International Journal of Computer Science and Engineering Innovations
International Journal of Computer Science and Engineering Innovations (IJCSEI) is a double-blind scholarly peer reviewed journal publishes original research, review articles, survey, short communications, technical reports in the all areas of computer science and engineering. The core computing and innovations in engineering research are the most received concepts the journal compromises, emphasizing modern scientific computing and technology, while also embracing cutting-edge advancements in computational techniques, system designs, and engineering solutions that address global challenges and drive technological progress across diverse industries. The novel insights keeps on moving the research to the next level of knowledge in technical core, where the IJCSEI wishes to surface the modern and scientific knowledge for the gradual progress. The journal follows the stringent peer review criteria, and review process and guarantees the publication of high quality articles suitable to the IJCSEI.
AI-Driven Compliance and Configuration Intelligence at Scale: An Explainable, Human-Centered Framework for Enterprise Infrastructure
Enterprise IT environments increasingly span heterogeneous on-premises, cloud, and hybrid platforms, where configuration changes occur continuously and at scale. Ensuring configuration and security compliance under these conditions remains a persistent challenge, particularly in regulated domains where transparency, auditability, and human accountability are mandatory. Traditional rule-based compliance mechanisms, while effective for baseline enforcement, struggle to provide timely detection of configuration drift, contextual risk assessment, and decision support. This paper proposes an AI-driven, explainable compliance and configuration intelligence framework designed to augment existing configuration management and governance tooling. Rather than replacing deterministic controls, the framework introduces data-driven risk inference, temporal drift analysis, and human-in-the-loop validation. We present a reference architecture derived from large-scale enterprise deployments and align it with established research in configuration management, explainable artificial intelligence (XAI), and human-AI interaction. The results demonstrate how explainable AI can enhance situational awareness, reduce compliance overhead, and improve governance without compromising control or trust.
Enhancing Software Delivery Performance through AI-Driven Observability and Intelligent Automation
Modern software delivery operates under simultaneous pressure for speed, stability, security, and cost efficiency. Although DevOps and continuous delivery practices have improved release velocity, many organizations still struggle to translate raw operational telemetry into timely decisions that reduce change failure, shorten recovery windows, and improve delivery throughput. This paper develops a Q1-style conceptual research framework that positions AI-driven observability as the missing decision layer between software delivery instrumentation and intelligent automation. Using a structured synthesis of forty references spanning software delivery performance, AIOps, defect prediction secure microservices, cloud-native monitoring, and cross-domain AI automation, the paper derives a reference architecture that integrates telemetry collection, dependency-aware context modeling, anomaly and risk prediction, policy-aware orchestration, and human-centered decision support. The framework links observability signals such as logs, metrics, traces, pipeline events, configuration changes, and service dependencies to delivery outcomes measured through throughput and stability metrics. In contrast to narrow monitoring or isolated machine learning approaches, the proposed model treats software delivery as a closed feedback system in which prediction, explanation, and automated action must remain aligned with governance, security, and service objectives. The paper contributes three outcomes: a reference architecture for AI-driven observability and intelligent automation, a mechanism-level explanation of how that architecture can improve delivery performance, and an evaluation model grounded in software delivery metrics, automation quality measures, and socio-technical controls. The result is a practical research agenda for building delivery systems that are not only observable, but also increasingly adaptive, trustworthy, and operationally efficient.
Optimized Deep Learning-Based Image Compression Using Convolutional Autoencoders
This study presents an optimized image compression framework based on deep learning, specifically a convolutional autoencoder. Traditional compression methods such as JPEG and PNG rely on fixed mathematical transformations, which often lead to quality degradation at high compression ratios. To address these limitations, the proposed system adopts a data-driven approach that learns compact and efficient image representations through end-to-end training. The framework integrates convolutional encoding, latent space quantization, entropy coding, and decoding mechanisms to ensure efficient storage and transmission. Experimental evaluation was conducted using benchmark datasets, and performance was assessed using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and compression ratio. The results demonstrate that the model achieves a compression ratio exceeding 133:1 with minimal perceptual quality loss, maintaining stable PSNR and SSIM values across varying compression strategies. Furthermore, the model exhibits fast convergence, strong generalization capability, and real-time processing performance, making it suitable for deployment in resource-constrained environments. Despite challenges such as computational training cost and minor loss of high-frequency details, the proposed approach significantly improves compression efficiency and visual quality. These findings highlight the potential of deep learning–based methods in advancing next-generation image compression systems.
