Pub Date : 2024-02-14DOI: 10.54209/infosains.v14i01.4080
P. Simangunsong, Paska Marto Hasugian
This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.
{"title":"Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method","authors":"P. Simangunsong, Paska Marto Hasugian","doi":"10.54209/infosains.v14i01.4080","DOIUrl":"https://doi.org/10.54209/infosains.v14i01.4080","url":null,"abstract":"This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.","PeriodicalId":508149,"journal":{"name":"Jurnal Info Sains : Informatika dan Sains","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837934","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 : 2024-02-14DOI: 10.54209/infosains.v14i01.4080
P. Simangunsong, Paska Marto Hasugian
This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.
{"title":"Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method","authors":"P. Simangunsong, Paska Marto Hasugian","doi":"10.54209/infosains.v14i01.4080","DOIUrl":"https://doi.org/10.54209/infosains.v14i01.4080","url":null,"abstract":"This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.","PeriodicalId":508149,"journal":{"name":"Jurnal Info Sains : Informatika dan Sains","volume":"27 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778125","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 : 2023-12-11DOI: 10.54209/infosains.v13i03.3505
Rakha Maulana, Bahteramon Bintang, Sanjaya Manurung, Nathanael Berliano, Novanka Putra, Aqwam Rosadi Kardian, Politeknik Siber dan Rekayasa Kriptografi, Sandi Negara
This paper presents a comprehensive analysis of the performance and efficiency of two widely used compression software, WINRAR and 7-Zip, across various data types. The study focuses on evaluating their effectiveness on different operating systems, specifically Windows 11 and Ubuntu 23.10. The analysis encompasses considerations such as compression ratios, resource utilization, and processing times. WINRAR and 7-Zip are examined in diverse scenarios, including the compression of text files (.txt), image files (.png), audio files (.flac), and video files (.mp4). The study reveals notable variations in compression outcomes influenced by intrinsic complexities of each file format. Moreover, the investigation extends beyond the initially studied operating systems, suggesting potential applications on other platforms like Kali Linux. The findings contribute insights into the nuanced performance of compression software across varied data types and operating environments, facilitating informed decision-making for users seeking optimal compression solutions.
本文全面分析了两款广泛使用的压缩软件 WINRAR 和 7-Zip 在不同数据类型下的性能和效率。研究重点是评估它们在不同操作系统上的有效性，特别是 Windows 11 和 Ubuntu 23.10。分析包括压缩率、资源利用率和处理时间等考虑因素。WINRAR 和 7-Zip 在压缩文本文件（.txt）、图像文件（.png）、音频文件（.flac）和视频文件（.mp4）等不同场景中进行了检验。研究显示，压缩结果受每种文件格式内在复杂性的影响而存在明显差异。此外，这项研究还超出了最初研究的操作系统的范围，显示了在 Kali Linux 等其他平台上的潜在应用。研究结果有助于深入了解压缩软件在不同数据类型和操作环境下的细微性能，从而为寻求最佳压缩解决方案的用户做出明智决策提供便利。
{"title":"Efficiency Analysis of Compression Software (WINRAR and 7-Zip) Across Diverse Data Types on Windows 11 and Ubuntu 23.10","authors":"Rakha Maulana, Bahteramon Bintang, Sanjaya Manurung, Nathanael Berliano, Novanka Putra, Aqwam Rosadi Kardian, Politeknik Siber dan Rekayasa Kriptografi, Sandi Negara","doi":"10.54209/infosains.v13i03.3505","DOIUrl":"https://doi.org/10.54209/infosains.v13i03.3505","url":null,"abstract":"This paper presents a comprehensive analysis of the performance and efficiency of two widely used compression software, WINRAR and 7-Zip, across various data types. The study focuses on evaluating their effectiveness on different operating systems, specifically Windows 11 and Ubuntu 23.10. The analysis encompasses considerations such as compression ratios, resource utilization, and processing times. WINRAR and 7-Zip are examined in diverse scenarios, including the compression of text files (.txt), image files (.png), audio files (.flac), and video files (.mp4). The study reveals notable variations in compression outcomes influenced by intrinsic complexities of each file format. Moreover, the investigation extends beyond the initially studied operating systems, suggesting potential applications on other platforms like Kali Linux. The findings contribute insights into the nuanced performance of compression software across varied data types and operating environments, facilitating informed decision-making for users seeking optimal compression solutions.","PeriodicalId":508149,"journal":{"name":"Jurnal Info Sains : Informatika dan Sains","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139184001","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：