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共 40873 条记录，以下是431-440 订阅

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Twitter Scraping and Sentiment Analysis on Stock Price

Twitter Scraping and Sentiment Analysis on Stock Price

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Iniyan, S. Thangakumar, J. Rajendran, P. Selvi Hindustan Institute Of Science And Technology Dept. Of Computer Science And Engineering Chennai603103 India

ISBN: (纸本)9798350364828

social media is useful in the community for sharing and discussing various events with others. The study's main objective is to predict the sign of the stock by sentimental analysis regarding the Hindenburg report on Adani group stocks by obtaining tweets from Twitter. The study's findings will aid in understanding the effect of the Hindenburg report on the stocks of the Adani group. Excel files are downloaded from Twitter containing tweets from social media users. Tweets are cleaned up by eliminating noise data after extraction. Machine learning algorithms, such as Logistic Regression (LR), Linear support classification (SVC), Random Forest (RF), and voting, were used to classify the data into positive, Neutral, and negative groups. © 2024 IEEE.

关键词： Logistic regression

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Tanaka, Yuuri Hashimoto, Yasuhiro The University of Aizu Dept. Computer Science & Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350362480

Human beings have long been regarded as "homo narrans,"employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling"ability in humans, who adjust readability based on the original story's readability. These insights advance our understanding of the "AI narrans"concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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GNN-based Disease Detection and Classification in Cassava Leaf 5

GNN-based Disease Detection and Classification in Cassava Le...

引用

5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Meenalochini, M. Amalroshini, A. Nandhini, K L Megha Murugan, C R Sogil Kongu Engineering College Dept. of Computer Science and Design Erode India

ISBN: (纸本)9798331518097

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Happy Chhikara, Rita Kashyap, Neeti The NorthCap University Dept. of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350375237

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms' capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape. © 2024 IEEE.

关键词： Internet of things

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Prompt-Based Domain Incremental Learning with Modular Classification Layer 27

Prompt-Based Domain Incremental Learning with Modular Classi...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Wang, Boyu Ma, Yue Qiu, Qinru Dept. of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9781643685489

Continual learning is referred to as machine learning model's ability to learn a sequence of tasks or data over time without forgetting previously learned knowledge. In particular, we focus on domain incremental learning where the model trained on one domain, e.g., real-life photos, has to be augmented to accommodate data from new domains, e.g., cartoon images. Typical incremental learning relies on rehearsal-based methods, which store trained samples in a buffer and replay them during training alongside new data. This results in significant memory overhead and raise concerns about data privacy. Recently, prompt-based methods address these challenges and outperform them by utilizing the pre-trained Vision Transformer (ViT) and replacing the replay buffer with a prompt pool. However, existing prompt-based models fail to capture domain-specific knowledge and perform poorly in domain incremental learning. In this paper, we propose "domain prompt incremental learning via dynamic neural network", which combines the advantages of architecture-based and prompt-based methods. Specifically, our framework maintains two types of prompt: a instance-level prompt that improves the model's generalization ability is shared across all input samples;and a domain prompt that encodes domain-specific knowledge is assigned for each task. Furthermore, a separated classification head is trained for each domain so that the model has a pre-trained ViT and an ensemble of classification layers, one for each domain. The experimental results shows that our approach outperforms state-of-the-art methods by 2.3% in average on two domain incremental learning (DIL) benchmarks. © 2024 The Authors.

关键词： Contrastive Learning

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Impact of Human-in-the-Loop Paradigm in AI Labeling and Annotation 8

Impact of Human-in-the-Loop Paradigm in AI Labeling and Anno...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Davda, Meeth Jitendra Sindhu, D.V. R V College of Engineering Dept. of Computer Science Bangalore India

ISBN: (纸本)9798331505462

Incorporating human expertise directly into artifi-cial intelligence workflows, a concept known as the Human-in-the-Loop (HITL) paradigm, has proven to be a crucial approach in improving the processes of AI data labeling and annotation. This survey paper delves into the impact of the HITL paradigm on the quality and efficiency of data annotation in AI systems, emphasizing the synergistic benefits of combining human insights with automated techniques. This paper reviews a variety of recent methodologies that integrate human evaluative input with automated systems, showcasing the enhancement of data accu-racy and system reliability across several applications, including healthcare, autonomous vehicles, and language processing. It classifies various HITL techniques, evaluates the applicability of each and offers potential solutions. © 2024 IEEE.