Architecting Secure Big Data Platforms: Insights from a Big Data and IT Security Specialist
Nowadays organizations gather process and derive value from massive volumes of structured and unstructured data differently due to the rapid expansion of big data platforms. Organizations that are increasingly operating in distributed ecosystems, consisting of cloud infrastructures, data lakes, real-time analytics pipelines, and third-party integrations, are facing security and privacy risks of big data and these risks have been increased drastically. For example, unauthorized access, data leakage, insider attacks, and compliance violations, among other threats, are making it so that perimeter-based security models are no longer able to protect sensitive data. The central message of this article is that instead of adding security afterward, we need to be embedding security mechanisms directly into the core of big data platforms in a security-by-design architecture. Considering the input of a Big Data and IT Security Specialist, the paper develops a secure big data architecture that, inter alia, includes data encryption, fine-grained access control, identity and key management, secure data ingestion, and continuous monitoring as part of this comprehensive framework. The case of practical implementation featuring an enterprise-scale big data deployment served as the ground for the application of the proposed model, wherein the security controls were related to the working of practical operational workflows as well as regulatory requirements. Among the most important conclusions drawn is that applying security-by-design principles to data protection and compliance not only leads to better security and compliance but also improves the platform's reliability and stakeholder trust. The paper wraps up by highlighting the practical implications for architects and security practitioners, basically providing doable tools for them to construct strong, secure, and scalable big data platforms in today's enterprise environments.
Beyond Logs: Building Intelligent Monitoring Systems with Splunk Clusters
Modern businesses go above and beyond just recording information and addressing problems as they arise. When collecting data from a large number of workstations, centralized logging solutions shine. However, when it comes to distributed, cloud-based, and microservices-based systems, they may not hold up as well. The proliferation of hybrid clouds, containers, and edge systems has led to a dramatic increase in the volume, velocity, and diversity of operational data. A system's security, speed, and reliability should not be assumed only because it offers static dashboards and keyword-based searches. Interaction between smart monitoring systems is essential for their ability to collect, measure, and store data in real time. They should also have the ability to anticipate problems and address them promptly. It is necessary to make this modification in Splunk clusters. The multi-site architectures, indexer clustering, and search head clustering that make up Splunk's business observability solutions make them very dependable and error-tolerant. In this post, we'll look at how Splunk clusters may help businesses create robust monitoring systems that can do more than just centralize log storage. Topics covered include automated frameworks, AI-driven operational insights, clustering approaches, scalable architecture, and advanced analytics.
Enterprise Content Management in the Cloud Era: An AEM and DevOps Perspective
Enterprise Content Management (ECM) aside from being just a traditional on-premise document repository is now an experience-driven platform that has the ability to power highly personalized omnichannel digital journeys. Organizations are expected to meet customers' ever-increasing demand for faster response times, scalable solutions, and constant customer engagement which is why ECM is in the process of radically changing into a cloud-native architecture becoming that essential core to the delivery of digital experience while at the same time moving beyond storage and workflow automation. Adobe Experience Manager (AEM) is one of the leading platforms in this cloud world which integrates content management, digital asset management, and experience orchestration, thus enabling enterprises to provide the same content regardless of channel be it web, mobile, or even the new emerging channels. Nevertheless, merely acquiring AEM will not suffice; the triumph of the contemporary ECM is thus largely determined by the manner in which content and code flow from inception to production. It is here that DevOps plays a crucial role, bringing in automation, collaboration, and reliability within the ECM lifecycle. When DevOps practices such as CI/CD pipelines, infrastructure as code, automated testing, and monitoring are combined, they make for a safer deployment environment, a faster release process, and a more stable platform these being very instrumental in the case of intricate AEM environments that have multiple teams, integrations, and a stringent governance requirement. The focus of this paper is on the nexus between cloud-native ECM, the capabilities of AEM, and the DevOps-led delivery models. It offers a down-to-earth view of how businesses can modernize their content operations while at the same time staying secure, compliant, and efficient. This study leverages a comparative review of the trends in ECM transformation, a detailed architectural examination of AEM in cloud and hybrid settings, and an AEM-specific DevOps framework.
Building and Supporting Cloud-Native Architectures on AWS: Real-World Engineering Practice
Cloud-native architectures represent a transformative evolution from traditional monolithic systems, enabling organizations to develop scalable, resilient, and agile applications through microservices, containerization, and automated orchestration. Amazon Web Services (AWS) has emerged as a leading platform for enabling this transformation by offering a comprehensive ecosystem of infrastructure, platform, and managed services. However, a gap persists between theoretical cloud-native design principles and practical implementation realities, where engineering teams must address operational complexity, reliability challenges, security governance, cost optimization, and organizational readiness. This study examines real-world engineering practices for designing and supporting cloud-native architectures on AWS through production deployment analysis, operational case studies, and implementation-driven insights. It focuses on distributed observability, resilience engineering, DevSecOps integration, automated CI/CD pipelines, and cost optimization in dynamic cloud environments. The findings present actionable architectural patterns, operational strategies, and governance frameworks that help organizations successfully adopt and sustain cloud-native systems, bridging the divide between conceptual design and operational execution.
