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Proceedings - 2024 international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

Proceedings - 2024 International Conference on Industrial Io...

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5th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

ISBN: (纸本)9798331539818

the proceedings contain 80 papers. the topics discussed include: a study on the rapid classification of medical consumable supply chains based on K-means clustering;optimized LSTM time series prediction model for industrial big data;elasticity evaluation of modern construction supply chain based on interval intuitionistic fuzzy numbers;intelligent optimization of material supply chain via machine learning;cold chain food safety detection technology combined with data mining technology;research on big data automatic mining method of high-dimensional text based on machine learning;application of time series analysis in predicting industrial data security events;and application of artificial intelligence and decision support system in green supply chain management.

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Application of deep learning technology in intelligent decision support and risk warning of information management system 5

Application of deep learning technology in intelligent decis...

引用

5th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Song, Ge WuXi Vocational Institute of Commerce JiangSu WuXi214153 China

ISBN: (纸本)9798331539818

the purpose of this study is to explore the design of intelligent decision support and risk warning system of information management system based on deep learning technology. By using the methods of deep learning, data mining, risk assessment and intelligent decision support, an innovative deep learning technology is proposed in the application of intelligent decision support and risk early warning of information management system, aiming at improving the intelligent decision ability and risk early warning ability of information management system. the deep learning method is used to train the deep learning model to learn a large amount of data in order to realize intelligent decision support and risk warning for the information management system. data mining method is used to find potential risk factors and decision support information by analyzing the correlation between data. the risk assessment method is used to assess and predict the potential risks facing the system. Finally, intelligent decision support method is used to provide personalized decision support and risk warning information for users. © 2024 IEEE.

关键词： Adversarial machine learning

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A Literature Review on Predictive data Analytics and learning Models in Stock Market Trend Analysis 5th

A Literature Review on Predictive Data Analytics and Learnin...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Kumar, Chanda Raj Manikandan, S. Department of Computer Science and Engineering Annamalai University Tamilnadu Chidambaram India

ISBN: (纸本)9789819780426

In today’s volatile financial landscape, where the stock market presents opportunities and risks, predictive data analytics and learning models have become indispensable tools for analyzing and predicting market trends. this study highlights the significant impact of these advanced tools in deciphering complex market fluctuations. By leveraging machine learning models, the research seeks to develop flexible strategies that utilize pattern recognition and statistical analysis to anticipate potential trends. Predictive data analytics is at the core of this research, serving as a fundamental pillar for extracting actionable insights and making data-driven decisions from vast datasets. the integration of Artificial Intelligence (AI) enhances this process, allowing for a deeper exploration of market complexities, uncovering hidden correlations, and identifying predictive markers that traditional methods might miss. this paper provides a thorough analysis and critical review of previous approaches and methods to make stock market predictions and trends using data analytics and learning techniques. the study examines the strengths and weaknesses of different approaches and discusses potential challenges associated with predicting stock portfolios and stock trend analysis through data analytics and learning techniques. the paper concludes by providing an analysis and outlook on the future direction of Stock Market Trends using data analytics and learning techniques. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive analytics

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A Framework for Monitoring Stability of Tailings Dams in Real-time Using Digital Twin Simulation and machine learning 5

A Framework for Monitoring Stability of Tailings Dams in Rea...

引用

5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Mwanza, Joseph Mashumba, Peter Telukdarie, Arnesh Univ Johannesburg Johannesburg Business Sch 69 Kingsway Ave ZA-2092 Johannesburg South Africa Natl Univ Sci & Technol Dept Appl Phys Bulawayo Zimbabwe

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. this paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. the framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML -based approaches may have advantages over traditional monitoring techniques and other AI -based methods. the paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. the paper serves as a basis for model development and future research.

关键词： Geotechnical Engineering Stability Artificial Intelligence Tailings Dam

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Predictive Analysis of Plant Macronutrient Consumption pattern Using machine learning 5

Predictive Analysis of Plant Macronutrient Consumption Patte...

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5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Nayana, V. Bhan, Madhu Sowmya, B.J. Ramiah Institute of Technology Department of Mca Karnataka Bangalore India Ramaiah Intitule of Technology Department of Mca Karnataka Bangalore India Ramaiah Intitule of Technology Department of Ai and Ds Karnataka Bangalore India

ISBN: (纸本)9798331528539

this study incorporates many machine learning (ML) methods to predict and analyze the nutrient intake as it explores the macronutrient consumption patterns of different plants in few different states of India. Important macronutrient factors for a wide variety of plant species were recorded using data gathered from a variety of agricultural online sources. Creating precise prediction models to improve nutrition management strategies was the main goal. K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Classification (SVC) were the three machine learning techniques used. Based on how well these algorithms predicted intake of macronutrients, their performance was assessed. the outcomes showed that, at 95.87%, Naive Bayes had the best accuracy, followed by KNN at 89.65% and SVC at 83.85%. Our results demonstrate that Naive Bayes is the best appropriate machine learning approach for this dataset analysis and show its promise for efficient nutrition prediction. Finally, by promoting sustainable farming practices, this research helps optimise nutrient management systems © 2024 IEEE.

关键词： KNN machine learning Macro Nutrients Naïve Bayes SVC

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On the Possibility of Developing a System for Predicting Students Academic Performance Using machine learning Methods 5

On the Possibility of Developing a System for Predicting Stu...

