检索结果-内蒙古大学图书馆

2025 IEEE international conference on Computational, Communication and information Technology, ICCCIT 2025

作者： Saravanan, V. Indumathi, G.S. James, Sathish Kumar Pandi, V. Samuthira Shakila, J. Banumathi, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India BNY Mellon Center PittsburghPA15219 United States Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Jeppiaar Engineering College Rajiv Gandhi Salai Department of CSE Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India

ISBN: (纸本)9798331512965

In an era where digital connectivity is ubiquitous, the imperative to safeguard cyberspace has never been more pressing. This research delves into the multifaceted landscape of cybersecurity, examining the strategies employed by governments, businesses, and individuals to protect sensitive information and critical infrastructure from cyber threats. By leveraging a mixed-methods approach, which includes a review of existing literature, case studies, and expert interviews, the research provides a nuanced understanding of the cybersecurity ecosystem. Key findings reveal that while technological advancements have bolstered defense mechanisms, including sophisticated malware, advanced persistent threats (APTs), and insider threats, continues to outpace traditional security measures. Moreover, the study underscores the critical role of human factors, such as user error and inadequate training, as significant vulnerabilities. The research concludes with a set of recommendations aimed at enhancing cybersecurity posture, including the adoption of a proactive, adaptive defense strategy, increased investment in cybersecurity education and awareness, and the fostering of international cooperation to combat transnational cybercrime. The insights gleaned from this analysis are intended to inform policymakers, industry leaders, and cybersecurity professionals, equipping them with the knowledge to fortify the digital frontier against evolving threats. © 2025 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

An Interactive Question Answer Based System on Alzheimer's Disease Using Retrieval Augmented Generation 37th

An Interactive Question Answer Based System on Alzheimer's D...

引用

37th international conference on computer Applications in Industry and engineering

作者： Sen, Sujoy Sarkar, Samay Ghosh, Partha Goto, Takaaki Sen, Soumya Univ Calcutta Kolkata India Acad Technol Adisaptagram W Bengal India Toyo Univ Saitama Japan

ISBN: (纸本)9783031762727;9783031762734

Alzheimer's Disease (AD) presents profound challenges to healthcare systems worldwide, necessitating efficient access to accurate information for optimal care delivery. Effective management of AD requires timely access to accurate information spanning disease etiology, diagnosis, treatment options, and caregiving strategies. However, the vast and constantly evolving body of AD-related literature poses a considerable barrier to efficient information retrieval, particularly for healthcare professionals operating in time-constrained environments. This paper outlines the objectives of a specialized Retrieval-Augmented Generation (RAG) system designed for answering questions related to Alzheimer's Disease (AD), employing prompt engineering and utilizing Pinecone as the vector database. With the goal of enhancing accessibility and comprehension of AD-related information, the system aims to efficiently retrieve relevant data from diverse sources and generate contextually relevant answers tailored to user queries. By leveraging advanced techniques in prompt engineering and vector similarity search, the RAG system empowers healthcare professionals, patients, and caregivers with timely access to accurate and comprehensive information, ultimately facilitating informed decision-making and improving patient outcomes in AD management.

关键词： Alzheimer's Disease Question Answering Retrieval-Augmented Generation Prompt engineering Pinecone Vector Database

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity with Machine Learning: Implementing AI Algorithms for Intrusion Prevention, advanced Data Protection, and Real-Time Threat Analysis

Cybersecurity with Machine Learning: Implementing AI Algorit...

引用

2025 IEEE international conference on Computational, Communication and information Technology, ICCCIT 2025

作者： Bonagiri, Krishna Krishnamoorthy, P. Keerthiga, V. Kirubakaran, D. David, Rajesh Nancharaiah, B. Quadrant Technologies RedmondWA98052 United States Sasi Institute of Technology & Engineering Department of Computer Science and Engineering Andhra Pradesh India Anand Institute of Higher Technology Department of CSE Kazhipattur Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India KUKA U.S. Holdings Company LLC AustinTX78758 United States Usha Rama College of Engineering and Technology Department of ECE A.P. Telaprolu India

