检索结果-内蒙古大学图书馆

12th International Young Scientists Conference in Computational science, YSC 2023

作者： Kumar, Prabhat Suresh, S. Department of Computer Science Institute of Science Banaras Hindu University Varanasi221 005 India Department of Computer Engineering & Applications GLA University Mathura281406 India Department of Computer Applications National Institute of Technology Kurukshetra136119 India

With sensors built into smartphones and wearable devices, Human Activity Recognition (HAR) makes it possible to identify regular activities. One of the challenges is to access the motion information from activity data and extract the features from this information. A general model trained without motion features may perform poorly while recognizing the contextual patterns in human activities. In this paper, we proposed the mHAR (motion HAR) model for recognizing static, gradual, and dynamic motion activity to address this challenge. We examine the impact of the proposed model against the general non-convolutional and convolutional recurrent networks in terms of accuracy rates and F1 score. As an experimental dataset, the publicly accessible WISDM was employed. Additionally, the findings of the experiments demonstrate that, in contrast to the unbalanced class problem, motion features play a significant role in activity recognition by achieving accuracy rates of 92.29% and an average F1 score of 83.33%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Enhancing Productivity and User Experience with Advanced Notepad: A Comprehensive Study 10

Enhancing Productivity and User Experience with Advanced Not...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Solanki, Chetna Bana, Garima Singh, Royal Goyal, Mayank Kumar Sharan, Bhagwati Gupta, Raj School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Engineering and Sciences Srm University-AP Department of Computer Science and Engineering Amaravati India

ISBN: (纸本)9789380544472

The current work outlines the creation of a system to facilitate collaborative software design. Based on the observation of system use during real-world design sessions, an iterative development strategy has been employed. As a result, we can fix interface flaws and gain additional knowledge about collaborative design. The proposed work is based on developing an advanced Notepad using Python and Tkinter Library to enhance productivity and user experience. It will provide the user with multiple themes and Fonts. The work is experimented using Visual Studio Code. The outcomes provide better quality of user experience. Additionally, we observe how conceptions change over time, frequently resulting in quick revisions that change the structure. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Techniques and applications in Multimodal Deep Learning 1

A Comprehensive Analysis of Techniques and Applications in M...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tomar, Vivek Sharma, Swati Arora, Sangeeta Tyagi, Aditya Dayal University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Science & Engineering Department Ghaziabad India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India Sharda University CSA Department Greater Noida India

ISBN: (纸本)9798350353648

In recent years, deep learning has gained popularity and been used in many different applications. Models that can process and link data utilizing several modalities are the aim of multimodal deep learning (MMDL). Even with all the advancements made, unimodal learning is still unable to fully capture the entirety of human learning. Multimodal learning facilitates a better understanding and analysis of situations in which information is processed through multiple senses. The various modalities covered in this article include picture, video, text, audio, flow, RGB, stance, depth, face expressions, body motions, and point clouds. There has been a thorough examination of the most current developments in multimodal deep learning applications in the last few years, as well as a detailed investigation of the baseline methodologies. A detailed taxonomy of distinct multimodal deep learning techniques is presented, providing further details on specific applications. Finally, key issues are outlined for each domain separately, along with potential avenues for further research. © 2024 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning Multimodal Natural Language Processing (NLP) UniModal

来源：评论

学校读者我要写书评

暂无评论

Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Image Forgery Detection Using Deep Learning 7th

Image Forgery Detection Using Deep Learning

引用

7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Koshy, Litty Chithu, K. Department of Computer Science and Engineering SCMS School of Engineering & Technology Ernakulam India Master of Computer Applications SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9789819908370

Since cameras are so widely available, taking pictures has become more and more common. In order to gain more information, it is frequently necessary to enhance photographs, which are crucial in our daily lives as memories or sources of wealth of information. There are many tools accessible to enhance the quality of photos;however, some of them are also widely used to alter images, leading to the dissemination of false information. This makes picture forgeries more severe and frequent, which is now a major cause of concern. To identify fake images, many conventional methods have been developed over time. Convolutional neural networks (CNNs) have drawn a lot of interest recently, and they have had an impact on the area of picture fraud detection as well. However, the majority of CNN-based picture forgery detection methods currently used in the literature are restricted to identifying a certain kind of fraud (either image splicing or copymove). As a result, a method that can quickly and precisely identify any hidden forgeries in a picture is needed. We present a powerful deep learning-based approach for detecting picture forgeries in this study. The RGB format images converted to error level analysis (ELA) are used to train our model. The suggested model is compact, and its effectiveness shows that it outperforms cutting-edge methods in terms of speed. The experiment's findings are promising, with a 91.37% total validation accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Generalized Framework for Brain Tumor and Pneumonia Detection Using Streamlite Application 4

