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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

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Cahyo, Nur Ryan Dwi Sari, Christy Atika Rachmawanto, Eko Hari Jatmoko, Cahaya Al-Jawry, Rabei Raad Ali Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Northern Technical University Department Computer Science Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Sciece DhiQar Iraq

ISBN: (纸本)9798350339215

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3;including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability. © 2023 IEEE.

关键词： Convolutional neural networks

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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Joint CFO and STO Estimation for Modified Walsh-Hadamard Code Division Multiplexing

Joint CFO and STO Estimation for Modified Walsh-Hadamard Cod...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Kitagawa, Masaki Kojima, Toshiharu Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350353983

In helicopter satellite communications, the periodic blockage of the transmission channel arises due to rotor blades. Modified Walsh-Hadamard code division multiplexing (MWHCDM) overcomes the periodic blockage. In MWHCDM, estimating the symbol timing offset (STO) is necessary to perform demultiplexing. The carrier frequency offset (CFO) significantly degrades the accuracy of STO estimation. Thus, STO estimation requires estimating and removing the CFO, although CFO estimation is only possible without STO. This paper proposes a joint CFO and STO estimation method to resolve this dilemma, combining the CFO and STO estimation to complement each other. computer simulation shows that the proposed method accurately estimates both CFO and STO. © 2024 IEEE.

关键词： Frequency estimation

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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Exploring the dynamics of creative tourism:A tourist-centric perspective to achieve economic sustainable development

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Journal of Mountain Science 2025年第1期22卷 278-295页

作者： ZOU Fuxia Atefeh AHMADI DEHRASHID Mehmet AKIF CIFCI Emrah ARSLAN Nina KHALIGHI College of Tourism and Aviation Management Hunan Women's UniversityChangsha 410004China Faculty of Natural Resources Department of ClimatologyUniversity of KurdistanSanandaj ***Iran Member of Department of Zrebar Lake Economic-social Research Kurdistan Studies InstituteUniversity of KurdistanSanandaj ***Iran Department of Computer Engineering Bandirma Onyedi Eylul UniversityBalikesir 10200Türkiye Engineering and Informatics Department Klaipėdos ValstybinėKolegija/Higher Education InstitutionKlaipeda 92294Lithuania Engineering and Informatics Department KTO KaratayKonya 42020Türkiye Department of Urban Planning and Design Faculty of Art and ArchitectureUniversity of KurdistanSanandaj ***Iran

Creative tourism is a dynamic and innovative approach to tourism,which points out the importance of people's active participation and their immersion in such *** a vernacular context,it should attract people(local and tourists)attention to accomplish its main *** its rich cultural and natural assets,Kurdistan province faces several challenges that impact its tourism *** achieve that,the study uses quantitative approach to thoroughly analyze and evaluate the components of creative tourism in this *** research focuses on tourists who visited the province's ten towns during the spring and summer of *** collection utilized a Likert-scale questionnaire ranging from"very good"to"very poor".The study employed a semistructured questionnaire developed through qualitative interviews alongside a researcher-made questionnaire validated by experts from the University of *** qualitative questionnaire achieved a high-reliability score of 93%using Cronbach's alpha ***-depth interviews and literary research were conducted to identify creative tourism components and indicators,informing the development of a quantitative *** analysis was performed using SPSS 20 and AMOS software to scrutinize the survey findings,providing insights into enhancing creative tourism strategies in Kurdistan *** results reveal the varying significance of these dimensions,with the cultural dimension identified as the most crucial(factor loading:0.95),followed by the social(0.92),economic(0.88),and managerial/political dimensions(0.83).The study highlights the importance of cultural planning,community engagement,and infrastructural support in fostering creative ***,it explores the impact of creative industries,such as music and arts rooted in Kurdish culture,on tourism *** diversification and spatialphysical considerations are critical factors in enhancing Kurdistan's appeal as a creative

关键词： Creative tourism Tourism development Local and regional development Structural equation Iran

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Systematic Approach for Securing and Preserving the Privacy of Blockchain Systems 18

Systematic Approach for Securing and Preserving the Privacy ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rajamanickam, Ramya Chaturvedi, Saumya National Informatics Centre Lodhi Road New Delhi India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

