检索结果-内蒙古大学图书馆

Predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques 1

International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Baby Lavanya, K. Ismail Ansari, M.D. Gundu, Venkateswarlu Krishna Mohan, G. Mouleswararao, B. Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation AP Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Customer churn poses a significant challenge to the telecom industry as it directly impacts revenue and customer retention efforts. To tackle this concern, the present research suggests an all-encompassing machine learning oriented method for forecasting customer attrition within the telecommunications industry. The dataset is employed for training and testing the Machine Learning models, and their effectiveness is assessed through conventional metrics like accuracy, precision, recall, and the area under the curve of the receiver operating characteristic (AUC-ROC). Conducting an analysis of feature importance enables us to pinpoint the foremost factors that impact customer churn. The findings illustrate that the logistic regression model attains accuracy in its predictive capabilities for customer attrition. The approach offers a practical and accurate solution, aiding telecom companies in reducing churn, increasing customer loyalty, and ultimately improving business performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Telecommunication industry

来源：评论

学校读者我要写书评

暂无评论

Improving Early Cardiomyopathy Diagnosis with CNNs: A Deep Learning-Based Approach 8

Improving Early Cardiomyopathy Diagnosis with CNNs: A Deep L...

引用

8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Abinaya, M. Duvvuri, Suneel Kumar Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Chengalpattu Department of Computer Science and Engineering Tamil Nadu India Rajahmundry Department of Computer Science and Applications Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Chengalpattu Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331520762

Cardiomyopathy is the type of heart disease that results from diabetes that influences the heart muscles, leading to structural and functional abnormalities and these situations can weaken the heart muscle, loss its ability to supply blood effectively. Cardiomyopathy affects millions worldwide every year, with an estimated 1 in 500 people impacted. In the United States, it leads to approximately 50,000 deaths are reported annually. The existing method with Logistic Regression for the detection of Cardiomyopathy obtained inaccurate results with a high false alarm rate. Introduced a deep leaning convolution neural networks algorithm for the preprocessing, extraction of data selection of features and diagnosis of disease with accurate results in the early stage. As a result, got the accurate performance for accuracy, precision, sensitivity, and recall with 97%, 96%, 95.6%, and 96% respectively. © 2025 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Fostering Learning with Facial Insights: Geometrical Approach to Real-Time Learner Engagement Detection 9

Fostering Learning with Facial Insights: Geometrical Approac...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Raji Gopinathan, N. Sherly, Elizabeth Cochin University of Science And Technology IIITM-Kerala Department of Computer Applications Trivandrum India Digital University Kerala School of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350394474

The global shift in the education paradigm demands technology-enabled learning platforms that monitor the performance and engagement of learners. In this paper, a new method of detecting learner engagement levels utilizing geometric angle features obtained from face images captured through a webcam attached to the learning device is proposed. Distinct ten angles resulting from lines joining seventeen landmark points serve as the feature set and employ machine learning algorithms for classification. The algorithms used for the classification purpose include Random forest, K-Nearest Neighbour, and Support Vector Machine. Random Forest outperforms the other classifiers in distinguishing between the two engagement levels being tested. It achieves the highest accuracy of 90% in distinguishing low and high engagement levels of students. The 3-Level classification model is also comprehensively compared with other state-of-the-art methods and found a notable accuracy than the deep learning models MobileNet and VGG16. This indicates that the proposed method can efficiently detect and output students' engagement levels which provides real-time support for teachers in classrooms and helps students while undergoing self-paced E-learning. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Current Perspectives and Virtualisation solutions with SDN for IoT 2

Current Perspectives and Virtualisation solutions with SDN f...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Bhardwaj, Shanu Kumar, Shailender Girdhar, Ashish Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9798350305418

During the past years, IoT has acquired a lot of consideration since it incorporates intelligent gadgets which empower many applications that work in our day-to-day existence. Due to this the rising number of clients and the interest in more different and specific applications have prompted a tremendous expansion in the network traffic. Managing different traffic requests from various applications is a difficult task for the current traditional networking architecture. Therefore, the paper provides a thorough analysis of the SDN and various other technologies-based network virtualization methods as well as current perspectives for the IoT. The representation provides the working of various up-going technologies such as Machine Learning, Edge Computing, and Virtualization with SDN to betray the performance of the SDN applications in today’s world. © 2023 IEEE.

