检索结果-内蒙古大学图书馆

7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Kranthi Reddy, S. Moinuddin, Khaja Waseem Akram, Md. Sachithananda, Sai Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819799251

Exposure to carcinogens, particularly those found in cigarette smoke, destroys lung cells DNA and promotes their unchecked proliferation, which is the main cause of lung cancer. Its growth is also greatly influenced by genetic predisposition, family history, and environmental factors such as radon and asbestos. Lung cancer can affect nonsmokers due to these genetic and environmental factors, even though smoking is the main cause. These malignancies have the potential to impair breathing and the exchange of oxygen and carbon dioxide by interfering with the lungs natural function. When applied to tabular datasets, machine learning and deep learning algorithms can help forecast the prognosis of lung cancer and the response to treatment, enabling tailored care plans and enhancing patient outcomes. This study suggests using a convolutional neural network (CNN) architecture to accurately identify lung cancer from medical photos. Clinicians might possibly improve patient outcomes and survival rates by using CNNs’ sophisticated feature extraction capabilities to produce more accurate diagnoses and customized treatment programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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

Enhancing Occluded Face Recognition Through Device-Edge Collaboration and Cross-Domain Feature Fusion with Heatmaps 3

Enhancing Occluded Face Recognition Through Device-Edge Coll...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Prasanth, Ashwin Annie Micheal, A. Department of Computer Science & Engineering Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331527549

Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a novel device-edge collaborative framework that advances occluded face recognition through an innovative cross-domain feature fusion technique. Our method employs a software-generated, occlusion-aware heatmap created from standard visible-light images, which emphasizes key facial landmarks even when regions of the face are partially covered. This heatmap provides a complementary data layer that enhances traditional visible facial features, allowing the cross-domain feature fusion process to synthesize a more comprehensive set of features for reliable recognition under occlusion. The combined feature set leverages both visible and simulated heatmap information to improve model robustness, even in complex occlusion scenarios. Additionally, a delay-optimized task scheduling method is applied on the edge, ensuring efficient resource allocation, faster processing, and minimal latency. Although this project is ongoing, preliminary evaluations indicate the potential effectiveness of this approach, with expected outcomes to include high recognition accuracy and reduced latency. This framework provides a scalable solution for occluded face recognition by integrating complementary data sources without requiring specialized hardware. © 2025 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Kookaburra Optimization Algorithm and Feature Aggregated Region based Differential network for Colon Cancer Detection from Histopathological Images 8

Kookaburra Optimization Algorithm and Feature Aggregated Reg...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Gowthamy, J. Ramesh, S.S. Subashka Rm Institute of Science and Technology Amapuram Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Ramapuram Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Colon cancer is a significant health concern, ranking as the third leading cause of cancer-related deaths in men and the second in women. Early detection is crucial in reducing the incidence and mortality rates associated with this malignancy. This paper introduces a novel Feature Aggregated Region-based Differential Network (FA-RDNet) designed for the accurate classification of colon cancer from histopathological images. Our approach leverages Multiple Instance Learning (MIL) to effectively handle large datasets structured as bags of instances, addressing the limitations of conventional MIL techniques. A key innovation of our work is the integration of an adaptive feature enhancement module that bilaterally updates multi-level deep features, allowing for efficient extraction of both low-level details and high-level semantic information. Furthermore, we introduce the Kookaburra Optimization Algorithm (KOA) to fine-tune the parameters of FA-RDNet, utilizing a unique two-phase mathematical modeling process inspired by the hunting behaviors of kookaburras. This enhances the model's optimization and classification accuracy. The effectiveness of our proposed system is validated using the publicly available LC25000 dataset, where it achieved a remarkable accuracy of 94.42%, outperforming existing state-of-the-art methods in colon cancer detection. The high computational efficiency and accuracy of FA-RDNet highlight its potential for practical applications in clinical settings, marking a significant advancement in the field of medical image analysis. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Gestational Diabetes Prediction Using ML Integrated with Web UI

Gestational Diabetes Prediction Using ML Integrated with Web...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Shrija, G. Sibi Prasad, D. Hemalatha, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Promoting maternal health is significant as GDM is a severe health concern for both infants and mothers. As opposed to the conventional systems based primarily on clinical diagnosis, this study attempts to develop a system that utilizes machine learning algorithms and Django web application to predict and manage GDM. The system analyzes key indicators such as clinical data, family members' medical history, and lifestyle trends to make predictions through an easy-to-use web-based interface. With an emphasis on the early detection and timely interventions, this study aims to improve wellbeing and health among mothers and her children. © 2025 IEEE.

