2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)最新文献_第4页

Deep learning Based Patient-Friendly Clinical Expert Recommendation Framework 基于深度学习的患者友好型临床专家推荐框架

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754157

Akhilesh Kumar, Sarfraz Khan, Rajinder Singh Sodhi, I. Khan, Sumit Kumar, Ashish Tamrakar

{"title":"Deep learning Based Patient-Friendly Clinical Expert Recommendation Framework","authors":"Akhilesh Kumar, Sarfraz Khan, Rajinder Singh Sodhi, I. Khan, Sumit Kumar, Ashish Tamrakar","doi":"10.1109/iciptm54933.2022.9754157","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754157","url":null,"abstract":"In recent years, with the popularization of the Internet and the development of technologies such as big data analysis, people's demand for mobile medical services has become more and more urgent, which is manifested in determining their diseases based on symptoms and selecting hospitals with better service quality according to the illnesses and doctors. An inquiry recommendation system is designed and implemented based on knowledge graphs and deep learning technology to solve the above problems. Based on the open medical data on the Internet, a “disease-symptom” knowledge map is constructed to help users self-examine according to symptoms. The knowledge map embedding model trains the embedded vector representation of entities in the knowledge map. The most similar is selected according to the Euclidean distance similarity of the vector. The disease entity enriches recommendation options, and the two are combined to achieve disease diagnosis services. At the same time, based on social media comment data, combined with the existing medical service quality evaluation indicators, the deep learning analysis method is used to automatically give a multi-dimensional score of the doctor's service quality and provide users with the doctor and hospital recommendation services. Finally, by constructing test sets and designing questionnaires, it is verified that the accuracy rates of disease diagnosis service and doctor-hospital recommendation service are 74.00% and 90.91 %, respectively.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"18 1","pages":"736-741"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79969623","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

Fraud App Detection of Google Play Store Apps Using Decision Tree 基于决策树的Google Play应用欺诈检测

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754207

K. Joshi, S. Kumar, Jyoti Rawat, Ansita Kumari, Aayush Gupta, Nikhil Sharma

引用次数: 1

An Enhanced Face Anti-Spoofing Model using Color Texture and Corner Feature based Liveness Detection 一种基于颜色纹理和角点特征的增强人脸抗欺骗模型

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754068

N. Nanthini, N. Puviarasan, P. Aruna

{"title":"An Enhanced Face Anti-Spoofing Model using Color Texture and Corner Feature based Liveness Detection","authors":"N. Nanthini, N. Puviarasan, P. Aruna","doi":"10.1109/iciptm54933.2022.9754068","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754068","url":null,"abstract":"In recent years, Biometric security systems have extended their uses. The systems are able to identify humans by analyzing their behavioural characteristics. Face recognition is the most popular biometric techniques, which widely used nowadays. They are treated as a suitable replacement for PINs and passwords for regular users. It is very easy to use a photo imposter to fake face recognition algorithm. To ensure the presence of real human face to a photograph or 2D masks, an enhanced face anti-spoofing model is proposed using Color Texture and Corner Feature based Liveness Detection (CTCF_LD). From the input video, the frames are extracted and cropped for the specific facial landmark points. The texture of the 2D masks and real face is analyzed by changing its colorspace. Then, the corner points are detected using various corner detection algorithms. Based on the corner points, the fake face is differentiated from the real face using a threshold value. Empirical study shows that the proposed CTCF_LD face anti-spoofing model with HSV_FCD algorithm gives better accuracy of 88%.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"184 1","pages":"63-68"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83039198","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

Methods of Congestion Control in Wired Networks with Reinforcement Learning – A Review 基于强化学习的有线网络拥塞控制方法综述

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9753983

Mettu Jhansi Lakshmi, Mahesh BabuArrama

引用次数: 0

Analysis of Network Technologies and Cyber security Assessment for Enhancing Machine Learning, Grid Computing and Cyber-Physical Connectivity Internetwork Effectiveness 提高机器学习、网格计算和网络物理连接互联网络有效性的网络技术分析与网络安全评估

