International Journal of Science and Research (IJSR), 2025
Serverless architecture is increasingly used by companies and organizations to reduce costs and e... more Serverless architecture is increasingly used by companies and organizations to reduce costs and enhance scalability. However, the difficulty of performing instantaneous and accurate incident analysis remains a major challenge, as the number of incidents and threats continues to rise. Currently, most serverless platforms rely on centralized Identity and Access Management (IAM) to control system access. This reliance raises concerns regarding the inability to audit access events and the potential impact of IAM failures on other services.
International Journal of Science and Research (IJSR), 2025
Phishing involves soliciting sensitive information by sending misleading emails that lure users t... more Phishing involves soliciting sensitive information by sending misleading emails that lure users to mimic legitimate websites, causing significant financial and data losses. The increase in phishing websites elevates the risk for users. Effective real-time phishing URL detection permits dynamic classification and blocking, acknowledging that malicious URLs may change over time. Accurately distinguishing between legitimate and phishing URLs constitutes a critical web-security challenge [2]. Real-time phishing URL detection methods aim to detect and dynamically block phishing URLs. Modeling real-time phishing URL detection as a reinforcement learning (RL) problem helps users avoid suspected phishing URLs because RL identifies the environment as phishing or legitimate, with the objective of dynamic classification and blocking. Reinforcement learning addresses classification as a control problem where an agent learns to make optimal decisions by interacting with an uncertain environment. Essential ingredients of an RL system include a policy, a reward signal, a value function, and an environment model. Since agents can learn value functions, policies, and models using various methods, many distinct RL algorithms have been proposed and applied to diverse problems. Leveraging diverse user interaction patterns with URLs is a promising approach for real-time phishing URL detection; if a website is widely known to be safe, it is unlikely to host malicious content.
International Journal of Computer Application , 2025
Observability is an extension of the monitoring approach with a slight difference. It does provid... more Observability is an extension of the monitoring approach with a slight difference. It does provide essential context to understand an issue but lacks clear dashboard-driven alerting. Observability is a broader term that can include monitoring. It is a term used to describe a system's ability to allow the user to ask any question of it and receive an answer. The concept of observability originated in control theory, where it was defined as a measure of how well internal states of a system can be inferred from knowledge of its external outputs. That's the technical definition, but what does it mean practically? Basically, the more observability hooks you expose regarding what's happening inside your system, the easier it will be to answer any questions regarding your system's state over time.
International Journal of Advanced Scientific and Technical Research , 2025
There is an unprecedented increase in data quantity and variety worldwide. Along with the growing... more There is an unprecedented increase in data quantity and variety worldwide. Along with the growing volume of training data for predictive models, the complexity of training rises accordingly, making large-scale data sets a prime Big Data challenge. Predictive learning models discover patterns in training data and label new instances accurately. Efficiently handling unstructured, large-scale big data sets necessitate new machine learning methods that combine boosting and classification algorithms. Extreme Learning Machine (ELM), based on generalized Single-hidden Layer Feedforward Networks (SLFNs), features short training times relative to traditional gradient-based methods, strong generalization to unseen multi-class examples, and parameter-free hidden nodes. ELM finds applications in document classification, bioinformatics, and multimedia recognition. Recent research focuses on distributed and parallel frameworks for predictive modeling.
International Journal of Computer Application , 2025
Kubernetes is primarily used for the deployment of the cloud-native application, containerbased a... more Kubernetes is primarily used for the deployment of the cloud-native application, containerbased application, or microservices application built about stateless application architectures. Kubernetes consists of thick client-server architecture, where services run on a cluster of machines, and the user communicates with the K8s master server. Deployments are described in JSON or YAML formats. These deployment descriptors are provided to K8s APIs. Kubernetes opens APIs for both consumers and deployers of services. Kubernetes provides an API abstraction layer to support the deployment of many distributed systems. Kubernetes features rich APIs for controlling various platform features, including application deployment, application scaling, and deployment checking and monitoring.
International Journal of Science and Research, 2024
In the world of electric vehicle (EV) navigation, the quest for the best routing algorithms is cr... more In the world of electric vehicle (EV) navigation, the quest for the best routing algorithms is crucial for creating efficient and sustainable transportation options. Conventional methods like Dijkstra's Algorithm show limitations in scalability and adaptability in changing conditions, which require frequent adjustments. This study supports using Ant-Colony Optimization (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA) as heuristic techniques to optimize EV routes. A comprehensive assessment of several optimization algorithms for solving the Electric Vehicle Routing Problem (EVRP) provided valuable information on their efficiency and effectiveness. This research evaluates Ant-Colony Optimization (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA) as possible approaches to optimize the Electric Vehicle Routing Problem (EVRP). ACO stands out as the top contender, showing better precision and dependability in providing the best routing options for various EVRP situations. Despite the extended computational duration, ACO stands out in pinpointing the most optimal travel distances, making it a strong option for EVRP optimization. Simulated Annealing shows respectable performance despite some variability, coming in behind the Genetic Algorithm in ranking for finding the shortest routes. ACO's effectiveness in addressing convergence problems and providing eco-friendly transportation solutions makes it the top choice for optimizing EV routing. The provided visual representation explains how ACO works by showing the most efficient routes between set map points, strategically connected to charging stations for user convenience.
