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Demand Forecasting in the Food Equipment Industry Using predictive data analytics Techniques 1

5th International Conference of the Industrial Engineering and Industrial Management (IEIM)

作者： Alqahtani, Ammar Y. Makki, Anas A. King Abdulaziz Univ Fac Engn Dept Ind Engn Jeddah Saudi Arabia King Abdulaziz Univ Fac Engn Rabigh Dept Ind Engn Jeddah Saudi Arabia

ISBN: (数字)9783031563737

ISBN: (纸本)9783031563720;9783031563737

Although food and restaurants are two of the most trending entertainment sectors in Saudi Arabia, where the equipment industry is a massive contributor to the enrichment of this trending environment, a major appliance manufacturer is facing difficulties forecasting the demand in the market. This study aims to study the sales department of one of the biggest appliance traders in the MENA region and forecast their sales for 2021. This study predicted sales using time series methods such as linear progression, exponential smoothing, Seasonal, and Trend decomposition using Loess (STL) and Auto-Regressive Integrated Moving Average (ARIMA). The program used in forecasting and using all these methods was Power BI, including R script commands. The results of the ARIMA method showed higher accuracy and less error than the other methods. The ARIMA method demonstrated an accuracy of 92.16% and a 9.24% error. For more accurate demand forecasting, businesses should consider the market changes for at least two years to understand consumer behavior that might change faster than expected, directly affecting the restaurant industry and the demand for equipment.

关键词： predictive data analytics Demand forecasting Equipment industry Restaurants industry Food industry Supply chain Logistics Appliances manufacturers

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Comparing Sensor-Based Computing and predictive data analytics for Usage in Context-Aware Applications 13th

Comparing Sensor-Based Computing and Predictive Data Analyti...

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13th International Symposium on Business Modeling and Software Design (BMSD)

作者： Shishkov, Boris Yosifov, Georgi Bontchev, Boyan Bulgarian Acad Sci Inst Math & Informat Sofia Bulgaria Univ Lib Studies & Informat Technol Fac Informat Sci Sofia Bulgaria Inst IICREST Sofia Bulgaria Sofia Univ St Kliment Ohridski Fac Math & Informat Sofia Bulgaria

ISBN: (纸本)9783031367564;9783031367571

Context-aware information systems count on sensors for establishing the user situation and adjusting their behavior accordingly. Nevertheless, there are quite some situations where sensors would be insufficiently effective: (a) Sometimes it is physically challenging to mount sensors at the optimal position, resulting in lower quality of the gathered data;(b) Mental aspects, such as user intentions, emotions, arousal, motivation, and engagement, are not easy to capture by means of sensors;(c) data fusion concerning data from different sensors could result in wrong inferences. We propose combining / augmenting sensor-based approaches with data analytics that can make predictions about larger groups of users. When the focal user is a member of that larger group and when there are more users with a similar profile, predictions about the focal user can be made. We combine context awareness conceptualizations and relevant data analysis techniques, and we reflect on these choices in a discussion concerning the usefulness of applying data analytics in facilitating context-aware computing. The paper illustrates the concepts with an example from the transport domain.

关键词： Context awareness Sensor-based computing predictive data analytics

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predictive data analytics for Breast Cancer Prognosis 3rd

Predictive Data Analytics for Breast Cancer Prognosis

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3rd International Conference on Advanced Computing and Intelligent Engineering (ICACIE)

作者： Chauhan, Ritu Kumar, Neeraj Amity Inst Biotechnol AUUP Sec 125 Noida India

ISBN: (纸本)9789811510816;9789811510809

The leading healthcare explosion in datasets has created furious interest among the researchers and scientists to determine automated technology for clinical future implications. Hence, data mining in the past decade has resolved and discovered hidden information and knowledge from large-scale databases. data mining tends to be widely anticipated technology which is utilized in varied application domain for retrieval of patterns for future decision-making. In the past several research studies are based on the prevalence and prognosis of breast cancer in females, but our proposed discuss the interception of disease for both the genders. Hence, we integrate the cancer data with predictive data analytics to determine the incidence and mortality rate of breast cancer both in males and females from the year 1990 to 2014.

关键词： predictive data analytics data mining Breast cancer Spectral analysis

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predictive data analytics for Agent-based Management of Electrical Micro Grids 41

Predictive Data Analytics for Agent-based Management of Elec...

