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2023 6th international conference on Energy Conservation and Efficiency, ICECE 2023 - proceedings

2023 6th International Conference on Energy Conservation and...

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6th international conference on Energy Conservation and Efficiency, ICECE 2023

ISBN: (纸本)9798350332193

the proceedings contain 26 papers. the topics discussed include: air quality of Karachi and Lahore: how does the energy industry of Pakistan affect it?;smart energy conservation in data centers using machine learning based software-defined networking;optimal dispatch of distributed generators in multiple microgrids using hybrid PSO-GWO;determination of thermal properties of eutectic phase change materials (EPCM) using the t-history method;multi-agent and reinforcement learning schemes for demand response estimation in distributed microgrids;analysis and design of the subnominal operation of voltage-source parallel resonant class e frequency multiplier;structural optimization of interuniversity campus with system analysis of the territorial dispersion of residential and educational facilities;and a case study on robust power/energy balancing driven cost optimization for sizing energy storage, power generators and consumers.

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Research on Intelligent Planning Algorithms Based on machine learning Optimization 4

Research on Intelligent Planning Algorithms Based on Machine...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Ding, Xinning Zheng, Boyu Beijing Foreign Studies University Beijing China

ISBN: (纸本)9798350342710

this paper solve the optimal cost problem of production planning model, construct a model based on linear programming and multi-objective planning, target the optimal cost in a one-week inspection interval, implement interactive linear and generic optimization solutions with the help of Lingo, determine the objective function as the total cost, construct the cost function, and establish the constraint function relationship in conjunction with reality. Firstly, it is concluded that the production preparation cost is lower than the relative inventory cost for WPCR and all (sub)components, and replacing the production preparation cost with the inventory cost is not an optimal solution. then, based on the multi-objective planning model, the model is further optimized on the basis of dynamic planning to build an integer planning model. the results show that after further optimization of the model, the total cost is reduced by 16.06% based on the dynamic planning model. In this paper, a new hair production combination algorithm is developed, which can provide a methodology for later production optimization. © 2023 IEEE.

关键词： Linear programming

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Social Media + machine learning to Offer Clues on Suicide Ideation Concerns 1

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the 4th international conference on Data Science, machine learning 2022

作者： Prayaga, Lakshmi Prayaga, Chandra Gunuru, Amrutha Department of Information Technology University of West Florida Pensacola United States Physics Department University of West Florida Pensacola United States Information Technology University of West Florida Pensacola United States

ISBN: (数字)9789819920587

ISBN: (纸本)9789819920570

Mental health concerns including suicide ideation are a growing concern especially among younger population. Social media also is providing a platform for people with mental health concerns to vent their frustrations and other psychological mental health concerns including suicide ideation. this research is a study on the application of topic modeling, a machine learning algorithm on big data gathered from social media to discover clues that may play a role resulting in adverse outcomes such as suicide ideation. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Overview and Analysis of the application of Digital Twin-based Human-machine Collaboration in the Product Development Process 24

Overview and Analysis of the Application of Digital Twin-bas...

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4th international Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Wang, Haoqi Lou, Gaopeng Lu, Xiaoping Dong, Liyang Zhengzhou University of Light Industry Zhengzhou China Co Ltd Qingdao China

ISBN: (纸本)9798400710100

In the era of Industry 5.0, the collaboration between humans and machines is vital for achieving smart manufacturing that is centered on human needs. this necessitates production services that are more efficient, precise, and safe. Digital twin (DT) technology plays a critical role in facilitating this collaboration by providing a virtual representation of physical systems, allowing for real-time data analysis and decision-making. this paper explores how DT-based human-machine collaboration (HMC) can significantly improve the product development process (PDP). By examining the advantages of DT-based HMC, the paper establishes correlations with key factors such as cost, time, and quality, grounded in the ten knowledge areas outlined in the Project Management Institute's Project Management Body of Knowledge. Additionally, the paper analyzes the practical applications of project management principles related to DT-based HMC within enterprises. through the application of these project management knowledge areas, the research assesses the likelihood of successful implementation of DT-based HMC in the PDP. the findings highlight that integrating DT technology with effective project management practices can lead to substantial enhancements in manufacturing processes, aligning them more closely with human-centric objectives. Ultimately, this study underscores the transformative potential of digital twins in fostering a collaborative environment that improves product development efficiency, quality, and overall competitiveness in the industry. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Advantages

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application of Data Visualization and machine learning Algorithms for Better Decision Making 5

Application of Data Visualization and Machine Learning Algor...

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5th international conference on Recent Trends and applications in computer Science and Information Technology, RTA-CSIT 2023

作者： Llaha, Olta Aliu, Azir South East European University Tetovo Macedonia

Nowadays, huge and accessible data is an ever increasing field of study. the change in technology is, in turn, increasing its degree of interactivity, configuring several scenarios of great complexity in which data is understood on the basis of our interaction with it at different levels. Data visualization involves presenting data in graphical or pictorial form which makes the information easy to take in. It helps to explain facts and determine courses of action. Criminology is an interesting application where data visualization plays an important role in terms of prediction and analysis. Crime analysis plays an important role in devising solutions to crime problems and formulating crime prevention strategies. the purpose of this paper is to evaluate the performance of machine learning algorithms, which can be used for analyzing data collected of the past crimes. We identified the most appropriate machine learning algorithm to analyze the collected data from sources specialized in crime prevention. this study helps the institutions against crime to better predict and classify it. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Decision making

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Data Security Risk Prediction of Labor Relationship Rights Protection Network Platform Based on machine learning 1

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Yu, Min China University of Labor Relations Beijing100048 China

