检索结果-内蒙古大学图书馆

7th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2019

作者： Guggari, shankru Kadappa, vijayakumar Umadevi, v. Department of Computer Science and Engineering B.M.S. College of Engineering Bengaluru India Department of Computer Applications B.M.S. College of Engineering Bengaluru India

ISBN: (纸本)9783030661861

Decision tree is a classification technique which is widely used in many real world applications. It suffers from few challenges like structural instability, overfitting, curse of dimensionality etc. To address some of these issues, vertical partitioning paradigm is used in the literature. In vertical partitioning paradigm, the feature set is split into multiple subsets and these subsets are used for subsequent processing instead of original feature set. In this paper, we propose a novel partitioning approach using highest-size frequent itemsets. The efficiency of the method is evaluated using 5 standard datasets from UCI repository. The proposed method achieves significant improvement in classification accuracy and demonstrates better or competitive structural stability as compared to classical decision tree methods. The statistical significance of the results obtained by the proposed method is demonstrated using t-test, wilcoxon signed rank and pearson correlation tests. © 2020, springer Nature switzerland AG.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Heart disease prediction using feature selection and ensemble learning techniques 3

Heart disease prediction using feature selection and ensembl...

引用

3rd International Conference on Intelligent Communication Technologies and virtual Mobile Networks, ICICv 2021

作者： Lakshmanarao, A. srisaila, A. Kiran, T.srinivasa Ravi Aditya Engineering College Department of Information Technology Surampalem A.P India V.R Siddhartha Engineering College Department of Information Technology Vijayawada A.P India P.B. Siddhartha College of Arts Science Department of Computer Science Vijayawada A.P India

ISBN: (纸本)9780738111834

Cardiovascular diseases (heart-related diseases) are the reason for the deaths of 18 million people every year in the world. According to WHO, 31% of the deaths worldwide are due to heart-related diseases. In this paper, we proposed a novel machine learning model for heart disease prediction. The proposed method was tested on two different datasets from Kaggle and UCI. We applied sampling techniques to the unbalanced dataset and feature selection techniques are used to find the best features. Later several classifier models were applied and achieved good accuracy with ensemble classifier. The experimentations on two datasets shown that the proposed model is effective for heart disease prediction. Python was used for all implementations. © 2021 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

INN: Experimental Evaluation of Machine Learning based Obesity and Overweight Prediction Using Intensive Neural Networks

INN: Experimental Evaluation of Machine Learning Based Obesi...

引用

Innovative Computing, Intelligent Communication and smart Electrical systems (ICsEs), International Conference on

作者： C vijayalakshmi v. Kalist G. Charulatha J Giri M O sabri Ulaganathan.M. s Department of Computer Science and Engineering B S Abdur Rahman Crescent Institute of Science and Technology Chennai Department of ECE Sathyabama Institute of Science and Technology Chennai Department of Biomedical Engineering Selvam College of Technology Namakkal Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Business Information Technology Department Faculty of Economic Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Obesity and overweight are significant public health concerns worldwide, contributing to a range of chronic diseases, including cardiovascular disease, diabetes, and certain cancers. This study presents an advanced predictive model for obesity and overweight detection using an Intensive Neural Network (INN) integrated with support vector Machine (svM). The model is designed to address the complex, non-linear relationships between various factors contributing to obesity, such as age, gender, height, weight, bMI, physical activity levels, dietary habits, and genetic factors. A dataset consisting of 28,765 manually collected data points was used to train and validate the model. Through meticulous data preprocessing, including handling missing values, noise reduction, feature scaling, and feature selection, the data was prepared for optimal model performance. The proposed INN + svM model achieved an outstanding accuracy of 94.76%, outperforming nine existing models, including Convolutional Neural Network (90.47%) and Deep Neural Network (89.12%). Additional metrics such as Precision (93.22%), Recall (92.88%), and F1-score (93.05%) further validate the model's superiority. The integration of svM with INN enhances the model's ability to classify difficult cases, resulting in a robust and reliable tool for predicting obesity and overweight conditions. The results suggest that this hybrid approach holds significant potential for improving early detection and intervention strategies in healthcare.

