2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)最新文献_第10页

Automatic Feature Extraction from Satellite Imagery for Remote Sensing 遥感卫星图像特征自动提取

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125969

D. D. Mary, Nandana. S Nair, Mohana, S. Revathy, L. Gladence, Bernatin T

引用次数: 0

An Augmented Reality based Intelligent Precision Agriculture using Cascade Advancement Technique 基于增强现实的梯级推进智能精准农业

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125750

M. Ulagammai, R. N. Moorthy

引用次数: 2

APD-based Laser Range-Gating System 基于apd的激光距离选通系统

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125929

Sohila Kanaparthi, Y. Rao, Kasyap Battula, Sivaditya Kamadi, Ramesh Varma Potti

{"title":"APD-based Laser Range-Gating System","authors":"Sohila Kanaparthi, Y. Rao, Kasyap Battula, Sivaditya Kamadi, Ramesh Varma Potti","doi":"10.1109/ICOEI56765.2023.10125929","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125929","url":null,"abstract":"One of the most important things to have in almost every aspect is security. Everyone in this world seeks security in any and every way possible. One of the ways is to also ensure the security for home during the night from all types of intruders. This proposed system is one of the surveillance systems that help in the detection of any intruders that may enter with the help of laser and gating technologies. Using this system, it is possible to detect intruders who are even traveling at high speeds. Here, a laser source is available all the time. When an intruder meets the laser light, immediately the available buzzer is turned on, and it captures the sense of the intruder. When a target meets the laser source, the laser signal is reflected, and an Avalanche Photo Detector detects the reflected signal. A delayed transmitted laser source signal is generated to synchronize the timing pulse. This type of exposure is known as a gate. With the synchronized gated pulse, the output signal is used to capture the target. This entire process will take place in the order of nanoseconds. The system is connected to ON the camera which is utilized for military surveillance purposes.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123425833","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

Resilient Detection of Cyber Attacks in Industrial Devices 工业设备网络攻击的弹性检测

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125932

Y. A. Meeran, S. Shyry

{"title":"Resilient Detection of Cyber Attacks in Industrial Devices","authors":"Y. A. Meeran, S. Shyry","doi":"10.1109/ICOEI56765.2023.10125932","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125932","url":null,"abstract":"With the advent of smartphones, laptops, and home computers, smart systems are becoming more and more flexible. As the use of internet increases, there will be more cyber threats occurring on most third-party connectivity websites. The powerful technique used to detect the threats present in the IoT applications are discussed in the proposed system. Based on the KAGGLE NIDS(Network Intrusion Detection System)(Intrusion Detection System) dataset, the number of possible attacks is calculated in the proposed architecture. A similar occurrence of intrusion creating a task is detected by the system, triggering the model to prevent the intrusion by notifying the user immediately. The existing attack detection systems have a number of limitations which includes the need of human intervention to detect the attacks encountered, slower detection rate and inaccuracy in detection. An advanced deep learning algorithm is proposed for detecting possible intrusions to overcome these limitations. The proposed design focuses on creating a Novel architecture using Adaptive convolutional neural network for improving the accuracy and significantly raising the detection rate above that of the current approaches there by aiding in the immediate detection of intrusions.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123160778","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

Autonomous Object Detection and Counting using Edge Detection and Image Processing Algorithms 基于边缘检测和图像处理算法的自主目标检测和计数

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125716

Swati Patil, Jay Chandrakant Shimpi, A. Tanawade, Pranali Gajanan Chavan, V. Tandulkar

{"title":"Autonomous Object Detection and Counting using Edge Detection and Image Processing Algorithms","authors":"Swati Patil, Jay Chandrakant Shimpi, A. Tanawade, Pranali Gajanan Chavan, V. Tandulkar","doi":"10.1109/ICOEI56765.2023.10125716","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125716","url":null,"abstract":"Machine vision applications are commonly utilised in manufacturing lines as low cost, high precision measuring devices. Output facilities can accomplish high production numbers without mistakes thanks to these solutions that offer contactless control and measurement. A camera may be used to carry out machine vision tasks including product counting., error checking., and dimension measuring. This study makes a recommendation for a vision system application that can do inanimate object item enumeration. The recommended solution uses Otsu thresholding., Hough transformations., edge detection methods., and other image processing algorithms to accomplish automatic counting without taking into account the kind or colour of the product. The system primarily uses one camera. The general idea is to get image with balanced contrast., brightness and appropriate HSV values in it. A picture of the items being captured using camera using android device., and different image processing techniques are then applied to the picture. Further., a real-time machine vision programme was deployed and took photos taken from an actual experimental setup. The practical experiments conducted have shown that the suggested technique yields quick., precise., and trustworthy results based on the comparative study of various detection techniques.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125680061","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 Deep Learning Based Model for Defect Prediction in Intra-Project Software 基于深度学习的项目内软件缺陷预测模型

