2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)最新文献_第5页

A Brief Analysis on Machine Learning Classifiers for Intrusion Detection to Enhance Network Security 浅谈用于入侵检测的机器学习分类器增强网络安全

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029091

Spandana Mande, Nandhakumar Ramachandran, Chanumolu Kiran Kumar, Ch Naga Priyanka

{"title":"A Brief Analysis on Machine Learning Classifiers for Intrusion Detection to Enhance Network Security","authors":"Spandana Mande, Nandhakumar Ramachandran, Chanumolu Kiran Kumar, Ch Naga Priyanka","doi":"10.1109/ICACRS55517.2022.10029091","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029091","url":null,"abstract":"Network-based applications present new threats and necessitate new security measures, which necessitate greater attention to speed and accuracy. The rapid expansion of malicious activity with the attacks poses substantial dangers to network security despite the numerous new security technologies that have been developed. Intrusion Detection Systems (IDS) are extensively relied upon by network managers to catch these kinds of network intrusions in the act. Detecting intrusions using machine learning is one of the most common ways, in which models are learned from data to distinguish between regular and anomalous traffic. Despite the prevalence of machine learning methodologies, there hasn't been much research into machine learning algorithms for intrusion detection. When it comes to protecting sensitive information and systems, the Network Intrusion Detection System (NIDS) is a must-have model. If a NIDS detects more threats than false alarms, it is considered to be a good tool for security. Intrusion detection systems that are able to adapt to the ever-changing nature of network threats are built using machine learning techniques. It's still unclear how effective and appropriate these machine learning approaches are for detecting advanced hostile attempts. In terms of accuracy, precision, recall, and training time cost, this study analyses the most common machine learning classifier approaches for intrusion detection. Developers can use this comparison to help them select the best NIDS development method. KDD99, CSE-CIC-IDS2018 and UNSW-NB15 datasets are used in the evaluation of the adopted basic machine learning classifiers. Classifiers like Decision Tree (DT), Random Forest, Support Vector Machine, Logistic Regression and K-Nearest Neighbour (KNN) are tested in the experiments. The classifiers working, limitations are briefly discussed in this manuscript.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133337288","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

Oculus: A New Dimension to Virtual Reality Oculus:虚拟现实的新维度

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029200

Akshiv Nagta, Bhanu Sharma, Sheena, Avinash Sharma

引用次数: 0

Issues and Future Challenges of Sentiment Analysis for Social Networks- A Survey 社交网络情感分析的问题和未来挑战-一项调查

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029070

R. Geethanjali, A. Valarmathi

{"title":"Issues and Future Challenges of Sentiment Analysis for Social Networks- A Survey","authors":"R. Geethanjali, A. Valarmathi","doi":"10.1109/ICACRS55517.2022.10029070","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029070","url":null,"abstract":"Sentiment Analysis is a approach that uses Natural Language Processing, Machine Learning, and Deep Learning to computationally identify, categorize, classify, and recover human emotions from unstructured text. The most efficient and popular method for learning from and training social media datasets is deep learning. In a range of applications, including audio, picture, and natural language processing, it has been demonstrated to be more successful. A text block is evaluated using the sentiment technique to determine whether it is positive, negative, or neutral. Not all members of the public express their feelings in the same way; as a result, some do so through comments and ratings while others do so through texts that don't reflect the right frame of mind. This evaluation lists the most typical and well-liked sentiment analysis algorithms for social media data. The classification of positive, negative, and neutral reviews using \"LSTM, Knearest method, Random Forest, Support Vector Machine, RNN, and MaLSTM\" is explored and examined in this work. All of these classification methods were evaluated along with their drawbacks and difficulties.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"424 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117352829","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

Real-Time Sidewalk Crack Identification and Classification based on Convolutional Neural Network using Thermal Images 基于卷积神经网络的热图像人行道裂缝实时识别与分类

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029202

G. Karthikeyan, Dhaarani T G, R. Anusuya, K. G, J. T, R. Prabu

{"title":"Real-Time Sidewalk Crack Identification and Classification based on Convolutional Neural Network using Thermal Images","authors":"G. Karthikeyan, Dhaarani T G, R. Anusuya, K. G, J. T, R. Prabu","doi":"10.1109/ICACRS55517.2022.10029202","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029202","url":null,"abstract":"The most common causes of pavement degradation include incorrect road construction and maintenance, overburden, inadequate road surface drainage, seepage, especially difficult environmental conditions like frost. Highway distresses raise fuel expenses, raise travel times, and have a negative impact on road safety. They also slow down traffic flow. The implementation of a lightweight classification algorithm for the identification of complex paving circumstances using RGB-thermal input and also an attention component embedded to modify the spatial as well as characteristics of the data of the images is recommended using a convolutional neural network based on a revised residual framework. With an attention mechanism as well as the RGB-thermal as input, the suggested model’s better predictive accuracy is 98.88 percent. The proposed technique improves the picture’s level of clarity and controls how picture streams are used, which improves the model’s actual quality. Additionally, it is contrasted to cutting-edge deep learning techniques, showing that our approach outperforms other picture categorization models in terms of character recognition, training time, and parameter count. In order to compare the information, the system learns from of the photos under various input information, a visualization approach combining GWM is provided.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115676896","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

PDF Malware Detection System based on Machine Learning Algorithm 基于机器学习算法的PDF恶意软件检测系统

