American International Journal of Computer Science and Technology
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Cloud Orchestration with Kubernetes/Docker
The increasing adoption of artificial intelligence (AI) workloads has placed significant demands on cloud-native infrastructure, particularly in terms of scalability, resource isolation, and automated management of containerised services. Container orchestration platforms such as Kubernetes and Docker have thereby become critical enablers for deploying AI/ML pipelines at scale. For example, research shows that Kubernetes is effective for container orchestration in AI cloud environments (Lokiny, 2022). Additionally, machine-learning–based orchestration frameworks for containers have been explored to improve scheduling, allocation and performance (Zhong et al., 2021). Yet, despite these advances, there remains a paucity of comparative analysis focused on AI workloads especially those that contrast orchestration platforms in hybrid or multi-cloud settings, and that evaluate metrics such as latency, throughput, fault-tolerance, and cost-efficiency. This paper presents a systematic comparative study of Docker- and Kubernetes-based orchestration frameworks for AI workloads, employing a multi-factor benchmark across scalability, resource utilisation, fault resilience, and operational cost. The experimental setup utilises micro-service and deep-learning inference pipelines deployed via Docker Swarm and Kubernetes across public cloud infrastructure. Results indicate that Kubernetes outperforms Docker Swarm in horizontal scaling and fault resilience, while Docker Swarm demonstrates marginal benefits in simplicity of deployment and lower management overhead in small-scale scenarios. Furthermore, the cost-performance trade-offs reveal that orchestration maturity and autoscaling policies favour Kubernetes when workloads grow beyond moderate scale. The paper discusses the implications for AI DevOps teams and cloud architects, offering guidelines for selecting and configuring orchestration technologies aligned with AI workload characteristics. In conclusion, as AI workloads continue shifting toward containerised, distributed, and hybrid-cloud environments, the orchestration strategy plays a pivotal role in ensuring performance, reliability, and cost-efficiency of the underlying infrastructure
Automated Returns Management and Reverse Logistics in SAP
Modern supply chains are significantly influenced by reverse logistics and returns management. The snowball effect that has resulted from the proliferation of online purchasing and the ease with which customers can request returns has compelled businesses and their customers to ensure that they have well-organized and automated return systems. This article is about returns management and reverse logistics with computers in a SAP environment--the subject however is restricted to these three SAP functionalities: SAP S/4HANA EWM and CRM toolsetne Mechanism for Returns: Using such SAP technologies (not yet fully automated) as processing credit memos or changing inventory, managing reverse logistics and permission Continuing Return Material Authorizations (RMA)--this report on how electronic technology has introduced new concepts into returns management looks at past difficulties and changing technologies in the future.It describes some of the benefits of automation in SAP, and how happy customers will purchase more products from you while also reducing your staff's workload. These benefits include improved operational efficiency--no longer having to return items cross country-andespecially lower costs The action returns automation policies was brought to the institutions which pioneered it together leverage points
The rapid increase of the digital infrastructure and the sophistication of the contemporary regulatory requirements have turned the compliance with the cybersecurity into a significant, yet increasingly difficult task on behalf of businesses. Conventional compliance administration, which is based on regular examinations, paper validation, and reactive responses, does not work due to the dynamism of cyber threats or emerging standards that include ISO 27001, NIST 800-53, and GDPR. The unified Security and Compliance Automation Framework proposed in this paper uses artificial intelligence (AI) and machine learning (ML) to automate compliance verification, policy enforcement, and threat response, using the DevSecOps-driven orchestration framework. The framework maximizes real-time ingestion of data, adaptive rule mapping, and proactive analytics to ensure continual observation of the hybrid and cloud-native infrastructures and control these platforms proactively. Evaluations on experimental result of a controlled hybrid cloud testbed show that the solution applies better compliance measurements (up to 93 percent), shorten audit cycles (by 68 percent), and decreases the time to incident response (by 42 percent) than manual old-fashioned methods. Its modular design allows its use in areas beyond healthcare including finance and enterprise IT regarding enforcing security controls and policy alignment. Overall, these results highlight the possibility of the framework to create a self-regulating and resilient compliance ecosystem in such a way that organizations can move forward to no longer need response models on defense but remain on a framework of guaranteed regulatory compliance and operational security posture
Agentic AI in Insurance: Moving Beyond Generative AI to Autonomous Decision-Making
The emergence of the Artificial Intelligence (AI) has revolutionised the operational and decision making of the international insurance sector. Although Generative AI has already transformed the technique of relying on information to produce content, claims consolidation, and client communication, the subsequent breakthrough is Agentic AI Stations that may release self-managed choices and aim-directed learning and take charge of actions without immediate human oversight. The paper presents the shift of AI used in the insurance industry towards agentic AI and its architectural, ethical, and operational consequences of implementing autonomous intelligence to underwrite, risk model, fraud detection, and claim management. Agents AGI works contrasts with classical super AI paradigm since it introduces goal-based reasoning, multi-agent interaction and self-improvement systems. In contrast to large language models (LLMs), like ChatGPT or Claude, which are designed to produce their own output given a prompt, agentic systems have the ability to start and finish activities, evaluate feedback, and autopilot judgments and plan with other virtual agents or human decision makers. Services in the insurance domain can use these systems to be able to independently analyze