2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)最新文献_第4页

A new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve accuracy 一个新的框架，分类癌性和非癌性巴氏涂片图像使用滤波技术，以提高准确性

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844216

Madderi Sivalingam Saravanan, J. C. Antony, V. P. Kumar, M. Veeramanickam, MAKARAND UPADHYAYA

引用次数: 1

Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays 基于胸片的新型冠状病毒异常检测和定位的小尺度CNN-N模型

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844184

Jagadeesh Marusani, B. Sudha, Narayana Darapaneni

{"title":"Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays","authors":"Jagadeesh Marusani, B. Sudha, Narayana Darapaneni","doi":"10.1109/ICAITPR51569.2022.9844184","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844184","url":null,"abstract":"Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130899922","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

E-Health Security on Could Computing and its Challenges 基于可计算的电子医疗安全及其挑战

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844196

A. Vinnarasi, R. Dayana, P. Malarvezhi, K. Vadivukkarasi

{"title":"E-Health Security on Could Computing and its Challenges","authors":"A. Vinnarasi, R. Dayana, P. Malarvezhi, K. Vadivukkarasi","doi":"10.1109/ICAITPR51569.2022.9844196","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844196","url":null,"abstract":"Healthcare credentials digitized on Cloud has reformed the access of healthcare solutions widely; where patient’s medical history is recorded periodically on Cloud and can be accessed based on necessity legally. Cloud services and storage has become a gateway for many services globally with multiple storage providers providing storage facilities to access. This paper enumerates the benefits of Electronic Healthcare on cloud and tribulations based on security and privacy, where the concern is a substance, which itself holds a pack of catalogues such as Confidentiality, Integrity, Data Violation, Reliability, Network eavesdropping, Denial of service, Collusion, etc. The foreground of this paper is a survey with different problems and solution possibilities discussed based on cloud storage and communication on security and privacy issues faced and discussed on work papers which are originally published for Electronics Health Records (EHR) or Electronics Medical Records (EMR). The pros and cons of each approach and results are conferred in this literature survey along with the definitions of cloud computing, its types and existing technologies.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127067467","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

TEmoDec: Emotion Detection from Handwritten Text using Agglomerative Clustering 基于聚类的手写体文本情感检测

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844210

Samayan Bhattacharya, Asraful Islam, Sk Shahnawaz

{"title":"TEmoDec: Emotion Detection from Handwritten Text using Agglomerative Clustering","authors":"Samayan Bhattacharya, Asraful Islam, Sk Shahnawaz","doi":"10.1109/ICAITPR51569.2022.9844210","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844210","url":null,"abstract":"Handwriting analysis is the practice of understanding a person better by examining their handwriting. The traditional approach involves the examination of several parameters like Pen-Pressure, Slant, Baseline, Zone, Margin, and Size of the handwritten text, by an expert. These parameters are indicative of the mental state of the person and can be used to judge the honesty, stress, and depression levels of the person. The disadvantage of this practice is that it is time-consuming and accuracies vary according to the skills of the examiner. In this paper, we propose a novel method based on the Agglomerative Hierarchical Clustering technique, that is able to identify the emotional state of the person by looking at the image of the handwritten text. Thus we are able to achieve reliable accuracies without the need for large annotated datasets by using unsupervised learning. After preprocessing, the image pixels are clustered, based on a threshold value of intra-cluster distances. All pixels with distance, from the centroid of the cluster, lower than the threshold value belong to that cluster. Each cluster corresponds to one of the predefined emotions. We test our model to predict 5 emotions, namely, Anger, Sadness, Depression, Happiness and Excitement. However, the proposed method can be used for more emotions as necessary by changing the threshold distance value. We achieve an accuracy above 75% for each of these emotions. Our work may potentially be used in mental health diagnosis, in the hiring process by the industry as well as in criminal investigation.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050828","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

Facial Expression Recognition using Convolutional Neural Network 基于卷积神经网络的面部表情识别

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844221

J. Avanija, K. Madhavi, G. Sunitha, Sreenivasa Chakravarthi Sangapu, Srujan Raju

引用次数: 0

Lateral Aerodynamic Parameters Estimation using Neuro Artificial Bee Colony Fusion Algorithm (NABC) 基于神经人工蜂群融合算法(NABC)的横向气动参数估计

