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ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification

Informatica (Slovenia) 2024年第7期48卷 103-112页

作者： Sabir, Aliea Ali, Huda A. Aljabery, Maalim A. Faculty of Computer Science and Information Technology Computer Information System Dept. University of Basrah Basrah Iraq Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah Iraq

Many things, such as goods, products, and websites are evaluated based on user's notes and comments. One popular research project is sentiment analysis, which aims to extract information from notes and comments as a natural language processing (NLP) to understand and express emotions. In this study we analyzed the sentiment of ChatGPT labeled tweet datasets sourced from the Kaggle community using five Machine Learning (ML) algorithms;decision tree, KNN, Naïve Bayes, Logistic Regression, and SVM. We applied three feature extraction techniques: positive/negative frequency, a bag of words (count vector), and TF IDF. For each classification algorithm. The results were assessed using accuracy measures. Our experiments achieved an accuracy of 96.41% with the SVM classifier when using TF- IDF as a feature extraction technique. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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AES-Based Steganography Using Blockchain: A Novel Approach for Secure Text Hiding in Encrypted Images

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Informatica (Slovenia) 2024年第21期48卷 67-78页

作者： Salim, Batool Arif Aljabery, Maalim A. Younis, Hameed Abdulkareem Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah Iraq

Steganography is a technique used to hide data within other data, emerging from the realization that information is valuable and must be concealed. By considering the potential of blockchain technology, which produces and stores data in an immutable chain, it is clear that steganography can be effectively applied alongside blockchain to hide information. This approach eliminates the need for traditional hiding methods. In this study, we aim to hide text messages within encrypted images using a new steganography-based blockchain, making them appear as ordinary encrypted images. The AES algorithm in CBC mode was used to encrypt both images and texts. Each image was split into 32-byte blocks, with a special block allocated for text, allowing for a text size of 32 characters. The robustness of the proposed technique against differential attacks was assessed using unified averaged changed intensity (UACI), number of pixels change rate (NPCR), entropy analysis, and correlation analysis. The outcomes are 99.6221% for NPCR, 33.5886 for UACI, and 7.9992 for the entropy value. Both statistical measures and differential metrics confirm the algorithm's effectiveness. This shows that the proposed encryption method generates random images and secure texts that are resistant to differential attacks and offer a prominent level of security. © 2024 Slovene Society Informatika. All rights reserved.

关键词： AES algorithm blockchain CBC mode correlation analysis entropy analysis NPCR secure texts steganography UACI

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CLASSIFICATION BY A STACKING MODEL USING CNN FEATURES FOR MEDICAL IMAGE DIAGNOSIS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer Science 2024年第1期86卷 3-18页

作者： Mutasher Rashed, Baidaa Popescu, Nirvana Computer Science Dept. National University of Science and Technology POLITEHNICA of Bucharest Romania Computer Science Dept. University POLITEHNICA of Bucharest Romania

Medical imaging coupled with Artificial Intelligence (AI) applications, in particular Deep learning (DL) and Machine Learning (ML), can speed up the disease diagnostic process. The purpose of this work is to present a novel disease detection system by suggesting a new Convolutional Neural Network (CNN) model and combining the CNN features with three of ML classifiers and suggesting a new classifier using the stacking model. The proposed system was used in binary and multi-classification and applied to two different medical datasets. The proposed model was evaluated using accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC, achieving robust results. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Convolutional neural networks

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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Predicting Crowd Movement Trajectories: A Graph Network-Based Approach Informed by Simulation Data 24

Predicting Crowd Movement Trajectories: A Graph Network-Base...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zong, Mengya Chang, Yuchen Dang, Yutian Wang, Kaiping Dept. of Computer Science Xi’an Univ. of Architecture and Technology Xi’an China Dept. of Architecture Xi’an Univ. of Architecture and Technology Xi’an China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

As-built assessment of transport hubs, based on large-scale simulation and passenger big data, has been an active area of research recently. Exploring the direction of crowd flow at future moments within a building is crucial for the rational assessment of the layout within the building. To tackle this problem, this paper proposes a two-stage framework for predicting passenger trajectories. In the first stage, our graph LSTM models the passenger’s trajectory sequence with the aim of capturing the temporal and spatial correlations in the passenger’s historical trajectory. In the second stage, different attentional weights are assigned to the previously embedded information to capture the interaction between pedestrians and the surrounding crowd. Finally, an LSTM decodes the information encoded for the passenger, predicting multiple plausible future trajectories. Experiments show that our model achieves excellent performance in the metrics ADE and FDE compared to the existing baseline of pedestrian trajectory prediction models. © ASCE.

