resources. While this evolution offers economic benefits by allowing consumers to sell excess energy back to the grid, it has also opened the door to fraudulent activities. Generation theft, a malicious act where consumers report falsified higher energy generation readings for financial gain, has emerged as a significant challenge, leading to estimated annual losses of approximately $6 billion in the United States alone. In response to this issue, our paper introduces a novel approach to the detection of fraudulent electricity generation within the grid. Our primary contribution is the creation of a benchmark dataset, based on actual power usage and generation data from the Ausgrid dataset, which now includes simulated fraudulent activities alongside normal usage patterns. Our investigation further explores the statistical properties of power consumption patterns among fraudulent users, revealing a distinct lack of periodicity and predictability. By employing the day-to-day correlation matrix as an input to a 2D Convolutional Neural Network (CNN), our detection system significantly improves its ability to distinguish between legitimate and fraudulent electricity use. Additionally, we propose a hybrid deep learning model that merges the strengths of 2D CNNs with the sequential pattern recognition capabilities of Long Short-Term Memory (LSTM) networks. This approach effectively captures both spatial and temporal dynamics within the dataset, leading to a remarkable performance with a detection rate of 97.04%, a false alarm rate of 0.67%, and an overall accuracy of 99.70%. © 2024 IEEE.

关键词： Cyber attacks

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Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

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Work-in-Progress: Integrating Generative AI with Evidence-based Learning Strategies in computer science and Engineering Education 53

Work-in-Progress: Integrating Generative AI with Evidence-ba...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Lauren, Paula Watta, Paul Lawrence Technological University Department of Mathematics and Computer Science SouthfieldMI United States University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI United States

ISBN: (纸本)9798350336429

Generative AI assistants are AI-powered applications that can provide personalized responses to user queries or prompts. A variety of AI assistants have recently been released, and among the most popular is OpenAI's ChatGPT. In this work-in-progress in innovative practice, we explore evidence-based learning strategies and the integration of Generative AI for computer science and engineering education. We expect this research will lead to innovative pedagogical approaches to enhance undergraduate computer science and engineering education. In particular, we describe how ChatGPT was used in two computing-based courses: a Junior-level course in database systems and a Senior-level class in mobile application development. We identify four evidence-based learning strategies: well-defined learning goals, authentic learning experiences, structured learning progression, and strategic assessment. We align these strategies with the two aforementioned courses and evaluate the usefulness of ChatGPT specifically in achieving the learning goals. Combining Generative AI with evidence-based learning has the potential to transform modern education into a more personalized learning experience. © 2023 IEEE.

关键词： Artificial Intelligence in Education (AIEd) Generative AI pedagogy

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An Ontological Framework for Lung Carcinoma Prognostication via Sophisticated Stacking and Synthetic Minority Oversampling Techniques

An Ontological Framework for Lung Carcinoma Prognostication ...

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2024 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2024

作者： Hossain Raju, Md Azad Imam, Touhid Islam, Jahirul Rakin, Abdullah Al Nayyem, Mohammad Navid Uddin, Mohammad Shihab University of South Dakota Department of Computer Science SD United States New Mexico Institute of Mining & Technology Department of Mathematics SocorroNM United States

ISBN: (纸本)9798331521240

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, predominantly due to its late-stage diagnosis and rapid progression. This study proposes an advanced ensemble learning model for early detection of lung cancer by analyzing demographic, clinical, and lifestyle features from a comprehensive survey dataset. To mitigate the issue of class imbalance, the Adaptive Synthetic Sampling (ADASYN) technique is employed, generating synthetic data for the minority class and improving model fairness. The ensemble model integrates XGBoost, LightGBM, and CatBoost algorithms in a stacking framework, which leverages the strengths of each algorithm to enhance overall predictive accuracy. To further refine the model, feature selection techniques such as Recursive Feature Elimination with Cross-Validation (RFECV) and LASSO Regression are utilized, minimizing the risk of overfitting and ensuring the model focuses on the most important predictive features. Hyperparameter tuning is optimized using Optuna, which applies Bayesian optimization techniques to identify the best configuration for model performance. Additionally, Stratified K-Fold Cross-Validation is employed to validate the robustness of the model, ensuring balanced representation across folds and enhancing generalizability. This approach demonstrates significant improvements in prediction accuracy compared to traditional machine learning models, emphasizing the potential of combining ensemble learning with robust optimization techniques for early lung cancer detection, ultimately contributing to more effective clinical decision-making and intervention. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Network Security: Deep Learning-Powered Intrusion Detection with Data Engineering Techniques