market environments, adjust their underwriting designs, make transactions within controlled limits, and respond to policy alterations. The transition of AI towards prompts (reactive) to intent (proactive) will require a new computational infrastructure that is the fusion of reinforcement learning (RL), knowledge graphs, explainable AI (XAI) and trust-based governance systems. In this paper, a multi-layered structure of Agentic Insurance Intelligence Framework (AIIF) is introduced that combines streams of risk data, behavioral economic and real time optimization models of policy to facilitate trustful autonomous practice. It is a qualitative and a quantitative study, which compares the traditional AI processes used in claims processing and agentic systems, which can reason under uncertainty. Results indicate that up to 45 percent accuracy of risk assessment, and 30 percent reduction in time to detect claims of fraudulent activities were recorded. Moreover, agentic reasoning layers have been shown to be more interpretable, regulatory-compliant as well as traceable of the decisions. The last section of this paper maintains that agentic AI is a not only a technological innovation but also a paradigm shift - of assistance to autonomy. Such architectures adopted by insurance businesses will result in a state of flexibility, efficiency and stability never felt in the dynamic risk environment
AI Tools for Automating Code Reviews, Providing Contextual Feedback, and Improving the Efficiency of the Review Process
Code review is an essential process in contemporary software development that guarantees code quality, safety, maintainability and teamwork. However, manual code reviews take a lot of time, are likely to fail because of mistakes, and depend on the reviewer's knowledge. As Artificial Intelligence (AI) becomes more popular, so does the trend toward automating and improving the code review process with Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM). This paper explores the advancements in AI tools aimed at automating code review processes, improving efficiency, and delivering relevant feedback. It highlights platforms like GitHub Copilot, Codacy, DeepCode, and CodeGuru, and discusses the technologies underpinning them, including static code analysis and NLP techniques. The study tests a dynamic system combining static analysis and contextual review on a public software repository, measuring review accuracy, time savings, false positives, and developer satisfaction. Results indicate that AI-powered review tools can reduce the review cycle by 40-60% and provide insights comparable to human reviewers. Challenges include addressing complex logic, ensuring useful feedback, and integrating seamlessly into developer workflows. The findings suggest significant potential for AI tools in code review, contingent upon team oversight, and signal avenues for future research in areas such as explainable AI and ethical considerations
Adaptive DevSecOps: Integrating AI-Driven Threat Detection in Continuous Delivery Pipelines
DevOps and Continuous Delivery (CD) have transformed the landscape of software development at a rapid rate, creating new security concerns that cannot be effectively resolved by older approaches. DevSecOps became a paradigm shift or model in which security was inserted into the software development process. Nevertheless, the growing complexity of microservices, cloud-native environments, and the changing threat environments all require more flexible and smarter solutions. The paper suggests an adaptive DevSecOps model powered by AI that incorporates real-time threat detection in CI/CD pipelines and allows for the prevention and mitigation of security threats proactively, without affecting the speed of development. We propose a system that integrates static and dynamic analysis that uses machine learning, behavioural anomaly detection and continuous learning techniques to secure pipelines. Model training and inference occur with logs, code repositories, and configuration data as input, and the model trains over time via feedback loops. The effectiveness and feasibility of the suggested approach have been confirmed by the experiments, which achieved extremely high detection accuracy (up to 97 percent) with low false positive rates and minimal latency. Real-world case study demonstrates system scalability and responsiveness in real-life scenarios. The integration issues, ethics, and challenges of adoption in the industry are also mentioned in the paper. Lastly, it presents research directions to be pursued in the future, which lie within the areas of explainability, adaptive response orchestration, and federated learning in collaborative threat inte
Adapting to Regulatory Changes with AI Automating Compliance in Guidewire's Claims Processes
It is still hard but important to make sure that insurance companies follow the rules in a world where the rules change quickly. With the rise of artificial intelligence (AI), it is now possible to automate these compliance processes on a large scale, especially on core platforms like Guidewire ClaimCenter. This paper looks at how AI can help the insurance industry deal with changes in regulations, focusing on how intelligent automation can make Guidewire's claims processes better. We examine an integrated strategy that includes Natural Language Processing (NLP), Machine Learning (ML), and Robotic Process Automation (RPA) to make it easier to keep up with compliance, map out regulations, sort through claims, and manage documents. The suggested method makes operations run more smoothly, lowers the risk of not following the rules, and makes sure that the rules are followed right away. We use a prototype integration in a simulated Guidewire environment to show how AI algorithms can automatically take in and understand changes to policies, update rule sets, and make logs that regulators can check. The results show that compliance mapping is 90% more accurate, the claims cycle is 60% faster, and manual work has gone down by 70%. This study demonstrates the feasibility of AI-driven compliance and provides insurers with a strategy to enhance the security of their claims systems amidst evolving regulations
Serverless Architecture Patterns: Deployment, Cost, and Latency Analysis
