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

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Global Solar Energy 10th

Global Solar Energy

引用

International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Nedbalek, Branislav Dudic, Branislav Kovac, Pavel Savkovic, Borislav Jesic, Dusan Comenius Univ Fac Management Bratislava 82005 Slovakia Univ Business Acad Fac Econ & Engn Management Novi Sad 21000 Serbia Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia Int Technol & Management Acad MTMA Novi Sad Serbia

ISBN: (纸本)9783031662676;9783031662683

In the global environment, the availability of the resource is solar energy as the most important renewable resource that is used for the production of heat and electricity. The supply of non-renewable energy sources is constantly decreasing, and energy obtained from the sun is inexhaustible, safe and renewable for most of the year. The sun as a source of energy is widely available everywhere in the world. The development of solar energy is a trend all over the world today. Solar systems are used to convert sunlight into thermal energy. The use of renewable energy sources has potential and a great perspective for the future. Investing in solar energy is one way to reduce your dependence on constantly changing energy prices on markets and exchanges.

关键词： renewable energy sources solar energy solar panels investment

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Performance Comparison of SAC Methods for Radar Dynamic Object Classification 18th

Performance Comparison of SAC Methods for Radar Dynamic Obje...

引用

18th International Conference on Intelligent Autonomous systems (IAS)

作者： Park, Yeong Sang Choi, Dooseop Min, Kyoung-Wook ETRI Daejeon South Korea

ISBN: (纸本)9783031449802;9783031449819

The sample consensus (SAC) methods have become increasingly important in the field of radar-based dynamic object classification for applications such as ego-motion estimation and obstacle avoidance. This paper presents a comprehensive performance comparison of several SAC methods for classifying dynamic objects using radar data. Our extensive experiments show that each SAC method's pros and cons, and provide insights into the effective use of SAC methods for radar-based dynamic object classification. It is expected that this study will help to increase the accuracy of dynamic object classification in real-world scenarios and guide future research in this area.

关键词： Dynamic object classification Radar sensor Sample consensus

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HR Analytics in Retail: Predicting Employee Churn with Machine Learning

HR Analytics in Retail: Predicting Employee Churn with Machi...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Erden, Serhan Berke Erisen, Mert Sarigul, Yavuz Nuri Eken, Buse Ozcan, Tuncay Istanbul Tech Univ Dept Management Engn TR-34367 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

The retail sector is facing operational inefficiencies and high recruitment costs due to the increasing turnover rate. This project focuses on identifying and analyzing the key factors leading to the departure of qualified personnel. The data discussed is a real-life HR analytical case of a company working in the retail industry. Using advanced machine learning algorithms such as LGBM, XGBoost, AdaBoost, and CatBoost, the study seeks to reveal the relationship between variables like education level, city, and age. The project provides actionable insights that can inform strategic decisions by using both qualitative and quantitative data sources. According to the results, the most successful model is discovered as CatBoost. The findings indicate that the employee's average sales and its coefficient of variation, trends of sales, and age of employee play crucial roles in employee churn. To interpret these, an increase in an employee's sales rates correlates with a higher likelihood of retaining their position. Actions taken in light of the project's findings can contribute to companies predicting employee churn in advance, thereby reducing turnover rates and improving operational costs.

关键词： Employee Churn Machine Learning HR Analytics Turnover Statistical Tests ANOVA Catboost LGBM XGBoost AdaBoost

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Securing networks: A Machine Learning Approach to Intrusion Detection systems 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Mathur, Tanay Jha, Anuja Appalla, Avani Vats, Prashant Manipal Univ Jaipur Dept CSE SCSE Jaipur Rajasthan India

ISBN: (数字)9789819713264

ISBN: (纸本)9789819713257;9789819713264

Our dependency on the Internet for daily work has dramatically increased as the globe observes the rapid improvements in technology. We are increasingly vulnerable to cyberattacks as a result of our increased dependence, and attackers target us more frequently. Our exposure to security attacks increases as our reliance on the Internet grows. As a result, there is an increasing need for products like antivirus software and other systems that can spot new security concerns. It is necessary to find a hardware or software solution that, when included in a network, effectively detects and blocks all forms of intrusions to solve this problem. This technology, also known as an intrusion detection system (IDS), is extremely useful for keeping track of network activity and identifying irregularities. Current studies in this area-Current research in this area attempts to create systems that, in addition to precisely identifying faults, also take proactive measures to eliminate dangers as soon as they are discovered. Using the UNSW-NB15 dataset and Convolutional Neural Network (CNN) technology, this study introduces an improved intrusion detection system (IDS) that aims to identify intrusions more precisely and effectively. The existing IDS's ability to keep up with developing intrusion strategies is limited since it relies on the KDD99 dataset. A fresh dataset that includes a wider variety of attack types and network situations is used as a result.

