检索结果-内蒙古大学图书馆

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Prabha, Sneh Yadav, Aditya Yadav, Aman Kumar, Akash Tyagi, Manu Department of Computer Science and Engineering Inderprastha Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Toxic comments are comments which are disrespectful, unreasonable and infuriating that make the reading uncomfortable. Sometimes these comments are inappropriate and not good for the public. These comments make daily content consumers uncomfortable and sometime even cause emotional as well as emotional harm. There are some people whose purpose for coming online is to create trouble for others and make their work and daily life difficult by making inappropriate comments. This kind of behavior can be described as anti-social and harmful to society. Many steps have already been taken to catch these troublemakers and punish them accordingly by blocking users who are found guilty. This cannot be done personally by checking one by one, to achieve this purpose we need to make a model. We have applied three Machine Learning Models logistic regression, Naive bayes, SVM and three Word Embedding Techniques, Word2Vec, FastText and Glove which can classify comments, and the content creator take appropriate action against users who makes these comments by reporting and even by deleting their account if needed. Out of all six techniques SVM has outperformed for classification of toxic and non-toxic comments and normal text classification in general. SVM gives the best results with 93.61% accuracy and F1 score of 93%. Proposed technique has improvised accuracy score as compared to existing techniques. © 2025 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

An Interactive Augmented Reality Application for Agriculture using Unity 17

An Interactive Augmented Reality Application for Agriculture...

引用

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

作者： Maasthi, Manasvi J. Vidyavardhaka College of Engineering Department of Computer Science and Engineering Mysuru India

ISBN: (纸本)9798331531195

Augmented reality is defined as a 3D computer generated imagery that is embedded into the real world. There are numerous areas in which augmented reality can be applied, including: education, medical care, entertainment, & so on. Unlike VR, AR does not require the use of a HMD (Head Mounted Device);a projection can be performed on a mobile phone making it affordable. Most farmers are facing problems today, for instance, loss of crops. In this Paper an application has been created aimed at enabling farmers to know important things like when to grow certain crops and what to use to assist in soil fertility, through the development of augmented reality applications using Unity and Vuforia. That way, the farmers will have an idea of what other crops they should be able to grow during the other season. So that they will not be undergoing any kind of loss. © 2025 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Road Lane Detection using Region-based Convolutional Neural Network (RCNN) 1

Road Lane Detection using Region-based Convolutional Neural ...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Thondralnayagi, A. Nandhakumar, T. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Road lane markings are one of the main factors that cause traffic accidents and jeopardize the safety of both drivers and pedestrians. Both computer vision systems find it challenging to recognize road lanes. The irregular maintenance, insufficient drainage, and traffic patterns. It negatively affects driving comfort, road safety, and vehicle condition, and it could also lead to a lot of accidents. Recently, several machine learning algorithms have been used, although their accuracy and efficiency have not improved significantly. We suggest applying Region-Based Convolutional Neural Networks (RCNN) for path identification to tackle this issue. RCNNs are another kind of neural network that is capable of extracting significant information from picture and time series data. This makes it highly helpful for tasks requiring images, such as shape identification, object classification, and recognition. Image from a dataset with noise removed using a Wiener filter. The next step is to extract features from images using Linear Discriminant Analysis (LDA). Determine which lane line in the RCNN is efficient and provides accuracy, precision, recall, and F1-score as 95.6%, 94.05%, 94.58%, and 94.23%, respectively. © 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

Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

引用

Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Helmet and Equipment Detection with Worker's Mobility Tracker In Mining Sector Using YOLOv8 & LSTM

Helmet and Equipment Detection with Worker's Mobility Tracke...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Ramyadevi, R. Keerthy, S. Catherina J, Sherin Joys Dhayanithi, T. Saveetha Engineering College Department of Computer Science and Engineering Kanchipuram India

