2021 Sixth International Conference on Image Information Processing (ICIIP)最新文献_第2页

Prediction of Heart Disease using Machine Learning Techniques 使用机器学习技术预测心脏病

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702625

H. Singh, Tushar Gupta, J. Sidhu

{"title":"Prediction of Heart Disease using Machine Learning Techniques","authors":"H. Singh, Tushar Gupta, J. Sidhu","doi":"10.1109/ICIIP53038.2021.9702625","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702625","url":null,"abstract":"Heart attacks and strokes account for 85 percent of these fatalities. Unhealthy food, lack of physical exercise, cigarette smoking, and excessive alcohol use are all major behavioral risk factors for CVDs. These variables can lead to high blood pressure, high blood glucose, high blood cholesterol, and obesity. It is critical to identify cardiac illness as soon as possible, as well as swiftly and correctly as possible. Complex medical data is analyzed by various data mining and machine learning techniques in literature. The findings of the in-depth examination of these research articles are extremely convincing and accurate, but the future scope of these papers reflects the need for more significant characteristics and abundant standardized data, as well as the employment of different algorithms to achieve better accuracy rates. This research paper compares Random Forest algorithm with nearest neighbor (KNN) and Naïve Bayes on standard datasets from Cleveland database and Statlog Heart Disease of University of California Irvine (UCI) repository. The major goal of the research study is to get meaningful outcomes. With only 13 characteristics, we were able to get some extremely encouraging outcomes. The results validate Random Forest Classifier with accuracy of 93.02 %, significantly outperformed Naive Bayes and KNN which have accuracy of 83.72% and 90.69% respectively.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067793","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

Analyzing and minimizing the effects of Vector-borne diseases using machine and deep learning techniques : A systematic review 使用机器和深度学习技术分析和减少媒介传播疾病的影响:系统综述

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702662

Inderpreet Kaur, A. Sandhu, Yogesh Kumar

{"title":"Analyzing and minimizing the effects of Vector-borne diseases using machine and deep learning techniques : A systematic review","authors":"Inderpreet Kaur, A. Sandhu, Yogesh Kumar","doi":"10.1109/ICIIP53038.2021.9702662","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702662","url":null,"abstract":"Among the numerous threats facing our world, Vector-borne illnesses pose the greatest threat. Although arboviruses have a long history of infecting humans, they have recently become more widespread and are affecting larger populations. This is due to a number of reasons, including increased air travel and uncontrollable mosquito vector populations. To halt the spread of fatal infectious diseases epidemics, machine learning and neural networks may be employed. Numerous studies omitted discussing the algorithms, data, and performance measures used in applications for predicting and detecting deadly infectious illnesses. To counteract the development of deadly disease epidemics, this article summarizes studies on two major methods (prediction and detection). This research will examine the current advances, difficulties, and future possibilities for utilizing machine and deep learning to identify and forecast fatal disease outbreaks in order to reduce the risk of spreading illness. This study examines previous studies, methodologies, datasets, variables, and performance metrics.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116948899","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: 4

Fog Load Balancing Broker (FLBB) 雾负载平衡代理(FLBB)

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702669

Mandeep Kaur, Rajinder Sandhu, R. Mohana

{"title":"Fog Load Balancing Broker (FLBB)","authors":"Mandeep Kaur, Rajinder Sandhu, R. Mohana","doi":"10.1109/ICIIP53038.2021.9702669","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702669","url":null,"abstract":"For efficient and timely execution of an IoT job allocation of an appropriate set of resources throughout its life span is significant. Initial allocation of resources is done through job scheduling techniques and for managing the resources during the execution load balancing techniques are implemented. Load Balancing in distributed systems is as important as is Job Scheduling. Modern systems are technically capable to place job requests on the most appropriate set of resources at the beginning of the execution process. But in distributed environments resources behave dynamically and the status of busy, available, or free resources keeps on changing very frequently. In such conditions single time allocation of resources to a job request, till the end of execution cannot be sufficient. For efficient utilization of available resources, timely execution, and efficient delivery of response resource allocations must be revised during the life span of a job request. This paper proposes a load balancing solution that takes care of the changing states of resources in the fog environments and relocates the job request from one environment to another wherever is found beneficial. The proposed framework performs its task in two steps: first checks if the relocation is feasible or not and second to select a job for relocation and shift it to some other environment. This framework is specifically designed for fog environments where load balancing is a pivot point for effective and efficient resource utilization, bandwidth and to achieve the desired quality of service (QoS).","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117127875","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

