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An automated Alert System for Financial Fraud Detection with learning Based Models

An Automated Alert System for Financial Fraud Detection with...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Mallanagouda Patil Aishwarya G Anukriti Sharma Siya Patil Computer Science and Engineering RV Institute of Technology and Managememt Bengaluru India Information Science and Engineering BMS College of engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

The rapid digitization has undoubtedly brought im-mense benefits particularly for the younger generation of the 21st century who often find themselves at utilizing digital technologies with ease. However, the elderly who often struggle to adapt to these technological advancements particularly when it comes to tasks such as mobile based transactions. The research in this paper focuses on combinations of various techniques involving the identification of unusual patterns or behaviors in financial transactions such as anomaly detection and continuous learning based models to analyze user behavior and transaction patterns to detect suspicious activities. The paper discusses the capabilities of learning based models, their ability to continuously learn and adapt, and how they can significantly improve the detection rate of fraudulent activities. The potential of the proposed approach lies in offering a real-time, intelligent, and automated solution in order to revolutionize fraud detection by doing outlier analysis. Results analysis shows that the proposed model can effectively identify fraudulent patterns in real time, adapt to evolving tactics, and minimize disruptions caused by false positives.

关键词： Adaptation models Analytical models Accuracy Collaboration Real-time systems User experience Fraud Security stakeholders Protection

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AI-Driven Appbox to Facilitate Self-Assessment in an intelligent Educational Environment

AI-Driven Appbox to Facilitate Self-Assessment in an Intelli...

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international conference on Information Technology Based Higher Education and Training, ITHET

作者： Malinka Ivanova Gabriela Grosseck Carmen Holotescu Department of Informatics Faculty of Applied Mathematics and Informatics Technical University of Sofia Sofia Bulgaria Department of Psychology Faculty of Sociology and Psychology West University of Timisoara Timisoara Romania Department of Information Technology Faculty of Engineering “Ioan Slavici” University of Timisoara Timisoara Romania

ISBN: (数字)9798331516635

ISBN: (纸本)9798331516642

intelligent learning environments integrate various functions for processing and analyzing learning data by leveraging learning analytics theory. Based on this data, numerous machine learning (ML) and artificial intelligence (AI) models are created to predict learning behavior and learning performance with main purpose to improve learning, assessment and teaching. The important role of self-assessment conducted by students in support of their learning progress and final results continues to be explored and discussed. Despite the availability of various educational software products, there is a need to develop intelligent applications to enhance students' self-assessment. The purpose of the paper is to present a conceptual framework and its subsequent implementation by developing a set with web applications based on ML and AI techniques. The applications are related to self-assessment of affective state, predicting and planning learning tasks, self-assessment of graphic objects, knowledge gaining from conversational assistant and self-assessment of learning performance.

关键词： Training Refining Training data learning (artificial intelligence) Machine learning Predictive models data models Software Planning Information technology

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EnLighten: A Photovoltaics learning Environment in Virtual Reality 21

EnLighten: A Photovoltaics Learning Environment in Virtual R...

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21st IEEE international conference on Advanced learning Technologies (ICALT)

作者： Arntz, Alexander Kessler, Dustin Eimler, Sabrina C. Hsch Ruhr West Univ Appl Sci Inst Comp Sci Bottrop Germany

ISBN: (纸本)9781665441063

Past examples have illustrated the potential for virtual reality (VR) in educational settings, allowing students the exploration of complex and otherwise hardly accessible learning objects and content. We introduce a VR-photovoltaics learning application. Replicated from a real photovoltaics(PV)-array, we designed a VR-application where students can inspect PV-modules and explore their distinct characteristics. By using current and historical data from the real PV-array through a network interface, combined with a dynamic weather system, a great versatility in scenarios to experiment and learn with can be created. In its modular conceptualization, the current version of the application offers flexible expandability for diverse learning scenarios with multiple data sources.

关键词： photovoltaics virtual reality adult education stEM-education engineering education

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Edge-Assisted Opportunistic Federated learning for Distributed IoT Systems

Edge-Assisted Opportunistic Federated Learning for Distribut...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Noor Khial Alaa Awad Abdellatif Amr Mohamed Aiman Erbad Carla Fabiana Chiasserini College of Engineering Qatar University Qatar College of Science and Engineering Hamad Bin Khalifa University Qatar Department of Electronics and Telecommunications Politecnico di Torino Italy

The paper introduces Opportunistic Federated learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collaboratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.

