检索结果-内蒙古大学图书馆

Analyze the impact of feature selection techniques in the early prediction of CKD

International Journal of Cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Review on Smart Healthcare employing Quantum Internet of Things

引用

IEEE engineering.Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

EAI Endorsed Transactions on Energy Web

引用

EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Panigrahi, Bhawani Sankar Kanna, R. Kishore Das, Pragyan Paramita Sahoo, Susanta Kumar Dutta, Tanusree Department of Computer Science & Engineering GITAM Institute of Technology GITAM University Vishakhapatnam India Chennai India Department of Computer Science & Engineering Silicon Institute of Technology Odisha Bhubaneswar India Odisha Sarang India Hyderabad India

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centers, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies. © (2024) Panigrahi, B.S. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Learning-Based Ensemble Algorithm with Optimal Selection for Outlier Detection

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第14期49卷 171-192页

作者： Ginni, Girish Reddy Lade, Srinivasa Chakravarthi Computer Science Engineering GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India Department of Computer Science and Engineering College GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India

In this paper, we propose a Learning-based Ensemble Method with Optimal selection strategy (LbEM-OSS), which presents a new outlier detection algorithm that captures only outstanding ones of constituent models. Using KNN to define local regions and Pearson correlation to evaluate the detectors makes the ensemble robust. Our method can adapt and generalize better across different high-dimensional datasets by generating pseudo-ground truths with average and maximum aggregation strategies. On a wide range of benchmark datasets, LbEM-OSS outperformed both statistics-based and neural ensemble methods, which achieved state-of-the-art ROC-AUC as high as 97.78% in the best-case and 4-8% AUC improvements over existing methods on average. These results portray its potential for noise, different dimensionality, and heterogeneous data nature. Moreover, it is highly scalable and accurate, which makes it an essential application in practical fields like fraud detection, network security, and healthcare. This research highlights the need for dynamic selection approaches within ensemble methods, providing the groundwork for future developments in sound outlier detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Optimal detection

来源：评论

学校读者我要写书评

暂无评论

Drug and Vaccine Extractive Text Summarization Insights Using Fine-Tuned Transformers

引用

Journal of Artificial Intelligence and Technology 2024年第4期4卷 351-362页

作者： Rajesh Bandaru Y.Radhika Department of Computer Science Engineering GITAM(Deemed to be University)VisakhapatnamIndia

Text representation is a key aspect in determining the success of various text summarizing *** using pretrained transformer models has produced encouraging *** the scope of applying these models in medical and drug discovery is not examined to a proper *** address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical *** research also aims to enhance sentence *** the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are ***,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.

关键词： BART BERT extractive text summarization LexRank TexRank

来源：评论

学校读者我要写书评

暂无评论

Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern Optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

引用

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

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. This early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. Thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. The method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. The classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. The method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An efficient missing value imputation and evaluation using GK-KH means and HTR-RNN

引用

International Journal of Business Intelligence and Data Mining 2023年第1期24卷 25-46页

作者： Syavasya, C.V.S.R. Muddana, A. Lakshmi Department of Computer Science and Engineering Gitam Deemed University Rudraram Hyderabad502329 India

The accuracy of the data mining (DM) outcomes might be affected by mining and analysing incomplete datasets with missing values (MV). Thus, a complete dataset is created by the imputation of MV, which makes the analysis easier. An effectual missing values imputation (MVI) is proposed and evaluated utilising Gaussian kernel-K harmonic means (GK-KH means) and hyperbolic tangent radial-recurrent neural networks (HTR-RNN) to combat this issue. At first, preprocessing is performed on the input data as of the CKD dataset wherein the duplicate form of the data gets eradicated. Next, the missing data are handled by ignoring them;and utilising GK-KH means, the MV is imputed. Next, the data are rationalised into a structured format. Then, SDRM-DHO selects the most optimal features as of the extracted features. Lastly, the HTR-RNN classifier accepts these chosen features as input. Proposed work performed well in more accurate missing value imputation. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Neurodegenerative disease detection using Artificial Intelligence

Comparative Analysis of Neurodegenerative disease detection ...

引用

2024 International Conference on Knowledge engineering.and Communication Systems, ICKECS 2024

作者： Vidya, J. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Neurodegeneration is one of the features of several debilitating diseases that are rising rapidly. As they are not curable and progressive, early detection may help the patients and the caretakers to maintain a good quality of life. Advancements in computational methods have made it possible to diagnose and classify NDD (neurodegenerative diseases) using the automated procedure, which will be more effective than manual detection. This paper aims to comprehensively describe the neurodegenerative disease classification using ML and DL techniques that may help the community working on early detection of the disease. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Spatial Steganography for Optimal Data Concealment with Image Quality Preservation

Enhancing Spatial Steganography for Optimal Data Concealment...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Padma, R. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350309140

In the domain of steganography, the act of concealing data within an image poses a significant challengebalancing between the precision of the carrier image and the capacity for embedding information. This article introduces an innovative spatial steganography approach employing evolutionary algorithms. Proposed method introduces novel approaches to strengthen the matching of the LSB between the carrier and the concealed image. This process leads to an up-scaled concealing efficiency while minimizing deformation. The technique involved optimized pixel traversal in both row and column wise directions, pixel relocation, pixel swapping confidential bits, and inverting confidential data. A generic steganography model rooted on evolutionary algorithms is formulated to explore optimal solutions. Experimental investigations of the RECS [11] and SDID [16] method confirm that the performance of the proposed method is comparatively better than the traditional methods in terms of higher Embedding rate by 29%, PSNR by 19%, and SSIM by 15% respectively. © 2024 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Design of a Physics-Informed Learning Model for the Electromagnetic Modeling of Functional Materials

Design of a Physics-Informed Learning Model for the Electrom...

引用

2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Narang, Naina Lingam, Greeshma Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam Vishakhapatnam India Department of Computer Science and Engineering GITAM Deemed to be University Hyderabad Hyderabad India

ISBN: (纸本)9798350379693

There is a crucial need for an intelligent system to assist material scientists in fabricating and testing functional materials, such as microwave-absorbing materials. Most researchers are searching for novel materials for reduced-size microwave absorbing materials needed in various civil and military applications. The existing models used by material scientists suffer from various challenges, such as trial and error, the requirement of large dataset, and sophisticated instrumentation. This paper proposes a physics-informed learning-based model to assist the researchers in selecting, modeling, and characterizing any material depending on the user-defined application. An example is demonstrated for dielectric material to achieve the minima of the reflection coefficient. © 2024 IEEE.

关键词： Military applications

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：