Building Robust Salesforce Platforms: Architecture Patterns and Real-World Experience
Salesforce has changed a lot and is now a very important enterprise platform that supports customer engagement, automation, analytics, and integration of different systems on a large scale. However, as companies grow across multiple clouds and business domains, their Salesforce environments become so customized and architecturally fragile. Uncontrolled development of Salesforce leads to performance bottlenecks, failure of integrations, gaps in governance, and accumulation of technical debt that weaken platform reliability and scalability. This article analyzes the use of field-tested architectural patterns and best practices in the creation of resilient Salesforce platforms by integrating enterprise architecture theory, cloud-native design principles, and Salesforce-specific frameworks. A structured approach is offered that is based on different aspects, such as modular domain-driven design, decoupled and event-driven integration, automation governance, DevOps-driven delivery, and observability-based reliability engineering. A real-world enterprise case study shows that intentional architectural transformation can lead to system stability, deployment reliability, and long-term maintainability. The paper points to the fact that creating a robust Salesforce platform is more of an architectural challenge than a tooling problem and it also points out some of the new trends such as AI-driven automation, multi-cloud orchestration, and low-code governance that will be major factors in shaping future enterprise Salesforce architectures.
Modern TechOps Architecture: Integrating AI, DevOps, and Observability at Enterprise Scale
Enterprise IT ecosystems are changing very fast with the help of cloud-native adoption, microservices expansion, hybrid and multi-cloud infrastructures, and increasing operational complexity that together challenge the traditional IT operations model to keep time and still maintain the reliability, scalability, and efficiency. To integrate AI, DevOps methodologies, and observability frameworks to set up adaptive and automated operational ecosystems, TechOps has been positioned as a next-generation operational paradigm. We suggest a combined architecture for TechOps that unites AI-powered automation, ongoing DevOps pipelines, and cutting-edge observability to significantly increase the reliability of the system at the enterprise scale, the efficiency of operations, and the effectiveness of incident management. The paper provides an assessment of the challenges of the current operational approaches and brings in a framework that is scalable and capable of enabling real-time monitoring, predictive failure detection, automated remediation, and continuous performance optimization. A case study based on an enterprise cloud infrastructure in the real world shows significant improvements in deployment speed, system resilience, and operational cost efficiency. The case study also demonstrates how integrated TechOps strategies help to reduce downtime, improve Mean Time to Resolution (MTTR), and strengthen proactive operational governance. Besides that, the suggested architecture solves other major problems of the industry such as tool fragmentation, data silos, and governance complexities, which typically stand in the way of digital transformation initiatives. Also, the paper contributes a formalized process of how to incorporate AI and observability into a DevOps-driven ecosystem and points out the next areas of work in autonomous infrastructure management, AI governance frameworks, and self-healing distributed systems.
Designing and Managing High-Availability Databases in Global Financial Systems
Designing and managing databases for global financial systems requires a lot more than quick queries and large storage capacity. Such banking systems need to be extremely reliable (almost 24/7), provide strong consistency guarantees, and be very resilient even when subjected to the highest operational stress. In the era of interlinked markets, payment platforms operate around the clock and coordinate real-time payments, trading, fraud detection, and regulatory reporting across time zones. So, even short downtime periods may turn into serious problems regarding lost revenue, customer dissatisfaction, regulatory breaches, and systemic risk. This paper studies the reasons for which high availability (HA) should be regarded as a must-have and how the main architectural principles borrowed from infosec are exploited to reach this goal. This research dives into HA from a fairly pragmatic standpoint: it addresses the most elementary components of a robust HA design multi-region replication strategies, automated failover mechanisms, disaster recovery planning (RPO/RTO targets), and how to strike a balance between consistency and performance in a distributed environment. The​‍​‌‍​‍‌ paper presents a method that integrates design review, reliability modeling, and a case study of a globally distributed and financially intensive workload to illustrate the reality of compromises and implementation choices. The paper's findings show that technology is only one factor in the equation of HA success; other essential factors are operational discipline, continuous monitoring, regular failover drills, and detailed recovery playbooks. The last section of the paper argues that carefully planned and well-executed HA partnerships are essential for maintaining transaction integrity, reducing the enterprise historical risk, and helping to keep the trust in the financial services of the ​‍​‌‍​‍‌world.
Scaling Software Quality: Real-World Experiences from Enterprise QA Management
The conventional quality assurance (QA) methods, usually manual, isolated, and reactive, are having a hard time keeping up with the needs of rapid delivery, continuous integration, and high reliability raised by the new environment. This article investigates the ways software quality can be effectively scaled through enterprise-level QA management and refers to the experiences of real-world transformations instead of only theoretical models. It outlines practical approaches, tools, and governance processes employed by large organizations to change QA from a mere testing function to a quality-driven engineering discipline. An extensive case study of a QA transformation initiative in an enterprise is at the core of this research. It shows how the use of standardized frameworks, test automation, metrics-driven decision-making and cross-functional collaboration came to be the solutions to quality bottlenecks on a large scale. The effects reveal that the measures taken have resulted in fewer defects through better defect prevention; thus, this is one of the ways performance was enhanced, and the rework and testing cost were significantly down, and the firmware update cycles were sped up, all of this happened without losing the quality of reliability. Instead of just presenting the outcomes, the article goes on to share the experiences, compromises, and organizational difficulties that were part of the journey to the new state of affairs. By marrying the theoretical aspects of software quality with the practical aspects of the enterprise, this paper serves to provide tangible value to such QA leaders, engineering managers, and practitioners who are looking to strike the right balance between quality, speed, and cost in large-scale software environments.
