The research aims to explore the transition from monolithic Digital Experience Platforms (DXPs) to Microservices-based DXPs, addressing scalability challenges. The study systematically decomposes monolithic structures...The research aims to explore the transition from monolithic Digital Experience Platforms (DXPs) to Microservices-based DXPs, addressing scalability challenges. The study systematically decomposes monolithic structures into Microservices, emphasizing business capability and subdomain decomposition. Concrete insights, challenges, and solutions encountered during this transformation process are presented. The research contributes valuable insights into the challenges and benefits of adopting Microservices in DXPs. Results highlight the importance of architectural patterns and strategic scaling dimensions for improved performance and scalability. The case study on Backbase’s Engagement Banking Platform showcases successful implementation, providing flexibility, integration, and efficient development in the evolving DXP landscape.展开更多
Data storage solutions are a crucial aspect of any application, significantly impacting data management and system performance. This article explores the rationale behind utilizing both SQL and NoSQL databases, addres...Data storage solutions are a crucial aspect of any application, significantly impacting data management and system performance. This article explores the rationale behind utilizing both SQL and NoSQL databases, addressing key questions about when each type is preferable. The background emphasizes the importance of selecting the appropriate database technology to meet specific application requirements. The purpose of this research is to provide a comprehensive guide for choosing between SQL and NoSQL databases based on various factors, including workload characteristics, scalability needs, and consistency requirements. To achieve this, we examine different strategies for implementing SQL and NoSQL databases in large-scale distributed applications and systems. The research method involves a comparative analysis of the features, advantages, and limitations of both database types. We specifically focus on scenarios involving read-heavy versus write-heavy systems and the trade-offs between availability and consistency. The results of this research indicate that SQL databases, with their relational structure and ACID compliance, are ideal for applications requiring complex queries and data integrity. In contrast, NoSQL databases, offering schema flexibility and horizontal scalability, are better suited for managing extensive datasets and high-velocity data ingestion. In conclusion, the selection of a database depends on the specific needs of the application. SQL databases are preferred for transactional systems with complex relationships, while NoSQL databases excel in scenarios demanding flexibility and scalability. The study provides insights into hybrid approaches, leveraging both database types to optimize system performance.展开更多
Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT...Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT scans can,however,be used to diagnose it.In this proposed model,Deep Learning technology is used to detect thyroid diseases.A Convolution Neural Network(CNN)based modified ResNet architecture is employed to detectfive different types of thyroid diseases namely 1.Hypothyroid 2.Hyperthyroid 3.Thyroid cancer 4.Thyroiditis 5.Thyroid nodules.In the proposed work,the training method is enhanced using dual optimizers for better accuracy and results.Keras,a Python library that is high level runs as the main part of the Tensor Flow framework.It is used in the proposed work to implement deep learning techniques.The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease.By applying Adam and SGD(Stochastic Gradient Descent)optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model.After retraining the model with SGD,the modified ResNet provides more accuracy of about 97%whereas the basic ResNet architecture attains 94%accuracy.A web-based frame-work is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.展开更多
Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, t...Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition(EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions(IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.展开更多
This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business v...This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.展开更多
文摘The research aims to explore the transition from monolithic Digital Experience Platforms (DXPs) to Microservices-based DXPs, addressing scalability challenges. The study systematically decomposes monolithic structures into Microservices, emphasizing business capability and subdomain decomposition. Concrete insights, challenges, and solutions encountered during this transformation process are presented. The research contributes valuable insights into the challenges and benefits of adopting Microservices in DXPs. Results highlight the importance of architectural patterns and strategic scaling dimensions for improved performance and scalability. The case study on Backbase’s Engagement Banking Platform showcases successful implementation, providing flexibility, integration, and efficient development in the evolving DXP landscape.
文摘Data storage solutions are a crucial aspect of any application, significantly impacting data management and system performance. This article explores the rationale behind utilizing both SQL and NoSQL databases, addressing key questions about when each type is preferable. The background emphasizes the importance of selecting the appropriate database technology to meet specific application requirements. The purpose of this research is to provide a comprehensive guide for choosing between SQL and NoSQL databases based on various factors, including workload characteristics, scalability needs, and consistency requirements. To achieve this, we examine different strategies for implementing SQL and NoSQL databases in large-scale distributed applications and systems. The research method involves a comparative analysis of the features, advantages, and limitations of both database types. We specifically focus on scenarios involving read-heavy versus write-heavy systems and the trade-offs between availability and consistency. The results of this research indicate that SQL databases, with their relational structure and ACID compliance, are ideal for applications requiring complex queries and data integrity. In contrast, NoSQL databases, offering schema flexibility and horizontal scalability, are better suited for managing extensive datasets and high-velocity data ingestion. In conclusion, the selection of a database depends on the specific needs of the application. SQL databases are preferred for transactional systems with complex relationships, while NoSQL databases excel in scenarios demanding flexibility and scalability. The study provides insights into hybrid approaches, leveraging both database types to optimize system performance.
基金Dr. Deepak Dahiya would like to thank Deanship of Scientific Re-search at MajmaahUniversity for supporting his work under Project No. (R-2022-45)。
文摘Thyroid disease is a medical condition caused due to the excess release of thyroid hormone.It is released by the thyroid gland which is in front of the neck just below the larynx.Medical pictures such as X-rays and CT scans can,however,be used to diagnose it.In this proposed model,Deep Learning technology is used to detect thyroid diseases.A Convolution Neural Network(CNN)based modified ResNet architecture is employed to detectfive different types of thyroid diseases namely 1.Hypothyroid 2.Hyperthyroid 3.Thyroid cancer 4.Thyroiditis 5.Thyroid nodules.In the proposed work,the training method is enhanced using dual optimizers for better accuracy and results.Keras,a Python library that is high level runs as the main part of the Tensor Flow framework.It is used in the proposed work to implement deep learning techniques.The comparative analysis of the proposed model and the existing work helps to show that there is a great improvement in the performance metrics in classifying the type of thyroid disease.By applying Adam and SGD(Stochastic Gradient Descent)optimizers in the training phase of the proposed model it was identified that these increase the operational efficiency of the modified ResNet model.After retraining the model with SGD,the modified ResNet provides more accuracy of about 97%whereas the basic ResNet architecture attains 94%accuracy.A web-based frame-work is also developed which yields the type of thyroid disease as the output for a given input scanned image of the system.
文摘Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition(EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions(IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.
文摘This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.
