Papers by Balakrishna Pothineni
European Journal of Computer Science and Information Technology, 2024
Detecting text generated by large language models (LLMs) is a growing challenge as these models p... more Detecting text generated by large language models (LLMs) is a growing challenge as these models produce outputs nearly indistinguishable from human writing. This study explores multiple detection approaches, including a Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, a Transformer block, and a fine-tuned distilled BERT model. Leveraging BERT's contextual understanding, we train the model on diverse datasets containing authentic and synthetic texts, focusing on features like sentence structure, token distribution, and semantic coherence. The fine-tuned BERT outperforms baseline models, achieving high accuracy and robustness across domains, with superior AUC scores and efficient computation times. By incorporating domain-specific training and adversarial techniques, the model adapts to sophisticated LLM outputs, improving detection precision. These findings underscore the efficacy of pretrained transformer models for ensuring authenticity in digital communication, with potential applications in mitigating misinformation, safeguarding academic integrity, and promoting ethical AI usage.
International Journal of Computer Science and Information Technology Research (IJCSITR) , 2024
The rapid advancement of digital ecosystems, fueled by globalization, cloud migration, and
expan... more The rapid advancement of digital ecosystems, fueled by globalization, cloud migration, and
expanding connectivity, demands cybersecurity frameworks capable of countering sophisticated
and ever-evolving threats. Traditional perimeter-based security models are increasingly
inadequate against the complexity of modern cyberattacks. This paper explores cutting-edge
cybersecurity paradigms, including Zero Trust Architecture (ZTA), serverless computing for
dynamic network profiling, enterprise-scale threat modeling, and resilient strategies designed for
next-generation connectivity. Through this examination, we propose a cohesive approach to
creating adaptive, scalable, and resilient cybersecurity systems, offering actionable insights to
address the intricate challenges posed by evolving threat landscapes and technological innovations
INTERNATIONAL JOURNAL OF RESEARCH And ANALYTICAL REVIEWS, 2024
User-facing experiences in Android development have rapidly evolved with the growing need for rea... more User-facing experiences in Android development have rapidly evolved with the growing need for real-time engagement
via push notifications, even when applications are not actively running. This paper explores the design and implementation
challenges of handling push notifications in Android applications, particularly in scenarios where the device is in sleep mode or
the app is running in the background. The paper addresses both theoretical and technical aspects, drawing on recent literature
from IEEE and other academic sources. It provides an enhanced review of strategies for optimizing notification delivery latency,
minimizing battery drain, ensuring data security, and enhancing user engagement. Experimental results include detailed
comparisons of Firebase Cloud Messaging (FCM) and background services, along with user feedback. Finally, this paper discusses
future enhancements, including the integration of machine learning models to improve notification efficiency and further reduce
battery consumption.
Journal of Software Engineering (JSE), 2024
The migration of legacy data warehouses like Teradata to cloud-native platforms such
as Google B... more The migration of legacy data warehouses like Teradata to cloud-native platforms such
as Google BigQuery is a transformative step toward modernizing data management and
analytics. This paper presents a structured framework to address technical, operational,
and strategic challenges, including schema incompatibilities, query translation, and
compliance requirements. Leveraging tools like Google’s Schema Conversion Tool,
Pub/Sub, and Dataflow, the framework ensures seamless schema transformation, data
migration, and query optimization. Emphasis is placed on data security, regulatory
compliance, and cost-efficiency. By aligning technical goals with business objectives
and fostering user adoption, this guide equips organizations to unlock the full potential
of scalable and real-time cloud analytics. Actionable insights and real-world use cases
provide a definitive roadmap for successful migrations
Journal of Software Engineering (JSE), 2024
This paper provides a detailed review of recent advancements in cloud
infrastructure automation,... more This paper provides a detailed review of recent advancements in cloud
infrastructure automation, artificial intelligence (AI) applications in cybersecurity, and
advanced IPTV technologies, highlighting their transformative roles in digital
innovation. Key areas explored include Terraform’s capabilities in Infrastructure as
Code (IaC), which significantly enhance automation in cloud environments, alongside
AI-driven security protocols that fortify threat detection and response mechanisms.
