Journal of Mathematical & Computer Applications
Journal of Mathematical & Computer Applications is an open access peer-reviewed journal from the publishers of Scientific Research and Community (SRC) which publishes articles in the form of written, video and power-point presentations from the arena of Mathematical & Computer Applications. The journal focuses to build up accessing the complete content of articles freely from online for reader’s perusal. Article acceptance will be in the form of research, review, case reports, analysis, magazines, editorials, opinion, communications, mini-review, short communications, picturized, book review, video type and power point presentations (PPTS). You can submit your articles through online: https://www.onlinescientificresearch.com/submit-online or through Email attachment: mathematics@onlinescientificresearch.info
Driving Digital Transformation through Intelligent ERP: A Perspective on SAP S/4HANA, AI, and Supply Chain Innovation
Digital transformation has become imperative for businesses aiming to remain competitive in a rapidly evolving technological landscape. Intelligent Enterprise Resource Planning (ERP) systems, particularly SAP S/4HANA integrated with artificial intelligence (AI), have emerged as pivotal tools enabling transformative efficiencies and innovation within supply chain operations. This paper explores SAP S/4HANA & #39s capabilities as an intelligent ERP, emphasizing its AI-driven functionalities such as predictive analytics, robotic process automation, and real-time decision-making. Through detailed analysis and real-world case studies, the research highlights the significant impact of integrating intelligent ERP on supply chain management, demonstrating measurable improvements in efficiency, responsiveness, and strategic decision-making. Finally, this study discusses implementation challenges and presents best practices to ensure successful adoption, concluding with insights into future trends shaping the intelligent ERP landscape.
Copyright (c) 2025 Journal of Mathematical & Computer Applications
AI-Powered Zero Downtime: Intelligent Approaches to Always-On Systems
In an era where user expectations demand 24/7 availability, zero downtime is no longer optional — it’s essential. This talk explores how AI and intelligent automation are transforming the way we design, monitor, and operate highly available systems. We’ll cover traditional zero-downtime strategies like blue-green deployments, canary releases, and feature flagging — then take it a step further by introducing AI-driven enhancements. From predictive failure detection using machine learning models, to adaptive traffic routing during live deployments, to AI-based observability for root cause analysis — modern systems are becoming smarter, not just more stable. You’ll learn how AI can proactively identify risks, optimize resource utilization, and even automate rollback or scaling decisions in real-time. By blending tried-and-tested DevOps practices with intelligent decision-making, organizations can achieve a new level of resilience, performance, and user trust. This session is ideal for engineers, architects, and tech leaders who want to future-proof their infrastructure and move beyond manual, reactive operations. Join us to discover how AI is accelerating the path to true zero downtime — turning it from an operational challenge into a strategic advantage.
A Deep Dive into Real-Time Data Streaming and Scalable Architectures
The Power of Real-Time Data Streaming in Modern Architectures In today’s hyperconnected world, businesses demand instant insights to drive decisions, optimize operations, and enhance customer experiences. Real-time data streaming has emerged as the backbone of this transformation, enabling organizations to process and analyze data as it happens—eliminating latency and unlocking unprecedented agility. This session dives deep into the tools, patterns, and challenges of building scalable real-time systems. We’ll explore: Why batch processing is no longer enough in an era of IoT, financial trading, and personalized user interactions Architecting with Apache Kafka & Flink to handle millions of events/sec with fault tolerance Proven use cases from pricing engines to fraud detection, where streaming delivers 3–10x performance gains Avoiding the pitfalls of state management, out-of-order events, and resource bottlenecks Attendees will leave with actionable strategies to modernize legacy pipelines, reduce cloud costs, and harness streaming data as a competitive weapon. Whether you’re building event-driven microservices or AI-powered analytics, this talk will reshape how you think about data in motion. (Ideal for: Architects, Data Engineers, and Tech Leaders ready to future-proof their systems.)
