检索结果-内蒙古大学图书馆

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Rajendar, Sivaramakrishnan Ranganathan, Sanjay Lakshmanan, Thavamuthubaskaran Nachimuthu, Vasanth Paramasivam, Sriram Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350396638

Stress is a common mental health problem that affects individuals from all walks of life. It can manifest in various forms, such as emotional, physical, or cognitive distress. The detection of stress in individuals is crucial for early intervention and treatment. However, traditional methods of stress detection, such as self-report surveys or physiological measures have limitations. In recent years, a social media platform like Twitter, have become a rich source of data for studying mental health. This study proposes the use of deep learning techniques, specifically the combination of LSTM and 1D CNN, to detect human stress levels from social media posts, particularly tweets. By leveraging Kaggle Twitter datasets alongside of the recent original Tweets, the model was well trained and validated, achieving high accuracy levels in stress detection. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Employee Behaviour and Attention of Career Building 7

Employee Behaviour and Attention of Career Building

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Suneetha, T.V. Shivani, Bhanda Shreshta, Chidrapu Begum, Mehrajunnisa Nargees Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Department Hyderabad India

ISBN: (纸本)9798350397253

More study into machine learning's potential uses in corporate settings is warranted in light of rising interest among business executives and stakeholders. The loss of skilled workers would result in less productivity of the organization. In this study, ML approaches is applied to investigate the root reasons for employee dissatisfaction. There were three major studies that employed the IBM Watson-generated dataset to foretell employee turnover. Many different machine-learning models, mostly built from scratch but some using the original lesson dataset, were trained in our initial experiment. Two such models were a small-sample size support vector machine (SVM) and a stochastic K-nearest neighbour (KNN) approach. The dataset is updated using aforementioned machine learning methods and employed the ADASYN synthetic methodology to reduce class disparity in our experiment. In the third trial, data types were manually standardised through an iterative and gradual process. Regarding this, it is found that KNN training on an ADASYN-balanced dataset with a K-size of 3 yielded the best results (0.93 F1-score). Using feature selection and random forest, F1-score up to 0.909 is achieved while using just 12 of the possible 29 characteristics. The accuracy achieved is 85% at value K= *** is concluded that this model would help a lot of organizations to Figure out the reasons behind the low performance of the employees. Also, it tries to improve whatever is causing the employees to leave the company. © 2023 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Morpho-contour exponential estimation algorithm for predicting breast tumor growth from MRI imagery

引用

International Journal of Information technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, B. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

Breast tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the body. Early detection of such metastatic sites can be facilitated by predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified breast tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces but also pinpoints regions indicative of infectious spread. This precision is achieved by enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the breast tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate by analyzing the distance between two distinct random points on the tumor's segmented region. Subsequently, this derived information, processed through a gradient boosting approach, predicts the likelihood of tumor growth across two different breast tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ability to accurately segment both benign and malignant breast tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for benign and malignant tumors in BUSnet, and even higher values for other networks. Secondly, we explored MoCEE's capability to estimate tumor size, considering different probability growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notably, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of probabilities. These findings highlight MoCEE's versatility and robustness, making it a valuable tool for precise breast tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient boosting approach Breast tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

来源：评论

学校读者我要写书评

暂无评论

Analytical Approach Towards Radiological Image-based Fracture Assessment Using Artificial Intelligence 1

Analytical Approach Towards Radiological Image-based Fractur...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Sylvia, Takhellambam Dubey, Sanjay Kumar Thongam, Khelchandra Amity University Computer Science & Engineering Dept. Uttar Pradesh Noida India National Institute of Technology Manipur Computer Science & Engineering Dept. Imphal India

ISBN: (纸本)9798350353648

The utilization of Artificial Intelligence (AI) techniques has been prevalent in various applications and the gradual increase in implementation towards healthcare sectors has scaled up. computer aided diagnosis has emerged as a major research area directed toward efficient use of resources. Fracture of bone has been a popular health problem associated with risk for mortality. Fractures are frequent in the long bones or the weight-bearing bones. AI techniques to accurately detect and classify fractures are still a challenging task beyond the conventional methods. Our objective is to assess the effectiveness of multiple algorithms in identifying and categorizing ankle fractures using radiological images. This review paper focuses on several available approaches of Deep-Learning (DL) and Machine-Learning (ML) regarding Ankle Fracture Classification and Identification based on Radiological Images for Medical Applications. The goal is to assist physicians and orthopaedics with simple and realistic decision support systems. In addition to a comprehensive analysis of the research, this study looked at several real-world examples of AI applications in the healthcare industry. Consequently, it is critical to have a fundamental grasp of these ML and DL algorithms to be ready for upcoming advancements in AI. We have reviewed and classified scientific conference and journal articles used for various AI techniques and offer future utility as a comparative analysis for further research in healthcare sectors. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

