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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Camera- and Sensor Data-Based Process Observation in Liquid Steel Production 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Thumfart, Maria Roessler, Roman Gruber, Christine Wachlmayr, Johann K1 MET GmbH A-4020 Linz Austria Voestalpine GmbH A-4020 Linz Austria

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

The Ruhrstahl-Heraeus (RH) degassing plant is of vital importance for ultra-clean steel production, due to its effective degassing and homogenizing, but its vacuum-based nature and the high temperatures of liquid steel renders the process rather inaccessible to direct observations. We developed a camera-based image analysis solution, which in combination with enhanced sensor data evaluation (e.g., chamber pressure, offgas composition or O-2-injection) determines the status of the process and detects irregularities and malfunctions of the plant. The proposed research helps to deeper understand the RH process in steelmaking and provides the basis for a future on-line decision support system for operation.

关键词： image analysis data analysis secondary metallurgy

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Patterns in Human Activity Recognition Through Machine Learning Analysis Towards 6G Applications

Patterns in Human Activity Recognition Through Machine Learn...

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Intelligent systems Conference

作者： Mashudi, Nurul Amirah Ahmad, Norulhusna Izhar, Mohd Azri Mohd Kaidi, Hazilah Md Mohamed, Norliza Noor, Norliza Mohd Univ Teknol Malaysia Fac Artificial Intelligence Kuala Lumpur Malaysia

ISBN: (纸本)9783031664274;9783031664281

Human activity recognition uses smartphone sensors to identify individual activities. The ability of current sensor-based human activity detection, such as smartphone-based activity recognition, to reliably identify physical activities has declined. Sixth-generation (6G) systems aim to integrate communication and sensing seamlessly. Human activity recognition is a sensing task with many applications in smart homes, emergency systems, and games. It can replace dedicated sensors by monitoring communication signals. Human activity recognition is crucial in several domains and practical applications. Thus, there is a high demand for a support system that can offer information on a user's present activity by concealing the complex activity recognition. This paper proposed several machine learning methods, such as decision trees, random forests, logistic regression, and K-nearest neighbours, to classify human activity recognition. The proposed methods evaluate the performance of human activity recognition using the WISDM dataset based on the acceleration and angular velocity of a smartphone and smartwatch. The proposed methods accurately identified 18 activities, including walking, jogging, stairs, sitting, standing, typing, brushing teeth, eating soup, eating chips, eating pasta, drinking from a cup, eating sandwich, kicking a soccer ball, playing catch tennis ball, dribbling basketball, writing, clapping, and folding clothes. A comparative analysis was conducted for each activity to determine the precision, recall, and F1 scores. The experimental results showed that K-NN achieved better accuracy at 76.48%, compared to a decision tree, random forest, and logistic regression. A comparative analysis is also performed with state-of-the-art methods.

关键词： Human activity recognition Machine learning Smartphone Smartwatch Accelerometer 6G

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Web Cloud Services with Distribution Modules (SaaS) and Java 11th

Web Cloud Services with Distribution Modules (SaaS) and Java

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Reyes, Nelson Salgado Roa, Henry N. Pontificia Univ Catolica Ecuador 12 Octubre Quito 1076 Ecuador

ISBN: (纸本)9783031456473;9783031456480

Currently, companies seek to automate their processes. It means the ability of technology to perform many of the tasks that humans perform daily in their workplace. This automatic function also controls, modifies, and clarifies the operational nature of the work and activities. This research presents a model for software development, which allows the rapid construction of corporate applications in the cloud (SaaS), using Java EE 11 technology, and based on agile development methodologies. For the design of this model, we consider appropriate processes and techniques on the software development stages. Particularly, on the analysis, construction, test, and deployment stages. For the development of this research, we studied several agile methodologies, but we only considered those that have the largest number of bibliographic sources such as SCRUM, XP (Extreme Programming), and other methodologies derived from Lean Development. Additionally, in each methodology, we analyze the principle of the agile manifesto to verify to what extent they comply with it. We also review the best practices and applicable design patterns in each methodology. In the end, we find common points between the different methodologies, which promote standardization in conjunction with the Java EE platform. As result, we present a solution based on three pillars for rapid development: (a) the automatic generation of initial code, (b) the use of predefined templates, and (c) the execution of automatic scripts. These three pillars are the basis for rapid development, with which the company may be able to support the development of business software in the cloud using Java EE technology, and with this, optimize costs, time, and resources.