Bridging Datacenters and Cloud: Insights from a Senior Cloud Systems Engineer at INFOR
Many businesses are undergoing profound transformations as they move their datacenter infrastructures to the cloud. The use of cloud computing has great potential for growth, increased dependability, and accelerated ideation. A transfer is much more complicated than just relocating mission-critical organizational systems. Working as a Senior Cloud Systems Engineer at INFOR, I see this change as a massive undertaking centering on hybrid designs that integrate traditional data centers with new cloud environments. Managing big hybrid enterprise systems is explored in this article from a technological, organizational, and operational perspective. Existing investments in on-premises infrastructure, regulations, and uptime expectations are examined to determine their influence on intents to transition to cloud computing. The primary challenges include creating effective hybrid connections, managing tasks that are sensitive to latency, ensuring that security and governance are consistent across the two settings, and adapting operational frameworks to include both legacy and new systems. In real-world circumstances, practical compromise is usually more important than architectural integrity, according to the results. It considers not just ideal state designs but also the practical procedures required for hybrid systems to operate, such as managing costs, monitoring, responding to incidents, and empowering teams. Anyone making changes similar to these platform developers, corporate architects, or IT managers should find this information useful. Making decisions based on facts and adhering to the law are two of the most important things this article stresses. In doing so, it exemplifies how companies may make prudent use of cloud technology to upgrade throughout data center-to-cloud migration while preserving stability, resilience, and operation.
Building Enterprise Grade CRM Systems: The Professional Journey of a Microsoft Dynamics Architect
Enterprise CRM solutions are crucial for gathering data, satisfying customers, and running a company. This means less time to prepare for and complete large-scale initiatives. As a business expands, its customer relationship management system must be capable of managing a large number of users, complex procedures, regulatory compliance, and integration with enterprise resource planning (ERP), analytics, self-service, and customer-facing applications. Microsoft Dynamics integrates extensively with comprehensive CRM solutions. It is well-known for its adaptability and compatibility with other Microsoft products and the cloud. Becoming a Microsoft Dynamics Architect requires more than just demonstrating proficiency in using the software to construct customer relationship management (CRM) solutions for businesses. Determine the true demands of the company and put in place effective control mechanisms. A Dynamics Architect's career prospects may improve if they assist companies in finding solutions to their challenges. It is important to strike a balance between customization and setup, construct systems with scalability, and adhere to safety and security regulations. In the end, it's certain that CRM systems aid in the company's long-term objective attainment. The goal of the training is to improve participants' performance on the job. Initially, architects oversaw issues. They are now in charge of specialized teams that advise customers on IT matters and educate them. This paper might be helpful for IT administrators, platform developers, and Microsoft Dynamics experts in creating solid customer relationship management solutions for big businesses. Expertise and a higher level of expertise are required for this position.
Transforming Healthcare IT with Cloud Engineering and DevOps Automation
Healthcare IT systems are under growing pressure to meet high security, availability, and compliance standards while offering the best possible patient experience. If your infrastructure is out of date, your apps aren't working effectively, and you have to manually install them, it may be tough to grow, stifling the execution of new ideas, and increasing operational risk. Healthcare firms can no longer depend on old IT procedures. They must implement data-driven care models, digital health platforms, and interoperable electronic health records. Cloud engineering and DevOps automation are two technologies that are currently useful in healthcare IT. Cloud-native designs allow you to easily add and remove resources as required, save money, and expand or shrink. DevOps, on the other hand, use automation, continuous integration and delivery (CI/CD), and infrastructure-like code to accommodate new ideas while ensuring stability. When combined, these technologies speed up app development, improve system dependability, and allow proactive security in line with HIPAA and GDPR, two healthcare-related laws. This article explains how cloud engineering and DevOps automation are changing the way hospital IT teams operate. It explores typical industry difficulties, presents examples of reference designs and automation phases, and investigates DevOps strategies that emphasize security and compliance. The paper offers practical advice and real-world examples for healthcare organizations wishing to continue offering scaled, patient-focused digital services while becoming more robust, adaptable, and legally compliant.
