2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)最新文献_第2页

Deep Learning Based Approach for Identification of Parkinson’s Syndrome 基于深度学习的帕金森综合症识别方法

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967676

R. Manimegalai, A. Ramya, S. Swedha, M. Pruthvi, S. Lokesh

{"title":"Deep Learning Based Approach for Identification of Parkinson’s Syndrome","authors":"R. Manimegalai, A. Ramya, S. Swedha, M. Pruthvi, S. Lokesh","doi":"10.1109/ICIIET55458.2022.9967676","DOIUrl":"https://doi.org/10.1109/ICIIET55458.2022.9967676","url":null,"abstract":"Parkinson’s Disease (PD) is a chronic condition of the nervous system characterized by neuronal degeneration, primarily in the substantia nigra part of the brain. In 2016, a total of 120,000 cases of Parkinson’s disease were recognized and documented. Nevertheless, specialists estimate that there are still undetected cases due to external variables such as medical expense and diagnosis accuracy. Because of the slow course of symptoms, early detection and diagnosis of PD have become a challenge in the medical field. Due to the loss of dopamine-producing neurons, patients face reduced motor activities. The symptoms are mainly divided into two categories: i) motor symptoms such as tremors, and slowness of movement; and, ii) non-motor symptoms such as insomnia, sadness, etc. Tremor is one of the first and most prevalent symptoms of Parkinson’s disease, and ignoring it at this point could have a serious detrimental influence on the patient’s health. In this work, a collection of Deep Learning (DL) models such as DenseNet, Resnet 15, and VGG 16 are used to predict Parkinson’s disease by using hand-drawn images and by using DaTscan brain images. The proposed model has achieved maximum accuracy of 91% for hand-drawn images and 95% for DaTscan brain images.","PeriodicalId":341904,"journal":{"name":"2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131196338","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

Attribute-based Maturity Grading of Mango Fruit by Machine Learning 基于属性的芒果果实成熟度机器学习分级

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967613

S. Kripa, V. Jeyalakshmi

{"title":"Attribute-based Maturity Grading of Mango Fruit by Machine Learning","authors":"S. Kripa, V. Jeyalakshmi","doi":"10.1109/ICIIET55458.2022.9967613","DOIUrl":"https://doi.org/10.1109/ICIIET55458.2022.9967613","url":null,"abstract":"Accurate and reliable fruit grading contributes to India’s economic prosperity. Classification and grading are needed before marketing agricultural products. The color, size, shape, and texture of a fruit, as well as its overall look, frequently characterize its quality. Fruit choosing should not be done solely on appearance. Ripeness is largely judged by internal fruit attributes, such as sweetness and acidity. According to studies, a fruit’s maturity is often appraised by an image that depicts its stage. Observing fruits of the same color at different stages of maturation makes determining ripeness difficult. The studied data, which includes qualities with yellow skin pigmentation at all stages of development, is collected from the mango fruit dataset “Nam Dok Mai Si Tong” to address this issue. This study suggests using indicators like Total Soluble Solids (TSS), Titrable Acidity (TA), and BrimA to define mango fruit development according to ripeness for a certain type. Metrics and ten-fold cross-validation is used to analyze ML models. The decision tree classifier has the highest accuracy (91.9% on average) for both unripe and ripe classifications compared to state-of-the-art, according to trial findings.","PeriodicalId":341904,"journal":{"name":"2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117152277","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 Cloud Application for ECG Arrhythmia Classification Using Deep Learning and N-Square Approaches 使用深度学习和n平方方法进行心电心律失常分类的云应用

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967615

J. Swarup Kumar, G. Jyothi, D. Indira, G. N. S. V. Sri, S. Mukesh, A. Yochana

{"title":"A Cloud Application for ECG Arrhythmia Classification Using Deep Learning and N-Square Approaches","authors":"J. Swarup Kumar, G. Jyothi, D. Indira, G. N. S. V. Sri, S. Mukesh, A. Yochana","doi":"10.1109/ICIIET55458.2022.9967615","DOIUrl":"https://doi.org/10.1109/ICIIET55458.2022.9967615","url":null,"abstract":"According to the World Health organization, coronary heart disease (sometimes called coronary artery disease) is the biggest cause of mortality worldwide (WHO). Around 17.7 million individuals died from Cardiovascular disease (CVDs) every year, and roughly 31% occurred in low and middle-income nations around the world. Arrhythmia is a variety of cardiovascular diseases that interrupts the heart’s normal rhythms. Some common types of irregular heartbeats include: There is no immediate danger from a single arrhythmia, but prolonged arrhythmia abnormalities can be life-threatening. Consuming these drugs increases the risk of developing heart disease. To treat and prevent cardiovascular disease, regular cardiac monitoring is essential. The heart’s rhythm and health can be visualized by the non-invasive device. Utilizing a deep two-dimensional convolution neural network, this sorting mechanism successfully categorizes electrocardiograms into the following five classes: fibrillatory, supraventricular, ventricular, ventricular, intraventricular, supraventricular, ventricular, and ventricular above, and finally unknown beats. In this piece, we try to extract the class pattern from an electrocardiogram (ECG) image using the N-Square technique and compressed image data.","PeriodicalId":341904,"journal":{"name":"2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122015509","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

Adaptive Predictive Control-Based Noise Cancellation With Deep Learning for Arrhythmia Classification from ECG Signals 基于自适应预测控制和深度学习的心电信号心律失常分类噪声消除