关键词： Cloud Computing TIC Software as a Service (SaaS)

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SenAgriPreci: Improving Precision Agricultural Yields Through a Crop Recommender System

SenAgriPreci: Improving Precision Agricultural Yields Throug...

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Intelligent systems Conference

作者： Balde, Fatoumata Asabere, Nana Yaw Diouf, Amie Gueye, Bamba Univ Alioune Diop Bambey Bambey Senegal Accra Tech Univ Dept Comp Sci Accra Ghana Univ Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031664274;9783031664281

Agriculture represents an important pillar in the global economy. It plays a preponderant role in social life. Africa alone represents 30% of the earth's arable land, or 30 million km2 of usable agricultural land. This sector employs more than 65% of the active Senegalese population. However, inappropriate weather or climate change have a deep effect on agricultural production or crop yields. Consequently, the conformance of precision agriculture requires effective decisions by stakeholders in the agricultural field. This can be achieved through an innovative recommender system. A recommender system filters data using different algorithms and suggests the most relevant items to users in a particular domain using relevant entities. To address the above issue, we propose a recommender system for farmers in Senegal called SENAgriPreci which enables users to make good and precise agricultural decisions using various parameters (Example: pH, crop type, soil type, temperature etc.). Using relevant datasets, namely: SAED and CRNA, we conducted benchmarking experiments to validate our proposed SENAgriPreci recommendation methodology. Experimental results in terms of Root Mean Square Error (RSME) validates that of our proposed recommendation method is more favorable and outperforms other related and contemporary recommendation methods in relation to accuracy and precise crop recommendations.

关键词： IoT Machine learning Agriculture Crop recommender system

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A Survey on the Usage of Centrality Measures in Error Prediction 1

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Computing Conference

作者： Marjai, Peter Kiss, Attila Eotvos Lorand Univ Dept Informat Syst Budapest Hungary J Selye Univ Komarno Slovakia

ISBN: (数字)9783031622731

ISBN: (纸本)9783031622755;9783031622731;9783031622724

The advent of extensive systems has led to a swift escalation in the volume of generated logs. Virtually every software application generates these files, which encompass runtime data of the software, like significant occurrences or potentially concerning behaviors such as errors. Log files serve as a valuable reservoir of information for comprehending and overseeing system operations, enabling the anticipation of forthcoming irregularities. In recent times, several approaches have emerged to address this objective. These include both supervised and unsupervised models, alongside deep learning techniques. Since error prediction is critical in fields like reducing downtime and performance optimization, root cause analysis and security monitoring, our goal is to examine new correlations between the lines of the log file that can help predict upcoming errors and aid the software operators in the above-mentioned tasks. Because of this, in this paper, we investigate the prediction capacity of a new heuristic method that builds a precedence graph based on the log templates that follow each other. The templates also called events are acquired with the use of a template miner. These events are then used to create the precedence graph. After the creation of the graph, different centrality measures are employed to identify the errors. We conclude that there is a connection between a node's anomaly detection ability and it's well-connectedness, while there is no connection between the node's position on shortest paths and it's ability to predict anomalous behaviors.