Ten Years of Engineering Excellence: My Journey as a Senior Developer in Enterprise and Data Platforms
Over the last ten years, enterprise software and data platforms have been drastically changed by the adoption of cloud computing, distributed architectures, and data-driven business strategies. This article is a reflection on a ten-year professional journey of a senior developer between large-scale enterprise applications and distributed data ecosystems. It delves into experiences of making resilient enterprise applications, scalable distributed data platforms, and cloud-native architectural patterns, besides it points out important engineering decisions, trade-offs, and operational challenges that have been faced and solved during system evolution. The conversation starts with the change from pure feature implementation to system engineering that puts scalability, reliability, maintainability, and business goals at the first place. Besides, the article uses real-life architectural patterns that have enabled sustainable platform growth, looks into failures and anti-patterns which have led to technical debt and discusses the evolving role of senior engineers in mentoring, cross-functional collaboration, and developing long-term platform strategy. By combining empirical lessons with standard engineering methods, this paper offers a compass to mid-to-senior engineers who find themselves in a technologically complex and organization-wise limited setting and still want to keep on the technical growth path.
Engineering Resilient Enterprise Solutions: A Senior Developer’s Perspective from the Airline Industry
Digital transformation is vital for the airline industry, which requires complex enterprise systems to be secure, scalable, and highly available in a constantly changing environment. From a senior developer's perspective, this research analyses how resilience can be deliberately designed in airline enterprise solutions to help operational continuity, regulatory compliance, and the changing needs of customers. The goal of this paper is to provide the practical insights that the authors have gained by being directly involved in the design, modernization, and support of large-scale airline systems, thus helping to close the gap between architectural theory and real-world practice. The key problems that were resolved include the limitations of the legacy systems, the integration of the different heterogeneous platforms, performance issues during peak load, and the need for the system to be backed up rapidly after a failure. The proposed approach puts major emphasis on modular architecture, cloud native design principles, automation, and being proactively monitored as the main facilitators of resilience. The paper presents a real-life case study from the airline industry which shows the application of these strategies and thus, the resulting measurable benefits in the system's stability, deployment agility, and incident response time. The research comes to the conclusion that resilient enterprise engineering should not only be regarded as a technical issue but also as a form of consciousness that is influenced by one's experience, teamwork, and continuous learning. Such a conception has repercussions for developers, architects, and organizations thus, it guides the digital transformation in the high stakes sectors like aviation.
From Microarchitecture to Silicon: Building High-Performance CPUs across Intel and Qualcomm
This paper outlines the complete cycle of creating efficient central processing units (CPUs), starting from initial microarchitectural ideas to actual manufacturable silicon devices. It does so by focusing on two main industry paradigms: the Intel x86 ecosystem and Qualcomm's ARM-based architectures. In the scenario where modern computing workloads are increasingly demanding higher performance with limited power and thermal budgets, CPU architects are compelled to make a very complicated balancing act among instruction-set philosophy, pipeline depth, memory hierarchy, power management, and physical implementation. Thus, fundamental decisions on architectural trade-offs, validation methods, and the physical outcomes of the chips are discussed. The major problems addressed revolve around keeping the ramping up of the performance at par with the power density limits, designing scalable products for different market segments, handling the complexity of the design, and ensuring the chips can be produced at a high yield in advanced process nodes. The article puts forward a vendor-neutral methodology for design and verification, which is a combination of microarchitecture, power mgmt, and physical design constraints initially optimized together and then supported by iterative modeling and silicon-aware verification. A side-by-side comparison that takes a look into the CPU development stories of Intel and Qualcomm is provided through a case study where the focus is on the design decisions, the performance-per-watt metrics, and the post-silicon validation activities, including bring-up days. The paper finds that while the architectural philosophies are different, the converging practices—such as workload-driven designs, aggressive power optimizations, and close couplings of hardware/software co-design—are the common ingredients for the success in both ecosystems. Subsequently, the paper articulates the implications for the future of CPU architectures.
Engineering Reliable Healthcare Platforms with Java and ETL Technologies
A reliable healthcare data platform cannot be compromised if healthcare services are to be safe, efficient, and compliant in a digital environment. However, creating such platforms has always been a challenge because of the vast amount, variation, and confidentiality of healthcare data. In this paper, we investigate the architectural, technological, and operational aspects of building trustworthy healthcare platforms with Java backend systems supplemented by ETL (Extract, Transform, Load) technologies. Today's healthcare ecosystems are continually generating large volumes of heterogeneous data in various formats from electronic health records, clinical devices, laboratory information systems, insurers, and patient-facing applications. To integrate these diverse data sources while preserving data correctness, ensuring high availability, security, and compliance with the law requires a very carefully designed system foundation. This research introduces a scalable and fault-tolerant platform architecture based on Java enterprise frameworks and distributed ETL pipelines, putting a major focus on the reliability engineering principles. Some of the critical design considerations were redundancy, transactional consistency, failure isolation, observability, and secure data processing, which are all consistent with such healthcare-specific requirements as privacy protection, interoperability standards, and audit readiness. The paper presents a case study that is inspired by the actual world, which demonstrates how the proposed architecture supports multi-source healthcare data integration. The illustrative use-case scenario confirms measurable improvements in data quality, system uptime, and processing latency when operating under realistic best-case scenarios. Moreover, the performance evaluation analyzes the impact of Java microservice architectural choices and ETL orchestration strategies on system reliability and operational stability. The results unveil trade-offs between scalability, complexity, and fault tolerance in healthcare data platforms that are available for the implementation.