关键词： Prediction models

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Graph-Based Deep Learning for Pedestrian Flow Prediction in Transport Hub: A Simulation-Informed Approach 24

Graph-Based Deep Learning for Pedestrian Flow Prediction in ...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chang, Yuchen Zong, Mengya Dang, Yutian Wang, Kaiping Dept. of Computer Science Xi’an Univ. of Architecture and Technology Xi’an China Dept. of Architecture Xi’an Univ. of Architecture and Technology Xi’an China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Transport hubs play a major role in the national transport network. Larger and deeper subway stations attract a considerable influx of passengers, particularly during peak hours. Therefore, long-term pedestrian flow prediction is important for subway station management. This paper proposes an end-to-end learning framework based on the Graph Convolutional Network and the Attention Mechanism. In the first phase, key functional facilities within the subway station are abstracted into a graph structure. Subsequently, pedestrian flow data for graph nodes are obtained using Anylogic pedestrian simulation. In the second phase, a Graph Convolutional Network is employed to extract spatial features among nodes to capture the spatial dependencies of each node. The temporal attention operation is utilized to capture long-term temporal dependencies among nodes. This aids in the rational allocation of subway personnel and reducing congestion. Results demonstrate the validity and robustness of our model for pedestrian prediction. © ASCE.

关键词： Subway stations

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Time series forecasting using transformer neural network

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International Journal of computers and Applications 2024年第10期46卷 880-888页

作者： Bhogade, Vaibhav Nithya, B. Dept. of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is *** model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Prediction models

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Predicting COPD Severity Using Machine Learning and GOLD Criteria 3

Predicting COPD Severity Using Machine Learning and GOLD Cri...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Kinikar, Atharva Chandwani, Maanav Rane, Tushar Pune Institute of Computer Technology Dept. Information Technology Pune India Pune Institute of Computer Technology Dept. Computer Engineering Pune India

ISBN: (纸本)9798350381931

Breathing problems that get worse over time are a symptom of chronic obstructive pulmonary disease, or COPD. It is a catch-all phrase for a number of conditions including asthma, bronchitis, and emphysema. It is defined by a progressive constriction of the airways and an early aging of the lungs. Experts have attested to the fact that early identification helps lessen the symptoms of COPD. Thus, using GOLD criteria, patients' COPD severity can be predicted using contemporary methods like machine learning algorithms. The proposed prediction models predict severity of COPD of the patient on a scale of 1 to 4 as prescribed by the GOLD system. Our model was trained to utilize a derived feature set and machine learning classifiers, such as SVC, Random Forest Classifier, XGBoost classifier, Naive Bayes, and Hard Voting Ensemble, with the best possible parameter choice to achieve a commendable accuracy of 97.6%. The global COPD epidemic will be lessened with the use of this method and medical professionals' experience. © 2024 IEEE.

关键词： Machine learning

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Small object detection in diverse application landscapes: a survey

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Multimedia Tools and Applications 2024年第41期83卷 88645-88680页

作者： Iqra Giri, Kaisar J. Javed, Mohammed Department of Computer Science Islamic University of Science & Technology Pulwama India Computer Vision & Biometrics Lab Dept. of IT Indian Institute of Information Technology Allahabad India

The importance of object detection within computer vision, especially in the context of detecting small objects, has notably increased. This thorough survey extensively examines small object detection across various applications, consolidating and outlining the available methodologies. Traditional papers on small object detection have focused on specific domains. However, this survey paper incorporates insights from a multitude of domains, providing a comprehensive understanding of the versatility and applicability of small object detection techniques. This paper sheds light on the key challenges faced and delves into potential solutions to address the challenges, offering insights into viable solutions to enhance small object detection performance, setting it apart from existing literature. The strategies identified in our survey encompass a spectrum of approaches, categorized as transformer-based, CNN, and traditional methods. Also, this paper collates prevalent datasets relevant to small object detection, simplifying access to these resources. Further, it provides a succinct overview of diverse evaluation metrics used for performance assessment in this field, enhancing understanding of the effectiveness and proficiency of these methods. This survey paper not only consolidates established knowledge but also highlights innovative viewpoints, providing a comprehensive and enlightening compilation that contributes to the advancement of small object detection in the field of computer vision. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Novel Identity Authentication Mechanism forUnmanned Maritime Vessels Communication Based on MITRE ATT&CK Framework

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Journal of Marine Science and technology (Taiwan) 2024年第2期32卷 170-180页

作者： He, Jun-Xian Chang, Shih-Hao Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Dept. of Computer Science and Information Engineering National Taipei University of Technology Taipei City Taiwan

With the development of the smart shipping industry, unmanned vessel technology is rapidly evolving, accompanied by a demand for robust Internet of Things (IoT) communication security practices. Key communication technologies for the operation of unmanned vessels include external vessel communication. One crucial aspect is the verification of the identities of the parties involved in these communication systems, as this ensures secure and trusted interactions between unmanned vessels and port management authorities. Identity authentication plays a vital role in ensuring the secure communication of unmanned vessels. This paper aims to analyze potential risks related to identity authentication technology in unmanned vessel communication systems. To effectively address these risks, we propose leveraging digital certificates and blockchain technology to achieve device non-repudiation and substitution. Based on this technology, we organize and implement defense strategies using the MITRE ATT&CK framework. This framework provides a comprehensive and systematic approach to understanding potential threats and designing appropriate defense measures. By utilizing this framework, security teams can better comprehend vulnerabilities and formulate effective countermeasures. To validate the effectiveness of the proposed defense framework, we conducted simulation experiments. These experiments helped us evaluate the efficiency and reliability of the strategies in real-world scenarios. Through these experiments, we could ascertain whether the countermeasures effectively mitigated the identified risks and ensured the secure communication of unmanned vessels. © 2024 National Taiwan Ocean University

关键词： Risk assessment

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