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Automated Decision-Making System for Decentralized Stock Trading using Blockchain smart Contracts and Quaternion Generative Adversarial Networks

Automated Decision-Making System for Decentralized Stock Tra...

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Ubiquitous computing and intelligent Information Systems (ICUIS), international conference on

作者： Mohideen AbdulKader M Kalaichelvi Nallusamy Jebamalar Zulaiha Maryam M Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Tamilnadu India Department of Information Technology Mohamed Sathak A. J. College of Engineering Siruseri Tamilnadu India Department of Mathematics & Actuarial Science B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai Tamilnadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In the field of financial technology, the creation of an automated decision system for the decentralized execution of stock market operations is a noteworthy development. The challenges of the generic approach, such as transparency, security, and efficiency in stock trading, are effectively solved with the help of predicting analytics combined with smart contracts based on blockchain technology. In this research, a Blockchain smart Contract Adoption with Quaternion Generative Adversarial Networks, (BSCA-QGAN), is presented for decentralized stock market prediction over Yahoo Finance data. The first pre-processing is data cleaning and normalization of data collected and then feature extraction is done by the Spike Driven Transformer (SDT) which is the integration of Transformers and Spiking Neural Networks. Also, the Quaternion Generative Adversarial Networks (QGANs) forecast the stock prices whereby time series with built-in quaternion math is modelled. The QGAN's hyperparameters are optimized using the White Shark Optimizer (WSO). An automated decision system for decentralized stock markets is integrated with blockchain smart contracts. The system achieved 99.72% prediction accuracy and minimal memory usage of 10MB.

关键词： Accuracy Quaternions smart contracts Decision making Generative adversarial networks Transformers Feature extraction Blockchains Security Stock markets

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on smart data Integration and Processing, STRAPS 2021, international Workshop on AI-enabled Process Automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented computing, ICSOC 2021

International Workshop on Artificial Intelligence for IT Ope...

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on smart data Integration and Processing, STRAPS 2021, international Workshop on AI-enabled Process Automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented computing, ICSOC 2021

ISBN: (纸本)9783031141348

The proceedings contain 32 papers presendted at a virtual meeting. The special focus in this conference is on Artificial Intelligence for IT Operations. The topics include: A Taxonomy of Anomalies in Log data;little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection;MMRCA: MultiModal Root Cause Analysis;IAD: Indirect Anomalous VMMs Detection in the Cloud-Based Environment;comparing Graph data Science Libraries for Querying and Analysing datasets: Towards data Science Queries on Graphs;gamification for Healthier Lifestyle – User Retention;streaming and Visualising Neuronal Signals for Understanding Pain;graph analytics Workflows Enactment on Just in Time data Centres Position Paper;say No2Ads: Automatic Advertisement and Music Filtering from Broadcast News Content;data Centred intelligent Geosciences: Research Agenda and Opportunities Position Paper;Design Patterns in an ERP Environment;towards a Semantics-Based Search Engine for smart Contract Information;a Blockchain-Based Autonomous System for Primary Financial Market;multi-agent Reinforcement Learning for Task Allocation in Cooperative Edge Cloud computing;occupancy Estimation from Thermal Images;constraint-Aware Trajectory for Drone Delivery Services;seamless Synchronization for Collaborative Web Services;Crowd4ME: A Crowdsourcing-Based Micro-expression Collection Platform;AI-Enabled Processes: The Age of Artificial Intelligence and Big data;hybrid Recommendation of Movies Based on Deep Content Features;continuously Testing Distributed IoT Systems: An Overview of the State of the Art;towards Scalable Blockchains Using Service-Oriented Architectures;snapshot of Research Issues in Service Robots;automatic Image Annotation Using Quantization Reweighting Function and Graph Neural Networks;interactive Process Improvement Using Simulation of Enriched Process Trees;a Reproducible Approach for Mining Business Activities from Emails for Process analytics;human-in

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Collaborative Intrusion Detection System for Intermittent 10 Vs Using Federated Learning and Deep Swarm Particle Optimization

Collaborative Intrusion Detection System for Intermittent 10...

