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Detection of Breast Cancer from Histopathological Images using Image Processing and Deep-Learning 利用图像处理和深度学习从组织病理图像中检测乳腺癌
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917784
Anusha Maria Thomas, Adithya G, A. S, R. Karthik
{"title":"Detection of Breast Cancer from Histopathological Images using Image Processing and Deep-Learning","authors":"Anusha Maria Thomas, Adithya G, A. S, R. Karthik","doi":"10.1109/ICICICT54557.2022.9917784","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917784","url":null,"abstract":"Breast cancer is the most commonly occurring cancer in women. Cancer patients frequently develop metastasis, which is responsible for more than 90% of their deaths. The mortality rate will be significantly reduced if it is identified and treated in an early phase. The categorization of cancer cells is critical for medical diagnosis, tailored therapy, and disease prevention. Classifying various types of these cells with great precision has remained a difficult issue. Deep learning has emerged as a significant tool for such challenging tasks in the fields of biology and medicine. In this research, we propose a novel model that throws light on image processing and deep learning for breast cancer classification from histopathological images. The proposed Vision transformer model outperforms the state-of-the-art convolution neural networks in classifying the breast cancer cell with an accuracy of 96%.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306913","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
Copy-Move and Image Splicing Forgery Detection based on Convolution Neural Network 基于卷积神经网络的复制移动和图像拼接伪造检测
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917679
Snehal Nikalje, Mrs Vanita Mane
{"title":"Copy-Move and Image Splicing Forgery Detection based on Convolution Neural Network","authors":"Snehal Nikalje, Mrs Vanita Mane","doi":"10.1109/ICICICT54557.2022.9917679","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917679","url":null,"abstract":"Digital images plays a very significant role in fields like journalism, medical imaging, criminal and forensic investigations and many more. Because of the easily available photo editing tools and software, images can be manipulated easily, that can disturb the contents of the images. Due to this, authenticity of the image gets lost and these can be misused by any person. The techniques that are commonly used for creating forged images are copy-move, image splicing and image enhancement forgery. Many techniques were developed to detect forgery, but these techniques are not robust against the structural changes occurred due to forgery in the images. In this paper, Convolution Neural Network(CNN) based image forgery detection method is proposed. In this method, Patch Sampling and Modulus LBP will be used to pretrain the neural network for Feature Learning and Feature Extraction. Then finally these features will be fed to SVM classifier that will help to detect forged images.Evaluation of the proposed method is done based on the parameters like precision, recall and accuracy, which shows that the proposed method is robust and insensitive against different operations as well as there is the improvement in the accuracy of the proposed method as compared to existing method.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115349942","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 Robust Pipeline Approach for DDoS Classification using Machine Learning 基于机器学习的DDoS分类鲁棒管道方法
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917596
Naman Agarwal, Abdul Quadir Md, Vigneswaran T, P. K, A. K. Sivaraman
{"title":"A Robust Pipeline Approach for DDoS Classification using Machine Learning","authors":"Naman Agarwal, Abdul Quadir Md, Vigneswaran T, P. K, A. K. Sivaraman","doi":"10.1109/ICICICT54557.2022.9917596","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917596","url":null,"abstract":"Remote and edge devices have less security features that are easily exploited by hackers. The security of businesses in major domains depends on the security features the infrastructure has to offer. Major breaches have been reported over the past years which have led to compromise of hidden data. DDoS attacks have been a major trend which has brought down many devices using similar techniques. Major vulnerabilities have been found in IoT systems which presents an open door for hackers. To address the upcoming trends in early vulnerabilities detection, a standard predictive model of DDoS attacks needs to be implemented. In this paper we propose a robust pipeline for DDoS classification and the performance of the models are calculated against the metrics such as precision, recall and f1-scores. After evaluating various machine learning models, the XGboost algorithm works well on our data set with an accuracy score of 99% outperforming other models.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117165568","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
Hybrid Network Security Model for Small and Mid Sized Enterprises 面向中小企业的混合网络安全模型
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917896
Vaishak Sreejith, Pudukaram Urjith Reddy, Bandari Harshith Rao, Sakthi Abirami Balakrishnan