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10126014

K.Guna sekaran, L. S. P. Annabel

{"title":"A Deep Learning Based Model for Defect Prediction in Intra-Project Software","authors":"K.Guna sekaran, L. S. P. Annabel","doi":"10.1109/ICOEI56765.2023.10126014","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10126014","url":null,"abstract":"Testing of software ensures the supply of meaningful software and hence prediction of defects in producing high quality software has become an inevitable one. Software defect prediction's main aim is to find out various bugs present in software and focus on testing efforts. Many of existing software defect prediction frameworks are much simple, making it difficult for developers to get detailed reference information. Nowadays, many deep learning models, like the Radial Base Functional Neural Network(RBF) and the Convolutional Neural Network (CNN), are applied to features which are created automatically from deep learning models and abstract syntax trees (AS Ts) to aid in the improved performance of predicting defects. But the results generated using RBF and CNN algorithms are not able to provide much accuracy due to its restricted size of dataset and improper baseline selections. To resolve these state-of-the-art problems, we have constructed a dataset taken from various defect datasets namely the Kamei Dataset, NASA Dataset and the PROMISE Source Code (PSC) dataset. In this research, the dataset is named as Combination Defect Analysis Dataset (CDA). Then, an Enhanced Convolutional Neural Network (ECNN) model is proposed for predicting defects in Intra-Project software (IPDP) and associated results to different models. Experimental results implied that Enhanced CNN(ECNN) model is efficient compared to the other associated models, along with it outclassing the other machine learning models suggested for IPDP.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130981286","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

A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection 基于卷积神经网络的乳腺癌检测分类方法

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125783

Md. Harun Or Rashid, S. M. Shahriyar, F. J. M. Shamrat, Tanzil Mahbub, Zarrin Tasnim, Md Zunayed Ahmed

{"title":"A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection","authors":"Md. Harun Or Rashid, S. M. Shahriyar, F. J. M. Shamrat, Tanzil Mahbub, Zarrin Tasnim, Md Zunayed Ahmed","doi":"10.1109/ICOEI56765.2023.10125783","DOIUrl":"https://doi.org/10.1109/ICOEI56765.2023.10125783","url":null,"abstract":"Thousands of women worldwide are diagnosed with breast cancer yearly, which may be fatal if not treated. The diagnosis of the condition may take years, by which time the patient has little choice except to have the affected breast removed. Early diagnosis and treatments are the best ways to stop this disease's spread. In this study, the authors presented a Computer Aided Diagnosis (CAD) system to assist in breast cancer diagnosis. The study uses the Wisconsin breast cancer dataset to classify benign and malignant data. For the classification, three pre-trained Deep learning algorithms: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), were used. A novel CNN model that exceeds the performance efficiency of three pre-trained models and requires minimal compilation time is proposed. A number of evaluation matrices are used to analyze the models' classification abilities. Upon closer inspection, it has been established that the proposed CNN model outperforms CNN, LSTM, and MLP models with validation accuracy of 97.85%. CNN and LSTM performed with accuracies of 94.12% with the Adagrad optimizer and 93.5% with the Adam optimizer, respectively. Furthermore, MLP performance with 92.44% accuracy using the Adam optimizer. The proposed CNN model achieves the lowest Loss value and compilation time. In addition, the models' recall value, precision, and f1-score are computed to pick out the most effective model for diagnosing breast cancer on numeric data.","PeriodicalId":168942,"journal":{"name":"2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134379350","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

Intrusion Detection and Prevention System to Analyse and Prevent Malware using Machine Learning 利用机器学习分析和预防恶意软件的入侵检测和防御系统

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125999

V. Ebenezer, Rosebel Devassy, G. Kathrine

引用次数: 2

Voice-based Image Captioning using Inception-V3 Transfer Learning Model 使用Inception-V3迁移学习模型的基于语音的图像字幕

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125754

Vaibhav Thalanki, R. N. Akshayaa, R. Krithika, R. Jothi

引用次数: 0

Underground Cable Fault Detector with Distance Locator 带距离定位器的地下电缆故障检测器

2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) Pub Date : 2023-04-11 DOI: 10.1109/ICOEI56765.2023.10125617

Mandaloju Bhavana, Gorantala Shravya, Maddela Poorna Chander, Tanneeru Subba Rao, Matla Rishika

引用次数: 0