2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)最新文献_第3页

Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model 基于大数据分析和深度学习模型的Twitter数据情感分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084281

Harika Vanam, Jeberson Retna Raj R

{"title":"Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model","authors":"Harika Vanam, Jeberson Retna Raj R","doi":"10.1109/ICECONF57129.2023.10084281","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084281","url":null,"abstract":"People from all over the world can express their thoughts and opinions through various online social media platforms. People use social media platforms online daily to communicate with one another and stay informed about current events. A large number of tweets covering a wide range of subjects are sent to Twitter daily. Twitter is one of the most well-known and widely used online social media platforms. Extracting features and locating trends can be accomplished through the use of machine learning algorithms. Tools and strategies designed specifically for working with large amounts of data are required to successfully extract useful information from the never-ending stream of data that is produced by Twitter. In this paper, we mainly focus on hashtag identification and identify the industry that possesses the highest share of voice. In this paper, we collect live data from Twitter by using Apache Spark. After that, we classify each tweet by making use of the machine learning techniques that are provided by the Apache Spark machine learning library. To test the model, Convolution neural network (CNN) and logistic regression (LR) are being utilized. The CNN method outperformed the Logistic Regression strategy by performing with an accuracy of approximately 95% on average and scoring 0.60 on the F1 scale. Both the accuracy and the F1 score are currently sitting at 0.59. According to the findings, real-time tweets can be evaluated considerably more quickly using the Apache Spark tool for big data as opposed to the conventional execution environment. The results show that real-time tweets can be evaluated much faster using the Apache Spark tool for big data instead of the usual execution environment.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"41 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":"131880097","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

Machine Learning Designed identification on cervical cancers in patient 机器学习设计宫颈癌患者识别

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083718

M. Sangeetha, G. Sasikala, K. Anitha, S. Ragavendiran, K. R. S. Kumar, M. Deivakani

引用次数: 0

An Enhanced Optimal Design of a Phase Changing Material Based Photo Voltaic System using Deep Learning 基于深度学习的相变材料光伏系统优化设计

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084115

Prasanna Lakshmi G S, M. K, S. Sangeetha, T. S. Krishnan, T. Udhayakumar, M. Anusuya

{"title":"An Enhanced Optimal Design of a Phase Changing Material Based Photo Voltaic System using Deep Learning","authors":"Prasanna Lakshmi G S, M. K, S. Sangeetha, T. S. Krishnan, T. Udhayakumar, M. Anusuya","doi":"10.1109/ICECONF57129.2023.10084115","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084115","url":null,"abstract":"In this paper, oscillations in heat flux are smoothed out using a PCM energy storage that is controlled by artificial neural networks (ANN). The purpose of this research is to evaluate how different levels of discharging heat flow might influence the use of PCM and ANN in various settings. We compared the standard deviations of the charging and discharging heat fluxes when they were managed by ANN and when they were managed just by PID. Investigations towards testing large-scale installations as pilot projects were carried out. The TES Unit, which had a heat capacity was fuelled by a heat flux that allowed for its intensity to be adjusted. The phase transition material in the Hitec salt was comprised of KNO3, NaNO2, and NaNO3, respectively. Sigmoid function areused in order to govern the three-layer ANN. The training procedure utilised resilient backpropagation as one of its methods. To ensure the quality of the training, compare the temperatures that were predicted with those that were actually recorded. It turned out that the prognosis was right on the money. The analysis reveals that a TES unit, in conjunction with a PCM, can be used to stabilise the changing heat flux.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"21 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":"114650047","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

Identify Facial Micro Expression Using Support Vector Machine Compared with Artificial Neural Network to Improve Recall Parameter 利用支持向量机与人工神经网络进行面部微表情识别，提高召回率

