检索结果-内蒙古大学图书馆

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvi, A. Abishak, D. Harish Sriraj, N. Navaneeth, S. Prasanna, R. Department of Artificial Intelligence and Data Science M Kumarasamy College of Engineering Karur India

ISBN: (纸本)9798331527549

This project focuses on developing a secure, AI-powered chatbot for retail chains, integrated within a web application to efficiently handle Frequently Asked Questions (FAQs). Utilizing IBM Watson’s advanced Natural Language Processing (NLP), the chatbot delivers precise, context-aware responses, enhancing the customer experience across retail environments. The platform offers a user-friendly interface for seamless interactions, a robust backend powered by IBM Watson’s AI model, and a database optimized for managing customer queries. A dedicated security module ensures data protection through encryption, authentication, and access control. This chatbot manages high volumes of inquiries with real-time responses, boosting customer engagement and operational efficiency while generating valuable insights for retail decision-making. Designed to be scalable and secure, the system upholds high standards of accessibility and data integrity, meeting the specific demands of the retail sector. © 2025 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Employee Attrition Analysis Using Machine Learning 2

Employee Attrition Analysis Using Machine Learning

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Solomon, Clorin Haffrin Mohankumar, Dharshini Sivanandam, Chakaravarthi Panimalar Engineering College Dept of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350328202

The production, morale and financial health of an organization can all be significantly impacted by staff attrition or the voluntary withdrawal of employees. An employee attrition study is a crucial step that enables organizations to identify the cause of the attrition and resolve the issue in order for them to grasp and successfully manage this challenge. In order to perform the research, extensive data on employee exits, job satisfaction, and other factors are initially acquired. data preprocessing and cleaning ensure that the data are accurate and suitable for analysis. It is possible to identify patterns and trends in attrition using statistical analysis and data visualization tools, and discover how turnover is connected to factors like income, work-life balance, performance, and a variety of other factors. In this project, we experiment with numerous algorithms in order to determine which one would best match the model. We are able to comprehend the top most important factors after selecting the ideal algorithm. We will examine employee attrition across a number of businesses in this article to determine its causes, which will assist the organization understand and manage a challenging position and resolve the issue. © 2024 IEEE.

关键词： Job satisfaction

来源：评论

学校读者我要写书评

暂无评论

Advancing Sign Language Prediction with Neural Network Models

Advancing Sign Language Prediction with Neural Network Model...

引用

2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Gouri, K.P. Av, Visesh Anoop, V. Parakkal, Ashlin Joji, Joman P Jyothi Engineering College Department of Artificial Intelligence and Data Science Thrissur India

ISBN: (纸本)9798350387230

Communication is a fundamental skill necessary for human interaction and societal integration. Individuals with disabilities, particularly those who are deaf or hard of hearing, face significant challenges due to limited access to specialized learning facilities and communication interfaces. This project leverages deep learning techniques to recognize hand gestures used in American Sign Language (ASL). Utilizing the publicly available ASL Alphabet dataset, this study implements Convolutional Neural Network (CNN) based on the architecture of VGG16 model. The VGG16 model, pretrained on the ImageN et dataset, was fine-tuned specifically for the ASL gesture recognition task. Advanced training techniques, including early stopping and learning rate reduction, were utilized to optimize the model's performance and prevent overfitting. data augmentation has been used to enhance the model's robustness. For enabling real-time recognition, the project integrates webcam functionality, allowing users to input live video streams for instant interpretation. Additionally, the project includes a user interface to provide a seamless interaction experience. The trained model achieved high accuracy score of 0.99 on the test set, demonstrating its effectiveness. This approach can be instrumental in developing applications such as sign language translators and gesture-to-text tools, thereby improving communication accessibility for individuals with disabilities. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity:Real-Time Anomaly Detection with Optimizing Encryption Key Selection 2

Cybersecurity:Real-Time Anomaly Detection with Optimizing En...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Nivaashini, M. Mithun, M. Shanmugam, Pabitha Mohammed Rafi, H. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331534950

In the present society where computers and technology are considered as almost indispensable, data theft is a serious concern for organizations since information is often subject to being threatened by hackers. It has however been realized that conventional approaches to guard data by encryption is not fully capable of protecting the information from complex attacks of today. To address these issues, this research provides a modern multi-layer encryption model to enhance security measures in its various layers. The above proposed system primarily identifies the occurrence of such sensitive data within the current databases and then encodes the same data, which further goes through a two-stage encryption process. The first operation to be done is encryption followed by data partition where the data is split into two sections and the two partitions encrypted using two different keys. This means that recombined data is secured, and the problem is that the data can only be decrypted using both keys. That is useful not only in increasing the level of data protection but also in creating an extra barrier in entering into the system. The model employs some of the most superior encryption methods across the range and also has enhanced keys management. Combined with this approach the system addresses the risks components of constructive security and provides a practical solution for real time protection of the data. Besides, this research offers a new technique of data encryption that will rectify the shortcoming of the existing methods of data encryption to ensure the data remains secure irrespective of the evolving and transforming threats in the world wide web. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Calorie Burn Estimation for Enhanced Physical Activity Monitoring 2

Machine Learning-Based Calorie Burn Estimation for Enhanced ...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Santhiya, S. Senthamarai, M. Jayadharshini, P. Juber, B. Akshay, J. Aneesh, S. Department of Artificial Intelligence Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode India

