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AI-Driven intelligent data analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency

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JOURNAL OF THE KNOWLEDGE ECONOMY 2024年第1期16卷 864-903页

作者： Zong, Zhijuan Guan, Yu Fuyang Normal Univ Fuyang 236041 Anhui Peoples R China

In the era of Industry 4.0, integrating digital technologies into industrial processes has become imperative for sustaining growth and fostering innovation. This research paper explores the profound impact of AI-driven intelligent data analytics and predictive analysis on economic efficiency and managerial practices within Industry 4.0. With a focus on knowledge, innovation, technology, and society, this study delves into the transformative potential of these advanced technologies. intelligent data analytics, powered by artificial intelligence (AI), has revolutionized the way industries harness vast datasets. Uncovering real-time patterns, correlations, and opportunities empowers decision-makers with accurate and timely insights. Predictive analysis, rooted in statistics and machine learning, aids in forecasting trends and managing risks, offering economic stability across sectors. Using a mixed-methods approach, the study combines qualitative interviews with 19 Chinese operations managers and quantitative data from an online survey of 286 managers. The study ranks various Industry 4.0 technologies through ordinal regression based on their impact on environmental sustainability and economic management. Results show that smart sensors, radio-frequency identification, AI, and analytics are the most influential technologies for enhancing economic and environmental outcomes. Conversely, technologies like additive manufacturing and automated robots yield less favorable results. The study also identifies a noticeable gap in professionals' understanding and adoption of AI and augmented reality. Environmental concerns around the disposal of electronic waste generated by these technologies are also highlighted. The research thus offers significant insights for companies seeking to adopt intelligent data analytics to enhance economic performance and environmental sustainability. On the managerial front, the fusion of these technologies enables agile and responsive frameworks,

关键词： Industry 4.0 intelligent data analytics Predictive analysis Artificial intelligence Economic efficiency Managerial practices

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Sensemaking in intelligent Health data analytics

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KUNSTLICHE INTELLIGENZ 2015年第2期29卷 143-152页

作者： Boman, Magnus Sanches, Pedro SICS Box 1263 S-16429 Kista Sweden KTH ICT SCS S-16429 Kista Sweden

A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results-the policymakerare replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.

关键词： Artificial intelligence Massive data Health data intelligent data analytics Syndromic surveillance Sensemaking

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Exploring the Cinemaverse: data-Driven Analysis of Movie Production, Revenue, and Trends Using Tableau Visualization Tool 15

Exploring the Cinemaverse: Data-Driven Analysis of Movie Pro...

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15th International Conference on Ubiquitous and Future Networks (ICUFN)

作者： Neware, Govindram Paul, John Poulose, Alwin Indian Inst Sci Educ & Res Thiruvananthapuram IIS Sch Data Sci Thiruvananthapuram 695551 Kerala India Univ Malaysia Pahang Al Sultan Abdullah Fac Mech & Automot Engn Technol Pekan 26600 Pahang Malaysia

ISBN: (纸本)9798350385304;9798350385298

Effective data visualization is essential for extracting meaningful insights from complex datasets in today's data-driven world. This paper showcases the power of data visualization by transforming the IMDB movies dataset into an interactive and informative dashboard using Tableau, a leading data visualization platform. The paper delves into the global distribution of movie releases, identifies the top-performing movie genres, examines the correlation between budget and box office success, highlights the most financially successful movies, and assesses the linguistic diversity of the dataset. The dashboard presented in the paper insights concisely through maps, treemaps, bar graphs, area charts, scatter plots, and pie charts. This paper also highlights the versatility of Tableau as a tool for creating interactive and visually appealing dashboards that a wide range of audiences can use.

关键词： data Visualization intelligent data analytics Tableau Visualization Tool data Analysis IMDB Movies dataset

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The Power of Visual Storytelling: Analyzing Customer Personalities with Tableau 15

The Power of Visual Storytelling: Analyzing Customer Persona...

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15th International Conference on Ubiquitous and Future Networks (ICUFN)

作者： Menaka, Jadov Poulose, Alwin Indian Inst Sci Educ & Res Thiruvananthapuram IIS Sch Data Sci Thiruvananthapuram 695551 Kerala India

ISBN: (纸本)9798350385304;9798350385298

The customer personality analysis serves as an in-depth exploration into the realm of a company's ideal customers, meticulously examining their characteristics, behaviors, and preferences. This paper's analytical approach aims to empower businesses by unraveling the intricacies of customer bases and enabling the customization of products based on diverse customer segments' specific needs and concerns. By leveraging advanced visualization techniques like histograms, bar charts, donut charts, and table charts within Tableau, this study seeks to enhance the understanding of customer attributes, facilitating more targeted and effective marketing strategies. Understanding customers is pivotal for businesses seeking to tailor their products and marketing efforts. Customer personality analysis delves into the wealth of data available, allowing for a nuanced understanding of customer segments. The objective is to shift from a generalized approach to a more targeted one, where resources are allocated efficiently and marketing efforts are tailored to resonate with specific customer groups.

