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Human Resource Network Information Recommendation Method Based on machine learning 1

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

作者： Wang, Xiao China University of Labor Relations Beijing100048 China

ISBN: (数字)9783031201028

ISBN: (纸本)9783031201011

Human resource network information recommendation method is affected by similarity information, which leads to poor recommendation effect. A human resource network information recommendation method based on machine learning is proposed. the machine learning statistical model is established to filter the noise information. Temporal behavioral preference features were constructed to calculate the average number of information clicks. Data preprocessing is accomplished through data format conversion, data cleaning and data specification. the network information resources were retrieved, the pattern matching results were obtained, the loss function based on content association rules was constructed, and the recommendation model was constructed combined with hybrid genetic algorithm. the experimental results show that the highest recommendation accuracy of this method reaches 94%, and the highest recall rate is 0.90, which indicates that the application of research methods to recommend human resources network information has a good effect. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Research on Food Image Recognition of Deep learning Algorithms 4

Research on Food Image Recognition of Deep Learning Algorith...

引用

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

作者： Luo, Lihua Guangdong University of Science and Technology College of Software Engineering Dongguan523000 China

ISBN: (纸本)9798350342710

In recent years, food image recognition has been a research field that has received much attention. Diet is an essential part of people's daily life. through food image recognition and classification, dietary evaluation is conducted to achieve the goal of healthy eating. Manual features and classification methods in traditional target detection algorithms can no longer meet the requirements of detection accuracy and speed, so it is necessary to introduce depth learning-based techniques into food image recognition. Deep learning is a complex machine learning algorithm that allows computers to simulate the human brain's thinking, judge new problems through data sets (human experience), and improve themselves to obtain relatively accurate models, just like humans. Among them, convolutional neural networks have developed most rapidly. they are applied to image recognition with the expectation of being able to automate recognition through machine learning, thereby reducing the workload of image recognition and providing more objective analysis results. Deep learning has achieved great success in intelligent image processing, image classification, and recognition and has excellent development potential and social value. this article implements automatic recognition of food images based on the recognition algorithm of the YOLOv5 convolutional neural network structure, which can detect food images with high detection efficiency. It explores the combination of deep learning to identify food images, providing a reference for people to eat healthily, especially for obese people to control calorie intake, and providing theoretical support for food image recognition. © 2023 IEEE.

关键词： Image recognition

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Research on renewable energy incremental classification model based on ensemble learning algorithm 8

Research on renewable energy incremental classification mode...

引用

8th international conference on Energy System, Electricity, and Power, ESEP 2023

作者： Cuihua, Tian Chaoxu, Lin Wulong, Liu School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361000 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Renewable energy is a green and low-carbon energy source, which is of great significance for improving energy structure, protecting the ecological environment, addressing climate change, and achieving sustainable development. this article investigates the application of ensemble learning models in the prediction of renewable energy increment. Traditional prediction methods have problems due to their own limitations, such as imbalanced data and rough feature selection, resulting in low model prediction accuracy. this article proposes an ensemble learning based energy increment prediction model (ERLSK model). the model includes four traditional machine learning classification models: KNN, random forest, SVM, and logistic regression. the best hyperparameter of each model are obtained by cross validation and grid search. By using different feature selection and model training methods, different prediction results are obtained, and these results are weighted and fused to obtain the final classification result. the experimental results indicate that the model is effective and feasible. © 2024 SPIE.

关键词： Climate change

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Semi-Automatic Liquid Filling System Using NodeMCU as an Integrated IoT learning Tool 1

引用

12th international conference on Robotics Vision Signal Processing and Power applications

作者： Adan, N. F. Zainal, Z. Sha'abani, M. N. A. H. Nor, M. F. Mohamed Ismail, M. F. Univ Tun Hussein Onn Malaysia Pagoh Higher Educ Hub Pagoh 84600 Johor Malaysia

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). the industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1-9 (2016)). thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. the integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. the system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and fields.

