2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)最新文献_第5页

Diagnosis of Glaucoma from Retinal Fundus Image Using Deep Transfer Learning 基于深度迁移学习的青光眼眼底图像诊断

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844194

Md. Shafayat Bin Mostafa, Debasish Bal, Khaleda Akhter Sathi, Md.Azad Hossain

{"title":"Diagnosis of Glaucoma from Retinal Fundus Image Using Deep Transfer Learning","authors":"Md. Shafayat Bin Mostafa, Debasish Bal, Khaleda Akhter Sathi, Md.Azad Hossain","doi":"10.1109/ICAITPR51569.2022.9844194","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844194","url":null,"abstract":"The phenemeon of retinal condition brought about by expanding intraocular strain inside the eye is knows as Glaucoma. The existing diagnosis process of glaucoma through various careful retinal tests such as ophthalmoscopy, tonometry, perimetry, gonioscopy, and pachymetry are costly as well as time-consuming. Moreover, the diagnosis processes are fully dependent on the Ophthalmologists knowledge of test report analysis. To overcome these issues, this work aims to propose the utilization of a profound deep learning model such as ResNetl52 as well as VGG16 for the primary feature extraction qualities, specifically cup-to-circle proportions, plate obligation scale harm, and unrivaled nasal fleeting lower regions to diagnose glaucoma. Performance evaluation of the model is performed based on the accuracy matrix that shows 87% and 72% of accuracy for the ResNetl52 and VGG16 models respectively. The ResNetl52 model outperformed the VGG16 model because of the capability of extracting deep structures of the retinal image with the aid of skip connections from the previous consecutive convolutional layers.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"23 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":"115620477","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

Machine Learning approaches for Microscopic Image analysis and Microbial Object Detection(MOD) as a decision support system 显微图像分析和微生物目标检测(MOD)的机器学习方法作为决策支持系统

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844212

Rapti Chaudhuri, S. Deb

{"title":"Machine Learning approaches for Microscopic Image analysis and Microbial Object Detection(MOD) as a decision support system","authors":"Rapti Chaudhuri, S. Deb","doi":"10.1109/ICAITPR51569.2022.9844212","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844212","url":null,"abstract":"Microbes are tiny living organisms beyond the scope to be seen by the naked eye that are coexisting all around the biosphere along with other animals. Significant identification of the microbes from the elementary forms of cell structure to deadly pandemic causing elements are vital for health and hygiene support systems. Manual identification of such creatures consumes infinite amount of time resulting in frequent contamination. In this work, the context is primarily focused on automated microscopic image analysis on visual features by incorporating systematic pattern matching approaches for rapid identification of the microbes. The microbes are classified and recognized using the state of art of real time object detection strategy. Experimental result analysis is done with bacterial images to confirm the precision and accuracy of the utilized technique. Visual and graphical representation of the result obtained confers the validity and correctness of the concerned procedure. Further, the challenges and the difficulties faced during the microbe identification and the techniques to tackle have also been discussed subsequently. The proposed solution is proved to be quick and swift analyzing technique of pathogenic microbes in an organized way and has potential to be used as a source of field-level pathology decision support system.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"85 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":"122793398","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 Ranking based Language Model for Automatic Extractive Text Summarization 基于排序的自动抽取文本摘要语言模型

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844187

Pooja Gupta, S. Nigam, R. Singh

引用次数: 2

Classification of Brain Tumours from The MR Images Using Neural Network and Central Moments 基于神经网络和中心矩的MR图像脑肿瘤分类

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844188

K. Kumar, Asna Maheen, P. Devulapalli

{"title":"Classification of Brain Tumours from The MR Images Using Neural Network and Central Moments","authors":"K. Kumar, Asna Maheen, P. Devulapalli","doi":"10.1109/ICAITPR51569.2022.9844188","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844188","url":null,"abstract":"MRI can detect a wide range of brain conditions, including swelling, tumours, cysts, bleeding, structural abnormalities, infections, inflammatory conditions, and blood vessel problems. The main goal was to generate the distribution of every zone selected by moving the window of size 16 by 16 pixels on the MR picture of brain, resulting in 64 histograms and each collected histogram will be evaluated as a series and for this the central moments of order one, two, and three will be calculated. A multilayer perceptron performs the classification i.e., brain tumour classification using MR images. Database used was made up of the collection of MR pictures of the brain that have been mixed with various types of brain tumours which belonged to unique people. The 3 steps which comprise the proposed system are given namely, pre-processing in this step the size of MR brain pictures where normalized and converted, feature extraction where histogram’s zone that are obtained after sliding a 16 by 16-pixel window on image and the order one, two and three of central moments are calculated, as well as the classification step carried out with the help of a perceptron with multiple layers","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":"133997325","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

Optimized Test Coverage With Hybrid Particle Swarm Bee Colony And Firefly Cuckoo Search Algorithms In Model Based Software Testing 基于模型的软件测试中混合粒子群蜂群和萤火虫布谷鸟搜索算法优化测试覆盖率