关键词： Convolutional Neural Network (CNN) Intrusion detection system (IDS) KDD99 UNSW-NB15

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Implementing Cyclical Learning Rates in Deep Learning Models for Data Classification 1

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

作者： Al-Khamees, Hussein A. A. Manaa, Mehdi Ebady Obaid, Zahraa Hazim Mohammedali, Noor Abdalkarem Al Mustaqbal Univ Coll Engn & Technol Comp Tech Engn Dept Hillah 51001 Babil Iraq Al Mustaqbal Univ Coll Sci Artificial Intelligence Sci Dept Hillah 51001 Babil Iraq Univ Babylon Coll IT Dept Informat Networks Hillah Babil Iraq

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

Neural networks are effectively used in a variety of applications including datamining. The neural network can realize different complex nonlinear functions by making them attractive to identify a system. One of the most important issues of classifying datasets through neural networks is the formation of an ideal network, that consists of many successive steps like set parameters. Perhaps the most prominent parameter is the learning rate. Indeed, choosing an appropriate learning rate value is one of the things that greatly helps to control the overall network performance. In contrast, any inappropriate value for the learning rate negatively affects the classification model and can therefore destabilize the model's performance and thus seriously deteriorate its quality. This paper presents a newmodel by adopting a cyclical learning rate instead of using a constant value for training deep neural networks by Multi-Layer Perceptron (MLP) architecture. This model is tested on various real-world datasets;Electricity, NSL- KDD, and four sub-datasets of HuGaDB (HuGaDB-01-01, HuGaDB-05- 12, HuGaDB-13-11, and HuGaDB-14-05). The proposed model achieves an accuracy of, 89.57%, 99.12%, 99.2%, 97.83%, 96.19%, and 99.85% for these datasets respectively. Accordingly, the proposed model outperforms many previous models. As a result, the deep neural network models can be more effective when they adopt an appropriate value for the learning rate.

关键词： Deep neural networks Data mining Cyclical Learning Rate Multi-Layer Perceptron (MLP)

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Multi-sensory Fault Diagnosis of Broken Rotor Bars Using Transfer Learning 1

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Aydin, Ilhan Akin, Erhan Firat Univ Dept Comp Engn TR-23119 Elazig Turkiye

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

Induction motors play a crucial role in industrial operations, constituting a significant portion of the workforce. The occurrence of faults in these motors can substantially impact their operational performance. Approximately 20% of these faults stem from broken rotor bars, underscoring the importance of early detection to prevent more severe issues. This study aims to leverage data from multiple sensors to detect broken rotor bar faults. Rather than evaluating sensor data independently, as commonly done in existing literature, this study adopts a novel approach. It employs wavelet transform to convert signals from two different sensors into time-frequency images, which are then concatenated to form a single dataset. Subsequently, a convolutional neural network based on MobileNetv2 architecture is developed to classify rotor faults using these images. Compared to single-sensor approaches, the proposed methodology yields a 2% enhancement in performance. Moreover, it demonstrates the capability to detect faults involving up to 4 broken rotor bars with an impressive accuracy rate of around 97.2%.

关键词： Fault diagnosis induction motors broken rotor bars deep learning envelope analysis wavelet transform

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Semi-synchronous Movements of Two Collaborative UR10e Robots Controlled by Matlab via TCP/IP Protocol 10th

Semi-synchronous Movements of Two Collaborative UR10e Robots...

引用

International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Karner, Timi Krapez, Luka Petauer, Matic Gotlih, Janez Belsak, Rok Univ Maribor Fac Mech Engn Smetanova Ulica 17 Maribor 2000 Slovenia

ISBN: (纸本)9783031662676;9783031662683

Collaborative robots are mainly used in the applications where human-robot cooperation is required. In some applications heavier objects should be manipulated for the worker to assembly the required piece. Matlab program and TCP/IP protocol was used to establish a successful communication and to develop a control algorithm to perform semi-synchronous movements with two collaborative UR10e robots. Main application was to manipulate an object with both robots simultaneously. Matlab main task was to calculate new position and orientation for the object manipulation and send it to both robots. Matlab program was also responsible for synchronization of both robots. A successful demonstration was presented for translational and rotational manipulation of the object around its own coordinate system while both collaborative robots grab, manipulate and release the object.

关键词： semi-synchronous movements collaborative robots Matlab TCP/IPprotocol

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Volumetric Attention Mechanism-Based Deep Learning for Breast Cancer Diagnosis in Digital Breast Tomosynthesis 9th

Volumetric Attention Mechanism-Based Deep Learning for Breas...