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029209

Pruthvi Priya P M, H. P

{"title":"PDF Malware Detection System based on Machine Learning Algorithm","authors":"Pruthvi Priya P M, H. P","doi":"10.1109/ICACRS55517.2022.10029209","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029209","url":null,"abstract":"In this digital system, the data is transferred through online in the several form. Safety measures are employed in critical places like healthcare, banks, etc. Nowadays the application of machine learning for solving problems has increased. Adversarial examples are the term used to describe such variations. Early research mostly concentrated on machine learning models for image process after moved to other applications, such as those for malware detection. Finding adversarial examples for ML-based PDF malware detectors is the part of work. Machine learning has apparently delivered extraordinary and, in some cases, human-competitive performance in classification tasks. Hackers can attack and extract the data easily. This proposed work has considered generative adversarial networks (GANs) to build variant PDF malware without any problem that may be identified as benign by using several existing classifiers while preserving the original harmful behavior to address the challenge. Features extraction method, which includes special features derived from malicious PDF files, to quickly produce an evasive variant PDF. The PDF GAN is used for the malware detection in PDF files.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125453836","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

Optimal Placement of Electric Vehicle Charging Station by Considering Dynamic Loads in Radial Distribution System 考虑径向配电系统动态负荷的电动汽车充电站优化配置

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029303

D. Srinivas, M. Ramasekhara Reddy

{"title":"Optimal Placement of Electric Vehicle Charging Station by Considering Dynamic Loads in Radial Distribution System","authors":"D. Srinivas, M. Ramasekhara Reddy","doi":"10.1109/ICACRS55517.2022.10029303","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029303","url":null,"abstract":"Electric vehicles with unique characteristics like lower noise, energy saving and pollution free due to reduction of carbon dioxide are consider to be the best choice for future automobile industry. The battery of electric vehicle (EV) must get recharged at charging stations when they are used for driving in urban area. Since electric charging stations will be used simultaneously by many EV. The existing distribution system might not be highly affected by the installation of charging stations if the numbers of electric vehicles are small. However, with the increase in the number of electric vehicles the characteristics of the electric vehicle charging patterns may have considerable impact on distribution systems which depends particularly on the electric vehicle charging location. There may be significant impacts like overloading and power losses in the system. These impacts could be mitigated by proper system planning and through strategic placement of Electric Vehicle Charging Station (EVCS) in the existing radial distribution network. In this research the optimal location EVCS are identified in radial distribution network and also the active power losses and system voltage profile are examined. This research proposes a heuristic algorithm called Particle Swarm Optimization (PSO) to optimize the IEEE 33 bus radial distribution system with electric vehicle charging stations. The prime objective is to place the EVCS at optimal location in existing radial distribution network by considering the real (active) power losses and also the voltage at the buses of the system.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"23 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120850014","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

Optically Controlled Microwave Sensor for Biomedical Applications 生物医学应用的光控微波传感器

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029248

Maryam Sami Majeed, M. Çevik

{"title":"Optically Controlled Microwave Sensor for Biomedical Applications","authors":"Maryam Sami Majeed, M. Çevik","doi":"10.1109/ICACRS55517.2022.10029248","DOIUrl":"https://doi.org/10.1109/ICACRS55517.2022.10029248","url":null,"abstract":"In this paper, a design of a microwave resonator is introduced for noninvasive blood glucose biomedical sensing. The proposed sensor is constructed from a printed transmission line of 50Ω characteristics impedance. The transmission line is loaded with a meander line of a sweep penny elegance chaos. The structure is splitted into two symmetric parts that are connected to each other through a light dependent resistor (LDR). The to maximize the field fringing through the sample under test, an interdigital capacitor is introduced in parallel to the meander line structure. The proposed sensor is found to provide multiple frequency resonances. However, the authors considered the frequency resonance at 3.46GHz to ensure the effective resonance of the detection with changing the blood glucose level. Therefore, the proposed sensor is fabricated and tested experimentally with 100 patients to realize the effects of varying the blood glucose level up on the proposed sensor transmission loss spectra in terms is of S11 magnitude. The proposed sensor performance is tested and optimized using circuit model theory. Next, the design performance is validated numerically using Computer Simulation Technology of Micro Wave Studio (CST MWS) commercial software package of finite integral technique algorithm. Finally, the experimental and theoretical results are found in good agreement to each other.","PeriodicalId":407202,"journal":{"name":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127939111","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 Hybrid Optimizer based Nonlinear Model Predictive Control for Rotary Inverted Pendulum 基于混合优化器的旋转倒立摆非线性模型预测控制

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029042

S. P. Diwan, S. Deshpande

引用次数: 0

Optimal Siting and Sizing of Dispersed Generation in Electrical Distribution Network with PSO 基于粒子群算法的配电网分散发电的优化选址与规模

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029192

Aishwarya M. Holi, Shubham B. Kattikar, Mihir V. Patel, R. Magadum, Santosh Nejakar

引用次数: 0

Design and Analysis of a Meta Material based Nested Circular Split Ring Resonator for Terahertz Applications 基于元材料的太赫兹嵌套圆裂环谐振器的设计与分析

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) Pub Date : 2022-12-13 DOI: 10.1109/ICACRS55517.2022.10029069

Swathi Dasi, G. Rao

引用次数: 0