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41st Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Queiroz, Jonas Dias, Artur Leitao, Paulo Polytech Inst Braganca Campus Sta ApoloniaApartado 1134 P-5301857 Braganca Portugal Artificial Intelligence & Comp Sci Lab Rua Campo Alegre 102 P-4169007 Oporto Portugal

ISBN: (纸本)9781479917624

Micro grids represent an emergent vision to address the challenges imposed by recent trends in smart electrical grids, where the large-scale integration of distributed energy production units plays an important role. Nevertheless, the realization of this vision requires the use of advanced intelligent approaches to manage the micro grids elements, such as distributed renewable energy production units, loads and storage devices. Multi-agent systems provide a suitable framework to design and implement such systems, where autonomous agents are endowed with predictive data analytics capabilities, e.g., for the prediction of renewable energy production and consumption, taking advantage of the large amount of data produced in these environments. In this context, this paper presents an agent-based system for the management of micro grids, where predictive capabilities were embedded in agents to provide real-time data analytics. The proposed approach was applied to an experimental case study where a set of predictive models was tested for short and long term forecasting of the energy produced by photovoltaic units.

关键词： electrical micro grid predictive data analytics multi-agent systems photovoltaic power generation forecast

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Machine Learning and predictive data analytics Enabling Metrology and Process Control in IC Fabrication 29

Machine Learning and Predictive Data Analytics Enabling Metr...

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29th Conference on Metrology, Inspection, and Process Control for Microlithography

作者： Rana, Narender Zhang, Yunlin Wall, Donald Dirahoui, Bachir Bailey, Todd IBM Corp SRDC Hopewell Jct NY 12533 USA

ISBN: (纸本)9781628415261

Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

关键词： predictive Metrology predictive data analytics Machine Learning Artificial Neural Networks Principal Component Analysis (PCA) Partial Least Square Model (PLS) Multivariate Linear Regression Models (MVR) Hybrid Metrology Bayesian Regularization Reference Metrology CD-AFM CD-SEM SEM Resist Shrinkage Scatterometry OCD MBIR Total Measurement Uncertainty (TMU) Prediction Accuracy Critical Dimensional (CD) Metrology EUV Resist Inline Process Control Metal Line Resistance Deep Trench Capacitance Chemical Mechanical Polishing (CMP) Reactive Ion Etching (RIE) Four Point Probe

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predictive data analytics for Agent-based Management of Electrical Micro Grids

Predictive Data Analytics for Agent-based Management of Elec...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Jonas Queiroz Artur Dias Paulo Leitao Polytechnic Institute of Braganca Campus Sta Apolonia

ISBN: (纸本)9781479917631

关键词： Electrical micro grid predictive data analytics Multi-agent systems Photovoltaic power generation forecast MICRO-GRID Multi-agent systems production unit System management Agent-based systems Large scale integrate circuit Intelligent agents real-time data Power production Sight Prediction models

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Adaptive Integration of Categorical and Multi-relational Ontologies with EHR data for Medical Concept Embedding

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2023年第6期14卷 1-20页

作者： Cheong, Chin Wang Yin, Kejing Cheung, William K. Fung, Benjamin C. M. Poon, Jonathan Hong Kong Baptist Univ Kowloon 224 Waterloo Rd Hong Kong Peoples R China McGill Univ 3661 Peel St Montreal PQ H3A1X1 Canada Hong Kong Hosp Author 147B Argyle St Hong Kong Peoples R China

Representation learning has been applied to Electronic Health Records (EHR) for medical concept embedding and the downstream predictive analytics tasks with promising results. Medical ontologies can also be integrated to guide the learning so the embedding space can better align with existing medical knowledge. Yet, properly carrying out the integration is non-trivial. Medical concepts that are similar according to a medical ontology may not be necessarily close in the embedding space learned from the EHR data, as medical ontologies organize medical concepts for their own specific objectives. Any integration methodology without considering the underlying inconsistency will result in sub-optimal medical concept embedding and, in turn, degrade the performance of the downstream tasks. In this article, we propose a novel representation learning framework called ADORE(ADaptive Ontological REpresentations) that allows the medical ontologies to adapt their structures for more robust integrating with the EHR data. ADORE first learns multiple embeddings for each category in the ontology via an attention mechanism. At the same time, it supports an adaptive integration of categorical and multi-relational ontologies in the embedding space using a category-aware graph attention network. We evaluate the performance of ADORE on a number of predictive analytics tasks using two EHR datasets. Our experimental results show that the medical concept embeddings obtained by ADORE can outperform the state-of-the-art methods for all the tasks. More importantly, it can result in clinically meaningful sub-categorization of the existing ontological categories and yield attention values that can further enhance the model interpretability.