Towards Tunable Advantages of Quantum-Like Teams. Interdependent Teams Counter the Disadvantages of Gen-AI
We provide a mathematical overview to enable any agentic system of “intelligent” machines, artificial intelligence (AI) and humans to interact in roles forming the structure interdependently performing team tasks. Our quantum-like model, maybe the only mathematical model of interdependence, captures the tradeoffs by a team’s choices as it expends its available energy on structure versus performance, forming an uncertainty (entropy) relationship. We address the support for our quantum-like model of uncertainty relations, the goals in this research, and the future for our research: 1. Redundancy reduces interdependence. With this finding, we confirm the existence of interdependence in all systems with teams. 2. Teams with orthogonal roles are best. This second finding is the root cause of humans, including scientists, not appreciating the role of interdependence in “squeezing” information states in teams. 3. Cognitive reports seldom equal behavior. The last finding solidifies our research, and explains why social scientists are absent from contributing to the mathematics of team science. In this talk, we review the need of a mathematics for team operations, the literature, the quantum-like mathematics in our model of agents, our hypothesis that freely organized teams enjoy significant advantages over command decision making (CDM) systems, and field results. We close with future plans and a generalization about squeezing states to control interdependent systems.
The Convergence of AI and Supply Chain Resilience: Current Practices and Future Paradigms
The integration of artificial intelligence (AI) into supply chain management signifies a transformative shift in business approaches to forecasting, planning, and operational efficiency. As organizations strive to enhance their competitive advantage and adapt to increasingly complex global markets, AI technologies present promising solutions to longstanding challenges within the supply chain domain. During this session we examine the potential impact of AI on supply chain management, with a particular emphasis on its applications in demand forecasting, inventory optimization, and adaptive decision-making processes. By leveraging machine learning algorithms and advanced data analytics, companies are positioned to achieve unprecedented levels of accuracy in predicting market trends and consumer behavior. Moreover, the implementation of AI-driven systems is anticipated to significantly enhance inventory management, reducing waste and optimizing stock levels across the supply chain network. As these technologies continue to evolve, the prospect of autonomous decision-making systems capable of rapid adaptation to market disruptions and changes presents an exciting frontier for supply chain resilience and agility.
AI for Financial Wellness – Moving from Debt Recovery to Empowerment
AI-Powered Financial Recovery: A New Era of Empowered Living As personal debt reaches unprecedented levels, traditional relief efforts often fail to effectively engage those most at risk. This work presents an AI-powered framework that leverages behavioural analytics, public financial data, and social media sentiment to proactively identify individuals in financial distress. By integrating classification models, NLP, and recommendation engines with real-time data pipelines, the system delivers personalized, ethical interventions that improve outreach and promote long-term financial wellness.
From Principles to Processes: Implementing responsible AI across national Borders
In an era in which artificial intelligence is fundamentally transforming business models, government, and society, we need clear, universally applicable guidelines. This speech outlines why international standards are more than just technical regulations: They build trust, ensure competitiveness, and enable cross-border cooperation in risk management, transparency, and liability. Established standards (ISO/IEC, OECD Principles, EU AI Act, NIST Guidelines) show us how we can align AI data processing with fundamental ethical values to make AI safe, fair, and sustainable. Adaptable governance models, auditability, human-centered AI design principles, and the building of multilateral coalitions are central to responsibly channeling innovation drivers. Ethics in AI is not a matter of ideology, but of operational implementation. How can the principles of responsible AI (fairness, transparency, safety, accountability) be transformed into concrete, measurable processes that work across diverse legal and cultural contexts? Risk-based regulation, auditability, human-centered design, grievance mechanisms, supply chain commitments, and certifiable compliance programs are key building blocks. Key messages Why international standards are essential, especially when AI processes data in security-relevant environments (cybersecurity, infrastructure, border protection)? Why AI-specific standardization is necessary and how it increases trust and resilience in security- critical ecosystems? How international standards facilitate cross-border collaboration, risk management, and compliance – building trust and enabling cross-border collaboration? What key standards exist (ISO/IEC, OECD Principles, EU AI Act, NIST) and what added value they offer for security policy, industry, and research. What action policymakers, businesses, and civil society should take promptly? In a globally connected world, AI ethics determine not only morality, but also economic sovereignty and social trust. International standards are not an obstacle, but rather the common toolbox that makes innovations safe and fair. Participants will learn why we have universally understandable yet flexibly adaptable layers of norms that guide regulation, business, and society toward a common ethical compass, transcending national egoism and technological fragmentation. Practical solutions provide inputs for implementation, as a lack of AI ethics is linked to liability issues.
