检索结果-内蒙古大学图书馆

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Khatri, Vinayak Agarwal, Gaurav Gupta, Amit Kumar Sanghi, Akash Invertis University Department of Computer Science and Engineering Bareilly India KIET Group of Institutions Department of Computer Applications Ghaziabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Rapid developments in machine learning (ML) and artificial intelligence (AI) have had a big impact on cyber security by providing creative answers to challenging security problems. This analysis examines how AI and ML are integrated into cyber security, emphasizing the uses, advantages, and drawbacks of each technology. To improve threat detection, malware categorization, phishing prevention, and network security, this study analyses many AI and ML methods, such as reinforcement learning, deep learning, supervised learning, and unsupervised learning. According to recent study findings, AI-driven intrusion detection systems can detect intrusions with up to 99% accuracy, which greatly lowers the number of false positives. Comparatively, compared to conventional techniques, machine learning algorithms for malware detection have increased detection rates by 20-30%. However, several technological obstacles stand in the way of AI's smooth integration with cyber security. These obstacles include problems with data quality, adversarial assaults, interpretability of models, and the computing demands of implementing AI at scale. There are other major obstacles, such as privacy and ethical considerations. Nonetheless, issues including adversarial attacks, ethical dilemmas, and poor data quality persist. This paper illustrates the usefulness of AI and ML technologies in cyber security through real-world case studies. Lastly, it emphasizes how AI and ML have the ability to revolutionize cyber security procedures by highlighting upcoming trends and research paths. This review attempts to provide useful insights for researchers, practitioners, and policymakers to harness AI and ML for a safer digital environment by providing a thorough overview and highlighting important discoveries. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations

引用

SN computer science 2025年第2期6卷 1-13页

作者： Dangi, Dharmendra Dixit, Dheeraj Kumar Bhagat, Amit Rao, Durgesh Gupta, Jeetendra Kumar Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science and Engineering MITS Madhya Pradesh Gwalior India Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Applications Atal Bihari Vajpayee Vishwavidyalaya Chhattisgarh Bilaspur India

Human motion detection is a critical application of biomechanics, leveraging computer vision to analyse and interpret human activities. This technology is essential across various fields, including sports training, where it aids in monitoring and optimizing athlete performance, and security, where it plays a role in tracking movements in sensitive areas. In this study, we employ two distinct datasets: the Movi dataset and the MoCap CMU dataset. To address the challenge of limited data, we utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for data augmentation, generating new, synthetic images to enhance the diversity and quantity of the training data. Subsequent analysis was conducted using multiple deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), VGG16, AlexNet, and AlexNet with K-Fold Cross Validation. The application of these models yielded promising results, with accuracies of 87.5%, 89.9%, 90.2%, 91.2%, and 98.3%, respectively. The findings demonstrate the effectiveness of DCGAN in improving model performance through data augmentation, as well as the potential of these deep learning models to accurately detect and classify human motion. This research contributes to advancements in biomechanics and its applications in sports science and security domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomechanics DCGAN Deep learning Human motion detection K-Fold cross validation Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Recent Trends in Sign Language Detection System Using Machine Learning Algorithms 2

Recent Trends in Sign Language Detection System Using Machin...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Sharma, Keshav Jain, Naman Upadhyay, Adarsh Mishra, Annu Chaturvedi, Ravi Prakash Pruthi, Jyoti Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Sharda School of Engineering & Technology Department of Computer Science & Applications Greater Noida India

ISBN: (纸本)9798350368109

This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recognition topics, from low-power bio-inspired armband solutions to sophisticated neural network structures. These technologies' incorporation into human-robot interactions, as well as their potential to improve communication accessibility, are highlighted. The communication gap limits social integration, access to services, information, and education for individuals who rely on sign language. To address this issue, our research aims to develop an innovative method for real-time translation of sign language into spoken and written languages. We propose a comprehensive system called "SignText"that utilizes Large Language Models (LLMs) and Python programming to interpret and translate sign language into subtitles. Through this approach, we aim to bridge the communication gap and enhance the lives of individuals who use sign language. Initial results demonstrate promising accuracy, with an average recognition rate of over 90% for basic signs and gestures, and up to 85% and 70% for more complex and similar signs, respectively. These results show significant progress toward fostering greater inclusion and accessibility in social interactions and daily life. © 2024 IEEE.

关键词： Human robot interaction

来源：评论

学校读者我要写书评

暂无评论

Microplastic Identification in Seawater using Generative Adversarial Networks

Microplastic Identification in Seawater using Generative Adv...

