检索结果-内蒙古大学图书馆

Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis

International Journal of Information Technology (Singapore) 2024年 1-17页

作者： R, Annamalai Sindhu, Kolla Gnapika Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

In the realm of maternal healthcare, accurate fetal plane detection is of paramount importance. This paper introduces a novel approach that leverages ensemble techniques to enhance the precision and dependability of fetal plane classification. We address two pivotal classification tasks: first, the categorization of fetal planes into six distinct classes, encompassing critical regions such as the fetal abdomen, brain, femur, thorax, maternal cervix, and other areas. The second task focuses on the nuanced classification of fetal brain planes, further refined into trans-thalamic, trans-cerebellum, and trans-ventricular subtypes. To address these challenges, we harnessed the power of six distinct pre-trained models, rigorously training each for 50 epochs. Our results underscore the consistent superiority of InceptionResNetV2, DenseNet121, and Xception. Through a pioneering ensemble approach, we synergistically harnessed their capabilities, leading to enhanced classification performance. This research promises to augment the precision of maternal-fetal plane classification, with the potential to revolutionize clinical practice and healthcare research. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： DenseNet121 Ensemble learning Fetal planes Histogram equalization InseptionResNetV2 Median filter Ultrasonography

来源：评论

学校读者我要写书评

暂无评论

Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

引用

Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Customer Churn Prediction in Subscription-Based Services 5th

Customer Churn Prediction in Subscription-Based Services

引用

5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Kolli, Mahi Varadharajan, Nayantara Ajith, Keerthana Dinesh Kumar, K. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Bangalore India

ISBN: (纸本)9789819788644

The rapid growth of subscription-based services in the telecom industry has led to a larger subscriber base for service vendors. However, customer churn, or the loss of clients, has become a critical issue for telecom companies. This paper addresses the problem of customer churn or attrition in the telecommunication industry and performs a churn prediction model that implements Machine Learning techniques on a big data platform. The study employs key algorithms, including Logistic Regression, Decision Tree, KNN Classifier, Random Forest and Support Vector Machine (RBF), to identify potential defectors and enable companies to implement retention techniques. This paper focuses on voluntary churn and implements engineering features and selection methods to improve predictive accuracy. As markets expand and customer acquisition costs increase, effective customer retention becomes paramount, prompting service companies to invest in prediction analytics for proactive churn management. The study leverages prior research and experiments with multiple machine-learning algorithms to build a predictive model of customer churn. The utilized model uses big data and innovative feature engineering and selection methods to predict customers who are most likely to churn. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Telecommunication industry

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Different YOLO Models for Real-Time Object Detection 4

An Analysis of Different YOLO Models for Real-Time Object De...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Jamgaonkar, Sawari Gowda, Jay Shyam Chouhan, Siddharth Singh Patel, Rajneesh Kumar Pandey, Ankur School of Computing Science and Engineering Vit Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Manipal University Jaipur Department of Computing Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331540364

Object detection in real-life applications plays a significant role, with advancements in technology enabling enhanced image classification and processing, reducing manual effort and increasing accuracy and speed. The proposed work involves a comparative analysis and hyper-parameterization of YOLO (You Only Look Once) modules, specifically YOLOv7, YOLOv8, and YOLOv9 models. These models utilise Convolutional Neural Networks (CNNs) to process image layers by dividing images into boxes and then segmenting them into pixels for identification and classification based on various parameters. Initially applied to the COCO dataset, these models were further tested on customised datasets. Among the three, YOLOv9 demonstrates superior performance, achieving a mean Average Precision (mAP) of 52 at 0.5:0.95, compared to YOLOv7 and YOLOv8, which achieved mAPs of 35.2 and 39, respectively. Overall, YOLOv9 significantly reduces parameter usage and improves accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Compressive sector selection and channel estimation for optimizing throughput and delay in IEEE 802.11ad WLAN

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 987-998页

作者： Venkatachalam, Ilayaraja Palaniappan, Senthilnathan Ameerjohn, Sirajudeen School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore 466114 India

To identify the best beam pair in IEEE 802,11ad Wireless Local Area Network (WLAN), it is necessary to evaluate every potential pair's spectrum responsiveness and Signal-to-noise Ratio (SNR). During beam monitoring, nevertheless, the pilot expenditure associated with SNR or channels predictions have been successfully minimized. Therefore, it is crucial to reduce the aided pilot in beam monitoring. In this study, we present a methodology for IEEE 802,11ad WLAN performance and latency optimization called Compressive Sector Selection and Channel Estimation (CSS-CE). 60 GHz transmissions suffer from scarce channel in beams synchronization. Thus, to lower the sample rate, Compressed Sensing (CS) is used. The next step is to recreate the compressed data utilizing Orthogonal Pairing Pursuit (OPP). Because different antenna sectors share similar spatial characteristics, compressive sensing typically probes portions of antenna sector areas. Next, the most suitable region is chosen to meet the additional transmission's latency as well as throughput limitations. The suggested CSS-CE paradigm outperforms the current methods in terms of performance and interruption, according to data from experiments. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Beam tracking Channel estimation Compressive sensing IEEE 802.11ad WLAN Orthogonal matching pursuit

