2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)最新文献_第4页

Image Super Resolution Based on Machine Learning for Enhancing Quality Image 基于机器学习的图像超分辨率提高图像质量

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084087

O. James, T. R. Singh, T. Singh

引用次数: 0

Performance analysis of Sentiment Classification using Optimized Kernel Extreme Learning Machine 基于优化核极限学习机的情感分类性能分析

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083939

D. Kavitha, M. Subbarao

{"title":"Performance analysis of Sentiment Classification using Optimized Kernel Extreme Learning Machine","authors":"D. Kavitha, M. Subbarao","doi":"10.1109/ISACC56298.2023.10083939","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083939","url":null,"abstract":"Experts’ attention has recently shifted to sentiment classification because of its applications in a variety of fields. In the past two decades, scientists have developed various multidisciplinary methodologies for sentiment analysis (SA) to enhance classification accuracy. Initial studies in sentiment analysis concentrated mostly on structured customer product and service reviews. However, academics have abruptly shifted their focus to unstructured data because of the availability of real-time messages via Twitter. Information considered to be more valuable is taken from tweets that express a point of view. There are a variety of programs, such as sentimentor, designed expressly for assessing the feelings of tweets. This paper provides a comprehensive overview of the several available SA methodologies, from the earliest to the most recent and accurate. Further the paper presents emotion detection in political tweets about an individual using various supervised machine learning (ML) approaches under different training rates. Kernel Extreme Learning Machine (KELM) by incorporating it with the Salp swarm optimization algorithm (SSA) is investigated under different training rates and with different data sets. Experimental results depicts that proposed KELM optimized by SSA attains improved performance compared to KNN, SVM, and Ensemble Classifiers.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125088806","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 Classification using Deep Learning Framework 使用深度学习框架的脑肿瘤分类

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083818

Anuksha Srivastava, A. Khare, A. Kushwaha

{"title":"Brain Tumor Classification using Deep Learning Framework","authors":"Anuksha Srivastava, A. Khare, A. Kushwaha","doi":"10.1109/ISACC56298.2023.10083818","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083818","url":null,"abstract":"Brain tumor classification plays a prominent rolein accurate identification of abnormal brain tissues and helps in clinical diagnosis of patient. This work presents a brain tumor classification approach based on deep learning framework. Deep learning-based approaches have been used in the work due to its self-learning capability and outperformance in classification problems. In this work, to study brain tumor classification, 2D MRI data are used. The proposed method consists of three stages: i) pre-processing, ii) design of a deep learning architecture for brain tumor classification, and iii) integration of conventional handcrafted features with deep learning features. A detailed study has been done by training the proposed architectures with raw MRI images, Local Binary Pattern (LBP) coded texture features, and Discrete Wavelet Transform (DWT) coefficients for brain tumor classification. SoftMax classifier has been used for classification purpose. To authenticate the proposed method, publicly available brain tumor dataset (Br35H) has been used. The accuracies achieved are 81.11% when the proposed architecture is trained with LBP coded texture feature, 94.11% when network is trained with DWT coefficient, and 94% when raw MRI image is used for training ResNet architecture respectively at epochs 50. Further, the effectiveness of the proposed method is demonstrated by comparing its results with other existing methods. The experimental results of the proposed method show the efficacy of the proposed method over the methods considered for comparison.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133252500","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

Hate Speech in Social Networks and Detection using Machine Learning Based Approaches 社交网络中的仇恨言论及其基于机器学习的检测方法

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084222

Chayan Paul

{"title":"Hate Speech in Social Networks and Detection using Machine Learning Based Approaches","authors":"Chayan Paul","doi":"10.1109/ISACC56298.2023.10084222","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084222","url":null,"abstract":"The use of social networking sites has increased considerably in last few years and as a result the user generated contents in the web also increased manifold. These data are mostly present in unstructured and quasi-structured formats. Many social media platforms are being affected due to the presence of hate speech. It is present in many forms such as verbal aggression and through photos which we know as memes and so on. This study considers twitter data for detecting hate speech on the internet. From the past few years, machine learning and natural language processing approaches are being used to detect hateful content on the web. This study aims to deal with the problem of hate speech detection in text data using machine learning algorithms. Feature selection for the dataset was performed before passing the dataset to the machine learning models. Different machine learning algorithms are implemented on an open-source twitter dataset. Performance of different algorithms are compared using standard performance measure metrics and presented in this paper. The experimental result shows artificial neural network outperforms the other algorithms considered in this study.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134433265","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