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5th international conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Tsygankov, Y.A. Solovev, A.Iu. Sergeychik, M.S. NUST MISIS Automated and Information Control Systems Department Stary Oskol Russia

ISBN: (纸本)9798350309126

this article considers the possibility of developing data mining techniques and applying machine learning models to identify hidden dependencies that uniquely characterize different categories of students in order to predict their academic performance. the application of machine learning in this subject area is conditioned by their ability to analyze large amounts of data, obtaining, as a result, generalizing models capable of classifying the objects of study and predicting the required indicators. the development of such a system will allow, based on the analysis of a priori data and current indicators, to predict the expected performance of students in disciplines of different orientation, providing information on the categories of performance in a visual form. © 2023 IEEE.

关键词： Students

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An Empirical Literature Review on Multi Class data Stream learning with Class Imbalance Problem in Real World data Sources 5

An Empirical Literature Review on Multi Class Data Stream Le...

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5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Muley, Rajesh Reddy Battula, Bhanu Prakash Sanaboina, Chandra Sekhar Jawaharlal Nehru Technological University Department of Computer Science and Engineering Andhra Pradesh Kakinada India Kkr & Ksr Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India University College of Engineering Kakinada Jawaharlal Nehru Technological University Department of Computer Science and Engineering Andhra Pradesh Kakinada India

ISBN: (纸本)9798331522100

data mining and knowledge discovery is one of the cutting-edge fields in science and technology for identifying pressing issues in real world. the amount of data collected has increased due to the quick development of technology and the low cost of internet access. One of the difficult problems is analyzing the massive volume of data that comes in from various data streams. machine learning research has primarily concentrated on binary classification data problems with high unbalanced data from the earliest literatures published on till recently. When it comes to processing data streams effectively, research on highly unbalanced multi-class data is still mostly underdeveloped. this study focuses on reviews of the models or methods for dealing with extremely unbalanced multi-class data, as well as their advantages and disadvantages and related fields. the objectives of this article are to: comprehend the trend of highly imbalanced multi-class data through examination of related literatures;to analyze the traditional and contemporary approaches to handling highly imbalanced multi-class data;and develop a framework for highly imbalanced multi-class data. After doing the chosen highly imbalanced multi-class dataset analysis and adapting it to the most recent machine learning algorithms or approaches, there will be talks on unresolved issues and the way highly imbalanced multi-class data should go in the future. © 2025 IEEE.

关键词： Labeled data

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Multifaceted Discrete Emotion recognition from EEG Physiological Signals via machine learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. the Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. this paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. this research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. the exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion recognition

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Identification of Atrial Septal Defects in Congenital Heart Disease Patients Using Deep learning-based Chest Radiographs 5

Identification of Atrial Septal Defects in Congenital Heart ...

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5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Shao, Hongmin Ling, Yunfei Pang, Jiyun Zhao, Qijun Qian, Yongjun College of Computer Science Sichuan University Chengdu China Sichuan University Department of Cardiovascular Surgery West China Hospital Chengdu China West China School of Medicine Department of Cardiovascular Surgery West China Hospital Sichuan University Chengdu China

ISBN: (纸本)9798350355925

Objective: Chest X-rays are a non-invasive and cost-effective diagnostic method;however, they are not commonly used for diagnosing atrial septal defects (ASD). Typically, the diagnosis of ASD requires more expensive tests. We have discovered that an enlarged heart shadow can be an initial indicator of ASD. We have developed and validated a model using deep learning techniques to determine whether a patient has an atrial septal defect based on their chest X-ray. Method: this study involved chest X-ray examinations of 1459 patients diagnosed with atrial septal defects at West China Hospital of Sichuan University from January 8, 2009, to June 25, 2022, as well as 1580 individuals who underwent general health check-ups at the West China Hospital's Examination Center from July 21, 2022, to August 13, 2022. Results: this study included chest X-ray examinations of 3,039 patients (44.3% male;median age 33 years with an interquartile range of 28-42 years). For the classification task, the deep learning model achieved an accuracy of 0.96, sensitivity of 0.97, specificity of 0.96, and an AUC (Area Under the Curve) of 0.9922. Conclusion: Current research indicates that chest X-ray analysis based on deep learning can determine the presence of atrial septal defects, a congenital heart condition. these findings suggest that deep learning methods can offer an objective diagnosis for atrial septal defects in routine health screenings, offering a low-cost and non-invasive approach. © 2024 IEEE.

关键词： Deep learning

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mining and Analyzing Statistical Information from Untranscribed Form Images 18th

Mining and Analyzing Statistical Information from Untranscri...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Andres, Jose Toselli, Alejandro H. Vidal, Enrique Univ Politecn Valencia PRHLT Res Ctr Valencia Spain TranSkriptorium AI Valencia Spain

ISBN: (纸本)9783031705489;9783031705496

Large amounts of historical structured documents are available in archives around the world. Here, we are interested in automatically extracting statistical information of selected attributes contained in these documents. To this end, we propose a pipeline relying on probabilistic indexing and machine learning to mine and analyze relevant information contained in historical form images. these ideas are assessed on a large collection of images containing almost 295 000 forms with results showing the adequateness of the proposed methods to perform the bigdata analytic task considered.

关键词： Probabilistic Indexing Big data Structured Documents Handwritten Text recognition

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