Pub Date : 2023-04-08DOI: 10.46565/jreas.202381468-475
Ramu Gautam, Shahram Latifi
Almost every dataset has missing data. The common reasons are sensor error, equipment malfunction, human error, or translation loss. We study the efficacy of statistical (mean, median, mode) and machine learning based (k-nearest neighbors) imputation methods in accurately imputing missing data in numerical datasets with data missing not at random (MNAR) and data missing completely at random (MCAR) as well as categorical datasets. Imputed datasets are used to make prediction on the test set and Mean squared error (MSE) in prediction is used as the measure of performance of the imputation. Mean absolute difference between the original and imputed data is also observed. When the data is MCAR, kNN imputation results in lowest MSE for all datasets, making it the most accurate method. When less than 20% of data is missing, mean and median imputations are effective in regression problems. kNN imputation is better at 20% missingness and significantly better when 50% or more data is missing. For the kNN method, k = 5 gives better results than k=3 but k=10 gives similar results to k=5. For MNAR datasets, statistical methods result in similar or lower MSE compared to kNN imputation when less than 25% of instances have a missing feature. For higher missing levels, kNN imputation is superior. Given enough data points without missing features, deleting the instances with missing data may be a better choice at lower missingness levels. For categorical data imputation, kNN and Mode imputation are both effective.
{"title":"COMPARISON OF SIMPLE MISSING DATA IMPUTATION TECHNIQUES FOR NUMERICAL AND CATEGORICAL DATASETS","authors":"Ramu Gautam, Shahram Latifi","doi":"10.46565/jreas.202381468-475","DOIUrl":"https://doi.org/10.46565/jreas.202381468-475","url":null,"abstract":"Almost every dataset has missing data. The common reasons are sensor error, equipment malfunction, human error, or translation loss. We study the efficacy of statistical (mean, median, mode) and machine learning based (k-nearest neighbors) imputation methods in accurately imputing missing data in numerical datasets with data missing not at random (MNAR) and data missing completely at random (MCAR) as well as categorical datasets. Imputed datasets are used to make prediction on the test set and Mean squared error (MSE) in prediction is used as the measure of performance of the imputation. Mean absolute difference between the original and imputed data is also observed. When the data is MCAR, kNN imputation results in lowest MSE for all datasets, making it the most accurate method. When less than 20% of data is missing, mean and median imputations are effective in regression problems. kNN imputation is better at 20% missingness and significantly better when 50% or more data is missing. For the kNN method, k = 5 gives better results than k=3 but k=10 gives similar results to k=5. For MNAR datasets, statistical methods result in similar or lower MSE compared to kNN imputation when less than 25% of instances have a missing feature. For higher missing levels, kNN imputation is superior. Given enough data points without missing features, deleting the instances with missing data may be a better choice at lower missingness levels. For categorical data imputation, kNN and Mode imputation are both effective.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135693593","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 : 2023-03-14DOI: 10.46565/jreas.202274430-435
Irna Tri Yuniahastuti, Tedy Ardiansyah
The calculation of the reliability at the PLTU Anggrek 2×25 MW Gorontalo obtained a LOLP value of 61.5 days/year. This value was still above the standardreliability value of PLN, this was due to the occurrence of PO (Planned Outage) and MO (Maintenance Outage) in unit 2 in June-July 2021, namely in theform of the first-year inspection in July. The FOR (Force Outage Rate) value was calculated based on the disturbance data from the generator in 2021. Thedaily load value was obtained from the load forecast value in January-July using FIS (Fuzzy Inference System) Mamdani type using the Matlab Toolbox. Theerror value in the proposed load forecast was 8%. The rules that had been compiled were used to predict expenses in August-December 2021. From the resultsof forecasting From the results of forecasting expenses, it was found that the trend of expenses was increasing every month.left with 10-pt Times New Roman bold. The abstract must be contained in one paragraph.
