2022 2nd Asian Conference on Innovation in Technology (ASIANCON)最新文献_第6页

Secure QR Code Scanner to Detect Malicious URL using Machine Learning 安全QR码扫描仪检测恶意URL使用机器学习

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908759

Atharva Pawar, Chirag Fatnani, Rajani Sonavane, Riya Waghmare, Sarang A. Saoji

{"title":"Secure QR Code Scanner to Detect Malicious URL using Machine Learning","authors":"Atharva Pawar, Chirag Fatnani, Rajani Sonavane, Riya Waghmare, Sarang A. Saoji","doi":"10.1109/ASIANCON55314.2022.9908759","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908759","url":null,"abstract":"Q-R codes are utilised for a variety of purposes, including accessing online web-pages and making a settlement. The Internet facilitates a wide range of illegal acts, including unsolicited e-marketing, financial embezzlement, and malicious distribution. Even though all the users identify the presence of Q-R codes visually, the information stored in those codes can only be accessed through an allocated Q-R code decoder. Q-R codes have also been shown to be used as an effective attack vector, For example techniques include social engineering, phishing, pharming, etc. Harmful codes are distributed under false pretences in congested areas, or malicious Q-R codes are pasted over current ones on billboards. Finally, consumers rely on decoder operating system to determine a random Q-R code is whether malicious or benign.For the purpose of this report, we consider the identification of malicious Q-R codes as a two-way classification problem in this research, and we test the effectiveness of many well-known M-L algorithms, including namely K-Nearest Neighbour, Random Forest, Binary LSTM and Support Vector Machine. This implies that the proposed method might be deemed an optimal and user-friendly QR code security solution. We created a prototype to test our recommendations and found it to be secure and usable in protecting users from harmful QR Codes.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Diabetes Prediction Using Machine Learning Analytics: Ensemble Learning Techniques 使用机器学习分析预测糖尿病:集成学习技术

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908975

D. Tripathi, S. Biswas, S. Reshmi, Arpita Nath Boruah, B. Purkayastha

{"title":"Diabetes Prediction Using Machine Learning Analytics: Ensemble Learning Techniques","authors":"D. Tripathi, S. Biswas, S. Reshmi, Arpita Nath Boruah, B. Purkayastha","doi":"10.1109/ASIANCON55314.2022.9908975","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908975","url":null,"abstract":"Diabetes is an incurable disease which is due to a high level of sugar in the blood over a long period of time. Hence, early prediction is required to reduce its severity significantly. Now-a-days Machine Learning (ML) community has been working on diabetes prediction and much research has been done for decades for its prediction. Keeping in view of its severity, this paper proposes a model, named Diabetes Expert System using Machine Learning Analytics (DESMLA) to explore the diabetes data to predict the disease more effectively. The Diabetes Dataset (DD) is imbalanced in nature; therefore, the DESMLA model uses the 5 most prominent oversampling techniques namely SMOTE, Borderline SMOTE, ADASYN SMOTE, K-Means SMOTE and Gaussian SMOTE to get rid of this class imbalance problem of the diabetes dataset. DESMLA model also performs feature selection to determine only the significant features for diabetes prediction as DD may contain some irrelevant and redundant features. DESMLA shows the comparison between filter and wrapper approaches for feature selection. From the experimental results, it is observed that DESMLA with wrapper approach produces better performance than that of filter approach. The performance improvement of DESMLA with class imbalance treatment and feature selection is observed which is promising and significant.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124670081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

(2,2) Visual Cryptography Based Biometric Authentication Mechanism for Online Elections (2,2)基于视觉密码的在线选举生物特征认证机制

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909485

Sneha Annappanavar, P. Chavan

引用次数: 0

A Systematic Literature Review of Machine Learning based Approaches on Pathology Detection in Gastrointestinal Endoscopy 基于机器学习的胃肠内镜病理检测方法的系统文献综述

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909267

Dinisuru Nisal Gunaratna, Pumudu Fernando

{"title":"A Systematic Literature Review of Machine Learning based Approaches on Pathology Detection in Gastrointestinal Endoscopy","authors":"Dinisuru Nisal Gunaratna, Pumudu Fernando","doi":"10.1109/ASIANCON55314.2022.9909267","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909267","url":null,"abstract":"Endoscopy is the most widely adhered medical procedure used to examine the gastrointestinal tract of a person. Accurate pathology detection during the endoscopic procedure is crucial as misidentifications or miss rates could reduce the chance of survival for the patient. After the successful collaboration of artificial intelligence with medicine, researchers around the world have tried different techniques in using this for gastroenterology. Our study demonstrates an extensive survey on existing pathology detection methodologies in endoscopic images using the publicly available datasets. The paper also discusses the content of the recently released datasets, preprocessing techniques tried on these datasets and how they affected the performance of the machine learning models. Furthermore, this study discusses how changing architectures of convolutional neural networks could affect the accuracy of models in relation to different datasets. Finally, the paper presents the results of each reviewed literature along with a brief discussion on the gaps that were identified.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123868204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of On-site Channel Model for 5G Indoor Applications 5G室内应用现场信道模型研究

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909153

Shivam Wadhwa, Shailesh Mishra

引用次数: 0

Network Data Feature Selection in Detecting Network Intrusion using Supervised Machine Learning Techniques 使用监督机器学习技术检测网络入侵中的网络数据特征选择

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909208

Arjonel M. Mendoza, Rowell M. Hernandez, Ryndel V. Amorado, Myrna A. Coliat, Poul Isaac C. De Chavez

