检索结果-内蒙古大学图书馆

6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Singh, Anshika Garg, Sharvan Kumar Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi India Department of Computer Science and Engineering Subharti Institute of Technology and Engineering Swami Vivekan and Subharti University Meerut India

ISBN: (纸本)9789811995118

Optical Character Recognition (OCR) is always a prominent area of research and has gained attention in academia and industries, and more advancements are being performed to improve accuracy and quality of text and images. OCR is a technique that converts the digital or optical image (which can be image of typed or printed document or scanned handwritten document or scene photo) into machine-encoded format. OCR takes an image containing text as input, scans the text character by character, analyzes it, and generates a text corresponding to the text in the image, thus reducing the storage size and making accessibility of data easy and faster. In this paper, we will be studying OCR using different techniques like Convolutional Neural Network, Recurrent Neural Network, pytesseract, Keras-OCR, EasyOCR and comparing their results on handwritten scanned images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A Study on the Transmission Dynamics of the Omicron Variant of COVID-19 Using Nonlinear Mathematical Models

引用

computer Modeling in engineering & sciences 2024年第6期139卷 2265-2287页

作者： S.Dickson S.Padmasekaran Pushpendra Kumar Kottakkaran Sooppy Nisar Hamidreza Marasi Department of Mathematics Periyar UniversitySalemTamilnadu636011India Faculty of Engineering and Natural Sciences Istanbul Okan UniversityIstanbulTurkey Department of Mathematics College of Science and Humanities in AlkharjPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Mathematics Faculty of MathematicsStatistics and Computer ScienceUniversity of TabrizTabrizIran

This research examines the transmission dynamics of the Omicron variant of COVID-19 using SEIQIcRVW and SQIRV models,considering the delay in converting susceptible individuals into infected *** significant delays eventually resulted in the pandemic’s *** ensure the safety of the host population,this concept integrates quarantine and the COVID-19 *** investigate the stability of the proposed *** fundamental reproduction number influences stability *** to our findings,asymptomatic cases considerably impact the prevalence of Omicron infection in the *** real data of the Omicron variant from Chennai,Tamil Nadu,India,is used to validate the outputs.

关键词： Omicron local stability reproduction number steady states global stability

来源：评论

学校读者我要写书评

暂无评论

Bank Note Authentication using Deep Learning

Bank Note Authentication using Deep Learning

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Mian, Reiaan Mazhar Khan, Sohaib Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

The swift progression of technology has resulted in a surge in digital transactions, necessitating robust authentication mechanisms to avert fraudulent activities. Because genuine and counterfeit banknotes seem so identical, it is difficult, time-consuming, and error-prone for humans to determine which one is authentic. Counterfeit notes are manufactured at an alarmingly high rate, which means that systems for authenticating bank notes are essential to ensure counterfeit notes are detected and verified quickly and in due time. Such measures guarantee multiple benefits, including safe transactions, uphold the integrity of financial institutions, and guardianship against fake currency notes in the market. In this work, we show that automated solutions may be deployed to improve the accuracy and efficiency of procedures for banknote authentication. Currently, there is a lack of effective and precise models, which are especially trained for verifying or detecting bank note authentication, despite presence of an extensive research in the fields of artificial intelligence and machine learning. Our study seeks to close this gap by creating and implementing a deep learning model that can accurately identify and differentiate between real and fake banknotes. In the study, we noted that the suggested model outperformed conventional techniques with a notable degree of accuracy with the use of an extensive dataset. The model’s potential for practical implementation in banking and financial establishments is shown by its exceptional accuracy and efficiency in detecting fake banknotes. The high accuracy of the model demonstrates that deep learning may be used successfully to improve security protocols, opening the door for more developments in financial technology and otehr allied fields. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Using Convolutional Neural Networks for Enhanced Pneumonia Detection via Chest X-Rays

Using Convolutional Neural Networks for Enhanced Pneumonia D...

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Babar, Waqar Ali, Raja Hashim Faheem, Alishba Mansoor, Syed Ahmed Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

As a deadly lung disease, pneumonia remains a leading cause of child mortality under five years old. Machine learning, especially deep learning have played a significant role in the improved performance for detection and identification of various diseases in the field of healthcare. Neural networks, especially the recent development of newer architectures, have revolutionized the object identification, and classification applications in clinical diagnosis of various diseases. This study presents the application of Convolutional Neural Networks (CNN) for the timely and accurate detection of pneumonia using chest X-rays, a development with considerable potential in aiding clinical diagnosis. The research deployed dropout regularization in model design to mitigate overfitting and relied on recall and F1 scores for thorough model evaluation. Although comparable studies achieved higher overall accuracy, our models registered a recall rate of 98%, crucial in reducing false negatives and enhancing patient safety. This suggests the potential of our CNN model as a vital tool for healthcare professionals in early pneumonia detection in children and adults, with the capacity to process a high volume of X-ray images rapidly and accurately. The successful model construction was enabled through various parameter tuning techniques, thus enhancing patient care efficiency and the potential to decrease mortality rates. © 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

VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

VigilantAI: Real-time detection of anomalous activity from a...

