Computer Science & Engineering: An International Journal最新文献

{"title":"An Ancient Number Recognition using Freeman Chain Code with Deep Learning Approach","authors":"Aditi M Joshi, Sanjay G Patel","doi":"10.5121/cseij.2022.12102","DOIUrl":"https://doi.org/10.5121/cseij.2022.12102","url":null,"abstract":"Sanskrit character and number documents have a lot of errors. Correcting those errors using conventional spell-checking approaches breaks down due to the limited vocabulary. This is because of high inflexions of Sanskrit, where words are dynamically formed by Sandhi rules, Samasa rules, Taddhita affixes, etc. Therefore, correcting OCR documents require huge efforts. Here, we can present different machine learning approaches and various ways to improve features for ameliorating the error corrections in Sanskrit documents. Simulation of Sanskrit dictionary for synthesizing off-the-shelf dictionary can be done. Most of the proposed methods can also work for general Sanskrit word corrections and Hindi word corrections. Handwriting recognition in Indic scripts, like Devanagari, is very challenging due to the subtitles in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, a new handwritten word dataset will be released for Devanagari, IIIT-HW-Dev to alleviate some of these issues. This process is required for successful training of deep learning architecture, availability of huge amounts of training data is crucial, as any typical architecture contains millions of parameters. A new method for the classification of freeman chain code using four-connectivity and eight-connectivity events with deep learning approach is presented. Application of CNN LeNet-5 is found to be suitable to get results in this cases as the numbers are formed with curved lines In contrast with the existing FCC event data analysis techniques, sampled grey images of the existing events are not used, but image files of the three-phase PQ event data are analysed by taking the advantage of the success of the deep learning approach on imagefile-classification. Therefore, the novelty of the proposed approach is that image files of the voltage waveforms of the three phases of the power grid are classified. It is shown that the test data can be classified with 100% accuracy. The proposed work is believed to serve the needs of the future smart grid applications, which are fast and taking automatic countermeasures against potential PQ events.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133960304","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}

{"title":"Aspect Category Detection","authors":"R. Patel, Dhaval Bhoi, Dr. Amit Thakkar","doi":"10.5121/cseij.2022.12112","DOIUrl":"https://doi.org/10.5121/cseij.2022.12112","url":null,"abstract":"The major subtask of Sentiment analysis based on aspects (ABSA) is the category of aspectdetection (ACD). Due to the subjectivity inherent in categorizing, as well as the occurrence of overlapping classes, it is a difficult challenge to solve. Rule-based techniques, as well as other machine learning approaches, have been used to tackle ACD, and a majority of them are statistical behavior. We employed an association rulebased method in this article. We developed a mixed principle strategy that incorporates both association rule mining and semantics associations to address the statistical limitations of association rules. We employed the concept of word-embed for semantic linkages. The experiments were carried out using the SemEval dataset, which is a standardized set of data for categorizing features industry. We discovered how semantic connections could help to enhance classification accuracy by complementing statistical associations. The proposed method outperforms several statistical methods.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114503639","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}

{"title":"An Investigation into Techniques used for Fetal Health Classification","authors":"Megha Chaturvedi, Shikha Agrawal, S. Silakari","doi":"10.5121/cseij.2022.12105","DOIUrl":"https://doi.org/10.5121/cseij.2022.12105","url":null,"abstract":"The natural birth of a mentally and physically sound child is the yearning of all mothers. Still, perinatal mortality is a huge concern that needs immediate heed. Prenatal attention towards the well-being of the mother and the child plays a vital role in this regard. Early detection of any abnormalities can give further insights into the pregnancy and will provide more time to parents and doctors to prepare for these unnatural circumstances. Cardiotocography (CTG) is a technique used for monitoring fetal heart rate, it is widely used to ensure fetal well-being during pregnancies at high risk. Usage of machine-learning techniques can automate this task and can reduce the chances of diagnostic errors. Deep Learning also has powerful algorithms for learning complicated characteristics and higher-level semantics. The principal objective of this paper is to dissect the boundaries of different classification algorithms and contrast their prescient exactnesses to discover the best classifier for ordering fetal wellbeing","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"665 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122546101","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}