{"title":"Hybrid Network Security Model for Small and Mid Sized Enterprises","authors":"Vaishak Sreejith, Pudukaram Urjith Reddy, Bandari Harshith Rao, Sakthi Abirami Balakrishnan","doi":"10.1109/ICICICT54557.2022.9917896","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917896","url":null,"abstract":"The majority of contemporary information security management studies are focused on the information security of large organizations and MNCs preventing security threats. However, the case of the management of Small and Medium sized (SME) companies are fairly different. SME businesses are more insecure since they cannot afford expensive technologies. There is a lack of effective procedures, improvement skills, and effort required for security management. Due to insufficient budgets, there will be a lack of huge investments in IT service infrastructures and workforce. These smaller companies are the potential targets for cyber criminals and the resources they lack make them easier to victimize as compared with the big companies. Malicious web content conjointly occurs as one of the key security issues. This paper proposes an effective hybrid security model that mimics the Amazon Web Services Identity & Access Management (AWS IAM) and compares it with the pre-existing models to showcase its adaptability, security and cloud friendly nature that compliments the developing SME nature.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116004690","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
Brain Tumor Detection Using Supervised Learning: A Survey 使用监督学习检测脑肿瘤:一项调查
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917753
Parth Shanishchara, Vibha Patel
{"title":"Brain Tumor Detection Using Supervised Learning: A Survey","authors":"Parth Shanishchara, Vibha Patel","doi":"10.1109/ICICICT54557.2022.9917753","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917753","url":null,"abstract":"With the advancement in technology, artificial intelligence and computer vision are being used extensively in health care sector. Specifically, there’s a lot of research happening in brain tumor detection and classification. A brain tumor can be defined as a chronic disease in which the brain tissues start to grow in an uncontrollable manner. There are very few technologies currently in use to detect brain tumors such as CT - Scans and MRIs. And, such technologies require expert diagnosis of the type and location of the tumor, and such tasks are time-consuming. This is the reason, there is a need for an automatic brain tumor detection system that can make the diagnosis faster. The survey paper will review the supervised machine learning algorithm and supervised neural network algorithms that can be employed to detect the tumor in 2D brain images. The experiments were carried out using SVM and other deep neural network approaches like ANN, CNN, VGG-16, ResNet, and InceptionNet. The dataset was downloaded from Kaggle. The average testing accuracy achieved was 97.76%","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116491877","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
SMS Based Remote Mobile Phone Data Access System 基于短信的远程手机数据访问系统
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917880
Ajay Joy, A. A, Ankitha K, Tintu Devasia
{"title":"SMS Based Remote Mobile Phone Data Access System","authors":"Ajay Joy, A. A, Ankitha K, Tintu Devasia","doi":"10.1109/ICICICT54557.2022.9917880","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917880","url":null,"abstract":"Presently cellphone and other cellular gadgets have emerged as one of the inevitable component in every aspect of our existence. Mobile phones are an effective communication tool that can make life easier. It allows to send and receive messages, connect with people in any part of the world with high reliability and security. Mobile tool safety is an essential element that secures all of the sensitive information of the consumer stored on the device. The proposed system,SMS Based Remote Mobile Phone Data Access System allows the user to perform major operations without using internet, with high security. The venture aims to develop a cell protection machine as a way to allow consumer to perform various operations such as, obtain contact details from mobile device for remote user, change the profile, track the location and lock the mobile phone via SMS.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122107543","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
Data Driven Modelling and Prediction Of Rainfall 数据驱动的降雨建模与预测
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917744
A. S, G. Devadhas, Shinu M M, Mary Synthia Regis Prabha D M, Dhanoj M