引用

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

作者： Rajasekar, M. Geetha, Angelina Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India Departmnet Of Computer Science And Engineering Hindustan Insitute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In recent years, microplastics-plastic particles smaller than five millimeters-have become a significant environmental contaminant. These tiny plastic pieces have been found permeating aquatic ecosystems globally, posing a threat to marine life upon ingestion. However, identification and quantification of microplastics in seawater samples remains an arduous task. The small size and translucent nature of microplastics makes them difficult to distinguish from natural particulates in seawater under a microscope. This has hindered efforts to accurately monitor microplastic pollution levels in the marine environment. Our goal in this study was to use cutting edge deep learning techniques to create an automated image based system for detecting microplastics in seawater. We chose to employ a generative adversarial network (GAN) architecture for its proven capability in generating highly realistic synthetic images. The GAN model was trained on a dataset of microscope images of microplastics and natural particulates extracted from seawater samples. Coastal seawater samples were first filtered to isolate particulates of a size range containing microplastics. Density separation was then used to separate the plastic microparticles from denser natural particulates. We implemented a deep convolutional GAN with a generator network to produce synthetic microplastic images, and a discriminator network to differentiate real from synthetic images. Through iterative adversarial training, the two networks evolved to produce and accurately classify images of microplastics with a high degree of realism. Advanced image processing techniques were used to enhance the training data. After training, the GAN model was evaluated on an annotated testing dataset of images containing real-world microplastics and natural particulates extracted from seawater. Our proposed GAN-based technique achieved a microplastic classification accuracy of 92.5% on the test set, demonstrating its effectiveness. The

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Blockchain Merkle-Tree Ethereum Approach in Enterprise Multitenant Cloud Environment

引用

computers, Materials & Continua 2023年第2期74卷 3297-3313页

作者： Pooja Dhiman Santosh Kumar Henge Sartaj Singh Avinash Kaur Parminder Singh Mustapha Hadabou School of Computer Applications Lovely Professional UniversityPhagwara144001India School of Computer Science and Engineering Lovely Professional UniversityPhagwara144001India School of Computer Science Mohammed VI Polytechnic UniversityBen Guerir43150Morocco

This research paper puts emphasis on using cloud computing with Blockchain(BC)to improve the security and privacy in a *** security of data is not guaranteed as there is always a risk of leakage of users’*** can be used in a multi-tenant cloud environment(MTCE)to improve the security of data,as it is a decentralized *** is saved in unaltered ***,Blockchain is not owned by a single *** encryption process can be done using a Homomorphic encryption(HE)algorithm along with hashing technique,hereby allowing computations on encrypted data without the need for *** research paper is composed of four objectives:Analysis of cloud security using Blockchain technology;Exceptional scenario of Blockchain architecture in an enterprise-level MTCE;Implementation of cipher-text policy attribute-based encryption(CPABE)algorithm;Implementation of Merkle tree using Ethereum(MTuE)in a Multi-tenant *** of these four objectives,the main focus is on the implementation of CP-ABE ***-ABE parameters are proposed for different levels of *** levels include inner tenant,outer tenant,Inner-Outer-Tenant,Inner-Outer-External-Tenant,Outer-Inner-Tenant,External-Outer-Inner-Tenant and the parameters such as token,private key,public key,access tree,message,attribute set,node-level,cipher-text,salting which will help in providing better security using CP-ABE algorithm in a multitenant environment(MTE)where tenants can be provided with different levels of security and achieved 92 percentage of authenticity and access-control of the data.

关键词： Blockchain(BC) merkle tree using ethereum(MTuE) multi-tenant environment(MTE) homomorphic encryption(HE) ciphertext policy attribute-based encryption(CP-ABE)

来源：评论

学校读者我要写书评

暂无评论

Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

IoT Based Agricultural Monitoring and Alert System 4

IoT Based Agricultural Monitoring and Alert System

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priya, V Vishnu Nisanth, G. Dhanushree, D. Sindhu, M. Varshini, T. Preteka, A.T. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Applications Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (纸本)9798331523923

The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such as soil moisture levels, soil temperature, water levels, irrigation pump operation status, and water quality, to improve precision farming practices. Enabling farmers to make well-informed decisions, maximize resources, and enhance crop output in real time is the primary objective of the AMAS system. The challenges of using IoT for agricultural monitoring, include issues with adoption among farmers, dependable technology, efficient data administration, and connectivity. Small-scale farmers' financial hardship and rural areas' restricted access to technology. Many obstacles stand in the way of the widespread adoption of IoT-based agricultural monitoring systems, such as smallscale farmers' limited financial resources, rural communities' restricted access to technology, and problems with connectivity, dependable data handling, and system scalability. Machine learning-based prediction models and IoT-integrated smart irrigation are two recent approaches that have showed promise in tackling these issues, but they still face obstacles such high implementation costs, technical complexity, and infrastructure dependence. By improving yields, increasing output, and facilitating remote monitoring, the AMAS system seeks to overcome these limits while taking into account the unique requirements and limitations of farmers. This study plan to enhance productivity, optimize yield, and implement remote monitoring. © 2025 IEEE.

