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Secure Host Login Technique based Key Agreement protocol for Software Defined network 4

Secure Host Login Technique based Key Agreement protocol for...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Bhardwaj, Aditya Mutaher, Hamza International Inistitute of Technology Dept. Computer Science and Engineering Bhubaneswar India School of Computing and Innovative Technologies British University Vietnam Hanoi Viet Nam

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Scalability is one of the Software Defined network features that makes it more efficient than the traditional network. Due to this feature, a huge number of hosts connect to the network and require network rules and policies from the network controller. The huge number of connected hosts makes the network vulnerable to unauthorized access, which may cause network damage. In this paper, a secure hosts login technique is proposed to assure the authenticity of the hosts that need to access the network and use its resources. The proposed technique uses the key agreement protocol to allow the hosts and controller to establish secure communication. Then the procedure moves to the registration phase and login phase, which are responsible for creating and verifying the hosts' credentials while claiming network access. The secrecy and authenticity of the proposed technique was simulated, verified, and proved using AVISPA tool. The result of the simulation also proved the immunity of the proposed technique against MITM and replay attacks. © 2022 IEEE.

关键词： Controllers

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Landslide Semantic Segmentation Using Satellite Imagery 19

Landslide Semantic Segmentation Using Satellite Imagery

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19th international conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2022

作者： Achariyaviriya, Witthawin Kondo, Toshiaki Karnjana, Jessada Nishio, Takayuki Sirindhorn International Institute of Technology School of ICT Thailand National Electronics and Computer Technology Center NSTDA Thailand Tokyo Institute of Technology Department of Information and Communication Engineering Japan

ISBN: (纸本)9781665485845

This work aims to improve the accuracy of landslide detection with single satellite imagery by using slope factor extraction and normalized difference vegetation index (NDVI) extraction and by combining three semantic segmentation models based on a convolutional neural network with a decision tree. The proposed method uses three semantic segmentation models with a decision tree. The first model is trained by a training set of color images. The second model is trained by a training set of slope factor images. The third model is trained by a training set of NDVI images. The slope factor and NDVI are extracted from color images that contain red, green, and blue bands. The results from three models are used to generate features for training the classification decision tree. Evaluation metrics (precision, recall, and F2 score) can be improved by using slope factor and NDVI. In combining three models, the F1 score and F2 score are increased more than using single of color images 16.71% and 24.15%, respectively. Moreover, the slope factor detection model and NDVI detection model can support some areas that color image detection model cannot detect. © 2022 IEEE.

关键词： Satellite imagery

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Performance Evaluation of Uplink Shared Channel for Cooperative Relay based Narrow Band Internet of Things network

Performance Evaluation of Uplink Shared Channel for Cooperat...

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2022 international conference on Business Analytics for technology and Security, ICBATS 2022

作者： Hassan, Mona Bakri Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Saeed, Rashid A. Mokhtar, Rania A. Khalifa, Othman O. Hashim, Aisha Hassan Abdalla Electronics Engineering Dept Khartoum Sudan Universiti Kebngsaan Malaysia Center for Cyber Security Faculty of Information Sceince and Technology Selangor Bangi43600 Malaysia Department of Electrical and Electronics Engineering Port Sudan Sudan Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia International Islamic University Malaysia Department of Electrical and Computer Engineering Lumpur Kuala Malaysia

ISBN: (纸本)9781665409209

Low Power Wide Area network (LPWAN) is one of the fastest growing network techniques provides efficient communciations for smart cities, e-Health, industry 4.0 and other applications. LPWAN enables long-rang communciations for M2M and cellular IoT networks. Narrowband-IoT (NB-IoT) is a type of LPWAN developed by 3GPP to connect a wide stream of IoT services and devices. NB-IoT systems rely on the mechanism of repeating the same signal every specified period of time in order to improve radio coverage better than it is in LTE systems. Repetition process is used to enhance the coverage of NB-IoT and for upgrade throughput as well. However, increasing the repetition of the signal significantly may give a negative result relative to the bandwidth limits. A cooperative relay (CoR) can be used beside repetition mechanism to helps reduce bandwidth stress. Moreover, the use of CoR for NB-IoT in physical uplink shared channel with repetitions will enhance the throughput. This paper will evaluate the performance of the CoR to enhance physical uplink shared channel in NB-IoT. The NB-IoT system model is simulated bu MATLAB to demonstrate the use of Cooperative relay (CoR) scheme in NPUSCH for NB-IoT for performance evaluation and comparison of using CoR scheme by considering metrics like data rate, throughput, and delay. The results conclude that in using CoR in NB-IoT gives high performance in overall NoT network throughput. © 2022 IEEE.

关键词： Wide area networks

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Evaluating deep neural network for automated sleep staging in real-life scenarios

Evaluating deep neural network for automated sleep staging i...