Serverless as one of the promising cloud computing technologies, Serverless allows resources to be provisioned automatically and assigned by cloud providers. Serverless architecture provides the ability to forget about infrastructure management and deliver auto-scaling capability, implement event-driven deployment, and charge at a very fine-grain level through the advent of Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS). The present paper gives a comprehensive overview of serverless architecture including its deployment models, cost considerations and changes in latency. Some of the following deployment patterns to be discussed in this paper will include microservices orchestration, fan-out/fan-in and event stream processing. An integrated evaluation model is conducted, that means the competitiveness of AWS Lambda, Azure Functions and Google Cloud Functions are compared. Cold start latency, execution time, and scalability are measured by evaluating real-world workloads. In the cost analysis, the time, memory, and calls of execution are considered. We also evaluate design trade-offs, and how to minimize latency and optimize costs. In our findings, albeit the strong benefits of serverless in terms of speed and cost-effectiveness, architectural and operational constraints will have to be taken into consideration in high-performance and real-time systems
Limitations of Code Analysis Tools in Detecting Runtime Errors, Performance Issues, and Other Vulnerabilities
Learning code is becoming a common, part of the software engineering procedures, therefore, code analysis tools are developed to enable programmers to identify errors in programs, preserve programming correctness, and locate possible vulnerability. However, in as much as they have become commonplace, these tools also have dire shortcomings in respect of runtime errors, bottlenecks, and unclear security vulnerability identification. The given paper talks about theoretical and practical drawbacks of both static and dynamic code analysis tools to the software systems in the real world. We look in detail at how they are detected, and we indicate both their failings and how these blind spots come about. Such restrictions fall into 3 general sets in which we consider shortcomings in the detection of runtime errors, inability to conduct admirable performance analysis and security weakness blind spots. On top of the literature review comes the historical perceptions and comparisons of some of the most popular tools such as SonarQube, Coverity, Fortify and Valgrind capabilities. Empirically We point out inconsistencies and boundaries in tool results by a methodology of empirical evaluation by a benchmark codebases characteristics of controlled experimentation. In our analysis, we identified that the code analysis environments tend to be unobehaved detecting concurrency related issues and memory inefficiency and context sensitive security vulnerability. We conclude by mentioning the perspective of the future improvement, including hybrid approaches to analysis, the possibility of introducing machine learning to identify the vulnerabilities, and better integration with the commercial set of run-time monitoring tools. In this paper, we have pointed out that the quality assurance of software must remain in much holistic form where the static analysis of the code is also combined with the dynamic instrumentation and analysis
Harnessing Generative AI in Guidewire: Transforming Insurance Operations with Intelligent Insights
The insurance market, which has long been associated with the need to use a lot of paperwork, manual processing and the use of legacy systems, is now in the transition to the digital world. One of the most disruptive technologies is Generative Artificial Intelligence (AI) that is causing a change. The provided paper talks about the ways to implement Generative AI in the Guidewire platform, which presupposes the opportunity to enhance the processing of claims, handling policies, communication with customers, detecting fraud, and underwriting. The work concentrates on the potentially smart application of automation, and discusses how Generative AI algorithms, in particular, large language models (LLMs) play a role in improving operational efficiency, predictive analytics, and personalized customer experiences. Mixed-method study, combining the qualitative value of case studies and the quantification of objective measures of the measurable metrics in the processes, we offer the proof of the objective value of AI-enabled systems. The research shows the ethical questions, safety of the data, the abject necessity to follow the regulations proposing the use of the strategic framework. The findings confirm the use of a hybrid of human and AI workflow whereby the humanity and AI integrate their benefits to bring about optimal results. The paper also enumerates a roadmap that can be followed by insurers intending to have a digital maturity of their business with the help of AI-based changes which incorporates guidelines, architectural drawings, performance benchmarks, and an illustration of future capabilities
Enhancing Supply Chain Resilience with AI/ML in Oracle Fusion SCM
With these increasingly complex yet volatile supply chains, organizations need a strong set of tactics that can help predict disruptions and streamline operations as well as being able to handle the risks effectively. The study involves the implementation of Artificial Intelligence (AI) and Machine Learning (ML) within the supply chain of Oracle Fusion Supply Chain Management (SCM) and how it can be improved through the introduction of the two technologies. The strategy depends on three significant applications namely, predictive maintenance, demand sensing, and supply risk analysis. Predictive maintenance employs Fusion SCM to improve the reliability of equipment by making use of IoT sensor readings and equipment history in the SCM and preventing unexpected malfunctions and planned downtime as well as reducing operational wastace. The advanced machine learning algorithms employed in demand sensing, such as time-series translation, deep learning to extract dynamic signal in the market and enhance their accuracy in demand consequently maintaining optimal levels of services and managing inventory. Moreover, supply risk analysis uses classification model and scenario-based simulation with the aim of determining the reliability of suppliers, geopolitical risk, and lead-time volatility enabling organizations to predict and efficiently respond to the vulnerability of their networks. A case study that was conducted on enterprise data shows that AI/ML integration into the Oracle Fusion SCM offers significant advantages in the accuracy of forecasts, the visibility of risks, and the resilience of operations versus traditional methods based on rules. The study identifies the ground-breaking contributions of AI-based analytics in enterprise resource planning systems, which has the potential to deliver data-driven, responsive, and absorbent supply chains in response to disruption in the still ever-changing world
Artificial General Intelligence in Software Engineering: Early Applications and Governance