关键词： Customer Personality Analysis Visualization Techniques data Visualization intelligent data analytics data analytics Techniques Interactive dashboards Decision-making Customer Behavior Campaign Metrics Demographics

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AI-Enabled IoT Framework for Smart Traffic Surveillance and Communication 1

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International Conference on intelligent and Fuzzy Systems (INFUS)

作者： Asuquo, Daniel Udo, Ifiok J. Ekpenyong, Moses Attai, Kingsley Univ Uyo Fac Comp Dept Informat Syst Uyo Nigeria Univ Uyo Fac Comp Dept Comp Sci Uyo Nigeria Ritman Univ Dept Math & Comp Sci Ikot Ekpene Nigeria

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

The rising increase in urban population and growing demand by passengers for seamless mobility requires efficient operation of road transport applications. The current traffic surveillance systems in most Nigeria cities are inefficient, costly, and characterized by errors thereby requiring the deployment of smart solutions. This paper proposes an AI-driven internet of things (IoT) solution to the present traffic flow management crises. We provide preliminary workflow activities for achieving our objective by designing a data dictionary of input features/parameters that impact traffic flow management. Specifications of the data dictionary assisted the simulation and implementation of a congestion prediction system modeled after four road networks within the Uyo Metropolis of Akwa Ibom State, Nigeria. Results of a deep learning model evaluation prove the success of our implementation with satisfactory values of root mean squared error (0.1332), receiver operating curve-area under the curve (0.9293) and accuracy (0.8668). A full-scale study including the implementation of our proposed framework is expected in future research.

关键词： Urban Mobility Real-time Traffic Management IoT Technology Smart Traffic Surveillance intelligent data analytics

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Machine learning based accident prediction in secure IoT enable transportation system

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JOURNAL OF intelligent & FUZZY SYSTEMS 2022年第2期42卷 713-725页

作者： Mohanta, Bhabendu Kumar Jena, Debasish Mohapatra, Niva Ramasubbareddy, Somula Rawal, Bharat S. Centurion Univ Technol & Management Dept CSE Bhubaneswar 752050 Odisha India Int Inst Informat Technol Dept CSE Bhubaneswar Odisha India VNRVJIET Dept Informat Technol Hyderabad India Gannon Univ Dept Cybersecur Erie PA USA

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

关键词： intelligent data analytics machine learning intelligent transportation system secure communication internet of things

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A new hybrid model combining EMD and neural network for multi-step ahead load forecasting

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JOURNAL OF intelligent & FUZZY SYSTEMS 2022年第2期42卷 1099-1114页

作者： Malik, Hasmat Alotaibi, Majed A. Almutairi, Abdulaziz BEARS NUS Campus Singapore Singapore King Saud Univ Coll Engn Dept Elect Engn Riyadh Saudi Arabia King Saud Univ Power Syst Reliabil & Secur Riyadh Saudi Arabia Majmaah Univ Coll Engn Dept Elect Engn Riyadh Saudi Arabia

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).

关键词： Feature extraction decomposition intelligent data analytics short-term forecasting power system planning

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Artificial Intelligence Ethics and Applications 3

Artificial Intelligence Ethics and Applications

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3rd International Conference on Industrial IoT, Big data and Supply Chain (IIoTBDSC)

作者： Xu, Qianwen Ariel Chang, Victor Gokaraneni, Nihal Ganatra, Meghana Wong, Siu Tung Li, Jie Aston Univ Aston Business Sch Dept Operat & Informat Management Birmingham W Midlands England Teesside Univ Sch Comp & Digital Technol Middlesbrough Cleveland England Kings Coll London Fac Nat Math & Engn Sci London England

ISBN: (纸本)9781665454551

intelligent data analytics is a valuable technique for analyzing the more profound values covered within the endless streams of information created in our lives nowadays. It has been applied to a variety of domains, such as healthcare, social media, manufacturing industries, etc. In particular, it significantly improves manufacturing systems by conducting scheduling, monitoring, quality evaluation, and failure detection, which are the most typical industrial activities. While intelligent data analytics brings numerous benefits to the manufacturing industry and helps address its challenges, several new ethical issues also arise. This paper identifies the challenges faced by the manufacturing sector and introduces how the applications of intelligent Big data analytics address these issues. In addition, it identifies the benefits it brings and the ethical issues it faces.

关键词： Artificial Intelligence Big data Smart Manufacturing Ethical issues intelligent data analytics

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Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

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SENSORS 2020年第23期20卷 6825-6825页

作者： Taffese, Woubishet Zewdu Nigussie, Ethiopia Univ Aalto Dept Civil Engn Espoo 02150 Finland Univ Turku Dept Future Technol Turku 20014 Finland

In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl-) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl- sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl- wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures.

关键词： sensors internet of things intelligent data analytics machine learning deep learning autonomous corrosion assessment corrosion reinforced concrete

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