关键词： Template miner Anomaly detection Centrality measure

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Analyzing University Dropout Rates Using Bayesian Methods: A Case Study in University Level in Mexico 12th

Analyzing University Dropout Rates Using Bayesian Methods: A...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Ramon de la Cruz, Jose Hernandez-Romero, Diana L. Arguijo, Pedro Sandoval Herazo, Luis Carlos Angel Melendez-Armenta, Roberto Inst Tecnol Super Misantla Tecnol Nacl Mexico Div Grad Studies & Res Km 1-8 Carretera Loma Cojolite Misantla 93821 Mexico Inst Tecnol Misantla Tecnol Nacl Mexico Div Grad Studies & Res Wetlands & Environm Sustainabil Lab Km 1-8 Carretera Loma Cojolite Veracruz 93821 Misantla Mexico

ISBN: (纸本)9783031602238;9783031602245

In Mexico's higher education, student dropout issue is critical, influenced by multiple factors. The Instituto Tecnologico Superior de Comalcalco (ITSC) recorded 3,499 students across eight engineering and two bachelor's programs in 2018, experiencing a significant dropout rate of 9%. This article presents aweb platform using Bayesian methods to analyze and interpret dropout data from ITSC. Specifically, the Computer systems Engineering program, most impacted by dropouts, was analyzed to identify underlying patterns and factors. Utilizing Naive Bayes models, the platform categorizes students and assesses dropout probabilities, offering insights for data-driven strategies to mitigate this challenge in higher education.

关键词： University Dropout Bayesian Approach Decision Trees

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Diffusion-Based Convolutional Recurrent Neural Network for Improving Sound Event Detection 1

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9th International Congress on Information and Communication Technology (ICICT)

作者： Al Dabel, Maryam M. Univ Hafr Al Batin Coll Comp Sci & Engn Dept Comp Sci & Engn Hafar al Batin Saudi Arabia

ISBN: (数字)9789819733057

ISBN: (纸本)9789819733040;9789819733057

The latent diffusion model and convolutional recurrent neural network in this article combine benefits of diverse networks to recognize sound events. The system was trained using large datasets and then used transfer learning to adapt to the intended job to overcome data shortages. The latent diffusion model compresses the audio mel-spectrogram into a discrete representation. A convolutional neural network (CNN) is then coupled as the RNN front-end to build a feature map. The feature map is then used to forecast temporal-spectral attention maps via an attention module. For adaptive feature refinement, the input spectrogram is multiplied by the created attention maps. Finally, trainable scalar weights combine back-end RNN fine-tuned features. We found that the recommended systems outperform the baseline and state-of-the-art utilizing the DCASE 2016 and DCASE 2017 challenges. Experiments on DCASE 2016 revealed a peak in system performance with F1 of 66.2% and ER of 0.42. DCASE 2017 dataset findings show F1 and ER at 68.1% and 0.40, respectively.

关键词： Sound event detection Latent diffusion model Spectrogram Deep neural network

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An Evolutionary Deep Learning for Respiratory Sounds Analysis: A Survey 2nd

An Evolutionary Deep Learning for Respiratory Sounds Analysi...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Albakaa, Zainab H. Alb-Salih, Alaa Taima Univ Kufa Fac Educ Kufa Najaf Governora Iraq Univ Al Furat Al Awsat Najaf Najaf Governora Iraq Univ Al Qadisiyah Coll Comp Sci & Informat Technol Al Diwaniyah Al Qadisiyyah G Iraq

ISBN: (纸本)9783031628801;9783031628818

The use of lung sounds in conjunctionwith respiratory auscultation can aid in the diagnosis of abnormalities. There is the possibility for highly developed AI combined with deep learning to automate the study of sound. A technique that utilizes neural networks and genetic algorithms to classify lung sounds is provided here. The lung sounds of people suffering from a variety of pulmonary diseases as well as healthy subjects were recorded via the chest wall. A CNN-based method for categorizing respiration sounds is proposed here. This method makes use of current breakthroughs in the field of picture classification. The Mel Frequency Cepstral Coefficients (MFCCs) play a crucial role in converting audio signals into visual representations. The level of accuracy achievedwhile classifying respiratory sounds (Normal, Crackles, Wheezes, Both) is far higher than was anticipated. When it comes to determining lung sounds, ML and DL, and especially EA-optimized models, are superior to conventionalmethods such as chestCT scanning in terms of effectiveness. In this study, EA is used to augment both ML and DL to better detect lung sounds.

