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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sibitenda, Harriet Hu, Ruofan Rundensteiner, Elke Diattara, Awa Traore, Assitan Ba, Cheikh University of Gaston Berger Computer Science Department Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester United States Data Science Program Worcester Polytechnic Institute Worcester United States Actroll Data Science Department France

ISBN: (纸本)9798350374889

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with 'Poverty' achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions. © 2024 IEEE.

关键词： Tweets

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On the Effectiveness of a Hybrid Model for Volatility Prediction 23

On the Effectiveness of a Hybrid Model for Volatility Predic...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Baddepudi, SaiAsrith E.V.N.M. Agarwal, Akshay Iiser Bhopal Data Science and Engineering Department India

ISBN: (纸本)9798350374889

Volatility prediction of the stock market has been a hot topic in economics and finance. However, it is challenging to forecast the volatility accurately due to the complex and dynamic nature of the stock market. It is observed that deep neural network architectures are universal approximators and effective for financial tasks;moreover, a single model is generally not found effective in solving the volatile nature of the input. Therefore, we propose a hybrid model that uniquely combines the strength of two state-of-the-art deep network models to estimate stock volatility precisely. The proposed hybrid model is evaluated on several real-world datasets covering two stock indexes and top volatile bank stock. The proposed algorithm surpasses the existing and state-of-the-art uni models and showcases its strength in effectively capturing the volatility of different stocks. © 2024 IEEE.

关键词： Prediction models

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Real-Time Stock Price Prediction Using Apache Kafka 5th

Real-Time Stock Price Prediction Using Apache Kafka

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Deepalakshmi, R. Harshini, N. Nivedha, S. Department of Data Science NMKRV College for Women Bengaluru India

ISBN: (纸本)9789819745395

Stocks play a vital role in the economy and serve as an indicator of business value and investor sentiment. To leverage improved decision-making aligned with investment strategies and market trends, real-time stock predictions enhance outcomes by offering immediate insights and enabling investors to make more informed decisions. Various machine learning techniques have been applied to this problem, but most of them rely on historical data and do not account for the dynamic and volatile nature of the stock market. In this project, we propose a novel approach for stock price prediction using real-time data streamed from Yahoo Finance with Apache Kafka. A simple linear regression model, trained on the historical data, enables to predict the closing price of the stock for every fifth minute based on the opening price. The machine learning algorithm provides a better and more efficient way to predict the prices and facilitate decision-making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Evolutionary Expert Model Merging with Task-Adaptive Iterative Self-Improvement Process for Large Language Modeling on Aspect-Based Sentiment Analysis

Evolutionary Expert Model Merging with Task-Adaptive Iterati...

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2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024

作者： Inoshita, Keito Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9798331521219

In recent years, natural language processing (NLP) has achieved notable advancements, particularly with large language models (LLMs) enhancing tasks such as sentiment analysis. Aspect-based sentiment analysis (ABSA), which involves identifying sentiment related to specific aspects within text, poses a more complex challenge compared to simple sentiment classification. To address this complexity, integrating multiple expert models has emerged as a promising approach. In this study, we propose the Evolutionary Expert Model merging with Task-adaptive Iterative Self-improvement Process (EEM-TISP) to improve ABSA task performance. EEM leverages evolutionary algorithms to merge expert models, optimizing task-specific outputs, while TISP iteratively refines these outputs, enhancing accuracy. Our results demonstrate that EEM significantly improves performance in simpler tasks, such as Aspect Term Sentiment Analysis (ATSA) and Aspect Category Sentiment Analysis (ACSA), surpassing individual expert models. TISP further boosts accuracy in these simpler tasks, though performance declines were observed in more complex ones. These findings indicate that while TISP is effective for simpler tasks, further optimization is required to tackle complex ABSA challenges, calling for future research into flexible prompt design and task-specific approaches. ©2024 IEEE.

关键词： Aspect-Based Sentiment Analysis Evolutionary Expert Model Merging Iterative Self-Improvement Large Language Model Task Adaptation

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Resources and Methods for Analysing Political Rhetoric and Framing in Parliamentary Debates 4

Resources and Methods for Analysing Political Rhetoric and F...

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4th Workshop on Creating, Analysing, and Increasing Accessibility of Parliamentary Corpora, ParlaCLARIN 2024

作者： Rehbein, Ines University of Mannheim Data and Web Science Group Germany

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A Time Series Sales data Forecasting Model Based on TCN and Its Three Variants 5

A Time Series Sales Data Forecasting Model Based on TCN and ...

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5th International Conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Chen, Sian Department of Data Science and Big Data Technology Brunel London School North China University of Technology Beijing China

ISBN: (纸本)9798331539818

The forecasting of sales data is a widely studied issue in the fields of artificial intelligence and time series forecasting. TCN (Temporal Convolutional Network) is the most adept at managing time series data among all classical models due to its distinctive architecture. In this paper, we design a TCN-based model for time series forecasting of sales data and propose three variants: a combined model of TCN with RNN, LSTM and GRU to enhance the performance of time series forecasting. Experimental results show that these models perform well in handling the sales data forecasting task, especially the combination of TCN with LSTM performs best in several metrics. © 2024 IEEE.

关键词： Spatio-temporal data

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Urban Functional Zone Delineation Aided by Vision Foundation Models 24

Urban Functional Zone Delineation Aided by Vision Foundation...

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24th IEEE International Conference on data Mining Workshops, ICDMW 2024

作者： Lian, Heng He, Yi William & Mary Department of Data Science United States

ISBN: (纸本)9798331530631

The delineation of urban functional zones is essential for urban planning and management. Most current methods rely on points of interest data, which tend to be concentrated in city centers, resulting in poor performance in suburban areas. Moreover, significant information from remote sensing images of target regions is often overlooked. To address these limitations, we propose a flexible approach that leverages existing points of interest data and semantic segmentation model to create a dataset linking image information to functions. The trained model can classify regions based on varying levels of detail - ranging from general classifications for entire areas to more detailed classifications for individual buildings or lands. In addition, we incorporate a super-resolution model to enhance satellite image resolution, addressing the challenge of poor semantic segmentation due to low image quality. This method improves functional zone delineation by combining points of interest data and imagery information, refining image quality for more accurate mapping and enhancing performance in suburban areas. © 2024 IEEE.

关键词： Semantic Segmentation

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Towards N-version Quantum Software Systems for Reliable Classical-Quantum Computing 35

Towards N-version Quantum Software Systems for Reliable Clas...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Saito, Shinobu Endo, Suguru Suzuki, Yasunari Ntt Computer and Data Science Laboratories Tokyo Japan

ISBN: (纸本)9798350367041

While quantum computers have attracted much attention, dealing with computational errors due to noise effects caused by the interaction between quantum hardware and the external environment is a significant challenge. In this paper, we propose an approach to apply N-version programming (NVP) to quantum software to improve the reliability of the entire quantum software system. First, we define architecture patterns for N-version quantum software systems (NVQS) based on a combination of quantum libraries and devices. Next, given that the output of the quantum software system is a probability distribution, we came up with an evaluation strategy inspired by the concept of NVP for clustering and selecting multiple probability distributions output from NVQS. Finally, we outline our future experimental plan. © 2024 IEEE.

关键词： Quantum computers

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Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines

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Discover Artificial Intelligence 2024年第1期4卷 73页

作者： Ahishakiye, Emmanuel Kanobe, Fredrick Department of Networks Data Science and Artificial Intelligence Kyambogo University Kampala Uganda Department of Computer Science Kyambogo University Kampala Uganda

Background: Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods: In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results: Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions: The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources. © The Author(s) 2024.

关键词： Lung cancer

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