检索结果-内蒙古大学图书馆

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Pokale, Komal Chaudhri, Shiv Nath Department of Artificial Intelligence and Data Science Maharashtra India Department of Computer Science and Design Maharashtra India

ISBN: (纸本)9798331505745

Fine-tuning a pre-trained model is a type of transfer learning: a pre-trained model that works on a large dataset is transferred onto a particular task or a smaller dataset, such as a hyperspectral image. The efficacy of such a technique is most visible in hyperspectral imaging since the data is usually too high-dimensional with a very scarce number of available annotated datasets. By using the pre-trained model, fine-tuning enables spatial and spectral features to be captured with lower computational overheads than fully training models. Domain adaptation approaches, including feature alignment and adversarial training, are also adapted to overcome limitations caused by the domain shift phenomenon due to the variability of the sensor, environmental changes, and temporal changes. Hyperspectral imaging is applied in diverse fields, including agriculture, environmental monitoring, mineral exploration, defense, medical diagnostics, and food quality assessment. Such applications are unique in the sense that HSI can integrate spatial and spectral information to achieve detailed material analysis. Advanced learning techniques will enhance the effectiveness of HSI models in real-world scenarios. © 2025 IEEE.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Mitigating class imbalance in heart disease detection with machine learning

引用

Multimedia Tools and Applications 2024年 1-26页

作者： Pandey, Arvind Shivaji, Borge Akshay Acharya, Malika Mohbey, Krishna Kumar Department of Statistics Central University of Rajasthan Ajmer305817 India Department of Computer Science Central University of Rajasthan Ajmer305817 India

Within the context of contemporary civilization, cardiovascular disease has emerged as a severe health issue that impacts individuals of all ages and from a variety of backgrounds. The results of several surveys indicate that cardiovascular disorders are the cause of 32% of all fatalities that take place all over the world. The difficulty in predicting cardiac illnesses, such as heart attacks, is one of the significant factors that contribute to deaths. This is because predicting these conditions is complicated and requires a lot of information and skill. In the discipline of computer science, machine learning has emerged as one of the most prominent subfields, and it has been linked to the effective resolution of a great deal of complex problems, notably in the field of medicine. In this study, we adopt various supervised classifiers and analyze their effectiveness in heart disease prediction. To address the imbalanced classification problem, we apply four sampling methods, namely, Synthetic Minority Oversampling TEchnique (SMOTE), random oversampling (ROS), random undersampling (RUS), and cost-sensitive learning. For the purpose of experimentation, we have used an extremely unbalanced dataset, the Behavioral Risk Factor Surveillance System (BRFSS) 2021 Heart Disease Health Indicators dataset. In light of the facts acquired, it can be concluded that the random over-sampling strategy performed noticeably better than the other sampling procedures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

引用

3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Lung Cancer Detection: A Comparative Approach Using Regressor Methods 4

Lung Cancer Detection: A Comparative Approach Using Regresso...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Puri, Chetan Sharma, Mansi Reddy, K.T.V. Department of Computer Science and Design Wardha India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331523923

Lung cancer detection is the detection of tumors or cancerous cells in lung tissue. It is done using several medical imaging modalities, such as nuclear and genetic tests, magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays. Detection of lung cancer at an early stage is very important as it increases the likelihood of successful treatment. For better diagnostic accuracy and patient outcomes, sophisticated detection methods now utilize regression models and machine learning algorithms. As one of the most common reasons for cancer fatalities globally, lung cancer highlights the urgent need for early and accurate diagnostic techniques. This research considers the use of regression-based strategies in lung cancer detection, suggesting their ability to improve diagnostic sensitivity and patient results. We created a strong predictive model that could effectively differentiate malignant nodules through sophisticated machine learning methods, such as support vector machines (SVM), decision trees, and linear regression. Regression analysis was used to assess how well benign and malignant lung lesions could be differentiated using a large clinical and medical imaging dastaset. Findings from research show that regression methods provide a sound method of enhancing early lung cancer detection, allowing for timely intervention and increased survival rates. The significance of machine learning in medical diagnosis is also illustrated through discussions on clinical implications and future research directions. The models that were tested, Random Forest had the best accuracy (94.6%), according to Stratified K-Fold cross-validation. The other models, including Gradient Boosting, Support Vector Classifier (SVC), and XGBoost, also showed high levels of accuracy, while the Multinomial Naïve Bayes model had the worst accuracy (75.7%). By reviewing clinical and imaging information and subjecting it to machine learning algorithms to identify patterns and associat

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

LSTM-based Framework for 5G Resource Allocation Prediction 11

LSTM-based Framework for 5G Resource Allocation Prediction

引用

11th International Symposium on Telecommunication, IST 2024

作者： Pourmahboubi, Amin Tabrizchi, Hamed University of Tabriz Faculty of Mathematics Statistics and Computer Science Department of Computer Science Tabriz Iran

ISBN: (纸本)9798350356250

During the rapid evolution of 5G networks, the efficient allocation of bandwidth, frequency spectrum, and computing power is critical to maintaining a high standard of service and performance. A methodology for predicting resource allocation percentages based on Long-Short-Term Memory (LSTM) is presented in this paper. The ability to predict resource allocation percentages enhances network operators' efficiency in terms of bandwidth, frequency spectrum, and computing power utilization. Network operators can maintain high service levels by accurately predicting resource needs and allocating resources dynamically. In high-latency and high-reliability applications such as VoIP calls, video streaming, and emergency services, this is particularly critical. From Iot sensor data to high-definition video calls and emergency services, our proposed model explores the specific resource requirements of a 5 G network. Our experimental results show that the LSTM model achieves a considerable improvement in performance when compared to the best-performing machine learning model, with an R2 score of 0.976 compared to 0.937 for the best-performing model. Predictive accuracy is improved by approximately 4.16 percent. This highlights the superiority of our proposed framework to recognizing patterns in time series data. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

