检索结果-内蒙古大学图书馆

AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making

international Journal of Advanced Computer Science and Applications 2025年第4期16卷 552-563页

作者： Gaidhani, Yashika Ramesh, Janjhyam Venkata Naga Singh, Sanjit Dagar, Reetika Rao, T. Subha Mastan Godla, Sanjiv Rao Baker El-Ebiary, Yousef A. Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of MBA-Sanjivani College of Engineering Savitribai Phule Pune University Pune India GD Goenka University Haryana Gurugram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Faculty of Informatics and Computing UniSZA University Malaysia

The advent of Artificial intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery. This research introduces an extensive AI-based predictive analytics framework aimed at improving customer engagement, retention and satisfaction using advanced Machine Learning (ML) and Natural Language processing (NLP) methodologies. By using XGBoost for churn prediction and BERT-based models for sentiment analysis, the system efficiently handles both structured and unstructured customer data. The methodology involves sophisticated feature engineering, customer segmentation via K-Means clustering, and Customer Lifetime Value (CLV) prediction to aid data-driven business strategies. An NLP-driven chatbot offers real-time, personalized support, response time and improving user experience. Evaluation metrics such as accuracy, precision, recall and F1-score demonstrate the better performance of the proposed system compared to conventional CRM approaches. This work also addresses important issues such as data privacy compliance, algorithmic bias and explainability of AI decision-making. Ethical deployment and transparency of AI are emphasized for building confidence in automated CRM systems. Future evolution will tackle the use of reinforcement learning to facilitate learning-based interaction schemes and federated learning for trusted, decentralized management of data. This architecture does not only provide better CRM functionality but also builds a platform towards intelligent, responsible and scalable solutions for customer relations across industries. © (2025), (Science and information Organization). All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Data Security Protection System in Human-Machine-Object Fusion Intelligent Demand Engineering System 3

Data Security Protection System in Human-Machine-Object Fusi...

引用

3rd international Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Qi, Yongjun Li, Chenggao Tang, Hailin Shen, Junxuan Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (纸本)9798331508456

In the engineering system of human-machine physical intelligence requirements, the multi-source and high complexity of data pose significant security risks. Therefore, this article designs a comprehensive data security protection system. Firstly, this article chooses a dynamic encryption strategy based on role-based access control. Then, this article combines differential privacy algorithm to construct a privacy protection model to ensure the privacy of data during sharing and processing. Subsequently, this article proposes a trusted data transmission protocol. Finally, this article introduces federated learning techniques to further improve the security and robustness of data in distributed scenarios. The experimental results show that the system performs well in terms of data security, privacy protection, and processing efficiency. Especially under high-intensity attacks, the system successfully detected and prevented approximately 98% of tampering. In the above data conclusions, the research method has significant advantages in encryption performance, achieving a better balance between privacy protection and data accuracy, demonstrating strong application prospects. © 2025 IEEE.

关键词： Requirements engineering

来源：评论

学校读者我要写书评

暂无评论

Affinity-based Optimizations for TFHE on processing-in-DRAM 25

Affinity-based Optimizations for TFHE on Processing-in-DRAM

引用

30th ACM international Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Nam, Kevin Jung, Heonhui Oh, Hyunyoung Paek, Yunheung Dept. of Ece Isrc Seoul National University Seoul Korea Republic of Department of AI-Software Gachon University Seongnam Korea Republic of

ISBN: (纸本)9798400710797

processing-in-memory (PIM) architectures are promising for accelerating intensive workloads due to their high internal bandwidth. This paper introduces a technique for accelerating Fully Homomorphic Encryption over the Torus (TFHE), a promising yet intensive application, on a realistic PIM system. Existing TFHE accelerators focus on exploiting parallelism, often overlooking data affinity, which leads to performance degradation in PIM due to excessive remote data accesses (RDAs). To address this, we present an affinity-based approach that optimizes the computation of TFHE on PIM. We apply algorithmic optimizations to TFHE, enabling PIM to effectively leverage its high internal bandwidth. We analyze the affinity patterns in the sub-tasks of TFHE and develop an offline scheduler that exploits our analysis to find optimal scheduling, minimizing RDAs while maintaining sufficient parallelism. To demonstrate the practicality of our work, we design a variant of an existing PIM-HBM device with minimal hardware modifications, and perform evaluations over a real FPGA-based PIM system. Our experiments demonstrate that our affinity-based optimizations outperform prior TFHE accelerators by 4.24-209× for real-world benchmarks. © 2025 ACM.

关键词： Memory architecture

来源：评论

学校读者我要写书评

暂无评论

AI-Boosted Video Annotation: Exploring Pre-Labeling with Cross-Modalities 21st

AI-Boosted Video Annotation: Exploring Pre-Labeling with Cr...

引用

21st international symposium on Distributed computing and Artificial intelligence, DCAI 2024

作者： Gutiérrez-Navarro, Juan Mora-Sánchez, Ángel Rodríguez-Jiménez, Silvia Blanco-Murillo, J.L. Information Processing and Telecommunications Center Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid Av. Complutense 30 Madrid28040 Spain Sigma Cognition Avenida de Manoteras 6 Madrid28050 Spain

ISBN: (纸本)9783031809453

Annotation in large-scale video datasets requires significant resources. To enhance the efficiency of this process, we suggest employing pre-trained cross-modal models within the Human-in-the-Loop (HITL) paradigm. We used a synthetic video dataset to generate precise semantic annotations and assess the effectiveness of different label representations in comprehending visual information across diverse vision tasks, including fine- and coarse-grained ones. We also introduce a framework for automating pre-annotation extraction from semantically similar frames. Our approach presents promising avenues for efficiently annotating video data, crucial for developing robust Machine Learning (ML) systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

From Voices to Beats: Enhancing Music Deepfake Detection by Identifying Forgeries in Background

From Voices to Beats: Enhancing Music Deepfake Detection by ...

