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Cloud engineering-based on Machine Learning Model for SQL Injection Attack

Cloud Engineering-based on Machine Learning Model for SQL In...

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Singh, Kavita Kokardekar, Sakshi Khonde, Gunjan Dekate, Prajakta Badkas, Nishita Lachure, Sagar Yeshwantrao Chavan College of Engineering Dept. of Computer Technology Mh Nagpur India

ISBN: (纸本)9798350325904

The entire IT sector has been altered by Cloud engineering, which is also continually coming up with new, creative solutions to everyday issues. A systematic method for the commoditization, standardization, and governance of cloud computing applications is provided by cloud engineering, which is the application of the engineering discipline to cloud computing. These days, a lot of businesses and individual users keep enormous amounts of data on the cloud. The information in the data could be sensitive or could be pertinent to the client or the user. Through a variety of harmful methods, the attackers attempt to obtain private data. One such attack is a SQL Injection Attack, in which a hostile user attempts to get unauthorized access to sensitive cloud data by injecting malicious SQL queries. In this paper, a machine learning model is proposed to identify this kind of attack. The Count-Vectorizer approach and the TF-IDF Vectorizer approach are used to test 3 different machine learning models on two different datasets, one with 4200 data items and the other with 30100 data items, both of which contain SQL queries. The models were built using the Random Forest, XgBoost, and Extra Trees Classifier algorithms, respectively. After deploying the models to the test, it was found that with the test size 0.2, Extra trees classifier shows the highest accuracy on dataset of 4200 entries on both the approaches TF-IDF Vectorizer (TV) and Count Vectorizer (CV) and for the dataset of 30100 entries the XgBoost Classifier gives high accuracy with Count Vectorizer approach while XgBoost gives highest accuracy with TF-IDF Vectorizer approach. © 2023 IEEE.

关键词： Cloud computing

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RL-based Virtual Network Embedding using VNF Sharing for Network Slicing in 5G Networks 36

RL-based Virtual Network Embedding using VNF Sharing for Net...

引用

36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Samar, Arihant Sivalingam, Krishna M. Indian Institute of Technology Madras Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665477161

This paper deals with network slicing in 5G networks, where a slice is defined as a set of virtual network function (VNF) instances that collaborate to create an end-toend (E2E) virtual network. A set of slices is implemented on a physical substrate network maintained by an infrastructure provider. The virtual network embedding (VNE) problem deals with the deployment of a network slice's virtual network request on the substrate. Typically, the resources allocated per slice's request are not shared with other slices due to privacy, security and performance considerations. However, there are situations in which VNF instances might be aggregated across many slices to further increase the utilization ratio of the substrate infrastructure. Given these shareable VNF nodes, deploying the network slices is effectively the embedding of the numerous virtual network where these slices are linked by the shared VNFs. This paper uses a reinforcement learning (RL) approach for the embedding problem. The approach incorporates sharing based virtual network functions in an existing RL scheme designed for virtual node embedding without much additional computation. The proposed scheme is implemented using a policy based RL method;the performance study shows an increase in the reward ratio by up to 20% compared to the non-sharing case, along with an increase in the acceptance percentage of slices. © 2023 IEEE.

关键词： 5G mobile communication systems

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Generalized Index Modulation System and Application for Satellite Communications 6

Generalized Index Modulation System and Application for Sate...

引用

6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Ghorib, Khaled A. Abdelaziz, Amr Darwesh, Ahmed F. Mohasseb, Yahya Military Technical College Dept. of Communication Engineering Cairo Egypt Arab Academy for Science Technology and Maritime Transport Dept. of Computer Engineering Cairo Egypt

