Pub Date : 2023-11-01DOI: 10.2174/266625581609231020155204
{"title":"Patent Selections","authors":"","doi":"10.2174/266625581609231020155204","DOIUrl":"https://doi.org/10.2174/266625581609231020155204","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"405 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112040","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 : 2023-11-01DOI: 10.2174/266625581609231020155306
{"title":"Acknowledgements to Reviewers","authors":"","doi":"10.2174/266625581609231020155306","DOIUrl":"https://doi.org/10.2174/266625581609231020155306","url":null,"abstract":"","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"405 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112039","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 : 2023-10-24DOI: 10.2174/0126662558257094231018062232
Tianqi Cheng, Lei Wang, Yuwei Wang, Xinping Guo, Chunxiang Liu
Introduction: To propose a medical image registration method with significant performance improvement. The spatial transformation obtained by the traditional deformable image registration technology is not smooth enough, and the calculation amount is too large to solve the optimization parameters. The network model proposed based on deep learning medical image registration technology has some limitations, which cannot guarantee the registration of topological structures, resulting in the loss of spatial features. It makes the model have topological conservation and transform reversibility, has the ability to learn more multi-scale features and complex image structures, and captures finer changes while clearly encoding global correlation. Method: Based on the core UNet model, a deformable image registration method with a new architecture Broad-UNet-diff is proposed. The model is equipped with asymmetric parallel convolution and uses diffeomorphism mapping. Result: Compared with the seven classical registration methods under the brain MRI datasets, the proposed method has significantly improved the registration performance. In particular, compared with the advanced TransMorph-diff registration method, the Dice score can be improved by 12 %, but only the 1/10 parameters are needed. Conclusion: This method confirms its convincing effectiveness and accuracy.
{"title":"Broad-UNet-diff: Diffeomorphic Deformable Medical Image Registration based on Multi-Scale Feature Learning","authors":"Tianqi Cheng, Lei Wang, Yuwei Wang, Xinping Guo, Chunxiang Liu","doi":"10.2174/0126662558257094231018062232","DOIUrl":"https://doi.org/10.2174/0126662558257094231018062232","url":null,"abstract":"Introduction: To propose a medical image registration method with significant performance improvement. The spatial transformation obtained by the traditional deformable image registration technology is not smooth enough, and the calculation amount is too large to solve the optimization parameters. The network model proposed based on deep learning medical image registration technology has some limitations, which cannot guarantee the registration of topological structures, resulting in the loss of spatial features. It makes the model have topological conservation and transform reversibility, has the ability to learn more multi-scale features and complex image structures, and captures finer changes while clearly encoding global correlation. Method: Based on the core UNet model, a deformable image registration method with a new architecture Broad-UNet-diff is proposed. The model is equipped with asymmetric parallel convolution and uses diffeomorphism mapping. Result: Compared with the seven classical registration methods under the brain MRI datasets, the proposed method has significantly improved the registration performance. In particular, compared with the advanced TransMorph-diff registration method, the Dice score can be improved by 12 %, but only the 1/10 parameters are needed. Conclusion: This method confirms its convincing effectiveness and accuracy.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"59 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315917","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}
Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.
{"title":"Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization","authors":"Dharmendra Kumar, Anil Kumar Solanki, Anil Kumar Ahlawat","doi":"10.2174/0126662558248113231012055802","DOIUrl":"https://doi.org/10.2174/0126662558248113231012055802","url":null,"abstract":"Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135619811","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 : 2023-10-19DOI: 10.2174/2666255816666230912153541
Wang Meng, Cao Wenhang, Dui Hongyan
Background: Data quality is crucial to the success of big data analytics. However, the presence of outliers affects data quality and data analysis. Employing effective outlier detection techniques to eliminate dirty data can improve data quality and garner more accurate analytical insights. Data uncertainty presents a significant challenge for outlier detection methods and warrants further refinement in the era of big data. Objective: The unsupervised outlier detection based on the integration of clustering and outlier scoring scheme is the current research hotspot. However, hard clustering fails when dealing with abnormal patterns with uncertain and unexpected behavior. Rough boundaries help identify more accurate cluster structures. Therefore, this article uses uncertainty soft clustering based on rough set theory to extend the clustering technology and designs appropriate scoring schemes to capture abnormal instances. This solves the problem of outlier detection in uncertain and nonlinear complex data. Methods: This paper proposes the flow of an outlier detection algorithm based on Kernel Rough Clustering and then compares the detection accuracy with five existing popular methods using synthetic and real-world datasets. The results show that the proposed method has higher detection accuracy. Results: The detection precision and recall of the proposed method were improved. For the detection accuracy, it is superior to popular methods, indicating that the proposed method has a good detection effect in identifying outlier. Conclusion: Compared with popular methods, the proposed method has a slight advantage in detection accuracy and is one of the effective algorithms that can be selected for outlier detection.