From Code to Strategy: A Senior Technical Manager’s Journey in Cloud Native Banking Systems
This essay looks at the career path of a senior technical manager who goes from a hands-on engineering job to a strategic leader who helps change cloud-native these banking systems. This look explores how early experiences with designing, building & running distributed systems affect their decision-making at both the architectural as well as organizational levels, especially in the banking sector, which is highly regulated and risk-sensitive. The abstract talks about how hard it is to update previous systems that weren't designed to be flexible, adapt very quickly, or be used all the time. It also discusses the choices that require to be taken to find a harmonious equilibrium among adherence to the laws, being stable, while remaining innovative. As banks shift from massive, on-premise systems to microservices-centered, cloud-native architectures, they continue to confront challenges with scalability, resilience, and value for money. The report says that higher-level technical jobs are now beginning to seem more and more like management positions. For instance, they need to make certain that their engineering groups are working toward corporate objectives, collaborate with those who benefit from risk, safety, and product, and convert tiny technical decisions into longer-term strategic advantages. The story additionally makes very clear that the way things are done in technology needs to fit with the scheme of things. It illustrates that cloud applications, automation, and platform engineering can be deployed as more than simply tools; they may be used as well to transform how things are done and how individuals act. This paper illustrates how astute technical leadership may expedite the digital transformation in cloud-based banking institutions by confronting actual-world obstacles and options while maintaining trust, dependability, and compliance with law. In the end, the cloud-native architecture is a technological and organizational catalyst that affects the manner in which banks develop platforms, operate teams, alongside compete in an economy that is growing more electronic.
Strategic Solution Architecture for Large-Scale Enterprises: A Practitioner’s View
In the fast-paced world of business, huge companies have to deal with the dilemma of encouraging innovation while making sure that their operations stay more stable. This makes strategic solution architecture more important than ever. This article looks at how solution architecture connects business strategy with technology execution. It also talks about how well planned designs may bring together more complex systems, improve processes as well as make these systems that can grow over time. The discussion, which is based on their actual life examples, looks at common problems that businesses have, such as combining many other different technologies, keeping previous systems running, and making sure that their compliance and security are in place on a wide scale. The technique provides a structured, sequential procedure that strikes a balance between technical rightness along with marketing aims. It achieves this through providing individuals tools, recommendations, and actual-world information to help them make choices. The main recommendations emphasize the requirement for open government, stakeholder participation & flexible regulations that can adapt to the latest developed technologies and evolving demands from the marketplace. By integrating planning for strategy with solid construction principles, organizations can enhance their existing operations more efficiently and get ready for growth in the future. This practitioner-oriented point of view gives practical advice & managerial insight, showing how essential solution design is for making these organizations more flexible, efficient, alongside creative.
Automating Public Services: Insights from a Lead RPA Developer at the Department of Economic Security
As the need for governmental services rises and gets more challenging, automation is growing into a crucial way to ensure that these things work more smoothly, more precisely and to the delight of those using them. This article comes from the personal knowledge of a top RPA (Robotic Process Automation) software engineer at the Department of Economic Security. It gives beneficial instances of how robotic process automation can make many routine administrative duties easier, speed up the pace of processing, and free up public servants to do things that are more important. The essay thoroughly analyzes methodologies including process evaluation, bot design & deployment techniques, complemented with a case study demonstrating the successful implementation of RPA in critical service activities. The results of these initiatives show measurable improvements in service accessibility, operational efficiency along with error reduction. They also demonstrate that there are many problems with their governance, managing change, and working with legacy systems. The findings demonstrate not only the short-term benefits of RPA, but also how it may change the way the public sector operates across the board. They also help spread automation projects to an array of these additional divisions and authorities. The talk also discusses prospective future approaches, such as autonomous automation, AI-enhanced choice-making when the coupling of RPA with various online services platforms. It views computerization in the public sector as an investment in strategy to enhance their service responsiveness, transparency, and focus on citizens, as opposed to merely a technological solution. This research adds to the discussion on how automation may modernize public administration, optimize service delivery & motivate creativity in governmental organizations by combining straightforward concepts with a progressive point of view.
