检索结果-内蒙古大学图书馆

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Premchander, Munimanda Shnain, Ammar Hameed Aadi, Nagender Aluvala, Srinivas Ramachandra, A.C. Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India Sr University School of Computer Science and Engineering Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350375442

In satellite anomaly detection, challenges include unbalanced sample distribution, a limited number of fault samples and subtle anomaly characteristics. Data-driven methods for satellite data anomaly detection often face issues such as high false positive rates and limited interpretability, which negatively impact accuracy. This paper, Multi-Head Attention Mechanism, Temporal Convolution Network (TCN), and Bi-Directional Long Short-Term Memory (Bi-LSTM) technique for anomaly detection in satellite data, achieve better accuracy. The MHA provides flexibility in focusing on different feature and parts of the data for satellite data where anomalies might be influenced by complex, long term patterns. The Bi-LSTM network capture context form both past and future time steps, which is enhance the models ability to understand temporal dependencies and detect anomalies in satellites. The TCS are effective in handling temporal data, especially when dealing with varying sequence length, which is essential for detection anomalies in satellite imagery data. The proposed MHA and TCN-LSTM technique is evaluated for detection anomaly, achieving a high accuracy of 0.92, recall of 0.91, F1-score of 0.92 and precision of 0.92 respectively. This performance is compared to existing techniques such as Random Forest (RF) and Convolutional Neural Network (CNN) and LSTM network. © 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

Real-Time Face Recognition Attendance System Based on Video Processing

Real-Time Face Recognition Attendance System Based on Video ...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar Y S, Rajath Sindhu, D. Shashikala, S.V. Arpitha, K. Chaithra, K.N. Adichunchanagiri University Bgs Institute of Technology Department of Computer Science Bengaluru India Adichunchanagiri University Bgs Institute of Technology Department of Information Science Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The advent of big data and the commercial value of facial recognition technology have created a bright future for the technology, with a large market potential. This article proposes a face recognition participation framework based on continuous video processing. The article explores four aspects of the framework: the accuracy of facial recognition in offline recordings, the accuracy of facial recognition in continuous video processing, the dropout rate of the face recognition participation framework with continuous video processing, and the interface settings of the face recognition participation framework with continuous video processing. By analyzing these aspects, the article proposes a framework for participation based on facial recognition technology and conducts research on the framework with continuous video processing. Experimental results show that the accuracy of the video facial recognition framework can reach 82%. Compared with the traditional registration method, the face recognition participation framework can reduce the dropout rate by more than 60%. The dropout rate of missing classes has also been significantly reduced, which can effectively address the problems of students missing classes and leaving classes early. © 2023 IEEE.

关键词： Attendance system Face recognition attendance Face recognition technology Video processing Video recognition

来源：评论

学校读者我要写书评

暂无评论

An Effective Energy Consumption and Weighted Clustering Using Improved K-Means Clustering

An Effective Energy Consumption and Weighted Clustering Usin...

引用

International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Hussein, Abbas Hameed Abdul Mohan, Pushpa Kurian, Smitha Bhukya, Shankar Nayak Mohan, M. College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Computer Science and Engineering HKBK College of Engineering Bangalore India Department of Computer Science HKBK. College of Engineering Karnataka Bengaluru India CMR Technical Campus Telangana Hyderabad India Department of ISE Nitte Meenakshi Institute of Technology Bangalore Bengaluru India

ISBN: (纸本)9789819778751

The Wireless Sensor Networks (WSNs) are essential to vast Internet of Things (IoT) ecosystem, energy-efficient practices must be implemented in order to enable sensor networks to be seamlessly integrated into intelligent environments. As amount of sensor nodes increases, the challenges associated with Energy Consumption (EC) and weighted clustering become more pronounced. In this research, propose an Improved K-Means clustering-based and Directional Mutation Rule-based Cooperative Optimization Algorithm (DMRC CoatiOA) for clustering and routing selection in WSNs. The primary objective of the proposed DMRC CoatiOA approach is to optimize the routing procedure in WSNs, aiming for enhanced efficiency. The performance evaluation of the proposed method demonstrates superior results, achieving an energy consumption of 1.80, network lifetime of 6200, average delay of 0.15, and throughput of 290 at 50 nodes. This outperforms existing methods such as Reinforcement-Learning based Energy-Efficient (EE) Optimized Routing Protocol for WSN (RLER) and EE Learning Automata and Grouping-Based Routing Algorithm for Wireless Sensor Networks (EDILA). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Automatic Gate Control System 6th