Resource-Aware Serverless Computing: Leveraging Stateless Paradigms for Cost-Optimized Cloud Applications
Resource-Aware Serverless Computing: Leveraging Stateless Paradigms for Cost-Optimized Cloud Applications. As cloud adoption continues to grow, so does the need for cost-effective and scalable application design. This talk explores how stateless paradigms, including serverless computing, stateless databases, and event-driven architectures, are being increasingly adopted to address performance, scalability, and cost challenges in modern cloud environments. We discuss how resource-aware design helps match application components to the appropriate compute, memory, and bandwidth resources. The focus is on stateless application design, which avoids persistent state within compute components, enabling seamless scaling and distributed execution. While stateless services offer many advantages, they also introduce challenges such as cold starts, repeated dependency uploads, and limited runtime control. To address these issues, we explore several optimization strategies, including code refactoring, reducing unnecessary complexity, and batch processing to minimize invocation overhead. We also examine how to orchestrate services efficiently in stateless environments using workflows and state coordination. The talk concludes with a case study on distributed machine learning training using serverless computing, illustrating how stateless, resource-aware strategies can significantly reduce infrastructure costs while maintaining performance.
Optimizing Deep Learning Models for Hardware Efficiency on Edge Platforms
Kaiqi Zhao is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Oakland University, Michigan, where she has been serving since August 2024. She is the founder and director of the Efficient AI Lab, which focuses on designing scalable, high-performance, and hardware-efficient machine learning models to enable intelligent computing on edge devices. Dr. Zhao earned her Ph.D. from the School of Computing and Augmented Intelligence at Arizona State University under the mentorship of Prof. Ming Zhao. Her research centers on model compression and efficient deep learning, with the goal of advancing the automation and deployment of compact, high-performing AI models for real-world applications. Her work bridges both fundamental research and practical impact. As the first author, she has published in top-tier AI and edge computing venues such as AISTATS, ICASSP (Oral), Interspeech (Oral), and ACM/IEEE SEC (Best Poster Award). She has also co-authored papers in leading systems and edge computing conferences, including IEEE ICDCS, USENIX HotEdge, and USENIX OpML. Notably, her research on Knowledge Distillation via Module Replacing for Automatic Speech Recognition has been integrated into the Amazon Alexa library for production use. Dr. Zhao is a dedicated contributor to the research community. She currently serves as a reviewer for the U.S. National Science Foundation (NSF, 2025) and as a Program Committee (PC) member for AAAI (2023–2025). She was also the Ph.D. Forum Chair for ACM/IEEE SEC in 2021. In addition, she regularly reviews for premier AI conferences including NeurIPS, ICML, ICLR, AAAI, AISTATS, ICASSP, and Interspeech (2022–2025), and top-tier journals such as the IEEE Internet of Things Journal (IF: 11.1), IEEE Transactions on Neural Networks and Learning Systems (IF: 10.2), IEEE Transactions on Circuits and Systems for Video Technology (IF: 8.3), and IEEE Transactions on Intelligent Vehicles (IF: 8.2). Dr. Zhao’s research is driven by a passion for building intelligent systems that are not only powerful but also efficient and deployable paving the way for next-generation AI solutions on edge platforms.
World First: Browse the 3D Web without Limits, Protected and Invisible by Patented Design. Access Knowledge, Consume Securely, Whatever the Sector or State of Your Data
In five years’, time, the web will be a 3D universe where we’ll navigate like drones, exploring digital territories, manipulating objects and ideas with immersive tactile interfaces. 3D tutorials and 3D printers will enable us to fix almost anything, while collaborative work will replace Taylorization. This freedom of discovery will become addictive and viral. Over 80% of the world will have access to ultra-fast Internet, browsing digital twins of cities, objects, molecules or organs, exploring everything from hardware to health, leisure to safety, right up to the limits of intellectual property. Everything will be accessible, fun and educational, with no apparent risk. But danger lurks: a simple incident (data theft, substitution of a virtual component) could lead to the production of defective, weakened or even dangerous objects, unknowingly integrated into the consumer chain. Traceability will become blurred, and these “modified” objects will represent a real threat. The whole world will become accustomed to “touching” virtually everything, comparing details and testing before buying. Companies that don’t respect 3D web standards will disappear, wiped off the map in a matter of months. This reality is no longer fiction: the era of the three-dimensional web is about to take hold, mixing the real and the fake, security and pitfalls. We need to anticipate the risks if we are to take full advantage of this revolution, where innovation rubs shoulders with counterfeiting, and where the invisible becomes an asset. Anticipating the worst to experience the best: that’s the challenge of this fascinating new era.