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967602

Rajesh. S. Pashikanti, A. Shinde, C. Patil

{"title":"Adaptive Predictive Control-Based Noise Cancellation With Deep Learning for Arrhythmia Classification from ECG Signals","authors":"Rajesh. S. Pashikanti, A. Shinde, C. Patil","doi":"10.1109/ICIIET55458.2022.9967602","DOIUrl":"https://doi.org/10.1109/ICIIET55458.2022.9967602","url":null,"abstract":"Arrhythmia is a disorder in the heart produced due to irregular electrical activities of the heart, and an electrocardiogram (ECG) represents a modality used by clinicians to discover arrhythmias. The occurrence of noise in ECG and irregular heartbeat is the complexity faced while diagnosing arrhythmias. Thus, there is a requirement for a technique that can attain high accuracy in arrhythmia classification. This paper utilizes the Deep Maxout network (DMN) for classifying arrhythmia using ECG signals. Here, the ECG signal is considered as an input to adaptive prediction noise control wherein the noise cancellation is performed using adaptive Least Mean Square (LMS) to discard noise and obtain a clean signal. From a clean signal, the features, like Empirical Mode Decomposition (EMD), wave component detection, wave features, such as RR interval, QT interval, PR interval, PP interval, and R peak whereas statistical features, such as kurtosis, eccentricity are mined for improved analysis. At last, the classification of arrhythmia is done using DMN. The greatest accuracy of 92.1%, the sensitivity of 92.6%, and the specificity of 91.9% are measured using DMN.","PeriodicalId":341904,"journal":{"name":"2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123954468","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

Anomaly Analysis in Machine Sensors 机器传感器中的异常分析

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967513

T. Parthasarathy, J. Kovilpillai, H. M. Irfan, B. Ramprasath

引用次数: 0

Examine and Develop Novel Substrate Integrated Waveguide Circulators in the Ku Band Ku波段新型基板集成波导环行器的研究与开发

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967495

Manish Sharma, N. Kashyap

{"title":"Examine and Develop Novel Substrate Integrated Waveguide Circulators in the Ku Band","authors":"Manish Sharma, N. Kashyap","doi":"10.1109/ICIIET55458.2022.9967495","DOIUrl":"https://doi.org/10.1109/ICIIET55458.2022.9967495","url":null,"abstract":"Severa1 researchers and scientists in the design and development of RF and microwave devices have been drawn to the development of ferrite circulators during the last several decades. In contemporary technology, circulators play an important part in communication systems such as satellite T/R modules, radar, and military applications. In the early days, the ordinary planar microstrip circulator had large insertion losses, return losses, and limited bandwidth. However, to get excellent wave transmission characteristics, a waveguide circulator was a good alternative, but due to their non-planarity and bulkiness, they are not suitable for modern communication devices and are also not economical, so researchers developed a revolutionary design of rectangular waveguide in planar technology, i.e., RSIW, with many advantages such as high Q-factor, low loss, relatively high power volume, and high microwave and millimeter wave integration, which gives them the hope of designing EM spectrum band devices in planar technology by modification in dimensional parameters by modification in RSIW.","PeriodicalId":341904,"journal":{"name":"2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448000","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 Study on Predicting Software Defects with Machine Learning Algorithms 基于机器学习算法的软件缺陷预测研究

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967593

C. Anjali, J. Dhas, J. Singh

引用次数: 0

Design of Diamond Shaped Monopole Antenna for Radio Navigation, Radio Location and Satellite Services 用于无线电导航、无线电定位和卫星服务的菱形单极天线设计

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967546

B. Tharini, A. Kannammal

引用次数: 0

Double Security to Colour Images with DCT based Watermarking and Chaos based Encryption 基于DCT的水印和混沌加密对彩色图像的双重安全性

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967591

M. Amrutha, A. Kannammal

{"title":"Double Security to Colour Images with DCT based Watermarking and Chaos based Encryption","authors":"M. Amrutha, A. Kannammal","doi":"10.1109/ICIIET55458.2022.9967591","DOIUrl":"https://doi.org/10.1109/ICIIET55458.2022.9967591","url":null,"abstract":"Copyright protection, content authentication, and other applications have benefited from color image watermarking. Color image encryption can be used to secure color images and prevent unauthorized access. To protect a color image’s copyright, the paper proposes a powerful watermarking in CIE La*b* space using a block-based DCT approach. Initially, the color image has been transformed to CIE La*b* space and the watermark has been implanted into the b* channel because any changes made to the b* channel are almost undetectable by the human eye. Before the watermark embedding the b* channel is sub-divided into non-overlapping blocks of size [8$times$ 8]. By making sure that the difference among two middle band frequencies is +ve within the situation where the encoded value is 1, the algorithm embeds a single bit of the watermark which is binary into the DCT coefficients of each sub-block of the original image. After watermarking, Encryption is done on the watermarked image before sending it to the receiver. This paper proposed a novel cryptographic system by constructing a new chaotic map for producing the key. The proposed method has been compared with different current works, and the experimental outcome shows the superiority of the work presented in this paper.","PeriodicalId":341904,"journal":{"name":"2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128274454","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

AntLion Optimization Algorithm based Type II Diabetes Mellitus Prediction 基于AntLion优化算法的2型糖尿病预测

2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) Pub Date : 2022-09-22 DOI: 10.1109/ICIIET55458.2022.9967563

A. Sasithradevi, C. Baskar, H. R. Deekshetha, S. Reshma, M. Vijayalakshmi, D. A. Perumal

引用次数: 0