An efficient charging scheduling scheme to enhance the wireless rechargeable sensor networks' lifespan 一种提高无线可充电传感器网络使用寿命的高效充电调度方案

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084182

Sk Md Abidar Rahaman, M. Azharuddin

引用次数: 1

Advancements in Cyber Security and Information Systems in Healthcare from 2004 to 2022: A Bibliometric Analysis 2004年至2022年医疗保健领域网络安全和信息系统的进展:文献计量学分析

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10084049

S. V. Nair, S. Saha

{"title":"Advancements in Cyber Security and Information Systems in Healthcare from 2004 to 2022: A Bibliometric Analysis","authors":"S. V. Nair, S. Saha","doi":"10.1109/ISACC56298.2023.10084049","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10084049","url":null,"abstract":"The main goals of the multifaceted healthcare system were to prevent, identify, and treat illnesses or conditions that affect human health. As the usage of IT in healthcare increased, the complexities in managing the IT infrastructure also increase, emphasizing the need of robust cyber security systems. The study aims to emphasize the advancements made in cyber security and information systems in healthcare, based on bibliometric analysis. 5,487 document's metadata was obtained from Scopus and data was analyzed using Vos Viewer. Ranking of articles was done with average yearly citations of the publications. Bibliometric analysis was performed based on “bibliographic coupling of countries”, “co-occurrence of all keywords”, “author-based co-authorship”, and “term co-occurrence based on text data”. It was found that United States had the maximum publications (1337). “Department of Information Systems and Cyber Security, The University of Texas at San Antonio, United States” is the most influential organization with 159 publications. IEEE Access is the most preferred platform for publication related to cyber security and information systems in healthcare (231 publications). 167 publications have received more than 100 citations. Choo K. K.R. is the most influential author with 185 publications.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125277379","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

A Critical Study of Biometrics and Their Fusion 生物识别技术及其融合的关键研究

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083801

Mayank Shekhar, Ripon Patgiri, Amit Kumar Trivedi, P. Dhar

引用次数: 0

Automated Nuclei Analysis from Digital Histopathology 数字组织病理学的自动核分析

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083938

Bijoyeta Roy, Pratima Sarkar, Mousumi Gupta

{"title":"Automated Nuclei Analysis from Digital Histopathology","authors":"Bijoyeta Roy, Pratima Sarkar, Mousumi Gupta","doi":"10.1109/ISACC56298.2023.10083938","DOIUrl":"https://doi.org/10.1109/ISACC56298.2023.10083938","url":null,"abstract":"In Histopathology analysis, an abnormal nuclear shape can be a strong parameter to detect malignancy. Similarly, by visualizing the growing amount of nuclei implies disease status such as grading of cancer. In clinical diagnosis there is a link between texture of the nucleus and disease current status. In digital pathology the most crucial & important step for computer-aided diagnostics system is automated cell nuclei segmentation. Regular studies are going on digital pathology for automatic analyzing the nuclei cell images. However, providing accurate cell nucleus segmentation rate through automation is still poor. Scientists are rigorously working on the development of computerized segmentation algorithm to provide faster diagnosis. This paper proposed a novel thresholding based unsupervised technique which is applicable to detect nuclei contours. Proposed approach is applied to seven multi organ histological tissue images. Threshold is calculated by taking into consideration the first nonzero gray level pixel value followed by finding the pixel connectivity with 8 neighborhood concept. Furthermore, the various nuclei feature including mean intensity, diameter, perimeter, centroid is computed to get better insight to cell functionality providing a support in early diagnosis to diseases. When compare with traditional adaptive thresholding this approach achieved better performance rate by 48.31 %.","PeriodicalId":211409,"journal":{"name":"2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130523586","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

Surface Electromyography based Hand Gesture Signal Classification using 1D CNN 基于表面肌电图的1D CNN手势信号分类

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083548

K. S, J. Sahoo, S. Ari

引用次数: 0

Depressive and Non-depressive Tweets Classification using a Sequential Deep Learning Model 使用顺序深度学习模型的抑郁和非抑郁推文分类

2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) Pub Date : 2023-02-03 DOI: 10.1109/ISACC56298.2023.10083981

A. Agrawal, Sharmistha Dey, Gopal Chandra Jana

引用次数: 0