引用

international Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhaolin Wei Dengpan Ye Jiacheng Deng Yuhan Lin Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Music deepfake detection is aimed at identifying whether songs are generated by AI. Current methods usually separate vocals from background music for detection, but this could leave residual forgery information in the background. Our study demonstrates for the first time that incorporating background forgery information with vocals can improve detection accuracy. Furthermore, our findings show that using background features can reduce EER by an average of about 2% on existing frameworks. Based on this observation, we propose a novel Hybrid Frontend that captures generalized features from both vocal and background music. The Hybrid Frontend comprises two branches: vocal and background music part. Specifically, the vocal part uses the Sinconv encoder as a deeply embedded feature extractor. The latter captures background variation by fine-tuning the pre-trained model with adapters. Experimental results demonstrate that our method outperforms the vocal-only detection on WildSVDD dataset, achieving an EER of 8.53%, which is 1.3% lower.

关键词： Deepfakes Adaptation models Signal processing Feature extraction Forgery Acoustics Multiple signal classification Data mining Speech processing Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

引用

international Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shihao Gao Caoshuo Li Cunli Mao Xulong Zhang Xiaoyang Qu Taisong Jin Jianzong Wang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Ping An Technology Co. Ltd. Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph’s semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches.

关键词： Acoustic distortion Semantics Contrastive learning Interference Signal processing Acoustics Speech processing

来源：评论

学校读者我要写书评

暂无评论

Efficient Dataset Distillation through Low-Rank Space Sampling

Efficient Dataset Distillation through Low-Rank Space Sampli...

引用

international Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hangyang Kong Wenbo Zhou Xuxiang He Xiaotong Tu Xinghao Ding Institute of Artificial Intelligence Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Huge amount of data is the key of the success of deep learning, however, redundant information impairs the generalization ability of the model and increases the burden of calculation. Dataset Distillation (DD) compresses the original dataset into a smaller but representative subset for high-quality data and efficient training strategies. Existing works for DD generate synthetic images by treating each image as an independent entity, thereby overlooking the common features among data. This paper proposes a dataset distillation method based on Matching Training Trajectories with Low-rank Space Sampling(MTT-LSS), which uses low-rank approximations to capture multiple low-dimensional manifold subspaces of the original data. The synthetic data is represented by basis vectors and shared dimension mappers from these subspaces, reducing the cost of generating individual data points while effectively minimizing information redundancy. The proposed method is tested on CIFAR-10, CIFAR-100, and SVHN datasets, and outperforms the baseline methods by an average of 9.9%.

关键词： Training Manifolds Costs Redundancy Noise Signal processing algorithms Vectors Trajectory Speech processing Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Data Security Protection System in Human-Machine-Object Fusion Intelligent Demand Engineering System

Data Security Protection System in Human-Machine-Object Fusi...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international Conference on

作者： Yongjun Qi Chenggao Li Hailin Tang Junxuan Shen Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

关键词： Differential privacy Protocols Federated learning Data security Heuristic algorithms Robustness Encryption Data communication Requirements engineering Protection

来源：评论

学校读者我要写书评

暂无评论

VedicFusion: Bridging Ancient Wisdom and Modern AI Through Natural Language processing

VedicFusion: Bridging Ancient Wisdom and Modern AI Through N...

引用

international symposium on Advanced computing and Communication (ISACC)

作者： Chintan Kasundra Shreekumar Kariya Meet Dhaduk Khushi Rupda Griva Vachhani Rituraj Jain Damodharan Palaniappan Department of Information Technology Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Vedic texts are wise and profound but hard to comprehend for modern readers. Advancements in AI, particularly in NLP, have presented a new opportunity to interpret complex texts. This paper introduces the concept of VedicFusion, a system that combines ancient Vedic wisdom with modern AI. Utilizing transformer-based models and a huge database of Vedic text, VedicFusion creates an interface for interpreting and exploring Vedic teachings. This tackles issues of cultural authenticity and contextual understanding. VedicFusion unlocks ancient wisdom, aiding in philosophical investigation, providing moral direction, and encouraging spiritual development. Its relevance supports AI work and research into ancient knowledge combined with modern technology in an accountable manner. Results show evidence of an increased perception and accessibility of the new teachings that have been derived because of these questions. Development of the database will expand while models for NLP refinement.

关键词： Ethics Philosophical considerations Systematics Databases User interfaces Transformers Natural language processing Cultural differences Artificial intelligence Context modeling

来源：评论

学校读者我要写书评

暂无评论

Breaking Language Barriers for Real-Time Translation Solutions in Tourism Using Cloud computing and AI

Breaking Language Barriers for Real-Time Translation Solutio...

引用

international symposium on Advanced computing and Communication (ISACC)

作者： S. Lourdu Jame C. Shobana Nageswari S. Jayaprakash E. Punarselvam R. Meenakshi M. Muthulekshmi Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. Cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions.

关键词： Cloud computing Adaptation models Translation Accuracy Scalability Computational modeling Real-time systems Natural language processing User experience Artificial intelligence

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：