Automatic Gate Control System

引用

6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Nishchay, V. Sujith Bhatt, P. Sreehari, S. Thippeswamy, M.N. Bhagat, Dipak Kumar Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Subex Limited Bengaluru Karnataka India

ISBN: (纸本)9789811613371

A cost effective way of implementing automatic gate control system is proposed in this paper. This will reduce the burden on security personnel. Vehicles are granted access inside the premises of the organization after identifying the number plate through image processing techniques. The algorithm to identify and extract the number plate will be deployed on a cloud where it will be used as a custom API service. An RFID reader is used for authentication in cases where the number plate is not recognized. Sensors are used which help in detecting the vehicle and help in closing and opening the gate at the right time. The pedestrian is granted access to the organization premises by reading the tag information embedded on the ID card using an RFID reader. A web page is developed to facilitate administrator control to add vehicle or pedestrian details. The automatic gate control system will be helpful to reduce the bottleneck created due to the large number of vehicles that enter the premises, especially at peak hours. The system is designed so that it should be cost effective without compromising on security. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Weed Recognition Using Image Patches Based Global Hybrid Attention with Densenet-169 Model 2

Weed Recognition Using Image Patches Based Global Hybrid Att...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Valiveti, Hima Bindu Almusawi, Muntather Satish, E.G. Aluvala, Srinivas Challaraj Emmanuel, E.S. Gokaraju Rangaraju Institute of Engineering and Technology Department of Ece Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Sr University School of Computer Science and Engineering Warangal India Kristu Jayanti College Department of Life Sciences Bengaluru India

ISBN: (纸本)9798350372892

Weeds are one of the main problem in agriculture that affect the crop yield. Accurate classification and recognition is major challenges in weeding because of same visual characteristics between plants and weeds. To solve this problem, in this research patch based image recognition is integrated with the Deep Learning (DL) approach to improving the classification and recognition of weeds. The weed image data are collected from the Deepweeds dataset. For pre-processing Enhanced Super Resolution Generative Adversarial Network (ESRGAN) is utilized for change the image size and then generated to small patches. Selection of relative important patches are performed by Fast Fourier Transform (FFT) and Laplacian Filter (LF) are trained to the DL model. The proposed Global Hybrid Attention mechanism with Densenet-169 model (GHA-Densenet-169) employed for classification of weeds with respective classes. The evaluation results of the proposed method using performance metrics are Accuracy, Recall, Precision and F1-score. The proposed approach attained high accuracy of 99.28%, recall of 99.26%, precision of 99.27% and f1-score of 99.25% which is greater than existing deep learning models such as Densenet-201, VGG-SVM, Adapting MobilenetV2, Resnet-SVM, and Swin Transformer and Two-stage Transfer Learning (ST-TSTL). © 2024 IEEE.

关键词： and deep learning fast fourier transform and laplacian filter global hybrid attention mechanism image patches weed recognition

来源：评论

学校读者我要写书评

暂无评论

Contrastive Loss Function Siamese Network for Change Detection on Remote Sensing Images 2

Contrastive Loss Function Siamese Network for Change Detecti...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Alkhayyat, Ahmad Shreyas, A.V. Raviprakash, M.L. Jayachandran, T. Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Nandha Engineering College Department of Ece Erode India

ISBN: (纸本)9798350373110

To observe the changes in the earth surface is increasing due to development of navigation software applications and also mainly prediction purposes for agriculture purpose and environmental hazards. To overcome the problem one of the most crucial technological choices for effectively identifying changes in observations in remote sensing photos is Change Detection (CD). Applications for CD are numerous and include mapping disasters, environmental monitoring, urban planning and land use study. On the other hand, the CD applications face significant hurdles due to the often-occurring class imbalance issue. In order to tackle this problem, a new type of Contrastive loss function Siamese network (CLFS-Net) that integrates the links between scenes and foregrounds to yield global-aware features for effective change detection. The CLFS-Net improve symbiotic connection learning between scene and foreground while simultaneously learning contextual relationships. The proposed CLFS-Network gained in terms of accuracy as 90%, precision as 95% and recall as 91% and f1 score as 95% respectively. © 2024 IEEE.

