2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)最新文献

{"title":"A New Hybrid Model ARFIMA-LSTM Combined with News Sentiment Analysis Model for Stock Market Prediction","authors":"Satyaveer, Prashant Patel, Harish Chandra, Prashant Pal, S. Singh","doi":"10.1109/ICAECT57570.2023.10118349","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118349","url":null,"abstract":"We have used the advantages of both data-driven and news-driven models and came up with a novel hybrid model. This model combined the data-driven ARFIMA-LSTM with the NEWS SENTIMENT analysis model. So, in order to obtain a better and more accurate prediction at each time scale, we combined the capabilities of a data-driven model (ARFIMA-LSTM) with a news sentiments-driven model. This model outperforms the traditional models, such as SVM, Random Forest, ARIMA, KNN, GRNN, and LSTM, and it accurately forecasts the market.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124534335","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}

{"title":"Development of an Efficient Cardiac Auscultation System Using Proper Denoising Techniques","authors":"Lubaib P, Dhoulath Beegum J, N. Muhsina, Manjusree S, A. Ss","doi":"10.1109/ICAECT57570.2023.10118133","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118133","url":null,"abstract":"Heart auscultation is a primary and cost-effective form of clinical examination of the patient. PCG signal is used as a diagnostic test for evaluating the status of the heart and it helps in identifying related diseases. There are several limitations and practical problems in the human auditory system when it comes to phonocardiogram (PCG) signal analysis in a clinical environment, despite the cognitive skills and expertise of the medical examiner. Thus, the assessments provided by cardiac auscultation are subjective and sometimes provide an inaccurate diagnosis. The objective of this research is to develop an efficient heart auscultation system by implementing necessary pre-processing and suitable filtering techniques. The problem of controlling the noise level has been one of the research topics over the years. The various filters used are IIR Butterworth and Chebyshev digital filters, FIR filters, LMS filter, Wiener filter, Wavelet transform based filter and cascading of these filters were also done and the filter which gives the highest value of SNR and good tone audibility score is chosen as the most efficient filter for noise removal. Based on the performance comparison of various filtering technique and calculation of heart rate, an efficient heart auscultation system is developed to assist medical practitioners in PCG signal analysis.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124616757","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}

{"title":"Thermal Image Processing System for the measurement of body temperature using a Drone","authors":"R. Perez-Siguas, E. Matta-Solis, Anika Remuzgo-Artezano, H. Matta-Solis, Hernan Matta-Perez, Luis Perez-Siguas","doi":"10.1109/ICAECT57570.2023.10117879","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117879","url":null,"abstract":"In December 2019, several cases of pneumonia caused by SARS-CoV-2 were identified in the city of Wuhan (China), which was declared by the WHO as a pandemic in March 2020 because it caused enormous problems to public health due to its rapid transmission of contagion. Being an uncontrolled case, precautions were taken all over the world to moderate the coronavirus that undoubtedly was very deadly for any person, presenting several symptoms, among them we have fever as a common symptom. A biosecurity measure that is frequently used is the taking of temperature with an infrared thermometer, which is not well seen by some specialists due to the error they present, therefore, it would not represent a safe measurement. In view of this problem, in this article a thermal image processing system was made for the measurement of body temperature by means of a drone to obtain the value of body temperature accurately, being able to be implemented anywhere, where it is intended to make such measurement, helping to combat the spread of the virus that currently continues to affect many people. Through the development of the system, the tests were conducted with various people, obtaining a more accurate measurement of body temperature with an efficiency of 98.46% at 1.45 m between the drone and the person, in such a way that if it presents a body temperature higher than 38° C it could be infected with COVID-19.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124954047","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}

{"title":"A typical Anchor-Shaped Narrowband SIW based Circular Cavity-backed Slot Antenna for Indoor/Outdoor WLAN Applications","authors":"Rimi Sengupta, S. Banerjee, M. Mitra","doi":"10.1109/ICAECT57570.2023.10117696","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117696","url":null,"abstract":"In this paper, a low profile substrate integrated waveguide (SIW) based circular cavity-backed antenna with slot is delineated and fabricated to operate in U-NII-2C band at a frequency of 5.49 GHz. A typical anchor-shaped slot is encompassed on the centre of the ground plane of the proposed antenna for radiation purpose. A gain of 3.6 dBi is obtained, portraying a narrow bandwidth of 70 MHz. Fabrication of parent antenna is done on Arlon AD270 substrate in the dominant TM010 mode. Parametric studies are performed with respect to return loss and gain of antenna. Simulations on the proposed design are performed in ANSYS make HFSS v15.0 and the antenna parameters are measured experimentally. The measured results show similar behavior to the simulated ones.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125743054","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}