{"title":"CALCULATION OF GENERATOR REABILITY INDEX WITH LOAD FORECASTING AT PLTU (ELECTRIC STEAM POWER PLANT) ANGGREK IN GORONTALO","authors":"Irna Tri Yuniahastuti, Tedy Ardiansyah","doi":"10.46565/jreas.202274430-435","DOIUrl":"https://doi.org/10.46565/jreas.202274430-435","url":null,"abstract":"The calculation of the reliability at the PLTU Anggrek 2×25 MW Gorontalo obtained a LOLP value of 61.5 days/year. This value was still above the standardreliability value of PLN, this was due to the occurrence of PO (Planned Outage) and MO (Maintenance Outage) in unit 2 in June-July 2021, namely in theform of the first-year inspection in July. The FOR (Force Outage Rate) value was calculated based on the disturbance data from the generator in 2021. Thedaily load value was obtained from the load forecast value in January-July using FIS (Fuzzy Inference System) Mamdani type using the Matlab Toolbox. Theerror value in the proposed load forecast was 8%. The rules that had been compiled were used to predict expenses in August-December 2021. From the resultsof forecasting From the results of forecasting expenses, it was found that the trend of expenses was increasing every month.left with 10-pt Times New Roman bold. The abstract must be contained in one paragraph.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87540448","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 : 2023-03-14DOI: 10.46565/jreas.202274443-448
Bopatriciat BOLUMA MANGATA, Miché Muselefu Kamasi, Parfum Bukanga Christian
The theme of this work is "market segmentation and offer targeting using neural discriminant analysis" for classification purposes. The data provided by modern applications are usually large in size and their classification may be disturbed. In this work, we will give a contribution of data mining to market segmentation and offer targeting using neural discriminant analysis. The main interest of this work is to offer decision-makers a better vision of their customers, allowing them to better manage and satisfy them by proposing products likely to be purchased by them; to make targeted marketing or a particular offer to a group of customers with similar characteristics and consumption behaviors; to know what to offer to the new customer who comes to the company by assigning him to a group of customers in which the habits, preferences towards a product or a group of products are known. Our approach therefore predicts the class of a new customer who comes to the company; the class is predicted after the customer has made his first purchase.. Individuals are placed in the class with the highest probability. In this case, the client will be assigned to the appropriate class with the marketing services.
{"title":"MARKET SEGMENTATION AND TARGETING OF OFFERS USING NEURAL DISCRIMINANT ANALYSIS","authors":"Bopatriciat BOLUMA MANGATA, Miché Muselefu Kamasi, Parfum Bukanga Christian","doi":"10.46565/jreas.202274443-448","DOIUrl":"https://doi.org/10.46565/jreas.202274443-448","url":null,"abstract":"The theme of this work is \"market segmentation and offer targeting using neural discriminant analysis\" for classification purposes. The data provided by modern applications are usually large in size and their classification may be disturbed. In this work, we will give a contribution of data mining to market segmentation and offer targeting using neural discriminant analysis. The main interest of this work is to offer decision-makers a better vision of their customers, allowing them to better manage and satisfy them by proposing products likely to be purchased by them; to make targeted marketing or a particular offer to a group of customers with similar characteristics and consumption behaviors; to know what to offer to the new customer who comes to the company by assigning him to a group of customers in which the habits, preferences towards a product or a group of products are known. Our approach therefore predicts the class of a new customer who comes to the company; the class is predicted after the customer has made his first purchase.. Individuals are placed in the class with the highest probability. In this case, the client will be assigned to the appropriate class with the marketing services.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76539570","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 : 2023-03-14DOI: 10.46565/jreas.202274427-429
Ghaniya Rashid, Baruani Swaleh
Calculation of throughput for OBSS Enabled and Disabled for 2.4GHz and in different Channels in Network Simulator-3 (NS-3 by increasing number of STAs and APs and finally compared the throughput with their graphs. The goal of spatial reuse in an Overlapping Basic Service Set OBSS), is to increase the number of parallel transmission without interferences. Then, we investigated the performance of the spatial reuse operation's throughputs using a simulation-based approach and plotted the graphs in MATLAB. The proposed system enhancing the use of parallel transmission and improvement of performance in wireless communication.
