Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20194
N. Naik, Tjprc
{"title":"Detection and Classification of Brain Tumor Using Naïve Bayes and J48","authors":"N. Naik, Tjprc","doi":"10.24247/ijcseitrdec20194","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20194","url":null,"abstract":"","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115733167","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20213
Shawni Dutta et al., Shawni Dutta et al.,
The field of image processing gaining importance is not only for its rapid and continuous progress but also for accurate and advanced analysis. Mammography is the most popular imaging technique for the detection of breast cancer Anatomical structure of a lesion is obtained properly compared to other imaging modalities like CT( Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron-emission tomography). In this work, an algorithm has been developed for the detection of breast cancer. The proposed method has consisted of three steps: preprocessing, segmentation and feature extraction. After segmentation of cancerous region, it is characterized with statistical features using first-order histogram and Gray Level Co-occurrence Matrix (GLCM)). Based on these two types of feature extraction methods, normal and cancerous mammograms have been diagnosed.
{"title":"Advance Technique for Early Detection of Breast Cancer Using Textual Analysis from Digital Mammogram","authors":"Shawni Dutta et al., Shawni Dutta et al.,","doi":"10.24247/ijcseitrdec20213","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20213","url":null,"abstract":"The field of image processing gaining importance is not only for its rapid and continuous progress but also for accurate and advanced analysis. Mammography is the most popular imaging technique for the detection of breast cancer Anatomical structure of a lesion is obtained properly compared to other imaging modalities like CT( Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron-emission tomography). In this work, an algorithm has been developed for the detection of breast cancer. The proposed method has consisted of three steps: preprocessing, segmentation and feature extraction. After segmentation of cancerous region, it is characterized with statistical features using first-order histogram and Gray Level Co-occurrence Matrix (GLCM)). Based on these two types of feature extraction methods, normal and cancerous mammograms have been diagnosed.","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131769745","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrjun20204
Chetan J. Shingadiya, Tjprc
Software testing is one of the most important parts of the software development process. In software development, developers always rely on software testing to deal with bugs. The problem of software testing in software development is one of the most important and research areas. Here, test set optimization plays an important role in system performance. The genetic algorithm is one of the techniques, widely used for optimization based on problems inspired by nature. In this article, we demonstrate the genetic algorithm with tournament selection techniques. We, evaluate system performance based on a number of test inputs
{"title":"A Genetic Algorithm for Test Suite Optimization","authors":"Chetan J. Shingadiya, Tjprc","doi":"10.24247/ijcseitrjun20204","DOIUrl":"https://doi.org/10.24247/ijcseitrjun20204","url":null,"abstract":"Software testing is one of the most important parts of the software development process. In software development, developers always rely on software testing to deal with bugs. The problem of software testing in software development is one of the most important and research areas. Here, test set optimization plays an important role in system performance. The genetic algorithm is one of the techniques, widely used for optimization based on problems inspired by nature. In this article, we demonstrate the genetic algorithm with tournament selection techniques. We, evaluate system performance based on a number of test inputs","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124094711","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20209
Krishna Sai et al., Krishna Sai et al.,
{"title":"Employee Salary Prediction using Multi Model Machine Learning Techniques, A Comparative Analysis","authors":"Krishna Sai et al., Krishna Sai et al.,","doi":"10.24247/ijcseitrdec20209","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20209","url":null,"abstract":"","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116071971","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20216
Anjali Singhal Anjali Singhal, Tjprc
An approach followed to design and develop any software has an important role in determining the reliability and quality of the final Software product. So proper guidelines are required to develop a software. There are different models, that are being followed by different companies as per their requirements. Among all, Agile methodologies are most popular because of its flexibility and adaptability. But that is possible because this model involves continuous customer interaction. There are short development cycles. After which there is interaction between the stakeholders. If there is an issue or change, that is incorporated in the next cycle. The Covid-19 pandemic has a destructive effect on the socioeconomic state. This has resulted in the closure of many companies. The companies which change strategies and adapt to the current situation survived. Agility assists companies in making new changes and adapting their business in this pandemic. This paper outlines on how adhering to agile methodology assist software companies in developing software and also helped government, business organization and their supply chain in coping up with the Covid-19 pandemic.