Furthermore, the paper examines Very Large Scale Integration (VLSI) implementations
in IPTV for optimized streaming and Oracle’s innovations in database management,
emphasizing sharding and Transparent Data Encryption (TDE) for secure, scalable
data handling. By focusing on machine learning, sharded architectures, AI-enhanced
Customer Relationship Platforms (CRP), and event-driven data integration, this paper
elucidates how these technologies contribute to advancing automation, data security,
and customer engagement across integrated digital ecosystems.
IJRAR, 2024
The rapid proliferation of data in modern enterprises necessitates advanced solutions for seamles... more The rapid proliferation of data in modern enterprises necessitates advanced solutions for seamless integration and
transformation. AWS Glue, a fully managed and serverless ETL (Extract, Transform, Load) service, emerges as a versatile tool for
addressing the challenges of data integration, scalability, and real-time processing. This paper explores AWS Glue's architecture,
features, and capabilities, highlighting its strengths and identifying areas for improvement. Comparative analyses with leading ETL
tools and real-world use cases illustrate AWS Glue's potential in data lake formation, real-time analytics, and migration workflows.
Additionally, the paper examines emerging trends and future directions in serverless ETL technologies, offering insights into their
role in shaping enterprise strategies. By leveraging AWS Glue, organizations can establish efficient, cost-effective, and scalable
data workflows, empowering them to extract actionable insights and maintain a competitive edge in data-driven ecosystems
International Journal of Advanced Research in Computer and Communication Engineering, 2024
In the evolving landscape of software development, architectural decisions significantly impact ... more In the evolving landscape of software development, architectural decisions significantly impact application
scalability, maintainability, and operational efficiency. Monolithic and Microservices architectures are two dominant
paradigms, each offering distinct advantages and posing unique challenges. While microservices provide modularity and
scalability, their complexity often leads organizations to reconsider monolithic designs for certain scenarios. This paper
critically examines both architectures, exploring their strengths, limitations, and trade-offs. Through case studies and
comparative analysis, it highlights contexts where reverting to a monolithic approach aligns better with operational goals.
Additionally, the paper outlines a structured framework for transitioning between these paradigms and discusses
emerging hybrid architectural models that blend simplicity with scalability. By offering a balanced perspective, this work
equips practitioners with actionable insights to make informed decisions tailored to their technical and business needs.
International Journal of Computer Science and Information Technology Research, 2024
With the increasing demands of data privacy regulations such as GDPR, HIPAA, and CCPA, ensuring r... more With the increasing demands of data privacy regulations such as GDPR, HIPAA, and CCPA, ensuring regulatory compliance during software development has become a critical yet challenging task. Manual compliance verification often introduces delays, inefficiencies, and the potential for human error, hindering development cycles. To overcome these challenges, this paper proposes a framework for automating compliance verification within Continuous Integration/Continuous Delivery (CI/CD) pipelines. By leveraging tools such as Open Policy Agent (OPA), OWASP ZAP, and Terraform, the framework integrates real-time compliance checks directly into the development workflow. This approach ensures consistent regulatory adherence, reduces reliance on manual processes, and accelerates software delivery. The proposed framework highlights how automation can minimize compliance bottlenecks, improve security, and enhance overall efficiency in modern software development pipelines.
