{"title":"AN ANALYTICAL STUDY ON CLOUD COMPUTING","authors":"Anamika Ahirwar, Nikita Prajapat, Simran Raj","doi":"10.51767/jc1204","DOIUrl":"https://doi.org/10.51767/jc1204","url":null,"abstract":"This paper is about the An Analytical Study on Cloud Computing. Cloud computing is the development of parallel computing, grid computing, distributed computing, and virtualization technologies which define the shape of a new area. In contrast to a variety of benefits related with cloud computing, there are certain challenges as well. These challenges include security, privacy and reliability of data, high costs of data transfers, and regularity in the availability of services, and bugs in large-scale distributed systems. Cloud computing is a method of computing in a place that provides users with the ability of information technology as a service and allows them to have access to these services on the Internet without having limited information. Cloud computing can help businesses transform their closing server infrastructures into dynamic environments, expanding and reducing server capacity depending on their demand. Cloud is a image to describe web as a space where computing has been pre-installed and close as a service; data, operating systems, applications, storage and processing power close on the web ready to be shared. In cloud computing data owners expand their complex data management systems from community sites to the public cloud for great pliability and cost-effective. The searching of this study that highlight there are five main issues related with cloud computing execution which are Mobility and Cloud Government Application security issues, Cloud Security data, cloud security platform, Cloud Security Services and Application, cloud network security issues and infrastructure issues.","PeriodicalId":408370,"journal":{"name":"BSSS Journal of Computer","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125554549","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":"PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY","authors":"Anshika Jain, Indore Davv, M. Ingle","doi":"10.51767/jc1201","DOIUrl":"https://doi.org/10.51767/jc1201","url":null,"abstract":"Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.","PeriodicalId":408370,"journal":{"name":"BSSS Journal of Computer","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129872119","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":"GOVT_EXAM ONTOLOGY IN SEMANTIC WEB FOR QUERY RETRIEVAL","authors":"Dhiraj Khurana, Cheshta Diwan","doi":"10.51767/jc1203","DOIUrl":"https://doi.org/10.51767/jc1203","url":null,"abstract":"The World wide web is growing day by day exponentially and it becomes difficult to find the relevant information even using efficient search engines. Search results always give lots of irrelevant data which often misled to actual needed information. One solution of this problem is semantic web which was proposed by Sir Tim Berner’s Lee. Semantic web is an extended version of World Wide Web that has opened the new doors for efficient information retrieval process. Ontology is one of the methodologies to implement the semantic web. In this paper govt exam ontology is created with the help of protégé ontology creation tool. In the given ontology various relationship between different govt exams of central and state level is provided and also the general information about these exams like exam type, subject level of exam and institutes for coaching is given. This domain specific ontology can help the potential information seeker to get the accurate information which can help to crack the exam.","PeriodicalId":408370,"journal":{"name":"BSSS Journal of Computer","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115031099","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":"Buffer Overflow Attack –Vulnerability in Heap","authors":"S. Kurariya","doi":"10.51767/JC1101","DOIUrl":"https://doi.org/10.51767/JC1101","url":null,"abstract":"We advise a paper which indicates a heap attack due to buffer over .Buffer overflow attack is the oldest and maximum pervasive attack technique. A buffer overflow takes place while a method or program attempts to put in writing more statistics to a set buffer, or duration block of reminiscence than the buffer is allotted to hold. Since buffers are layout to incorporate a defined quantity of records, the extra information can revoke statistics values in memory deal with which is adjacent to the destination buffer till and unless this system include enough bounds checking to flag or discard information while an excessive amount of is despatched to a reminiscence buffer. Buffer overflow attacks are classify into two categories: Heap and stack based attack .The time period heap outline pool of memory utilized in dynamic allocation for run time environment and the heap based totally attack define the flooded reminiscence space reserved for a software, but the problem arrives when appearing such an assault it make the situation critical","PeriodicalId":408370,"journal":{"name":"BSSS Journal of Computer","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130971857","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":"Hybrid Recommendation System for Better Mining Rules Generation of User and Consumer Data","authors":"G. Gupta, Atul D. Newase","doi":"10.51767/JC1110","DOIUrl":"https://doi.org/10.51767/JC1110","url":null,"abstract":"In today's world, data and information play an essential role in each field, including online and software data. However, it is a challenging task to abstract & sorted consumer data for use. To solve this data overloading and sorting of useful data, a Hybrid Recommendation System (HRS) comes into existence. HRS's focus is to suggest the best applicable and useful items to the related customers or users. The recommendations can be applied to decision-making processes, like which types of things to get, which new videos to watch, which online latest games and software to search, or the best product. The benefits of the Hybrid Recommendation System persist on the quality efficiency of the system. The efficient things can be calculated in easy to use, reliable, accurate, and expandable. This proposed HRS's primary goal is to better mining rules based on user and consumer data to improve the Hybrid Recommendation System's accuracy.","PeriodicalId":408370,"journal":{"name":"BSSS Journal of Computer","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127705695","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":"Structuring the Federation of Cloud Service for Big Data","authors":"Ishani Sood, Varsha Sharma","doi":"10.51767/JC0901","DOIUrl":"https://doi.org/10.51767/JC0901","url":null,"abstract":"The demand for agile and flexible business application for big data has sparked\u0000interest in using cloud computing technology. Due to the limitation of provider’s\u0000capability and the magnanimity of task quantity for big data, a single cloud\u0000provider may not offer enough service resources. However, a service federation\u0000among multiple providers can effectively solve this problem. This paper proposes\u0000the framework of cloud federation and introduces the basic processes of building\u0000service federation among multiple cloud providers. Moreover, we propose a multiobjectives task assigning model in the federation. A genetic algorithm based\u0000heuristic approach is developed as the optimization method. Eventually, some\u0000simulation experiments are conducted to illustrate the effectiveness of the model.","PeriodicalId":408370,"journal":{"name":"BSSS Journal of Computer","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125009601","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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