关键词： edge computing internet of things machine learning network traffic software-defined networking

来源：评论

学校读者我要写书评

暂无评论

Osteoarthritis Prediction in Knee Joint Using Deep Learning Techniques 27

Osteoarthritis Prediction in Knee Joint Using Deep Learning ...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shubham Kumar Chouhan, Kuldeep Agrawal, Arun Prakash Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Osteoarthritis is a disorder that transpires when the articular cartilage in the knee breaks down and changes to the underbelly of the cartilage. Normal joint fracture protection for the bones is provided by the slick substance known as articular cartilage. Early prediction can slow down the progression of the condition. Medical professionals' expertise is utilized to predict severity. In this research, we address the issue that arises when a model's validation accuracy is lower than its training accuracy. Specifically, we find that as dataset size increases, the validation accuracy of Osteoarthritis prediction falls. We used a Deep Convolutional Neural Network to develop an automatic osteoarthritis severity assessment system using the Kellgren and Lawrence (KL) grading system. With this method, X-ray images are used to predict the severity of osteoarthritis. This study's objective is to predict the severity stage of knee osteoarthritis in the form of prediction accuracy (in %) of osteoarthritis using a transfer learning and deep learning approach. As a result, EfficientNetB5 provides a prediction accuracy of over 97.45% as demonstrated in this work. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management

引用

IEEE Systems Journal 2023年第3期17卷 3894-3905页

作者： Singh, Ashutosh Kumar Swain, Smruti Rekha Saxena, Deepika Lee, Chung-Nan National Institute of Technology Department of Computer Applications Kurukshetra136119 India Goethe University Department of Computer Science Frankfurt60323 Germany National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

Virtual machine placement (VMP) is the process of selecting the most appropriate physical machine (PM) to place users' requested virtual machine (VM) in large cloud data centers. Several methods have been framed to deal with this problem. However, the current solutions only consider limited resource types, resulting in an unbalanced load that activates unnecessary PMs inside the data center. In this article, we suggest a flower pollination-based nondominated sorting optimization (FP-NSO) algorithm that maximizes resource usage and minimizes energy consumption and carbon emission of the data center. Multiple resource-constraint metrics are associated with our algorithm that assists in finding the most suitable PMs for deploying VMs in a cloud environment. The VMP is carried out by employing the combined concept of flower pollination optimization and nondominated sorting technique-based genetic algorithm (NSGA-II). The algorithm is evaluated using the Google cluster dataset. The performance metrics like resource utilization, power consumption, and carbon emission values are computed for static and dynamic scenarios. The obtained results are compared with existing approaches. There is a significant reduction in power consumption, carbon emission, and execution time up to 16.69%, 48.60%, and 75.87%, respectively, and an improvement in resource utilization is up to 78.18%. © 2007-2012 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Segmentation of Water Bodies from Satellite Images Using Spatial-Channel Attention UNet

Segmentation of Water Bodies from Satellite Images Using Spa...