ISBN: (纸本)9798350378511

The generalized index modulation (G-IM) technique provides an improvement in bit error rate (BER), spectral efficiency (SE), and/or energy efficiency (EE) over traditional digital modulation schemes. However, G-IM techniques suffer from the propagation of errors due to the incorrect detection of indices for higher modulation order (M > 4). To alleviate the risk of error propagation, the enhancement of the correct indices detection probability is a crucial requirement for G-IM systems. This paper presents an error propagation-free generalized index modulation (G-IM) system with adaptive coding modulation in which the indices bits are coded independently on the information bits. The main focus of this study is to derive a quantitative relation between the target system BER and the required indices error rate and, hence, to derive the overall system error probability. In particular, an analytical total BER formula for the error propagation-free G-IM system is provided and compared to the total BER of the classical counterpart modulation techniques. Hence, a direct relationship between the BER of index bits and the ordinary modulation BER is represented, providing an approximate threshold after which the target BER of the G-IM system outperforms the traditional modulation techniques. To validate our established results, the G-IM system with low density parity check code with binary phase shift keying modulation technique (LDPC-BPSK), as a simple satellite coding-modulation scheme which is used in satellite digital video broadcasting TV (DVB-T2) and deep-space communications, is described as a possible direct application for the G-IM system. once the index BER exceeds the threshold, the LDPC-BPSK modulated G-IM system significantly outperforms the classical BPSK modulation while keeping the same EE or SE yet on the expense of added encoder complexity. Moreover, once the index error tends to the zero point, the total G-IM BER reaches its lower limit, a 3 dB gain, thanks t

关键词： Binary phase shift keying

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Advanced Graduate Admission Prediction 8

Advanced Graduate Admission Prediction

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Nalam, Srujan Aleemuddin, Mohd Kadari, Yuva Teja Nanda Kumar, M. Sreenidhi Institute of Science and Technology Dept. of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798350334012

The process of choosing and narrowing down universities for graduate program admission plays a crucial role in the overall application procedure. This project aims to examine the uses of machine learning models to predict the likelihood of a student being accepted into a master's program. Hence this will provide students with an early indication of their admission prospects. In the past, models were constructed using different algorithms such as random forest, multiple linear regression and k-nearest neighbor. Results have shown that logistic regression out performs these algorithms. The admission of students into educational institutions is a critical issue, and this study addresses the application of machine learning algorithms to predict the chance of admission of students into master's programs. These models include SVM, Gaussian Naive Bayes, and Logistic Regression, with experiments demonstrating that the Logistic Regression model surpasses the others. This will give students a better understanding of their admission prospects in advance. © 2023 IEEE.

关键词： Students

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Enhanced Communication Through Isolated Sign Language

Enhanced Communication Through Isolated Sign Language

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Chowdary Kandepu, Tharun Durga LakshmiDevi, N. Mathi, Mani Chandan Midathada, Sai Kumar Kishore Mallireddy, Hemanth Sai Marapatla, Harsha Siril Gmr Institute of Technology Dept of Computer Science and Engineering Rajam India Rajam India

ISBN: (纸本)9798350300857

Isolated sign language recognition is a vital area of research that aims to bridge the communication gap between the hearing-impaired community and the general population. Sign language is the main medium of communication for the deaf and dumb persons. Isolated sign language is a useful and important communication method, breaking barriers, and promoting inclusion in various aspects of daily life for the deaf and hearing-impaired communities. The GISL (Google Isolated Sign Language) comprises a wide range of isolated sign language gestures performed by individuals from different cultural backgrounds. The dataset is annotated with corresponding labels to facilitate supervised learning algorithms. The dataset consists of recorded video samples of human gestures which resembles the communication through human actions. To evaluate the performance of the model, accuracy is computed to assess the model's performance in recognizing isolated sign language gestures. The model is also tested for real-time processing capabilities, ensuring its usability in practical scenarios. © 2023 IEEE.

关键词： Audition

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A Systematic Study of Challenges in Cervical Cytology Image Segmentation of Intelligence based Cervical Cancer Screening 4

A Systematic Study of Challenges in Cervical Cytology Image ...

引用

4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Anu, P.M. Lakshmi, C. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350305258

Cervical Cancer (CC) is the fourth frequent dreadful cancers that prevails in women across the globe. Cervical cytology is being adopted as the standard technique for the screening of pre-cancerous stage and cancerous lesions detection. For accurate segmentation in cervical cytology, the smear images play a crucial role in any Artificial Intelligence based computer Aided Diagnosis (CAD) method. This relies upon the precise segmentation of nucleus present in the cervical cytology smear images. The motive of this study deals with a systematic analysis of the various challenges that are encountered while segmenting the Cervical Cell Nucleus and the countermeasures that are incorporated to eliminate the misdiagnosis and missed diagnoses in the early stage of screening. With the motive of this, a systematic study of the published articles from the sources - Scopus, PubMed and Web of Science are analyzed and reviewed. The importance of this study is due to the intensity of the disease that still positions cervical cancer as a bothering disease that disrupts the life of about five lakh female population, every year across the world. © 2023 IEEE.