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2022 international conference on computer network Security and Software Engineering, CNSSE 2022

作者： Cao, Dingbang Hu, Congjia Hu, Jiaming Li, Dailun Li, Wenkang School of Computer Engineering and Science Shanghai University Shanghai200444 China School of International Education Guangxi University of Science and Technology Guangxi Liuzhou545000 China Electronic and Electrical Engineering University College London Gower Street LondonWC1E6BT United Kingdom Faculty of Computer Science McGill University MontrealQCH3A0G4 Canada School of Computing University of Leeds West Yorkshire LeedsLS2 9JT United Kingdom

ISBN: (纸本)9781510655980

Using artificial intelligence in automated sleep staging has become very popular. In this paper, we evaluate a deep learning model, Tiny Sleep Net, to illustrate the limitations of the deep learning model in the task of sleep staging predication. During the experiment, we changed the hyperparameter to achieve various study rates, changed the number of epochs and the balance of datasets. The results showed that the actual result of this model is quite acceptable, which is 82.3%. In contrast, the whole model is relatively fragile when some parameters are changed, witnessing an incredibly low accuracy when predicting. In addition, compared with the overall accuracy, the difference in the per-class accuracy can be drastic. Therefore, these results showed that the neural network is susceptible to the hyperparameter and the exact number of epochs generated. Therefore, in exact situations when the input dataset is not balanced or hyperparameters have minus changes, the final prediction accuracy will decrease dramatically. © 2022 SPIE.

关键词： Sleep research

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A Comparative Analysis of ResNet Architectures

A Comparative Analysis of ResNet Architectures

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2022 international conference on Smart Generation Computing, Communication and networking, SMART GENCON 2022

作者： Nagpal, Piyush Bhinge, Shivani Atul Shitole, Ajitkumar International Institute of Information Technology Department of Electronic and Telecommunication Hope Foundation Pune India International Institute of Information Technology Department of Computer Engineering Hope Foundation Pune India

ISBN: (纸本)9781665454995

Neural networks today are becoming increasingly complex, from a few layers to more than 100 layers. The principal advantage of a totally deep neural network is that it may represent very complicated functions. Functions can be learned at different levels of abstraction, such as low-level boundary functions and high-level complex functions. However, the use of deep networks is not always efficient. A huge barrier to training them is vanishing gradients: very deep networks often have a gradient signal that goes to zero quickly, thus making gradient descent prohibitively slow. Deep residual networks are nearly like networks in which convolution, pooling, activation, and fully connected layers are superimposed. The only construct of a simple network that can be created as a residual network is the identifying link between the layers. Different types of ResNet can be developed depending on the depth of the network, such as ResNet-50 or ResNet-152. The number at the end of ResNet suggests the variety of layers in the community or the depth of the network. ResNet can be designed to any depth using ResNet's basic building blocks. In this article, we demonstrated a residual network with depths between 34 and 152 and tried to differentiate the architectures by training them on the same dataset. © 2022 IEEE.

关键词： Gradient methods

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Credit Approval Decision using Machine Learning algorithms 10

Credit Approval Decision using Machine Learning Algorithms

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10th international conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Mridha, Krishna Barua, Dipayan Shorna, Meghla Monir Nouman, Hasan Nouman Kabir, Md Hasanul Singh, Ajay Vikram Marwadi University Computer Engineering Rajkot India Marwadi Univeristy Informaation Technology Rajkot India Daffodil International University Computer Science and Engineering Dhaka Bangladesh Henan Polytechnic University Computer Science & Technology China Aiit Amity University Uttar Pradesh Noida India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

The credit approval process for customers seeking a loan has been an important and risky full decision to take for banking institutions. The standard procedure involves a host of information regarding the customer based on what the bankers decide whether to grant a loan which often is time-taking and exhaustive. Therefore, this paper proposes a solution based on machine learning algorithms that reduce risk factors associated with the customer's behaviors that could suggest selecting a trusted person to save the bank's effort and assets. The method utilizes customer information such as monthly income, prior loan history, investment, education, gender, etc., up to twelve attributes that are further used to construct a machine learning model of the decision-making on credit approval. Five different machine learning algorithms named Gradient Booster (GB), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), and Gradient Booster Classification (GB) are built with a 90% training dataset. Among these, LR has reported the best accuracy as much as 80.43% for the available dataset. We applied these methods to a dataset collected from Kaggle, which contains 615 rows and 13 columns where rows represent customers and columns feature and decisions on credit in 'yes' or 'no'. Future work includes the attempt to improve prediction accuracy with the application deep learning algorithm. © 2022 IEEE.

