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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 drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM

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Intelligence-Based Medicine 2025年 11卷 100206-100206页

作者： Naveed, Sajid Husnain, Mujtaba Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan Department of Software Engineering Faculty of Computing The Islamia University of Bahawalpur 63100 Pakistan

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects. © 2025 The Authors

关键词： Drug response recommendation system Drug SMILES Gene expression Glioblastoma K-mer fragmentation LightGBM

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Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era

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European Physical Journal C 2025年第4期85卷 1-20页

作者： Caron, Sascha Dobreva, Nadezhda Ferrer Sánchez, Antonio Martín-Guerrero, José D. Odyurt, Uraz Ruiz de Austri Bazan, Roberto Wolffs, Zef Zhao, Yue High-Energy Physics Radboud University Nijmegen Netherlands Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Faculty of Engineering Technology University of Twente Enschede Netherlands Institute of Physics University of Amsterdam Amsterdam Netherlands Amsterdam Netherlands SURF Amsterdam Netherlands Department of Electronic Engineering ETSE-UV University of Valencia Valencia Spain Instituto de Física Corpuscular University of Valencia Valencia Spain Valencia Spain

High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step of the data processing pipeline. One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine Learning-assisted solution is expected to provide significant improvements, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. This is the topic of this paper. We take inspiration from large language models. As such, we consider two approaches: the prediction of the next word in a sentence (next hit point in a track), as well as the one-shot prediction of all hits within an event. In an extensive design effort, we have experimented with three models based on the Transformer architecture and one model based on the U-Net architecture, performing track association predictions for collision event hit points. In our evaluation, we consider a spectrum of simple to complex representations of the problem, eliminating designs with lower metrics early on. We report extensive results, covering both prediction accuracy (score) and computational performance. We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments. The results highlight distinct advantages among different designs in terms of prediction accuracy and computational performance, demonstrating the efficiency of our methodology. Most importantly, the results show the viability of a one-shot encoder-classifier based Transformer solution as a practical approach for the task of tracking. © The Author(s) 2025.

关键词： Data reduction

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and Technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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Towards distinguishing trust based attacks in an IoV network

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Journal of King Saud University - Computer and Information Sciences 2025年第4期37卷 1-15页

作者： Wang, Yingxun Mahmood, Adnan Mohd Sabri, Mohamad Faizrizwan Zen, Hushairi Faculty of Engineering Universiti Malaysia Sarawak Sarawak Kota Samarahan 94300 Malaysia Faculty of Computer and Information Engineering Qilu Institute of Technology Jinan 250200 China School of Computing Macquarie University Sydney 2109 NSW Australia Faculty of Engineering and Technology i-CATS University College Sarawak Kuching 93350 Malaysia

The paradigm of the Internet of Vehicles (IoV) promises significant innovative advancements and is receiving an increased attention of the researchers from both academia and industry. The IoV creates an intelligent network between different road entities and their corresponding roadside infrastructure via state-of-the-art sensing and communication technologies and, therefore, facilitates in addressing a range of safety-critical and non-safety vehicular applications. Accordingly, the security of an IoV network is of importance with internal attacks being of considerable concern. Although the conventional cryptography-based schemes are extremely intelligent in tackling the external attacks, trust has been recently employed to handle the internal attacks. Nevertheless, trust-based attacks also present a formidable challenge. Therefore, in this paper, an IoV-based trust management heuristic has been envisaged that takes into account both direct trust and indirect trust to ascertain the behaviors of the vehicles vis-à-vis time in a bid to detect various trust-based attacks, i.e., zig-zag attacks, self-promoting attacks, on-off attacks, and opportunistic attacks, along with the attackers’ multiple attacking strategies. The experimental findings further demonstrate that the envisaged IoV-based trust management heuristic exhibits prompt and accurate detection of the trust-based attacks in contrast to the state-of-the-art trust management mechanisms. © The Author(s) 2025.

关键词： Internet of vehicles Non-safety vehicular applications Safety-critical vehicular applications Trust-related attacks Vehicles’ behaviors

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Probabilistic characteristic analysis of seismic performance of water distribution system based on quasi-Monte Carlo simulation

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Earthquake engineering and engineering Vibration 2025年第2期24卷 595-611页

作者： Hou Benwei Yuan Minghao Diao Kegong Ma Xitao Zhou Baojin Du Xiuli College of Achitecture and Civil Engineering Beijing University of TechnologyBeijing 100124China Faculty of Computing Engineering and MediaDe Montfort Universitythe GatewayLeicester LE19BHUK Beijing Urban Construction Design&Development Group Co. LtdBeijing 100045China

Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable *** this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is *** framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster *** performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair ***,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are *** framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and *** results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.