来源：评论

学校读者我要写书评

暂无评论

Detection and Classification of Brain Tumor Using Deep Learning Model 1

引用

4th International Conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2023

作者： Routhu, Manoj Sitaram Govindaraju, Rohith Vigna Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

Accurate and efficient brain tumor classification is paramount for timely clinical diagnosis and effective treatment planning. In this groundbreaking research, we introduce an innovative Convolutional Neural Network (CNN) architecture intricately integrated with customized preprocessing techniques, resulting in an exceptional classification accuracy of 98.5% on the challenging br35H dataset. By harnessing the power of MRI scans and leveraging diverse datasets, our model expedites brain tumor assessments and sets the stage for advanced classification methodologies. With the global incidence of brain tumors rising, the need for technology-driven diagnosis becomes increasingly evident, and CNNs emerge as pivotal tools in enhancing diagnostic precision. This study not only underscores the profound significance of CNN models but also transcends geographical boundaries, reducing the frequency of misdiagnoses and ultimately empowering global healthcare. This paper comprehensively explores our methodology, delving into the intricate details of data collection processes, model development strategies, and experimental findings. Moreover, it sheds light on the implications of deploying CNN models in medical imaging. By contributing to the ongoing discourse on transformative healthcare technologies, this research aims to propel the adoption of CNN-based approaches, ushering in a new era of precise and efficient brain tumor classification for the benefit of healthcare professionals, patients, and society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-class Chest Radiograph Classification Using Deep Convolutional Generative Adversarial Networks 6th

Multi-class Chest Radiograph Classification Using Deep Convo...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Kaushik, Hariharan Natarajan, B. Annamalai, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai India

ISBN: (纸本)9789819778614

The urgency for precise diagnostics during the COVID-19 pandemic has driven advancements in imaging and deep learning tools. However, progress is impeded by limited access to medical imaging data. This study employs cutting-edge deep learning techniques to identify chest diseases, including COVID-19 and various lung conditions. State-of-the-art Generative Adversarial Networks (GANs), including CycleGAN, Super-Resolution GAN (SRGAN), and Deep Convolutional GAN (DCGAN), are used to synthesize medical images, augmenting the dataset and enhancing model robustness. VGG-19 achieves a remarkable 91% accuracy and an F1-score of 0.98 across diverse chest-related diseases. Saliency Maps and Grad-CAM graphics improve interpretability, showcasing the model’s healthcare potential. The study encompasses diseases such as Pneumonia and COVID-19, contributing to a comprehensive classification task. These findings underscore the impact of deep learning in medical imaging, promising improved patient care and healthcare accessibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

引用

Computer Systems science & engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

来源：评论

学校读者我要写书评

暂无评论

Deciphering Parkinson's Disease Progression Using Deep Learning Techniques

引用

SN Computer science 2024年第8期5卷 1042页

作者： Pandey, Pranjal Mohan Mitra, Sakalya Singh, Shalu Singh, Ajeet School of Computing Science and Engineering VIT Bhopal University Kothri-Kalan Madhya Pradesh Sehore 466114 India

Parkinson's disease (PD), a prevalent central nervous system disorder among the elderly, is characterized by the depletion of dopamine-producing brain cells in the substantia nigra pars compacta, leading to motor and non-motor impairments. Traditional diagnostic tools like the Hoehn and Yahr scale and the MDS-UPDRS rely on physical and cognitive assessments for judging the possibility of the disease. These methods are not accurate enough and have not shown promising results over the years. Deep learning solutions, however show promise in early PD detection. Despite significant advances, the following gaps remain: (a) leveraging deep learning for genetic data modelling, (b) utilizing scale-invariant and high-level hand-drawn image features for PD classification. This research addresses these gaps by employing an artificial neural network on the AMP PD dataset, which focuses on protein abundance data, and the New Hand PD Dataset by analyzing hand-drawn images using scale and rotation invariant combined with high-level features. This research demonstrates that deep learning models can outperform traditional benchmark models, offering new avenues for early diagnosis and personalized treatment of PD. By integrating genetic factors and local image features, this work aims to enhance understanding and provide innovative solutions for PD diagnosis at an early stage. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brain disorder Deep learning Parkinson's disease Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Hate Speech in Hindi using Supervised Learning Models

Automated Detection of Hate Speech in Hindi using Supervised...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rajak, Nikhil Phulre, Ajay Kumar Chandra Patel, Subhash Patel, Narottam Das Khekare, Ganesh Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350375442

This study addresses the pressing issue of hate speech on online platforms. Traditional text-based analysis is no longer adequate for identifying hate speech due to the growth of internet platform data. The project aims to create scalable automated methods for web platforms to detect hate speech in real-time. To find the best algorithms for recognizing hate speech, the study compares many machine learning models using a large dataset in Hindi from Twitter. Where the study matters is in the creation of advanced content filtering systems that offer a more secure and friendly online environment. Support Vector Machine (SVM) has the best performance, with outstanding precision (0.9468) and F1-score (0.944), showcasing the top-performing ones and talking about the difficulties and ways ahead in this field. The study recommends improving Hindi hate speech detection systems. With problems such as imbalanced data sets and the dynamic nature of online language, language research using hybrid models, constant model adjustments, and language trend monitoring are all necessary. © 2024 IEEE.

关键词： Adversarial machine learning

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：