关键词： Decision making

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LSMM: A Life-cycle Security Management Mechanism for Secure Image Sharing 2

LSMM: A Life-cycle Security Management Mechanism for Secure ...

引用

2nd international conference on computer Science and Blockchain, CCSB 2022

作者： Zhang, Yaoyao Yao, Zhongyuan Pan, Heng Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

During the storing, transferring, processing, and exchanging phases of the full life-cycle of secure image sharing, there may exist security issues such as information leakage, infringement and abuse. To address those problems, a life-cycle security management mechanism is proposed. By utilizing the Commutative Encryption and Watermarking (CEW), the mechanism ensures that the ownership and confidentiality of one image can hardly be tampered with and compromised. Besides, with the help of the blockchain, the mechanism is enabled to record any changes during the whole process of the image sharing, which makes copyright traceability possible and thus reduces the occurrence of infringement and abuse. The experiment result shows that, in our mechanism, the quality of plaintext image with watermark obtained by decryption is high, and the invisibility of watermark is preserved. Also, the efficient analysis and experimenting of the mechanism exhibits that it is feasible in practice. © 2022 IEEE.

关键词： Life cycle

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Research Trend in the international Literatures on Machine Translation Based on CiteSpace

Research Trend in the International Literatures on Machine T...

引用

2022 Global conference on Robotics, Artificial Intelligence and Information technology, GCRAIT 2022

作者： Zhou, Li Xie, Baoxia School of Foreign Language South China University of Technology Guangzhou China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

With the continuous development of computer, machine translation has experienced from 'mechanical brain' to neural network machine translation based on deep learning. It is constantly innovating in technology and upgrading in algorithm. With the attempt to clarify the development characteristics, development status, advantages and limitations of the machine translation system at each stage, this study collects the machine translation research data from the databases of SCI and SSCI since the 1990s and analyzes them with the assist of CiteSpace so as to looking forward to the future of machine translation. © 2022 IEEE.

关键词： Deep learning

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Evaluation Method of English Teaching Quality Based on SOFM Neural network 2nd

Evaluation Method of English Teaching Quality Based on SOFM ...

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2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Sheng, Jing Wuchang University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9783031239465

With the globalization of world economy, culture, science and technology, international cooperation and exchanges are becoming more and more frequent, and English will occupy a more and more important position. As a public compulsory basic course for Non-English Majors in Colleges and universities in China, College English plays a very important role in Expanding College Students’ knowledge, improving foreign language comprehensive quality and cultivating language application ability. Therefore, the quality of College English teaching is also included in one of the important indicators of college curriculum construction evaluation. This paper analyzes four problems existing in College English teaching, and points out that emphasizing summative assessment and neglecting formative assessment are the main reasons affecting the quality of English teaching. Therefore, this paper proposes an English teaching quality evaluation method based on SOFM neural network to strengthen the monitoring of the formation process of teaching quality, so as to improve teaching quality. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： international cooperation

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1st international conference on Artificial Intelligence and Internet of Things, ICAII 2022

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and Internet of Things, ICAII 2022

ISBN: (纸本)9783031373022

The proceedings contain 23 papers. The special focus in this conference is on Artificial Intelligence and Internet of Things. The topics include: On Assaying the T-score Value for the Detection and Classification of Osteoporosis Using AI Learning Techniques;evaluation of Healthcare Data in Machine Learning Model Used in Fraud Detection;a New Approach to Heart Disease Prediction Using Clustering and Classification algorithms;skin Lesion Classification: Scrutiny of Learning-Based Methods;Lung Disease Classification Using CNN-Based Trained Models from CXR Image;A Novel Framework for Satellite Image Denoising and Super Resolution Using CNN-GAN;deep Learning Based Enhanced Text Recognition System;meta-transfer Learning for Contextual Emotion Detection in Face Affirmation;hand-Written Devanagri Character Recognition Using Convolutional Neural network in Python with Tensorflow;the Design and Development of computerized Geogebra Mathematical Model for Painting Color Learning System;bisimulations for Fuzzy Geometric Models;research Study: Data Preprocessing Using Machine Learning for Prediction of Booking Cancellations;Advanced MRI Segmentation Algorithm for the Detection of Brain Tumor Using U-Net Architecture with Transfer Learning EfficientNet-B7;audio Analysis for Early Diagnosis of Alzheimer’s Disease: A Study on Fisher’s Linear Discriminant and Mel-Frequency Cepstral Coefficients;automation in Agriculture Using Deep Machine Learning: A Survey;A Survey on Security Threats in VANET and Its Solutions;An Analysis of Advanced Computations and Semantic Reasoning to Create an Intelligence System Using WSN;ioT & Smart City Viability: An Empirical Study;design of a Contextual IoT Framework for the Improved User Experience and Services;human Motion Detection Using Ultra-Wide Band Radar.

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