引用

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

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

引用

IEEE Transactions on engineering Management 2024年 71卷 12448-12462页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja'fari, Forough Pinto, Pedro Chuang, Huan-Ming National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrotechnics and Telecommunications Department Porto4900-347 Portugal Sharif University of Technology Department of Computer Engineering Tehran11155-9161 Iran National Yunlin University of Science and Technology Department of Information Management Yunlin640 Taiwan

The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of 'selecting the feature with the lowest number of distinct values,' 'selecting the feature with the lowest entropy,' and 'selecting the feature with the highest entropy.' For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria. © 1988-2012 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Exchange Rate Forecasting in Money Market

Exchange Rate Forecasting in Money Market

引用

2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Panda, Manaswinee Madhumita Panda, Surya Narayan Pattnaik, Prasant Kumar Chitkara University Institute of Engineering and Technology Chitkara University Computer Science and Engineering Punjab India Kiit University Computer Science and Engineering School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350325904

Changes in currency exchange rates can impact a country's economy, making it important to carefully consider and anticipate foreign currency exchange rates in the money market. Therefore, accurately predicting FOREX fluctuations is a significant concern. Convolutional neural networks (CNN) and random forest regression have been suggested as prediction algorithms for foreign exchange. The efficiency of the approach is assessed by forecasting the worth of the New Zealand dollar against the US dollar (NZU/USD)for 2, 4, 6, 8, 10, 12, and 14 weeks into the future. The best weight for the suggested Model is calculated utilizing the adaptive learning rate method (ADAM) optimization approach. The new approach provides an additional precise way to estimate foreign exchange rates (Forex) based on the estimation criteria. The CNN model with the layer for random forest regression performs better than the other three models. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Study of Spam Email Filtering Methods using Supervised Machine Learning Techniques 7

Study of Spam Email Filtering Methods using Supervised Machi...

引用

7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Ch, Mallikarjuna Rao Sahithi Reddy, S. Konlyada, Sathvika Konagala, Vineela Chenda, Vanditha Das Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Department Hyderabad India

ISBN: (纸本)9798350397284

The development of the Internet paved the way for the commercial exchange of data-extensive messages in the form of E-Mails. But, a big issue is spam E-Mails which are unwanted and incessant emails that the user may or may not have signed up for. Hence, users need to have a filter that distinguishes spam emails from ham to avoid unnecessary and potentially harmful messages. This study proposes a framework for automatically detecting spam emails using supervised machine learning techniques. The models are trained on an openly available dataset with additional methods that help in gaining insight into the data. The performance and the accuracy of different models for segregating incoming emails are then tested and compared. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Enhanced CNN-SVM Fusion for In-Depth Facial Acne Analysis 3

Enhanced CNN-SVM Fusion for In-Depth Facial Acne Analysis

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Prabha, Chander Monga, Sakshra Srivastava, Prakash Singla, Gagan Saluja, Nitin Kumar, Vinod Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science & Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350384598

The purpose of this work is to present and access a comprehensive analysis of an advanced facial acne classification method that is focused on machine learning. Hormonal acne, pustules, papules, cysts, blackheads (open comedones), and whiteheads (closed comedones) were all included in the dataset that was used to train and evaluate the algorithm. Accuracy, recall, and F1-Score provide evidence that the model correctly recognizes a variety of acne types, with scores ranging from 93.98% to 95.86%. Other evaluation metrics also suggest that the model displays this accuracy and efficiency. As an additional point of interest, the support percentage demonstrates how samples are spread within each acne class, hence demonstrating the balanced distribution of classes within the dataset. The model demonstrated that it was very accurate, obtaining a total score of 98% for its performance. In addition, aggregate performance metrics such as micro, weighted, and macro averages provide further evidence that the algorithm is capable of providing a diagnosis of acne that is both accurate and trustworthy across all types of acne. The research contributes to the development of automated systems for detecting acne on the face, and dermatologists may find it beneficial to utilize the data acquired to improve their ability to diagnose and treat patients who suffer from facial acne and health issues. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Exploration and Prospects of Large Language Models with ReAct Integration in Maritime Risk Identification

Exploration and Prospects of Large Language Models with ReAc...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Chen Shu, Zhong-Cheng Guo, Lei Chen, Yu-Han School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9789819639601

Maritime risk identification technology is more and more important for ships sailing on the sea, and can ensure the safety of ships sailing on the sea. However, traditional risk identification methods rely on historical data and qualitative experience, which is difficult to deal with complex and changeable environment. Recently, advancements in artificial intelligence, particularly large language models, have introduced new solutions to these challenges. While these models excel in data processing and pattern recognition, their effective application within the maritime domain remains problematic. This paper presents the ReAct (Reasoning and Acting) model, which enhances the adaptability of large language models by simulating human reasoning and actions. This paper first reviews the traditional methods and existing problems of maritime risk identification, and then discusses the basic principle of ReAct model and its potential in risk identification. Finally, the study proposes the integration of ReAct with large language models, highlighting the advantages and future prospects of this approach. This research aims to provide new theoretical and technological support for maritime risk identification, ultimately contributing to improved risk management and accident prevention. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Risk management

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：