引用

2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Anitha, M. Chitra, P. Meenaragavi, P. Thiagarajar College of Engineering Department of Computer Applications Madurai625 015 India Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350378177

Precise division of aquatic areas is essential for efficient environmental supervision and administration. It enables the precise delineation and tracking of aquatic ecosystems, facilitating the assessment of water quality and the identification of environmental hazards such as pollution and habitat degradation. By offering high-resolution data and enabling thorough coverage of large areas, advanced technologies like satellite images play a crucial role in improving water body segmentation and expanding the capacity to monitor and manage water resources effectively and sustainably. In this study, the focus is on leveraging Sentinel-2 satellite imagery for accurate water body segmentation. The dataset is meticulously curated and comprises Sentinel-2 satellite images. Masks are then created to isolate water bodies from the original images using advanced deep-learning architectures, including UNet, Deeplabs, and SegNet. Furthermore, the performance of UNet is enhanced by incorporating attention mechanisms such as channel attention, spatial attention, and combined spatial and channel attention. These algorithms are used to generate output masks, which are then compared with the existing masks from the dataset. Subsequently, the evaluation was performed using metrics such as accuracy, binary cross- entropy loss, and mean intersection over union, providing comprehensive insights into their efficacy. Notably, the findings highlight the remarkable accuracy achieved by the UNet model enhanced with spatial and channel attention mechanisms, reaching an impressive 96.7%. This notable progress emphasizes the possibility of achieving better accuracy in segmenting water, which in turn aids real-time monitoring and informed decision-making in environmental management. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Low-Cost Weather Monitoring System with Intelligent Design a...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Vishnu Vilashini, S. Kishore, S. Nair, Rekha. R. Babu, Tina Bhadani, Anant Thanushree, G.R. Dixit, Anuj Kumar Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Karnataka India Allaince University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system's advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak's cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management. © 2023 IEEE.

关键词： NodeMCU Real-time monitoring Recursive feature elimination ThingSpeak Weather prediction XGBoost Regression

来源：评论

学校读者我要写书评

暂无评论

A holistic securing approach to speech steganography system with message embedding and pause optimization

引用

Multimedia Tools and applications 2025年 1-27页

作者： Kumar, Surendra Yadav, Arun Singh Dwivedi, Mridula Kumar, Mohit Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281401 India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow226007 India Lucknow India Department of IT Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

Nowadays, prevention control of message sharing as well as security enhancement in speech transmission which occurs due to users having a passion for novel inventions, communication system benefits. In this paper, the main objective is to design a speech steganography framework with covert message data transmission including speech. The secret message data is encrypted with the help of the Hyperelliptic Curve Cryptography (HECC) algorithm to provide a maximum security environment. The designed speech steganography framework is integrated with the pause removal mechanism respectively. Since, the Stegospeech is sent into the channel, as the Stegospeech as of now obtained the secret Message data its transfer speed will increment. Then at that point, it needs high bandwidth for transmission as well as needs higher energy utilization. In this manner, to decrease the data transfer speed, the best procedure is to eliminate the undesirable pause in the speech, the pause removal strategy carried out utilizing the combinations of Ensemble Empirical Mode Decomposition (EEMD) with Hilbert-Huang Transform (HHT). Then, some techniques are used to embed the message into the speech like 4-level Discrete Wavelet Transformation (DWT), Matrix definition, and Single Value Decomposition (SVD). They are likewise utilized for the secret message extraction from speech signal as well as message information from the Stegospeech signal. The presented strategy is carried out and analyzed towards the different invasions in the output. The Correlation Coefficient (CC), the Peak Signal-To-Noise Ratio (PSNR), the Signal-To-Noise Ratio (SNR), the Number of Pixels Changing Rate (NPCR), and the Unified Average Changing Intensity (UACI) are some of the popular standards that are utilised when comparing two encrypted voice signals. The numbers 33.3% and 100%, respectively, for UACI and NPCR are considered to be optimum states. It is noticed that the presented technique provides better Statistical resul

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Deep Learning Pretrained Image Classifiation Models 1

Performance Analysis of Deep Learning Pretrained Image Class...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Deepa Alli, A. Sheetal Prasad Km, Shiva Christ University Department of Computer Science Bengaluru India Presidency College Department of Computer Applications Bengaluru India Presidency College Department of Computer Science and Engineering Bengaluru India Rao Bahadur y Mahabaleswarappa Engineering College Department of Computer Applications Karnataka Ballari India

ISBN: (纸本)9798350316469

Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning Image classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：