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844223

Prashant Kumar, S. Sonkar, A. K. Ghosh, Deepu Philip

引用次数: 0

COVID-19 Diagnosis with HRCT Images Using Deep Transfer Learning 基于深度迁移学习的HRCT图像诊断COVID-19

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844195

Manzoor Mohammad, B. Swapna

引用次数: 0

A comprehensive review on various optimization routing algorithms in VANET VANET中各种优化路由算法的综合综述

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844205

J. Sumadeep, K. Vadivukkarasi, R. Dayana, P. Malarvezhi

引用次数: 1

Comparison of three classifiers used in the detection of benign tumor and malignant melanoma skin diseases 三种分类器在皮肤良性肿瘤和恶性黑色素瘤检测中的比较

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844220

R. Sahoo, Abhyarthana Bisoyi, Aruna Tripathy

{"title":"Comparison of three classifiers used in the detection of benign tumor and malignant melanoma skin diseases","authors":"R. Sahoo, Abhyarthana Bisoyi, Aruna Tripathy","doi":"10.1109/ICAITPR51569.2022.9844220","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844220","url":null,"abstract":"It is an unknown fact that many skin diseases have similar type of shape, size and symptoms. Hence, it is a cumbersome task to recognize and classify these diseases by the doctors. So, for the correct identification of skin disorders, doctors need to check the patient’s history alongside certain laboratory testing and physical examinations. But all these processes are time consuming and also costlier for a common man. Hence, this paper discusses a MATLAB based software system introduced to reduce the complexity and thereby providing accurate results. This system includes image preprocessing, features extraction and classification for prediction of the type of skin disorders. Besides feature extraction, the paper mainly focusses on the classification based on three classifiers—SVM (Support vector machine), KNN (K- nearest neighborhood) and NB (Naïve Bias classifier)—and provides a comparative result based on various parameters. It can be concluded from the comparison tables that among the three classifiers, SVM provides the highest accuracy of 98.73% while KNN with 93.67and and NB with 84.81%. This classification helps a doctor to achieve the exactness of the type of skin disorder. In this system the patient needs to provide the image of the infected portion as input and the proposed system shall detect the disease.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123820387","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

Detection and Classification of Canine Hip Dysplasia According to FCI Grading System Using 3D CNN’s 基于3D CNN的FCI分级系统对犬髋关节发育不良的检测与分类

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844209

Sai Parichit Akula, Pratixith Akula, Nagendra Kamati

{"title":"Detection and Classification of Canine Hip Dysplasia According to FCI Grading System Using 3D CNN’s","authors":"Sai Parichit Akula, Pratixith Akula, Nagendra Kamati","doi":"10.1109/ICAITPR51569.2022.9844209","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844209","url":null,"abstract":"Dysplasia refers to abnormal growth or development that worsens with time. The current standard diagnostic techniques involves harsh radiation methods, ultrasound imaging of the hip, and metric extraction. This has been shown to be unreliable due to human error, in probe positioning, resulting in misdiagnosis, time-consuming, and tiresome labor. In comparison to a normal 2D CNN, MRI with 3D CNN has been offered as a more effective option since it can examine the whole set of 3D MR images as a single unit. In this paper, we developed a deep learning model that can classify canine hip dysplasia according to its severity using 3D sequences of hip joint magnetic resonance data. The severity of each hip was graded on a scale of A–E by the Federation Cynologique Internationale (FCI). We used the Danish Kennel Club dataset, which included 11,759 ventro-dorsal pelvic images (23 518 hip joint images), with X-ray and MRI images accessible for each hip joint. In addition, to assist breeders discover better and healthier parents from their stock and to prevent hip dysplasia in future generations, Another model was trained by reclassifying the samples into \"non-dysplastic\" (A+B) and \"dysplastic\" (C–E) groups. When compared to earlier models, our models attain an accuracy of 89.7% and 70.0% respectively, and outperform in terms of computing time and performance. This also shows that 3DCNN’s have a greater potential of improving diagnostic accuracy and may be employed as a clinical help in veterinary medicine for hip dysplasia than traditional X-ray approaches.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"17 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116693378","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