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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Mayuranathan, M. Akilandasowmya, G. Jayaram, B. Velrani, K. Sundara Kumar, M Jogendra Vidhya, R.G. Srm Valliammai Engineering College Department of Computer Science Engineering India Sri Sairam Institute of Technology Department of Computer Communication and Engineering India Rmk Engineering College Department: Computer Science and Business Systems Tamilnadu Gummidipoondi Taluk Tiruvallur District Kavaraipettai India School of Computing Sathyabama Institute of Technology Information Technology India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350325799

Recently, AI and NLP have been used to improve customer care. Customer support relies on event extraction and relevant data. In customer support applications, AI-based algorithms extract events. Event extraction identifies and extracts specific events or activities from textual data like customer service chats, emails, and social media interactions. Customer support teams may discover issues, trends, and improve the customer experience by automatically extracting events. Customer support event extraction can be done using AI-based models. Event extraction relies on named entity recognition (NER), sentiment analysis, and text classification. NER classifies customer names, product names, and problem keywords. Text classification classifies client issues into preset categories like billing, technical support, or product feedback, while sentiment analysis assesses the customer's emotional tone. Event extraction AI models use machine learning algorithms. Support vector machines (SVM) and random forests can be trained on annotated customer support data to reliably classify events. Recurrent neural networks (RNNs) and transformers can capture complicated patterns in textual data, making them promising event extraction methods. Customer support applications benefit from AI-based event extraction algorithms. It frees up support teams to focus on client issues by automating evaluation and categorization. It also helps firms identify emerging trends and enhance their products and services to answer client complaints. AI-based customer support event extraction models face hurdles. Labeled training data is costly and time-consuming to obtain. Ensuring model generalizability across multiple domains or languages is ongoing research. In conclusion, AI-based event extraction in customer support systems can improve customer experiences and support team efficiency. © 2023 IEEE.

关键词： Support vector machines

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An total generalized variation for impulse noise removal

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Multimedia Tools and Applications 2024年 1-27页

作者： Yin, Mingming Adam, Tarmizi Paramesran, Raveendran Fikree Hassan, Mohd Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Faculty of Computing Universiti Teknologi Malaysia Johor Malaysia School of Information Technology Monash University Malaysia Selangor Malaysia

Impulse noise corrupts digital images, and hinders the extraction process of information and features in computer vision applications. To restore digital images, total variation (TV) based methods using different data fidelity have been proposed. However, the restored images by using these TV models are always blocky and smeared because of staircase artifacts. Besides, using the $$\ell _2$$ -norm as the data fidelity term for impulse noise removal produces unsatisfactory restoration results. Meanwhile, the $$\ell _1$$ -norm data fidelity which is normally used for impulse noise produces over-penalized solutions and is not robust to outlier characteristics of impulse noise. To address this, in this paper, we present a new model which uses the non-convex $$\ell _0$$ -norm as the data fidelity norm and the total generalized variation (TGV) for the regularizer. The non-convex $$\ell _0$$ -norm is more suitable for impulse noise, while the TGV regularization has the ability to preserve edges and reduce the staircase effect better than the TV regularizer. In terms of objective and subjective evaluations, experimental results show that the proposed method is more highly effective and competitive than the latest state-of-the-art algorithms such as the $$\ell _0$$ total variation, the $$\ell _1$$ total generalized variation, the $$\ell _1$$ overlapping group sparse total variation, and the nonconvex-nonconvex TV. Specifically, at noise level corruption of 70% and 90%, the experimental results show that the PSNR and SSIM values achieved by the proposed method are the highest, with noticeable gaps compared to other methods. This highlights a significant improvement in denoising performance at high noise levels.

