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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Unravelling the Research themes of Financial Literacy Using Topic Modelling and Text Mining: A machine learning Perspective 4

Unravelling the Research Themes of Financial Literacy Using ...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Olasiuk, Hanna Kumar, Sanjeev Singh, Sudhanshu Nagpal, Ankita Ganushchak, Tetiana Op Jindal Global University Jindal Global Business School Haryana Sonipat India Galgotias University School Of Business Uttar Pradesh Gautam Buddh Nagar India Birla Institute Of Technology And Science Pilani Department Of Management Pilani Campus Rajasthan India State University Of Trade And Economics Economic And Finance Enterprise Department 19 Kyoto st. Kyiv Ukraine

ISBN: (纸本)9798350384628

Financial literacy is essential in business, economics, and finance as it empowers individuals and organisations with the knowledge and skills needed for sound financial decision-making. It enables effective risk management, investment maximisation, and economic growth through responsible financial practices. Despite significant research growth in financial literacy over the past two decades, there exists a gap in comprehensive reviews of its primary themes. the study addresses this gap by utilising a blend of bibliometric analysis and a machine learning technique (structural topic modelling) on a dataset of 1303 articles from Elsevier's Scopus database (2002-2022). It primarily focuses on financial literacy, highlighting key themes, publication trends, and notable journals. the outcomes of structural topic modelling unravel four distinct thematic clusters: i) Financial literacy and financial planning, ii) Financial literacy and financial education, iii) Financial literacy and technology, and iv) Financial literacy and financial behaviour. "Journal of Financial Counselling and Planning"is a prominent source, contributing 92 articles (7.1% of the total). Future studies on financial literacy should explore the evolving requirements of people and communities, encompassing an understanding of digital platforms and blockchain technologies, enabling them to make well-informed financial choices. these results provide valuable insights into the ever-changing landscape and emerging research trends in financial literacy. they hold significant relevance for diverse audiences, including scholars, policymakers, financial institutions, and professionals. © 2023 IEEE.

关键词： machine learning

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Moment Distribution of Hyper-Erlang Distribution and Its Approximation application 4

Moment Distribution of Hyper-Erlang Distribution and Its App...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Yang, Dan Li, Mengjun National University of Defense Technology College of Systems Engineering Changsha China

ISBN: (纸本)9798350373233

Moment distributions can be used to study many important problems in the field of social sciences, such as the Gini coefficient of residents' income, Lorenz curve, etc. the set of Hyper-Erlang distributions is a dense subset of the set of non-negative probability distribution functions, which has a wide applicability, so Hyper-Erlang distributions are chosen as the object of this study. In this paper, we give the analytical expression of the moment distribution of HyperErlang distribution and prove that the moment distribution function of Hyper-Erlang distribution is closed within the set of Hyper-Erlang distribution. Combined with the HyperErlang distribution parameter estimation algorithm, eight commonly used distributions are fitted, and the practicality and effectiveness of the Hyper-Erlang moment distribution method proposed in this paper are verified through the comparison of the original moment distribution of the commonly used distributions and the approximation effect of the Hyper-Erlang moment distribution. the approach proposed in this paper can be used to give a moment distribution expression for any data, and valid approximate moment distribution expressions for all types of probability distributions for which no analytical moment distribution expression can be derived. © 2023 IEEE.

关键词： Parameter estimation

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JumpCrypt: Based on Precomputing Federated learning with Homomorphic Encryption 25

JumpCrypt: Based on Precomputing Federated Learning with Hom...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Yun Wang, Chong Xu, Chao Wang, Jing School of Computer Science and Engineering Southeast University Nanjing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

ISBN: (纸本)9798350330014

Federated learning (FL) enables organizations or individuals to train a machine learning model collaboratively by aggregating local gradient updates from each client without sharing privacy-sensitive data. To ensure the security of gradient updates, the FL framework employs additively homomorphic encryption (AHE) to prevent revealing the participants' identities during aggregation. However, strategy imposes heavy communication and computation costs. thus, this paper proposes a system solution for cross-silo FL named JumpCrypt. Our scheme uses pre-computation to build a plaintext and noise pool for faster encryption. Meanwhile, gradient sparsity is employed to reduce communication overhead. Experimental results show that our scheme achieves a 3x speedup in training compared with BatchCrypt while reducing communication overhead by 30%. © 2023 IEEE.

关键词： Sensitive data

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Development and application of Network Data Mining in English Corpus Software Design 4

Development and Application of Network Data Mining in Englis...

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4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Xu, Wei Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798350333558

With the development of technology and the progress of the times, the existence of data information has penetrated into various industries in society along with network and computer technology. Today, online office and decision-making are commonplace. How to use network data mining technology in the design of English corpus software to accurately find valuable data and information. this article first explores big data, then explains some of the changes that big data has brought to the field of corpus design, and then explores the important achievements and breakthroughs that can be achieved by combining big data methods in corpus design. the questionnaire survey method was used to investigate and statistically analyze the data mining methods used in the corpus. From the survey results, it can be seen that there are more staff using the PageRank and Apriori algorithms, as well as the C.45 algorithm, while the proportion of staff using K-means is relatively small, indicating that these three algorithms have higher efficiency and wider usage. therefore, data mining based on big data can significantly improve the efficiency of corpus design. © 2023 IEEE.