ISBN: (纸本)9798331529833

Monitoring personal protective equipment and compliance with safety procedures for mining operations is essential to ensuring personnel safety. Nevertheless, sending humans underground is impractical, as mining conditions are fast-changing and dangerous. Most of the existing systems are still not able to detect helmets, PPE, and the worker mobility in real-time, which are two critical aspects of safety. To overcome these challenges, this project bases its approach on enhancing monitoring and security by integrating machine learning technologies. Using YOLOv8, a cutting-edge object detection model, the system enables workers' helmet and PPE detection in real-time. This work also uses Long Short-Term Memory (LSTM), a special type of RNN, to track the behavior of workers within and over time. By reading the frames at 10 millisecond intervals, the system detects movement deviations be it unsafe movements or fatigue induced deviations and provides proactive measures for safety. The devised method reaches an outstanding accuracy of 95.9%, combining YOLOv8 and LSTM, which reduces human error and improves safety at the workplace. This state-of-the-art solution is not only providing continuous monitoring but is also increasing operational efficiency, thereby transforming safety solutions in the mining space. © 2025 IEEE.

关键词： Protective clothing

来源：评论

学校读者我要写书评

暂无评论

Classification of Pancreatic Cancer using Kolmogorov-Arnold Networks: An Upgrade on Traditional Machine Learning for Medical Classification 6

Classification of Pancreatic Cancer using Kolmogorov-Arnold ...

引用

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

作者： Vinod, Ratnaparkhi Gaurang Kumar, Manemoni Arun Chowdary, Chinthalapudi Vaahnitha Suvarchala, K. Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331522667

Pancreatic cancer is one of the most fatal cancers, and medical doctors basically face major problems in its early diagnosis. This article throws light on the work involving the application of Kolmogorov-Arnold Networks (KANs), where we deploy a new machine learning paradigm in the diagnosis of pancreatic cancer. The process of classification basically happens in pancreatic cancer using urinary biomarkers. Unlike Logistic Regression and XGBoost, models have struggled with complex and non-linear medical data, KANs can manage large and complex datasets with ease. Using a dataset of urinary biomarkers, trained a KAN model with EBM techniques for feature selection to increase the model's interpretability and feature relevance. The model presented in this paper showed superior performance compared to traditional models in terms of high accuracy and generalization capability. KANs therefore will be very essential in handling efficiently the challenge presented by the vastness of data in medical studies regarding pancreatic cancer detection. Findings from the studies indicate a better potential in reliable use for progression in developing improved detection of pancreatic cancer using the KAN method. The publication contributes to current research that makes innovative uses in machine learning a reality for improvements in diagnosis as well. © 2025 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Early Stage Pepper Leaf Disease Detection using Deep Learning based Capsule Network with Convolutional Neural Network 1

Early Stage Pepper Leaf Disease Detection using Deep Learnin...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jeevitha, S. Venkadesh, R. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Agriculture plays a vital part in the economy of some countries, providing benefits such as food, national income, and employment. This results in substantial growth in agricultural produce, financial losses, and a decline in quality and quantity. Farmers have traditionally detected diseases through eye tests, but this method has drawbacks. However, previous research suggests that binary classification needs improvement because some types are more challenging to determine. To address these issues, various pepper diseases are classified based on DL techniques to address these issues. First, image pre-processing uses a Median Filter (MF) to reduce noise and increase brightness. Furthermore, the Active Contour Segmentation (ACS) method detects affected and unaffected areas in the image. Moreover, the Texture Color Intensity Feature Selection (TCIFS) method is employed to select the optimal features of pepper disease. Finally, the proposed Capsule Network with Convolutional Neural Network (CapsNet-CNN) technique detects pepper leaf disease. The proposed method attains an accuracy of 98.32%, recall of 97.43%, and precision of 98.04% compared to other methods. © 2025 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Classification using Adaptive Recurrent Neural Network Method 1

