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检索条件"机构=Department of Computer science with Data Analytics"

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Creation and Classification of Kannada Meme dataset: Exploring Domain and Troll Categories 2nd

Creation and Classification of Kannada Meme Dataset: Explo...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Kundargi, Sahana Y. N, Lohith M, Anand Kumar Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9783031584947

In this pioneering research, the first-ever Kannada memes dataset is established, marking a groundbreaking contribution. This dataset encompasses 2002 memes, spanning various categories such as movies, politics, sports, trolls, and non-troll memes. The classification models have been meticulously fine-tuned for memes, incorporating image-based models using DenseNet169 and text-based models with BERT for text encoding. An innovative multimodal approach combines insights from images and text, acknowledging the comprehensive nature of meme content. Throughout the study, model strengths and weaknesses are assessed, emphasizing their reliance on cutting-edge technologies like Deep Learning and Natural Language Processing. Valuable improvements are recommended, such as the implementation of oversampling techniques and regular dataset updates to enhance relevance and accuracy. This work extends beyond immediate research, contributing to the development of adaptive meme classification systems, particularly for Kannada-speaking audiences within the evolving meme culture landscape. Notably, the results indicate that multimodal models achieved the best scores for domain classification, while image-based models excelled in troll meme classification, further highlighting the significance of this approach within the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification (of information)

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Automated Machine Learning in Critical Energy Infrastructure for Net Zero Carbon Emissions 32

Automated Machine Learning in Critical Energy Infrastructure...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Moraliyage, Harsha Haputhanthri, Dilantha Samarajeewa, Chamod Mills, Nishan De Silva, Daswin Manic, Milos Jennings, Andrew La Trobe University Centre for Data Analytics and Cognition Melbourne Australia Virginia Commonwealth University Department of Computer Science Richmond United States

ISBN: (纸本)9798350399714

Increasing energy consumption and diversified energy generation have led to the proliferation of energy management (EM) systems for optimized grid operations and net zero carbon emissions. Artificial Intelligence (AI) is centric to EM systems to process large volumes of high velocity data for anomalies, predictions, optimization and other actionable insights that unravel the complexities of grid operations. Managing Energy AI capabilities itself is becoming an increasingly complex task that requires extensive resourcing and expertise. In this paper, we aim to address this gap by formalizing the role of Automated Machine Learning (AutoML) by proposing a novel framework for its key functionalities in critical energy infrastructure. This framework provides a generic and cohesive abstraction to assist with integrating and managing the complexities of AI capabilities. The framework is empirically evaluated in the microgrid setting of a multi-campus, multi-functional tertiary education institution. Results from these experiments confirm the performance contributions of the proposed framework in addressing the complexities of AI capabilities of EM systems as they transition towards microgrid energy optimisation and net zero carbon emissions. © 2023 IEEE.

关键词： Cloud computing architecture

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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EmoZen: A Robust Word Embedding for Implicit and Explicit Expressions of Emotion 49

EmoZen: A Robust Word Embedding for Implicit and Explicit Ex...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Rathnayaka, Prabod Gamage, Gihan De Silva, Daswin Alahakoon, Damminda Manic, Milos La Trobe University Centre for Data Analytics and Cognition Melbourne Australia Virginia Commonwealth University Department of Computer Science Richmond United States

ISBN: (纸本)9798350331820

Machine perception of emotions is integral to the development of human-centric Artificial Intelligence (AI) in sustainable industrial applications. Human expressions of emotions are not always direct. Word embeddings are mature techniques that can extract the semantics of such indirect expressions from text data. However, they are not primed to extract emotions. In this paper, we propose a novel approach that generates robust word embeddings for implicit and explicit expressions of emotion. This approach consists of two techniques, mask and rogue, we evaluate both techniques on two benchmark datasets for emotion classification. Our results confirm the effectiveness of the proposed approach in extracting emotions from diverse contexts. We have shared the emotion word embedding for public use. © 2023 IEEE.

关键词： Embeddings

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Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks

Exploring the Intersection of IoT and Blockchain: An Analysi...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ashok, Harishma Ganesan, Swathi Somasiri, Nalinda PSG Institute of Advanced Studies Data Analytics Coimbatore India York St John University Department of Computer Science London United Kingdom

ISBN: (纸本)9798350348217

Internet of things (IoT) is one of the leading technologies of transforming the internet into the next level. Security of IoT is becoming a major challenge and its measures of stopping these insecurities have been developing. Improving IoT security is important since IoT provides a great environment for systems in the industrial world and other several applications. The challenges need to be addressed so that consumer's data can be safe. Measures available are to improve the protection of access by unauthorized persons, providing a safe way to transfer data, and ensuring synchronization of the system. Blockchain has also provided a safe way for IoT devices, each device is given the public key, IoT devices scramble and send the message dependent on the public key of the objective device so just the objective device can unscramble the message. © 2023 IEEE.

