检索结果-内蒙古大学图书馆

EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

引用

Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Batra, Devanshu Chaurasia, Brijesh Kumar Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Glaucoma is currently one of the most significant causes of permanent blindness. Fundus imaging is the most popular glaucoma screening method because of the compromises it has to make in terms of portability, size, and cost. In recent years, convolution neural networks (CNNs) have revolutionized computer vision. Convolution is a "local" CNN technique that is only applicable to a small region surrounding an image. Vision Transformers (ViT) use self-attention, which is a "global" activity since it collects information from the entire image. As a result, the ViT can successfully gather distant semantic relevance from an image. This study examined several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad. With 1750 Healthy and Glaucoma images in the IEEE fundus image dataset and 4800 healthy and glaucoma images in the LAG fundus image dataset, we trained and tested the ViT model on these datasets. Additionally, the datasets underwent image scaling, auto-rotation, and auto-contrast adjustment via adaptive equalization during preprocessing. The results demonstrated that preparing the provided dataset with various optimizers improved accuracy and other performance metrics. Additionally, according to the results, the Nadam Optimizer improved accuracy in the adaptive equalized preprocessing of the IEEE dataset by up to 97.8% and in the adaptive equalized preprocessing of the LAG dataset by up to 92%, both of which were followed by auto rotation and image resizing processes. In addition to integrating our vision transformer model with the shift tokenization model, we also combined ViT with a hybrid model that consisted of six different models, including SVM, Gaussian NB, Bernoulli NB, Decision Tree, KNN, and Random Forest, based on which optimizer was the most successful for each dataset. Empirical results show that the SVM Model worked well and improved accuracy by up to 93% with precision of up to 94% in the adaptive equalization preprocess

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study for Prediction of Hematopoietic Stem Cell Transplantation-Related Mortality 6th

A Comparative Study for Prediction of Hematopoietic Stem Cel...

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Hanselia, Rishabh Choubey, Dilip Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bihar India

ISBN: (纸本)9789819933143

Multipotent hematopoietic stem cells are transplanted during hematopoietic stem cell therapy (HSCT) in order to replicate inside of a patient and produce more healthy blood cells. These cells are often taken from bone marrow, peripheral blood, or umbilical cord blood. Patients with particular blood or bone marrow malignancies, like multiple myeloma or leukemia, are those who have the procedure the most frequently. Though a lifesaving procedure, it comes with its risk;hence, to minimize the risk, prediction of survivability and the factors affecting it is crucial. In this study, the authors have done an extensive and rigorous analysis of various works done in predicting the mortality of patients undergoing hematopoietic stem cell transplantation that involves the use of machine learning and data mining techniques and have compared them. This study gives an overview of the available machine learning and data mining techniques in improving risk prediction for patients undergoing HSCT that provides an alternative to traditional risk scores and indexes. Though these techniques already outperform the in-use risk scores and indexes, they can be still improved upon by the use of a specialized and large amount of data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Post Flood Damage Assessment Using Aerial Imagery 2

Post Flood Damage Assessment Using Aerial Imagery

引用

2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Dixit, Tarun Singh, Pramod Kumar ABV-Indian Institute of Information Technology and Management Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350305142

Visual Scene understanding is one of the most efficient methods to assess the damage caused by natural disasters. Many datasets exist that contain aerial images from satellites or UAVs. Floodnet is one of those datasets containing images useful to assess the damage done by a flood. Different network architectures have been developed for classification and semantic segmentation using supervised learning to achieve reasonable accuracy. However, they cannot address the problem of considerable class imbalance in the Floodnet dataset. This imbalance arises due to the few labeled images, as labeling an aerial image is an expensive and high-effort task. This imbalance in classes prevents the model from achieving good accuracy. This work uses a semi-supervised learning approach to address the classification and semantic segmentation problem. The classical Pseudo Labeling approach has been implemented on the best-performing model in classification and semantic segmentation objectives. Pseudo-labels are assigned to unlabeled images by predicting classes from labeled images based on the probability distribution. These pseudo-labels are then trained with ground-truth data to achieve better results. Classification models train themselves in a semi-supervised manner based on the pseudo-labels' generation algorithm. Semantic Segmentation models also generate pseudo-label masks for pixel-level identification. Classification model Resnet50 trained in a semi-supervised manner achieves accuracy and Fl-score of 94.97% and 0.971, respectively. Semantic Segmentation model Segformer achieves a Mean Intersection over Union (MIoU) score of 0.596. © 2023 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

An Efficient Approach to Resolve Social Dilemma in P2P Networks 37th

An Efficient Approach to Resolve Social Dilemma in P2P Net...

引用

37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Kishor, Avadh Niyogi, Rajdeep Department of Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Gwalior Gwalior India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9783031290558

The premise of a peer-to-peer (P2P) system is based on the voluntary contribution of peers. However, an inherent conflict between individual rationality and social welfare engenders a new situation called the free-rider problem, in which some peers consume the resource without contributing to return. The tendency of free-riding in a P2P system is a critical issue, as it threatens the viability of the whole system. This can be reduced by employing an effective mechanism that deters the free-riding by incentivizing peers to contribute to the system. In this paper, we consider a social planner that acts as a decision-maker among individual agents. We cast the problem as a constrained optimization problem. A metaheuristic algorithm called the artificial bee colony algorithm is used to solve the problem. The empirical results show that each peer contributes to its full capacity and receives a fair (equal) profit share. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques

引用

Cyber-Physical Systems 2023年第3期9卷 244-259页

作者： Kshirsagar, Deepak Kumar, Sandeep Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Engineering and IT College of Engineering Pune Pune India

The use of machine learning models in intrusion detection systems (IDSs) takes more time to build the model with many features and degrade the performance. The present paper proposes an ensemble of filter feature selection techniques (EFFST) to obtain a significant feature subset for web attack detection by selecting one-fourth split of the ranked features. The experimentation on the CICIDS 2017 dataset shows that the proposed EFFST method provides a detection rate of 99.9909%, with J48 using 24 features. The system’s performance is compared to the original features and traditional relevant feature selection methods employed in IDSs. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

引用

Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Spectral Unmixing Using Shape-Driven Blind Deconvolution in Convolutional Autoencoder 13

Spectral Unmixing Using Shape-Driven Blind Deconvolution in ...

引用

13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Banda, Nithish Reddy Galla, Akhil Samrat, B. Ghorai, Mrinmoy Indian Institute of Information Technology Sri City Computer Science and Engineering Department India

ISBN: (纸本)9798350395570

This research paper involves the integration of spectral shape information into a convolutional autoencoder (CAE) for blind unmixing in hyperspectral image analysis. We propose a novel approach that incorporates shape-based similarity measures in the CAE to capture and represent underlying spectral characteristics. Extensive experiments on benchmark datasets demonstrate improved quality in blind unmixing. The impact of architectural choices is also investigated, providing insights for optimal configuration at the end. This research contributes to advancing blind unmixing techniques in the perspective of shape-driven information and offers a promising avenue for enhancing accuracy and efficiency in hyperspectral image analysis © 2023 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Explainability to Business: Demystify Transformer Models with Attention-based Explanations 2

Explainability to Business: Demystify Transformer Models wit...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Thiruthuvaraj, Rajasekhar Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science & Engineering Kottayam India

ISBN: (纸本)9781665456302

Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations. © 2023 IEEE.

关键词： Forecasting

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：