Pub Date : 2022-03-30DOI: 10.54692/lgurjcsit.2022.0601276
Mubashir Ali
In recent times, Lung cancer is the most common cause of mortality in both men and women around the world. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. The possibility of a patient's survival rate increasing if lung cancer is identified early. To detect and diagnose lung cancer in its early stages, a variety of data analysis and machine learning techniques have been applied. In this paper, we applied supervised machine learning algorithms like SVM (Support vector machine), ANN (Artificial neural networks), MLR (Multiple linear regression), and RF (random forest), to detect the early stages of lung tumors. The main purpose of this study is to examine the success of machine learning algorithms in detecting lung cancer at an early stage. When compared to all other supervised machine learning algorithms, the Random forest model produces a high result, with a 99.99% accuracy rate
{"title":"Lung Cancer Detection using Supervised Machine Learning Techniques","authors":"Mubashir Ali","doi":"10.54692/lgurjcsit.2022.0601276","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0601276","url":null,"abstract":"In recent times, Lung cancer is the most common cause of mortality in both men and women around the world. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. The possibility of a patient's survival rate increasing if lung cancer is identified early. To detect and diagnose lung cancer in its early stages, a variety of data analysis and machine learning techniques have been applied. In this paper, we applied supervised machine learning algorithms like SVM (Support vector machine), ANN (Artificial neural networks), MLR (Multiple linear regression), and RF (random forest), to detect the early stages of lung tumors. The main purpose of this study is to examine the success of machine learning algorithms in detecting lung cancer at an early stage. When compared to all other supervised machine learning algorithms, the Random forest model produces a high result, with a 99.99% accuracy rate","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794906","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 : 2022-03-30DOI: 10.54692/lgurjcsit.2022.0601275
Hafiz Muhammad Tayyab Khushi
Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation.
{"title":"Impulse Noise Removal Using Soft-computing","authors":"Hafiz Muhammad Tayyab Khushi","doi":"10.54692/lgurjcsit.2022.0601275","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0601275","url":null,"abstract":"Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121002724","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504255
Attique Ur Rehman, Sajid Mahmood, Ghulam Mustafa, Sumet mehta
In this era of technology, a lot of advancements have been done in almost every field such as medical, science, aerospace and other fields. With the increasing advancements in technology, a lot of data is being produced at the same time. For instances in the field of medicine there is a huge amount of data that is being generated as there are hundreds and thousands of patients who came for their checkup. So now the question arises where this huge amount of data is being stored. This huge amount of data is called as Big Data. And the major problem faced is how to manage and organize this huge amount of data along with its security and not being lost. Big data is used for extracting a lot of useful information but it is not easy to organize it. If the data is being lost than there are a lot of problems that can occur on a huge level as a lot of data being stored in big data is very confidential. This data can be stored on cloud which is the new advancement in the field of technology as it is highly reliable for huge amount of information. So, in this survey paper we will discuss about the solutions of organizing and handling big data proposed by different authors.
{"title":"A Comparative Analysis on Handling Big Data Using Cloud Services","authors":"Attique Ur Rehman, Sajid Mahmood, Ghulam Mustafa, Sumet mehta","doi":"10.54692/lgurjcsit.2021.0504255","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504255","url":null,"abstract":"In this era of technology, a lot of advancements have been done in almost every field such as medical, science, aerospace and other fields. With the increasing advancements in technology, a lot of data is being produced at the same time. For instances in the field of medicine there is a huge amount of data that is being generated as there are hundreds and thousands of patients who came for their checkup. So now the question arises where this huge amount of data is being stored. This huge amount of data is called as Big Data. And the major problem faced is how to manage and organize this huge amount of data along with its security and not being lost. Big data is used for extracting a lot of useful information but it is not easy to organize it. If the data is being lost than there are a lot of problems that can occur on a huge level as a lot of data being stored in big data is very confidential. This data can be stored on cloud which is the new advancement in the field of technology as it is highly reliable for huge amount of information. So, in this survey paper we will discuss about the solutions of organizing and handling big data proposed by different authors.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122840039","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504247
Hafiz Muhammad Tayyab Khushi
In today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the "spatial domain" or the "transform domain" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition" and a more compact work load, as well as enhanced system consistency, are among the new features.