{"title":"Adhering Agile Methodology in Covid-19","authors":"Anjali Singhal Anjali Singhal, Tjprc","doi":"10.24247/ijcseitrdec20216","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20216","url":null,"abstract":"An approach followed to design and develop any software has an important role in determining the reliability and quality of the final Software product. So proper guidelines are required to develop a software. There are different models, that are being followed by different companies as per their requirements. Among all, Agile methodologies are most popular because of its flexibility and adaptability. But that is possible because this model involves continuous customer interaction. There are short development cycles. After which there is interaction between the stakeholders. If there is an issue or change, that is incorporated in the next cycle. The Covid-19 pandemic has a destructive effect on the socioeconomic state. This has resulted in the closure of many companies. The companies which change strategies and adapt to the current situation survived. Agility assists companies in making new changes and adapting their business in this pandemic. This paper outlines on how adhering to agile methodology assist software companies in developing software and also helped government, business organization and their supply chain in coping up with the Covid-19 pandemic.","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116151135","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20212
Tharun P Tharun P
The previously trained on massive text corpora such as GPT-3 is powerful and has an open domain with more than 175 billion parameters. However, as our semantic search will make it possible to search with the keyword that will give you what it was searched for, it is still challenging for such models to train and get the accuracy level challenging model Coherently for prolonged passages of textual content, in particular at the same time as the models This focuses on the target area of the small figure. In the next few steps, the key formulas for domain precise content materials will become more and more complex. Wikipedia's semantic file search, to calculate the semantic relevance of language text, requires multiples of data set search. Which is, Important common sense and global knowledge on specific topics. By recommending Semantic Search Analysis (SSA), which is a fully specialized technique for representing text in the main superior domain obtained from Wikipedia. Using the previously trained strategy, we mainly construct the average value of the content of the text explicitly on the adaptive model from Wikipedia. Results display that our version outperforms different models.
{"title":"Semantic Search over Wikipedia Documents Meaning of Queries Based Pre-Trained Language Model","authors":"Tharun P Tharun P","doi":"10.24247/ijcseitrdec20212","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20212","url":null,"abstract":"The previously trained on massive text corpora such as GPT-3 is powerful and has an open domain with more than 175 billion parameters. However, as our semantic search will make it possible to search with the keyword that will give you what it was searched for, it is still challenging for such models to train and get the accuracy level challenging model Coherently for prolonged passages of textual content, in particular at the same time as the models This focuses on the target area of the small figure. In the next few steps, the key formulas for domain precise content materials will become more and more complex. Wikipedia's semantic file search, to calculate the semantic relevance of language text, requires multiples of data set search. Which is, Important common sense and global knowledge on specific topics. By recommending Semantic Search Analysis (SSA), which is a fully specialized technique for representing text in the main superior domain obtained from Wikipedia. Using the previously trained strategy, we mainly construct the average value of the content of the text explicitly on the adaptive model from Wikipedia. Results display that our version outperforms different models.","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121790633","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20214
Kambar Priyanka et al., Kambar Priyanka et al.,
{"title":"Nail Deformities Detection and Classification Using Image Processing Technique","authors":"Kambar Priyanka et al., Kambar Priyanka et al.,","doi":"10.24247/ijcseitrdec20214","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20214","url":null,"abstract":"","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622014","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}
Pub Date : 1900-01-01DOI: 10.24247/IJCSEITRDEC20196
N. Naik, Tjprc
{"title":"Marksheet Verification Using Blockchain","authors":"N. Naik, Tjprc","doi":"10.24247/IJCSEITRDEC20196","DOIUrl":"https://doi.org/10.24247/IJCSEITRDEC20196","url":null,"abstract":"","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124983711","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}
Pub Date : 1900-01-01DOI: 10.24247/IJCSEITRJUN20213
Drumil Joshi et al., Drumil Joshi et al.,
This paper aims to introduce a reliable forecasting model for the consumption of electricity using renewable sources (namely: offshore wind, onshore wind and solar power) in EU countries, based on live data from the ENTSOE transparency platform as its input. The primary use behind this data science and machine learning methodology, is to help judge the availability of renewable energy resources. Aforementioned software is put to work by inputting desired country and associated parameters. It learns by carefully observing past patterns and their seasonality to make accurate predictions for the future. The ML algorithms used in this process are linear regression, extra trees regression, random forest regression, support vector machine (SVM) and gradient boosting, and precision is substantiated by getting a minimal Symmetric Mean Absolute Error (SMAPE) of 1-2.
{"title":"An Efficient Supervised Machine Learning Model Approach for Forecasting of Renewable Energy to Tackle Climate Change","authors":"Drumil Joshi et al., Drumil Joshi et al.,","doi":"10.24247/IJCSEITRJUN20213","DOIUrl":"https://doi.org/10.24247/IJCSEITRJUN20213","url":null,"abstract":"This paper aims to introduce a reliable forecasting model for the consumption of electricity using renewable sources (namely: offshore wind, onshore wind and solar power) in EU countries, based on live data from the ENTSOE transparency platform as its input. The primary use behind this data science and machine learning methodology, is to help judge the availability of renewable energy resources. Aforementioned software is put to work by inputting desired country and associated parameters. It learns by carefully observing past patterns and their seasonality to make accurate predictions for the future. The ML algorithms used in this process are linear regression, extra trees regression, random forest regression, support vector machine (SVM) and gradient boosting, and precision is substantiated by getting a minimal Symmetric Mean Absolute Error (SMAPE) of 1-2.","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127756314","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}
Pub Date : 1900-01-01DOI: 10.24247/ijcseitrdec20192
Surinder Kumar, Tjprc
{"title":"Fault Tolerance in Defence C4I Systems","authors":"Surinder Kumar, Tjprc","doi":"10.24247/ijcseitrdec20192","DOIUrl":"https://doi.org/10.24247/ijcseitrdec20192","url":null,"abstract":"","PeriodicalId":185673,"journal":{"name":"International Journal of Computer Science Engineering and Information Technology Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124237196","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：