关键词： Internet of things

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A Cybersecurity Framework for Home Energy Management Systems using Artificial Intelligence 32

A Cybersecurity Framework for Home Energy Management Systems...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Gunasekara, Lakshitha Moraliyage, Harsha Silva, Daswin De Mills, Nishan Alahakoon, Damminda Jennings, Andrew Manic, Milos La Trobe University Centre for Data Analytics and Cognition Melbourne Australia Virginia Commonwealth University Department of Computer Science Richmond United States

ISBN: (纸本)9798350399714

Home Energy Management Systems (HEMS) have become a pivotal component of the smart grid infrastructure as they facilitate energy exchange and trading. With the increasing use of micropower renewables generation from wind and solar within residential houses and buildings, HEMS now plays a critical role in not just managing energy usage but assisting the smart grid with schedule optimisation. However, HEMS are typically installed with limited security measures, which makes them vulnerable to cyber-attacks. A coordinated attack on multiple HEMS has the potential to compromise the entire power grid. In this article, we aim to address this gap by proposing a robust security framework for HEMS. The framework centralises security in the design of HEMS and leverages Artificial Intelligence for the security requirements. The proposed framework consists of five layers with built-in security measures that use AI to mitigate cyber threats. This is a key contribution that has been neglected in related work in optimizing HEMS operations. © 2023 IEEE.

关键词： Smart power grids

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Augmenting Industrial Chatbots in Energy Systems using ChatGPT Generative AI 32

Augmenting Industrial Chatbots in Energy Systems using ChatG...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Gamage, Gihan Kahawala, Sachin Mills, Nishan De Silva, Daswin Manic, Milos Alahakoon, Damminda Jennings, Andrew La Trobe University Centre for Data Analytics and Cognition Melbourne Australia Virginia Commonwealth University Department of Computer Science Richmond United States

ISBN: (纸本)9798350399714

Chatbots, the automation of communicative labor, have been widely deployed in industrial applications and systems. Built upon the Generative Pre-trained Transformer 3 (GPT-3), ChatGPT is a Generative Artificial Intelligence (AI) primed to transform all pre-existing chatbot capabilities with human-like conversation skills. It has already disrupted many disciplines including tertiary education and academic research methods, with increasing adoption in simple to complex tasks. However, the augmentation of pre-existing industrial chatbots with generative AI capabilities has not been fully investigated and demonstrated in recent literature. In this paper, we address this gap by presenting the augmentation of a pre-existing chatbot using ChatGPT generative AI capabilities. Our contribution encompasses the ten primary human-like conversation capabilities of ChatGPT, its augmentation of the pre-existing functionalities and the adopted prompt engineering strategies. Each capability is empirically demonstrated on Cooee, a functionally deployed chatbot in the microgrid energy systems of the La Trobe Energy analytics Platform (LEAP). © 2023 IEEE.

关键词： Machine learning

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QUALYST: data Quality Assessment System for Thailand Open Government data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Accelerated Malaria Diagnosis with Deep Convolutional Neural Network 1

Accelerated Malaria Diagnosis with Deep Convolutional Neural...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Krishna Kumar, V. Geetha Devasana, M.S. Saravana Balaji, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India De Montfort University Kazakhstan Department of Business Data Analytics Almaty Kazakhstan

ISBN: (纸本)9798350359756

The disease known as malaria is a communicable illness that is spread by the bites of mosquitoes. There are already diagnostic approaches that include manually counting the amount of red blood cells (RBC) that are contaminated. This is accomplished by doing a microscopic examination of the stained blood cells of the individual who is suffering from the disease. However, this is a hard job that requires careful attention both visually and intellectually. In order to accomplish this, a comprehensive manual examination must be undertaken by an expert who is informed about the subject matter. It is possible that the implementation of deep learning algorithms will simplify the process of disease diagnosis and make this analysis more straightforward. Furthermore, because they are diagnosing malaria, they are expected to manage a vast quantity of data, which includes photographs of microscopic blood smears. This is because they are responsible for finding the disease. In order to get a better level of accuracy, it is necessary to train models that are more in-depth. This requires fast computing since it requires a substantial amount of computer resources. An accelerated convolutional neural network technique that is based on a graphics processing unit (GPU) is utilized by the Accelerated Malaria Diagnosis System (AMDS) that has been created in order to diagnose malaria. © 2023 IEEE.

关键词： Diseases

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Cardiovascular Disease Prediction Using Machine Learning Approaches

Cardiovascular Disease Prediction Using Machine Learning App...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Islam, Taminul Vuyia, Adifa Hasan, Mahadi Rana, Md Masum Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Tennessee Department of Data Analytics ChattanoogaTN United States University of South Dakota Department of Computer Science VermillionSD United States

ISBN: (纸本)9798350323917

As of the release of COVID-19, cardiovascular disease has surpassed all other causes of mortality among both sexes. In most cases, this condition is associated with atherosclerosis and the formation of blood clots. Heart disease, stroke, and other CVDs are major causes of mortality worldwide. Because even a small inaccuracy might lead to exhaustion or death, increased precision, perfection, and accuracy are required for diagnosing and predicting heart-related disorders. In this paper, data was collected from 1189 patients with some attributes related to heart disease and kept 80% data for training, and 20% data for testing and determining their accuracy using different models. In this study, enhanced preprocessing steps were used to increase the accuracy of cardiovascular disease prediction. It aids in determining whether a patient has heart disease and helps a doctor to determine whether or not a patient has cardiovascular disease. The applied models are Extreme Gradient Boosting, Random Forest, CART, Extra Tree Classifier, and Gradient Boosting Machine. This work compared the performance of several Machine Learning algorithms that make use of the accuracy of the metrics, F1-score, recall, and precision to demonstrate the validity of our findings. The Extreme Gradient Boosting model has achieved the best 91.9% accuracy in this research. © 2023 IEEE.

关键词： Forecasting

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