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A Novel Approach of Ecommerce for Sales Prediction Using Hybrid ABC and AdaBoost Approach

A Novel Approach of Ecommerce for Sales Prediction Using Hyb...

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Electronics, Computing, Communication and control Technology (ICECCC), International conference on

作者： N. Kogila R. Rajkumar Sudha Rajesh S. Vennila Department of Commerce B. S. Abdur Rahman Crescent Institute of Science & Technology Chennai Tamil Nadu India Department of Commerce St. Joseph's College of Arts & Science (Autonomous) (Affiliated to Annamalai University Chidambaram) Cuddalore Tamil Nadu India Department of Computational SRMIST Kattankulathur Intelligence School of Computing Chennai Tamil Nadu India Department of Computer Application St. Ann's College of Arts & Science (Affiliated to Annamalai University) Tindivanam Tamil Nadu India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

For online stores to offer speedier shipping times, precise sales forecasting is essential. Online advertising campaigns and the competitive nature of comparable but distinct products are only two of many potential variables that could make accurate sales forecasting more difficult. Typically, these characteristics are disregarded in this approach since traditional methods that rely on time series analysis primarily take sales data into account. Preprocessing, feature selection, and training the model must be carried out in an exact sequence. data quality concerns, missing information, and product grouping are all part of the preprocessing process. In feature selection, a statistical method known as principal component analysis (PCA) is used to reduce the dimensionality of a dataset that contains multiple linked variables. To train the model, Hybrid-ABC-AdaBoost was utilized. Incredibly, the data show that an accuracy of 97.22 % was reached.

关键词： Training Support vector machines Accuracy data integrity Computational modeling Time series analysis Feature extraction

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data modeling of Energy Consumption in High Energy-Consuming Industries Based on Semi-Supervised Learning Algorithm 2

Data Modeling of Energy Consumption in High Energy-Consuming...

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2022 2nd International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2022

作者： Wang, Hongkai He, Dong Mao, Dong State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Zhejiang Hangzhou310016 China

ISBN: (纸本)9783800760268

The implementation of emission reduction measures in high-energy-consuming industries will increase the financial cost of enterprises in the short term and affect the business results of enterprises. The positive effects brought by the implementation of emission reduction measures by enterprises are sufficient to make up for the costs incurred by enterprises in taking emission reduction measures, thereby enhancing the market value of enterprises. To overcome the adverse effects of the volatility and uncertainty of energy consumption in high-energy-consuming industries on energy consumption, this paper proposes a cloud model load group dynamic regulation algorithm that takes into account delay compensation, and designs a load regulation with LA as the middleman Under the constraints of load adjustable margin, considering the delay in the aggregation process of high energy-consuming industry load groups, the LS-SVM method is used to make ultra-short-term predictions for user load groups in the aggregation delay interval. The real-time fluctuation of the load group uses the two-dimensional cloud task generator to dynamically compensate the control amount to improve the control effect. This solution integrates the load resources of the user group, realizes the consumption of energy consumption fluctuations, and optimizes the resource optimization on the demand side. The configuration has played a significant role in improving the balance of power supply and demand in the regional power grid and has played a role in ensuring the stability of the power grid. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Energy utilization

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Different Patterns of Medication Administration Between Inside and Outside the Patient Room Using Electronic Medical Record Log data 12th

Different Patterns of Medication Administration Between Insi...

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12th International conference on Digital Human modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM) Held as Part of the 23rd International conference on Human-Computer Interaction (HCii)

作者： Kasaie, Alireza Kim, Jung Hyup Guo, Wenbin Nazareth, Roland Shotton, Thomas Despins, Laurel Univ Missouri Ind & Mfg Syst Engn Dept Columbia MO 65211 USA Univ Missouri Sinclair Sch Nursing Columbia MO USA

ISBN: (纸本)9783030778194;9783030778200

The primary object of this study is to analyze the different patterns of medication administration in a medical intensive care unit (MICU) using an electronic medical record (EMR) log data and how these patterns can be different inside and outside the patient room in terms of average process time. To analyze ICU nurses' workflow related to the EMR documentation, the real-time measurement system (RTMS) datawas used, and multiple hierarchical task analysis (HTA) charts were developed. The results revealed that there was no significant different pattern between themedication administration inside and outside the patient room. However, we found a significant difference inmedication administration's average process time between inside and outside the patient room. The findings of this study highlight the behavioral differences in performing medication administration in an ICU.

关键词： Electronic medical record Medication administration Health information technology

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IFIP WG 5.7 International conference on Advances in Production Management Systems, APMS 2022

IFIP WG 5.7 International Conference on Advances in Producti...