ISBN: (纸本)9798331512965

In an era where cyber attacks are getting increasingly complex, the incorporation of machine learning (ML) algorithms into cybersecurity processes has emerged as a crucial technique for boosting protection against potential intrusions and data breaches. For the purpose of preventing intrusions, providing superior data protection, and conducting real-time threat analysis, this research investigates the application of machine learning approaches that are driven by artificial intelligence. Organizations are able to increase their preventative defenses against cyber attacks by utilizing algorithms that are capable of analyzing massive volumes of data and identifying patterns that are indicative of harmful behavior. Several machine learning (ML) approaches, including supervised and unsupervised learning, anomaly detection, and neural networks, are investigated in this study. The purpose of the study is to demonstrate the usefulness of these approaches in identifying and responding to a wide range of security concerns. In addition to this, the research investigates the significance of continual learning and adaptation in machine learning models, which enables these models to develop alongside developments in new threats. The findings highlight the potential for machine learning to change cybersecurity frameworks by delivering speedy and automatic reactions to incidents. This would lead to the protection of sensitive information and an overall improvement in the system's resilience. In the end, this research highlights the importance of implementing tactics that are based on artificial intelligence in order to meet the intricacies of modern cyber threats and maintain robust security in a world that is becoming increasingly digital. © 2025 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

来源：评论

学校读者我要写书评

暂无评论

A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

Technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Towards Hybrid Meta-Heuristic Analysis for the Optimization of Fundamental Performance in Robotic Systems

引用

international Journal of advanced computer science and Applications 2025年第4期16卷 1136-1149页

作者： Dabbaghi, Boudour Hamidi, Faiçal Aoun, Mohamed Jerbi, Houssem Research Laboratory MACS LR16ES22 University of Gabes Gabes Tunisia Laboratory of Information-Communication and Knowledge Sciences and Techniques University of Western Brittany Lorient France Department of Industrial Engineering College of Engineering University of Hail Hail1234 Saudi Arabia

This paper examines the concept of implementing a hybrid optimization approach through combining analytical and meta-heuristic approaches to improve the performance of practical engineering systems. Designed in support of artificial intelligence strategy, the proposed approach ensures high stability and efficiency under actuators saturation constraint. This is a well-known and sensitive problem in robotics and control. Specifically, this paper deals with the problem of computing the stability region for controlled systems. While addressing this issue, research approaches take into consideration the fact that actuator saturation may occur. It is imperative to maintain this propriety and ensure the reliability of design control systems, particularly those developed to control robot actuators. Models of the studied systems are based on differential algebraic representations and polytypic regions in state space. The developed technique combines LMI with an improved meta-heuristic based optimization approach that fast searches and enlarge domains of attraction for robot actuators. The direct Lyapunov theory is used to analyze and validate stability key performance. A numerical example study has been conducted to validate the proposed approach’s efficacy and efficiency. A comparative benchmarking study has been carried out to highlight the main concepts and results of this study. © (2025), (science and information Organization). All rights reserved.

关键词： Robots

来源：评论

学校读者我要写书评

暂无评论

advanced Feature engineering and Model Improvement for Sarcasm Detection Using Deep Learning Architectures

Advanced Feature Engineering and Model Improvement for Sarca...

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Roy, Manish Chandra Kishoro Bisoy, Sukant Das, Pradipta C.V. Raman Global Universtiy Depatment of Computer Science & Enginnering Odisha Bhubaneswar India Veer Surendra Sai University of Technology Department of Information Technology Odisha Burla India

ISBN: (纸本)9798331529833

Sarcasm detection, crucial for enhancing sentiment analysis and human-computer interaction, poses challenges due to its nuanced, context-dependent nature. This study investigates advanced feature engineering and model enhancement techniques to improve sarcasm detection using deep learning. Traditional models often falter with sarcasm's complexities, so we propose a hybrid approach combining contextual embeddings, syntactic patterns, and sentiment-aware features. We test deep learning models like LSTM, GRU, and Transformer-based architectures, incorporating attention mechanisms and pre-trained models like BERT for focused analysis. Our method uses sarcasm-specific cues, with data preprocessing and augmentation. We employ Recursive Feature Elimination (RFE) and PCA to optimize feature space, and evaluate on public sarcasm datasets. Results show improved accuracy and robustness, highlighting applications in social media analysis, customer feedback, and content moderation. © 2025 IEEE.

关键词： Contrastive Learning

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：