Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of 44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also effectively meet real world application requirements.
{"title":"Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System","authors":"I. Hussain, Qian-hua He, Zhu-Liang Chen","doi":"10.5121/IJCSA.2018.8301","DOIUrl":"https://doi.org/10.5121/IJCSA.2018.8301","url":null,"abstract":"Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of 44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also effectively meet real world application requirements.","PeriodicalId":175732,"journal":{"name":"International Journal on Computational Science & Applications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132042590","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}
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
{"title":"Analysis of Software Quality Using Software Metrics","authors":"Ermiyas Birihanu Belachew, Feidu Akmel Gobena, Shumet Tadesse Nigatu","doi":"10.5121/IJCSA.2018.8502","DOIUrl":"https://doi.org/10.5121/IJCSA.2018.8502","url":null,"abstract":"Software metrics have a direct link with measurement in software engineering. Correct measurement is the \u0000prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.","PeriodicalId":175732,"journal":{"name":"International Journal on Computational Science & Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123169650","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: 10.5121/ijcsa.2021.11401
James Jin, G. S, Yu Sun
With more than seven billion people actively using the Internet, the number of cyber attacks has increased, and personal data breaches have become a concern among the general public. The COVID-19 pandemic has only increased the use of online platforms and services for work and leisure activities, which opens the door to more scams, viruses, and other cyber security breaches. Guided by SEO techniques and research regarding dangerous website and domain patterns, we have designed and implemented a visual system that tracks suspicious links on an active webpage and marks them in order to alert users to proceed with caution. Our AI utilizes linear regression to best detect trends in URL parsing, comparing them with registered unsafe links to see if they pose similar threats. The results reveal that AI isn’t entirely accurate since some trends are hard to decipher; however, it can reliably flag certain redirects and out-of-domain links that would otherwise remain hidden to users.
{"title":"An Artificial Intelligence URL Parser for Safer Web Browsing and Detection of Suspicious Links","authors":"James Jin, G. S, Yu Sun","doi":"10.5121/ijcsa.2021.11401","DOIUrl":"https://doi.org/10.5121/ijcsa.2021.11401","url":null,"abstract":"With more than seven billion people actively using the Internet, the number of cyber attacks has increased, and personal data breaches have become a concern among the general public. The COVID-19 pandemic has only increased the use of online platforms and services for work and leisure activities, which opens the door to more scams, viruses, and other cyber security breaches. Guided by SEO techniques and research regarding dangerous website and domain patterns, we have designed and implemented a visual system that tracks suspicious links on an active webpage and marks them in order to alert users to proceed with caution. Our AI utilizes linear regression to best detect trends in URL parsing, comparing them with registered unsafe links to see if they pose similar threats. The results reveal that AI isn’t entirely accurate since some trends are hard to decipher; however, it can reliably flag certain redirects and out-of-domain links that would otherwise remain hidden to users.","PeriodicalId":175732,"journal":{"name":"International Journal on Computational Science & Applications","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":"127461501","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: 10.5121/ijcsa.2021.11402
Amanda Zhu, Baoyu Yin, Yu Sun
For this project, I decided to relieve the tension of procrastination that commonly happens in students and adults. To find a solution to this, I created a program that uses Google Cloud Vision API (Optical Character Recognition) to detect the distracting forms of media such as Twitter, YouTube, and Facebook, and counts the number of times the user visits these websites. After a certain number of visits, the program sends a notification to remind the user to stay focused. If the user ignores the notification message while staying on the unapproved website, the program forces the tab to close. This application was applied to a small user study where a qualitative evaluation of the approach was conducted. After collecting data for two weeks, it concluded that the program was able to effectively reduce and limit the uses of online distractions, allowing the user to manage their time more efficiently by staying off websites they should not visit.
{"title":"Procrash: A Solution To Procrastination by Limiting Online Distractions using Optical Character Recognition","authors":"Amanda Zhu, Baoyu Yin, Yu Sun","doi":"10.5121/ijcsa.2021.11402","DOIUrl":"https://doi.org/10.5121/ijcsa.2021.11402","url":null,"abstract":"For this project, I decided to relieve the tension of procrastination that commonly happens in students and adults. To find a solution to this, I created a program that uses Google Cloud Vision API (Optical Character Recognition) to detect the distracting forms of media such as Twitter, YouTube, and Facebook, and counts the number of times the user visits these websites. After a certain number of visits, the program sends a notification to remind the user to stay focused. If the user ignores the notification message while staying on the unapproved website, the program forces the tab to close. This application was applied to a small user study where a qualitative evaluation of the approach was conducted. After collecting data for two weeks, it concluded that the program was able to effectively reduce and limit the uses of online distractions, allowing the user to manage their time more efficiently by staying off websites they should not visit.","PeriodicalId":175732,"journal":{"name":"International Journal on Computational Science & Applications","volume":"141 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":"132622264","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}
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