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A machine learning predictive model for bushfire ignition and severity: The Study of Australian black summer bushfires

Decision Analytics Journal

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Decision Analytics Journal 2025年 14卷

作者： Henderson, Kasinda Chakrabortty, Ripon K. School of Engineering & Technology UNSW Canberra ACT-2610 Australia School of Systems & Computing UNSW Canberra ACT-2610 Australia

Australian bushfires are catastrophic, and their impacts span social, economic, and environmental factors. To reduce the damages experienced by bushfires, predicting Australian bushfire ignition allows for an early warning system to give first responders and disaster managers prompt and accurate information. Traditional methods of bushfire ignition prediction suffer from incorporating large-dimensional data and take extensive computational time. Applying machine learning (ML) models enhances accuracy and reduces the computational time required to predict bushfire ignition. This study proposes a predictive model that can take meteorological and topographical data and determine the probability of Australian bushfire ignition and severity using historical fire detection gathered from the Black Summer Bushfire Disaster. The Black Summer Bushfire Disaster occurred between December 2019 and February 2020. The fires affected numerous towns throughout Victoria, New South Wales, and the Australian Capital Territory;hence, the varying topographical and meteorological conditions allow fire ignition and severity influences to be explored. The proposed methodology incorporates Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Convolutional Neural Networks (CNN), and K-nearest Neighbour (kNN) Algorithms. The proposed method relies on five datasets. Meteorological data is sourced from the Bureau of Meteorology (BOM), Australia. Topographic data is sourced from Geoscience Australia and the National Aeronautics and Space Administration's (NASA's) Aqua and Terra satellites, which utilize a Moderate Resolution Imaging Spectroradiometer (MODIS). Active Fire Point Data is also sourced from NASA MODIS, which can detect fires. The proposed methodology aims to act as an early warning system by providing a fire occurrence and fire intensity warning map and the probability of fire occurrence and fire intensity depending on the current meteorological climate. ©

关键词： Australian bushfire Black summer disaster Machine learning (ML) Meteorological data Predictive analytics Topographic data

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Automatic Title Generation with Attention-Based LSTM 3rd

Automatic Title Generation with Attention-Based LSTM

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3rd International Conference on Information technology, InCITe 2023

作者： Dhilsath Fathima, M. Seeni Syed Raviyathu Ammal, M. Singh, Prashant Kumar Shome, Sachi Kharsyienlieh, Manbha Hariharan, R. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Mohamed Sathak Engineering College Tamil Nadu Kilakarai India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819959969

The objective of this article is to develop a generalised architecture that can generate headings for any type of document, including articles, book paragraphs, newspaper articles, and legal documents. The title of a document provides a concise overview of its content, allowing readers to quickly understand the main idea without focusing on the specifics. The more attractive the title is, the more the reader is bound to read it. Title generation will be helpful to the editors of newspapers or technical writers to easily find the central theme of the whole article. The Sequence-to-Sequence (Seq-to-Seq) model is implemented in this proposed work for generating the title for the project, article, and research articles. The Seq-to-Seq model utilizes an encoder-decoder recurrent neural network that has an attention layer and Long Short-Term Memory (LSTM). The main strength of the developed framework is the adoption of an attention layers to the encoder-decoder architecture, which assigns more weightage to background words. This proposed model achieves 90% prediction accuracy by including an attention layer into the LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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What Affects the Adoption of Metaverse in Education? A SEM-based Approach 20

What Affects the Adoption of Metaverse in Education? A SEM-b...

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20th International Joint Conference on Computer Science and Software engineering, JCSSE 2023

作者： Rohan, Rohani Roy, Pranab Vanijja, Vajirasak Funilkul, Suree Mukherjee, Subhodeep Pal, Debajyoti School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand Jk Laxmipat University Department of Computer Science and Engineering Jaipur India Department of Operations Management Visakhapatnam India Kmutt Innovative Cognitive Computing Research Center Bangkok Thailand

ISBN: (纸本)9798350300505

Metaverse is an emerging 3D digital space that augments the real world and the virtual world. It has been envisioned to be the trendsetter for future education having tremendous potential. However, the success of any new technology depends on its mass adoption and widespread societal diffusion. Therefore, it becomes important to understand the factors that can lead to its success by ensuring widespread adoption. In this work, we investigate the different psychological gratifications of the educational usage of Metaverse under the lens of the Uses and Gratification (UGT) theory. Particularly, we propose and test a research model by using the Structural Equation Modelling (SEM) approach by considering four fundamental psychological gratifications of autonomy, achievement, affiliation, and dominance, together with two additional factors of hedonic motivation and student personality. Data is collected from two Asian universities, and results show the importance of autonomy, hedonic motivation, and personality in predicting Metaverse adoption. © 2023 IEEE.