关键词： IoT NodeMCU Blynk learning tool TVET

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Optimized Ensemble machine learning Model for Chronic Kidney Disease Prediction 4

Optimized Ensemble Machine Learning Model for Chronic Kidney...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Choudhary, Chahil Nagra, Lovepreet Singh Das, Prasenjit Singh, Joybir Jamwal, Suksham Singh Chandigarh University CSE Department Punjab Mohali India

ISBN: (纸本)9798350306118

the prevalence of chronic kidney illness has been increasing by a rate of 41.5 over the past few years, posing a significant challenge for healthcare systems worldwide. the disease is characterized by a gradual loss of kidney function over time, resulting in the accumulation of waste products and fluids in the body. Early detection and management of CKD are crucial for improving patient outcomes and reducing healthcare costs. machine learning (ML) techniques have been increasingly employed for predicting the risk of CKD and developing effective screening tools. In this study, an ensemble ML model for CKD forecast is projected, which attained an accurateness of 96.5% by utilizing the Kaggle-based chronic kidney disease dataset. the model was constructed using two algorithms, random forest, and bagging, which were trained on a large dataset of CKD patients' clinical and demographic information. the ensemble model demonstrated superior performance compared to each algorithm separately, indicating the efficacy of the ensemble approach in enhancing predictive accuracy. the results of this research indicate that the suggested ensemble model could be a valuable tool for early identification and management of CKD, thus reducing the burden of CKD on individuals and healthcare systems. © 2023 IEEE.

关键词： Large datasets

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ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

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Recognition and Optimization of Wear Behaviour of Al 6081 with Polymeric Nanocomposites Using machine learning 4

Recognition and Optimization of Wear Behaviour of Al 6081 wi...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumar, Jaidev Pawar, Sanjay R. Ashish Kamalakar, V. Karuppathal, R. Bhange, Anup D Hariom Saraswati P. G. College Department of Chemistry Uttarakhand Haridwar247667 India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai India Kl Deemed University Department of Cse Andhra Pradesh Vaddeswaram India VelTech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Physics Tamil Nadu Chennai600062 India PSNA College of Engineering and Technology Department of Information Technology Tamilnadu India Kdk College of Engineering Department of Computer Science and Engineering Maharashtra Nagpur440009 India

ISBN: (纸本)9798350300888

the Al 6081 alloy is broadly used in numerous engineering requests due to its excellent mechanical properties. However, its resistance of wear parameter is not satisfactory. In this study, the wear resistance of Al 6081 alloy reinforced with Graphene nanoplatelets (GNPs) at three different percentages (0.3%, 0.6%, and 0.9%) was investigated using Taguchi L27 array design. machine learning techniques such as Signal to Noise ratio and Neural Networks were used to forecast the responses and optimize the wear behaviour of the Al 6081 alloy. the wear track was identified using Scanning Electron Microscopy (SEM) images. the results of the study indicate that the addition of GNPs improves the wear resistance of the Al 6081 alloy. the percentage of GNPs reinforcement that gives the best wear resistance is determined using the Taguchi L27 array design. It is found that 5% GNPs additives reduces the wear compared to other composition, 1% and 3%. this study provides a deeper understanding of the wear behaviour of Al 6081 alloy reinforced with GNPs and can be used as a reference for future research in this field. the use of machine learning techniques in this study provides a new approach for the optimization of wear behaviour in Al 6081 alloy reinforced with GNPs, which can be applied in various industrial applications. Overall, the consequences of this study validate that incorporating 0.9% GNPs in Al 6081 alloy is an operative way to advance its wear resistance properties. © 2023 IEEE.

关键词： Wear resistance

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Research on Multi-Model Fusion for Multi-Indicator Collaborative Anomaly Prediction in IoT Devices 27

Research on Multi-Model Fusion for Multi-Indicator Collabora...