{"title":"Data Driven Modelling and Prediction Of Rainfall","authors":"A. S, G. Devadhas, Shinu M M, Mary Synthia Regis Prabha D M, Dhanoj M","doi":"10.1109/ICICICT54557.2022.9917744","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917744","url":null,"abstract":"The prediction of weather and is difficult because these phenomena are highly non-linear and complicated phenomena. Technology based on artificial intelligence enables knowledge processing and is utilised in predicting. Synthetic neural network (ANN) has emerged as an alluring substitute for conventional statistical techniques for anticipating the behaviour of nonlinear systems The purpose of this paper is to prevent tools to model and predict rainfall behavior form past observations based on past observation. There are two fundamentally different approaches that are used in the paper to develop a model, both based on statistical methods based on ANNs. The prediction efficiency was evaluated based on 115years of mean annual rainfall between 1901and 2015.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019324","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
Abstractive Text Summarization Using Hybrid Methods 基于混合方法的抽象文本摘要
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917994
Gautam Daga, Subhradeep Saha, Yash Shah, S. Nirmala
{"title":"Abstractive Text Summarization Using Hybrid Methods","authors":"Gautam Daga, Subhradeep Saha, Yash Shah, S. Nirmala","doi":"10.1109/ICICICT54557.2022.9917994","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917994","url":null,"abstract":"Text summarization involves the process of constructing a concise, condensed, short, coherent, and fluent summarized version of a comparatively longer document with more text and involves the process of collection and segregation of the text’s major note points. There are broadly two different kinds of approaches that can be used for the process of text summarization - a) Extractive Summarization(selects important sentences as a subset of original text) and b) Abstractive Summarization(introduces new words to create a meaning summary). Hybrid methods try to incorporate a combination of both the above models, extractive and abstractive to summarize the text where the final summary can either be abstractive or extractive based on the relative positioning of the two models.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124695976","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 Review On Thermal Infrared Semantic Distribution for Nightfall Drive 夜幕驱动热红外语义分布研究进展
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917651
Maheswari Bandi, R. R
{"title":"A Review On Thermal Infrared Semantic Distribution for Nightfall Drive","authors":"Maheswari Bandi, R. R","doi":"10.1109/ICICICT54557.2022.9917651","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917651","url":null,"abstract":"The technique of turning infrared (IR) radiation (heat) into visual images is known as thermal infrared. Semantic segmentation aims to divide an input image based on information that is semantic and forecast each pixel's semantic category based on a label set. As modern life becomes increasingly intellectualized, more applications emerge, for example, augmented reality, self-driving cars, and CCTV monitoring, and so on, require inferring meaningful for future processing, semantic data from photographs. This study looks at contemporary deep learning-based lexical background subtraction. As a result of semantic segmentation necessitates a significant the amount of annotations at the pixel level, this study investigates research to rely on unsupervised semantic distribution reduce the fine-grained annotation needs as well as the economic and time expenses of human annotation. The aim of this effort is to enhance the decomposition model's generalization ability and robustness.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125066037","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: 7
Depression Analysis using Electroencephalography Signals and Machine Learning Algorithms 使用脑电图信号和机器学习算法分析抑郁症
2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) Pub Date : 2022-08-11 DOI: 10.1109/ICICICT54557.2022.9917751
N. V. Babu, E. G. Kanaga
{"title":"Depression Analysis using Electroencephalography Signals and Machine Learning Algorithms","authors":"N. V. Babu, E. G. Kanaga","doi":"10.1109/ICICICT54557.2022.9917751","DOIUrl":"https://doi.org/10.1109/ICICICT54557.2022.9917751","url":null,"abstract":"Depression has been defined as a silent disease that affects everyone regardless of physical or biological state. More than 40% of the population is openly afflicted by the disease. Depression has become a troubling trend, affecting not just a person’s psychological well-being but also his or her physical well-being. Electroencephalography (EEG), for example, may identify the effects of depression in the brain. Doctors and researchers can use the tests to analyse the electrical activity of the brain. The electroencephalography signals are used to analyse depression in the proposed work. Data Collection, Data Preprocessing, Feature Extraction, and Classification are the tasks. In the procedure, three main sorts of data are employed. A total of five machine learning algorithms are deployed. Each dataset is compared to the associated algorithms. In all three datasets, the Random Forest method outperformed the other algorithms in terms of accuracy. Furthermore, depression is divided into three categories during the procedure.","PeriodicalId":246214,"journal":{"name":"2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129795040","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
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