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An I4.0 data intensive platform suitable for the deployment of machine learning models: a predictive maintenance service case study 3

An I4.0 data intensive platform suitable for the deployment ...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Dinten Herrero, Ricardo Zorrilla, Marta Univ Cantabria Grp Ingn Software & Tiempo Real Avda Los Castros 48 Santander 39005 Spain

The Artificial Intelligence is one of the key enablers of the Industry 4.0. The building of learning models as well as their deployment in environments where the rate of data generation is high and their analysis must meet real time requirements lead to the need of selecting a big data platform suitable for this purpose. The heterogeneous and distributed nature of I4.0 environments where data becomes highly relevant requires the use of a data centric, distributed and scalable platform where the different applications are deployed as services. In this paper we present an I4.0 digital platform based on RAI4.0 reference architecture on which a predictive maintenance service has been built and deployed in Amazon Web Service cloud. Different strategies to build the predictor are described as well as the stages carried out for its construction. Finally, the predictor built with k-nearest algorithm is chosen because it is the fastest in producing an answer and its accuracy of 99.87% is quite close to the best model for our case study. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Big data platform data Stream mining Predictive Maintenance

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A Reactive Approach for High-Accuracy and data-Driven Customer Behaviour Analysis and Prediction 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Sirohi, Priyank Singhal, Niraj Rizvi, Syed Vilayat Ali Kumar, Pradeep Meerut Meerut India Sir Chhotu Ram Institute of Engineering and Technology Chaudhary Charan Singh University Meerut India Department of Computer Science and Engineering JSS Academy of Technical Education Uttar Pradesh Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Understanding the differences of high accuracy and data driven customer behaviour is one of the essential components of success in the e-commerce industry because customer behaviour varies from person to person depending on their segmentations. Owners will be able to recognise their desired customers by comprehending customer behaviour. They will be able to better target their marketing efforts, boost sales, and control costs. Applications of artificial intelligence in this area have a significant positive effect on operations. The most attributes that can influence a customer’s behaviour can be found and predicted by business partners using a data mining prediction model. The current study therefore identifies better ways to improve business decision-making through the use of AI and data analytics, which will aid in comprehending customer behaviour. This research, brings forth ideas and concepts to make models more data driven and more accurate through a more reactive approach on model designing, through clever infrastructural designing. This paper proves that the proposed method can assure users to get better results through reactive-data methodologies. This paper proves it practically by taking a simple classification problem, in contract to customer-behaviour prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data accuracy

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Advancements in Prosthetic Hand Control: An IoT-Convolutional Neural Network Fusion for Gesture recognition 3

Advancements in Prosthetic Hand Control: An IoT-Convolutiona...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dhanalakshmi, S. Hemalatha, R. Arul Mary Rexy, V. Kanthimathi, T. Juliet, P. Sudha Thamizhamuthu, R. Sri Muthukumaran Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Shree Sathyam College of Engineering and Technology Department of Electronics and Communication Engineering Salem Tamil Nadu Sankari India Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh Guntur India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350319125

This research presents a novel method for enhancing prosthetic hand control by integrating Convolutional Neural Network (CNN)-based gesture recognition with IoT hardware and software. Prosthetic devices are very helpful for those who have lost limbs, but limitations in functionality, such as poor fine motor control, might limit their use. It proposes a unique solution to this issue by using IoT sensors to capture user hand motions and a CNN to interpret these movements in real time. Sensors attached to the user's prosthetic limb or other relevant locations provide input for the IoT-based system. By putting this information into a CNN, the device may learn to detect and classify distinct hand gestures, allowing for more complex control of the prosthetic hand. By bridging the gap between human intent and prosthetic hand motion, the control mechanism becomes more intuitive and responsive. Early evaluations show promise for the CNN's utility, with tests indicating it can accurately detect and react to human motions. It also uses assistive technology by proposing a method for realistic and effective prosthetic hand control. This method has the potential to greatly improve the lives of those who have lost a limb by combining IoT with deep learning. © 2023 IEEE.

