检索结果-内蒙古大学图书馆

4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Arthi, T.S. Partheeban, N. Singh, Bhoomika Singh, Aparajitha Tripathi, Nikhil Department of Computer Science and Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitations in terms of time, cost, and potential subjectivity. This study proposes a lightweight Machine Learning (ML) model for IDC classification using digital whole-mount breast cancer slide images. The model aims to achieve high accuracy while being suitable for deployment in resource-constrained environments. We evaluate our model on a publicly available breast histopathology dataset and compare its performance with other Deep Learning (DL) models, focusing on both accuracy and computational efficiency. The results demonstrate that our model achieves a high classification accuracy (around 89.3 percent) with minimal processing power and memory requirements. Furthermore, successful deployment on a Raspberry Pi embedded system validates its potential for real-world application in resource-limited settings, potentially leading to improved breast cancer diagnosis accessibility. Index Terms—Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Deep Learning (DL), Invasive Ductal Carcinoma (IDC), Multilayer Perceptron (MLP), Raspberry Pi, Tiny Machine Learning (TinyML). © 2025 the Author(s).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

引用

Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

引用

computer Modeling in engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review of Driver Drowsiness Detection Systems and Applied Technologies 5

A Comprehensive Review of Driver Drowsiness Detection System...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Ansh Tiwari, Abhinav Chauhan, Atul Singh Gupta, Sachi Galgotias College of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

Drowsiness among drivers is a significant contributing factor to traffic injuries, leading to a yearly toll of severe injuries and fatalities. This study reviews the literature in-depth on driving force sleepiness detection systems, considering physiological signals, facial expressions, and riding habits. It not only describes the current approaches for each class but also offers a comparative analysis of recently published studies, closely contrasting correctness, dependability, hardware specifications, and intrusiveness. The research also examines the advantages and disadvantages of each technique's brilliance, highlighting the need for a hybrid machine that builds on each one's advantages to guarantee performance, resilience, accuracy and real-time use. This crucial issue requires attention and creativity, as demonstrated by a different investigation looking for ways to reduce driving induce sleepiness by strengthening deeply ingrained patterns. This paper investigates several Convolution Neural network configurations, visual Geometry Group architectures (VGG16 and VGG19), a Generative antagonistic network (GAN), and a Multi-Layer Perceptron (MLP) and offers enhancing statistics to model real-world scenarios. Using eye photo datasets, general performance criteria including as accuracy, precision, F1-score, recall, and specificity are assessed. The findings highlight the ability for extensive makeup in fashions with accuracy and an F1-score of © 2024 IEEE.

关键词： Analysis of Driving Patterns Assessment of Facial Capabilities Assessment of Physiological Alarms Dlib Motive Force Detection of Sleepiness Safe Riding

来源：评论

学校读者我要写书评

暂无评论

Facial Emotion Recognition and Detection

Facial Emotion Recognition and Detection

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Sunil Gupta, Neeraj Kumar Tiwari, Shreyash Shukla, Swapnil Galgotias College of Engineering and Technology Department of Computer Science and Engineering UttarPradesh Greater Noida India

ISBN: (纸本)9798350373783

Facial emotion detection and recognition remains a challenging and interesting in the world of computer vision. Recognizing face expression was always tough nut to crack out as the way a facial expression expressed by a person varies from one man to another. Deep learning is a most reliable technology for the study of facial expression and deep neural network is used to classify the images according to different emotions. Convolutional neural network is the technology helped to classify the images. In this paper we are introducing a new junction of these technologies for the recognition of facial emotions. In recent years, facial emotion recognition has witnessed a great surge in popularity, motivating researchers to dive deeper into this technology, getting promising outcomes in the past. There are several models which provide an overview regarding facial emotions. The main difficulty in this to identify the subtle emotions and the need of some large datasets which are also discussed in this paper. We also highlight some of the important aspect of facial emotion recognition system in many other real-life fields and have lot of scope in future research. The idea is to give overview about this field and and its role and advancement in branch of artificial intelligence and computer vision. © 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

Predicting relevant captions using image caption generator in social media platforms 4th

Predicting relevant captions using image caption generator i...