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844208

Sirisha Potluri, J. Ravindra, Gouse Baig Mohammad, Guna Sekhar Sajja

{"title":"Optimized Test Coverage With Hybrid Particle Swarm Bee Colony And Firefly Cuckoo Search Algorithms In Model Based Software Testing","authors":"Sirisha Potluri, J. Ravindra, Gouse Baig Mohammad, Guna Sekhar Sajja","doi":"10.1109/ICAITPR51569.2022.9844208","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844208","url":null,"abstract":"Software testing process is a very vital process in the software industry to obtain high quality software. From last four decades, several techniques for software testing were recommended to guarantee high-quality software delivery by satisfying all the client requirements. Model-based testing is a great breakthrough in the field of software test automation and is based on the automatic test case generation through various models. Though we have several model based testing models available in the literature, in this research an optimized novel hybrid approach is proposed by using Particle swarm bee colony and Firefly cuckoo search algorithms. One of the best substantial advantages of the proposed model is that it optimizes time and cost involved in software testing process. By using this approach, we can ensure automatic test case creation and execution to make the overall testing process more efficient by reducing the errors. Another improvement of the proposed work is that it produces the required number of test cases to test and ensure the system that it works perfectly and never undergo undesirable performance. Obtaining required number of test cases is promoting the proposed model towards cost optimization in software testing.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"18 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":"114309053","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

Nano Sensors for Detection of Diabetes- A Review 用于糖尿病检测的纳米传感器——综述

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844204

Bhushan N Chopda, Yashwardhan Kaushik, Mayank Manchanda, Sarthak Jain, Princy Randhawa, Gouse Baug Mohammad

{"title":"Nano Sensors for Detection of Diabetes- A Review","authors":"Bhushan N Chopda, Yashwardhan Kaushik, Mayank Manchanda, Sarthak Jain, Princy Randhawa, Gouse Baug Mohammad","doi":"10.1109/ICAITPR51569.2022.9844204","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844204","url":null,"abstract":"Blood sugar is the primary source of energy generated by the human body through meals. Diabetes mellitus, or diabetes, is a condition in which the body’s insulin is ineffectively used or the pancreas is unable to create enough insulin. If a patient is being treated with insulin, continuous blood sugar monitoring is important because it can help patients make important decisions regarding food, physical activity, and medicines, as well as allow them to proactively manage their blood sugar levels to lower the risk of hypoglycaemia. Multiple methods/techniques are used to monitor blood glucose levels, and various developments have occurred over time. Traditional techniques of monitoring glucose and insulin levels rely on a painful and inconvenient procedure to keep track of these values. Several studies have primarily focused on the development of better sensing technology to address the challenges. The primary goal of this work is to give a high-level review of Nano sensors that are used to monitor blood sugar levels.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"42 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":"116548178","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

Recognition of Type of Skin Disease Using CNN 用CNN识别皮肤疾病类型

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844199

Medishetty Maniraju, Rudrangi Adithya, Gandu Srilekha

{"title":"Recognition of Type of Skin Disease Using CNN","authors":"Medishetty Maniraju, Rudrangi Adithya, Gandu Srilekha","doi":"10.1109/ICAITPR51569.2022.9844199","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844199","url":null,"abstract":"According to a study conducted by National Centre for Biotechnology Information (NIH), the cost associated with lost productivity and treatment among those who sought medical care for skin cancer exceeded 1.2 billion dollars and also more than 5.1 million people got serious effects like infections, hair loss, itches, burns of skin cancer. The study also concludes that most of those cases can be decremented by early detection of the cancer. The diseases like basal cell carcinoma, melanoma, pyogenic granulomas, are cancerous diseases and non-cancerous diseases like dermatofibroma, melanocytic nevi, have a variety of harmful impacts on the skin and continue to spread overtime, if treatment of skin disease at early stage is not done then it leads to complication in the body and including spreading of the infection from one another. To overcome this an early detection of skin disease plays a very major impact in today’s world. Now a days image processing has become widely used in developing a solution to this type of problems. Developing a high accurate methodology can be used to decrement the count of skin infections and their huge loses. This paper presents a seven types of skin disease detection using CNN. The dataset used is HAM10000.We obtain high accuracy by making the dataset ordered by adding duplication. The input image undergoes different layers such as maxpool2d, conv2d, batch normalization, flatten, dense, Softmax etc.., As this classification is among seven different types of skin diseases out of which four are cancerous and other three are non-cancerous, the output is one among these seven.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"37 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":"128378262","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

PHP web shell detection through static analysis of AST using LSTM based deep learning PHP web shell检测通过静态分析AST使用基于LSTM的深度学习

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844206

Bronjon Gogoi, Tasiruddin Ahmed, Ratnaboli Ghorai Dinda

{"title":"PHP web shell detection through static analysis of AST using LSTM based deep learning","authors":"Bronjon Gogoi, Tasiruddin Ahmed, Ratnaboli Ghorai Dinda","doi":"10.1109/ICAITPR51569.2022.9844206","DOIUrl":"https://doi.org/10.1109/ICAITPR51569.2022.9844206","url":null,"abstract":"Web shells are used by attackers to maintain persistent access on a compromised web server. Attackers exploit commonly occurring vulnerabilities like SQL Injection, cross site scripting and uploads a web shell that can be used to execute commands or perform a host of other functions. Web shells are a post-exploitation tactic that allows an attacker to remotely access and possibly control an internet-facing server. A web shell may remain hidden and the attacker can silently use the web shell to maintain remote access to the web server. Common methods of detecting web shells include looking for common strings in PHP source files, analyzing logs etc. But such methods have high false positives as they consider any script with a particular string or a function to be a web shell without taking into account other features of a web shell. In this paper, a machine learning based approach is proposed for the detection of web shells written in PHP language. The proposed approach analyses the function call and the use of super global variables commonly used in PHP web shells using a deep learning technique. The proposed approach has the advantage that it has low false positives and can detect web shells with an accuracy of 0.97.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"85 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":"128570541","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

Literature Survey On Video Surveillance Crime Activity Recognition 视频监控犯罪活动识别的文献综述

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) Pub Date : 2022-03-10 DOI: 10.1109/ICAITPR51569.2022.9844189

Kishore Kumar Kamarajugadda, H. Reddy

引用次数: 0