关键词： support vector machines Measurement Obesity Noise reduction Predictive models Genetics Feature extraction Data models Reliability Public healthcare

来源：评论

学校读者我要写书评

暂无评论

CLIP-AE: A Multi-modal Unsupervised Images Enhancement Method based on High-order Adaptive Curve for visual Disbalance Defects

引用

IEEE Transactions on Multimedia 2025年

作者： Wu, Jiaqi Zhang, shihao Hou, Mingshuo Wang, Zehua Chen, Wei Tian, Zijian Yu, F. Richard Leung, victor C. M. School of Artificial Intelligence Beijing100083 China University of British Columbia Department of Electrical and Computer Engineering 2332 Main Mall Vancouver VancouverBCV6T 1Z4 Canada The University of British Columbia Department of Electrical and Computer Engineering 2332 Main Mall Vancouver VancouverBCV6T 1Z4 Canada China University of Mining and Technology School of Computer Science & Technology Jiangsu Xuzhou221116 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

For visual disbalance defects (vDDs) in low-light images, such as brightness unevenness and color imbalance, existing enhancement methods struggle to extract defect features from local regions and apply adaptive enhancement based on varying degrees of these defects. To address these challenges, we propose an unsupervised multi-modal enhancement method based on a high-order adaptive curve, named CLIP-AE. specifically, we introduce a multi-modal recurrent optimization approach utilizing contrastive language-image pre-training (CLIP). This method iteratively optimizes variable embedded prompts and an Adaptive Enhancement Module (AEM) to establish dependencies between the prompts and detailed style features in the images, guiding the AEM to perform adaptive image enhancement. Additionally, we implement a progressive feature alignment strategy to enhance the model's ability to perceive style features and improve optimization efficiency by using multiple enhanced images with identical content features and incremental style features. In the AEM, the optimized Hyperparameters Generative Network (HGN) generates the optimal hyperparameters, which drive a High-Dimensional Nested Gamma correction (HDN-Gamma) to perform pixel-wise adaptive enhancement for vDDs. HDN-Gamma further maps pixel values using specific enhancement curves to avoid artifacts. Extensive experiments demonstrate that our method effectively improves visual disbalance defects and reduces artifacts. Compared to seven state-of-the-art algorithms, our method shows significant improvements (PsNR: 16.46%, 16.89%, and 15.14%;ssIM: 9.26%, 8.02%, and 9.85%;MUsIQ: 6.37%, 6.54%, and 7.45%) on the LOL, sICE, and MIT-Adobe FiveK datasets. Our approach offers a novel solution for applying multimedia technology in low-light image enhancement tasks. © 1999-2012 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

smart Heart Disease Prediction system with IoT and Fog Computing sectors Enabled by Cascaded Deep Learning Model

引用

Computational Intelligence and Neuroscience 2022年 2022卷 1070697页

作者： Raju, K. butchi Dara, suresh vidyarthi, Ankit Gupta, v. Mnssvkr Khan, baseem Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India Department of Computer Science and Engineering B. V. Raju Institute of Technology NarsapurTS 502313 India Department of Cse and It Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College BhimavaramAP 534204 India Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diabetes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. because of the emergence of smart wearable gadgets, fog computing and "Internet of Things"(IoT) solutions have become necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attributes is also gathered. All these features are gathered and subjected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized by the Galactic swarm Optimization (GsO). Through the performance analysis, the precision of the suggested GsO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PsO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LsTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models. © 2022 K. butchi Raju et al.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Machine learning systems for detecting schizophrenia 4

Machine learning systems for detecting schizophrenia

引用

4th International Conference on IoT in social, Mobile, Analytics and Cloud, IsMAC 2020

作者： swati, N.v. Indiramma, M. B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781728154640

schizophrenia is a neurological disorder which has drawn a lot of attention around the world. It includes symptoms such as social withdrawal, hallucinations, delusions, confused thinking leading to other major disorders including obsessive-compulsive disorder and panic disorder. Diagnosis of psychological disorders is difficult based on one's behavioral changes. It is difficult to diagnose the exact psychological disorder due to similar symptom exhibition. More the time is taken to diagnose;more is the chance of leading to permanent disablement. In this regard, ongoing research projects are ranging from craniological studies using fMRI to retinal imaging analysis to diagnose various psychological disorders. Machine learning for image analysis in the medical domain has proven successful. This paper discusses the evolution of schizophrenia detection technologies in relation to structural changes in brain and eye using different machine learning classification algorithms. © 2020 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Non blind Digital Medical Color Image Watermarking Using svD and FFT

Non Blind Digital Medical Color Image Watermarking Using SVD...