关键词： Cervical Cancer Cervical Cytology Cervical Nucleus Challenges in Cervical Cytology Segmentation Intelligence based Cervical screening

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Blockchain-Based Paper Review System 9

Blockchain-Based Paper Review System

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Peter, Allen Xavier Hassan, Sahil Sudev Krishnan, K. Jose, Tony Rakhee, M. Muthoot Institute of Technology and Science Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Paper review and publishing is an important part of a researcher's life. It is where a researcher's time and resources that he has spent bears fruit. And this is enabled by getting his work reviewed by different peers. In our current state of things, researchers face a lot of issues while they try to publish their research manuscript. These issues include quality of peer-reviews, rise of predatory journals, bias between reviewers, and other issues regarding academic integrity. These issues in the traditional system are what make the system inefficient. Therefore, we propose a more efficient blockchain-based system that will try to tackle these issues. Hyperledger Fabric is one of the most popular Blockchain platforms which provides a permissioned blockchain. Our proposed system would leverage the permissioned blockchain. This is to ensure that not everyone from outside the system has access to the data present in the system and only the members of the blockchain are able to access the data. The workflow of the peer-review system would be stored as transactions. The blockchain would be used to keep track of these transactions and order them properly in order to maintain provenance. This system would prove to be a lot more efficient than the traditional existing systems. © 2023 IEEE.

关键词： Blockchain

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Indian Sign Language Recognition for Static Gestures using DenseNet169 Model 8

Indian Sign Language Recognition for Static Gestures using D...

引用

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Keren Beulah, I. Raimond, Kumudha Litisha Miraclin, G. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350396638

It is usually difficult to communicate with someone who has hearing loss. Sign language is a way of communication used by dumb and deaf people. Each country has its own unique sign language. Being an Indian, it's a pride and identity to develop a system for Indian sign language (ISL) recognition. This proposed system aims to examine the difficulties in classifying signs in ISL and intends to take a step forward in this field by utilizing machine learning (ML) models for gesture prediction. This model is trained using a convolutional neural network (CNN), and then the Densenet169 architecture is utilized to recognize the images. This model has been tested and has yielded the highest accuracy of 99.95% in the classification of signs. © 2023 IEEE.

关键词： Machine learning

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rabbi, Md Fozle Dola, Sraboni Das Taha, Noorjahan Binte Kader Al Mahmud, Tamim Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350316001

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed 'FlowerConvNet'. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy. © 2023 IEEE.

关键词： Learning systems

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Smart Contract Cloning Identification Using BERT Model Based Representation Learning 20

Smart Contract Cloning Identification Using BERT Model Based...

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20th IEEE India Council International Conference, INDICON 2023

作者： Kumar, Vishnu Pranav, Pisipati Srinivas, M. National Institute of Technology Warangal Dept. of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350305593

The use of Smart Contracts and Blockchains has experienced a significant rise, leading to the automation of various financial and corporate operations. However, the occurrence of flaws and vulnerabilities in smart contracts has resulted in significant financial losses, raising concerns about their security. It is crucial to enhance the maintenance and reliability of smart contract codes. To detect the clones and bugs in a smart contract, in this paper, we propose a deep learning based model that learns the embedding as a continuous numeric-valued vector for each smart contract. Our approach involves five steps. First, we divide the smart contract Solidity program into streams along with their corresponding structural information. Second, we tokenize the textual data of the smart contract and create a corpus. Third, we employ the BERT word embedding model on the smart contract's tokens corpus to learn feature representation for each token, generating a fixed-length vector of size 768. Fourth, we compute the embedding of each smart contract by considering the weighted sum of tokens within the contract. Finally, to measure clone similarity, we use hierarchical clustering on the feature vectors of smart contracts. The experimental results obtained using our approach on 22,000 Ethereum blockchain Solidity contracts show a clone ratio of 92.14%, and a precision of 93% in detecting clone-related bugs, representing significantly higher accuracy compared to conventional algorithms. © 2023 IEEE.

关键词： BERT Model Blockchain Bug Detection Clone Detection Ethereum Smart Contract Solidity programming

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