ISBN: (数字)9783031201028

ISBN: (纸本)9783031201011

In order to improve the effect of data security risk prediction of labor relations rights protection network platform, this paper introduces machine learning algorithm into this field, and designs a new network platform data security risk prediction method. Determine the data risk index of the network platform of labor relationship rights protection, calculate the weight of the risk data in the website data, and find the risk data characteristics in the website. Build the decision tree, calculate the data entropy involved in the decision tree, summarize the characteristics of the risk data, create the nodes of the decision tree, and get the status of the risk data of the labor relationship rights protection network platform. the obtained risk data status is brought into the Bayesian network probability definition to analyze the risk degree of the risk data. the experimental results show that the design method can effectively shorten the evaluation time and improve the risk prediction accuracy. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Research on Eliminating Mismatched Feature Points: A Review 1

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13th international conference on computer Engineering and Networks (CENet)

作者： Chen, Dunhua Peng, Jiansheng Yang, Qing Guangxi Univ Sci & Technol Coll Automat Liuzhou 545000 Peoples R China Hechi Univ Dept Artificial Intelligence & Mfg Hechi 547000 Peoples R China

ISBN: (数字)9789819992478

ISBN: (纸本)9789819992492;9789819992478;9789819992461

the mismatch point elimination algorithm is a commonly used method in the field of computer vision and image processing to deal with the presence of mismatches or outliers in matched point pairs. these mismatch points may be caused by noise, occlusion, illumination changes or image distortion. In this paper, we first explain why there is a need to eliminate the mismatch points and the current state of research, and then introduce various types of feature points and describe the extraction methods of various feature points. Next, we review several methods of false match feature point elimination, such as geometric consistency verification-based methods, graph optimization-based methods, motion statistics-based methods, and learning-based methods, analyze their advantages and disadvantages as well as make comparisons, and give an outlook on future research directions. In the conclusion, we summarize the full paper and discuss the application trends of the mismatching feature point elimination algorithms. the purpose of this paper is to provide readers with a clearer and deeper understanding of false match feature point elimination algorithms, and hopefully give some reference significance to later researchers.

关键词： ORB features Mismatches RANSAC machine learning

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Human Activity Recognition using Ontology 4

Human Activity Recognition using Ontology

引用

4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Geethalekshmy Abishek, V.S. Nair, Aditya Department of Computer Science and Application Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Human activity recognition (HAR) is an expanding field with various applications, including ambient assisted living, assistive healthcare, home security, and camera-less patient monitoring. HAR utilizes state-of-the-art sensor technology to record and analyze human activity while maintaining non-intrusiveness. Recent advancements in machine learning, ontology-based methods, and sensor technologies have significantly improved the accuracy and real-time capabilities of HAR. the potential of HAR lies in its ability to infer physiological states, activities, and motions, enabling personalized support and enhancing decision-making. By detecting the subject's activity, assistance protocols can be initiated to address their needs or set reminders, which can also be triggered using sensors. Our goal here is to create such an HAR application which aids a subject in activities of daily life. In numerous contexts, the ongoing development of HAR systems holds promise for enhancing daily living, safety, and independence. © 2023 IEEE.

关键词： activities of daily living (ADL) Human activity recognition (HAR) knowledge base ontology rules sensors

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Study on machine learning Techniques used for Agricultural Yield Estimation 4

Study on Machine Learning Techniques used for Agricultural Y...

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4th international conference on Inventive Research in Computing applications, ICIRCA 2022

作者： Manasa, C.M. Prince, Blessed Pavithra, N. Presidency University Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

this paper presents the various crop yield prediction algorithms using machine learning techniques. In the Indian economy, agriculture plays an important role and it is the most generally practiced occupation in India. the majority of the land is utilized for agriculture exercises to meet the food necessities of the Indian population. the focus of this paper is on the application of machine learning algorithms in the agricultural domain for crop yield prediction. Various Data Mining and machine learning techniques, such as K Means, Decision Tree, Random Forest classification, K Nearest neighbor, XGboost, artificial neural network, and SVM have been used for recent data applications. Techniques used in agriculture with the rapid development of data mining technology, it has made significant progress. Artificial intelligence, and computer science Data Mining is a new research area in crop yield analysis in agriculture. Yield prediction is critical, based on the available data, there is an agricultural problem that needs to be solved. the yield prediction problem can be solved by using data mining and machine learning techniques this paper helps the farmers which are the advancements that assist with forecasting the yield dependent on various parameters. © 2022 IEEE.

关键词： Crops

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Anomaly Localization in Audio via Feature Pyramid Matching 47

Anomaly Localization in Audio via Feature Pyramid Matching

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47th IEEE-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Valjakka, Jorma Myllari, Juha Myllyaho, Lalli Kivimaki, Juhani Nurminen, Jukka K. Univ Helsinki Helsinki Finland

ISBN: (纸本)9798350326970

Sound anomaly detection is a task that aims at identifying unusual or abnormal sounds within audio data. these sounds could be caused by different factors, such as background noise, equipment malfunctions, or unexpected events. Anomaly detection in sound is a well-studied topic, with a lot of research being done in the field. Anomaly localization refers to the process of identifying the specific location or region within a sample where an anomaly (or outlier) occurs. When applied to audio signals, anomaly localization can involve analyzing the spectral content of the sound to detect regions that deviate from the typical or expected pattern. In this study, we present a simple yet effective model based on the Student-Teacher Feature Pyramid Matching Method for locating anomalies in audio data. Utilizing the MIMII dataset by augmenting it with synthetic anomalies, we evaluate the method's accuracy. Our results demonstrate that the proposed model can accurately locate artificially created anomalies within the spectrograms, both in terms of time and frequency. this approach offers a promising solution for identifying and determining the precise location of anomalies in various audio applications.

关键词： Audio processing Anomaly detection (AD) Anomaly localization (AL) Deep learning machine learning

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