{"title":"Wi-Fi Spatial Re-Use: A Survey","authors":"Ghaniya Rashid, Baruani Swaleh","doi":"10.46565/jreas.202274427-429","DOIUrl":"https://doi.org/10.46565/jreas.202274427-429","url":null,"abstract":"Calculation of throughput for OBSS Enabled and Disabled for 2.4GHz and in different Channels in Network Simulator-3 (NS-3 by increasing number of STAs and APs and finally compared the throughput with their graphs. The goal of spatial reuse in an Overlapping Basic Service Set OBSS), is to increase the number of parallel transmission without interferences. Then, we investigated the performance of the spatial reuse operation's throughputs using a simulation-based approach and plotted the graphs in MATLAB. The proposed system enhancing the use of parallel transmission and improvement of performance in wireless communication.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135837797","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}
The most vexing problem plaguing Rumuomoi's 11kV distribution network is voltage sag and swell, which degrades power quality. There has been no effective mitigation control implemented. The purpose of this research is to address the issue of power quality by implementing artificial neural network (ANN) control with an embedded dynamic voltage restorer (DVR). To begin, the artificial neural network is trained using the input and desired data obtained during simulation using a proportional integral (PI) controller. To limit the amount of data obtained during training, the Levenberg-Marquardt feed forward back method is utilized, and the result for each iteration is determined in Matlab software. The desired dynamic voltage restorer system was tested using a replicated model of Rumuomoi 11kV and it was determined that Bus 7 is 0.938p.u, Bus 8 is 0.9244p.u, Bus 9 is 0.9148p.u, Bus 10 is 0.9035p.u, Bus 11 is 0.8912p.u, and Bus 12 is 0.8811p.u, all of which exceeded the statutory limit condition of 0.95-1.01p.u. There were no bus voltage violations after network optimization with DVR, demonstrating that DVR is effective at enhancing power quality by removing voltage sag and swell in the distribution network.
{"title":"IMPLEMENTING ARTIFICIAL NEURAL NETWORK BASED DVR TO IMPROVE POWER QUALITY OF RUMUOLA-RUMUOMOI 11kV DISTRIBUTION NETWORK","authors":"Kingsley Okpara Uwho, Hachimenum Nyebuchi Amadi, Okechi Chikezie","doi":"10.46565/jreas.202274404-419","DOIUrl":"https://doi.org/10.46565/jreas.202274404-419","url":null,"abstract":"The most vexing problem plaguing Rumuomoi's 11kV distribution network is voltage sag and swell, which degrades power quality. There has been no effective mitigation control implemented. The purpose of this research is to address the issue of power quality by implementing artificial neural network (ANN) control with an embedded dynamic voltage restorer (DVR). To begin, the artificial neural network is trained using the input and desired data obtained during simulation using a proportional integral (PI) controller. To limit the amount of data obtained during training, the Levenberg-Marquardt feed forward back method is utilized, and the result for each iteration is determined in Matlab software. The desired dynamic voltage restorer system was tested using a replicated model of Rumuomoi 11kV and it was determined that Bus 7 is 0.938p.u, Bus 8 is 0.9244p.u, Bus 9 is 0.9148p.u, Bus 10 is 0.9035p.u, Bus 11 is 0.8912p.u, and Bus 12 is 0.8811p.u, all of which exceeded the statutory limit condition of 0.95-1.01p.u. There were no bus voltage violations after network optimization with DVR, demonstrating that DVR is effective at enhancing power quality by removing voltage sag and swell in the distribution network.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79277945","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}
Screw compressors belong to the group of positive displacement machines, which are widely used in various industries due to the advantages of these compressors compared to other industrial compressors. The distance between the moving and fixed parts of the rotating equipment is inevitable. This distance causes current leakage in all types of compressors, which reduces the efficiency of the compressor. In this research, the effect of grooving the flow passage on the flow characteristics of the passing fluid has been investigated using ANSYS CFX software. The simulation quality of positive displacement machines strongly depends on the correct prediction of the leakage current between the existing switches. It seems that creating a groove in the path of the passing current increases the thickness of the boundary layer, which acts as a barrier against the return leakage current between the rotor and the stator in the compressor. In this paper, a simplified model of the problem, which is a grooved cylinder in which a piston rotates, is investigated. First, the flow of fluid from a cylinder whose inner surface is smooth is examined, and then in boundary conditions and similar geometric dimensions, the effect of different types of grooves with different geometries on the flow characteristics will be studied. The variable is the change in drag force on the inner surfaces of the cylinder. The results show that in the best case, ie a cylinder with a single helical groove, the drag force compared to smooth geometry is 1.79 times and the flow rate is reduced by 13.21%. It is expected that the results of this study can be generalized to reduce leakage current between the rotor and the screw compressor housing.