Large Data, Little Victories: How Data-Driven Understandings Can Help Small Businesses make Better Decisions
This thorough cross-sectional study reveals important insights into the complex links between Data Driven Insight (DDI), Decision Making Process (DMP), Resource Allocation (RA), and Business Outcome (BO). It was carried out by watching 150 organizations during the influential year of 2024. The results highlight the critical role of the Decision-Making Process and reveal a statistically significant positive correlation between DDI and BO, as well as a minor positive correlation between the two. Interestingly, the study finds that effective resource allocation is another important component that has a moderately positive correlation with business results. The measurement instrument’s robustness is confirmed by the reliability statistics, which gives confidence to the collected data. Moreover, the ANOVA regression results highlight the substantial role that the interaction between DDI, DMP, and RA plays in explaining the variance in BO. These significant realizations stress the vital role that sound decision-making and wise resource management play in determining a company’s success. In the changing world of 2024 and beyond, researchers and practitioners may devise strategies and make well-informed decisions that improve overall organizational performance, thanks to the actionable knowledge they possess.
Identity Security Platform Overview Integrating MFA (Multi-Factor Authentication) And SSO (Single Sign On)
CyberArk Identity Security Platform provides a one-shop integration of multiple features layering different functionalities allowing for consumers to access different avenues from a single user-friendly, look-a-like interface. The platform is allowing for building a secure environment where the tool learns and adapts limiting access to resources functioning with zero trust. The underlying pieces of the platform are its ability to connect to multiple directory services, allow for validating identity by using multi-factor authentication, manage a complete identity life cycle and incorporate Single sign-on.
Prolegomena to Information Technology: Higher Degree Capstone Units
This paper is not about Information Technology (IT) per se, but per accidens in the special features of the teaching of IT higher degree “capstone” units and the supervision of doctoral candidates. Usually, the students are very intelligent and very well prepared in their Bachelor degrees, but IT is developing at such a pace that probably no graduate curriculum anywhere is ever completely up-to-date at any instant in time. This paper outlines one approach to the problem through a series of ten “prolegomena”, each one of which consists of a central issue and eight related IT topics. The aim of each prolegomenon is not to teach and learn a new topic so much as to sensitise the students to those IT topics which are not covered in their coursework at a particular university. This is an attempt to avoid the not uncommon phenomenon of university graduates who do not know what they do not know! The process is also a limited introduction to managing continuing professional development during their future working careers. The students in these capstone units are placed in groups of four where they learn the essentials of teamwork, since they will be working in teams when they graduate. The capstone unit focuses on an industry-level project which can be elaborated in a minor thesis over two semesters. The projects come from industry colleagues in the USA and Europe. Naturally the prolegomena here are institution-related since they are trying to touch on the gaps within the syllabus of a particular institution. Interested readers will have to modify them to fit the context in their own institutional environment. Other features of capstone units include assisting the students to acquire those general employability skills related to their institution’s graduate attributes; these are to help them in handling interviews both for their initial employment and later promotion opportunities.
Computational Approach in Mathematical Modeling of Cell Mass Concentration Effect on Heat Transfer Through a Blood Channel
This study involves a mathematical formulation of models representing the cell-induced mass concentration effect on heat transfer through a blood channel with the aim of understanding the role mass concentration plays on blood circulation. The models were derived as partial differential equations in dimensional form, scaled, and reduced to ordinary differential equations. The direct method of solving differential equations was adopted, and profiles were used. and the ODEs for the temperature and concentration profiles were obtained, respectively. In order to investigate the impact of the cell mass concentration effect on the heat transfer through the blood channel, we carried out a numerical simulation using Wolfram Mathematica, where the pertinent parameters such as the Prandtl number, Schmidt number, oscillatory parameter, radiation parameter, and mass concentration parameter on the flow profiles were used. In conclusion, the results suggested that the fluid temperature could increase with heat source, concentration rate, and Prandtl number, but decreases with oscillatory effects. Secondly, concentration decreases with increasing reaction rate, oscillation, and Schmidt number.
