检索结果-内蒙古大学图书馆

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Shabu, S.L. Jany Indhuja, V. Jeevitha, R. Refonaa, J. Dhamodaran, S. Poornima, D. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505745

Sleep apnea disorder manifests as obstructive sleep apnea, which affects immense portions of society because it causes life-threatening health problems that include cardiovascular disease alongside diabetes and decreased productivity. The diagnosis of OSA through manual methods is both time demanding and expensive and contains a significant potential for human errors while requiring heavy resources. This research presents deep learning automation as the novel approach for automatic OSA detection through polysomnographic assessments. Our proposed system implements Genetics Algorithm optimized machine learning algorithms which include Logistic Regression, Support Vector Machines, Random Forests, Artificial Neural Networks and more. The analysis utilizes data from the Sleep Health and Lifestyle Dataset for preprocessing operations and features extraction that leads to better sleep disorder and stage classification accuracy. Through hyperparameter adjustment the Random Forest predictive model reached 95% F1 score which proved most effective for OSA detection. Through automation the classification process benefits from reliability with scalability and efficiency and produces better patient outcomes. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Automated Immune Cell Classification using Image Processing Techniques 6

Automated Immune Cell Classification using Image Processing ...

引用

6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Arva Hari Vardhan Abdurrahman, Usama Micheal, A. Annie Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated preprocessing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening;it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders. © 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

Synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security 1

引用

4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (数字)9789819752003

ISBN: (纸本)9789819751990

Image steganography is a technique for encrypting data, allowing covert communication, and enhancing data security inside image files. This study provides an in-depth analysis of image steganography, covering its core ideas, methods, uses, difficulties, and developments. Numerous embedding algorithms and methodologies, including transform domain methods and LSB-based strategies, are examined in this study. The paper highlights the need for effective defenses against steganalysis attacks, while also discussing the challenges and potential weaknesses in steganography. It examines common steganography techniques and discusses countermeasures such as adaptive embedding, key-based embedding, and algorithmic resistance. Additionally, the paper also highlights the significance of assessing the efficacy and caliber of image steganography techniques. Objective metrics such as PSNR and MSE are discussed, providing insights into the trade-offs between security and image fidelity. For those interested in understanding image steganography, its applications, and its challenges, this study offers a complete resource. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Impact of Demographic and Operational Variables on 1-to-Many Face ID Search

IEEE Transactions on Technology and Society

引用

IEEE Transactions on technology and Society 2024年第2期5卷 217-230页

作者： Pangelinan, Gabriella Bhatta, Aman Wu, Haiyu King, Michael C. Bowyer, Kevin W. Florida Institute of Technology Department of Electrical Engineering and Computer Science MelbourneFL32901 United States University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many face identification has heightened scrutiny in the media and spawned lawsuits. We analyze the accuracy of 1-to-many face identification across African-American and Caucasian demographic groups and in the presence of blur and reduced resolution in the probe image, as might occur in "surveillance camera quality"images. Because of the high accuracy of modern face recognition algorithms with "government ID quality"images and the size of available datasets, we use the following indirect metrics to assess the relative propensity for false positive identifications: (1) the traditional d-prime statistic between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated distribution and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation patterns in 1-to-many accuracy largely, but not perfectly, follow that observed in 1-to-1 accuracy. We show that, different from the case with 1-to-1 matching accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. We also show that increased blur or reduced resolution of the face in the probe image can significantly increase the false positive identification rate. This point is important because the reputation of modern face recognition algorithms for high accuracy in 1-to-many face identification comes from statistics based on analyzing "government ID quality"rather than "surveillance camera quality"images. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling of Social Media Data Using Machine Learning Techniques 2

Predictive Modeling of Social Media Data Using Machine Learn...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Anitha, T. Srihitha, G. Sai Aiswarya, G.R.P Lakshmi Raghi, K.R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505745

Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analysis of YouTube data using a hybrid machine learning framework. Various video metadata, including descriptions, titles, tags, views, likes, and comments, are analyzed to forecast audience engagement and trends. The pipeline begins with data preprocessing, including handling missing values, removing duplicates, and preparing the data for analysis. Exploratory Data Analysis (EDA) is performed to uncover hidden patterns and relationships, followed by feature selection techniques to identify the most influential variables. The dataset is then split into training and testing subsets, and advanced machine learning models are employed to predict video performance and user engagement. The results are evaluated using key performance metrics, delivering actionable insights to improve content strategies and audience targeting. This study highlights the effectiveness of machine learning techniques in optimizing YouTube content and enhancing decision-making in digital marketing. © 2025 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Automated Brain Tumor Detection and Classification in MRI Scans using a Deep Convolutional Neural Network and U-Net Architecture 6

Automated Brain Tumor Detection and Classification in MRI Sc...