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IFIP WG 5.7 International conference on Advances in Production Management Systems, APMS 2022

ISBN: (纸本)9783031164064

The proceedings contain 69 papers. The special focus in this conference is on Advances in Production Management Systems. The topics include: Digital Transformation in the Milk Production Chain;supplying School Canteens with Organic and Local Products: Comparative analysis;school Feeding and Family Farming: Partnership for the Generation of Employment and Income;simulation-Based Game Theoretical analysis of Japanese Milk Supply Chain for Food Waste Reduction;assessing Energy Efficiency in processes of the Agri-Food Sector: From Delivery of Natural Resources to Finished Products;technologies Used for Animal Welfare Monitoring;Managing Technological Obsolescence in a Digitally Transformed SME;expense and Revenue Factors of Smart Factories: analysis of the Economic Effects of Condition Monitoring;procurement 4.0: A Systematic Review of Its Technological Evolution;The Digital Twin Application for Micro-Tool Wear Monitoring with Open-Source CAD System;introducing a Fast Lane to Multi-Project Environments in Factories to Focus on Digital Transformation;business process Digitalization Tracking and Monitoring: An Heuristic Software-Based Approach;crowdsourced Sentiment-Driven process Re-design with SentiProMoWeb: Towards Enterprise Social Information Systems;digital Technologies as an Essential Part of Smart Factories and Their Impact on Productivity;KNOWO: A Tool for Generation of Semantic Knowledge Graphs from Maintenance Workorders data;digital Transformation in the Engineering Research Area: Scientific Performance and Strategic Themes;a Proposal of data-Driven and Multi-scale modeling Approach for Material Flow Simulation;distributed Optimization for Supply Chain Planning for Multiple Companies Using Subgradient Method and Consensus control;an Ant Colony Optimization with Turn-Around-Time Reduction Mechanism for the Robust Aircraft Maintenance Routing Problem;exploring a Commercial Game for Adoption to Logistics Training;towards Cognitive Intelligence-Enabled Manufacturi

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Quality-Relevant process Monitoring Based on Improved Concurrent Canonical Correlation analysis 10

Quality-Relevant Process Monitoring Based on Improved Concur...

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10th IEEE data Driven control and Learning Systems conference, DDCLS 2021

作者： Wang, Xuemei Wu, Ping Lou, Siwei Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9781665424233

How to use existing technology to improve product quality while ensuring production stability and safety has become a hot topic in various fields. Being one of the most powerful multivariate analysis tools in process monitoring, canonical correlation analysis (CCA) is an exploratory statistical method that emphasizes the correlation between a set of poly dimensional data obtained at the same experimental level. For overcoming the drawbacks of CCA, a concurrent canonical correlation analysis (CCCA) modeling method was put forward, which decomposes the input and output variables into five segments. However, CCCA based quality-related process monitoring scheme would make ambiguous decisions due to the decomposition is incomplete. It is intended that this paper will explore an improved concurrent canonical correlation analysis (ICCCA) technique used for process monitoring. Specifically, a CCA model is established where the latent structure of process data is extracted. Subsequently, an algorithm of total principal component regression (TPCR) is implemented grounded on the established CCA model. Complete decomposition of the input and output variables into six parts was performed for quality-related process monitoring. Taking the Tennessee Eastman process (TEP) as an industry benchmark, the superiority of ICCCA is verified by comparing it with other monitoring methods. © 2021 IEEE.

关键词： process monitoring

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Reverse blockchain technology and its application to improve the quality of control systems in conditions of network instability

Reverse blockchain technology and its application to improve...

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Blockchain Computing and Applications (BCCA), International conference on

作者： Volodymyr Khilenko Andrii Zinchenko Oleksii Stepanov Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Department of Information Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

The work proposes a technology for using a blockchain database to improve the quality of control systems for dynamic objects and technological processes in situations where the stability of communication systems is not ensured or loss of information and control signals is possible. The developed technology (hereinafter referred to as "reverse blockchain technology") involves the calculation of management signals based on a comparison of data from the blockchain database and forecast results obtained at the time of loss of the information signal. The description of the dynamics of the control object is considered in matrix form. Model experiments were carried out to select computational methods for processing signals and forming forecasts for the dynamics of an object. Recommendations are formulated to improve the reliability of control systems in conditions of instability of communication channels.

关键词： Protocols databases Computational modeling process control Predictive models control systems Stability analysis Mathematical models Blockchains Reliability

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

43rd IBIMA Conference on Artificial intelligence and Machine...