Copyright (c) 2026 Journal of Mathematical & Computer Applications
Parallel Computing in Terms of Gaisi Takeuchi's Quantum Set Theory1
The foundations and results of parallel computing in terms of Gaisi Takeutis’ quantum set theory are presented in this review paper. This approach is based on 1) the extension of the von Neumann’s uniqueness theorem for the solutions of the canonical commutations relations in quantum mechanics of finitely many degrees of freedom to quantum local field theories of infinitely many degrees of freedom [2]; 2) the results of the mathematician Gaisi Takeuti about quantum set theory in extension theory; and 3) the results of the physicist Lev Davidovics Landau about the behavior of the quantum liquids He3 and He4 resulting in a more general approach than the one based on the notion of the quantum bit [1-5].
The Education System and Artificial Intelligence - How It’s Going to Change the World
We posit two types of spontaneous symmetry breaking: one corresponding to the presence of a trivial vector bundle, the second corresponding to the presence of a nontrivial vector bundle. In their avatars, these resemble respectively the cases of type I and type II superconductivity. In the non-trivial case, the topology of the base space of the bundle enters fundamentally. Our applications will be to flavor symmetry breaking among the light quarks, so that the base space will be spacetime itself. In this case it is the SU(2)L and SU(3)L flavor symmetries themselves that are broken and the nature of such breaking is strongly restricted by the topology involved. Despite the negligible nature of any ambient gravitational intensities, we speculate that the topological change that must accompany the advent of mass could be the origin of this breaking and may give rise rise to both the Cabibbo angle and CP violation in the kaon system. We derive formulas for these parameters on the basis of this assumption.
Quantitative Verification of Microwave Heating Based on IR/FIR Spectrum Analysis
When DFT/MM-derived IR and FIR spectrum of materials is analyzed using their molecular models, their microwave thermal effect will be predicted and verified more quantitatively than the well-known heat output formula, P=1/2σ|E|2+πfε0εr”|E|2+πfμ0μr”|H|2.
OAuth 1.0 vs. OAuth 2.0: An In-Depth Analysis of Token Handling ,Client Authentication, and Developer Usability
OAuth (Open Authorization) is a widely adopted framework that allows third-party applications to access user resources without exposing user credentials. OAuth 1.0 and OAuth 2.0 are the two major versions of the protocol, each with distinct characteristics, advantages, and trade-offs. This paper provides an in-depth comparative analysis of OAuth 1.0 and OAuth 2.0, with particular emphasis on token handling, client authentication, and developer usability. By examining these critical components, the paper aims to provide a comprehensive understanding of how each version operates, the implications for security, and the experience for developers implementing these protocols.
Enhancing Regulatory Audit Readiness through Automated Data Lineage and Impact Analysis Using Azure Purview and SAP HANA
Data integrity is critical in Good Automated Manufacturing Practice (GxP) environments. This paper explores a cloud-based, automated data governance solution using Azure Purview and SAP HANA to enhance regulatory audit readiness. The proposed architecture automatically classifies metadata, tracks end-to-end data lineage, and visualizes access logs across regulated workflows in real time. It supports compliance with FDA 21 CFR Part 11, ISO 13485, and EU MDR, enabling faster, more transparent, and reliable audit responses while reducing manual overhead and human error.
Computer Vision in Automated Defect Detection for Manufacturing
Defect detection in additive manufacturing (AM) is crucial for maintaining quality and reliability across industries. Traditional methods primarily rely on manual inspection or isolated machine learning models, which often fail to generalize across varying defect types. This paper introduces a hybrid framework that integrates multiple Machine Learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), autoencoders, and explainability techniques such as SHAP and Grad-CAM. By leveraging the complementary strengths of these methods, the framework ensures both high detection accuracy and interpretability. The experimental evaluation is conducted using the MVTec Anomaly Detection dataset, which includes diverse defect types, and the results demonstrate significant improvements in classification performance and interpretability. The proposed approach paves the way for a more reliable and transparent AI-based defect detection system applicable across various manufacturing environments.
Integration of Email Security with ATD for Phishing and Malware Prevention
Email is the vector that is most exploited to deliver malware attacks and phishing. Traditional email security solutions only provide baseline protection, and they fail to detect any targeted, sophisticated, or zero-day threats. This white paper covers the integration of Advanced Threat Detection (ATD) with email security systems to address these gaps. The paper analyzes how combining behavior analysis, sandboxing, threat intelligence, and machine learning enhances the detection of malware attached to and socially engineered phishing emails. This paper discusses the real-world case studies, architectural models, practical recommendations, and performance trade-offs. The industry implementations present that the ATD-integrated email security improves the chance of decreasing the incident response time, improves the catch rates, and minimizes the false positives. This white paper discusses the future directions, XDR integration, AI-driven detection, and broader communication security as the next evolution in email threat defense.
