检索结果-内蒙古大学图书馆

AI-driven churn prediction in subscription services: addressing economic metrics, data transparency, and customer interdependence

引用

Neural Computing and Applications 2025年第14期37卷 8651-8676页

作者： Talaat, Fatma M. Aljadani, Abdussalam Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Management College of Business Administration in Yanbu Taibah University Al-Madinah Al-Munawarah41411 Saudi Arabia

This study aims to improve customer churn prediction by integrating machine learning algorithms and evaluating their performance using criteria like accuracy, profit, and Customer Lifetime Value (CLV). Using a Telecom Customer Churn Prediction dataset, the study analyzes the efficiency of various models such as XGBoost, Linear Regression, Decision Tree Regressor, Lasso, Random Forest, and Gradient Boosting Regressor. This study introduces an Adaptive Profit-Centric Churn Prediction Engine (APCPE), an innovative churn prediction system that evaluates profitability and adapts to changing customer behavior. Each model’s accuracy, profit, and CLV are assessed, revealing that the proposed APCPE is the most accurate model, with a 97.01% accuracy rate, a profit of $606.3125, and a CLV of $1212.625. Convergence curves, including R2 convergence curves, show the model’s performance and convergence rates for each algorithm. Convergence curves, including R2 convergence curves, show the model’s performance and convergence rates for each algorithm. The study digs deeper into interdependence modeling, which involves finding and measuring consumer interactions or dependencies such as social network linkages and product co-occurrence. The study adds insights into modeling customer behavior dependencies and their impact on churn prediction, providing a holistic approach to improving corporate tactics for customer retention. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

EdgeCrypt Tracker: Object Tracking With Differential Encryption for IoAAV Surveillance

引用

IEEE Internet of Things Journal 2025年第9期12卷 11511-11528页

作者： Thiyagarajan, Karthik Hammad, Issam Lu, Rongxing Dalhousie University Faculty of Engineering Mathematics and Internetworking HalifaxNSB3H 4R2 Canada University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada

This article proposes EdgeCrypt Tracker, an object-tracking algorithm combined with differential encryption to provide better accuracy and runtime efficiency for battery-operated Internet of Autonomous Aerial Vehicles (IoAAV). Specifically, EdgeCrypt Tracker operates directly on high-efficiency video coding (HEVC) and has three stages: 1) preprocessing;2) object tracking;and 3) differential encryption. The preprocessing stage separates intra frames and removes artificial camera motion caused by camera movement from inter frames. Next, the object tracking stage utilizes a hybrid neural network, combining a single-shot multibox detector (SSD) network with a MobileNetV3 backbone that processes intra coded blocks and a fast gated recurrent neural network (FastGRNN) network that processes inter coded blocks. Finally, the tracked information is passed to the differential encryption stage, which encrypts all syntax elements within moving objects and alternate syntax elements related to the background. Experimental results demonstrate that EdgeCrypt Tracker achieves an average object tracking accuracy of 92%, real-time inference with a 35% lower encryption overhead compared to state-of-the-art methods. This work demonstrates the potential of integrating object tracking and encryption within video compression for secure, efficient AAV-based surveillance. © 2014 IEEE.

关键词： Nuclear power plants

来源：评论

学校读者我要写书评

暂无评论

Hybrid Power-Frequency Multiple Access With SIC-Free for Holographic Reconfigurable Intelligent Surface-Based Systems

引用

IEEE Wireless Communications Letters 2025年第5期14卷 1566-1570页

作者： Le, Anh-Tu Vu, Thai-Hoc Da Costa, Daniel Benevides Voznak, Miroslav VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava708 00 Czech Republic University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia