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Papers by Balakrishna Pothineni
expanding connectivity, demands cybersecurity frameworks capable of countering sophisticated
and ever-evolving threats. Traditional perimeter-based security models are increasingly
inadequate against the complexity of modern cyberattacks. This paper explores cutting-edge
cybersecurity paradigms, including Zero Trust Architecture (ZTA), serverless computing for
dynamic network profiling, enterprise-scale threat modeling, and resilient strategies designed for
next-generation connectivity. Through this examination, we propose a cohesive approach to
creating adaptive, scalable, and resilient cybersecurity systems, offering actionable insights to
address the intricate challenges posed by evolving threat landscapes and technological innovations
via push notifications, even when applications are not actively running. This paper explores the design and implementation
challenges of handling push notifications in Android applications, particularly in scenarios where the device is in sleep mode or
the app is running in the background. The paper addresses both theoretical and technical aspects, drawing on recent literature
from IEEE and other academic sources. It provides an enhanced review of strategies for optimizing notification delivery latency,
minimizing battery drain, ensuring data security, and enhancing user engagement. Experimental results include detailed
comparisons of Firebase Cloud Messaging (FCM) and background services, along with user feedback. Finally, this paper discusses
future enhancements, including the integration of machine learning models to improve notification efficiency and further reduce
battery consumption.
as Google BigQuery is a transformative step toward modernizing data management and
analytics. This paper presents a structured framework to address technical, operational,
and strategic challenges, including schema incompatibilities, query translation, and
compliance requirements. Leveraging tools like Google’s Schema Conversion Tool,
Pub/Sub, and Dataflow, the framework ensures seamless schema transformation, data
migration, and query optimization. Emphasis is placed on data security, regulatory
compliance, and cost-efficiency. By aligning technical goals with business objectives
and fostering user adoption, this guide equips organizations to unlock the full potential
of scalable and real-time cloud analytics. Actionable insights and real-world use cases
provide a definitive roadmap for successful migrations
infrastructure automation, artificial intelligence (AI) applications in cybersecurity, and
advanced IPTV technologies, highlighting their transformative roles in digital
innovation. Key areas explored include Terraform’s capabilities in Infrastructure as
Code (IaC), which significantly enhance automation in cloud environments, alongside
AI-driven security protocols that fortify threat detection and response mechanisms.
Furthermore, the paper examines Very Large Scale Integration (VLSI) implementations
in IPTV for optimized streaming and Oracle’s innovations in database management,
emphasizing sharding and Transparent Data Encryption (TDE) for secure, scalable
data handling. By focusing on machine learning, sharded architectures, AI-enhanced
Customer Relationship Platforms (CRP), and event-driven data integration, this paper
elucidates how these technologies contribute to advancing automation, data security,
and customer engagement across integrated digital ecosystems.
transformation. AWS Glue, a fully managed and serverless ETL (Extract, Transform, Load) service, emerges as a versatile tool for
addressing the challenges of data integration, scalability, and real-time processing. This paper explores AWS Glue's architecture,
features, and capabilities, highlighting its strengths and identifying areas for improvement. Comparative analyses with leading ETL
tools and real-world use cases illustrate AWS Glue's potential in data lake formation, real-time analytics, and migration workflows.
Additionally, the paper examines emerging trends and future directions in serverless ETL technologies, offering insights into their
role in shaping enterprise strategies. By leveraging AWS Glue, organizations can establish efficient, cost-effective, and scalable
data workflows, empowering them to extract actionable insights and maintain a competitive edge in data-driven ecosystems
scalability, maintainability, and operational efficiency. Monolithic and Microservices architectures are two dominant
paradigms, each offering distinct advantages and posing unique challenges. While microservices provide modularity and
scalability, their complexity often leads organizations to reconsider monolithic designs for certain scenarios. This paper
critically examines both architectures, exploring their strengths, limitations, and trade-offs. Through case studies and
comparative analysis, it highlights contexts where reverting to a monolithic approach aligns better with operational goals.