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international conference on data Science and Advanced analytics (DSAA)

作者： Farhan Ullah Gautam Srivastava Leonardo Mostarda Diletta Cacciagrano Division of Computer Science University of Camerino Camerino Italy Dept. of Math and Computer Science Brandon University Brandon MB Canada Department of Mathematics and Computer Science University of Perugia Perugia Italy School of Software Northwestern Polytechnical University Xian China

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

intelligent vehicles have significantly influenced the advancement of intelligent Transportation Systems (ITS). smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance.

关键词： Training Cloud computing Analytical models Privacy smart cities Intrusion detection Feature extraction data models Security Optimization

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data-Driven Insights into Automotive Customer Complaints: A Machine Learning Approach to Predictive analytics

Data-Driven Insights into Automotive Customer Complaints: A ...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Anil Kumar Adike S. Silvia Priscila Department of Computer Science Bharath Institute of Higher Education and Research Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Automotive customer complaints forecast as a difficult-to-solve and data-driven machine learning classification problem. Key findings from background research include the problems associated with data imbalance, shortcomings of the models in terms of temporal pattern analysis and variability of prediction errors. It incorporates clustering and LSTM models to analyze product complaint characteristics, improving accuracy and flexibility. The proposed model operates based on a diverse dataset of automotive complaints, and the model accuracy is 94.5%, the precision is 92.7 % , the recall is 93.1%, the F1 score is 92.9%, and the AUC is 95.3%. Compared to other methods, the advantage of this particular work is that it achieves far better accuracy on complex and imbalanced data sets than previous approaches. The findings proved that the chosen model's approach is rather effective for the prognosis of different kinds of complaints to assist in the usage of the anticipative maintenance approaches. It is useful for automakers, because, knowing that the issues, and indicators of the low degree of customer satisfaction define, manufacturers can come to them before high-risk issues occur.

关键词： Deep learning Analytical models Accuracy Predictive models Transformers Maintenance Automobiles Usability Long short term memory Automotive engineering

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

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computing for Sustainability and intelligent Future (COMP-SIF), international conference on

作者： Gomathi Thiyagarajan Swathi. Y Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates.

关键词： Process mining data analysis Consumer behavior data visualization Electronic commerce Sustainable development

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Neural Networks in Workforce Planning and Management

Neural Networks in Workforce Planning and Management

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Pallavi Shetty Ch. Sharonu Pushpa Gazala Amanullah Khan Deeksha Joshi Melanie Lourens Joshuva Arockia Dhanraj Department of MCA NMAM Institute of Technology Nitte (Deemed to be University) Department of Data Science MLRIT Hyderabad Indira Institute of Management Sciences School of Management Graphic Era Hill University Haldwani Uttarakhand India Faculty of Management Sciences Durban University of Technology South Africa Research and Development Cell Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The primary objective of this study is to examine the utilization of neural networks in workforce planning and management, with an emphasis on their capacity to enhance labor allocation, anticipate personnel requirements, and enhance overall organizational efficiency. Neural networks, a type of artificial intelligence, are extremely appropriate for managing the complexities of human management because of their robust capabilities in data processing and pattern detection. By examining historical data and current trends, these algorithms can identify areas where skills are lacking, forecast the need for workers, and suggest the optimal employment levels. By incorporating neural networks into workforce planning, we can achieve a workforce that is more adaptable and efficient, as well as improve employee satisfaction, reduce costs, and improve the precision of our decision-making.

关键词： Neural networks Employment Decision making Market research data processing Planning Resource management Personnel Artificial intelligence Predictive analytics

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Non-intrusive Load Monitoring Based data-Free Incremental Electrical Appliance Identification 8th

Non-intrusive Load Monitoring Based Data-Free Incremental El...