Importance of Histopathology Images in disease detection and Cancer Survival Prediction Analysis 组织病理学图像在疾病检测和癌症生存预测分析中的重要性

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702635

S. Varanasi, K. Malathi

{"title":"Importance of Histopathology Images in disease detection and Cancer Survival Prediction Analysis","authors":"S. Varanasi, K. Malathi","doi":"10.1109/ICIIP53038.2021.9702635","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702635","url":null,"abstract":"Illness has been portrayed as a heterogeneous polluting such as a huge stage of subtypes. The early preference and doubt for a damage kind have end up a want in contamination research, as it is able to empower the going with the clinical courting of sufferers. The significance of depicting pollutants sufferers into high or all-round secure social affairs hosts pushed one-of-a-kind appraisal gatherings, from the biomedical and the bioinformatics subject, to have a look at using AI and ML technique. thinking about everything, these methods were used as an association to reveal the new flip of occasions and remedy of peril inflicting situations. moreover, the prerequisite of ML gadgets to look key highlights from complicated datasets uncovers their importance.We constructed up a Deep studying layout to count on illness specific excitement throughout 10 ruinous improvement kinds from the most cancers Genome Atlas (TCGA). We applied a hopelessly orchestrated framework without pixel-degree explanations and endeavored three irrefutable regular fine disturbance limits.Our assessment indicates the ability for this manner to cope with oversee direct give head prognostic information in distinct peril sorts, and even inner express pathologic tiers. anyways, given the normally unnoticeable number of cases and saw scientific activities for a essential learning undertaking of this type, we noticed absolute sureness levels for model execution, as a result inclusive of that destiny work will profit through more obvious datasets gathered for the reasons for fidelity acting.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122084510","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

Ancient Sanskrit Line-level OCR using OpenNMT Architecture 使用OpenNMT架构的古梵文行级OCR

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702666

Ronak Shah, M. Gupta, Ajai Kumar

{"title":"Ancient Sanskrit Line-level OCR using OpenNMT Architecture","authors":"Ronak Shah, M. Gupta, Ajai Kumar","doi":"10.1109/ICIIP53038.2021.9702666","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702666","url":null,"abstract":"There have been several Optical Character Recognition (OCR) related works happened for Indian languages like Hindi, Marathi, Bangla, etc. But there is very little OCR-related work done for the Sanskrit language of Devanagari script. Sanskrit is a very complex language. The large word length and old degraded documents add more challenges to Sanskrit OCR research. Due to these challenges, the word accuracy of available OCR systems is not very high for such documents. Most of the work happened to recognize Sanskrit character recognition only. There is only one attempt to recognize the whole Sanskrit line for 10 fonts.This paper shows the study of different hyperparameters of OpenNMT architecture for Sanskrit OCR of synthetically generated color line images. A neural encoder-decoder model with attention is presented to converting line images into editable text. An attention-based approach can tackle this problem in a better way in comparison to other neural techniques using CTCbased models. The main aim of this paper is to give a detailed analysis of data preparation and various hyperparameters (like the number of LSTM layers, LSTM direction, size of character embedding vector, batch size, number of iteration, and hidden unit size) of encoder-decoder in OpenNMT, and accuracy of various combinations. This paper also concludes the best accuracy model for Sanskrit OCR using OpenNMT. The text recognition performance of the proposed method on the test set is achieved 99.44%. Our major contribution is to show text recognization of degraded line images with a variety of fonts using OpenNMT architecture. Our contribution helps the researcher community in deciding hyperparameters of encoder-decoder architecture for Sanskrit language OCR.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984663","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

An Optimized Convolution Neural Network Framework for Facial Expression Recognition 一种优化的卷积神经网络框架用于面部表情识别