关键词： Active learning

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Deploying NLP techniques in Twitch application to comprehend online user behaviour 1

Deploying NLP techniques in Twitch application to comprehend...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sethurajan, Monikka Reshmi Natarajan, K. Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Sentiment analysis of emotion entails identifying and analyzing subjective information from language, such as views and attitudes, and helps to improve data visualization by employing a variety of strategies, tactics, and tools. New media channels have significantly changed how people interact, exchange ideas, and share information. Numerous businesses have begun to mine this data, concentrating on social media since it is a popular platform for customers to voice their ideas about various brands or goods and because it gives users an audience, enhancing the visibility and potential effect of this input. So far, as the internet expands and modern technology advances, new avenues have emerged with a higher ability to offer businesses pertinent feedback on their goods. The goal of this study is to investigate the many forms of online behaviour by analyzing chat interactions from the well-known streaming service Twitch. Emotes were occasionally employed in place of letters, to get attention, or to communicate emotions. We propose a system that may take in chat logs from a certain stream, use a sentiment analysis algorithm to classify each message, and then display the data in a way that might permit users to analyze the results according to its polarity (positive message, negative message, or neutral message). This application must be sufficiently versatile to be used with any platform broadcast type and to handle the datasets at very huge level. © 2023 IEEE.

关键词： Sentiment analysis

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SQL Injection Detection using Machine Learning and Convolutional Neural Networks 5

SQL Injection Detection using Machine Learning and Convoluti...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Misquitta, Jason Asha, S. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server's validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not. © 2023 IEEE.

关键词： Personal computing

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Multilingual Approaches to Named Entity Recognition 3

Multilingual Approaches to Named Entity Recognition

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Savaram, Padmaja Tabassum, Suhana Bandu, Sasidhar Lakshitha, B. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Dept. of Computer Science KSA Al-kharj Saudi Arabia University College of Engineering Osmania University Dept. of Computer Science and Engineering Hyderabad India Prince Sattam Bin Abdulaziz University Dept. of English Al-Kharj Saudi Arabia Vnrvjiet Jntu Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518592

Named Entity Recognition (NER) represents a fundamental operation within Natural Language Processing (NLP), focused on the extraction and classification of specific entities embedded in textual data. Given the rising linguistic diversity present in digital communications, there is an escalating need for NER systems to be proficient in identifying and categorizing entities across a spectrum of languages. However, developing NER models for resource-poor languages presents significant challenges due to limited labeled data and linguistic resources. This paper examines methodologies for enhancing the ability of NLP models to perform NER across diverse languages by transferring knowledge from high-resource languages to low-resource languages. We delve into advanced approaches such as cross-lingual transfer learning, multilingual embeddings, and cross-lingual model adaptation. Cross-lingual transfer learning utilizes pre-trained models from high-resource languages to initialize NER systems for low-resource languages, thereby facilitating the effective transfer of linguistic knowledge across language boundaries. Multilingual embeddings provide a shared representation space for words across languages, facilitating the transfer of linguistic knowledge. Additionally, cross-lingual model adaptation techniques aim to adapt existing NER models to new languages through fine-tuning or domain adaptation. By enhancing the generalizability of NER models through cross-lingual knowledge transfer, we enable these models to perform effectively across diverse linguistic contexts, including both resource-rich and resource-poor languages. These advancements contribute to broader accessibility and applicability of NER technology across languages and cultures, facilitating more inclusive and comprehensive language processing applications. © 2024 IEEE.

关键词： Embeddings

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Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks 6

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Chowdhury, Rituparna Das, Rudra Faruk Ananna, Fowzia Binta Saha, Arnob Nawar, Sadia Hosen, Md. Hamid East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. © 2024 IEEE.

关键词： Lime

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