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8th international conference on Energy and Environmental Science, ICEES 2024

作者： Zhou, Rui Fang, Xiaohan The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China

ISBN: (纸本)9783031639005

In the realm of modern energy management systems, Non-Intrusive Load Monitoring (NILM) technology provides an innovative solution for power suppliers. It allows for remote monitoring and analysis of users’ electricity consumption patterns without requiring direct access to each device. This technology works by collecting comprehensive energy data at the user’s meter entry point and using algorithms to differentiate and identify the energy consumption characteristics of each device. However, with the increasing number of smart devices used by residential and commercial users, traditional NILM methods face the challenge of dealing with more complex and diverse electricity usage patterns. To address these challenges, we propose a novel incremental NILM framework. This framework has the ability to independently learn new load categories without load data, which is crucial for adapting in real-time to the changing variety of load devices, while also effectively addressing user privacy concerns. We introduce a VI-SCRM for extracting V-I trajectory features, incorporating a reconstruction convolution module. This method reduces the redundancy of V-I trajectory features and eliminates insignificant features, thereby improving algorithm efficiency. Additionally, this approach streamlines the model training process and prevents the model from ‘forgetting’ previously acquired knowledge. By implementing this framework in NILM, the system can continually adjust to new types of devices. Our experimental results confirm the effectiveness of this approach and highlight its potential advantages in practical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolution

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Software Module Clustering Using the Hierarchical Clustering Combination Method 7

Software Module Clustering Using the Hierarchical Clustering...

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7th international conference on Cloud computing and Big data analytics, ICCCBDA 2022

作者： Xia, Hong Zhang, Yongkang Chen, Yanping Zhang, Hengshan Wang, Zhongmin Wang, Fengwei School of Computer Science and Technology Xi'An Univ. of P. and Telecom. Shaanxi Key Lab. of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications School of Computer Science and Technology Xi'an China ZTECorporation Shenzhen China

ISBN: (纸本)9781665487115

The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and clustering results, a single hierarchical clustering algorithm cannot integrate the number of clusters, arbitrary decision-making, recovery quality and other indicators on different evaluation standards, there is no comprehensive clustering algorithm selection method. We propose a hierarchical clustering combination algorithm and use principal component analysis to combine the results of multiple hierarchical clusters, the combined result retain the basic information of each clustering algorithm as much as possible. The experimental results show that compared with the single software hierarchical clustering method and other combined methods, the result of our method is closer to expert decomposition, and it has good performance on a variety of indicators. © 2022 IEEE.

关键词： Cloud computing Software architecture Clustering methods Software algorithms Decision making Clustering algorithms Big data

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Machine Learning Adoption in Blockchain-Based smart Applications 5

Machine Learning Adoption in Blockchain-Based Smart Applicat...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Suthar, Vishal Bansal, Vipul Reddy, Ch.Srinivasa Gonzales, Jose Luis Arias Singh, Devendra Singh, Devesh Pratap Government Polytechnic College Department of Computer Engineering Mandore Rajasthan Jodhpur India Kurukshetra University Department of Information Technology Haryana India Vignan's Institute of Information Technology Department of IT Duvvada India Pontifical Catholic University of Peru Departamento of Business Peru Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. These dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. The assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector Machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). The difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered. © 2022 IEEE.

关键词： Blockchain

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The IMOCO4.E reference framework for intelligent motion control systems 28

The IMOCO4.E reference framework for intelligent motion cont...

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Mohamed, Sajid Van Der Veen, Gijs Kuppens, Hans Vierimaa, Matias Kanellos, Tassos Stoutjesdijk, Henry Masiero, Riccardo Määttä, Kalle Van Der Weit, Jan Wytze Ribeiro, Gabriel Bergmann, Ansgar Colombo, Davide Arenas, Javier Keary, Alfie Goubej, Martin Rouxel, Benjamin Kilpeläinen, Pekka Kadikis, Roberts Armendia, Mikel Blaha, Petr Stokkermans, Joep Čech, Martin Beltman, Arend-Jan ITEC B.V. Sioux Technologies China VTT Finland GNT India Philips Medical Systems CRIT Normet India Philips Consumer Lifestyle Edilasio Portugal Kion Group China WEG Automation Europe Italy Fagor AOTEK Tyndall National Institute Ireland Univ. of West Bohemia Czech Republic Univ. of Modena Italy Institute of Electronics & Computer Science Latvia TEKNIKER Spain Brno Univ. of Technology Czech Republic

ISBN: (纸本)9798350339918

intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial 'know-how' of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale 'intelligent Motion Control under Industry4.E' (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework's applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases. © 2023 IEEE.

关键词： Edge computing

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