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FDRS: Federated Diversified Recommender System Based on Heterogeneous Graph Convolutional Network 25

FDRS: Federated Diversified Recommender System Based on Hete...

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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： Li, Nianzhe Liu, Hanwen Meng, Shunmei Li, Qianmu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350330014

Currently, researchers are working to improve the accuracy of recommender systems. However, recommender systems ignore users' diversity requirements in recommendation lists, thus reducing user satisfaction. Additionally, in the big data environment, users' data information is scattered in different enterprises or organizations, but these enterprises or organizations are reluctant to share users' data information due to the existing laws and regulations, which leads to the recommendation process facing the problem of the sparsity of users' available data. Based on the above research, we propose a novel framework of a federated diversified recommender system, i.e., FDRS, which is a diversified recommendation method following the principle of vertical federation learning. Specifically, FD RS first constructs a heterogeneous users-items graph, then uses a heterogeneous graph convolutional network to learn user representations based on heterogeneous information, and finally, based on vertical federation learning, FDRS performs model parameter aggre-gation, rating prediction, and diversity recommendation. We compare our approach with existing recommendation methods on the Epinions dataset. The experimental results show that our proposal greatly alleviates the accuracy and diversity dilemmas while preserving data privacy. © 2023 IEEE.

关键词： Recommender systems

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HTPS: Heterogeneous Transferring Prediction System for Healthcare datasets

HTPS: Heterogeneous Transferring Prediction System for Healt...

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31st Euromicro international conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Fojcik, Marcin Cupek, Rafal National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland

ISBN: (纸本)9798350337631

Medical internet of things leads to revolutionary improvements in medical services, also known as smart healthcare. With the big healthcare data, data mining and machine learning can assist wellness management and intelligent diagnosis, and achieve the P4-medicine. However, healthcare data has high sparsity and heterogeneity. In this paper, we propose a Heterogeneous Transferring Prediction System (HTPS). Feature engineering mechanism transforms the dataset into sparse and dense feature matrices, and autoencoders in the embedding networks not only embed features but also transfer knowledge from heterogeneous datasets. Experimental results show that the proposed HTPS outperforms the benchmark systems on various prediction tasks and datasets, and ablation studies present the effectiveness of each designed mechanism. Experimental results demonstrate the negative impact of heterogeneous data on benchmark systems and the high transferability of the proposed HTPS. © 2023 IEEE.

关键词： Health care

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Dementia Prediction Using Machine learning

Dementia Prediction Using Machine Learning

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2022 international conference on ENTERprise Information Systems, CENTERIS 2022 - international conference on Project MANagement, ProjMAN 2022 and international conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Dhakal, Sara Azam, Sami Hasib, Khan Md. Karim, Asif Jonkman, Mirjam Farhan Al Haque, A.S.M. College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Science and Engineering Ahsanullah University of Science Technology Dhaka Bangladesh Department of Electronic Systems Aalborg University Copenhagen Denmark

Dementia is a chronic and degenerative condition, which has become a major health concern among the elderly. With ever-continuing cases of dementia, it has become a very challenging task in the 21st century to provide care for patients with dementia. This paper proposes a framework for the prediction of dementia using the data collected from the OASIS (Open Access Series of Imaging studies) project which was made available by the Washington University Alzheimer's Disease Research Centre. Different techniques have been implemented for data imputation, pre-processing and data transformation to create suitable data for training the model. Machine learning approaches like Adaboost (AB), Decision Tree (DT), Extra Tree (ET), Gradient Boost (GB), K-Nearest Neighbour (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and SVM (Support Vector Machine) has been used for a combination of features. These techniques have been applied to the full set of features and features selected from Least Absolute Shrinkage and Selection Operator (LASSO) techniques. A comparison between the accuracy, precision, and other metrics based on the results of the classification algorithms has been provided. The experimental results show that the highest accuracy of 96.77% was obtained by Support Vector Machine (SVM) with full features. The proposed methodology is promising and if developed and deployed can be helpful for the rapid assessment of Alzheimer's Disease (AD). © 2023 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Adaptive Splitting Algorithm for Neural Network Modelling in AMP 21