Artificial General Intelligence (AGI) represents a paradigm shift in the field of artificial intelligence research, known as advanced artificial intelligence, which is capable of generalised reasoning, autonomous learning, and domain-free applications, extending beyond the realm of narrow AI applications. Within the software engineering universe, AGI has shown some promising early signs of default and is being deployed in various important moments throughout the development lifecycle, such as code generation, testing, architecture design, and DevOps optimisation. This article explores the guiding principles that define AGI, distinct from conventional AI, and identifies the primary strengths of this technology in the evolving landscape of software engineering. We examine the role that AGI-based tools will play in aiding software development in a more autonomous, creative, and intelligent manner, enabling systems to be developed with the ability to adapt to new needs, detect errors, and even transform older code. We also look at assistants and collaborative multi-agent systems that use AGI to help developers and make their work easier, which improves productivity and the ability to make decisions. The paper talks about these technical advances and the urgent issues they raise about government, ethics, and risk management. Open-sourced decision-making, human control, and governance mechanisms are identified as the critical factors for the secure implementation of AGI. The paper ends by talking about the future of AGI in software engineering. It stresses the need for systems that are not only powerful and flexible, but also explainable, accountable, and in line with human values. This synthesis offers conceptual insights to researchers and practitioners that are practically applicable to the challenges and opportunities of integrating AGI into real-world software development environments
Predictive Maintenance and Health Monitoring
Predictive maintenance (PdM) is a new trend in the modern industrial work as it allows companies to decrease downtimes, lengthen equipment lives and systems and apply more aggressive maintenance planning. Health monitoring of industrial machines is nowadays transformed due to the implementation of advanced sensors, Internet of Things (IoT), and machine-learning algorithms with the dawn of Industry 4.0. The following paper is the comprehensive review of predictive maintenance and health monitoring of industrial equipment and methodology. The paper highlights the three main areas that focus on data collection, feature identification, health conditions evaluation, fault anticipation, and maintenance arrangements. Vibration analysis, thermal imaging, acoustic monitoring, sensor fusion, and many other techniques are discussed to increase the accuracy of fault detection. There are machine learning models such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), and deep learning systems that are used in anomaly detection and prognosis. The paper is also presented with a detailed case study of implementing PdM within a manufacturing environment in which it was shown that the downtime rates caused by actual reasons were reduced dramatically as well as the expense of maintenance. Mean Time Between Failures (MTBF), Remaining Useful Life (RUL) and predictive accuracy, performance metrics are evaluated to measure the adequacy of the proposed methodology. Additionally, the challenges and future trends are discussed like real-time monitoring, edge computing, and digital twins. This paper is a step-by-step guide on how researchers and industry practitioners can integrate predictive maintenance and health monitoring to the modern industrial setting
Auto ML Pipelines for Real-Time Underwriting Risk Scoring
The rapid advancement of machine learning has significantly influenced the insurance industry by enhancing underwriting processes through more efficient and accurate risk assessment. In this paper, we provide an automated machine learning (AutoML) pipeline that performs time underwriting risk scoring, feature selection optimization, model adjustment and implementation without the support of a large number of people. The suggested framework brings together data preprocessing, feature engineering, model selection, and real-time scoring to make risk scores that are easy to understand and work quickly. Our tests on a large insurance dataset suggest that our method would be better at predicting outcomes and have less lag time than a conventional underwriting process. The practice will help insurers make better decisions, which will lower both the financial risk and the problems with customer service
Serverless Insurance Platforms: Leveraging AWS Lambda for Guidewire Claim Events
The insurance industry has undergone major digital transformation in recent years, yet traditional systems continue to face challenges such as scalability limitations, high operational costs, and latency. This paper explores the adoption of a serverless architecture using AWS Lambda integrated with Guidewire ClaimCenter to enhance claims processing efficiency. Guidewire ClaimCenter generates continuous event streams—such as claim creation, status updates, fraud alerts, and settlements—that require real-time processing for improved automation and faster settlement cycles. The proposed framework leverages AWS Lambda, Amazon SNS, API Gateway, and DynamoDB to establish an event-driven, scalable, and cost-effective architecture. Lambda functions deliver micro-level modularity, enabling dynamic business logic execution without dedicated server infrastructure. This model reduces total cost of ownership, enhances fault tolerance, and optimizes resource utilization through AWS’s auto-scaling and pay-per-use features. Using a hybrid research approach—including architecture design, simulated claim events, and performance analysis—the study shows significant improvements over traditional service-oriented systems, with 42% lower processing latency, 37% cost reduction, and 99.99% availability. Beyond technical gains, the framework also improves compliance, disaster recovery, and customer trust. Future enhancements include machine learning-based anomaly detection, blockchain-enabled claim auditing, and cross-platform integration for a more intelligent and resilient insurance ecosystem
Predictive Cash Flow Management in Oracle ERP Cloud Using Machine Learning