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9753885

B. Dhanalakshmi, Chaitanya Singh, S. Surya, Atharav Hedage, Shikha Kuchhal, Korakod Tongkachok

{"title":"Analysis of Network Technologies and Cyber security Assessment for Enhancing Machine Learning, Grid Computing and Cyber-Physical Connectivity Internetwork Effectiveness","authors":"B. Dhanalakshmi, Chaitanya Singh, S. Surya, Atharav Hedage, Shikha Kuchhal, Korakod Tongkachok","doi":"10.1109/iciptm54933.2022.9753885","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9753885","url":null,"abstract":"The fast improvement of the Internet has incredibly worked with individuals spreading information worldwide, prompting the Internet turning into a pivotal apparatus in many fields like science, trade, schooling, etc. With the Internet common in such countless parts of day to day existence, network security has become basic and requests consistent consideration. For this situation, an IDS's motivation is to recognize between real organization associations and possibly hurtful ones. The recommended method in this examination centers around an interruption identification and avoidance system that comprises of Cloudlet Controller (CC), Trust Authority (TA), and Virtual Machine Manager to recognize questionable practices in a cloud climate. Cloud clients are migrated to different regions in the climate, as indicated by our proposed approach. Whenever information bundles from different clients are gathered, network traffic happens. To resolve this issue, we assembled the Cloudlet Controller, which gets bundles from different clients through switch. CC gets parcels until they arrive at their edge level; if the cloudlet is over-burden, the bundles are shipped off inactive cloudlets. In this philosophy, network traffic is decreased, and it is significantly helpful in identifying interruptions in a straightforward way. In this review, a cross breed arrangement based interruption location and counteraction framework on the cloud is worked to distinguish gatecrasher exercises in the framework. A cross breed characterization based interruption location framework is created with a few cloudlets, a cloudlet regulator, a confided in power, and a virtual machine supervisor to distinguish interloper assaults in the framework, as well as a deterrent instrument to safeguard bundles from gatecrashers.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"148 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89116609","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

ARIA The Bot 机器人艾瑞亚

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9753961

Shubham Singh, Dilpreet Kaur Arora, Iram Nabi Dar, Abdul Moghni, Satyam Kumar, Ajit Kumar

{"title":"ARIA The Bot","authors":"Shubham Singh, Dilpreet Kaur Arora, Iram Nabi Dar, Abdul Moghni, Satyam Kumar, Ajit Kumar","doi":"10.1109/iciptm54933.2022.9753961","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9753961","url":null,"abstract":"Aria The bot is a windows personal voice assistant software which performed various task which is given by the user in the form of the voice command this concept is taken by the movie named “IRON MAN” character Jarvis which perform all the task assigned by Tony Stark. In this project we are going to make a windows voice assistant which is going perform all the task such as opening of file, perform searching on the web, getting result from the Wikipedia, reading pdf, opening of application and suggesting some jokes to you. By taking input by voice we are going to convert voice into text and according to dictionary which is present in the database we are going to perform task which is assign to the keyword, we are importing so much of library such as “os” for performing operating system tasks. “speech recognition” for to analyzing voice command, “pyttsx3” for converting voice command in to the text string, “Time” for fetching the current time and date, “web browser” for performing web based task on default web browser, we also add activation command to run the aria the bot which is used as state for this project.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"114 1","pages":"167-174"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90091344","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 novel Genetic Algorithm based Encryption Technique for Securing Data on Fog Network Using DNA Cryptography 一种基于遗传算法的基于DNA加密的雾网络数据保护技术

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754031

D. Garg, K. Bhatia, Sonali Gupta

{"title":"A novel Genetic Algorithm based Encryption Technique for Securing Data on Fog Network Using DNA Cryptography","authors":"D. Garg, K. Bhatia, Sonali Gupta","doi":"10.1109/iciptm54933.2022.9754031","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754031","url":null,"abstract":"The data generated by the intelligent systems is enormous, hence storing and analyzing it over the cloud environment is a wise decision. However, most state of the art applications demands minimal network latency in the network so that the response can reach the user within fraction of seconds. To meet this requirement, fog computing came into existence, where computing and analysis of data is carried out at the fog nodes instead at the cloud. In such a scenario, security is of utmost concern since each fog node and each IoT device generating the data is vulnerable to attack. Industries such as healthcare in particular, need to practice security measures because it contains Patient's Identifiable Information (PII). Encryption is one of the most effective techniques to provide security to data. Various encryption techniques are available but they all suffer from some limitations. In this paper, a new encryption technique is proposed, which is based on genetic science and works in two phases. In the first phase, the plaintext is converted to a complex cipher text by making use of a complicated key. The key is randomly selected from the DNA population and is made further complex by using logical operators. The cipher text obtained in the first phase is made more impenetrable in the second phase, by using genetic science principles of crossover and mutation. The simulation and results of the proposed technique indicate that it provides more security to the data as compared to the existing encryption techniques.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"1 1","pages":"362-368"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82904141","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: 5

Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image 递归CNN模型检测x射线安全图像中的异常检测

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754033

R. S. Kumar, S. A, A. Balaji, G. Singh, Ashok Kumar, Manikandaprabu P

{"title":"Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image","authors":"R. S. Kumar, S. A, A. Balaji, G. Singh, Ashok Kumar, Manikandaprabu P","doi":"10.1109/iciptm54933.2022.9754033","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754033","url":null,"abstract":"To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a multi-channel region proposal network (MCRPN). Multi-layer feature extraction is achieved using the complementarily of distinct levels of convolution features in visual semantics, and the richer semantic components of VGG16 high-level layers and the shallower edge features of low-level layers are fused; To construct a multi-scale contraband detection network, the multi-scale candidate target regions are mapped to the corresponding feature maps; dilated convolutions are introduced into the multi-channel, and a multi-branch dilated convolutions module (DCM) is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm achieves an average detection accuracy of 84.69 percent and a test performance improvement of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm's recognition accuracy has been increased to a considerable level.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"1 1","pages":"742-747"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91317748","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

Novel lung cancer detection using ANN classifier in comparison with Decision Tree to measure the Accuracy, Sensitivity, Specificity and Precision 用人工神经网络分类器与决策树进行肺癌检测的准确性、敏感性、特异性和精密度的比较

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754184

D. Preethi, K. Ganapathy

{"title":"Novel lung cancer detection using ANN classifier in comparison with Decision Tree to measure the Accuracy, Sensitivity, Specificity and Precision","authors":"D. Preethi, K. Ganapathy","doi":"10.1109/iciptm54933.2022.9754184","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754184","url":null,"abstract":"The aim of this work is to predict the performance of the Artificial Neural Network algorithm for novel lung cancer detection. A total of 1339 samples are collected from two lung cancer datasets found in Kaggle. The G power for samples is calculated from clincalc which contains two different groups from which group 1 is taken as ($mathrm{n}1=670$) and for group 2 ($mathrm{n}2= 670$), alpha (0.05), power (80%) and enrollment ratio. The collected samples are divided into training dataset $(mathrm{n}=937 [75%])$ and test dataset $(mathrm{n}=402 [25%])$. Accuracy, sensitivity, specificity and precision score values are calculated for evaluating the performance of the Artificial Neural Network algorithm. By comparing these two algorithms Artificial Neural Network had given better accuracy, specificity, sensitivity and precision of 97.95%, 96.55%, 98.55% and 98.55% than Decision Tree of 61.22%, 40.90%, 67.10% and 71.68%. By using the SPSS tool, the Significance value is calculated as 0.02. From this proposed work it is observed that the Artificial Neural Network (ANN) had given better accuracy than the Decision Tree algorithm.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"14 1","pages":"528-534"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90765881","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

Cancer Prediction using Machine Learning 利用机器学习进行癌症预测

2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) Pub Date : 2022-02-23 DOI: 10.1109/iciptm54933.2022.9754059

G. Sruthi, Chokkakula Likitha Ram, Malegam Koushik Sai, Bhanu Pratap Singh, Nikhil Majhotra, Neha Sharma

{"title":"Cancer Prediction using Machine Learning","authors":"G. Sruthi, Chokkakula Likitha Ram, Malegam Koushik Sai, Bhanu Pratap Singh, Nikhil Majhotra, Neha Sharma","doi":"10.1109/iciptm54933.2022.9754059","DOIUrl":"https://doi.org/10.1109/iciptm54933.2022.9754059","url":null,"abstract":"Machine learning is increasingly being employed in cancer detection and diagnosis. Cancer prediction will become quite easy in the future and we can predict it without the need of going to the hospitals. As we can see many technologies are being used and tested in the medical field. So, by this we can say that this will make us easier in the future to detect cancer. We are testing which algorithm will give us good result among CART, SVM AND KNN. We are making a cancer prediction using machine learning, in which we are including three types of cancer they are breast cancer, lungs cancer and prostate cancer. In breast cancer, we are using SVM algorithm and for lung and prostate we are using Random forest algorithm. We are going to give different attributes for three cancer system where the user has to enter data to get result. For breast cancer we are considering attributes like clump thickness, uniform cell size, uniform cell shape etc. and the prediction result will be whether the cancer is malignant or benign. For lung cancer, we are considering smoking, yellow fingers, anxiety, peer pressure etc. In prostate cancer, we are considering are radius, texture, perimeter, area etc. and the result for both cancer is likelihood of being affected by the cancer.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"7 1","pages":"217-221"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78849073","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