引用

6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Devadas, Prathima Mathivanan, G. Department of Computer science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorization of brain cancers using medical imaging technologies. The framework uses a deep convolutional neural network (DCNN) with an exclusive segmentation technique, notably the U-Net architecture, to discrete tumor areas in magnetic resonance imaging (MRI) scans. The DCNN model, which contains multiple convolutional layers, is used for feature extraction, followed by fully connected layers for tumor classification. To increase segmentation accuracy, the U-Net model integrates both encoder-decoder structures and skip connections to capture fine-grained spatial details. The suggested algorithm is trained and evaluated on a large dataset of brain MRI images, achieving high accuracy, sensitivity, and specificity for both tumor detection and classification. Experimental results show that the model outclasses standard methods, providing a dependable and operative solution for the early detection and diagnosis of brain cancers, potentially aiding clinical decision-making processes. © 2025 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Demonstration of an Application Layer Omni-frequency IoT Protocol 17

Demonstration of an Application Layer Omni-frequency IoT Pro...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Gupta, Nayancy Peddamalla, Lokesh Mendu, Veman Sai Prabhath Banda, Gourinath Indian Institute of Technology Indore Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

The advent of the Internet of Things (IoT) has transformed the way devices communicate, with an ever-increasing need for seamless interoperability and energy-efficient communication. This paper presents a unified omni-frequency protocol designed to operate independently of specific radio-wave protocols, allowing efficient and secure communication between users, devices (referred to as "things"), and cloud servers. The proposed protocol operates at the Application Layer and introduces two key components: the original equipment manufacturer (OEM) Server, which centrally manages user and device data, and the OEM Host, which verifies device credentials. This system enables efficient Internet Protocol(IP)-based and non-IP based communication, improving energy management and distribution of computational tasks to the edge. An Android application was developed to serve as the user interface, supporting multiple communication methods, including IP, Bluetooth classic version 5.0, Near-Field Communication (NFC) and ad hoc networking. The versatility of this unified approach optimizes the interactions, data processing, and energy utilization of IoT in various practical environments. © 2025 IEEE.

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Cloud Based Soil Nutrient Prediction System for Precision Agriculture

Data-Driven Cloud Based Soil Nutrient Prediction System for ...

引用

2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Mishra, Amit Dass, Jeevesh Saxena, Siddhant Prakash, Ishan Satya Sargun Garg, Arshia Verma, Karun Bhattacharya, Jhilik Thapar Institute of Engineering & Technology Department of Electronics and Communication Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Computer Science Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Electronics and Computer Engineering Punjab Patiala India

ISBN: (纸本)9798331533311

Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine learning-based model that will predict the NPK fertilizer to achieve the higher crop yields. Field sensor data along with meteorological data and remote sensing data from various test fields are integrated. A random forest and decision tree machine learning model are applied to the integrated data to develop a software tool for precision agriculture, enabling accurate estimation of nutrient requirements. A mobile application is used to share the nutrient prediction with farmers, enabling them to make well-informed choices regarding resource utilization efficiency and environmental friendliness. This research can transform agricultural practices globally and contribute towards food security on earth. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Detection of Bias Origins in Training Dataset of Cardiovascular Disease using Machine Learning Classifier

Detection of Bias Origins in Training Dataset of Cardiovascu...

引用

2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Prakhar, Jyoti Haider, Md. Tanwir Uddin Department of Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (纸本)9798331536336

Today, decision support systems are essential in various industries, where vital decisions are made using datasets such as health care systems. So, if the dataset is biased within healthcare systems, then it will hinder accurate disease identification. Thus, it emphasizes the need for the detection of bias origin to mitigate it and to enhance model performance and ensure rational decision-making. Seeing this fact, in this paper we have proposed a methodology for the detection of bias origin within cardiovascular disease. The proposed methodology identifies the biases’ origin due to the underrepresented class of the protected attribute in training dataset using machine learning models. For experimental evaluation, we selected a cardiovascular disease dataset and trained it using an ML algorithm. Then, we assessed it on a new test dataset i.e. CVD dataset consisting of four groups of females and males with cardiovascular disease or not. Furthermore, the performance metrics, including accuracy and mean absolute error (MAE), are evaluated to validate the under- represented samples, which are the primary cause of bias in the system. The results demonstrate that the proposed methodology is straightforward and effective in detecting the origin of bias. © 2025 IEEE.

关键词： Cardiology

来源：评论

学校读者我要写书评

暂无评论

Improving Face Recognition for Smart Home Security: A Study of Resnet, Siamese Networks, and Attention Mechanisms

Improving Face Recognition for Smart Home Security: A Study ...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Sood, Hemaang Nikhil, B.V.S. Dengla, Palak Saeed, Sanya Nagabhushan, S.V. Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This study presents a smart doorbell system designed to improve home security through advanced deep learning techniques. At its core, the system utilizes the YOLOv5 (You Only Look Once) algorithm for real-time face detection, ensuring accurate and efficient visitor identification. To further strengthen security, the system employs multifactor authentication (MFA), integrating face recognition alongside speech recognition and or/a manual keypad. The study evaluates three neural network architectures for face recognition-ResNet with cosine similarity, a Siamese network with CNN, and an attention-enhanced Siamese network-to determine the most effective model for secure and reliable authentication. Performance comparisons highlight progressive improvements in accuracy and robustness,. Additionally, a convenient interface enables seamless remote monitoring and control, enhancing both security and convenience. By addressing key limitations of existing smart security systems, this research provides insight into improving intelligent home access control. © 2025 IEEE.

关键词： Authentication

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：