This book offers an in-depth examination of the transformative impact Artificial Intelligence (AI... more This book offers an in-depth examination of the transformative impact Artificial Intelligence (AI) and Machine Learning (ML) have on DevOps and Site Reliability Engineering (SRE). It sits at the intersection of the cutting edge in AI and at how actual operations can use smart technology to refine your CI/CD pipeline, tell when incidents are rolling your way, help to automate resolution and improve the eyes on monitoring. Readers will learn complete details on AI-driven observability, finding anomalies, performance tuning, and capacity planning—helping organizations to predict failures, improve up times and accelerate software with a rock rock-solid foundation. With clear and detailed explanations, bolstered by case studies with leaders from the industry, and actionable frameworks to implementation, DevOps engineers, SRE professionals, and IT executives will learn how to effectively operationalize AI within their environments. It also includes critical content on AI ethics, transparency, and governance—a must for today's high-stakes production environments. Readers will walk away fully prepared to use AI to automate the repetitive and time-consuming tasks based on data and to make data-informed decisions that strengthen their infrastructure and deliver operational excellence.
This book is a complete guide for professionals and data enthusiasts who want to make the most of... more This book is a complete guide for professionals and data enthusiasts who want to make the most of Microsoft’s cloud-native ecosystem for big data analytics. It covers essential services like Azure Synapse Analytics, Microsoft Fabric, and Power BI. The bookprovides a full framework for scalable data processing and smart decision-making. Readers will learn best practices for data ingestion, transformation, storage, modeling, and visualization. They will also see how to combine data engineering, data science,and business intelligence workflows within a single Microsoft environment. With practical examples and architectural designs, this book helps readers build secure, effective, and cost-efficient analytics solutions that meet the needs of today’s enterprises.
The tremendous societal and economic promise of AI has been emphasized repeatedly of late. Ubiqui... more The tremendous societal and economic promise of AI has been emphasized repeatedly of late. Ubiquitous applications of AI permeate the fabric of every society, discuss every facet of our future and consider every sensitive socioeconomic issue [1-2]. Societal sectors such as health and medicine, transportation, communications, infrastructure, education, defence, governance, environment and others use, interact with and deliberate on the benefits and drawbacks – sometimes specific, sometimes general – of AI. These applications create many benefits, but also create certain ethical challenges, dilemmas and concerns. Industry developers aspire to produce autonomous systems that can make ethical decisions. Much work has considered how humans might produce such ethical systems.
Artificial Intelligence (AI) has emerged as one of the most transformative forces in business in ... more Artificial Intelligence (AI) has emerged as one of the most transformative forces in business in this century. As companies combine datasets, AI infrastructure, and powerful AI algorithms to produce smarter and more generalized AI models, business leaders in every industry must be prepared to fundamentally reorganize their operations around models and products created with the help of this new technology. Generic AI assistants will affect the way knowledge workers do research, draft documents, analyze information, and create visual aids.
In today's interconnected and competitive world, cross-industry knowledge transfer is vital for i... more In today's interconnected and competitive world, cross-industry knowledge transfer is vital for innovation, often yielding superior results to any organization. At the same time, organizations are increasingly called upon to foster inclusive innovation in order to embed fairness for economic and social impact. In this chapter, we provide a backdrop to discussion in the rest of the book, briefly describing our perspective on innovation as a business model design activity. We draw on a number of key, partially overlapping literatures - especially design, innovation, knowledge, sensemaking and business model - to illuminate how organizations can draw on the rich knowledge bases, reflexive practices and inclusive innovation action effects observable in other industries, people and the world around them. These literatures help us to model how the acts of design during the life of a business model can evoke a shared 'sense' of something greater than the individual, potentially transcending a singular industry character. Cross-industry transfer in micro-foundational terms, thus offers researchers and practitioners a heuristic for actively developing principles and practices that draw upon relational knowledge bases and inclusively innovative intent.