In contemporary business, cash flow is important in ensuring the health and viability of the business. Manual inputs and rigid forecasting models, which characterize the traditional cash flow forecasting methods, have limited the use of traditional forecasting methods, as most of them are inaccurate and thus cause inaccuracies, which are reflected in the operations of the business. In this paper, a machine learning-redesigned, predictive model is proposed to automate cash flow with automation at a higher degree in the module Accounts Receivable (AR) and the Accounts Payable (AP) in Oracle ERP Cloud. The model uses an analysis of the past trends of AR/AP along with payment behavior of the customers and payment terms, to predict the future flows in and out more accurately and with less manual input. Our approach is to employ supervised learning models, specifically Gradient Boosting Machines (GBM), the Long Short-Term Memory (LSTM) network and ensemble techniques, which are trained on ERP transactional data. The proposed system deploys on Oracle ERP Cloud APIs, making it easier to consume and update data in the model at run-time. Our experimental results demonstrate that our predictive model is able to increase accuracy of cash flow forecasting significantly over that achieved using a rule-based approach. The system offers dashboard visualization to the finance team, such as scenario analysis and risk scores. The proposed research also brings much value to the existing literature because it shows that ML can be used to enhance ERP systems and provide timely financial forecasts to open up the possibilities of intelligent enterprise resource planning and financial automation. The feasibility and effectiveness of the proposed approach are tested with the help of a prototype implementation that is carried out using Python, Oracle Cloud Infrastructure, and Oracle Autonomous Database
How IaC Tools Can Be Used to Automate Infrastructure Provisioning and Management within the CI/CD Pipeline
The drastic trend of DevOps or cloud-native technologies has necessitated the implementation of Infrastructure as Code (IaC) to streamline the infrastructure provisioning and management of the firms. IaC provides repeatable and consistent environments, reduces errors that are introduced manually to configure systems, and increases the scale and maintainability of infrastructure. As the best practice, the incorporation of IaC in the pipeline of Continuous Integration and Continuous Deployment (CI/CD) has become a part of delivering highly agile and robust deployment systems. This paper presents a detailed discussion on the automation of infrastructure provisioning tools (using Terraform, Ansible, and AWS CloudFormation) within the CI/CD cycle of events. We present the layered architectural pattern, which incorporates IaC into various pipelines of CI/CD. We also examine the mathematical models on resource dependency management and workflow optimization. To make the approach valid, different case studies and benchmarking outcomes are shown. It is estimated that the use of IaC substantially decreases deployment duration by as much as 65 percent, lowers configuration drift and improves fault recovery duration by 80 percent. To summarize, IaC has become the epitome of current DevOps approaches, and it allows achieving dynamic, scalable, and even secure infrastructures
From Code Reviews to Culture: Scaling Quality without Bottlenecks
In Continuous integration and Delivery is a constant movement, which reflects on the main issue of supporting quality of code at scale without introducing bottlenecks. The paper discusses the way in which the acceleration of the delivery speed can occur with the help of the increase of the code reviewing activities combined with the introduction of an engineering culture centered on quality. In particular we are taking into consideration the impact of tooling, team norms and mentoring structures to develop a balance between a short development time and the software integrity and maintainability. The issue with the old fashioned code review strategies is that when subjected to an environment of scale, they will either have time to finish them out or create less than satisfactory code. By discussing the existing bottlenecks and implementing a more specialized tooling (e.g. automated review tools, static analyzers), and the development of a specific team behaviour and mentorship program, we would propose a highly focused strategy that would not only enable us to create more reliable codebase faster but would not slow it down either. The step-by-step methodology of the study includes requirement analysis, integration of tools and culture of the team development, and empirical evaluation in form of the simulation case study. It is implemented with a tiered enforcement design that implements immediate feedback frameworks such as Gerrit, GitHub actions, SonarQube and ESLint. The measurement tools include; defect density, review turnaround time measures and scores of team satisfaction. According to the case study, the reduction in the number of defects during the post-deployment to the highest possible 45 per cent and the shortening of the review cycles to 30 per cent are attainable. The doubled implications of the research are as follows: Scale Software development can increase and not reduce its quality, and cultural investment in peer reviews and mentoring will pay in the long-term benefits of technical excellence. Finally, we can make recommendations on the implementation of a balanced quality-delivery model and pinpoint directions to pursue in the future, such as review assistant with the help of AI and domain-specific review patterns
Edge-Native Software: Designing Resilient Apps for Low-Latency, Distributed Environments
The emergence of edge computing has also changed the way contemporary applications are designed, with the focus put on achieving high-resilience, low-latency software programs that are deployed on decentralized and resource-limited systems. Edge-native software is software purposely made to operate at the edge of a network, to allow processing of real-time data, lower bandwidth consumption, and greater fault resiliency. This paradigm is particularly critical to those areas, including autonomous systems, industrial automation, smart cities, and energy infrastructure, that need sub-second responsiveness and system trustworthiness. In this paper, an in-depth design framework of edge-native applications based on microservices architecture is described. Some of the main principles that we discuss are decentralization, data locality, stateless vs. stateful design, redundancy, fault domain isolation and so on. Reference architecture is proposed to support scalable deployments in the event of network partition, and self-healing and management qualities in processing consistent data across heterogeneous edge nodes. We consider powered-up technologies such as lightweight container orchestration (i.e., K3S, Docker), edge communication (i.e., MQTT, gRPC), and Chaos engineering. The performance of the architecture in the latency, fault, and resource stress situations is confirmed by experimental results on the Kubernetes-based edge testbed. The results show that edge-native solutions have the potential to surpass the cloud-based model in measures of latency, throughput, fault recovery, and energy efficiency. We also comment on representative use cases like cars on autopilot and predictive maintenance, and discover such issues as the complexity of orchestration, safety, and managing AI models at the edge