关键词： water distribution system probabilistic analysis post-earthquake performance seismic resilience quasi Monte Carlo Sobol’s sequence

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Adaptive Disturbance Stability Control for Unmanned Aerial Vehicles based on Radial Basis Function Neural Networks and Backstepping Sliding Mode Control

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wei, Longxin Tan, Min Keng Lim, Kit Guan Teo, Kenneth Tze Kin Universiti Malaysia Sabah Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Fujian Polytechnic of Information Technology Faculty of The Internet of Things and Artificial Intelligence Fuzhou China

This paper focuses on the stability control of unmanned aerial vehicles (UAVs) under external disturbances. UAVs are frequently subjected to external disturbances, with a significant impact stemming from wind effects, which adversely affect the stability of trajectory tracking control. Current research on UAV control has not effectively addressed these external disturbance issues;while some controller algorithms may exhibit good suppression capability under minor disturbances, their effectiveness decreased significantly as the magnitude of disturbances increases. In order to enhance the interference immunity of the controller, this study proposes a method based on Radial Basis Function Neural Networks (RBFNN). The RBFNN is employed to estimate external disturbances, and an adaptive weight adjustment algorithm is utilized to accommodate various types of interference. This approach effectively suppresses disturbances by integrating the backstepping sliding mode control method into the design of both position and attitude controllers. This combined method not only enables real-time estimation of complex disturbance signals but also facilitates rapid adjustments to the control strategy, ensuring stable flight control. Simulation experiments demonstrate that the proposed control scheme maintains excellent trajectory tracking and attitude control performance under various disturbance conditions. Under specified disturbance conditions, the proposed controller exhibits strong anti-disturbance capabilities in both trajectory and attitude angle tracking, maintaining a tracking error of less than 1% under all applied disturbances, and outperforms other advanced controllers in overall performance. © 1965-2011 IEEE.

关键词： Drones

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Boosting EEG-Based Emotion Recognition with Kolmogorov-Arnold Networks

Boosting EEG-Based Emotion Recognition with Kolmogorov-Arnol...

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Advanced Research in computing (ICARC), International Conference on

作者： Sivaraj Nimishan Selvarajah Thuseethan Roshan G. Ragel Shanmuganathan Vasanthapriyan Faculty of Engineering University of Peradeniya Sri Lanka Faculty of Science and Technology Charles Darwin University Australia Faculty of Computing Sabaragamuwa University of Sri Lanka Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

As biomedical signal processing continues to advance, the need for reliable and deployable solutions for identifying human emotional states grows. Electroencephalography (EEG) is a promising modality for emotion recognition; however, existing systems often face challenges related to accuracy and efficiency. In this work, the use of the Kolmogorov-Arnold Network (KAN) for EEG-based emotion recognition is studied. KANs are a novel type of neural network that utilize edge-based activation functions, learning complex patterns with fewer parameters than traditional Multi-Layer Perceptrons (MLP). In addition, to enhance the quality of EEG signals, wavelet denoising is utilized. The proposed KAN model is evaluated on two publicly available datasets: GAMEEMO and LUMED. The results demonstrate that KAN significantly outperforms MLP in both accuracy and F1 score. On the GAMEEMO dataset, KAN achieved an average accuracy of 80.79% and an F1 score of 78.63%, while on the LUMED dataset, it reached an accuracy of 90.95% and an F1 score of 89.53%. This is a significant improvement of over 4% in both metrics compared to MLP. These results highlight the potential of KANs as an efficient and effective approach for EEG-based emotion recognition.

关键词： Measurement Emotion recognition Accuracy Noise reduction Neural networks Signal processing Multilayer perceptrons Electroencephalography Robustness Testing

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Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges

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Computers in Biology and Medicine 2025年 189卷 109834-109834页

作者： Ibrahim, Mahmoud Khalil, Yasmina Al Amirrajab, Sina Sun, Chang Breeuwer, Marcel Pluim, Josien Elen, Bart Ertaylan, Gökhan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands VITO Belgium Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

This paper presents a comprehensive systematic review of generative models (GANs, VAEs, DMs, and LLMs) used to synthesize various medical data types, including imaging (dermoscopic, mammographic, ultrasound, CT, MRI, and X-ray), text, time-series, and tabular data (EHR). Unlike previous narrowly focused reviews, our study encompasses a broad array of medical data modalities and explores various generative models. Our aim is to offer insights into their current and future applications in medical research, particularly in the context of synthesis applications, generation techniques, and evaluation methods, as well as providing a GitHub repository as a dynamic resource for ongoing collaboration and innovation. Our search strategy queries databases such as Scopus, PubMed, and ArXiv, focusing on recent works from January 2021 to November 2023, excluding reviews and perspectives. This period emphasizes recent advancements beyond GANs, which have been extensively covered in previous reviews. The survey also emphasizes the aspect of conditional generation, which is not focused on in similar work. Key contributions include a broad, multi-modality scope that identifies cross-modality insights and opportunities unavailable in single-modality surveys. While core generative techniques are transferable, we find that synthesis methods often lack sufficient integration of patient-specific context, clinical knowledge, and modality-specific requirements tailored to the unique characteristics of medical data. Conditional models leveraging textual conditioning and multimodal synthesis remain underexplored but offer promising directions for innovation. Our findings are structured around three themes: (1) Synthesis applications, highlighting clinically valid synthesis applications and significant gaps in using synthetic data beyond augmentation, such as for validation and evaluation;(2) Generation techniques, identifying gaps in personalization and cross-modality innovation;and (3) Evalu

关键词： Generative adversarial networks

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Readability-Guided Idiom-Aware Sentence Simplification (RISS) for Chinese 23rd

Readability-Guided Idiom-Aware Sentence Simplification (RISS...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Chinese Language and Culture Guangdong University of Foreign Studies Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9789819783663

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

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