引用

Innovation and Novelty in engineering and Technology (INNOvA), International Conference on

作者： Dayanand. G. savakar Pandurang biradar G.K. Kulkarni Department of Computer Science Bagalkot University Jamakhandi Karnataka India Department of Computer Science Rani Channamma University Belagavi Karnataka India Department of M C.A BLDEA's V.P.Dr.P.G. Halakatti College of Engineering &Technology Vijayapur Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Medical images are highly sensitive and require robust methods to ensure authenticity, Data verification, and resistance to various attacks. The Proposed method “Non blind Digital Medical Color Image Watermarking Using svD and FFT” typically outlines a method that integrates singular value decomposition (svd) and fast fourier transform (fft) for embedding watermarks in medical images [4], [6]. This method guarantees that the watermark remains resilient against different types of attacks while maintaining the image's clarity. The non-blind nature of the watermark extraction process means that it can be done without relying on the original image, making it particularly valuable in medical imaging where maintaining the integrity and authenticity of the data is of utmost importance. Experimental results demonstrate that the method achieves high imperceptibility with minimal visual impact on the original image, ultimately; this approach ensures reliable watermark extraction, assuming the original image is available, making it suitable for secure medical image sharing and telemedicine applications.

关键词： visualization Technological innovation Fast Fourier transforms Telemedicine Watermarking Color Reliability biomedical imaging singular value decomposition Resilience

来源：评论

学校读者我要写书评

暂无评论

A survey on bipolar Disorder Classification Methodologies using Machine Learning

A Survey on Bipolar Disorder Classification Methodologies us...

引用

2020 International Conference on smart Electronics and Communication, ICOsEC 2020

作者： vasu, v. Indiramma, M. B.M.S College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781728154619

This paper focuses on a scenario based bipolar disorder (bD) classification methodology. bD is a psychological brain disorder that affects 45 million people worldwide. It is also called manic depression and causes serious changes in energy level, behavior and shift in moods. Many researchers reported volumetric changes in brain neural networks with bD compared to a healthy person (HP). The methodology currently used for the identification of disorder using functional magnetic resonance imaging (FMRI) data analysis requires specialists in the field and the process is generally time consuming. The present breakthroughs on the prediction using retinal vasculature, where the brain and retinal share the same biomarker structure, machine learning and deep learning, which remains as the most used approach for classification. This paper reviews feature selection and various methodologies used on bP classification and identifies the best methodology that can help the future research work.. © 2020 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Implementation of a Methodology for Detection and Prevention of security Attacks in vehicular Adhoc Networks

Implementation of a Methodology for Detection and Prevention...

引用

2020 IEEE International Conference for Innovation in Technology, INOCON 2020

作者： Naveen, R. Chaitanya, N.s.v. Nikhil srinivas, M. vineeth, Nandhini B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781728197449

In the current generation, road accidents and security problems increase dramatically worldwide in our day to day life. In order to overcome this, vehicular Ad-hoc Network (vANETs) is considered as a key element of future Intelligent Transportation systems (ITs). With the advancement in vehicular communications, the attacks have also increased, and such architecture is still exposed to many weaknesses which led to numerous security threats that must be addressed before vANET technology is practically and safely adopted. Distributed Denial of service (DDos) attack, replay attacks and sybil attacks are the significant security threats that affect the communication and privacy in vANET. An algorithm to detect and prevent various kinds of security attacks in vANET communication has been designed and proposed in this work. An analysis has also been done by applying four protocols on an existing scenario of real traffic simulator using OpenstreetMap and the best suitable protocol has been selected for further application. The evaluation has been done using sUMO, Ns3 and Java simulation environment. simulation results and extensive performance analysis shows that our proposed Algorithm performs well in detecting and preventing the attacks in vANET communication. © 2020 IEEE.

关键词： vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Retinal vessel segmentation using Deep Learning-A study

Retinal Vessel Segmentation using Deep Learning-A Study

引用

2020 International Conference on smart Electronics and Communication, ICOsEC 2020

作者： Islam, Mohammed Mujahid Ul Indiramma, M. B.M.S College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9781728154619

The Retinal vasculature conditions are important and reliable biomarkers for several cardiovascular and ophthalmologic diseases including Retinopathy of Prematurity, Diabetic Retinopathy, Glaucoma, Macular Degeneration, etc. The retinal-related diseases can be detected and diagnosed in the early stage before developing complexities and causing loss of sight by thoroughly understanding the vasculature condition. Analyzing these conditions is time consuming and tedious task. There have been many studies conducted to automatically extract retinal vessel information from fundus image and this is called retinal vessel segmentation. This paper provides a survey of the existing work that had been conducted to achieve this objective using Deep Learning methods. Deep Learning has become the state-of-The-Art for solving computer vision problems. This paper briefly discusses U-Net based network architectures proposed for object segmentation and the usage of these networks in medical fields for vessel segmentation. It further compares the various architectures based on AUC and F1 score.. © 2020 IEEE.

关键词： Image segmentation

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：