关键词： Lung Sound Machine Learning (ML) Deep Learning (DL) Evolutionary Algorithms (EA) Evolutionary Deep Learning (EDL)

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Biologicalisation in Learning Factories - Learning Concept and Lab-Demonstrator for Bio-Fluids for Metal Processing 14th

Biologicalisation in Learning Factories - Learning Concept a...

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14th International Conference on Learning Factories (CLF)

作者： Hagen, J. Schoemig, O. Arafat, R. Juraschek, M. Brand, J. Herrmann, C. Tech Univ Carolo Wilhelmina Braunschweig Inst Machine Tools & Prod Technol Chair Sustainable Mfg & Life Cycle Engn Langer Kamp 19B D-38106 Braunschweig Germany Fraunhofer Inst Surface Engn & Thin Films IST Riedenkamp 2 D-38108 Braunschweig Germany

ISBN: (纸本)9783031653995;9783031654008

New technologies and concepts are continuously introduced in production engineering to get closer to the goal of sustainability. However, the skill development in engineering is often connected to a delay in taking these developments into account. It is therefore important to recognize emerging trends and technologies at an early stage and to include them into engineering education. Currently, one emerging topic is the biologicalisation of production processes. This involves the integration of biological components into industrial manufacturing systems. This paper addresses the question of how engineers can be trained in this topic and how competencies can be built up. A particular field, in which biologicalisation can be applied, is the development and testing of new cutting fluids and lubricants. In this concept, students can be introduced to the technology field of biologicalisation in an application-oriented way. For that purpose, a biologicalisation framework for production engineering is presented together with a practical, lab-scale learning station. This allows students to develop their own bio-integrated metal working fluid (MWF) concept and test its functionality as well as long-term stability. Real-time data acquisition enables the students to identify and implement improvement measures and experience the challenges as well as potentials of biologicalisation.

关键词： Learning Factories Biologicalisation Engineering Education Metal Working Fluids

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Empirical Evaluation of Hybrid Time Series Forecasting Method Between ARIMA and RBFNN Under Parallel Model 7th

Empirical Evaluation of Hybrid Time Series Forecasting Metho...

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7th International Conference on Green Technology and Sustainable Development

作者： Thanh Son Nguyen Chi Cong Pham Tri Quoc Nguyen Dang Minh Duc Nguyen HCMC Univ Technol & Educ Ho Chi Minh City Vietnam Ho Chi Minh City Open Univ Ho Chi Minh City Vietnam Int Univ VNU HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9783031761966;9783031761973

Commonly, a time series is a series of values observed over time. This type of data is used in almost every field ranging from science, engineering, business, finance, economics, medicine to government. The time series prediction problem is the problem of using a model that analyzes past data to predict future values. Accurately forecast time series data is crucial to support decision making in many areas of life. Therefore, improving the accuracy of this problem is a challenging task for researchers in this field. Many traditional time series models or artificial neural network models have been used for time series prediction. Among them, Auto Regressive Integrated Moving Average (ARIMA) model and Radial Basis Function Neural Network (RBFNN) has been widely studied and used for forecasting in specific fields such as finance, stock prices, air pollutants, etc. In this study, we introduce the method of combining two models ARIMA and RBFNN in parallel. This hybrid method can combine the advantages of ARIMA and RBFNN models to create a more effective model for the problem of forecasting time series data. The parallel hybrid method between ARIMA and RBFNN is experimentally evaluated on on real time series data from different fields. The evaluation is based on two criteria: execution time and accuracy. Experimental results demonstrate the power of the proposed method on real-time series datasets in terms of accuracy when compared with each model used individually, but the execution time is a bit longer.

关键词： Time series prediction ARIMA RBFNN

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