2022 Smart Technologies, Communication and Robotics (STCR)最新文献

Gaussian Mixture Model based Hybrid Machine Learning for Lung Cancer Classification using Symptoms 基于高斯混合模型的混合机器学习肺癌症状分类

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009440

H. Rajaguru, Sannasi Chakravarthy S R, S. Chidambaram

{"title":"Gaussian Mixture Model based Hybrid Machine Learning for Lung Cancer Classification using Symptoms","authors":"H. Rajaguru, Sannasi Chakravarthy S R, S. Chidambaram","doi":"10.1109/STCR55312.2022.10009440","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009440","url":null,"abstract":"Being a fatal disorder, lung cancer becoming a primary reason for mortality in people who are affected with various symptoms. This implies that there is always a necessity in the medical field to have a promising approach for detection and timely treatment for such disorders. Also, it is required to be done at an earlier stage to attain a reduced mortality rate among cancer patients. The work intended to propose a hybrid machine learning (ML) strategy for the classification of lung cancer. The approach incorporates both Non-Linear Regression (NLR) and Gaussian Mixture Model (GMM), combinely termed as NLR-GMM algorithm. The algorithm takes the key advantages of both machine learning models for better classification of lung cancer data. For this, the work employs the lung cancer dataset constituted using its symptoms. The data set is preprocessed and visualized for analysis. Then classification is performed using the proposed hybrid ML approach which provides a maximum performance of 92.88% of classification accuracy. The results are compared with the existing ML algorithms such as Gaussian Naïve Bayes and K-Nearest Neighbor algorithms for checking the proposed strategy.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127274988","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 Dual Narrowband High Frequency Smart Antenna 双窄带高频智能天线的设计

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009566

T. Perarasi, K. Ali, M. Moses, C. Poongodi, R. Gayathri, D. Deepa

{"title":"Design of Dual Narrowband High Frequency Smart Antenna","authors":"T. Perarasi, K. Ali, M. Moses, C. Poongodi, R. Gayathri, D. Deepa","doi":"10.1109/STCR55312.2022.10009566","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009566","url":null,"abstract":"In this paper, an antenna for a narrow band in dual mode of operation for radio frequency applications is presented and is assumed to be Smart. The performance of antenna is analyzed to enhance radiation characteristics of autonomous vehicle system that are predominant in today’s technology operated at 76 GHz and 78 GHz. A bowtie structured antenna which are apt design for size reduction are operated and designed in two bands by changing its mechanical functionalities and other structure is also compared. For the effective transmission of signals in all the directions for vehicle, a dual-mode design is proposed with the directivity of 5 dB. Is radiation characteristics validates the better coverage and its radiation efficiency is estimated as 73% as compared with existing 51%. From value of insertion loss of -13.3 dB it is validated that the value of VSWR is lesser than unity. Current distribution provides its coverage and it is absolutely working better at operating frequency of autonomous vehicle band. Gain is increased by approximately by 2 dB and front to back ratio as 3.13 dB which helps in size reduction of around 11.2%.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125346990","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

CNN based Image Steganography Techniques: A Cutting Edge/State of Art Review 基于CNN的图像隐写技术:前沿/艺术评论

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009102

S. Thenmozhi, Bharath M. B

引用次数: 0

Rat Swarm Optimizer based Transform for Performance Improvement of Machine Learning Classifiers in Diagnosis of Lung Cancer 基于大鼠群优化的机器学习分类器在肺癌诊断中的性能改进

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009353

K. B, Meghana G, Roshni M, B. N

{"title":"Rat Swarm Optimizer based Transform for Performance Improvement of Machine Learning Classifiers in Diagnosis of Lung Cancer","authors":"K. B, Meghana G, Roshni M, B. N","doi":"10.1109/STCR55312.2022.10009353","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009353","url":null,"abstract":"Usage of Machine Learning algorithms for assisting healthcare providers is increasing day by day. But the performance and robustness of the machine learning algorithms are the main concerns while implementing them for critical healthcare applications such as detection of cancer. This work concentrates on the performance improvement of supervised classifiers through the feature transform based on Rat Swarm Optimizer in diagnosing lung cancer using histopathological images. Rat Swarm Optimizer used for the transformation of features. These transformed features are more capable of providing better classification accuracy when compared to normal features. The dataset is downloaded from the publicly available website and three classes are present: normal, lung squamous cell carcinomas, and lung adenocarcinomas. In each class, 1000 histopathological images are considered. Four supervised classifiers namely Histogram-Gradient boosting classifier, Random forest classifier, K-Nearest Neighbor classifier, and Linear Discriminant Analysis classifiers are tested. The highest accuracy of 90.66% is offered by Histogram-Gradient boosting classifier and this is increased to 95.82% when Rat Swarm Optimizer is used as transform before classification.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115065451","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

Performance Investigation of Handwritten Equation Solver using CNN for Betterment 利用CNN改进手写方程求解器的性能研究

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009300

K. Priyadharsini, Sudharsan Perumal, J. D. Dinesh Kumar, K. Darshan, S. Vignesh, P. Vinoth

引用次数: 0

Descriptive Analytics Solution for Attack Detection by Utilizing DL Strategies 利用DL策略进行攻击检测的描述性分析解决方案