关键词： Electrotherapeutics

来源：评论

学校读者我要写书评

暂无评论

Classifying the state of knowledge-based question answering: patterns, progress, and prospects

引用

International Journal of computers and Applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

引用

Intelligent Automation & Soft Computing 2023年第3期35卷 2893-2907页

作者： C.Santhosh Kumar K.Vishnu Kumar Department of Computer Science and Engineering Priyadarshini Engineering College635751TamilnaduIndia Department of Computer Science and Engineering KPR Institute of Engineering&TechnologyCoimbatore641407India

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to ofﬂoading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and conﬁdentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational ofﬂoading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

关键词： Peer-to-peer computing energy and delay aware ofﬂoading edge-cloud enabled healthcare system parkinson’s disease prediction

来源：评论

学校读者我要写书评

暂无评论

Detecting Phishing Websites using Naive Bayes Classification 3

Detecting Phishing Websites using Naive Bayes Classification

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Talukder, Ahmed Rohan Alam, Faria Mim, Sazia Tabasum Emon, Md. Abdul Al American International University Computer Science and Engineering Bangladesh Military Institute of Science and Technology Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350388282

Phishing is one form of cyber attack used to obtain sensitive information from targeted individuals. Through this process, the attacker masquerades as a domain similar to an official legitimate website. Perpetrators can execute attacks with minimal expenses and effort, allowing them to initiate numerous assaults rapidly. The swift pace of phishing underscores the importance of automated detection processes in ensuring the protection of Internet users. Recently, many promising approaches, such as the content-based approach, heuristic-based approach, Fuzzy's rule-based approach and machine-learning approach have been used to distinguish between phishing and legitimate ones. The existing studies do not focus on a probabilistic approach. Therefore, this study uses the Naive Bayes classification to detect phishing sites. The study compares three naive Bayes approaches: Bernoulli Naive Bayes, Multinomial Naive Bayes, and Complement Naive Bayes. The evaluation result of the Complement Naive Bayes Classifier and Multinomial Naive Bayes is superior to Bernoulli Naive Bayes. Both of the classifiers give 96 percent accuracy. As for detecting phishing websites, text classification and feature extraction are essential tasks;Naive Bayes classification provides an appropriate output. © 2024 IEEE.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Real-Time Hand Gesture Recognition and Sentence Generation for Deaf and Non-Verbal Communication

Real-Time Hand Gesture Recognition and Sentence Generation f...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Manikanta, K. Lavanya Shyam, N. Saraswathi, S. Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering India Sathyabama Institute Of Science And Technology India

ISBN: (纸本)9798350364828

To bridge the communication gap between deaf people and non-sign language users, the research project Real-Time Hand Gesture Recognition and Sentence Generation for Deaf and Non-Verbal Communication was created. The proposed system makes use of computer vision technology for real-time hand gesture identification and natural language processing to produce coherent and understandable words. In order to recognize hand gestures in real-time, the study employs deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These models are trained on a dataset of hand gestures and are able to accurately classify different hand gestures in real-time. The system can recognize a wide range of hand motions, from simple ones like pointing and waving to complex ones involving several fingers. The system creates sentences that are consistent with the hand gesture's intended meaning once it has been identified. Natural language processing techniques are utilized to convert the recognized hand signal into understandable speech. Based on established rules and grammar suggestions, the program generates appropriate sentences while taking the context of the conversation into account. The proposed device may change communication for nonverbal and deaf individuals who have trouble expressing themselves. It gives these populations and the rest of society a quick, efficient way to communicate. © 2024 IEEE.

关键词： Recurrent neural networks

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：