{"title":"A New Robust Multi focus image fusion Method","authors":"Hafiz Muhammad Tayyab Khushi","doi":"10.54692/lgurjcsit.2021.0504247","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504247","url":null,"abstract":"In today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the \"spatial domain\" or the \"transform domain\" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement\" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition\" and a more compact work load, as well as enhanced system consistency, are among the new features.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125387719","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504254
Sarah Chaudhry, AbdulHafeez Muhammad, Asghar Ali Shah, Fakhra Batoo
Information and Communication Technology (ICT) has revolutionized the lives of the people. The technology is embedded in daily life of literate or semiliterate/illiterate users. However, the user interface (UI) requirements for semiliterate/illiterate users are different from that of an educated person. The researchers of Human Computer Interaction for Development (HCI4D) face challenges to improve the usability of a UI for the semiliterate users. Therefore, a Systematic Literature Review (SLR) is conducted to provide a set of design factors and guidelines for UI development of semiliterate users. The study is based on extensive research gathered from literature to understand the user-centered design (UCD) approach, enhancing user experience (UX) for semiliterate users. This study analyses fifty two research articles that are published during 2010-2020. The findings shed light on the systematization of UI design guidelines for semiliterate/illiterate users. These guidelines can help in taking advantage of ICT during the COVID-19 pandemic. The analysis shows that seventeen main design factors are indispensable for designing UI of semiliterate users. The most suggested design factors include localization and graphics, which should be incorporated in UI for the target population. Moreover, the lag in the design factors as personalization and consistency open a road for future research.
信息通信技术(ICT)彻底改变了人们的生活。该技术已嵌入识字或半文盲/文盲用户的日常生活中。然而，半文盲/文盲用户的用户界面(UI)需求与受过教育的人不同。人机交互促进发展(HCI4D)的研究人员面临着提高半文盲用户界面可用性的挑战。因此，本文通过系统性文献综述(Systematic Literature Review，简称SLR)，为半文盲用户的UI开发提供一套设计因素和指导方针。该研究基于从文献中收集的广泛研究，以了解以用户为中心的设计(UCD)方法，为半文盲用户增强用户体验(UX)。本研究分析了2010-2020年间发表的52篇研究论文。研究结果揭示了为半文盲/文盲用户系统化UI设计指南。这些准则有助于在2019冠状病毒病大流行期间利用信息通信技术。分析表明，对于半文盲用户的UI设计，17个主要设计因素是不可或缺的。最受建议的设计因素包括本地化和图像，它们应该被整合到目标用户的UI中。此外，个性化和一致性等设计因素的滞后为未来的研究开辟了道路。
{"title":"Human-Computer User Interface Design for Semiliterate and Illiterate Users","authors":"Sarah Chaudhry, AbdulHafeez Muhammad, Asghar Ali Shah, Fakhra Batoo","doi":"10.54692/lgurjcsit.2021.0504254","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504254","url":null,"abstract":"Information and Communication Technology (ICT) has revolutionized the lives of the people. The technology is embedded in daily life of literate or semiliterate/illiterate users. However, the user interface (UI) requirements for semiliterate/illiterate users are different from that of an educated person. The researchers of Human Computer Interaction for Development (HCI4D) face challenges to improve the usability of a UI for the semiliterate users. Therefore, a Systematic Literature Review (SLR) is conducted to provide a set of design factors and guidelines for UI development of semiliterate users. The study is based on extensive research gathered from literature to understand the user-centered design (UCD) approach, enhancing user experience (UX) for semiliterate users. This study analyses fifty two research articles that are published during 2010-2020. The findings shed light on the systematization of UI design guidelines for semiliterate/illiterate users. These guidelines can help in taking advantage of ICT during the COVID-19 pandemic. The analysis shows that seventeen main design factors are indispensable for designing UI of semiliterate users. The most suggested design factors include localization and graphics, which should be incorporated in UI for the target population. Moreover, the lag in the design factors as personalization and consistency open a road for future research.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121525146","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504243
Waseem Iqbal
The internet of things (IoT) is an innovative and advanced high-level IT development that provides the connection between a large network of devices equipped with numerous computing capabilities, actuation, and sensing with the help of internet connection, consequently providing multifarious novel services regarding smart systems. All around the globe the attractive big data analytics and IoT services are allowing initiatives regarding smart systems. Business processes are commonly executed inside the application systems where computers, objects of IoT as well as humans participate. However, for the system-supported processes, the use of IoT technology is still facing the problem of the absence of a standard system architecture that is essential to manage the coordination in a smart IoT environment. Business process management (BPM) is regarded as a substantial technique for designing, controlling, and improving the processes of a system. This article introduces a BPM modeling approach for IoT-based systems operation exploits IoT using BPM by adopting an IoT framework architecture and considering IoT data for interaction in a defined process model. The methodology has been carried out on top of current BPM modeling notions and system techniques for formal representations of the system and also to get through the challenges of collaboration and connection.