A Framework of Economic Indicators for Transit-Oriented Development (Fei-Tod): A Case Study of Bandar Sunway, Malaysia
Transit-Oriented Development (TOD) has emerged as a potential solution to urban sprawl and traffic congestion in rapidly urbanizing Malaysian cities. However, the absence of standardised economic assessment tools has hindered effective TOD implementation and evaluation. This study aims to address this gap by developing a comprehensive Framework of Economic Indicators for Transit-Oriented Development (FEI-TOD) tailored to the Malaysian context, with a specific focus on Bandar Sunway as a case study. The research employs a mixed-method approach, integrating a systematic literature review using the PRISMA method, a three-round Delphi survey with TOD experts, and structured interviews with key stakeholders in Bandar Sunway. This methodological triangulation ensures the incorporation of global best practices, expert consensus, and local insights. The study systematically identifies common elements of successful TOD projects globally, compiles and evaluates potential economic indicators, and develops a context-specific framework for Bandar Sunway. The resulting FEI-TOD comprises 45 indicators across five categories: land use, density, transit facilities, value earnings, and economic attributes. This framework contributes to both the theoretical understanding of TOD's economic impacts and the practical implementation of sustainable urban development strategies in rapidly growing Malaysian cities. The FEI-TOD serves as a decision-support tool for urban planners, policymakers, and developers, facilitating evidence-based decision-making in TOD planning and implementation. The study's findings have significant implications for urban policy and practice in Malaysia, aligning with national urban development goals and the United Nations' Sustainable Development Goals, particularly SDG 11. The adaptability of the FEI-TOD suggests potential for broader application across diverse urban contexts in Malaysia and other rapidly urbanising regions.
Understanding Distance Metrics in KNN Imputation: Theoretical Insights and Applications
K-Nearest Neighbors (KNN) imputation is a widely used technique for handling missing data in machine learning and statistical modeling. The success of KNN imputation heavily depends on the choice of distance metric, as it determines the "closeness" of neighbors. This paper provides a comprehensive overview of the key distance metrics used in KNN imputation, including their theoretical background, mathematical formulations, use cases, and the implications of their selection on imputation outcomes [1-6].
Self-Cooling Simulated Annealing (SCSA) Algorithm for Nonlinear Least Square’s Data Analysis
A Self-Cooling Simulated Annealing (SCSA) algorithm is introduced for the optimization of nonlinear least-squares problems. In contrast to conventional simulated annealing techniques that require a predefined cooling schedule, the SCSA algorithm autonomously regulates system temperature based on the lowest figure of merit (ie. χ²) achieved at each iteration. The algorithm incorporates two key enhancements to improve efficiency: the separation of linear and nonlinear parameters, which reduces the dimensionality of the stochastic search space, and an adaptive Gaussian sampling mechanism that dynamically updates parameter-specific variances based on recent optimization history. A thermal resistance parameter (K) regulates the cooling rate and can be adjusted according to problem complexity. Performance benchmarking against standard Monte Carlo and gradient-based methods demonstrates that SCSA offers greater robustness in avoiding local minima and provides reliable convergence across varying levels of optimization difficulty. These characteristics make the method broadly applicable to nonlinear data analysis and other complex optimization tasks.
Geometric Constructions of Set Systems and Their Hypergraph Representations
We study t-designs of parameters (n, k, λ) over finite fields as group divisible designs and set systems admitting a transitive action of a linear group represented by an hypergraph G whose vertex set of size n is partitioned into sets of size k in such a way that every t sub set is contained in at least λ subsets of G. We relate the problem to the representation theory of the general linear group GL(n, Fq) and the constructions of AG codes over finite fields. As a byproduct we construct a RS code based encryption scheme
The Role of Machine Learning in Modern Software Development
The integration of machine learning (ML) into software applications is increasingly essential for enhancing functionality, decision-making, and performance in diverse fields. As industries strive to leverage intelligent systems, ML enables software to adapt dynamically, process large datasets efficiently, and deliver insights with minimal human intervention. This paper investigates the methodology of incorporating machine learning models into software systems, focusing on model selection, training, optimization, and real-time deployment. We present case studies on fraud detection systems and embedded applications, highlighting challenges, optimization techniques, and the role of continuous learning in maintaining model accuracy. Furthermore, the importance of explainable machine learning models for fostering user trust and understanding is emphasized. The paper concludes with discussions on the advantages and limitations of ML integration and future directions in enhancing model performance, scalability, and privacy-preserving capabilities, particularly in resource-constrained environments.