On the Saturation Effects of Spectral Algorithms in Large Dimensions 38

On the Saturation Effects of Spectral Algorithms in Large Di...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lu, Weihao Zhang, Haobo Li, Yicheng Lin, Qian Department of Statistics and Data Science Tsinghua University Beijing100084 China

The saturation effects, which originally refer to the fact that kernel ridge regression (KRR) fails to achieve the information-theoretical lower bound when the regression function is over-smooth, have been observed for almost 20 years and were rigorously proved recently for kernel ridge regression and some other spectral algorithms over a fixed dimensional domain. The main focus of this paper is to explore the saturation effects for a large class of spectral algorithms (including the KRR, gradient descent, etc.) in large dimensional settings where n ΢ dγ. More precisely, we first propose an improved minimax lower bound for the kernel regression problem in large dimensional settings and show that the gradient flow with early stopping strategy will result in an estimator achieving this lower bound (up to a logarithmic factor). Similar to the results in KRR, we can further determine the exact convergence rates (both upper and lower bounds) of a large class of (optimal tuned) spectral algorithms with different qualification τ's. In particular, we find that these exact rate curves (varying along γ) exhibit the periodic plateau behavior and the polynomial approximation barrier. Consequently, we can fully depict the saturation effects of the spectral algorithms and reveal a new phenomenon in large dimensional settings (i.e., the saturation effect occurs in large dimensional setting as long as the source condition s > τ while it occurs in fixed dimensional setting as long as s > 2τ). © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

引用

Multimedia Tools and Applications 2025年第15期84卷 14433-14487页

作者： Ait Mehdi, Mohamed Belattar, Khadidja Souami, Feryel Faculty of Computer Science Data Science and Artificial Intelligence Department Laboratory for Research in Artificial Intelligence University of Science and Technology Houari Boumediene BP 32 El-Alia Algiers Bab-Ezzouar16111 Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers16000 Algeria

Object localization is a critical task in image analysis, often facilitated by artificial intelligence techniques. While the Maximally Stable Extremal Regions (MSER) detection algorithm is a popular choice for local detection, its exhaustive approaches can be algorithmically complex and prone to suboptimal results with improper parameter selection. Various metaheuristic algorithms have been proposed for medical object localization to address this. In this context, four contributions are presented. Firstly, recent metaheuristics, including the Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Heap-based Optimizer (HBO), and Gradient-based Optimizer (GBO), are adapted to tackle the MSER localization problem. These algorithms are rigorously evaluated across diverse medical image datasets using multiple metrics, with their performance statistically validated through the Friedman mean rank test. The second contribution introduces a novel objective function to improve the localization process. The third contribution involves a comparative analysis of the recent algorithms against seven standard metaheuristics specifically designed for MSER localization. Lastly, we present an improved computer-aided diagnosis system that integrates an SVM-based model with Local Binary Patterns (LBP) descriptors extracted from each MSER alongside DenseNet-121 features. Experimental results demonstrate that the HBO and SMA optimizers outperform conventional algorithms, showing a higher diversity, improved exploitation and exploration capabilities, a well-balanced exploration-exploitation trade-off, elevated fitness values, faster convergence speeds, and enhanced computational efficiency. Additionally, we integrate these findings into an improved computer-aided diagnosis system, yielding classification results surpassing state-of-the-art models. These findings highlight the potential advantages of this approach, especially in object detection applications. © The Author(s), under

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Resume Ranking and Shortlisting with DistilBERT and XLM 2

Resume Ranking and Shortlisting with DistilBERT and XLM

引用

2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Mukherjee, Anuska Umme Salma, M. Christ Deemed to Be University Department of Statistics and Data Science Karnataka India

ISBN: (纸本)9798350383287

The research presented in this paper offers a solution to the time-consuming task of manual recruitment process in the field of human resources (HR). Screening resumes is a challenging and crucial responsibility for HR personnel. A single job opening can attract hundreds of applications. HR employees invest additional time in the candidate selection process to identify the most suitable candidate for the position. Shortlisting the best candidates and selecting the appropriate individual for the job can be difficult and time-consuming. The proposed study aims to streamline the process by identifying candidates who closely match the job requirements based on the skills listed in their resumes. Since it is an automated process, the candidate's individual preferences and soft skills remain unaffected by the hiring process. We leverage advanced Natural Language Processing (NLP) models to improve the recruitment process. Specifically, our emphasis lies in the utilization of the distilBERT model and the XLM (Crosslingual Language Model). This paper explores the application of these two models in taking hundreds of resumes for the job as input and providing the ranked resumes fit for the job as output. To refine our approach further, two types of metrics for resume ranking, such as Cosine similarity score and Spatial Euclidean distance, are used, and the results are compared. Intriguingly, distilBERT and XLM result in different sets of top ten ranked resumes, highlighting the nuanced variations in their ranking approaches. © 2024 IEEE.

关键词： Personnel

来源：评论

学校读者我要写书评

暂无评论

Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

来源：评论

学校读者我要写书评

暂无评论

Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

引用

SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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：