Companies today use increasingly more machine learning in their products and services. From recom... more Companies today use increasingly more machine learning in their products and services. From recommendation engines to computer vision to natural language processing, the usage of machine learning is exploding. But while the adoption of machine learning continues to grow, deploying and maintaining machine learning systems in production remains painfully challenging [1-3]. Respondents to industry surveys report that deploying machine learning is harder than any other part of the software development lifecycle. The gap between niche research and core business functions threatens to hollow out investment in new ideas: a significant amount is spent on machine learning annually, but a large percentage is not delivering any value [2,4,5]. Firms are spending huge sums on machine learning, but the vast majority of projects are failing. Organizations that successfully implement strong systems to support the MLOps function should be rewarded with vectors of highly leveraged development teams, and a reliable return on their investment in large scale machine learning studies.
The historical development of Artificial Intelligence (AI) has been an
incremental one, where cas... more The historical development of Artificial Intelligence (AI) has been an incremental one, where cascades of many breakthroughs in technology have led to advances in intelligence for all-parallel, all-digital simulators for systems with pre-defined rules sets [1-2]. Notable exceptions exist which display truly general native intelligence, and which do not require nearly infinite compute resources, but none have yet been able to successfully navigate in physical environments to create lasting organizations, replicate themselves, and achieve self-sustainability quality, as these forms of life do [3-5]. In mimicking the intelligence of these complex naturally evolved Living Systems, AI systems have become general production-quality, affordable tools for high-volume data and signal processing tasks, and semi-autonomous decision-support systems for moderately larger artifice processes in many mission-critical domains undertaken by humans and human-centered organizations, supported by their technical systems.
The foreseeable future will likely see a profound impact caused by the advancing technology of Ar... more The foreseeable future will likely see a profound impact caused by the advancing technology of Artificial Intelligence systems, in our day-to-day activities and routines, just like the same effect brought about by the introduction of the internet and World Wide Web [1-2]. The AIs currently in use, and the coming future of general intelligence machine systems, demand and expect appropriate – or even the best possible – Governance and Compliance Laws to be established to avert any possible negative consequences that could arise from their interaction with humanity, nature, the economy, governments, and society [2-4]. In other words, they need to answer two vital questions: who governs whom? And who governs what? That said, the question of Governance indeed takes centre stage.
Artificial Intelligence (AI) and Machine Learning (ML) have matured over the past two decades as ... more Artificial Intelligence (AI) and Machine Learning (ML) have matured over the past two decades as decision-making technologies for systems and processes in varied domains [1-3]. AI and ML solutions are increasingly being moved from on-premises deployments to the Cloud, for multiple logical and practical reasons related to business agility, economical operations, performance at scale, availability, and security [2,4]. With the advent of the Internet of Things (IoT) and the increasing use of embedded or device-level intelligence for real-time decision-making and information filtering, there is a recent movement to define solutions that blend the Cloud with Edge devices. The goal is to leverage the benefits of both Cloud and Edge in a Hybrid architecture to solve specific business problems. From a research and education perspective, there are key questions that await answers: what is Cloud-Native AI Architectures? What is Cloud + Edge-AI Architectures? What are Hybrid Models? How do we design, code, test, deploy, and manage lifecycle for Cloud-native, Edge-AI, and Hybrid Models? What types of business problems are best solved using one of these architectures, and how do I know? What AI models work best in these environments? How do I put in Cloud, Edge, and Hybrid deployment best practices? To operationalize and automate these questions, what tools, techniques, and platforms do we need? This book attempts to answer these questions in its humble way, while remaining technology-agnostic wherever possible. The book takes the position that we will never be able to automate
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Papers by Swarup Panda
Books by Swarup Panda
environment and others use, interact with and deliberate on the benefits and
drawbacks – sometimes specific, sometimes general – of AI. These applications create many benefits, but also create certain ethical challenges, dilemmas and concerns. Industry developers aspire to produce autonomous systems that can make ethical decisions. Much work has considered how humans might produce such ethical systems.
practitioners a heuristic for actively developing principles and practices that draw upon relational knowledge bases and inclusively innovative intent.
incremental one, where cascades of many breakthroughs in technology have led to advances in intelligence for all-parallel, all-digital simulators for systems with pre-defined rules sets [1-2]. Notable exceptions exist which display truly general native intelligence, and which do not require nearly infinite compute resources, but none have yet been able to successfully navigate in physical environments to create lasting organizations, replicate themselves, and achieve self-sustainability quality, as these forms of life do [3-5]. In mimicking the intelligence of these complex naturally evolved Living Systems, AI systems have become general production-quality, affordable tools for high-volume data and signal processing tasks, and semi-autonomous decision-support systems for moderately larger artifice processes in many mission-critical domains undertaken by humans and human-centered organizations, supported by their technical systems.