{"title":"Analysis of the Effect of Grooving the Flow Passage on the Flow Characteristics of the Fluid Passing Through the Cylindrical with a Rotating Cylinder","authors":"Meysam Hassani, Mahmood Ebrahimi, Masoud Rahmani, Amin Moslemi Petrudi","doi":"10.46565/jreas.202274435-442","DOIUrl":"https://doi.org/10.46565/jreas.202274435-442","url":null,"abstract":"Screw compressors belong to the group of positive displacement machines, which are widely used in various industries due to the advantages of these compressors compared to other industrial compressors. The distance between the moving and fixed parts of the rotating equipment is inevitable. This distance causes current leakage in all types of compressors, which reduces the efficiency of the compressor. In this research, the effect of grooving the flow passage on the flow characteristics of the passing fluid has been investigated using ANSYS CFX software. The simulation quality of positive displacement machines strongly depends on the correct prediction of the leakage current between the existing switches. It seems that creating a groove in the path of the passing current increases the thickness of the boundary layer, which acts as a barrier against the return leakage current between the rotor and the stator in the compressor. In this paper, a simplified model of the problem, which is a grooved cylinder in which a piston rotates, is investigated. First, the flow of fluid from a cylinder whose inner surface is smooth is examined, and then in boundary conditions and similar geometric dimensions, the effect of different types of grooves with different geometries on the flow characteristics will be studied. The variable is the change in drag force on the inner surfaces of the cylinder. The results show that in the best case, ie a cylinder with a single helical groove, the drag force compared to smooth geometry is 1.79 times and the flow rate is reduced by 13.21%. It is expected that the results of this study can be generalized to reduce leakage current between the rotor and the screw compressor housing.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"1103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135837796","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 : 2023-03-14DOI: 10.46565/jreas.202274420-425
V. Pandey
The following work encompasses the design of a disc braking system for a single seated solar electric racing vehicle and serves as a guide for developing a desired braking system for various student racing competitions. The basic aim of this work is to design and fabricate a safe, reliable and an efficient hydraulic braking system according to the required standards .Reliability, lightweight materials, manufacturing ease and costs, maximum braking force, safe stopping distance and manageable temperature are some of the considerations done for the braking system. Modelling and simulation are done in Spaceclaim 2020 and Ansys 2020R2.Topology optimization of brake disc is done to reduce the weight of the braking system done. This brake system is designed according to the rules and regulations of ESVC organized by ISIE and accredited by Ministry of New and Renewable Energy, Government of India and Ministry of MSME, Government of India.
{"title":"DESIGN AND ANALYSIS OF A DISK BRAKING SYSTEM FOR A SINGLE SEATED SOLAR ELECTRIC STUDENT RACING VEHICLE","authors":"V. Pandey","doi":"10.46565/jreas.202274420-425","DOIUrl":"https://doi.org/10.46565/jreas.202274420-425","url":null,"abstract":"The following work encompasses the design of a disc braking system for a single seated solar electric racing vehicle and serves as a guide for developing a desired braking system for various student racing competitions. The basic aim of this work is to design and fabricate a safe, reliable and an efficient hydraulic braking system according to the required standards .Reliability, lightweight materials, manufacturing ease and costs, maximum braking force, safe stopping distance and manageable temperature are some of the considerations done for the braking system. Modelling and simulation are done in Spaceclaim 2020 and Ansys 2020R2.Topology optimization of brake disc is done to reduce the weight of the braking system done. This brake system is designed according to the rules and regulations of ESVC organized by ISIE and accredited by Ministry of New and Renewable Energy, Government of India and Ministry of MSME, Government of India.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"358 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80177991","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}
This article on the vectorization of learning equations by neural network aims to give the matrix equations on [1-3]: first on the Z [8, 9] model of the perceptron[6] which calculates the inputs X, the Weights W and the bias, second on the quantization function [10] [11], called loss function [6, 7] [8]. and finally thegradient descent algorithm for maximizing likelihood and minimizing Z errors [4, 5].