关键词： Software design

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Lumpy Skin Disease Prediction Using machine learning 4

Lumpy Skin Disease Prediction Using Machine Learning

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Patil, D.H. Pawar, Mahesh Jaiswal, Mukesh Rane, Prasad Jagtap, Sandesh Department of Information Technology JSPM's Rajarshi Shahu College of Engineering Pune411033 India

ISBN: (纸本)9798350305258

LSD, a viral disease that can spread rapidly among cattle, has the potential to cause significant financial losses for the livestock industry. Early detection and accurate identification of sick animals are necessary to stop the spread of an infection and lessen its effects. the most common hosts of the viral illness lumpy skin disease (LSD) are cattle and buffalo. Although there is no risk to human health, it can seriously affect animal welfare and reduce output. Contact with sick animals, their bodily fluids, or contaminated objects can spread LSD. Additionally, biting insects and parasites like ticks, flies, and mosquitoes can spread it. LSD infection often results in an acute illness with fever, depressive symptoms, and distinctive skin nodules. the skin nodules can cause discomfort, reduced milk production, and abortion in animals that are pregnant. LSD cannot be treated specifically, although supportive care can increase the animal's comfort and chances of life. the best defence against LSD is vaccination. Our method involves gathering a diverse and substantial dataset of images featuring both healthy cattle and those infected with LSD. Advanced learning techniques like Convolutional Neural Networks (CNN) is used to perform preprocessing on images to extract features. Our next step involves utilizing models of machine learning like Random Forests Neural Networks and Support Vector machines (SVM) to classify cattle images as either healthy or infected with LSD. We have employed a range of performance indicators and cross- validation techniques to evaluate the effectiveness of our proposed methodology, which demonstrates its exceptional speed, affordability, and accuracy. Conventional convolutional neural networks represent the prevailing architecture in recent strides within image processing and computer vision. Based on our research, we have found that the application of image recognition and machine learning techniques shows promising potential for the early

关键词： F1 LSD Lumpy skin disorders Neural Networks Random Forest Classifier

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A Comprehensive Analysis of machine learning and Deep learning Approaches towards IoT Security 4

A Comprehensive Analysis of Machine Learning and Deep Learni...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Desanamukula, Venkata Subbaiah Asha Priyadarshini, M. Srilatha, D. Rao, K Venkateswara Lakshmi Kumari, R.V.S. Vivek, Kolla Lakireddy Bali Reddy College of Engineering Department of CSE Mylavaram India Vignan's Lara Institute of Technology & Science Department of CSE Vadlamudi India Vasi Reddy Venkatadri Institute of Technology Department of Eee Namburu522508 India Gayatri Vidya Parishad College of Engineering for Women Department of Eee Visakhapatnam India Qis College of Engineering and Technology Department of CSE Ongole523002 India

ISBN: (纸本)9798350300093

through the IoT, millions of smart devices may be networked together and set to automatically exchange information with one another. By 2022, the number of connected devices is projected to have reached 80 billion, making IoT one of the fastest-growing digital sectors. Furthermore, additional security concerns have arisen due to the interconnectedness of IoT devices and the many different fields that must work together to install such devices. the underlying weaknesses of IoT devices render useless the implementation of security mechanisms such as protection from cyber threats, authorization, access control, networking, and information protection. To protect the IoT ecosystem, it is necessary to improve upon current security checks. As a result of recent developments in machine learning and Deep learning (ML-DL), artificial intelligence has moved from the realm of science fiction to that of everyday life in several crucial contexts. therefore, ML-DL techniques play a crucial role in advancing the protection of IoT systems beyond just ensuring encrypted communications between IoT components. To better secure IoT devices, this analysis helps to give a complete overview of ML strategies and current breakthroughs in DL strategies. Vulnerabilities to the protection of the IoT are addressed, including those that have always been present as well as those that are relatively new. the merits, demerits, and overall viability of the ML/DL approach to IoT protection are then described and analyzed. the use of ML/DL to enhance IoT security is explored, along with its advantages and disadvantages. these prospects and difficulties suggest avenues for future study. © 2023 IEEE.

关键词： Authorization

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Reinforcement learning for Optimizing Conditional Tabular Generative Adversarial Networks Hyperparameters in Financial Fraud Detection 4

Reinforcement Learning for Optimizing Conditional Tabular Ge...