Enhancing Network Security: Deep Learning-Powered Intrusion ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Sharath, T. Muthukumaravel, A. Bharath Institute of Higher Education and Research Department of Computer Science Tamilnadu Chennai India Bharath Institute of Higher Education and Research Faculty of Arts and Science Tamilnadu Chennai India

ISBN: (纸本)9798350390759

The research work focuses on improving network security by integrating Deep Learning techniques, data engineering methodologies, and adaptive defensive measures. The context emphasizes the growing intricacy of cyber risks and the need for sophisticated Intrusion Detection System. The methods used include optimizing DL architectures via pruning and quantization, including ensemble learning, implementing dynamic data augmentation, and improving model interpretability. The observed findings show significant improvements in the performance of intrusion detection. The accuracy of the model climbed from 92.5% to 96.1%, the precision improved from 89.7% to 93.8%, the recall went from 94.2% to 97.2%, and the F1-score enhanced from 91.8% to 95.5%. These metrics indicate the system's capacity to identify and counteract incursions with greater precision and effectiveness. It enhances the methodical and efficient method of detecting intrusions, specifically targeting the ever-changing nature of cyber threats. The integrated architecture greatly improves the robustness and flexibility of IDS, allowing for the implementation of proactive defensive measures in contemporary network settings. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning Technique for Oropharyngeal Cancer Detection in Medical Imaging 7

Deep Learning Technique for Oropharyngeal Cancer Detection i...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kumar, R. Pazhanirajan, S. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chidambaram India

ISBN: (纸本)9798350340600

Oropharyngeal carcinoma is a life-threatening disease that requires prompt diagnosis and treatment. This study uses deep learning algorithms to present a novel method for improving medical imaging to detect oropharyngeal cancer. Deep learning has demonstrated immense potential for medical image processing applications in recent years. This research capitalizes on this opportunity by devising a dependable model for detecting oropharyngeal cancer. Examining oropharyngeal cancer with an emphasis on the critical significance of early detection. This study emphasizes the need for more precise and efficient diagnostic techniques. This work presents a comprehensive strategy from data acquisition and cleansing to the development of the model's underlying architecture, training, validation, and explication. The results section presents quantitative and qualitative data demonstrating the model's ability to detect oropharyngeal cancer. The proposed model has the potential to improve the quickness and accuracy with which oropharyngeal cancer is diagnosed, thereby benefiting patients. It emphasizes the need for additional research and the implementation of deep learning innovations in healthcare. © 2023 IEEE.

关键词： Medical imaging

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Abnormal Events Detection in Surveillance Environments using Machine Learning

Abnormal Events Detection in Surveillance Environments using...

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2024 International Conference on science, Technology, Engineering and Management, ICSTEM 2024

作者： Priya, D. Divya Theam, Lim Jit Vinisha, J. Krishnan, Kesavan Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Kampar Perak Malaysia Department of Information Technology St.Joseph's Institute of Technology Tamilnadu India Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350376913

The ability of intelligent CCTV systems to detect unusual events, detect human behaviour and recognize objects is essential. This system enables the detection of anomalous events in the environment, anomalous human behaviour and the level of alertness in the environment. The system uses machine learning and machine vision capabilities to detect and identify specific anomalies in CCTV video feeds. This system often uses frames through frame processing, and supervised learning is often used as a training method. However, unsupervised and semi-supervised learning replace supervised learning in the system learning process due to the large variety of anomalies and the impossibility of predicting and preparing for all of them. This system can decrease or do away with the amount of labour-intensive human work needed to manually spot anomalies in the live CCTV feed and provide alerts. The initial monitoring of unusual events and low-quality archiving of typical scenarios also increases the efficiency of the system’s storage. In addition, this system provides an extension for developing a distributed anomaly classification system so that only abnormal events are directed to different unique systems for categorization. Regular recordings, on the other hand, will be archived with low quality. Furthermore, this approach offers a distributed anomaly classification system development path. © 2024 IEEE.

关键词： Anomaly detection

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches 15

Reassessing Glove Embeddings in Deep Learning: A Comparative...

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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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