Cross-Channel Sales Data Fusion and Optimization in Omnichannel Retail Systems
The customer purchasing patterns are quickly evolving and it is the high digitization of trade that has compelled retailers to resort to omnichannel retailing mechanisms. The tactics combine different intermediaries of sales such as physical outlets, online stores, mobile apps and social networks in such a manner that the customers experience uninterrupted shopping. It is a hassle to handle and optimize the cross-channel sales information because these systems are convenient but in data isolation and other formats that are inconsistent across platforms. The provided paper proposes an integrated approach to the cross-channel sales information integration and profitability in the omnichannel retail architecture. The model provided encompasses the solution to heterogeneous data sets by utilizing the top-notch data preprocessing, normalization and feature engineering. The general purchasing system with a hybrid engine that works with machine learning (ML), linear programming (LP), and time-series forecasting is suggested to optimize all the activities of the purchase process such as pricing, inventory processes, and personalized marketing. The methodology is verified by using real retail data in businesses as well as simulated multi-channel data. The results indicate increased level of predictability, stock turnover and income. The fusion engine is one of such innovations, where the incoming sales streams are adjusted dynamically and provide the opportunity to conduct real-time analytics and optimization. Besides customer interactions information may be utilized to enact feedback loops via the system to personalize and engage the customer. The research delivers a scalable and interchangeable design of omnichannel data optimization and viable information on the matter of implementation as well as possibilities. This paper shows the importance of data synergy and computational intelligence in application in the modern retail environment
Generative AI with RDS as a Vector Store
Generative Artificial Intelligence (AI) can be seen as a radically new paradigm in the study of data-driven applications, which enables machines to create content on their own in diverse fields, i.e., computer vision, scientific simulations, and natural language processing. A critical issue related to implementing the Generative AI systems is the practical storage, retrieval and control of high-dimensional vectors representations characteristic of the embedding-based AI systems. It is common that older databases cannot scale or provide fast similarity search, which is required in large scale collection of vector operations. The paper examines the concept of integrating Amazon Relational Database Service (RDS) as a vector store to the Generative AI systems, its practicability, performance, and its application to practice. We would propose a new design where the familiar RDS handles such structured relational data that high dimensional vectors can be both stored and read effectively. Our propositions that are founded on advanced indexing techniques, dimensionality reduction, and query optimization operate to indicate that the RDS can be an effective and consistent backend to Generative AI applications. The research paper entails a detailed experiment of text, image, and multimodal embeddings and compares the performance of retrieval, storage efficiency, and computational overhead with special purpose vector databases (such as faiiss and milvus). Conclusions RDS is capable of executing operations of a vector search with very low latency with the appropriate changes and adjustments and can offer a comparatively inexpensive alternative to organizations that already deploy the AWS services. Hybrid systems based on RDS and custom differentiated vector engines are also discussed in the paper to create the most preferable combination of consistency, scalability, and performance. The findings have valuable implications to both researchers and practitioners who may wish to implement Generative AI systems without necessarily incurring the overhead cost of the specialized infrastructure, further encouraging the practical application of AI-based solutions to the industry
Role-Based vs. Attribute-Based Access Control in Multi-Tenant Full-Stack Applications
The access control plays a critical role in securing the digital systems, especially multi-tenant full-stack applications contains various organizations or users operating under the same infrastructure. This article examines Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) in the context of applications. RBAC provides a straightforward way of assigning permissions depends upon the user roles, it is simple and easy to understand, but ABAC takes into consideration various attributes of users, resources, and the environments, giving a fine-grained level of control. Nonetheless, using one or another of these models in multi-tenant systems presents trade-offs that incorporate scalability, flexibility, complexity of enforcement, and overhead administration. In this paper, both RBAC and ABAC are considered in different multi-tenant full-stack settings. Since RBAC lacks the flexibility of policy enforcement provided by ABAC, we suggest a hybrid model; we would take the hierarchical characteristic nature of RBAC and combine it with the granularity of the issue that is presented by the acronym, which is ABAC. The steps will involve the development of a working prototype system, policy schema definition, incorporation of an authentication layer and experimentation on real-world datasets in order to emulate multi-tenant settings. Scalability, response time, and policy evaluation time are the metrics used in evaluation. Administrative overhead is also used as an evaluation metric. The most important conclusions are that ABAC is more flexible in dynamic, attribute-rich conditions, whereas RBAC does offer good policy enforcement practices and easy integration. The weaknesses of the two are alleviated in the hybrid approach, and contextual attributes can result in dynamic role assignment. There are also case studies of multi-tenant access management at various organizations with the help of various models. In the paper, future work on machine learning-based policy suggestion engines and interoperability frameworks across cloud ecosystems will be highlighted, as well as several recommendations about how to address the need
Quantum-Native Development: Tooling, Patterns, and Debugging in Hybrid Quantum-Classical Apps