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yee Cheah, Patience Chew Yang, Yue Lee, Boon Giin School of Computer Science University of Nottingham Ningbo China Ningbo China

ISBN: (纸本)9798350395914

Financial fraud datasets typically exhibit a pro-nounced class imbalance, leading to elevated false positive rates when employing machine learning classifiers. To mitigate this issue, the Conditional Tabular Generative Adversarial Network (CTGAN), an oversampling technique tailored for tabular data, has emerged as a promising solution by generating additional minority samples. However, it remains pertinent to investigate whether optimizing the hyperparameters governing CTGAN can yield further improvements in performance. In light of the recent application of reinforcement learning (RL) for hyperparameter tuning, this study delves into the potential of harnessing RL mod-els to fine-tune the hyperparameters of CTGAN. the overarching objective is to design an RL-based methodology that automates the optimization of CTGAN hyperparameters. Although the initial findings from this endeavor may be inconclusive, they pave the way for future research avenues, exploring the potential of RL in the context of CTGAN hyperparameter tuning and scrutinizing the nuanced effects of CTGAN hyperparameters on classification performance. © 2024 IEEE.

关键词： Generative adversarial networks

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13th international conference on Image Processing and Communications, IP and C 2023, 13th international conference on computer Recognition Systems, CORES 2023

13th International Conference on Image Processing and Commun...

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13th international conference on Image Processing and Communications, IP and C 2023, 13th international conference on computer Recognition Systems, CORES 2023

ISBN: (纸本)9783031416293

the proceedings contain 23 papers. the special focus in this conference is on Image Processing and Communications, international conference on computer Recognition Systems. the topics include: Coregistration of Selected Sequences in MRI Imaging Based on Edge Analysis of Image Difference;actinic Keratosis Prediction Based on Deep learning Methods;a New Assessment of Convolutional Neural Networks for Texture Directionality Detection;knowledge Distillation with Relative Representations for Image Representation learning;An Approach for CT Image Conversion Using Filtering Based on Quaternion Mathematics;exploring Text-Guided Synthetic Distribution Shifts for Robust Image Classification;Color Tracking application Using AI-Based Docker Container Scheduling in Fog Computing;air Pollution Monitoring and Information Distribution System;Detection of Conspiracy theories from Tweets Using NLI-Based Zero Shot Text Classification Models;hollow n-grams Vectorizer for Natural Language Processing Problems;battery Management System for Polish Hybrid Residential Photovoltaic Power Plants;document Annotation Tool for News Content Analysis;dynamic Time Warping Technique Applied to the User’s Intent Recognition for Myoelectric-Based Control of Upper Limb Prosthesis;A Hybrid Long Short-Term Memory Network Based on Wind and Rain Sensitive and Its application to PM2.5 Prediction;a Non-deep Approach to Classifying Movie Genres Based on Multimodal Data;incremental Extreme learning machine for Binary Data Stream Classification;Interpreting Intrusions - the Role of Explainability in AI-Based Intrusion Detection Systems;real-Time Deformation of three-Dimensional Volumetric Models Using Mesh Models;Hyperparameters Optimization Using GridSearchCV Method for TinyML Models;analysis of the Possibility to Employ Relationship Between the Problem Complexity and the Classification Quality as Model Optimization Proxy;deep learning for Pit Detection in Cherries.

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Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile application Traffic 26

Multi-Feature Fusion Based Approach for Classifying Encrypte...

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26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yang, Qingya Fu, Peipei Shi, Junzheng Wang, Bingxu Li, Zhen Xiong, Gang Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350331684

With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN. © 2023 IEEE.

关键词： Classification (of information)

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Extreme learning machine Optimization for Employee Satisfaction With Modified Metaheuristic 4

Extreme Learning Machine Optimization for Employee Satisfact...

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4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Abdulla, Tasnim Jovanovic, Luka Radomirovic, Branislav Njegus, Angelina Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia

ISBN: (纸本)9798350385793

In the contemporary business landscape, the success of a company is intricately linked to the engagement and satisfaction of its workforce. this study analyzes the signifi-cance of developing a contented and engaged employee base, emphasizing the direct impact of workplace satisfaction on productivity, turnover rates, and overall organizational dynamics. Organizational culture emerges as a pivotal factor influencing the recruitment, retention, and satisfaction of talented employees. To address the complexities of identifying and mitigating employee dissatisfaction, this research work proposes a comprehensive solution harnessing the capabilities of cloud-based technologies, specifically text mining, Natural Language Processing (NLP), and modified metaheuristic techniques. the study explores the application of an extreme learning machine as a classifier for assessing employee satisfaction within a cloud computing framework. Acknowledging the critical role of hyperparameter selection in model performance, metaheuristic optimizers and cloud platforms implementation are employed to enhance accu-racy and effectiveness. Furthermore, a novel modification to a metaheuristic algorithm for satisfying the unique requirements of this research is introduced. this research study demonstrates the efficiency of the optimized models, achieving an accuracy rate surpassing 84%. By integrating cloud computing technologies into the proposed framework, organizations gain a powerful and scalable tool for proactively identifying and addressing employee dissatisfaction, ultimately contributing to the improved employee well-being and organizational success in the cloud era. © 2024 IEEE.

关键词： Job satisfaction

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