{"title":"Investigating Outlier Detection Techniques Based on Kernel Rough Clustering","authors":"Wang Meng, Cao Wenhang, Dui Hongyan","doi":"10.2174/2666255816666230912153541","DOIUrl":"https://doi.org/10.2174/2666255816666230912153541","url":null,"abstract":"Background: Data quality is crucial to the success of big data analytics. However, the presence of outliers affects data quality and data analysis. Employing effective outlier detection techniques to eliminate dirty data can improve data quality and garner more accurate analytical insights. Data uncertainty presents a significant challenge for outlier detection methods and warrants further refinement in the era of big data. Objective: The unsupervised outlier detection based on the integration of clustering and outlier scoring scheme is the current research hotspot. However, hard clustering fails when dealing with abnormal patterns with uncertain and unexpected behavior. Rough boundaries help identify more accurate cluster structures. Therefore, this article uses uncertainty soft clustering based on rough set theory to extend the clustering technology and designs appropriate scoring schemes to capture abnormal instances. This solves the problem of outlier detection in uncertain and nonlinear complex data. Methods: This paper proposes the flow of an outlier detection algorithm based on Kernel Rough Clustering and then compares the detection accuracy with five existing popular methods using synthetic and real-world datasets. The results show that the proposed method has higher detection accuracy. Results: The detection precision and recall of the proposed method were improved. For the detection accuracy, it is superior to popular methods, indicating that the proposed method has a good detection effect in identifying outlier. Conclusion: Compared with popular methods, the proposed method has a slight advantage in detection accuracy and is one of the effective algorithms that can be selected for outlier detection.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667701","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 : 2023-10-16DOI: 10.2174/0126662558256721231009045901
ChunXiang Liu, Yuwei Wang, Tianqi Cheng, Xinping Guo, Lei Wang
Aim: To deal with the drawbacks of the traditional medical image fusion methods, such as the low preservation ability of the details, the loss of edge information, and the image distortion, as well as the huge need for the training data for deep learning, a new multi-modal medical image fusion method based on the VGG19 model and the non-subsampled contourlet transform (NSCT) is proposed, whose overall objective is to simultaneously make the full use of the advantages of the NSCT and the VGG19 model. Methodology: Firstly, the source images are decomposed into the high-pass and low-pass subbands by NSCT, respectively. Then, the weighted average fusion rule is implemented to produce the fused low-pass sub-band coefficients, while an extractor based on the pre-trained VGG19 model is constructed to obtain the fused high-pass subband coefficients. Result and Discussion: Finally, the fusion results are reconstructed by the inversion transform of the NSCT on the fused coefficients. To prove the effectiveness and the accuracy, experiments on three types of medical datasets are implemented. Conclusion: By comparing seven famous fusion methods, both of the subjective and objective evaluations demonstrate that the proposed method can effectively avoid the loss of detailed feature information, capture more medical information from the source images, and integrate them into the fused images.
