Journal of Information Systems and Telecommunication最新文献_第2页

An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier 基于PCA和SVM分类器的手写体卡纳达语数字识别方法

Journal of Information Systems and Telecommunication Pub Date : 2021-01-01 DOI: 10.52547/jist.9.35.169

R. G, Prasanna G B, Santosh V Bhat, Chandrashekara Naik, C. H N

{"title":"An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM\u0000 Classifier","authors":"R. G, Prasanna G B, Santosh V Bhat, Chandrashekara Naik, C. H N","doi":"10.52547/jist.9.35.169","DOIUrl":"https://doi.org/10.52547/jist.9.35.169","url":null,"abstract":"Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70688694","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

Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT) 模式识别技术在心脏再同步化治疗(CRT)中的应用评价

Journal of Information Systems and Telecommunication Pub Date : 2020-07-22 DOI: 10.29252/JIST.8.31.197

M. Nejadeh, P. Bayat, J. Kheirkhah, H. Moladoust

{"title":"Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)","authors":"M. Nejadeh, P. Bayat, J. Kheirkhah, H. Moladoust","doi":"10.29252/JIST.8.31.197","DOIUrl":"https://doi.org/10.29252/JIST.8.31.197","url":null,"abstract":"Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables.","PeriodicalId":37681,"journal":{"name":"Journal of Information Systems and Telecommunication","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44097340","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

Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm 基于粒子群优化和引力搜索算法的萤火虫算法改进

Journal of Information Systems and Telecommunication Pub Date : 1900-01-01 DOI: 10.52547/jist.9.34.123

M. Tourani

引用次数: 0

Optimal Clustering-based Routing Protocol Using Self-Adaptive Multi-Objective TLBO For Wireless Sensor Network 基于自适应多目标TLBO的无线传感器网络最优聚类路由协议

Journal of Information Systems and Telecommunication Pub Date : 1900-01-01 DOI: 10.52547/jist.9.34.113

Ali Sedighimanesh, H. Zandhessami, M. Alborzi, Mohammadsadegh Khayyatian

引用次数: 0