Scaling Workforce Operations through Technology: Insights from 25 Years in UKG and Enterprise Systems
In the past twenty-five years, globalization, complicated laws as well as the rapid growth of business technologies have all caused huge changes in how work is done. This essay looks at how workforce management has changed over time based on a lot of actual world experience using UKG and many other business technologies in a number of fields. It sees workforce operations as more than just an administrative task; they are a strategic asset that has a huge effect on their productivity, compliance, employee experience, and the organization's ability to bounce back. This abstract looks at how systems like UKG have helped businesses move from disconnected, manual processes to unified, data-driven operational frameworks in retail, manufacturing, healthcare as well as services. This also shows that there have been problems for more than 20 years, such as not wanting to adapt, the mismatch between business processes as well as system architecture, worries about data quality, and the tension between standardization and local flexibility. The essay utilizes an experience-based analytical approach, combining reflective practitioner these insights with illustrated case evidence to reveal many patterns that transcend industries and countries. It doesn't simply focus on technology features; it also looks at how people, processes, and platforms work together to achieve sustainable scalability. The findings of this research demonstrate that successful workforce transformation is contingent upon governance, the clarity of the operating model, and leadership commitment to ongoing enhancement, rather than the selection of technologies. The paper ends by giving organizations that are starting or improving their workforce technology initiatives some strategic advice. It also gives them practical advice on how to use UKG & enterprise these ecosystems to create more flexible, compliant, and future-ready workforce operations in a business world that is getting more dynamic all the time.
Engineering for Scale: Performance Testing and Capacity Planning in OpenShift and Pivotal Cloud Foundry
As enterprises rush their cloud-native adoption, they put an enterprise container platform under an increased pressure to deliver consistent performance even with the unexpected and varying workloads that keep increasing rapidly. Hence, one can clearly see a demand for engineering work that goes beyond just deployment automation, specifically, a comprehensive performance testing and capacity planning based on data performance. Here we introduce a practical, scalable approach to engineering for performance in two very popular enterprise platforms: Red Hat OpenShift and Pivotal Cloud Foundry (PCF). We put forward a methodology integrating workload modeling, stress and endurance testing, service-level-objective (SLO) validation, and profiling of resource utilization spanning compute, memory, storage, and network layers. We illustrate the case of an enterprise application environment where bottlenecks unfold differently in OpenShift and PCF due to the differences in orchestration behavior, routing, autoscaling mechanisms, and platform abstractions. Our results highlight common failure modes such as the noisy-neighbor effect, the saturation of ingress components, and misaligned autoscaling thresholds leading to overprovisioning or performance degradation. The paper offers a testing framework which can be repeated, a capacity forecasting model that is business-demand oriented, and platform configuration tuning instructions that are both essential and feasible. Being fruits of the joint effort the paper lists improved reliability, predictable scaling, and quantifiable cost efficiency as the outcomes that shift teams to work stress-free in growth planning while still focusing on user experience.
Deep Reinforcement Learning (DRL) has emerged as a groundbreaking approach for creating intelligent agents capable of learning complex strategies and behaviors in gaming environments. By combining reinforcement learning with deep neural networks, DRL enables agents to learn from interactions with the environment, optimize long-term objectives, and adapt to dynamic scenarios without explicit programming. Game AI applications span board games, real-time strategy games, first-person shooters, simulation environments, and multi-agent competitive and cooperative settings. DRL algorithms, including Deep Q-Networks (DQN), Policy Gradient Methods, Actor-Critic architectures, and Proximal Policy Optimization (PPO), have demonstrated remarkable performance, surpassing human-level capabilities in several domains. Beyond entertainment, DRL for game AI provides a testbed for research in planning, decision-making, multi-agent collaboration, and generalizable intelligence. Challenges include sample inefficiency, high computational requirements, reward shaping, and ensuring stability in training. Future directions point to hierarchical DRL, multi-agent coordination, transfer learning, explainable game AI, and scalable architectures. Deep Reinforcement Learning is redefining how autonomous agents learn, adapt, and innovate in complex game environments.
Ensemble Learning Methods for Improving SMS Spam Classification Accuracy
SMS spam classification remains a critical challenge due to the short length, informal structure, and high variability of text messages. The performance of single machine learning and deep learning models is promising; however, the class imbalance problem, overfitting issue, and poor generalization across datasets frequently occur. In this paper, we investigate how ensemble learning can enhance the peak performance and stability in SMS spam filtering. Several ensemble models (bagging, boosting, stacking and voting based) are examined using heterogeneous base learners such as Naïve Bayes, Support Vector Machines, Random Forests or neural network models. The experimental results on benchmark SMS spam datasets show that the ensemble models consistently achieve higher performance compared with single classifiers in different measures, including accuracy, precision, recall and F1-score. The results demonstrate the advantages of ensemble learning in learning complementary decision boundaries and mitigating model bias and variance. The present work demonstrates the promise of ensemble methods for the development of trustworthy, scalable SMS spam filters to be efficiently applied in a production environment.