Additionally, the paper outlines a structured framework for transitioning between these paradigms and discusses
emerging hybrid architectural models that blend simplicity with scalability. By offering a balanced perspective, this work
equips practitioners with actionable insights to make informed decisions tailored to their technical and business needs.
expanding connectivity, demands cybersecurity frameworks capable of countering sophisticated
and ever-evolving threats. Traditional perimeter-based security models are increasingly
inadequate against the complexity of modern cyberattacks. This paper explores cutting-edge
cybersecurity paradigms, including Zero Trust Architecture (ZTA), serverless computing for
dynamic network profiling, enterprise-scale threat modeling, and resilient strategies designed for
next-generation connectivity. Through this examination, we propose a cohesive approach to
creating adaptive, scalable, and resilient cybersecurity systems, offering actionable insights to
address the intricate challenges posed by evolving threat landscapes and technological innovations
via push notifications, even when applications are not actively running. This paper explores the design and implementation
challenges of handling push notifications in Android applications, particularly in scenarios where the device is in sleep mode or
the app is running in the background. The paper addresses both theoretical and technical aspects, drawing on recent literature
from IEEE and other academic sources. It provides an enhanced review of strategies for optimizing notification delivery latency,
minimizing battery drain, ensuring data security, and enhancing user engagement. Experimental results include detailed
comparisons of Firebase Cloud Messaging (FCM) and background services, along with user feedback. Finally, this paper discusses
future enhancements, including the integration of machine learning models to improve notification efficiency and further reduce
battery consumption.
as Google BigQuery is a transformative step toward modernizing data management and
analytics. This paper presents a structured framework to address technical, operational,
and strategic challenges, including schema incompatibilities, query translation, and
compliance requirements. Leveraging tools like Google’s Schema Conversion Tool,
Pub/Sub, and Dataflow, the framework ensures seamless schema transformation, data
migration, and query optimization. Emphasis is placed on data security, regulatory
compliance, and cost-efficiency. By aligning technical goals with business objectives
and fostering user adoption, this guide equips organizations to unlock the full potential
of scalable and real-time cloud analytics. Actionable insights and real-world use cases
provide a definitive roadmap for successful migrations
infrastructure automation, artificial intelligence (AI) applications in cybersecurity, and
advanced IPTV technologies, highlighting their transformative roles in digital
innovation. Key areas explored include Terraform’s capabilities in Infrastructure as
Code (IaC), which significantly enhance automation in cloud environments, alongside
AI-driven security protocols that fortify threat detection and response mechanisms.
Furthermore, the paper examines Very Large Scale Integration (VLSI) implementations
in IPTV for optimized streaming and Oracle’s innovations in database management,
emphasizing sharding and Transparent Data Encryption (TDE) for secure, scalable
data handling. By focusing on machine learning, sharded architectures, AI-enhanced
Customer Relationship Platforms (CRP), and event-driven data integration, this paper
elucidates how these technologies contribute to advancing automation, data security,
and customer engagement across integrated digital ecosystems.
transformation. AWS Glue, a fully managed and serverless ETL (Extract, Transform, Load) service, emerges as a versatile tool for
addressing the challenges of data integration, scalability, and real-time processing. This paper explores AWS Glue's architecture,
features, and capabilities, highlighting its strengths and identifying areas for improvement. Comparative analyses with leading ETL
tools and real-world use cases illustrate AWS Glue's potential in data lake formation, real-time analytics, and migration workflows.
Additionally, the paper examines emerging trends and future directions in serverless ETL technologies, offering insights into their
role in shaping enterprise strategies. By leveraging AWS Glue, organizations can establish efficient, cost-effective, and scalable
data workflows, empowering them to extract actionable insights and maintain a competitive edge in data-driven ecosystems
scalability, maintainability, and operational efficiency. Monolithic and Microservices architectures are two dominant
paradigms, each offering distinct advantages and posing unique challenges. While microservices provide modularity and
scalability, their complexity often leads organizations to reconsider monolithic designs for certain scenarios. This paper
critically examines both architectures, exploring their strengths, limitations, and trade-offs. Through case studies and
comparative analysis, it highlights contexts where reverting to a monolithic approach aligns better with operational goals.
Additionally, the paper outlines a structured framework for transitioning between these paradigms and discusses
emerging hybrid architectural models that blend simplicity with scalability. By offering a balanced perspective, this work
equips practitioners with actionable insights to make informed decisions tailored to their technical and business needs.