{"title":"Automatic Scikit-learn based detection and classification of Breast Cancer using Machine./ Learning techniques","authors":"I. S, Hemapriya B, K. Devi, H. K","doi":"10.1109/ICAECT57570.2023.10117662","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117662","url":null,"abstract":"The most severe disease nowadays is breast cancer. The abnormal development of breast cells is what causes breast cancer. The breast ducts may include this type of malignancy. Women over 40 are the ones who get diagnosed with breast cancer most frequently. Women over 50 have the highest incidence (about 77% of invasive cases). A clear understanding of breast cancer and its kinds, as well as the creation of prophylactic measures, have made significant strides. Many genes have been linked to breast cancer. Malignant and benign cells both fall within this category. Cancerous malignant conditions exist. Non-cancerous benign cancer. Malignant cells affect other cells of the body as well as the tissues continue to divide uncontrollably. While benign tumors are significantly less dangerous than malignant ones since they do not spread to other parts of the body. Treatment is not necessary for these cancers. Malignant tumors can be treated with chemotherapy, radiation therapy, and immunotherapy, among other forms of treatment. The algorithms used to develop this prediction system are machine learning-based. Machine learning became a research focus and was found to be an effective method. Recently, many scholars have become interested in the developing technique of machine learning. Scikit-learn is used to create a model that categorizes breast cancer as either malignant or benign. A type of library that is included with the collab platform is called Scikit-learn. Mammograms have historically been the primary method of finding breast cancer. Any calcium deposits or calcifications in the breasts might be seen on a mammogram. Considering how pricey it is, few people can afford it. It will be great if an early diagnosis occurs so that treatment can get underway right away. Many women's lives could be saved by this. This dataset analysis will benefit from the use of machine learning algorithms. Six machine learning (ML) methods are compared in this paper: Naive Bayes (NB), Random Forest (RT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). These methods will be applied to accurately forecast outcomes. Due to early detection, the 5-year survival rate for breast cancer patients is above 82 %. The eventual goal of this project is to assist clinicians in analyzing enormous databases of available cancer data and discovering patterns using patient data and that cancer data. This endeavor to a modest stride in the protracted battle against breast cancer.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126412593","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}

{"title":"A Novel Approach Using Extractive and Abstractive Summarization for the Genre Classification of Short Text","authors":"Chaitanyasuma Jain, Jatinderkumar R. Saini","doi":"10.1109/ICAECT57570.2023.10118150","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118150","url":null,"abstract":"Genre classification for text documents is useful in media monitoring and detection of misinformation. Recent work in text classification for genre has shown that advanced algorithms such as neural networks and transformers are well-suited for the purpose. However, for shorter text documents, such as those obtained from social media or news articles, training of deep learning models becomes challenging since they require a large amount of input. Furthermore, genre classification of text summaries, such as headlines of news, is an important direction which has not been explored at large. In this work, the effect of Extractive and Abstractive Summarization on classification for genre of text documents was evaluated. Gensim summarizer was used to obtain extractive summaries and the Pegasus summarizer to obtain abstractive summaries. For classification, two classes of genres, Fiction and Non-fiction, were considered while the gold standard Brown Corpus was used for experimentation. The features used for genre classification were frequencies of various Part-of-Speech (PoS) tags derived from five Penn TreeBank annotated tags. Logistic Regression (LR) and Support Vector Machines (SVM) were used for classification purposes. The results of classification were better for summaries obtained using the extractive technique, indicating that the features of extractive summaries remain in agreement with the documents from which the summary is constructed as compared to abstractive summaries. Further, the SVM classifier performed better than the LR classifier. For exhaustive coverage of the research goal, further experimentation with the number of words of the output summaries of the extractive technique was performed to arrive at a threshold value of the length of summaries. The value indicated that summaries as short as 80 words can be successfully classified using this method.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114234757","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}