引用

4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Gupta, Aryan Bhadauria, Devansh Singh Atray, Manav Kaur, Inderjeet Department of Computer Science & Engineering Galgotias College of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032911571

Creating natural language descriptions or captions for images is a formidable task that requires a combination of computer vision techniques to understand image content and natural language processing models to express this understanding in coherent sentences. This paper delves into the development of a web-accessible Image Caption Generator, aiming to improve user experience by automatically generating captions for uploaded images. The versatility of this generator extends its applicability to various uses, such as image indexing, assisting visually impaired individuals, and enhancing interactions on social media platforms. This paper results in caption generator without the need for complex data pre-processing or a specialized model pipeline. Notably, a singular end-to-end model seamlessly predicts captions, streamlining the entire process. The paper integrates social media platforms with the proposed Image Caption Generator giving users automated and contextually relevant captions, fostering a more immersive and inclusive digital experience. © 2025 the Author(s).

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region

引用

Environmental science and Pollution Research 2024年第49期31卷 59430-59445页

作者： Kumar, Gaurav Patil, Deshbhushan Kumar, Akshay Gupta, Rajiv Department of Computer Science and Engineering SCSE Galgotias University Greater Noida India Department of Civil Engineering Walchand College of Engineering Sangli India Civil Engineering Department Birla Institute of Technology and Science Pilani Rajasthan Jhunjhunu333031 India

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Surface waters

来源：评论

学校读者我要写书评

暂无评论

Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques

引用

Multimedia Tools and Applications 2025年第16期84卷 16757-16788页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Agrawal, Akshat Rani, Uma Mohan, Anand Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering World College of Technology & Management Haryana Gurugram India Department of Physics Kunwar Singh College Bihar Darbhanga India

The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily functions. While thyroid nodules are predominantly benign, certain cases may manifest malignancy, leading to thyroid cancer. Despite the generally favorable prognosis, early detection and intervention remain paramount for optimal outcomes. Given the limitations in clinical data, this paper introduces an innovative approach employing a modified XGBoost model that integrates both clinical and molecular properties into a unified predictive model. Utilizing fine-needle aspiration biopsies and gene expression analysis, our model aims to enhance cancer prediction in thyroid nodules. We explore the influence of incorporating high-quality clinical data on the prioritized gene set, employing a nature-inspired method for hyperparameter optimization. The modified XGBoost model emerges as highly recommended for non-parametric feature selection, extraction, and prediction due to its remarkable accuracy. Our proposed model attains an impressive 94.6% accuracy, surpassing other machine learning models such as C5.0 (92.5%), CART (88.7%), CHAID (83.4%), Quest (81.5%), LSVM (77%), and random tree (24.1%). The real-time efficiency of our model is demonstrated in the results section. Experiments showcase the superior performance of combined molecular and clinical data-based prediction models. Emphasis is placed on the importance of scrutinizing the origins of clinical data and maintaining vigilance over variable quality. This study signifies a substantial advancement in thyroid cancer prediction, offering a robust and accurate model that integrates diverse data sources, ultimately contributing to improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Autogenie: Revolutionizing Vehicle Recommendations with Hybrid ML-Based System 5

Autogenie: Revolutionizing Vehicle Recommendations with Hybr...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Varun Singh, Sidharth Garg, Yash Galgotias College of Engineering and Technology Department of Computer Science and Engineering Gautam Budh Nagar India

ISBN: (纸本)9798350383522

In an ever-changing car market, it is still difficult to choose the best car from a wide range of options. 'AUTOGENIE' offers an innovative solution to this problem combining matrix factorization, collaborative filtering, and content filtering in a hybrid machine learning system In this varied environment, traditional recommendation methods have problems in making recommendations true and best to give in. When a particular algorithm is used, the recommendations it makes fail in some way-whether accuracy, precision, or personal preference-that upsets the user's AUTOGENIE filling this void provides through complementary methods, and improves the accuracy of recommendations by analyzing large data sets and identifying hidden variables Furthermore, this hybrid machine learning algorithm is also able to adapt to simple data and changes the user Preferences Using advances in deep metric multiplication for cross-domain recommendation, AUTOGENIE works exceptionally well for customizing recommendations despite changing user preferences and limited data It uses the information provided are personalized recommendations, so they reflect recommendations that are highly relevant to them. This paper describes the methodology of AUTOGENIE, including its new methodology for studying hybrid devices, experimental results and implications. By combining collaborative filtering, matrix factorization, and content filtering, AUTOGENIE provides a convenient way forward for highly customized vehicle recommendations in the dynamic automotive industry in. © 2024 IEEE.

关键词： Collaborative Filtering Content Filtering Hybrid ML Approach Matrix Factorization Precision Personalization Vehicle Recommendations

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：