Adaptive Splitting Algorithm for Neural Network Modelling in...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Kaifei Ma, Yuxiang Wu, Yulei Chen, Tao Ma, Huijie Henan University School of Computer and Information Engineering Kaifeng475004 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom

ISBN: (纸本)9798350329223

Artificial Intelligence algorithms usually consume a lot of energy and computing resources during training, so data is usually sent to high-performance devices to complete model training. However, such a computing mode cannot make good use of computing resources in the network and is not conducive to the privacy protection of user information. In recent years, some studies have used end devices to participate in model training, among which federated learning is a typical representative. Due to the heterogeneous computing resources of the end devices, how to split the neural network model to achieve an effective balance between training time and energy consumption is an important issue. In this paper, we propose a novel model splitting method that includes the steps of collecting data, correcting the data, and determining the splitting layers to complete the splitting learning. We consider four factors (training time, energy consumption, CPU utilisation, and memory utilisation) to jointly decide the splitting layer and propose a scheme for adaptively revising the model. We use the VGG16 model for evaluation. The results show that the efficiency of our method is improved by 64.5% compared to the recently proposed ARES scheme. At the same time, our proposed method does not affect the convergence and accuracy of the model. © 2023 IEEE.

关键词： Neural network models

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IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge 21

IDPS Signature Classification with a Reject Option and the I...

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21st IEEE international conference on Machine learning and Applications (IEEE ICMLA)

作者： Kawaguchi, Hidetoshi Nakatani, Yuichi Okada, Shogo Nippon Telegraph & Tel NTT Network Innovat Ctr Tokyo Japan Japan Adv Inst Sci & Technol JAIST Sch Informat Sci Nomi Ishikawa Japan

ISBN: (纸本)9781665462839

As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are being incurred to manually manage signatures, which are malicious communication pattern files. Network security experts need to classify signatures by importance for an IDPS to work optimally. In this study, we propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. Experts classify some signatures with predefined if-then rules. We first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as the term frequency-inverse document frequency (TFIDF) features of the message text in each signature and the text expanded by web scraping, respectively. Because failures need to be minimized when classifying IDPS signatures, we consider introducing an RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments conducted on two real datasets composed of signatures labeled by experts. In the experiment, the SF and WMF combination outperforms the SF and KF combination. We also show that using a deep ensemble improves the performance of the RO. An analysis shows that experts refer to the text contained in the signatures and information from the web.

关键词： machine learning feature engineering reject option real data Snort IDPS

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Educational Design Patterns for student-Centered 21st Century learning in Technology-Enhanced learning Environments 23rd

Educational Design Patterns for Student-Centered 21<i>st</i>...

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23rd international conference on Interactive Collaborative learning (ICL) / 49th IGIP international conference on engineering Pedagogy - Educating Engineers for Future Industrial Revolutions

作者： Dolezal, Dominik Roschger, Christoph Hahnenkamp, Andreas Mairinger, Maximilian Zimmermann, Daniel Satek, Alexander Koppensteiner, Gottfried Motschnig, Renate Univ Appl Sci Technikum Wien A-1200 Vienna Austria TGM Vienna Inst Technol A-1200 Vienna Austria Univ Vienna A-1010 Vienna Austria

ISBN: (纸本)9783030682019;9783030682002

Learners of the 21st century face new challenges posed by the requirements of the digital era. However, education itself does not seem to have changed significantly. Thus, reports of a gap between graduates' competences and the skills truly needed in professional life accumulate. This paper proposes three best practices to modern 21st century education formalized as educational design patterns. Relevant literature is reviewed and the used pattern mining process is outlined. Furthermore, three educational software services fostering the application of the proposed patterns are introduced and evaluated within the scope of usability tests with 56 secondary-level students and a focus group of three teachers. The results show that the educational software services are well suited to reasonably support class and the educational design patterns provide educators with field-tested best practices for 21st century education.

关键词： 21st century skills student-centered learning Educational design patterns

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