The recent surge in interest in quantum computing has led to the emergence of quantum-native software development: a multidisciplinary field that combines quantum information theory, classical software engineering, and system-level orchestration. Hybrid quantum-classical systems, particularly noisy intermediate-scale quantum (NISQ) hardware, which is of interest in the short term, demand interoperability between quantum computation hardware and classical computing resources. The paper takes a broad look at the entire scenario of quantum-native development, in particular, in terms of tooling, design patterns, and debugging paradigms. We conduct a comprehensive survey, including the examination of compiler platforms, SDKs, debugging tools, and simulators. Their new tendencies include quantum workflow orchestration, hybrid execution graphs, circuit reuse, and noise-aware algorithm design, all of which are covered in detail. We also examine the difficulty of debugging with two paradigms: quantum mechanics and classical determinism. Through comparative studies of current frameworks, such as Qiskit, Cirq, and PennyLane, and practical tests of benchmarks, including VQE and QAOA, we will provide guidance to developers, researchers, and system architects working on quantum computers. The article proposes a taxonomy of tools as layered, a pattern repository, and a framework for a debugging pipeline to facilitate future work in both research and implementation. The conceptual contributions are presented in context through tables, flowcharts, and visual references. Overall, the purpose of this paper is to take a step towards the process of quantum software engineering, which would enable the developer community to explore a fresh landscape of quantum-native development
Predictive Analytics for Supply Chain Disruption Using SAP HANA and Deep Learning
Global supply chain disruptions have gained popularity in the past few years, with reasons like geopolitical tensions, pandemics, and natural disasters. Such disturbances may have considerable effects on manufacturing, logistics, and inventory management. The ability to overcome these challenges proactively is provided via predictive analytics, which can be enabled through the power of the latest tools (including SAP HANA computing and deep learning algorithms). The paper addresses the incorporation of the in-memory data processing functions in SAP HANA with deep learning models relating to the prediction of disruptions in the supply chain and their management. By utilising comprehensive data collection, preprocessing, model training, and evaluation, we demonstrate that predictive analytics can enhance the resilience of supply chains. The research enhances the emerging sphere of supply chain digitization by introducing a real-time predictive system that is compatible with enterprise systems based on SAP HANA
Dynamic Premium Pricing Using Real-Time Geospatial Risk Analysis in P&C Insurance
Traditional Property and casualty P&C insurance pricing has involved the utilization of the risky-based pricing which is conducted with the use of historical data, and demographic averages. However, risk in the modern context is dynamic nature and requires a responsive system, real-time, location-aware system being responsive to geospatial risk variables. As this paper will show, this can be achieved by a framework of Dynamic Premium Pricing with Real-Time Geospatial Risk Analysis (DR-GRA) to achieve fairness, profitability and customer-centricity of the insurance pricing. The paper suggests a structure of geospatial integration of data comprising of IoT devices, weather fore-casts, satellite images, and mobility profiles that would alter the premiums in real-time. This strategy utilizes machine learning (ML) models, predictive hazard model, geospatial risk heatmaps and exposure assessment on an individualized basis. The suggested system is compared with traditional actuarial modus operandi using simulated data of floods, wild fires and urban crimes. The results show that DR-GRA is effective to boost the price accuracy rate (32 percent), and enhance reliability of the forecasts of losses and alleviates the adverse selection. The plan has countered the regulatory compliance, privacy and ethical forces and offer insurers a competitive advantage in regards to precision underwriting. The paper concludes by pointing out that geospatial analytics can revolutionize the insurance sector, make the industry resilient and accommodative to changes in climate risks
Generative AI Evolution: Co-Pilot Tools for Claims Processing and Underwriting Enhancement
This is because, with the introduction of artificial intelligence (AI) that is generative a lot of industries have been transformed among which is insurance that is a major beneficiary. In the current paper, the authors observe the progress of the generative AI tools that are designed to act as a co-pilot in the claims processing and underwriting, enhancing effectiveness, accuracy and decision-making. A wide range of architecture types such as transformer-based models, large language models (LLMs), and diffusion networks have enabled insurers to automate some of the most complex tasks, such as document analysis, fraud detection, risk assessment, etc. We discuss the integration of these AI systems into the actual work processes, which will result in performance increases, the reduction of manual operations, and cost-efficiency. Among the critical issues, such as data privacy, model bias, and compliance, among others, are discussed as well as possible mitigation measures are provided. Case study and industry survey, as well as experimental studies, are given to measure AI impact. Outcomes: economic Enhancements Economic results in improved relations between both companies have shown that AI tools, which generate content can significantly reduce the time it takes to justify claims, increase precision in the process of underwriting and provide predictive analytics to better control risks. Lastly, the paper covers the developments in the future of generative AI with emphasis on explainable AI (XAI), continuous AI, and human-AI interface architectures
AI-Augmented Supply Chain Demand Forecasting in Oracle Fusion SCM