{"title":"Network Data Feature Selection in Detecting Network Intrusion using Supervised Machine Learning Techniques","authors":"Arjonel M. Mendoza, Rowell M. Hernandez, Ryndel V. Amorado, Myrna A. Coliat, Poul Isaac C. De Chavez","doi":"10.1109/ASIANCON55314.2022.9909208","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909208","url":null,"abstract":"Network attacks have become necessary in today’s time due to increased network traffic. To determine whether network traffic is normal or anomalous a supervised machine learning system is developed. A network intrusion detection system (IDS) is a must-have piece of a security system. This proposed study aims to discover new patterns automatically from substantial quantities of network data, reducing time manually compiling intrusion and normal behavior patterns. The best model in terms of detection success rate was discovered using a supervised learning algorithm and feature selection method. AdaBoost outperforms Neural Network, kNN, and Naive Bayes in supervised machine learning with feature selection in this study, with a detection accuracy of 100.00%, 99.30%, 91.60%, and 99.70%, respectively. The Network Intrusion Detection dataset is used to classify network intrusions to evaluate the study and it has also been used in past studies. On the other hand, the proposed model proved to be more effective than other studies in terms of intrusion detection. The proposed approach can be used in various fields, including finance, health, and transportation. Furthermore, additional parameter tuning could be added, and different feature selection techniques could be used to improve the performance of the classifiers.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129700560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interference Mitigation Approach using Massive MIMO towards 5G networks 面向5G网络的大规模MIMO干扰缓解方法

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909360

Mithra Venkatesan, A. Kulkarni, Radhika Menon, Shashikant Prasad

{"title":"Interference Mitigation Approach using Massive MIMO towards 5G networks","authors":"Mithra Venkatesan, A. Kulkarni, Radhika Menon, Shashikant Prasad","doi":"10.1109/ASIANCON55314.2022.9909360","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909360","url":null,"abstract":"There is a drastic increase in the number of users who subscribe to the mobile broadband every year. On the other hand, 4G networks have reached the theoretical limits on the data rate and therefore it is not sufficient to accommodate the above increasing traffic. To overcome this problem, new Generation of mobile communication known as fifth generation (5G) comes into the picture. Large network capacity, ultra-low latency and heterogeneous device support are the important features in 5G Technology. Massive MIMO in 5G Technology is built on multi-tier architecture using several low power Base Stations (BSs) inside small cell. Simultaneous usage of the same spectrum causes interference which further reduces the system throughput and network capacity. Thus resource management is an integral part of 5G Heterogeneous Networks (HetNets) so that interference between several base stations and different devices can be minimized. Proposed scheme introduces feedback on the existing cell association and antenna allocation algorithms and also introduces the evolutionary game theory for interference mitigation in HetNets as Game theory can be efficiently modelled for a competitive and compatible environment. Impact of feedback and game theory into RATs on data rate experienced by users and revenue generated by base station from users respectively are observed. Feedback mechanism along with Game theory approach enables to make efficient and effective resource allocation decisions. This facilitates the existing Cell Association algorithms to maximize the data rate of users in different classes and the antenna allocation algorithm to maximize the total profit of the Base station. Both users and base stations are self-interested to maximize their own benefits in terms of data rate and revenue.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129599432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Secure Communication Methodologies for WSN Assisted IoT Applications WSN辅助物联网应用的安全通信方法设计

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9908931

G. P, Sanjay Kumar, Jambi Ratna Raja Kumar, Saju Raj T

引用次数: 1

Analysis of Multivariate Chaotic Time Series using Neural Networks 多元混沌时间序列的神经网络分析

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909083

Avani Sharma, Sumit Dhariwal

引用次数: 0

Cryptocurrency Analysis and Forecasting 加密货币分析与预测

2022 2nd Asian Conference on Innovation in Technology (ASIANCON) Pub Date : 2022-08-26 DOI: 10.1109/ASIANCON55314.2022.9909168

Payal Pagariya, Sadhvee Shinde, Rupali Shivpure, Sakshi Patil, Ashwini Jarali

{"title":"Cryptocurrency Analysis and Forecasting","authors":"Payal Pagariya, Sadhvee Shinde, Rupali Shivpure, Sakshi Patil, Ashwini Jarali","doi":"10.1109/ASIANCON55314.2022.9909168","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909168","url":null,"abstract":"Cryptocurrencies are becoming a well-known and commonly acknowledged kind of substitute trade money. Most monetary businesses now include cryptocurrency. Accordingly, cryptocurrency trading is widely regarded as the most of prevalent and capable types of lucrative investments. However, because this financial sector is already known for its extreme volatility and quick price changes, over brief periods of time. For such constantly changing nature of crypto trends and price, it has become a necessary part for traders and crypto enthusiast to get a detailed analysis before investing. Also, the construction of a precise and dependable forecasting model is regarded vital for portfolio management and optimization. In this paper we propose a web system, which will help to understand cryptocurrency in a more statistical way. Proposed system focuses mainly on four coins : Bitcoin, Ethereum, Dogecoin and Shiba Inu performing analysis and forecasting on all the four coins. System will also do statistical comparison between the coins. Analysis and comparison is carried out using python libraries and modules whereas LSTM and ARIMA are used for forecasting. Extensive research was conducted using real-time and historical information, on four key cryptocurrencies, two of which had the greatest market capitalization, notably Bitcoin and Ethereum, while the other, Dogecoin and Shiba Inu, that had a significant growth in market capitalization over the previous year. In comparison to old fully-connected deep neural networks, the suggested model may employ mixed crypto data more proficiently, minimizing overfitting and computing costs.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1