关键词： Motivation

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Using Pattern Analysis and Machine Learning to Categorise users of Online Directories based on their Surfing Habits

Using Pattern Analysis and Machine Learning to Categorise us...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Athinarayanan, S. Joel, M. Robinson Jumlesha, Shaik Susmitha, K. Charitha, Limgamgunta School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Cse Chennai Tamil Nadu Avadi600062 India Aits Department of Computer Science and Engineering Andra Pradesh Tirupati India Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350313987

The major goal is to group users together, including in student organizations. Assume that the notion is required to cluster consumers in educational institutions. A cluster of websites or web pages organized by user interest makes up an online use directory. Web mining is the process of using strategies to extract websites from the web proxy log file. We are employing the Apriori Algorithm and the Fuzzy Clustering technique for it. Typically, data cleaning, a preprocessing step, is followed by aggregation of the data after cleanliness in web use mining. We do a pattern analysis following the data's classification. The outcomes of the studies are contrasted with another intriguing technique that is made public. In comparison to other efforts, the outcomes produced by the suggested system exhibit better performance. The technique we utilised, called WDUM or web directories user mining, produces better results than earlier techniques. We compare user growth and penetration performance to the threshold values. Thus, after preprocessing, the log files are retrieved, and the Apriori Algorithm and fuzzy logic classification are applied. These outcomes demonstrate the greatest methods for capturing user interest. Finally, the projected user clicked the prediction findings using machine learning techniques. © 2023 IEEE.

关键词： Clustering Algorithm Machine learning and Web proxy log Pattern analysis Web Usage Mining

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Accelerating Frequency-domain Convolutional Neural Networks Inference using FPGAs

Accelerating Frequency-domain Convolutional Neural Networks ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yi Chen Bosheng Liu Yongqi Xu Jigang Wu Xiaoming Chen Peng Liu Qingguo Zhou Yinhe Han School of Computer Science and Technology Guangdong University of Technology Guangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Low-end field programmable gate arrays (FPGAs) are difficult to deploy typical convolutional neural networks (C- NNs) owing to the limited hardware resources and the increasing model computational complexity. Fast Fourier transform (FFT) is a promising solution for saving both computation and memory footprint by convolving in the frequency domain. However, few FPGA accelerators can take full advantage at the computation level, because of the distinct element-wise complex calculation in the frequency domain. In this work, we present an FPGA-based 8-bit inference accelerator (called FAF) that packs frequency-domain calculations into digital signal processing (DSP) blocks to fully utilize DSPs for performance boost. We then provide a mapping dataflow to maximize the reduction of redundant packing operations by frequency-domain data reuse. Evaluations based on representative CNN benchmarks show that our work can achieve 1.5-6.9× better power efficiency compared with representative FPGA baselines.

关键词： Couplings Fast Fourier transforms Frequency-domain analysis Computational modeling Digital signal processing Logic gates Throughput

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Adaptive Datum Points Location Optimization Method for Flexible Workpieces based on Centroid Characteristics 18

Adaptive Datum Points Location Optimization Method for Flexi...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Luo, Jia Cao, Yanlong Liu, Fan Zhi, Junnan Yang, Jiangxin Li, Tukun Fu, Hongsheng Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou310027 China School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom

This paper proposes a method for adapting the layout of flexible workpieces of various shapes, in order to reduce the difficulty of location design for personalized shapes. Different from the previous location design that relied on the experiences of engineers, this method establishes an optimization model that determines the number of datum points and the optimal solution based on the centroid characteristics of the workpieces. The optimization index of this model is to control the minimum deformation of the overall flexible workpieces and the minimum variance of the deformation at the maximum deformation point. Taking the characteristics of the workpieces as a priori constraints for location determination, this model obtains the optimal solution combined with finite element analysis. Finally, the effectiveness of the method is verified through a numerical example. © 2024 The Authors. Published by Elsevier B.V.

关键词： adaptive location optimization finite element analysis flexible workpieces

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Semantic Analysis Using Deep Learning for Predicting Stock Trends

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Procedia Computer Science 2024年 235卷 820-829页

作者： Manisha Galphade V.B. Nikam Dhanalekshmi Yedurkar Prabhishek Singh Thompson Stephan Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set.

关键词： Deep learning emotion mining financial analysts investors sentiment analysis

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