Heart Disease Classification using Adaptive Recurrent Neural...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Divya, M. Karthikeyan, A. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Cardiovascular diseases are still among the leading causes of mortality, which require fast and reliable classification methods for diagnosing and preventing the disease. This work thus presents an elaborate framework involving an ARNN for heart disease categorization. First, the technique of Variational Autoencoders (VAE) is used to detect and remove irrelevant or undesirable data records, purging the dataset. Subsequent to this, the Information Gain Feature Selection (IGFS) technique is employed to select the most informative features that lower dimensionality and increase the model's accuracy. Finally, the ARNN is used for classification using the fact that it is a dynamic network that can accommodate temporal characteristics of the data. The experimental results also show that the accuracy, precision, and recall of the approach are higher and higher than those of the conventional method. The proposed framework demonstrates tremendous promise for real-world implementation in medical diagnostics and is a sound and scalable approach to heart disease prediction. Even more striking, these results stress the meaning of applying modern techniques to machine learning in healthcare-sector development. The ARNN achieve 95.6% accuracy for predict the cardiovascular disease. © 2025 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Enhancing Dyslexia Detection: A Robust Machine Learning Approach 3

Enhancing Dyslexia Detection: A Robust Machine Learning Appr...

引用

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

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Rakesh, K. Revanth, K. Saran, S. Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Dyslexia, recognized as a common learning disability, adversely affects reading, spelling, and word decoding capabilities, frequently resulting in considerable academic and social obstacles if not identified at an early stage. Conventional diagnostic approaches, encompassing neuroimaging and standardized assessments, prove to be effective yet are characterized by high costs and limited scalability. In response to this challenge, the present study introduces a hybrid classification model that integrates Random Forest and XGBoost methodologies to improve dyslexia detection through binary classification. The dataset employed comprises both numerical and categorical variables, such as "Gender" and "Native Language," which were processed via one-hot encoding to facilitate the transformation of categorical information. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to mitigate class imbalance, while Principal Component Analysis (PCA) was implemented to diminish dimensionality, thereby enhancing model performance. Preliminary assessments of individual classifiers indicated that Random Forest surpassed both Decision Tree and Neural Network (MLPClassifier), achieving an accuracy of 95.31%, an F1-score of 0.95, and precision metrics of 0.94 for class 1 and 0.97 for class 0. Expanding upon these findings, a hybrid model that merges Random Forest and XGBoost through the application of a VotingClassifier was constructed. This hybrid framework attained an elevated accuracy of 96.23%, accompanied by consistently robust precision, recall, and F1-scores of 0.96. The results underscore the efficacy of the hybrid methodology in offering a scalable and dependable solution for the early detection of dyslexia within both clinical and educational contexts. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Integrated Hydroponics Automation System Utilizing IoT for Enhanced Plant Growth 3

Integrated Hydroponics Automation System Utilizing IoT for E...

引用

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

作者： Nilahni, N. Sivasakthi, P. Kavitha, P. Madhumathi, S.M. Sakthi Lakshmi Priya, C. Saranyavaishalini, V.G. Department of Computer Science and Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798331527549

Growing plants in nutrition solution with any growing medium or roots dipped in distilled water is Hydroponics. Hydroponics, the practice of growing plants in a nutrient-rich solution without soil, offers significant advantages such as higher yield, reduced water usage, and efficient resource management compared to traditional farming. However, existing hydroponic systems often require separate monitoring and control platforms for different setups, such as Wick and Drip systems, increasing complexity and cost. This paper proposes a Combined Hydroponics Automation System utilizing IoT, integrating both Wick and Drip systems into a single, unified platform. The system employs Nodemcu ESP8266 to monitor key parameters such as pH, humidity, moisture, and water levels, with data transmitted to a cloud platform for real-time access and control. The unified approach reduces development and operational costs while simplifying the monitoring process. The outcome demonstrates improved efficiency in plant management, with streamlined data visualization, remote control, and optimized resource allocation, making it a cost-effective solution for urban and resource-limited farming environments. © 2025 IEEE.

关键词： Hydroponics

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：