关键词： Assistive technology Limb loss machine learning Real-time control Sensor data

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The Privacy Computing for Digital Economy Development under New Technology Based on Spatial-Temporal Information Matrix, Especially among RCEP Nations 3

The Privacy Computing for Digital Economy Development under ...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Liu, Haitao Zheng, Shenjun Hong, Yong Cao, Yujuan Lu, Yun Tian, Tian Guangxi Artificial Intelligence and Big Data Applying Institute Nanning530201 China Hangzhou ChinaOly Technology Co. Ltd. Hangzhou310015 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Guangxi Academy of Social Sciences Nanning530201 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the coming of intelligent era, the wide using of new generation information technology, such as Internet on Things, AI, Big data and Cloud computing, digital economic development is more and more prosperous for every economic entity, specially, for some emerging economic entities, such as RCEP nations. However, the problem of objective evaluation of ability of digital economic development of different nations and dependence of digital economic development among each nation are very hard but necessary under not leaking digital economic data of all parts. For solving this problem, an improvement Meta learning approach was proposed creatively which constructed temporal dimension factor and spatial dimension factor based on spatial-temporal information matrix method. It could make knowledge transfer from source network to target network according to the orthogonal rule between temporal dimension factor and spatial dimension factor. 3 different type and smart test ways were designed in order to validate the performance of the approach of this paper comprehensively. Digital economic development data of RECP nations were collected and employed for testing. The amount and accuracy of knowledge transfer from different source network to various target network were validated one by one. The results not only validate the effectiveness of improving Meta learning approach of this article but also reveal that the diversity of digital economy development among RECP nations but impacting tightly each other. Moreover, there also have relative contribution on opening knowledge transfer from source network to target network under privacy data not be leaked. This content would be researched at more complex industrial data forward. © 2022 IEEE.

关键词： Economic and social effects

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Quantum Computing and machine learning Algorithms-A Review 3

Quantum Computing and Machine Learning Algorithms-A Review

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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

作者： Manan, Iram Rehman, Faisal Sharif, Hanan Riaz, Naveed Atif, Muhammad Aqeel, Muhammad Khawaja Fareed University of Engineering and Information Technology Department of Information Technology Rahim Yar Khan Pakistan Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan NUST Department of Computer Science and Information Technology Islamabad Pakistan

ISBN: (纸本)9781665461412

The goal of machine learning (ML) is to develop models that automatically learn from the past without being explicitly programmed. ML has numerous applications, such as pattern recognition, forecasting upcoming trends, and judgement. It can handle massive amounts of huge vectors and tensors representing multidimensional data. To handle these processes on traditional computers, enormous time and computational resources are required. Unlike traditional computers, which use binary bits to compute, quantum computers (Q.C.) use qubits, which can simultaneously hold 0 and 1 combinations through superposition and entanglement. This makes *** an excellent choice for implementing ML algorithms because they are skilled at handling and post-processing large tensors. Although many of the models used for ML on quantum computers are based on concepts from their classical computing counterparts, the use of quantum computers has made them the better of the two. This paper assess the speed and complexity benefits of using quantum computers and gives a general overview of the state of knowledge regarding the use of ML on Q.C. © 2022 IEEE.

关键词： Quantum entanglement

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AI based Security in computer vision and machine learning algorithms for hand sign recognition 3

AI based Security in computer vision and machine learning al...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Thorat, Nilesh N. Kulal, Nilesh M. Baburao, Khedekar Vilas Hirve, Sumit Arun Kolekar, Sachin Sangore, Rohidas Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350354348

In order to facilitate intuitive control and communication across a variety of areas, hand gesture recognition is an essential component of human-computer interaction systems. In this paper, a real-time hand gesture recognition system developed with the MediaPipe library, and convolutional neural networks (CNNs) is presented. The system utilizes a CNN model provided by MediaPipe for accurate detection of hand landmarks in camera feed images. Through extensive preprocessing and feature extraction, hand gestures, including finger gestures and palm orientations, are classified in real-time. The system offers dynamic selection between two modes: logging key points and logging point history, facilitating data collection for training or analysis *** features of the system include bounding rectangle calculation, landmark visualization, and gesture classification, providing users with immediate feedback on detected hand gestures. The modular design and parameter configurability make the system versatile for a wide range of applications, from interactive interfaces to sign language interpretation. The outcomes of the experiments showcase the practicality and efficacy of the suggested method, indicating that it has the potential to improve the experiences of people interacting with computers. Incorporating AI security measures, the system can significantly enhance security protocols in various applications. For instance, it can be used for gesture-based authentication, where specific hand gestures act as passwords to grant access to secure systems. This method can be further secured by combining it with traditional authentication techniques, creating a robust multi-factor authentication framework. Additionally, the real-time detection capability allows for immediate response to unauthorized access attempts, triggering alerts or lockdown procedures when unfamiliar gestures are detected. © 2024 IEEE.

关键词： Convolutional neural networks

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English Teaching Evaluation Algorithm and Modeling based on machine learning and data mining Technology

English Teaching Evaluation Algorithm and Modeling based on ...