Blockchain technology provides a decentralized, irreversible, and verifiable ledger for recording digital asset transactions. This technology has the potential to drastically alter several cutting-edge scenarios. But recently, blockchain has found itself under scrutiny over security and privacy. Multiple heists and hacks have taken place in blockchain systems in this short period of blockchain evolution. Innovative secure privacy blockchain solutions utilizing the crypto-privacy approaches have become the need of the hour to handle privacy issues. The below work discusses ways to bring in state-of-the-art technologies to exculpate the concerns raised on privacy and security uncertainties. Here, the primary emphasis is given to combat the chaos arising in blockchain privacy and security thus contributing to the users and developers of blockchain. We categorically explain that blockchain alone is not sufficient in the current era of heists and attacks, giving the recommendation of the framework in the coming sections, thereby placing the techniques for privacy-preserving blockchain tightly bound to the system. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain Blockchain Security Privacy Privacy-Preserving

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Behavior Trees and State Machines in Robotics Applications

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IEEE Transactions on Software engineering 2023年第9期49卷 4243-4267页

作者： Ghzouli, Razan Berger, Thorsten Johnsen, Einar Broch Wasowski, Andrzej Dragule, Swaib Chalmers University of Technology Department of Computer Science and Engineering Gothenburg40530 Sweden Ruhr University Bochum Faculty of Computer Science Bochum44801 Germany Chalmers University of Gothenburg Department of Computer Science and Engineering Gothenburg40530 Sweden University of Oslo Department of Informatics Oslo0316 Norway IT University of Copenhagen Software Development Group Copenhagen2300 Denmark

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the commu

关键词： Modeling languages

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Neural Collaborative Filtering for Improved Tourism Destination Recommendation 8

Neural Collaborative Filtering for Improved Tourism Destinat...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Marzuki, Imam Hariadi, Mochamad Rachmadi, Reza Fuad Arif, Yunifa Miftahul Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Indonesia Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350368970

This study evaluates the effectiveness of Neural Collaborative Filtering (NCF) in enhancing tourism destination recommendation. The researchers employed a methodology encompassing a comprehensive literature review, integration of user preference dataset, and the application of the NCF algorithm. The evaluation utilized MAE and RMSE metrics, demonstrating significant improvement compared to the Neural Network method. The results highlight the superior accuracy and reliability of the NCF-based recommendation system in providing tourism destination recommendation. This study emphasizes the practical significance of implementing NCF, offering a more sophisticated and efficient approach to enhancing user experience. The implication of this research contribute to academic needs and are expected to provide practical benefits for recommendation system in the tourism sector. The researchers elucidate critical differences in the NCF algorithm compared to benchmarks, and the introduction explores gaps in NCF applications for tourism, reinforcing the importance of this study. Experimental results show that the NCF model achieved an MAE of 0.306 and an RMSE of 0.371, outperforming the Neural Network's MAE of 0.794 and RMSE of 0.960, confirming superior predictive accuracy. © 2024 IEEE.

关键词： Collaborative filtering

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Selected Mapping Without Side Information for PAPR Reduction in Modified Walsh-Hadamard Code Division Multiplexing

Selected Mapping Without Side Information for PAPR Reduction...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Maruyama, Daihi Kojima, Toshiharu Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350353983

Modified Walsh-Hadamard code division multiplexing (MWHCDM) is effective for helicopter satellite communications, where the rotor blades block the transmission channel pe-riodically. This paper addresses reducing peak-to-average power ratio (PAPR) for MWHCDM, applying a selected mapping (SLM) method without side information, proposed for orthogonal frequecy division multiplexing (OFDM) previously, to MWHCDM. The SLM method is especially suitable for the primary modu-lation of quaternary phase-shift keying (QPSK), although the primary modulation of MWHCDM is binary phase-shift keying (BPSK). A novel multiplexing processing based on the structure of MWHCDM signals resolves this mismatch. In addition, introducing amplitude information to the evaluation function employed for blind estimation of phase sequence enhances the estimation performance. computer simulation shows that the proposed SLM method achieves excellent PAPR reduction and phase sequence estimation performance, causing no degradation in bit error rate (BER) performance. © 2024 IEEE.

关键词： Bit error rate

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