引用

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

作者： Oladimeji, Oladosu Oyebisi McLoughlin, Ian Unnikrishnan, Saritha Atlantic Technol Univ ATU Fac Engn & Design Dept Comp & Elect Engn Sligo Ireland Atlantic Technol Univ Dept Comp Sci & Appl Phys Galway Ireland

ISBN: (纸本)9789819735556;9789819735563

Breast cancer is the leading cause of cancer-related deaths among females. In clinical practice, digital breast tomosynthesis (DBT), Automated Breast Ultrasound, and advanced MRIs are rapidly replacing traditional image modalities like full-field digital mammography due to their superior diagnostic capabilities. However, detecting breast cancer from these advanced modalities is challenging due to their high resolution, large volume, and complexity. This research proposes a novel deep learning model to enhance the accuracy of breast cancer detection in DBT. The proposed model utilizes a volumetric attention mechanism to learn the image features efficiently. The model was trained and validated using a public dataset of 31 benign cases and 26 cases of breast cancer in the Left Medio-Lateral Oblique (LMLO) view of DBT image with fivefold cross-validation. A mean accuracy, AUC, MCC, and F1-score of 92%, 93%, 88%, and 90% were obtained, respectively. The proposed model's performance was compared to several state-of-the-art CNN-based models as baseline. The results demonstrate that the proposed model surpasses these baseline models in terms of accuracy, precision, recall, F1-score, and AUC. The promising results obtained demonstrated that the proposed model has the potential to be integrated into advanced CAD systems for breast cancer diagnosis.

关键词： Volumetric attention mechanism Digital breast tomosynthesis 3D medical imaging Breast cancer

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Intuitionistic Fuzzy AHP Approach as a Tool for Prioritization of Indicators for Development of Contemporary Wineries 1

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Stanojevic, Ana Milosevic, Mimica Milosevic, Dusan Jevremovic, Ljiljana Milojkovic, Aleksandar Univ Nis Fac Civil Engn & Architecture Nish Serbia Univ Union Nikola Tesla Fac Informat & Comp Sci Cara Dusana 62-64 Belgrade Serbia Univ Nis Fac Elect Engn Nish Serbia

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

Wineries represent a long-tradition typology of industrial architecture, evolved along with changes and technological advancements related to wine-making and wine consumption. Initially built as simple wine cellars, as part of complexes or as individual buildings, today, wineries are modern facilities where the production meets the tourism component. The research focuses on developing contemporary wineries and analyzing the factors that transformed these facilities from primarily industrial to publicly accessible centres of wine culture at the turn of the 20th to 21st century. The paper aims to identify the critical indicators for developing contemporary winery design concepts by applying a multi-criteria Intuitionistic Fuzzy Analytic Hierarchy Process (AHP) approach. Membership and non-membership functions can be numbers, intervals, and fuzzy numbers. Factors related to the development of contemporary wineries are divided into five independent groups: economic, environmental, socio-cultural, technological, and institutional factors. The criteria recognized within these groups will be ranked using Intuitionistic AHP, Interval Intuitionistic AHP and Fuzzy Intuitionistic AHP methods. Obtained results should address how differences in intuitionistic approaches affect the final ranking.

关键词： Intuitionistic AHP Interval Intuitionistic AHP Fuzzy Intuitionistic AHP Contemporary Wineries Development Industrial Architecture

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Enhancing Quality of Experience in Omnidirectional Video: Strategies Overview 1

引用

13th Computer Science Online Conference

作者： Kockovic, Ladislav Vargic, Radoslav Medvecky, Martin Slovak Univ Technol Bratislava Bratislava Slovakia

ISBN: (数字)9783031703003

ISBN: (纸本)9783031702990;9783031703003

This article delves into various strategies for enhancing Quality of Experience (QoE) in 360 degrees video, a rapidly evolving domain of video technology. It addresses the challenges posed by interactive and immersive content, emphasizing the critical role of factors like bandwidth, storage, and processing power. The study compares the impact of 3 Degrees of Freedom (3DoF) versus 6 Degrees of Freedom (6DoF) on user engagement and investigates the intricacies of transmitting different video types. Furthermore, the article scrutinizes the suitability of specific codecs, including H.264, H.265, H.266, MPEG immersive video, and VP9, for 360 degrees video. It also explores the potential of neural networks, particularly NeRF (Neural Radiance Fields) and NeRV (Neural Representations for Videos), in video coding. Additionally, the use of MPEG-DASH for adaptive streaming in omnidirectional video is examined. The article concludes by highlighting the synergistic effects of combining codecs, adaptive streaming, and mobile edge computing, underscoring their promise in delivering high-quality 360 degrees video. It calls for further research to optimize these elements for an even more immersive user experience.

关键词： 360 degrees video Quality of Experience (QoE) interactive content neural networks adaptive streaming mobile edge computing

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