关键词： Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

MERN Stack Web-Based Education Management Information Systems for Pacific Island Countries

引用

SN computer science 2023年第1期4卷 70页

作者： Badru, Lawal Olarotimi Vasudevan, Vani Lingam, Govinda Ishwar Khan, M.G.M. The School of Information Technology Engineering Mathematics and Physics The University of South Pacific Suva Fiji Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India The School of Pacific Arts Communication and Education The University of South Pacific Suva Fiji

Generation of quality data to aid planning, assessing the education performance, monitoring programs implementation and learning outcomes are the basic functions of the Education Management Information System (EMIS). This study examines the existing EMIS in the Pacific Island Countries PICs and proffers solution. A SWOT analysis on the selected PICs EMIS through the published technical reports and policy documents from government and donors’ between years 2000 and 2021, revealed that EMIS in PICs have not progressed beyond the stages of collecting demographic data and generating basic indicators. Fiji EMIS which has prospect of leading other PICs EMIS could only generate few indicators manually, and the findings indicate that these indicators are not being considered in decision making. To solve these defects, we proposed data-driven microservices architecture developed with MERN (MongoDB, ExpressJS, ReactJS, and NodeJS) stack on 13 NoSQL collections, tested with pseudonymised data from Fiji Ministry of Education (consisting of 98.6% Learners, 100.2% Schools and 99.5% Teachers in post). Our solution renders dynamic visualized production-ready education data and 28 UNESCO standard indicators to guide decision making and this may serve as a model for PICs EMIS. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Education indicators Fiji Microservices architecture School management solution Web-based application

来源：评论

学校读者我要写书评

暂无评论

Stock Price Prediction using LSTM and TLBO

Stock Price Prediction using LSTM and TLBO

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar M, Kishore Veena, R.C. Ila Chandana Kumari, P. Shivakanth, Gandla Rai B, Karunakara Cmr Technical Campus Department of Cse Hyderabad India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science of Engineering Telangana India K L University Vadeeshwaram Dept of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The stock market's inherent complexity and volatility make accurate predictions difficult. Complex patterns in unstructured data may be simulated and analyzed with the help of Deep Learning. It is possible to employ deep learning models for a wide variety of purposes, including but not limited to image recognition, audio recognition, and natural language processing (NLP). As a result, everyone with a stake in the market is always trying to make educated guesses about what the stock price will do next. We used many deep learning techniques, including Long Short-Term Memory, to model our problem (LSTM). To successfully predict the closing prices of three distinct markets, an Extreme Learning Machine (ELM) model is combined with a variety of metaheuristic optimization techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Symbiotic Organism Search (SOS). These algorithms are helpful in creating ELM network weights and biases in the most effective way possible. The Teaching and Learning Based Optimization (TLBO) paradigm underpins most of the models and methodologies now in use for feature selection. To that end, this study sets out to identify the most effective machine learning model for making such predictions. The primary objective is to develop a smart technology that can predict the movement of stock market values based on financial time series and news headlines. Investors would be able to make more informed decisions based on the binary results of the proposed method. Processing time, error rate, and accuracy in forecasting the direction of stock price movement for five companies across sectors are used to evaluate the performance of the proposed LSTM-TLBO model. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Attention based ConVnet-Recurrent Neural Network for Facial Recognition and Emotion Detection

Attention based ConVnet-Recurrent Neural Network for Facial ...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 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

Fraud Detection using Enhanced Secure Machine Learning Algorithm for Wireless Communication 2

Fraud Detection using Enhanced Secure Machine Learning Algor...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Deepa, N. Alkhayyat, Ahmad Lande, Jayapal Armoogum, Sheeba Bhoomika, S.S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Mauritius Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350372892

In recent years of technology, the wide usage and expansion of credit cards causing significant improvement in the fraud cases. Which are affecting the financial conditions, so it is very important to aware and stop the fraud transactions. In this paper, Fraud detection for wireless communication is implemented by using a proposed method called as eXtreme Gradient Boosting (XGBoost) is based on Gradient-boosted Decision Tree (GDBT) and it increases the training speed and to improve accuracy by making multiple decision trees iteratively, and reducing each iterations loss function. Initially, the proposed method implemented on dataset which is collected from Kaggle. That contains the credit card transactions made by the European credit cardholders in the year of 2013 September for 2 days. In data preprocessing stage Synthetic Minority Oversampling Technique (SMOTE) is used to balance the imbalanced class distribution in the dataset by generating synthetic samples that will improve the classification accuracy. And for dimensionality reduction Auto encoders are used significantly there are two phases namely encoders and decoders, which reduces the size of input data. In the classification phase, XGBoost algorithm is used as it achieves high accuracy of 98% when compared with all the start-of-arts namely Variational Autoencoder Generative Adversarial Network (VAEGAN) and Convolutional Neural Network, Bi directional- Long Short Term Memory (CNN-Bi-LSTM). © 2024 IEEE.

关键词： Dimensionality reduction

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：