关键词： Soil moisture

来源：评论

学校读者我要写书评

暂无评论

Classification of Ultrasound Breast Cancer Image Using Tuning Up the Hyper-Parameter of Convolutional Neural Network

SSRN

引用

SSRN 2023年第7期17卷 264-269页

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management & Research Gwalior India Department of Computer Engineering Madhav Institute of Technology and Science Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are some pre-trained models used by authors for the detection of breast tumor, but these models require extensive computation power due to their many layers and parameters. To address this issue, it is required to proposed convolutional neural network (CNN) model with fewer training parameters for classification of ultrasound images dataset to determine that a particular image is either benign or malignant. In this paper, CNN model is proposed with changes in some hyperparameters like the number of filters, filter size, batch normalization, learning rate, epoch, and batch size, to achieve better accuracy with less computational power. The proposed model was compared to other pre-trained models, including ResNet50, Efficient Net, and VGG16, using two databases (database A for training and validation data, and database B for testing data). Our proposed classifier outperformed than pre-trained classifiers in terms of accuracy. © 2023, The Authors. All rights reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data

引用

Multimedia Tools and applications 2025年 1-21页

作者： Henge, Santosh Kumar Mandal, Sanjeev Kumar Sharma, Amit Upadhyay, Aditya Kundu, Shakti Iyer, Srinivasan Saini, Ashok Kumar Yadav, Manoj Kumar Singh, Bhupinder Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Bangalore India Department of Computer Applications Lovely Professional University Punjab Jalandhar India Center for Distance and Online Education Manipal University Jaipur Rajasthan Jaipur India Haryana Gurugram122413 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Jalandhar India

In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and complex natures of data sources become the issue and we need to propose an effective method for adaptive algorithms to accurately forecasting and make decision efficiently. The system framed with two stages of execution: The first stage has covered time series-based predictions for different stock values involving the LSTM Model;the second stage integrated the experimental scenarios based on trend, value, indicators, and it's supporting impacted data metrics, which experimented on various large-cap stock companies. The dataset is sourced online by yahoo finance API with consecutive days data and dependable, non-dependable influenced indicators, which helped to intend trend-based evaluation performances. The proposed model experimented on distinct stock values and test cases executed based on indicator-based prediction with a 76.92% accurate performance rate using a Random Forest Classifier and 76.92% of Logistic Regression. The value-based prediction test case has achieved a performance rate of 97.5% using SVR Regressor and 97.2% using Linear Regressor. It computed value of root mean square error for the Ten large cap companies for performance evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive algorithms

来源：评论

学校读者我要写书评

暂无评论

Spider monkey ınspired optimization algorithm based clustering for face recognition

引用

International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Gopal, Veni devi Devi, G. Shree Geetha, Angelina Department of Computer Science and Engineering B S Abdur Rahman Institute of Science & amp Technology Chennai India Department of Computer Science and Engineering Hindustan Institute of Science and Technology Chennai India Dept of Computer Applications BSA Crescent Institute of Science and Technology Chennai India

Face recognition is a fast-growing technology that is widely used in forensics such as criminal identification, secure access, and prison *** contrasts from other classification issues in that there are normally a more number of classes in face recognition. Thus, the validation accuracy of the conventional subsystem face recognition is unsuitable. In order to overcome the problems that are likely to be encountered, the proposed method utilizes the Spider Monkey inspired Optimization (SMO) algorithm for face clustering and recognition. At first feature reduction is done by Advance Principal Component Analysis (APCA) which is a hybridization of PCA and linear discriminant analysis (LDA). After reducing the features, the selected features are grouped and faces are recognized by means of SMO algorithm. From the food search behavior of spider monkeys, SMO algorithm is developed. The performance of the proposed method is evaluated by means of sensitivity, specificity, and accuracy. Here the proposed method accomplishes 98.66% accuracy. Using the MATLAB platform, the recommended technique is implemented. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Accuracy Linear discriminant analysis Principal component analysis Sensitivity Specificity Spider monkey optimization

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：