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Javed, Danish Arshad, Usama Peerzada, Shuhrabeel Saud, Muhammad Ramiz Ali, Nisar Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized Artificial Intelligence for enhancing public safety through realtime detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. One of the major problems faced in this field is the availability of a large robust labeled dataset on which a deep learning model can be trained. For this purpose, we have created our own dataset by converting various CCTV footage and movies into images, and then labeling them with the correct class. In addition, we also augmented data by using various data augmentation techniques for the chosen images. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85%. Our system stands out by not only spotting these activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in bolstering security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public’s sense of safety. © 2024 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources 41

CaPS: Collaborative and Private Synthetic Data Generation fr...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Pentyala, Sikha Pereira, Mayana De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Electrical Engineering Universidade de Brasilia Brazil Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data sharing and data usability and resulting in massive amounts of data confined within the walls of organizations. While synthetic data generation (SDG) is an appealing solution to break down these walls and enable data sharing, the main drawback of existing solutions is the assumption of a trusted aggregator for generative model training. Given that many data holders may not want to, or be legally allowed to, entrust a central entity with their raw data, we propose a framework for collaborative and private generation of synthetic tabular data from distributed data holders. Our solution is general, applicable to any marginal-based SDG, and provides input privacy by replacing the trusted aggregator with secure multi-party computation (MPC) protocols and output privacy via differential privacy (DP). We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate SDG algorithms MWEM+PGM and AIM. Copyright 2024 by the author(s)

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

ASL Recognition using Deep Learning Algorithms

ASL Recognition using Deep Learning Algorithms

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Khan, Ayesha Ali, Raja Hashim Akmal, Urooj Ramazan, Alishba Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

There is a considerably vast communication gap between deaf and hearing individuals, and one potential solution is the development of American Sign Language (ASL) recognition technology. While methods utilizing deep learning and convolutional neural networks (CNNs) have shown promising results in general ASL recognition tasks, the potential of optimized CNN architectures for ASL recognition remains *** research identifies the ideal CNN structure to achieve best ASL recognition in static images. The VGG16 model has come out to be very effective;however, analyzing multiple CNN implementations may provide interesting perspectives which could in turn expose features that could potentially be helpful in ASL recognition. This experiment compares the performance of a fine-tuned ResNet50 CNN model with a baseline VGG16 model in ASL recognition. Through analysis of each model’s respective strengths and weaknesses, the study aims to identify areas within the VGG16 architecture that may be further optimized to produce precise *** research project provides guidance for picking suitable CNN architectures and offers valuable insights for improving VGG16 model to achieve even better accuracy and robust results. © 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

Enhancing Fake News Detection Using BERT: A Comparative Analysis of Logistic Regression, RFC, LSTM and BERT

Enhancing Fake News Detection Using BERT: A Comparative Anal...

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ramzan, Alishba Ali, Raja Hashim Ali, Nisar Khan, Ayesha Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

The research analyzed how to identify fake news effectively, understanding the developing capabilities to differentiate between misinformation and the correct nature of news in today’s complex media environment. Numerous approaches were tested: mostly the ways of determining if a certain message is valid or false. With application of Logistic Regression, Random Forest and lastly LSTM models, the research has been able to achieve 98.7%, 98.8%, and 95% accuracies respectively, showing that these traditional and advanced techniques are very effective. However, it became apparent that these models performed poorly as soon as tested on a new dataset and the discrepancy was not being able to understand context while being trained. Understanding the context is the key in news identification, so the research turned to BERT, the Transformer model pre-trained with huge contextual data to perform this task. Drawing on its deep and diverse knowledge base, BERT has shown a remarkable aptitude for sorting news articles into fake/real categories based on their context. Logistic Regression, Random Forest, and LSTM models demonstrated that although they were able to build models that were highly accurate up to 99% on familiar data, their accuracy dropped disproportionately as soon as new data was given. BERT, despite having a lower overall accuracy of 84% demonstrated a better sensitivity to contextual nuances in the news data. Here, the importance of contextualized conception in the sphere of fake news detection should be emphasized as a way to take advantage of BERT’s complicated comprehension which is a promising alternative of more accurate and effective identification as the media landscapes of the modern times may be complex and diverse. Despite the fact that the classical models surpass in planned environments, BERT’s capability of contextualization made it irreplaceable for evidence as the news sources in uncommon circumstances can digress from base. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

引用

computers, Materials & Continua 2022年第4期71卷 457-471页

作者： Milos Mravik T.Vetriselvi K.Venkatachalam Marko Sarac Nebojsa Bacanin Sasa Adamovic Department of Computer Science Singidunum UniversityBelgrade11000Serbia Department of Computer science and Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

At present,the prevalence of diabetes is increasing because the human body cannot metabolize the glucose *** prediction of diabetes patients is an important research *** researchers have proposed techniques to predict this disease through data mining and machine learning *** prediction,feature selection is a key concept in ***,the features that are relevant to the disease are used for *** condition improves the prediction *** the right features in the whole feature set is a complicated process,and many researchers are concentrating on it to produce a predictive model with high *** this work,a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression(L2)to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data *** is a major problem in feature selection,where the new data are unfit to the model because the training data are *** regression is mainly used to overcome the overfitting *** features are selected by using the proposed feature selection method,and random forest classifier is used to classify the data on the basis of the selected *** work uses the Pima Indians Diabetes data set,and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed *** accuracy of the proposed algorithm in predicting diabetes is 100%,and its area under the curve is 97%.The proposed algorithm outperforms existing algorithms.

关键词： Ridge regression recursive feature elimination random forest machine learning feature selection

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

引用

computer Systems science & engineering 2022年第9期42卷 1095-1107页

作者： Ayman Altameem Jaideep Singh Sachdev Vijander Singh Ramesh Chandra Poonia Sandeep Kumar Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering Manipal University JaipurJaipur303007India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

关键词： Machine learning brain signal hand motion recognition braincomputer interface convolutional 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：