{"title":"Business Process Model for IOT Based Systems Operations","authors":"Waseem Iqbal","doi":"10.54692/lgurjcsit.2021.0504243","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504243","url":null,"abstract":"The internet of things (IoT) is an innovative and advanced high-level IT development that provides the connection between a large network of devices equipped with numerous computing capabilities, actuation, and sensing with the help of internet connection, consequently providing multifarious novel services regarding smart systems. All around the globe the attractive big data analytics and IoT services are allowing initiatives regarding smart systems. Business processes are commonly executed inside the application systems where computers, objects of IoT as well as humans participate. However, for the system-supported processes, the use of IoT technology is still facing the problem of the absence of a standard system architecture that is essential to manage the coordination in a smart IoT environment. Business process management (BPM) is regarded as a substantial technique for designing, controlling, and improving the processes of a system. This article introduces a BPM modeling approach for IoT-based systems operation exploits IoT using BPM by adopting an IoT framework architecture and considering IoT data for interaction in a defined process model. The methodology has been carried out on top of current BPM modeling notions and system techniques for formal representations of the system and also to get through the challenges of collaboration and connection.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114059110","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504248
Rana Mohtasham Aftab
In response to any terrorist attack on hospitals, airports, shopping malls, schools, universities, colleges, railway stations, passport offices, bus stands, dry ports and the other important private and public places, a proper plan will need to be developed effective response. In normal moments, security guards are deployed to prevent criminals from doing anything wrong. For example, someone is moving around with a weapon, and security guards are watching its movement through closed circuit television (CCTV). Meanwhile, they are trying to identify his weapon in order to plan an appropriate response to the weapon he has. The process of manually identifying weapons is man-made and slow, while the security situation is critical and needs to be accelerated. Therefore, an automated system is needed to detect and classify the weapon so that appropriate response can be planned quickly to ensure minimal damage. Subject to previous concerns, this study is based on the Convoluted Neural Network (CNN) model using datasets that are assembled on the YOLO and you only see once. Focusing on real-time weapons identification, we created a data collection of images of multiple local weapons from surveillance camera systems and YouTube videos. The solution uses parameters that describe the rules for data generation and problem interpretation. Then, using deep convolutional neural network models, an accuracy of 97.01% is achieved.
{"title":"An Expert System for Weapon Identification and Categorization Using Machine Learning Technique to Retrieve Appropriate Response","authors":"Rana Mohtasham Aftab","doi":"10.54692/lgurjcsit.2021.0504248","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504248","url":null,"abstract":"In response to any terrorist attack on hospitals, airports, shopping malls, schools, universities, colleges, railway stations, passport offices, bus stands, dry ports and the other important private and public places, a proper plan will need to be developed effective response. In normal moments, security guards are deployed to prevent criminals from doing anything wrong. For example, someone is moving around with a weapon, and security guards are watching its movement through closed circuit television (CCTV). Meanwhile, they are trying to identify his weapon in order to plan an appropriate response to the weapon he has. The process of manually identifying weapons is man-made and slow, while the security situation is critical and needs to be accelerated. Therefore, an automated system is needed to detect and classify the weapon so that appropriate response can be planned quickly to ensure minimal damage. Subject to previous concerns, this study is based on the Convoluted Neural Network (CNN) model using datasets that are assembled on the YOLO and you only see once. Focusing on real-time weapons identification, we created a data collection of images of multiple local weapons from surveillance camera systems and YouTube videos. The solution uses parameters that describe the rules for data generation and problem interpretation. Then, using deep convolutional neural network models, an accuracy of 97.01% is achieved.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130336586","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504251
Muhammad Usman Ashraf
A software game is an application that is not only applicable for entertainment purposes but also used in domains like business, education and health care. Software game development is a multidisciplinary process that involves art, sound, artificial intelligence (AI), control systems and human factors which makes it different from traditional software development practice. Distributed software development (DSD) facilitates decentralized zones for the availability of multidisciplinary human resources at less cost. Past studies explored many influencing factors for game development, however, how these factors majorly affect the game development in Distributed Software Development (DSD) environment yet not been studied as per our knowledge. In this research, we not only identified the most influencing factors for game development in DSD but also gauge a relationship matrix between these factors with games’ technical requirements. In our evaluation, we took twenty-nine top-rated animated games to establish a mapping of these factors present in these games. To calculate the variation in a given project budget, we execute Monte-Carlo simulations between the independent variable (influencing factors) and dependent variable (overall cost) that forecast the valuation of each variable impact on the overall nominal cost of the project. Empirical results of our research conclude that among all identified factors, ‘Physical Resources’ and ‘Freelancers’ have a significant impact on the overall project cost. Our research findings quantitatively assist the software project managers to estimate the cost deviations due to influencing factors in Distributed Software Development (DSD) environment.