{"title":"Multimodal Medical Image Fusion based on the VGG19 Model in the NSCT Domain","authors":"ChunXiang Liu, Yuwei Wang, Tianqi Cheng, Xinping Guo, Lei Wang","doi":"10.2174/0126662558256721231009045901","DOIUrl":"https://doi.org/10.2174/0126662558256721231009045901","url":null,"abstract":"Aim: To deal with the drawbacks of the traditional medical image fusion methods, such as the low preservation ability of the details, the loss of edge information, and the image distortion, as well as the huge need for the training data for deep learning, a new multi-modal medical image fusion method based on the VGG19 model and the non-subsampled contourlet transform (NSCT) is proposed, whose overall objective is to simultaneously make the full use of the advantages of the NSCT and the VGG19 model. Methodology: Firstly, the source images are decomposed into the high-pass and low-pass subbands by NSCT, respectively. Then, the weighted average fusion rule is implemented to produce the fused low-pass sub-band coefficients, while an extractor based on the pre-trained VGG19 model is constructed to obtain the fused high-pass subband coefficients. Result and Discussion: Finally, the fusion results are reconstructed by the inversion transform of the NSCT on the fused coefficients. To prove the effectiveness and the accuracy, experiments on three types of medical datasets are implemented. Conclusion: By comparing seven famous fusion methods, both of the subjective and objective evaluations demonstrate that the proposed method can effectively avoid the loss of detailed feature information, capture more medical information from the source images, and integrate them into the fused images.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136142330","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 : 2023-10-09DOI: 10.2174/0126662558236061230922074642
T. Swathi, N. Kasiviswanath, A. Ananda Rao
Background: In the global financial market, the stock price index is used to analyse the performance of securities and the stock market. It can be obtained by accumulating stock price movements of every firm in the exchange market. A proper stock price prediction (SPP) model becomes essential for investors in turning the security market into a profitable place. Objective: Earlier works in the SPP models involve different approaches, such as statistical models, fundamental examination, time-series prediction, and machine learning (ML). Result and Method: Deep learning is a kind of ML model that tries to define high level conceptual concepts by the use of a learning process at distinct levels and stages. This study, in this view, provides a new sine cosine optimization (SCO) model with a deep learning-enabled stock price prediction (SCODL-SPP). The SCODL-SPP model intends to predict the closing prices of the shares using a deep learning model. The proposed SCODL-SPP model involves primary data pre-processing using a min-max normalization approach. A stacked long short-term memory (SLSTM) model is used to forecast stock values. Because hyperparameters in DL models are crucial, selecting them optimally can help improve prediction performance. Conclusion: The SLSTM Model's hyperparameters are optimised using the SCO algorithm in this research. According to the experiments, the SCODL-SPP model outperforms other models in terms of prediction accuracy.
{"title":"A Novel Sine Cosine Optimization with Stacked Long Short-term Memory-enabled Stock Price Prediction","authors":"T. Swathi, N. Kasiviswanath, A. Ananda Rao","doi":"10.2174/0126662558236061230922074642","DOIUrl":"https://doi.org/10.2174/0126662558236061230922074642","url":null,"abstract":"Background: In the global financial market, the stock price index is used to analyse the performance of securities and the stock market. It can be obtained by accumulating stock price movements of every firm in the exchange market. A proper stock price prediction (SPP) model becomes essential for investors in turning the security market into a profitable place. Objective: Earlier works in the SPP models involve different approaches, such as statistical models, fundamental examination, time-series prediction, and machine learning (ML). Result and Method: Deep learning is a kind of ML model that tries to define high level conceptual concepts by the use of a learning process at distinct levels and stages. This study, in this view, provides a new sine cosine optimization (SCO) model with a deep learning-enabled stock price prediction (SCODL-SPP). The SCODL-SPP model intends to predict the closing prices of the shares using a deep learning model. The proposed SCODL-SPP model involves primary data pre-processing using a min-max normalization approach. A stacked long short-term memory (SLSTM) model is used to forecast stock values. Because hyperparameters in DL models are crucial, selecting them optimally can help improve prediction performance. Conclusion: The SLSTM Model's hyperparameters are optimised using the SCO algorithm in this research. According to the experiments, the SCODL-SPP model outperforms other models in terms of prediction accuracy.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135146975","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}
aims: Load forecasting with for efficient power system management background: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method:: 1D CNN BI-LSTM model incorporating convolutional layers. method: 1D CNN BI-LSTM model incorporating convolutional layers Result:: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion:: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.
{"title":"Load Forecasting with Hybrid Deep Learning Model for Efficient Power System Management","authors":"Saikat Gochhait, Deepak Sharrma, Rutvij Jhaveri, Rajkumar Singh Rathore","doi":"10.2174/0126662558256168231003074148","DOIUrl":"https://doi.org/10.2174/0126662558256168231003074148","url":null,"abstract":"aims: Load forecasting with for efficient power system management background: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method:: 1D CNN BI-LSTM model incorporating convolutional layers. method: 1D CNN BI-LSTM model incorporating convolutional layers Result:: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion:: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134944942","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}
Abstract: One of the most promising fields of research in recent years is Mobile Ad Hoc Networks (MANET). The well-known advantages of the internet for specific types of applications lead to the fact that it is a wireless ad-hoc network. As a result, such networks can be utilized in circumstances where no other wireless communication infrastructure is present. A MANET is a network of wireless devices without any centralized control. A device can directly communicate with other devices using a wireless connection. For nodes that are located far from other nodes, multi-hop routing is employed. The functionality of route-finding is performed by routing protocols. The mobility model creates the movement pattern for nodes. This article discusses early research to address concerns about performance indicators for MANET routing protocols under the Column Mobility Model (CMM). Moreover, we discuss concerns regarding the designs of the related work, followed by the designed CMM model on the behavior of routing protocols.