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009596

T. Subburaj, T. Nagalakshmi, N. Krishnamoorthy, J. Uthayakumar, R. Thiyagarajan, S. Arun

{"title":"Descriptive Analytics Solution for Attack Detection by Utilizing DL Strategies","authors":"T. Subburaj, T. Nagalakshmi, N. Krishnamoorthy, J. Uthayakumar, R. Thiyagarajan, S. Arun","doi":"10.1109/STCR55312.2022.10009596","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009596","url":null,"abstract":"An intrusion detection system that employs a variety of system tasks and log files that are being generated on the host machine to detect HIDS refers to high-intensity distributed denial-of-service attacks. To enhance the capacity of intrusion detection systems, Big Data with Deep Learning Methods are combined. Deep Neural Network (DNN) and highly proficient approaches, Random Forest as well as Gradient Boosting Tree, are utilized to categories internet traffic datasets. Deep learning algorithms are widely used to develop an intrusion detection system (IDS) task of automatically recognizing and characterizing attacks at the host addressing performance in real time. Researchers utilize a homogeneity measure to analyze characteristics to identify its most productivity and organizational from dataset. As according to extensive experimental research, DNNs outperform classical machine learning classifiers in terms of performance. The findings shows that DNN has a good precision for different classifiers detection on datasets with accuracy rate for multi-class categorization. Employing Apache Flink to simplify the process and handling the streaming capabilities.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129096002","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

A Customized Deep Learning Algorithm for Prediction of Eye Diseases from Color Fundus Photography 彩色眼底摄影预测眼病的定制深度学习算法

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009058

Shivappriya S N, Pasupathy S A, H. R, Shanmuga Priya J, Pavenashri Raj, Vikram L

引用次数: 0

Detection and Diagnosis of Lung Cancer using Machine Learning Convolutional Neural Network Technique 基于机器学习卷积神经网络技术的肺癌检测与诊断

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009607

M. Ramkumar, C. Ganesh Babu, A. R. Abdul Wahhab, K. Abinaya, B. Abinesh Balaji, N. Aniruth Chakravarthy

{"title":"Detection and Diagnosis of Lung Cancer using Machine Learning Convolutional Neural Network Technique","authors":"M. Ramkumar, C. Ganesh Babu, A. R. Abdul Wahhab, K. Abinaya, B. Abinesh Balaji, N. Aniruth Chakravarthy","doi":"10.1109/STCR55312.2022.10009607","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009607","url":null,"abstract":"The diagnosis and analysis of the lung diseases has been an appealing task for the clinical experts in the dawning and in the latter days. To certain extent, the analysis has to be done in an appropriate way to eliminate the risk of human lives by the prior detection of tumorous growth. Henceforth, there are various diagnosis technique available in the world and yet various stochastic expedient has been carried out. In the validating conviction, the enactment of the neural network technique has been initiated to examine the cancerous growth in the gathered image datasets. With the help of Artificial intelligence and deep learning technique the cancerous growth can be evaluated. In accordance to knock back the performance measures the supervised learning technique is implemented with the use of the deep learning technique. Convolutional Neural Network the stratagem for the tumor detection. The substructure of this work includes following constraints such as image acquisition, image pre-processing, image enhancement, image segmentation, feature extraction, neural identification. To put it succinctly, machine learning technique gives an innovational approach to enrich the decision support in lung tumor medicaments at less cost.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"26-27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123647321","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

RPL Attacks Detection and Prevention in IOT Networks with Advanced GRU Deep Learning Algorithm 基于先进GRU深度学习算法的物联网网络RPL攻击检测与防范

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009350

T. Thiyagu, S. Krishnaveni

{"title":"RPL Attacks Detection and Prevention in IOT Networks with Advanced GRU Deep Learning Algorithm","authors":"T. Thiyagu, S. Krishnaveni","doi":"10.1109/STCR55312.2022.10009350","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009350","url":null,"abstract":"Cyber-attacks on the Internet of Things have improved significantly more in previous years, with the expansion of intelligent internet-connected systems and functions. The Internet of Things aims to create a better environment for people to automatically understand their needs and act accordingly. This project aims to identify a wormhole attack against Routing Protocol for Low Power and Lossy Networks (RPL) of the Internet of Things, which is the technology behind most of the devices and sensors in the Internet of Things. This study proposes a deep learning-based advanced gated recurrent unit (AGRU) network model. The proposed model is compared to Logistic regression and One Support Vector Machine using different weight states and node power consumption. As a result, the model’s predictions and promises regarding IoT security and source effectiveness seem to be accurate. In terms of source efficiency and IoT security, it is evident that the results confirmed the commitment and expectations of the study. According to previous literature studies, RPL flood attacks are associated with a reduced error rate in detecting attacks.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121182802","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

Implementation Techniques for GIFT Block Cypher: A Real-Time Performance Comparison GIFT分组密码的实现技术:实时性能比较

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009581

Insha Syed, Mir Nazish, Ishfaq Sultan, M. T. Banday

{"title":"Implementation Techniques for GIFT Block Cypher: A Real-Time Performance Comparison","authors":"Insha Syed, Mir Nazish, Ishfaq Sultan, M. T. Banday","doi":"10.1109/STCR55312.2022.10009581","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009581","url":null,"abstract":"Lightweight cryptography is gaining popularity for securing private and sensitive data collected by smart IoT devices. It provides security solutions tailored for constrained devices with the low area, low power and low latency requirements. The PRESENT is one of the most popular block cyphers that are efficient in hardware and offer an optimum level of security. However, the PRESENT cypher does not provide much security against the linear cryptanalytic attack. These security concerns have been addressed through the design of the GIFT block cypher that makes an appropriate choice and efficient use of lighter s-box and bit-permutations, thereby making the overall design more secure and hardware efficient than the PRESENT block cypher. However, the realisation of the linear layer by the bit-permutation method makes the GIFT cypher inefficient in software. This paper describes the software-efficient lookup table, bit-slicing and fix-slicing implementation techniques for the GIFT block cypher. These techniques have been simulated in KEIL MDK IDE and implemented on the ARM Cortex-M3-based LPC1768 hardware platform. Performance comparison of these techniques has been carried out using ULINKpro and ULINKplus debug adapters in terms of various metrics such as power, energy, execution time and memory code size.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114369685","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