{"title":"Measuring the Impact of Factors Affecting Game Development in Distributed Software Development","authors":"Muhammad Usman Ashraf","doi":"10.54692/lgurjcsit.2021.0504251","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504251","url":null,"abstract":"A software game is an application that is not only applicable for entertainment purposes but also used in domains like business, education and health care. Software game development is a multidisciplinary process that involves art, sound, artificial intelligence (AI), control systems and human factors which makes it different from traditional software development practice. Distributed software development (DSD) facilitates decentralized zones for the availability of multidisciplinary human resources at less cost. Past studies explored many influencing factors for game development, however, how these factors majorly affect the game development in Distributed Software Development (DSD) environment yet not been studied as per our knowledge. In this research, we not only identified the most influencing factors for game development in DSD but also gauge a relationship matrix between these factors with games’ technical requirements. In our evaluation, we took twenty-nine top-rated animated games to establish a mapping of these factors present in these games. To calculate the variation in a given project budget, we execute Monte-Carlo simulations between the independent variable (influencing factors) and dependent variable (overall cost) that forecast the valuation of each variable impact on the overall nominal cost of the project. Empirical results of our research conclude that among all identified factors, ‘Physical Resources’ and ‘Freelancers’ have a significant impact on the overall project cost. Our research findings quantitatively assist the software project managers to estimate the cost deviations due to influencing factors in Distributed Software Development (DSD) environment. \u0000 ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126421824","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 : 2021-12-27DOI: 10.54692/lgurjcsit.2021.0504249
Umer Iqbal
The main challenge for embedding encrypted message in an input image is to get better the security of the confidential information through hybrid-based image steganography method. Moreover, earlier LSB based solutions existed in which either secret information embedded without encryption or embedded un-randomly in an image and existing MSB based information concealing solutions minimizes information capacity and image quality too. Most of existing steganographic systems either based on LSB or MSB but only some hybrid solutions are available in which either the confidential message is not encoded before embedding it into the image and the embedding system is also not random based. The existing well known hybrid based image steganography techniques are not only deficient in performance but also deficient in embedding of encoded data in an image. To overcome these issues, a Hybrid-LSB-MSB based image steganography and multi-operation data encryption method is proposed in this article. Proposed method is not only randomly embeds the confidential information in a cover image but also provided the facility to encode the confidential information before substituting. The Hybrid-LSB-MSB based proposed image steganography method is compared with earlier Hybrid based image steganography method by using Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) values including payload capacity. Higher PSNR and Lower MSE values signify effective steganography quality. The experimental results show that proposed method retains higher PSNR and lesser MSE values as contrasted to the existing methods thereby effective in steganographic properties.
{"title":"Hybrid Image Steganography Method with Random Embedding of Encrypted Message","authors":"Umer Iqbal","doi":"10.54692/lgurjcsit.2021.0504249","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0504249","url":null,"abstract":"The main challenge for embedding encrypted message in an input image is to get better the security of the confidential information through hybrid-based image steganography method. Moreover, earlier LSB based solutions existed in which either secret information embedded without encryption or embedded un-randomly in an image and existing MSB based information concealing solutions minimizes information capacity and image quality too. Most of existing steganographic systems either based on LSB or MSB but only some hybrid solutions are available in which either the confidential message is not encoded before embedding it into the image and the embedding system is also not random based. The existing well known hybrid based image steganography techniques are not only deficient in performance but also deficient in embedding of encoded data in an image. To overcome these issues, a Hybrid-LSB-MSB based image steganography and multi-operation data encryption method is proposed in this article. Proposed method is not only randomly embeds the confidential information in a cover image but also provided the facility to encode the confidential information before substituting. The Hybrid-LSB-MSB based proposed image steganography method is compared with earlier Hybrid based image steganography method by using Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) values including payload capacity. Higher PSNR and Lower MSE values signify effective steganography quality. The experimental results show that proposed method retains higher PSNR and lesser MSE values as contrasted to the existing methods thereby effective in steganographic properties. ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122995075","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 : 2021-09-12DOI: 10.54692/lgurjcsit.2021.0503212
Salman Qadri
The purpose of this study is to highlight the significance of machine vision for the Classification of kidney stone identification. A novel optimized fused texture features frame work was designed to identify the stones in kidney. A fused 234 texture feature namely (GLCM, RLM and Histogram) feature set was acquired by each region of interest (ROI). It was observed that on each image 8 ROI’s of sizes (16x16, 20x20 and 22x22) were taken. It was difficult to handle a large feature space 280800 (1200x234). Now to overcome this data handling issue we have applied feature optimization technique namely POE+ACC and acquired 30 most optimized features set for each ROI. The optimized fused features data set 3600(1200x30) was used to four machine vision Classifiers that is Random Forest, MLP, j48 and Naïve Bayes. Finally, it was observed that Random Forest provides best results of 90% accuracy on ROI 22x22 among the above discussed deployed Classifiers
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