The rising instability and complexity of supply chains globally require strong forecasting skills to control the service levels, as well as maximize inventories. Oracle Fusion Supply Chain Management (SCM) provides embedded Artificial Intelligence (AI) and Machine Learning (ML) models that improve accuracy in demand forecasting and also offers the use of past data, seasonal trends and current market changes. The paper demonstrates the possibilities of AI-enhanced demand forecasting in the Oracle Fusion SCM by comparing the efficiency of the Oracle embedded ML models with pre-built custom models and external platforms such as TensorFlow and PyTorch, developed using the Oracle Cloud Infrastructure (OCI) Data Science. This study provides a complete evaluation framework by investigating accuracies on forecasting, scalability, interpretability, integration complexity, and performance across Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and several other measures. To test the embedded and the custom solutions, we apply real-world data of a global retail supply chain use case. As our results show, native ML in Oracle is a convenient way of getting the job done and integrating with other services, but under some conditions, custom models with OCI Data Science can be better. We conclude by providing an advisory to help us select between native and custom ML based on business requirements, data access, and the maturity of this operation
Advancements in AI Coding Assistance Tools and Their Potential Impact on Collaborative Software Development
The AI is soon to change software engineering because AI putpins such as GitHub Copilot, OpenAI Codex, TabNine, and Amazon CodeWhisperer are offering software developers more of a cushion than they have had before. They are streamlined to write code quicker, automate duties that require duplication, provide real-time bug detection and document creation with machine learning (ML) and natural language processing (NLP). Not only the productivity of one person can be altered, but team collaboration and team communication, minimizing knowledge siloes, and making distributed Agile practices can be radically enhanced by them. Throughout this paper, the author grants an in-depth discussion of the latest advances in the development of AI-aided code tools and how they could affect the collaborative software development processes. The research utilizes mixed research design where the review of the literature, methodological framework, and discussion of the experimental results can be applied to determine the technical capability and effects to organizations. The involvement of the state-of-the-art in AI code assistants in and before 2025, (2) a methodology to incorporate AI in collaborative workflow, (3) a performance, challenge, consideration analysis is important. We assume that AI assistance in the process of code creation can yield quantifiable features of productivity and quality improvement and that the issues of bias, intellectual property and overreliance on automation demonstrate that there is much more that can be done to alleviate these problems. As the results have shown, the appropriate implementation of the AI assistants in the working environment which is framed in terms of collaboration requires the hybrid human-AI workflows, the strong models of governance, and the flexible system of software development
The global markets between 2017 and 2025 have been experiencing dynamic growth with several factors being the main drivers, such as technological changes, consumer preferences, trade pattern changes, and post-pandemic cycles of recovery. The main factors that have contributed to the growth of the global markets are Digital transformation across different sectors, the increased use of automation and AI, and the rise of sustainable practices as both the governments and businesses are turning towards greener models. On the regional side, the Asia-Pacific is confidently leading the way as the major growth centre; the area has been propelled by the rapid industrialisation, the digital adoption, and the rising consumption of the middle class, while North America and Europe kept their ground with modest growth supported by innovation and high-value services. The African and Latin American emerging markets appeared to have future growth opportunities, but their development has not always been balanced due to situations of infrastructure gaps and policy instability. As for the sectors, technology, healthcare, and renewable energy have led the way, while the traditional sectors of manufacturing and retail have also progressed through digitisation and omnichannel strategies. One of the main methods used in the report for drawing insights is a combination of historical data analysis, market surveys, and case studies used to demonstrate how the companies in the different industries have dealt with both disruptions and opportunities. These illustrations show how the companies that were the first to embrace cloud technologies, e-commerce, and AI-driven analytics reaped the most significant benefits, whereas those with the slowest adoption have been pressured in terms of their profits. As per the prediction, which is beyond 2025, it is mentioned that the trend will keep on going with even more emphasis on resilience, digital ecosystems, and cross-border collaboration
Role Evolution: Developer, Analyst, Lead, Senior
The journey from Developer to Analyst then to Lead and finally to Senior is a progression of changes not only in the designation but also in the skills, mindset, and impact that changes both the personal and organizational levels. The developer builds the technical-ness, logical-ness, and detail orientation through the activities of his job, these being the skills that basically constitute the common ground of confidence and capability for providing visible results. The analyst role makes adaptability and broader vision the more important considerations whereby one’s competency of comprehending systems, business needs, and transforming numbers into practicable insights becomes more profound and makes a link between technical and strategical objectives. The transition to the position of Lead signifies that you are no longer a sole contributor but rather the power behind the wings of other people, off the hook and supporting the leaders empathically while also feeling personally responsible and accountable for the results. The promotion to the Senior position, in the end, is linked with a perspective and vision, not a shift of focus only on the obtaining of results, but rather on influencing future outcomes, coaching younger colleagues, and making certain that the organization’s advancement accompanies individual ideals. The skills required at each job phase are substantially different: Developer is the one who perfects the trade, while the Analyst makes it more analytical. The Lead is the one who brings the team together and the Senior is the one who sets the direction and the sustainability program. This career path is essentially a great instance of how wearing and rubbing one's skills turns one into a leader who not only manages but also greatly affects the culture of the company. The reason for employing this career path as a case in point is to show the inter-relationship of skill development, leadership, and strategic thinking that results in growth not only in the performance but also of the people