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

ISBN: (纸本)9783031790850

The proceedings contain 62 papers. The special focus in this conference is on Artificial intelligence and Machine Learning. The topics include: Comprehensive analysis of Football Player Performance with WorldfootballR: data Insights and Key Variables;Decision Making process for a Sustainable Horticulture Using AI;drug Delivery System – The Construction and Use of the Graphical Interface in modeling the controlled Release of Drugs in the Human Body;Enabling German SMEs and Crafts Through data-Driven Innovation: Developing a Scoring Model and Chronological Framework for Enhanced Decision-Makin;Exploring the Impact of AI-Driven Gamification on Visitor Engagement in Museums: A Critical Review;framework for Automating Bank Transactions and Invoice Mapping Using Multi-criteria Optimization and Neural Networks;measurements of Clothing Circumference Using Vision Methods;mechatronic Mannequin as A Way to Reduce the Number of Returns in Fashion E-Commerce;ontological modeling of the Financial Intermediation Models for Creating a System of Texts’ Semantic analysis;optimizing Delivery Routes with Spatially Distributed Orders;review of Artificial Intelligence Models for Constructing a Sales Forecasting Module to Enhance Decision-Making;Stock Price Prediction Using Sentiment analysis and LSTM Networks;unveiling Online Shopping Quality: Taxonomy-Based Multi-label Classification of Shopping App Reviews;unveiling the Dynamics in Shopping Behaviors: A Review;control of Technical Parameters of Industrial Products Through Machine Vision;automatic Coffee Roasting process Assistance System Based on Fuzzy Logic Techniques;a Simulation Study on the Influence of Prioritized Multicast Connections on Traffic Effectiveness Network Nodes;Advantages of Introducing the DVB-T2 Standard in Terrestrial Television;consensus Mechanisms in Blockchain Networks;Self-optimizing SD-WAN.

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

43rd IBIMA Conference on Artificial intelligence and Machine...

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

ISBN: (纸本)9783031774928

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Motion control of Railway Machinery Room Inspection Robot Based on Deep Learning

Motion Control of Railway Machinery Room Inspection Robot Ba...

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Intelligent Technologies (ACOIT), Asian conference on

作者： Dehua Zhu Guoneng Shuohuang Railway Development Co. Ltd China

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In this study, the motion control process of the inspection robot in the railway machinery room is described, and the effective training and deployment of the deep learning model are used to achieve the ultimate goal. Firstly, after repeated comparative experiments, CNN was determined to be the core model of this study, and the advantages of the model in time series data and image data processing were used to develop the research objectives. In this paper, the cross-validation method is used to continuously optimize the network structure and hyperparameters and improve the generalization ability of the model under complex environmental conditions. In addition, mature attention mechanism and data augmentation technology are introduced to enhance the accuracy and robustness of the model’s prediction. Finally, the optimized CNN model is directly deployed to the embedded computing unit of the inspection robot system in the railway machinery room, so that it can collect equipment data in real time and realize important decisions such as effective fault prediction and remaining service life estimation. In order to ensure the reliability of decision-making, a special fault diagnosis module is designed in this paper, which can confirm the model output twice. It can be seen in practice that this method can significantly improve the maintenance efficiency and prolong the service life of the equipment.

关键词： Deep learning Training Computational modeling Time series analysis Inspection Predictive models Rail transportation data models Machinery Robots

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Comparison of Turbulence Approaches Modelling the Flow Around the NACA 0012 Profile

Comparison of Turbulence Approaches Modelling the Flow Aroun...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Soufinae Elouardi Bouchaib Radi Rabii El Maani Laidi Zahiri Laboratory of Modeling and Simulation of intelligent industrial systems (MS2IS) Hassan II University of Casablanca Higher Normal School of Technical Education Mohammedia Morocco Laboratory of Engineering Industrial Management and Innovation (IMII) FST Settat Hassan 1st University Settat Morocco Laboratory of Computer and Mathematical Process Engineering ENSA Khouribga Sultan Moulay Slimane University Khouribga Morocco

Turbulence refers to the instability of air movements in which the speed has a whirling character at all points. This is a phenomenon often observed and generates sometimes violent movements that can disrupt flight in the case of aerodynamic interactions with aircraft. Bodily accidents due to turbulence are numerous, but they can be avoided with good control and prediction of the fluid behavior around this structure. In order to do this, designers are increasingly using numerical modeling to boost these aircraft's efficiency. We carried out a numerical analysis of the two-dimensional transonic flow on a NACA 0012 airfoil using the ANSYS/FLUENT software in order to compare the capacity of different turbulence models to predict results that are similar to those of experimental studies. The results were compared to the provided experimental data in the high Reynolds Number range of 9.10 6 . Then, a comparison based on the RANS, LES and Hybrid approach in order to obtain a better control of the profile performance. We demonstrated that Spalart-Allmaras model of Dettached Eddy Simulation gave good results for unsteady flow over the wing.

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