Azure Modernization Pathways: A Strategic Guide to Enhancing Legacy Application Performance
Business operations rely heavily on legacy applications, which often experience performance decline due to outdated technology that restricts agility and scalability, hindering innovation. This research examines how Microsoft Azure enhances legacy systems. It leverages its platform features, along with strategic modernization methods and specific services, to deliver measurable improvements in scalability, reliability, and cost efficiency. Microsoft Azure serves as a strategic catalyst, helping organizations transform their outdated applications into high-performing, resilient, and future-ready assets.
Intelligent Flaky Test Detection Using Historical Failure Patterns: An Ai-Driven Approach to Enhance Software Reliability
The burgeoning complexity of modern software systems, coupled with accelerated Continuous Integration/Continuous Deployment (CI/CD) pipelines, has exacerbated the pervasive challenge of flaky tests – non-deterministic failures that undermine developer confidence and impede release velocity. This paper introduces a novel, AI-driven framework engineered to proactively identify, diagnose, and mitigate flaky test failures by intelligently analyzing vast repositories of historical CI/CD data and a diverse array of external contextual signals. Our framework employs a sophisticated ensemble of machine learning models, including deep learning architectures for temporal pattern recognition and graph neural networks for dependency analysis, to precisely isolate the latent root causes of flakiness. Beyond mere detection, the system leverages Explainable AI (XAI) techniques to provide transparent insights into failure mechanisms and proposes intelligent remediation strategies, ranging from automated test quarantines and dynamic test re-prioritization to prescriptive recommendations for test refactoring or code modification. By continuously learning from evolving failure patterns, these AI models not only dramatically improve the stability and throughput of software delivery pipelines but also furnish invaluable, real-time historical insights into test reliability trends, empowering data-driven decision-making, fostering proactive quality assurance, and ultimately cultivating a culture of enhanced software quality and predictability
Engineering Intelligence: Fusing Agile, Devops and AI for Next Gen Software Development
As the complexity of software systems and customer expectations evolve organizations constantly seek to be at the forefront of innovative approaches for seamless, efficient and intelligent software delivery. The integration of Agile, DevOps and AI methodologies is an innovative evolution in the software engineering landscape. The article seeks to find the interplay between the three aspects and how they affect the software development lifecycle (SDLC) processes and product quality.
Orchestrating Microservices Across Hybrid Clouds with Kubernetes Federation
The proliferation of microservices and the increasing adoption of hybrid cloud strategies have introduced complex challenges in managing distributed workloads across multiple environments. Kubernetes Federation offers a compelling approach to orchestrating microservices across heterogeneous cloud infrastructures by enabling unified control, policy propagation, and service discovery across federated clusters. This paper investigates the practical and architectural considerations involved in implementing Kubernetes Federation in hybrid cloud scenarios. It outlines the architecture of Federation v2, emphasizing its components, capabilities, and limitations. Real-world use cases such as cross-cloud load balancing, disaster recovery, compliance enforcement, and multi-region deployments are explored to highlight the benefits of federated orchestration. An implementation framework is presented, including setup, deployment pipelines, and observability mechanisms. The paper also presents performance evaluations based on metrics like latency, availability, and failover efficiency, supported by empirical results from simulated environments. Key challenges such as network complexity, security considerations, and operational overhead are discussed in detail. The study concludes with recommendations for optimizing hybrid cloud operations using Kubernetes Federation, along with future research directions including integration with service meshes and autonomous orchestration. By bridging the gap between isolated clusters and unified governance, Kubernetes Federation emerges as a critical enabler of scalable, resilient, and policy-compliant microservice orchestration in hybrid cloud ecosystems.
Copyright (c) 2022 Journal of Mathematical & Computer Applications