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702639

Sakshi Indolia, S. Nigam, R. Singh

引用次数: 1

A Novel Technique to Classify Face Mask for Human Safety 一种新的人体安全口罩分类技术

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702607

P. Nagaraj, Gunta Sherly Phebe, Anupam Singh

{"title":"A Novel Technique to Classify Face Mask for Human Safety","authors":"P. Nagaraj, Gunta Sherly Phebe, Anupam Singh","doi":"10.1109/ICIIP53038.2021.9702607","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702607","url":null,"abstract":"Computer vision learning is a major area of focus because to the growing prevalence of the globally epidemic COVID-19, which will benefit healthcare management by increasing wellness in the general population. During the event, recognizing little things is a really troublesome errand of PC vision, as it incorporates getting arrangement and finding things underneath of pictures. Instead of rivals, the most impressive feature was being able to tell whether something is a face or a veil. Regardless, those that spread the disease benefit from the YOLOv3 advancements. In respect to GPU performance, the implementation of YOLOv3, which involves face veil identification, has a good performance. Though it is light on memory and appropriate with the current trend. For our face-cover photo, we got the same number of people who wear veils and who don’t. Constant video information ended up as part of the assessment since it concluded over concerns including privacy, location, and permission. The findings of the trials indicate that in preparation for 4,000 children, typical misfortune levels are 0.0730. After the new mAP (My Autonomous Programming) scores from 4,000 ages have been reported: they have a rating of 0.96. This technique of representation accomplished facial cover recognition with a yield of 96% identification accuracy.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818362","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: 3

A Review of Articles Representing Renogram Processing Techniques 再现图处理技术的文章综述

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702598

Pradnya N. Gokhale, B. R. Patil

{"title":"A Review of Articles Representing Renogram Processing Techniques","authors":"Pradnya N. Gokhale, B. R. Patil","doi":"10.1109/ICIIP53038.2021.9702598","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702598","url":null,"abstract":"The renography represents time activity process detected when one measures the activity in the kidneys after the dose injection of radiolabeled radio tracer(e.g.99mTc-DTPA,99mTc-MAG3). Interpretation of this renal scan helps to diagnose whether the drainage function from the kidney is normal or abnormal. This renal tracer’s data is processed by mathematical models and data processing techniques like Rutland-Patlak and deconvolution methods to produce renograph. This research study is carried out to review previously published research articles incorporating various methods, their applications and image processing algorithms as well as techniques that were applied to process renal radiotracer’s transit time data. This review includes various types, advantages, gaps and possible scopes for existing renogram data processing techniques. After analysis process of 142 articles it is found that, maximum of the articles are associated with renal scan’s processing methods that are limited to renal patient’s related disease categories and having absence of quantifiable measurement and study of parenchymal radio tracer’s transit time counted from renal cortex to renal pelvis path while limited numbers of articles are purely related to applied algorithms for detecting obstruction level qualitatively.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121609435","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

Exponentially Biased Discriminant Analysis Based Classification of Covid 19 Chest Images Using Generalized Regression Neural Network 基于指数偏倚判别分析的广义回归神经网络Covid - 19胸部图像分类

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702585

G. K, H. V

引用次数: 0

Shape Feature Based Multi-class Classification Approach towards Odia Characters employing Extreme Learning Machine 基于形状特征的极限学习机Odia字符多类分类方法

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702563

Sradhanjali Nayak, Pradyut Kumar Biswal, S. Pradhan, Om Prakash Jena

{"title":"Shape Feature Based Multi-class Classification Approach towards Odia Characters employing Extreme Learning Machine","authors":"Sradhanjali Nayak, Pradyut Kumar Biswal, S. Pradhan, Om Prakash Jena","doi":"10.1109/ICIIP53038.2021.9702563","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702563","url":null,"abstract":"Character recognition of Odia alphabets using computer-aided techniques has become a challenging research issue due to its complexity. Odia is recognised as one of the classical languages. Though various image processing methods have been used for classification or Odia character recognition but still there is scope for improvement. The multi-class classification demands the implementation of an elevated constructive learning algorithm. In this paper, we have proposed a conjunctive approach of shape-based feature extraction and Extreme Learning Machine (ELM) to classify the Odia alphabets. The proposed method is implemented over 1500 Odia alphabet images comprising of 52 classes. ELM brings an integrated learning domain with extensive feature transformation which will act as a catalyst for effective fulfillment of classification purposes in the multi class domain. ELM based technique is tested for different activation functions and the output result shows the effectiveness of ELM classifier over traditional Naive Bayes and support vector machine (SVM) classifier. The ELM based technique gives more promising results in comparison with the above two classifiers for the multi class handwritten Odia alphabet classification.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127605137","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