{"title":"Simulative Analysis of Column Mobility Model for Proactive and Reactive Routing Protocols in Highly Dense MANET","authors":"Satveer Kour, Himali Sarangal, Manjit Singh, Butta Singh","doi":"10.2174/0126662558264941231002055909","DOIUrl":"https://doi.org/10.2174/0126662558264941231002055909","url":null,"abstract":"Abstract: One of the most promising fields of research in recent years is Mobile Ad Hoc Networks (MANET). The well-known advantages of the internet for specific types of applications lead to the fact that it is a wireless ad-hoc network. As a result, such networks can be utilized in circumstances where no other wireless communication infrastructure is present. A MANET is a network of wireless devices without any centralized control. A device can directly communicate with other devices using a wireless connection. For nodes that are located far from other nodes, multi-hop routing is employed. The functionality of route-finding is performed by routing protocols. The mobility model creates the movement pattern for nodes. This article discusses early research to address concerns about performance indicators for MANET routing protocols under the Column Mobility Model (CMM). Moreover, we discuss concerns regarding the designs of the related work, followed by the designed CMM model on the behavior of routing protocols.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135546438","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 : 2023-10-01DOI: 10.2174/2666255816666230619121018
Poonam Narang, Pooja Mittal
Background: The trend of software development has always been challenging for industry experts and software developers. There is tremendous growth in software development methodologies under the influence of evolving technologies and the rising demands of society. The 2019 pandemic forced software developers to shut down their offices and begin working from home, thereby, highlighting the critical necessity for a shared development and operations teams platform. As a result, the development trend moves from waterfall and Agile towards DevOps. Objective: The objective of the research is to review and comparatively analyze the availability factor of different selective and required features in software development methodologies. Software development industries will be benefited in appropriate methodology selection based on the requirement. Methods: The analysis is based on review of different development methodologies based on existing literature study, Google, and Stack Overflow Trends followed by tabular comparison of Waterfall, Iterative, Prototype, Spiral development models under Traditional and Rapid Application Development (RAD), Scrum, Kanban, XP for Agile methods with DevOps automation culture on essential features. Results: The moving trend towards DevOps, from Traditional and Agile development, demonstrate the most recent market swings for these models. Although Traditional models adhere to outdated software development methodologies, they are included in this high-quality survey and evaluation because of their widespread use in the software industry and prominent researcher’s survey work. Conclusion: Software developers, students, and researchers will all find it simple to comprehend the workings of development processes as a result of this analytical review. Additionally, it will also make it easier for these target audiences to choose relevant and effective models for software development.
{"title":"Trends of Software Development Methodologies Toward DevOps: Analysis and Review","authors":"Poonam Narang, Pooja Mittal","doi":"10.2174/2666255816666230619121018","DOIUrl":"https://doi.org/10.2174/2666255816666230619121018","url":null,"abstract":"Background: The trend of software development has always been challenging for industry experts and software developers. There is tremendous growth in software development methodologies under the influence of evolving technologies and the rising demands of society. The 2019 pandemic forced software developers to shut down their offices and begin working from home, thereby, highlighting the critical necessity for a shared development and operations teams platform. As a result, the development trend moves from waterfall and Agile towards DevOps. Objective: The objective of the research is to review and comparatively analyze the availability factor of different selective and required features in software development methodologies. Software development industries will be benefited in appropriate methodology selection based on the requirement. Methods: The analysis is based on review of different development methodologies based on existing literature study, Google, and Stack Overflow Trends followed by tabular comparison of Waterfall, Iterative, Prototype, Spiral development models under Traditional and Rapid Application Development (RAD), Scrum, Kanban, XP for Agile methods with DevOps automation culture on essential features. Results: The moving trend towards DevOps, from Traditional and Agile development, demonstrate the most recent market swings for these models. Although Traditional models adhere to outdated software development methodologies, they are included in this high-quality survey and evaluation because of their widespread use in the software industry and prominent researcher’s survey work. Conclusion: Software developers, students, and researchers will all find it simple to comprehend the workings of development processes as a result of this analytical review. Additionally, it will also make it easier for these target audiences to choose relevant and effective models for software development.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136216777","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：