A Generalized Framework for Brain Tumor and Pneumonia Detect...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Kishore, S. Nair, Rekha R Mehra, Vishal Babu, Tina Dayananda Sagar University Department of Computer Applications School of Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering School of Engineering Bengaluru India

ISBN: (纸本)9798350335750

One of the dangerous disease pneumonia, which can affect one or both lungs, is frequently brought on by viruses, fungi or bacteria. Radiotherapists with advanced training are required to evaluate chest X-rays used to diagnose pneumonia. Brain tumours are another fatal disease that can have a profound impact on a patient's quality of life and alter everything for them and their loved ones. Therefore, creating an automatic system for diagnosing pneumonia would help treat the disease quickly, especially in remote areas. The work proposes a general framework for MRI and CT images that can automatically detect diseases such as brain tumours and pneumonia with single framework using VGG16. The proposed work focusing on developing streamlit web application for deployment. The proposed work gained an accuracy of 97.87% compared to Inception, Xception and ResNet for brain tumor detection and 94.3% for Pnemonia detection model. The training and testing data are compared using a CNN-based classifier to determine the optimal outcome. © 2023 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

引用

computer Systems science & engineering 2023年第8期46卷 1415-1429页

作者： R.Sheeba R.Sharmila Ahmed Alkhayyat Rami Q.Malik Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Applications Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India College of Technical Engineering The Islamic UniversityNajafIraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and *** address these shortcomings,big data analytics is the most superior technology that has to be *** though big data and IoT could make human life more convenient,those benefits come at the expense of *** manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT *** cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security *** study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)*** the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big *** MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature ***,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL *** comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

关键词： Big data analytics internet of things security intrusion detection deep learning

来源：评论

学校读者我要写书评

暂无评论

Analysis of Important Facts between Heart Disease Features using Machine Learning Classifiers 14

Analysis of Important Facts between Heart Disease Features u...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Pal, Gyanendra Kumar Gangwar, Sanjeev Department of Computer Science & Engineering VBS Purvanchal University Jaunpur India Department of Computer Applications VBS Purvanchal University Jaunpur India

The heart disease is very dangerous disease so diagnosis expert cannot easily manage the effects of risk factors. Machine learning algorithms provide way to predict complex problem in dataset. In this paper, we have simplified dataset by two different features selection methods as Lasso Regression and Random Forest. The Lasso algorithm easily reduces the weak features and prepared strong important collection of features. The Random Forest randomly selects the important features and reduces dataset complexity. In this experiment, we have used Random Forest as predictor algorithm and test dataset model on different K-Fold cross validation as K=2, 4, 6, &. 10., and analyzed simplify dataset by Lasso and Random Forest. Finally, we find Random Forest perform better on dataset which is simplified by Random Forest features selection method. In our experimental results, the proposed Random Forest model performs excellent works in terms of accuracy and ROC. The Random Forest approach with Random Forest features selection calculated classification accuracy as 99% and ROC values calculated 93% on 10-fold cross validation. © Grenze Scientific Society, 2023.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

An Automated Machine Learning NLP Model for Enhancing the Translation of English Causative Sentences into Malayalam Language

An Automated Machine Learning NLP Model for Enhancing the Tr...

引用

2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Tom, Rony Mathew, Juby Amal Jyothi College of Engineering Department of Computer Applications Kerala Kanjirappally India Amal Jyothi College of Engineering Department of Computer Science & Engineering Kerala Kanjirappally India

ISBN: (纸本)9798350303452

This research paper introduces an innovative Neural Machine Translation (NMT) model designed to accurately translate causative English sentences into Malayalam. The study comprehensively explores the various forms of causative sentences utilized in both English and Malayalam, focusing on syntactic and semantic differences. Additionally, it delves into the limitations of current translation models such as Google Translator in accurately translating causative constructs. To address these challenges and enhance translation accuracy, a collaborative effort with professional linguists in both languages led to the creation of a rich dataset. The dataset comprises a diverse collection of causative sentences, covering tense variations, singular and plural forms, noun and verb classes, as well as sentence forms involving non-human and animate-human subjects. This model integrates essential components including tokenization, Part of Speech (POS) tagging, tense identification, suffix identification and addition, and sentence reordering for improved translation. Comparative evaluations between the Google Translator and the newly developed system were conducted based on translation accuracy and fluency. The evaluation revealed that the novel NMT model consistently outperforms existing translation systems, demonstrating superior translation results. The findings underscore the effectiveness and potential of the proposed NMT model in advancing causal English to Malayalam translation, offering valuable insights for the improvement of machine translation systems. © 2023 IEEE.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the Local Binary Pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： Local binary pattern

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：