{"title":"MATRIX EQUATIONS IN DEEP LEARNING RESOLUTION FOR M DATA HAS N PARAMETERS","authors":"Tshibengabu Tshimanga Yannick, Mbuyi Mukendi Eugène, Batubenga Mwamba-nzambi Jean-Didier","doi":"10.46565/jreas.202274400-403","DOIUrl":"https://doi.org/10.46565/jreas.202274400-403","url":null,"abstract":"This article on the vectorization of learning equations by neural network aims to give the matrix equations on [1-3]: first on the Z [8, 9] model of the perceptron[6] which calculates the inputs X, the Weights W and the bias, second on the quantization function [10] [11], called loss function [6, 7] [8]. and finally thegradient descent algorithm for maximizing likelihood and minimizing Z errors [4, 5].","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85570814","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 : 2023-03-03DOI: 10.46565/jreas.202273387-390
Mohammed Abid Chaudhary
Limiting gun violence has become a major concern in recent years. Deep Learning models can help reduce gun violence by automatically detecting weapons from security cameras. In this project, we will be implementing the use of Deep learning algorithms, to detect any firearms / weapons to improve response time and reduce potential harm. The proposed system in this project is a weapon detection system based on CNN. The project will involve the use of various resources for implementation, such as: Tensorflow, OpenCV, Python, CNN, Google Colab, Numpy, Pandas
{"title":"WEAPON DETECTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK","authors":"Mohammed Abid Chaudhary","doi":"10.46565/jreas.202273387-390","DOIUrl":"https://doi.org/10.46565/jreas.202273387-390","url":null,"abstract":"Limiting gun violence has become a major concern in recent years. Deep Learning models can help reduce gun violence by automatically detecting weapons from security cameras. In this project, we will be implementing the use of Deep learning algorithms, to detect any firearms / weapons to improve response time and reduce potential harm. The proposed system in this project is a weapon detection system based on CNN. The project will involve the use of various resources for implementation, such as: Tensorflow, OpenCV, Python, CNN, Google Colab, Numpy, Pandas","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73522008","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 : 2023-02-25DOI: 10.46565/jreas.202273373-382
M. Mutanga
As ubiquitous computing becomes an increasingly inherent component of everyday life due to the rapid growth of communication technologies and globalization, threats against information systems have taken a more latent yet lethal dimension. This emergent digital security challenge has correspondingly motivated a proactive change in the software engineering process in recent decades. This change has inspired more intense research scrutiny on security as a crucial component of any software system. Moreover, in today’s virtual world of hyperconnectivity, the most significant vulnerabilities in modern information systems security are software centred. Nevertheless, research shows that software developers often lack the required knowledge and skills in secure software systems development (SSD). Such knowledge ensures that all the resultant software components of each development lifecycle are correctly implemented rather than merely following the SSD lifecycle. Also, the knowledge engenders software security consciousness as a professional attitude amongst developers. Therefore, investigating students’ awareness of SSD principles can generate insight into evolving the undergraduate software development curriculum – a path to building future career developers. The study used a voluntary online survey to recruit a sample of 76 undergraduate developers and employed a descriptive approach to data analysis. Among other findings, the study revealed that participants' perception of the threat of software vulnerability impacts their attitude towards security on online and mobile platforms. And that though over 90% of the undergraduate developers took software vulnerability threats either “serious” or “extremely serious”, this disposition did not reflect the depth of their knowledge and experience in SSD.
{"title":"Secure Software Development Awareness: A Case Study of Undergraduate Developers","authors":"M. Mutanga","doi":"10.46565/jreas.202273373-382","DOIUrl":"https://doi.org/10.46565/jreas.202273373-382","url":null,"abstract":"As ubiquitous computing becomes an increasingly inherent component of everyday life due to the rapid growth of communication technologies and globalization, threats against information systems have taken a more latent yet lethal dimension. This emergent digital security challenge has correspondingly motivated a proactive change in the software engineering process in recent decades. This change has inspired more intense research scrutiny on security as a crucial component of any software system. Moreover, in today’s virtual world of hyperconnectivity, the most significant vulnerabilities in modern information systems security are software centred. Nevertheless, research shows that software developers often lack the required knowledge and skills in secure software systems development (SSD). Such knowledge ensures that all the resultant software components of each development lifecycle are correctly implemented rather than merely following the SSD lifecycle. Also, the knowledge engenders software security consciousness as a professional attitude amongst developers. Therefore, investigating students’ awareness of SSD principles can generate insight into evolving the undergraduate software development curriculum – a path to building future career developers. The study used a voluntary online survey to recruit a sample of 76 undergraduate developers and employed a descriptive approach to data analysis. Among other findings, the study revealed that participants' perception of the threat of software vulnerability impacts their attitude towards security on online and mobile platforms. And that though over 90% of the undergraduate developers took software vulnerability threats either “serious” or “extremely serious”, this disposition did not reflect the depth of their knowledge and experience in SSD.","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79738278","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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