This paper proposes a hybrid power-frequency multiple access (HPMA) strategy to enhance the reliability, energy, and capacity of wireless communication systems. HPMA, a technique that reconciles all mutual benefits of orthogonal multiple access (OMA) and non-OMA (NOMA), removes the need for successive interference cancellation (SIC) and bypasses the user pairing inherent to NOMA. Focusing on holographic reconfigurable intelligent surfaces-aided systems servicing two users under imperfect channel estimation conditions, we establish theoretical frameworks for outage probability (OP) and ergodic capacity (EC), where both metrics are quantified with exact closed-form expressions and asymptotic analyses for scenarios with high signal-to-noise ratio (SNR) as well as for low-rate region cases. The former scenario provides insights into system diversity, modulation, and coding gains, and the ergodic slope;whereas the latter scenario elucidates trade-offs between energy and reliability and the method for maximizing energy-spectral efficiency. Numerical results demonstrate that HPMA outperforms conventional OMA and NOMA, improving OP in most SNR ranges in addition to enhancing EC and energy efficiency at low to moderate SNRs. © 2012 IEEE.

关键词： Hybrid power

来源：评论

学校读者我要写书评

暂无评论

The impact of backbone selection in YOLOv8 models on brain tumor localization

引用

Iran Journal of computer science 2025年 1-23页

作者： Ranjbarzadeh, Ramin Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

This study investigates the utilization of the You Only Look Once (YOLOv8) deep learning framework for accurately identifying the location of brain tumors in medical imaging. We investigate the effects of model size and pretraining on the accuracy and computational efficiency of tumor detection by utilizing different setups of the YOLOv8 model. These setups include various configurations, ranging from very small to large, and can be pretrained on the COCO dataset or not. The experimental results, carried out on Google Colaboratory using NVIDIA Tesla T4 GPUs, show that pretrained models often achieve better performance by utilizing the extensive feature representations learned from the COCO dataset, resulting in increased precision in tumor location. For instance, the YOLOv8-XS model pretrained on COCO achieves an IoU of 0.278 and a Dice coefficient of 0.435, whereas its non-pretrained counterpart attains only 0.241 IoU and 0.388 Dice, indicating a 15% improvement in tumor localization accuracy. Similarly, pretrained YOLOv8-L achieves 0.269 IoU, outperforming standard object detection models such as Mask R-CNN (IoU: 0.212) and Faster R-CNN (IoU: 0.228). These results highlight the impact of pretraining on model performance, particularly for lightweight architectures, while also revealing diminishing returns for larger models. The research uncovers a subtle connection between the complexity of the model, pretraining, and the time required for training. It emphasizes the possible advantages and constraints of pretraining for various sizes of models. © The Author(s) 2025.

关键词： Brain tumor localization COCO dataset Deep learning Object detection Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

引用

38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

引用

ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Xu, Huali Zhi, Shuaifeng Sun, Shuzhou Patel, Vishal Liu, Li College of Computer Science Nankai University Tianjin China College of Electronic Science and Technology National University of Defense Technology Changsha China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Johns Hopkins University Baltimore United States

While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This article presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim at providing both a position article and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements. © 2025 Copyright held by the owner/author(s).

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

引用

4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

来源：评论

学校读者我要写书评

暂无评论

A comprehensive survey on 6G-security: physical connection and service layers

引用

Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Secure and Optimized Cloud Storage with Privacy Preservation using Improved SHA - 256 2

Secure and Optimized Cloud Storage with Privacy Preservation...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Joshi, Aditi Devarajan, Ganesh Gopal Karuppiah, Marimuthu Sharma, Yogesh Kumar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad201204 India Presidency School of Computer Science and Engineering Presidency University Karnataka Bengaluru560064 India Department of CSE Dewan V S Institute of Technology U.P Meerut India

ISBN: (纸本)9798331530389

With the increased reliance of enterprises on cloud storage solutions for data management, maintaining safe and efficient storage while emphasizing privacy protection is crucial. With an emphasis on user privacy, this study examines the methods, tools, and difficulties currently involved in attaining safe and effective cloud storage. It looks at access control protocols, encryption techniques like SHA-256, and new technologies that are intended to improve the security and effectiveness of cloud storage systems. The study also addresses the changing trends influencing the security of cloud storage in the future and emphasizes the significance of adhering to privacy laws. © 2025 IEEE.

关键词： Cloud storage

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：