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084307

S. Soharika, N. Bhavani

{"title":"Identify Facial Micro Expression Using Support Vector Machine Compared with Artificial Neural Network to Improve Recall Parameter","authors":"S. Soharika, N. Bhavani","doi":"10.1109/ICECONF57129.2023.10084307","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084307","url":null,"abstract":"Aim: The creation of facial recognition systems, which are crucial in the modern world, is the aim of this research. The Novel Support Vector Machine and Artificial Neural Network are used in this study to build a rapid facial recognition technique in Python. In this research article, we compare the performance of Novel Support Vector Machine and Artificial Neural Network in facial recognition. Materials and Methods: The detailed studies of suggested algorithms are reviewed. The testing was carried out using a publicly accessible face database. Each algorithm is put to the test using ten different photos, each with a varied face expression and lighting. For the SPSS study, nearly 10 samples were taken to evaluate, compare, and understand the accuracy of proposed algorithms. For accuracy prediction, a G power of 80% is used in the SPSS software. The parameters considered are CI and alpha, which were determined as 0.003 (p < 0.05). For SVM group 1, 10 samples are taken and for Artificial Neural Network algorithm group 2, 10 samples are taken to compare the Recall for facial expressions. Result: The results of combining several feature extraction methods and classifiers were given and examined. SVM was shown to have the best accuracy, with a score of 97.01 % Conclusion: Emotion recognition is a promising technique for improving the efficiency of current image-based recognition techniques. In this research project.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"40 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":"114719316","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

MRI Image Analysis for Brain Tumor Detection Using Convolutional Neural Network 基于卷积神经网络的脑肿瘤MRI图像分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083560

B. Ayshwarya, M. Dhanamalar, Vinod Kumar

{"title":"MRI Image Analysis for Brain Tumor Detection Using Convolutional Neural Network","authors":"B. Ayshwarya, M. Dhanamalar, Vinod Kumar","doi":"10.1109/ICECONF57129.2023.10083560","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083560","url":null,"abstract":"Brain tumors are a dangerous type of cancer with one of the lowest chances of being alive after five years. Magnetic resonance imaging (MRI) is frequently used by neurologists to determine the type of brain tumor present. Using computer-aided tools can speed up the diagnosis process and minimize the burden on health care systems. Deep learning for medical imaging has demonstrated outstanding achievements, especially in the automatic and fast diagnosis of many malignancies. However, in order to get decent results with deep learning models, we require a good quality of data images. To improve the quality of MRI images, a three-step preprocessing method is proposed in this research, coupled with a new Deep Convolutional Neural Network (DCNN) architecture. Batch normalization is used in the design to speed up training and increase the learning rate while also simplifying the initialization of the layer weights. With a modest number of convolutional, max-pooling layers and training iterations, the suggested architecture is computationally light. The proposed architecture is compared to other models in this paper to show its effectiveness. When tested on a dataset of 3394 MRI images, the system achieved a remarkable competitive accuracy. The proposed architecture has been shown to be strong and has helped improve the accuracy of detecting a wide range of brain diseases in a short amount of time.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","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":"129798897","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

Enhancing Energy Level and Network Lifetime for Asynchronous Duty Cycled WSN with Expectation-Maximization Algorithm 利用期望最大化算法提高异步占空比WSN的能量水平和网络寿命

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084142

R. Purushothaman, R. Narmadha

{"title":"Enhancing Energy Level and Network Lifetime for Asynchronous Duty Cycled WSN with Expectation-Maximization Algorithm","authors":"R. Purushothaman, R. Narmadha","doi":"10.1109/ICECONF57129.2023.10084142","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084142","url":null,"abstract":"Generally in Asynchronous duty cycled Wireless Sensor Network (WSN), the energy level and the network life time will be considered as the most important factors which decides the performance of the network. If the delays in the network are more, then accordingly the energy level and the life time will gets degraded. To compensate the delays between the senders and to decrease the variety of duplicate packets here we are proposing an algorithm named as Expectation-Maximization (EM) Algorithm. The algorithm consists of two stages. First and foremost, each node characterizes an applicant region using a standard mathematical form of four corners. Bundles generated by the node will be channeled through any route in the area. As applicants, local nodes may be chosen. The texture of the activist group determines the size of the Candidate Zone (CZ). Second, rising stars within the Candidate Zone (CZ) are favored due to the Opportunistic Routing (OR) metric, which is a replication of four mixtures: directional conveyance, transmission distance circulation, opposite distance dissemination, and energy appropriation. The Expectation-Maximization Algorithm is used in this cycle. Resource management in wireless sensor networks is one of the fundamental issues that should be considered to work on the life expectancy of sensor organizations. In general, execution assessment and recreation of enormous scope situation shows that our conventional method performs better with OR-EM. The objective of the proposed work is done by calculating most significant energy dispersed by a node in the path aside fixing the sink node to the destination node. The experimental results prove that our proposed method using Expectation-Maximization algorithm has improved in terms of energy level by 13.5% while comparing without Expectation-Maximization algorithm.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"46 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":"127087557","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

Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning 基于深度学习增强特征提取技术的单色图像去雾

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083630

Nisarg Doshi, Sagar Bhavsar, D. Rajeswari, R. Srinivasan

{"title":"Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning","authors":"Nisarg Doshi, Sagar Bhavsar, D. Rajeswari, R. Srinivasan","doi":"10.1109/ICECONF57129.2023.10083630","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083630","url":null,"abstract":"Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"71 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":"127266677","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 Machine Learning Approach to Segment the Customers of Online Sales Data for Better and Efficient Marketing Purposes 一种机器学习方法对在线销售数据的客户进行细分，以实现更好、更有效的营销目的

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084339

Mathesh T, Sumathy G, Maheshwari A

引用次数: 3

An Effective Analysis to Reduce Hypertension with Adaptive Lifestyle 适应性生活方式降低高血压的有效分析

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10083791

G. Sivakarthi, R. Perumalraja.

{"title":"An Effective Analysis to Reduce Hypertension with Adaptive Lifestyle","authors":"G. Sivakarthi, R. Perumalraja.","doi":"10.1109/ICECONF57129.2023.10083791","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10083791","url":null,"abstract":"Different lifestyle behaviors tend to produce hypertension in people, and the factors behind the lifestyle are highly correlated with effective blood pressure (BP) control. Hypertension may also lead to unnoticed minor fluctuations in Blood Pressure that lead to major issues, viz., fatal diseases like stroke prognosis, cardiovascular disease, artery damage, blood clots, osteoporosis, etc. The proposed work aimed to identify the unnoticed minor fluctuations in patient-specific life style activities. Initially, the patient's lifestyle study was conducted on five members for three months. From the study, it is observed that the normal blood pressure range for most individuals, regardless of age, is 120/80 mm Hg or even below, and we classified blood pressure as having a systolic pressure of 140 mm Hg and a diastolic pressure of 90 mm Hg. Once the range of hypertension was measured, we gave some recommendations to the patients who were associated with high blood pressure in regular lifestyle activities. Suggestions were made to avoid certain activities and keep blood pressure within the normal range. Based on that BP range, the goal was to identify the factors and lifestyle changes that could keep the blood pressure under control. A survey was conducted in our home by our family members for three months (April-June). To test our proposed work method, we recruited five patients from our family. An initial evaluation of patients' lifestyles was calculated by the patient, who entered the prescribed details. Among the 5 patients, 3 had initial and final blood pressure readings that could be monitored for up to 90 days based on the patients' lifestyle factors.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"26 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":"129102956","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

Fundus Image Based DR Detection Using Image Processing 基于眼底图像的图像处理DR检测

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) Pub Date : 2023-01-05 DOI: 10.1109/ICECONF57129.2023.10084241

T. Singh, A. Suresh

{"title":"Fundus Image Based DR Detection Using Image Processing","authors":"T. Singh, A. Suresh","doi":"10.1109/ICECONF57129.2023.10084241","DOIUrl":"https://doi.org/10.1109/ICECONF57129.2023.10084241","url":null,"abstract":"Almost any field of application that aims to address societal issues now relies heavily on image processing, thanks to recent breakthroughs in the field. Medical image processing is among the most widely appealing and vital disciplines of research in recent times, owing to its enormous potential as well as the vast volume of underlying information that needs to be recovered using systematic image processing techniques. However, this research work, has taken processing of retinal images as its prime issue of concern to predict and aid early detection of retinal disorders. This arises from the motivation that certain symptoms appearing in a particular part of the human body may indicate onset of certain diseases in which diabetes could be quoted as the best example. Retina of the eye is an essential component from an image processing aspect, as it reveals several disorders associated with it at an earlier stage, if properly processed and analysed. Some well-known retinal disorders include diabetic retinopathy, retinal tear and detachment, cataract, retinitis pigmentosa, age related macular disorders (AMD) etc. if these conditions are left undetected, they may result in increasing disorders affecting other parts of the human body. In view of the above issues and prospects of retinal image analysis, this thesis has taken retina as the prime subject of study for early detection of retinal disorders.","PeriodicalId":436733,"journal":{"name":"2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)","volume":"79 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":"129105848","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