{"title":"Random Forest Regressor Approach for Predicting Resale Value of used Vehicles (RFRVP)","authors":"M. Rane, Mandar Patil, Nikhil Rane, Amruta Amune","doi":"10.1109/ICAECT57570.2023.10118285","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118285","url":null,"abstract":"Nowadays, within last decade, car production has been growing substantially. In 2016, over than 70 million passenger cars have been produced. Different websites, like Cars24, CarDekho, OLX, and Quikr provide the platform to this buyers who are able to sell their cars, but determining the car's cost appears to be the most difficult choice ever. Algorithms for machine learning might be able to solve this problem. combining recent used-car sales data and machine learning techniques like supervised learning a fair price for the car could be predicted. To Forecast the selling cost of the old Cars, we used machine learning-based algorithms which includes Random Forest, Linear, Decision Tree, and Ridge Regression also we use sophisticated Python module Sklearn. The findings shows that Machine Learning algorithms has high accuracy for predicting if the data set is big. They give result with high precision which is independent of the dataset size. I'll also evaluate the prediction r2 scores of various models to see which one is the best.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127284604","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}

{"title":"Malicious Host Detection in Software Defined Networks Using Machine Learning Algorithms","authors":"P. J., M. Sharief, Ujjal Mukherjee, M. Anand Kumar, G. M S","doi":"10.1109/ICAECT57570.2023.10118020","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118020","url":null,"abstract":"The automation and flexibility provided by the Software Defined Networks (SDN) model are crucial for addressing the challenges currently associated with the network operation. SDN network operates separately with control planes in which the control programs are used to forward the network packets from the data plane, where forwarding hardware only executes these decisions. The control programs are closely associated with each device dispersed across the network prior to the invention of SDN. SDN is being used in a large number of real-world applications as a promising network virtualization technology. The concentration of network control operations provides the features of network programmability, flexible network management, agility, and encourages network innovations are the main innovation of SDN. Software defined networks mainly focuses on providing networking functionalities by utilizing a centralized SDN controller that interacts with programmable network components. SDN made networks easier to use and more convenient, but it also invented different security flaws. These flaws could result in security risks that would be disastrous for the architecture of the SDN in particular and the entire network in general. For future networks, SDN security is essential. A reliable real-time approach is required to detect infected SDN devices. The number of unauthorized requests made to the controller can be monitored and limited using a few known techniques. These countermeasures, however, are unable to effectively defend against a series of attacks that each employs a different IP address. In order to identify malicious devices in the SDN network and to prohibit hosts and services in the SDN environment, this research presented a machine-learning approach. The results of the experiments demonstrate how well the suggested strategy classifies harmful devices.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127686750","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}

{"title":"Image Synthesis using Ensemble of Loss Functions: A Comparative Analysis using GAN Architecture","authors":"P. Sarma, Joseph Kiran Chayengia, Gitarthi Gogoi","doi":"10.1109/ICAECT57570.2023.10118052","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10118052","url":null,"abstract":"In this paper authors tries to show success of using ensemble of loss function on GAN network model. The model was able to generate quality synthetic data on MNIST handwritten dataset. The performance of the simple vanilla GAN model is evaluated for binary entropy loss and least square loss combined and compared for both of these loss functions separately. Instead of using only one single activation function we have used multiple activation functions such as Sigmoid., Tanh and ReLU in the last layer of the discriminator. Minimum loss value is counted among the pairs of transformed output vectors that are changed using different activation functions. In this case study we have used MNIST handwritten dataset. At last observations are made that training a model using ensemble of L2 loss and binary cross entropy loss functions produced significantly better results compared to using only L2 and only binary cross entropy loss function.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127827805","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}

{"title":"Detection of multiple classes of disturbances with Hilbert Transform in the Presence of Harmonics","authors":"Hemlata Meena, Hemant Kumar Meena, D. Saxena","doi":"10.1109/ICAECT57570.2023.10117809","DOIUrl":"https://doi.org/10.1109/ICAECT57570.2023.10117809","url":null,"abstract":"This article suggests a detection and classification strategy for complex power quality disturbances (PQDs) determined from Hilbert-Transform (HT) and Support Vector Machine (SVM). This work outlined the basic concepts of HT and examined its superiority in detecting complex power quality signals in the presence of noise. Continuously increment in the use of non-linear equipment connected to the electric power system introduces distortion currents into the power system and generates harmonics. Because of this, power quality (PQ) has received much attention over the last several years. Harmonics cause severe damage to power system devices and introduces inaccuracies in measurement instruments. MATLAB computations to analyze power system harmonics and experimentations employing various features were used to verify the efficiency of the suggested technique.","PeriodicalId":338586,"journal":{"name":"2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131452308","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}