关键词： Electronic health record representation learning data mining with ontologies predictive data analytics

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Visual data and predictive analytics for Proactive Project Controls on Construction Sites 25th

Visual Data and Predictive Analytics for Proactive Project C...

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25th Workshop of the European-Group-for-Intelligent-Computing-in-Engineering (EG-ICE)

作者： Lin, Jacob J. Golparvar-Fard, Mani Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9783319916354;9783319916347

This paper presents the theoretical foundation for a project controls system that improves understanding of how construction performance can be captured, communicated, and analyzed in form of a visual production system;predicts and effectively communicates the reliability of the weekly work plan and look-ahead schedules, supports root-cause assessment on plan failure at both project and task-levels;facilitates information flows;and decentralizes decision-making. Our model-driven system builds upon novel visual data analytics to map the current state of production in 4D ( 3D+time), compare to 4D BIM, and expose waste at both project and task-levels. Using predictive analytics and based on actual progress and productivity data, reliability in the future state of production is forecasted to highlight potential issues in a location-driven scheme and support collaborative decision making that eliminates root causes of waste. To evaluate the performance of our system, several case studies are conducted on real-world commercial building projects. It is shown that the developed system provides visual interfaces between people and information on and offsite, enables effective pull flows, decentralizes work tracking, facilitates in-process quality control and hand-overs among contractors, and most importantly transforms retroactive and task-driven workflows in contractor coordination meetings to proactive location-driven practices.

关键词： Visual production management predictive data analytics Lean construction

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COVID-19: Future Preference of University Students to Promote Online Education in Pakistan

COVID-19: Future Preference of University Students to Promot...

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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Asim, Hira Asmat, Ayesha Ilyas, Kiran Hassan, Malik Tahir Univ Management & Technol Dept Software Engn Lahore Pakistan Univ Management & Technol Dept Comp Sci Lahore Pakistan Univ Management & Technol Dept Informat & Syst Lahore Pakistan

ISBN: (纸本)9781665412858

COVID-19 is a deadly virus that has been transmitted globally in no time. Due to the continuous spread of COVID-19, the Higher Education Commission (HEC) of Pakistan has decided to shift towards online classes. In this study, a survey has been conducted to assess the online learning experience of the students. This paper aims to find the preferences of students regarding e-learning in the upcoming semester(s) for the promotion of digital learning in Pakistan. A well-structured questionnaire is circulated and a total of 519 responses are collected from the prestigious universities of Pakistan after the conclusion of the online semester. It has been observed that only 26% of the students are willing to take online classes in the future. To dig down the reasons behind this low percentage, comparative as well as sentiment analyses of the results have been conducted. The comparative analysis has indicated that the students are not satisfied with their learning outcomes, online examination, and grading in this regard. Similarly, the sentiment analysis of the comments has revealed that 60% of the students have provided negative feedback regarding online classes. Whereas, it has also been observed that many students are in the favor of attending short courses, seminars, and conferences online. Based on the analyses, suitable recommendations have also been provided to promote e-learning in Pakistan.

关键词： COVID-19 Sentiment Analysis Digital Future Distance Learning Transformative Technologies Knowledge Discovery Knowledge Acquisition predictive data analytics

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predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit

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COMPUTERS & CHEMICAL ENGINEERING 2020年 139卷 106912-106912页

作者： Dias, Tiago Oliveira, Rodolfo Saraiva, Pedro Reis, Marco S. Univ Coimbra Dept Chem Engn CIEPQPF Rua Silvio LimaPolo 2 Pinhal de Marrocos P-3030790 Coimbra Portugal Petrogal SA Rua Belchior Robles P-4451852 Leca Da Palmeira Portugal Dean NOVA IMS Campus Campolide P-1070312 Lisbon Portugal

The Research Octane Number (RON) is a key parameter for specifying gasoline quality. It assesses the ability to resist engine knocking as the fuel burns in the combustion chamber. In this work we address the critical but complex problem of predicting RON using real process data in the context of a catalytic reforming process from a petrochemical refinery. We considered data collected from the process over an extended period of time (21 months). RON measurements are obtained offline, by laboratory analysis, with a significant delay and at much lower rates when compared to process measurements. The proposed workflow covers all the way from data collection, cleaning and pre-processing to data-driven modelling, analysis and validation for a real industrial refinery located in Portugal. The accuracy achieved with the best soft sensors open up perspectives for industrial applications and the results obtained also provide relevant information about the main RON variability sources. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： predictive data analytics Soft sensors Research Octane Number Catalytic reforming Big data

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