Adversarial Attacks and Robustness in SMS Spam Classification Models
Short Message Service (SMS) spam classification systems based on machine learning and deep learning have achieved high accuracy under standard evaluation settings; however, their vulnerability to adversarial attacks poses significant risks to real-world deployment. Attacks on SMS spam classifiers are realized as precision-level adversarial examples, where carefully crafted perturbations (e.g., character substitution/reordering, word disguising and synonym injection, benign token insertion) maintain human readability while drawing models to misclassify. In this paper, we study universal adversarial attacks on SMS spam clasiﬁcation models and their adaptation to various feature representations and learning architectures: from classic machine learning models to deep neural networks and transformer-based solutions. And it introduces defense terms like adversarial training, data augmentation, ensemble learning, input sanitization and defensive distillation. Primary challenges such as trade-offs between robustness and accuracy, language diversity, and limited computation in mobile contexts are considered. The findings of the paper show possible future research challenges worth addressing for building robust SMS spam detection systems that are still performing effectively under adversarial conditions.
Cybersecurity Risks and Solutions for Digitally Growing SMEs
Accelerated by the digital transformation wave, small and medium-sized enterprises (SMEs) realise improvement in operational efficiency and market competitiveness, but are encountering more cybersecurity risks. This paper investigates the key cybersecurity concerns encountered by SMEs experiencing digital growth and their solutions. The research is a qualitative-based systematic review of pertinent literature, white papers, reports (industry), and known SME cyberattacks. The results indicate that the common SME cybersecurity threats were phishing, ransomware, poor access controls, insider threats, and insecure cloud permeations. The research also names employee education, 2FA and MFA security solutions, regular data backups, secure communications networks, and incident response planning as crucial in the struggle to achieve cyber resilience. The research finds that a natural, holistic cybersecurity concept is needed for SMEs to ensure the continued success of digital progress – to safeguard sensitive data and uphold accompanying customer confidence in the context of an ever more agitated global information landscape.
Logical Foundations of Computation: Techniques for Reliable and Efficient Programming
Mathematical logic forms the backbone of the field of computer science, providing precise reasoning about computational processes, program correctness, and designing system architecture. In computer programming, logical principles extend beyond theory and are actively applied as practical tools to improve program efficiency and reliability. Propositional as well as predicate logic serve as the building blocks for modeling data, algorithm verification, and understanding program behavior. Formal proof systems increase rigour in both software verification and compiler development. Programming paradigms like imperative, functional, and logic programming each incorporate logical principles in distinct ways, illustrating the flexibility of logic as a foundation for computation. Applications across different areas like compiler design, database systems, program verification, and artificial intelligence highlight the continuing relevance of logic in the modern computing landscape. Moreover, advances in automated reasoning, formal methods, and intelligent systems are expanding the impact of logic beyond its conventional boundaries. This paper explores the basic principles of mathematical logic, their role in various programming paradigms, and their wide-ranging applications in present-day computer science.
Automated MRI-Based Mapping of Dentate Nucleus Using Advanced Neural Networks
The dentate nucleus (DN) plays a central role in cerebellar output and is implicated in various neurological disorders. Accurate in vivo segmentation of the DN is challenging due to its small size and low visibility on conventional MRI sequences. In this study, we developed a fully automated deep learning pipeline to segment the DN using quantitative susceptibility mapping (QSM) MRI. A diverse dataset of 328 individuals, including healthy controls and patients with cerebellar ataxia or multiple sclerosis, was collected from nine international sites. Manual annotations provided reference standards with high reliability. A two-step approach combining localization and segmentation was implemented, with the nnU-Net framework yielding the best performance. The model achieved Dice scores of 0.90 ± 0.03 (left DN) and 0.89 ± 0.04 (right DN) on internal testing and outperformed existing tools in external validation. These results demonstrate that automated neural network-based DN segmentation is accurate, generalizable, and suitable for large-scale clinical studies. The model is publicly accessible for research applications.
Office Electronic Communication Methods and Employee Timely Delivery in State-Owned Universities in Bayelsa State
The research inquiries into the connection between electronic communication practices in offices and prompt delivery of employees in state-owned universities in Bayelsa State. This research encompasses a cross-sectional survey research design, and the population of the research was permanent administrative employees of state-owned universities (Niger Delta University, University of Africa, and Bayelsa Medical University) in Bayelsa state. To constitute the study participants, a purposive sampling method was chosen; thus, 90 administrators (30 from each state-owned university) were chosen. This research paper was primarily based on primary data, which was gathered using structured questionnaires. The reliability of the instrument was estimated using Cronbach's Alpha and Pearson's Correlation Coefficient, as the inferential analysis was done using Statistical Package for the Social Sciences (SPSS) version 23. The results showed that the email services were significantly related to timeliness. Instant text messages were significant in terms of timeliness, and chat applications presented very high relationships with timeliness. In conclusion, the study shows that the implementation of office electronic communication techniques is of great importance in terms of timely delivery to employees in state-owned universities in Bayelsa State. It was suggested that Universities ought to modernise and leverage their email systems with improved user interfaces, quicker delivery times, and task management systems to improve their contribution towards timeliness.