ISBN: (纸本)9798331541217

The rapid pace of technological advancements has created a multitude of prospects and challenges, especially in regard to mental health and personal fitness. More often than not, the fast pace of modern living forces people to make unhealthy food choices that are more readily available, leading to increased caloric intake, which represents one of the primary causes of the growing rates of obesity. This situation strongly emphasizes the urgent need for effective control over calorie intake and expenditure along with other resources to promote healthier lifestyle habits. The study takes a comprehensive approach to these issues by propounding intricate methods in machine learning designed to calculate caloric expenditure incurred during various phases of physical activity. It primarily helps people by providing information to maintain consistency in their exercise routines and promote healthy eating habits. The model proposed in this study combined complex algorithms like XGBoost, Decision Trees, and Support Vector Regression (SVR) for a highly massive dataset of over 15,000 data points. Interestingly, XGBoost reflected an observed mean absolute error of as low as 1.48, thus improving the accuracy of calorie expenditure estimation and encouraging users to engage in regular physical activity. This approach is much more impactful than just analyzing caloric intake. The Machine Learning algorithm, as it learns and evolves from experience with its user, can output thematic insights and recommendations that nudge individuals toward active fitness and nutritional oversight management. The findings highlight the impact of technology on constructive behavioral modification and the potential for improving lives as individuals transition to healthier lifestyles. © 2024 IEEE.

关键词： Support vector regression

来源：评论

学校读者我要写书评

暂无评论

FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

引用

Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Detection using Ultrasound Images based and Convolutional Neural Network 6

Breast Cancer Detection using Ultrasound Images based and Co...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Hiwase, Kunal Wande, Radha Tale, Abhay Kalekar, Harsh Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798331522667

Breast cancer forms one of the most common types of cancer among women worldwide;therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized. © 2025 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic Programming

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on artificial intelligence 2025年第1期6卷 169-183页

作者： Zhang, Fangfang Shi, Gaofeng Mei, Yi Zhang, Mengjie Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Emerson New Plymouth4310 New Zealand

Dynamic flexible job shop scheduling is an important combinatorial optimization problem that has rich real-world applications such as product processing in manufacturing. Genetic programming has been successfully used to learn scheduling heuristics for dynamic flexible job shop scheduling. Intuitively, users prefer small and effective scheduling heuristics that can not only generate promising schedules but also are computationally efficient and easy to be understood. However, a scheduling heuristic with better effectiveness tends to have a larger size, and the effectiveness of rules and rule size are potentially conflicting objectives. With the traditional dominance relation-based multiobjective algorithms, there is a search bias toward rule size, since rule size is much easier to optimized than effectiveness, and larger rules are easily abandoned, resulting in the loss of effectiveness. To address this issue, this article develops a novel multiobjective genetic programming algorithm that takes size and effectiveness of scheduling heuristics for optimization via multitask learning mechanism. Specifically, we construct two tasks for the multiobjective optimization with biased objectives using different search mechanisms for each task. The focus of the proposed algorithm is to improve the effectiveness of learned small rules by knowledge sharing between constructed tasks which is implemented with the crossover operator. The results show that our proposed algorithm performs significantly better, i.e., with smaller and more effective scheduling heuristics, than the state-of-the-art algorithms in the examined scenarios. By analyzing the population diversity, we find that the proposed algorithm has a good balance between exploration and exploitation during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

引用

Computer Modeling in engineering & sciences 2024年第6期139卷 3209-3231页

作者： Weiya Shi Shaowen Zhang Shiqiang Zhang College of Artificial Intelligence and Big Data Henan University of TechnologyZhengzhou450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural *** these efforts,the detection of small objects in remote sensing remains a formidable *** deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep ***,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection ***,the sensitivity of small objects to the bounding box perturbation further increases the detection *** this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote *** address feature loss in deep layers,we have devised a cross-layer attention fusion *** noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid ***,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)*** efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available *** experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： Small object detection attention mechanism cross-layer fusion discrete cosine transform

来源：评论

学校读者我要写书评

暂无评论

Adaptive Wavelet Convolution Stochastic Analysis Graph Neural Network for Remaining Useful Life Prediction

引用

IEEE Sensors Journal 2025年第11期25卷 19428-19441页

作者： Hu, Qidi Su, Binyi Kong, Jinzhen Liu, Weipeng Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin300130 China Hebei University of Technology School of Mechanical Engineering Tianjin300130 China

The prediction of the Remaining Useful Life (RUL) of stochastically degrading devices is of critical importance for Prognostics and Health Management (PHM). However, most of the existing approaches overlook the inherent coupling of trends and fluctuations in the degradation process. Meanwhile, these methods are struggling to effectively model the random fluctuations component in the degradation process and may lead to inaccurate prediction. To address these limitations, we propose a modified RUL prediction framework WTSAGNN. The framework extends the spatiotemporal dependencies of multi-sensor signals into the frequency domain. By leveraging the proposed adaptive orthogonal wavelet convolution to more effectively decouple the trend and fluctuation components of the degradation process. Furthermore, we introduce a stochastic differential network (SDN) to replace the traditional multilayer perceptron (MLP). The proposed method enhances the robustness of neural networks against stochastic variations in degradation processes through explicit stochastic modeling. Experimental results demonstrate that WTSAGNN outperforms existing state-of-the-art(SOTA) methods on benchmark datasets, achieving up to a 9.1% improvement in prediction accuracy with only a limited increase in computational overhead. In addition, the proposed modules are plug-and-play, allowing seamless integration with existing data-driven prediction frameworks. © 2025 IEEE.

关键词： Graph neural networks

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：