引用

data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Wei Sun Heilongjiang International University Harbin China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

Traditional English teaching evaluation has problems such as subjectivity, limitations, quantitative bias, and lacks feedback and support. The evaluation algorithm based on data analysis technology provides an accurate, comprehensive, and personalized evaluation method for teachers and students, which can understand their learning needs and ability levels by collecting and analyzing students' learning data. This study adopts a method based on data analysis technology to develop English teaching evaluation algorithms. Firstly, this paper collected students' learning data, including information on language expression, learning behavior, and academic performance. Then, machine learning and data mining techniques are used to analyze and model these data to predict students' English proficiency level and learning needs. The research results indicate that the F1 value range of the algorithm in this paper is 0.85-0.94, and the English teaching evaluation algorithm based on data analysis technology has achieved significant results in evaluating students' English proficiency.

关键词： machine learning algorithms Accuracy data analysis Education machine learning learning (artificial intelligence) Predictive models Prediction algorithms data models data mining

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machine learning Techniques for Pose Based Human Activity recognition

Machine Learning Techniques for Pose Based Human Activity Re...

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Disruptive Technologies (ICDT), international conference on

作者： Ravindra Singh Koranga Arun Kumar Rai Lalit Mohan Shubham Singh Pritika Priya School of Computer Science and Engineering Galgotias University Greater Noida India CSE Birla Institute of Applied Sciences Bhimtal India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Activity recognition is the task of identifying human actions and physical activities. It can have applications in broad areas including medical, sports and fitness, healthcare, behaviour analysis and security. Activity recognition is a widely sought after area of research in both machine learning (ML) and smart devices. The activity recognition plays an important role in the field of machine learning. There are several machine learning techniques which can be applied for pose based activity recognition. These techniques involve training machine learning models for the dataset. The dataset is obtained by using OpenPose framework. This framework provides keypoints from an input image. These keypoints represent the raw data determining the pose of a human. The machine learning model can predict the pose after training on the dataset. models

关键词： Training Pose estimation machine learning Medical services Predictive models Security Object recognition Human activity recognition Smart devices Sports

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A Method to Detect the Onsets and Ends of Paroxysmal Atrial Fibrillation Episodes Based on Sliding Window and Coding 3

A Method to Detect the Onsets and Ends of Paroxysmal Atrial ...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Jia, Haijun Liu, Sen Wang, Ya'nan Wang, Daomiao Yang, Cuiwei Fudan University Center for Biomedical Engineering School of Information Sci. & Tech. Shanghai China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide and it significantly impacts patients' morbidity, mortality and quality of life. AF is usually divided into paroxysmal AF (AFp), persistent AF (AFf), and permanent AF. It's vital to detect the onsets and ends of AFp episodes accurately for treatment of patients. However, the detection of the onsets and ends of AFp episodes is not concerned in existing detection algorithms for AFp. In order to solve the problem, a method to detect the onsets and ends of AFp episodes based on sliding window and coding is proposed in this paper. Before using the method to detect the onsets and ends, it's required to train the classifier which is used to detect whether AF attacks in windows with fixed-number of RR intervals. The method based on sliding window and coding offers a systematic and effective method to detect which heart beat is the onset or end of AFp episodes. The classifier achieves accuracy of 0.968, sensitivity of 0.975 and specificity of 0.960 respectively. The proposed method achieves recall rate of 0.818 and score U of 1.746. The results show that the method based on sliding window and coding can reduce the impact of erroneous prediction offered by the classifier. In addition, real-time detection is achieved because both the trained classifier and proposed method have low time complexity. © 2022 IEEE.

关键词： machine learning

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PR-GB-NET: Probabilistic Graph Bridging Network with data Resampling for Scene Graph Generation 3rd

PR-GB-NET: Probabilistic Graph Bridging Network with Data Re...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Lu, Hongyu Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

Scene graphs are structured and powerful representations of images that extract semantic information from images to enhance visual understanding and image reasoning. To improve the utilization of predicate prior knowledge in scene graphs, we use a probability mass function (PMF) for the predicate, given a subject-object pair during the initialization of the graph neural network. The PMFs are updated through a gating function during message passing of the graph neural network. To mitigate the effects of long-tailed distributions in datasets, we employ a data resampling strategy. During each training iteration, we try to balance the head, body and tail predicates as much as possible. Experiments show that the proposed methods achieve competitive performance compared to previous methods on dataset we created for home environment, demonstrating their effectiveness and generality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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