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

Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

引用

Energy engineering 2025年第1期122卷 153-166页

作者： Rahool Rai Fareed Hussain Mangi Kashif Ahmed Sudhakar Kumaramsay Faculty of Mechanical and Automotive Engineering Universiti Malaysia Pahang Al-Sultan AbdullahPekan26600PahangMalaysia Faculty of Engineering Science&Technology Hamdard UniversityKarachi74600Pakistan Electrical Engineering Department SUKKUR IBA UniversitySukkur65200Pakistan Berlin School of Business and Innovation BSBI Faculty of Computer Science and InformaticsBerlin12043Germany Automotive Engineering Centre University Malaysia Pahang Al Sultan AbdullahPekan26600PahangMalaysia Centre for Research in Advanced Fluid&Processes(Fluid Centre) Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)Paya BesarKuantan26300PahangMalaysia

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

关键词： PV module efficiency water film thickness reynolds number CFD analysis PV/T renewable energy

来源：评论

学校读者我要写书评

暂无评论

Stock Price Forecasting in Finance with Artificial Neural Networks 5

Stock Price Forecasting in Finance with Artificial Neural Ne...

引用

5th Asia Conference on Information engineering, ACIE 2025

作者： Daloglu, Turgut Osi, Garibe Aslan, Murat Beskirli, Mehmet Cizre District Directorate of National Education Şirnak University Şirnak Turkey Şirnak University Department of Computer Engineering Şirnak Turkey Karamanoǧlu Mehmetbey University Faculty of Medicine Departmen of Medical Education and Informatics Karaman Turkey

ISBN: (纸本)9798331528409

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies. © 2025 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

An Intuitionistic Fuzzy Credibility Model for Exploring Document Ranking Through Similarity Measures in Information Retrieval Systems

引用

International Journal of Fuzzy Systems 2025年 1-24页

作者： Kausar, Rukhsana Malaika Riaz, Muhammad Simic, Vladimir Pamucar, Dragan Institute of Mathematics University of the Punjab Lahore Pakistan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan School of Engineering Technology Sunway University Selangor Malaysia

In the realm of information retrieval (IR) and multi-criteria decision-making (MCDM), the selection of an appropriate similarity measure is crucial for evaluating keyword significance within large corpora and understanding user behavior. This study explores two foundational methodologies for document ranking: term frequency-inverse document frequency (TF-IDF) and best matching 25 (BM25). These methods provide key insights into the efficiency of information retrieval systems. Additionally, this research introduces an innovative similarity metric grounded in intuitionistic fuzzy credibility (IFC) principles, offering a deeper perspective on document context. Furthermore, the study presents a unique aggregation operator, the intuitionistic fuzzy credibility weighted aggregation (IFCWA) operator, employing the entropy weight method (EWM) to assign weights to decision-makers and criteria. It addresses critical gaps in current assessment methodologies by delving into the pivotal role of these methodologies in assessing keyword significance within textual datasets and their implications for user behavior within the Amazon marketplace. As the digital marketplace continues to evolve, our research aims to bridge critical gaps in keyword significance assessment methodologies, contributing significantly to the development of more sophisticated and effective information retrieval systems that enhance user engagement and decision-making processes. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2025.

关键词： Online searching

来源：评论

学校读者我要写书评

暂无评论

Improvement of the Teaching Process Using the Genetic Algorithm 8th

Improvement of the Teaching Process Using the Genetic Algori...

引用

8th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2024

作者： Šimić, Goran Jevremović, Aleksandar Strugarević, Danilo School of Electrical and Computer Engineering at Academy of Technical and Art Applied Studies Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Belgrade11000 Serbia Academy of Applied Preschool Teaching and Health Studies Kruševac37000 Serbia

ISBN: (纸本)9783031723926

Teaching is a process that requires permanent observation and improvement. With the rapid development of e-learning, there was a need to review, improve and optimize the process of evaluating students’ performance. The main objective of this study is to develop and implement the procedure for automatized generation of assessment tests. For this purpose, the proposed solution includes the application of a genetic algorithm. The procedure is developed on a sample of 29 existing test cases of "Internet programming" course. The optimization resulted in better selection and reduced number of questions used for evaluation of students` performance. The results of the study and the topicality of the issue point to the need for further research, both in the aspect of students` performance assessment, as well as in other aspects of higher education. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Conceptual framework e-learning Genetic algorithm Higher education Students` performance

来源：评论

学校读者我要写书评

暂无评论

Knowledge Augmented Significant Language Model-Based Chatbot for Explainable Diabetes Mellitus Prediction

Knowledge Augmented Significant Language Model-Based Chatbot...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Mazen Elfayoumi Mohamed AbouElazm Omar Mohamed Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user's understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT- 3.5, and Gemma2. This approach augments the chatbot's responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures.

关键词： Technological innovation Accuracy Large language models Computational modeling Retrieval augmented generation Medical services Predictive models Chatbots Diabetes Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

End-to-End Optimized Ensemble Model for Alzheimer's Disease Detection

End-to-End Optimized Ensemble Model for Alzheimer's Disease ...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Hassan Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities-demographics, cognitive scores, neuropsychological assessments, and MRI data-via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management.

关键词： Support vector machines Accuracy Biological system modeling Neural networks Predictive models Boosting Feature extraction Alzheimer's disease Tuning Optimization

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency

Leveraging Large Language Models for Smart Pharmacy Systems:...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Osheba Ahmed Abou-El-Ela Osama Adel Youssef Maaod Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.

关键词： Drugs Electric potential Hospitals Large language models Mortality Medical services Chatbots Real-time systems Safety Electronic medical records

来源：评论

学校读者我要写书评

暂无评论

Practical big data techniques for end-to-end machine learning deployment: a comprehensive review

引用

Discover Data 2025年第1期3卷 1-18页

作者： El-Sayed, Ahmed Abougabal, Mohamed Lazem, Shaimaa Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Informatics Research Institute City of Scientific Research and Technological Applications New Borg El-Arab Egypt

In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fog Computing-based Real-Time Emotion Recognition using Physiological Signals

Fog Computing-based Real-Time Emotion Recognition using Phys...

引用

International Conference on Advanced Communication Technology (ICACT)

作者： Omur Fatmanur Erzurumluoglu Kadir Ozlem Asli Tuncay Atalay Ozgur Atalay Gökhan Ince Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Textile Technologies and Design Istanbul Technical University Istanbul Turkey

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system’s ability to support multiple users simultaneously.

关键词： Emotion recognition Accuracy Computational modeling Smart homes Signal processing Brain modeling Real-time systems Physiology Time factors Edge computing

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：