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27th international conference on computer Supported Cooperative Work in Design (CSCWD)

作者： Wang, Donghao Wang, Tengjiang Qi, Hao Liu, Shijun Pan, Li Shandong Univ Sch Software Jinan Peoples R China Inspur Gen Software Co Ltd Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In the era of Industry 4.0, the widespread application of Internet of things (IoT) technology enables us to monitor the operational status of production equipment through sensors and creates new requirements for equipment anomaly prediction and analysis. It can shift maintenance tasks from passive to proactive, reducing downtime and repair costs associated with equipment failures, and ensuring production safety and efficiency. However, the main challenge in industrial IoT applications lies in acquiring a sufficient amount of anomaly data. To address this issue, we propose a method that analyzes normal operating data of equipment to reveal trends in equipment status, combining multiple-model fusion prediction and multi-index coordinated decision-making. Firstly, a multi-model fusion strategy is adopted for prediction, integrating models such as XGBoost, LightGBM, and LSTM to enhance the accuracy of equipment attribute prediction. Secondly, through a multi-index coordinated approach, considering combinations of multiple indicators, an adaptive dynamic threshold rule is formulated based on the residuals between predicted and actual values to achieve early warning of equipment anomalies. Finally, we validate the effectiveness of this method using a mine ventilation fan as an example. the experiment demonstrated that this method can detect equipment anomalies one day earlier than traditional threshold alarm methods, achieving proactive equipment maintenance.

关键词： multi-model fusion anomaly warning machine learning multi-indicator adaptive threshold

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Financial Decision Support system for Small and Medium-Sized Enterprises 4th

Financial Decision Support system for Small and Medium-Sized...

引用

4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Kannammal, A. Priyasri, C. Shruthijeya, J. Blessie, E. Chandra Coimbatore Inst Technol Dept Comp Decis & Comp Sci Coimbatore Tamil Nadu India Coimbatore Inst Technol Dept Comp Artificial Intelligence & Machine Learn Coimbatore Tamil Nadu India

ISBN: (纸本)9783031837920;9783031837937

this paper introduces a web-based financial analysis and planning tool designed to address the financial management challenges faced by small and medium-sized enterprises (SMEs). the platform integrates several advanced functionalities including a real-time dashboard for financial metrics, budgeting and forecasting modules, scenario analysis capabilities, cost and revenue management, and KPI tracking. Unlike existing solutions, which often offer fragmented functionalities or lack integration, this platform developed by machine learning algorithms provides a cohesive system that combines these elements into a single interface, enhancing the comprehensiveness and usability of financial management for SMEs. By enhancing accessibility to real-time data and predictive insights, this platform enables SMEs to optimize resource allocation, mitigate financial risks, and drive sustainable growth. the proposed system stands as an innovative and integrated solution designed to empower SMEs with efficient financial planning and management tools.

关键词： Financial Management Budgeting Forecasting Scenario Analysis Cost Analysis Revenue Analysis KPI Tracking Web-Based application

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2023 4th international conference on Big Data, Artificial Intelligence and Internet of things Engineering, ICBAIE 2023

2023 4th International Conference on Big Data, Artificial In...

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4th international conference on Big Data, Artificial Intelligence and Internet of things Engineering, ICBAIE 2023

ISBN: (纸本)9798350343618

the proceedings contain 83 papers. the topics discussed include: application of nonnegative matrix factorization in intelligent patrol inspection of substations;deep learning-enhanced fingerprint generation and security verification in the context of Siamese network matching models;a study on scratch defect detection of ceramic blades based on improved YOLOv5;feature-aligned feature pyramid network and center-assisted anchor matching for small face detection;identification of user-transformer relationship based on dynamic time warping algorithm and two-stage clustering algorithm;degradation adaption based heterogeneous face hallucination;lightweight IOT-based authentication scheme in 5G circumstance;and research and improvement of secure hash algorithm based on MD5.

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