International Journal of Intelligent Systems and Applications in Engineering

A Hybrid Deep Learning Approach for Predicting Patient Health Outcomes in Mobile Healthcare Applications

Akhil Tirumalasetty — 2026-02-14

Along with mobile health care apps, deep learning has transformed health monitoring and prediction. A hybrid approach based on deep learning for mobile health systems for precise patient health outcome prediction is proposed in this paper. It exploits Convolutional Neural Networks (CNN) to extract the features followed by Long Short Term Memory (LSTM) networks to learn from the sequential pattern for efficient analysis of the patients' vitals, past medical history and real-time sensor data. Also Attention Mechanism plays very significant role in highlighting important health parameters thus interprets and explains levels of data which helps in decision improvement through the model. We train the hybrid model on heterogeneous healthcare data and test it with accuracy, precision, recall and F1-score. The experimental results demonstrate significant benefits in terms of predictive consistency and real-time flexibility than traditional deep learning models. This framework could change the base of mobile healthcare applications to initiate early disease detection, personal treatment recommendations, and timely involvement in the patient journey that would facilitate healthier and more effective healthcare.

Efficient Large-Scale Data based on Big Data Framework using Critical Influences on Financial Landscape

Bhanu Prakash Paruchuri — 2026-02-14

One of the most recent commercial and technological concerns in the technological era is big data. Hundreds of millions of events occur on an ongoing basis. The financial sector is significantly involved in the computation of big data events. As a result, hundreds of millions of financial transactions occur in the financial industry each day. Financial practitioners and analysts perceive it as an emerging challenge in the data administration and analytics of a variety of financial products and services. In addition, financial services and products are significantly affected by big data. Determining the financial concerns that big data significantly affects is, thus, an important topic to research with the impacts. This paper used these concepts to show the current state of finance and how big data affects financial markets, institutions, internet finance, financial management, internet credit service companies, fraud detection, risk analysis, financial application management, and more. The connection between big data and economic aspects can be better understood by doing an exploratory literature review of secondary data sources. Because big data in finance is a relatively new concept, further research directions will be proposed at the end of this study.

A Physics-Informed Neural Network Framework for MHD Casson Ternary and Tetra Hybrid Nanolubricant Flow

Praveen Kumar U M — 2026-02-14

The heat and mass transport properties of Casson hybrid nanofluids flowing across a stretched surface in the presence of thermal radiation, Joule heating, and a magnetic field are examined in this work. We look at two sophisticated nano-lubricant arrangements. , ZnO, and SiC nanoparticles suspended in engine oil make up the first ternary hybrid nanofluid. Graphene nanoplatelets (GNPs) are added to the ternary mixture to create the second tetra hybrid nanofluid. Comparing the effects of nanoparticle composition on energy dissipation mechanisms, flow behavior, and thermal conductivity is the aim. Joule heating, radiative heat flux, thermo-diffusion, and chemical reaction effects are all included in the mathematical formulation. The controlling nonlinear partial differential equations are reduced to a linked system of ordinary differential equations by means of appropriate similarity transformations. A Physics Informed Neural Network (PINN) method designed especially for nanofluid lubrication systems is used to solve these equations. By directly integrating the governing physical laws into the loss function, the suggested PINN architecture enables the simultaneous elimination of boundary condition errors and equation residuals. Computational efficiency and solution stability are improved by this two-way optimization. Also wed did Numerical Validation of the PINN Solver Comparing the tetra hybrid nanofluid to the ternary formulation, numerical results show that the former offers noticeably greater thermal enhancement and lower entropy generation. GNPs' remarkable heat conductivity and enormous surface area are primarily responsible for this performance enhancement. On the other hand, the ternary hybrid nanofluid shows moderate temperature gradients and comparatively constant viscosity behavior. For complicated nonlinear thermal-fluid problems in lubrication applications, the PINN framework provides a dependable computational tool with good convergence and prediction accuracy overall.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8084

Distributed AI Systems: Building Scalable and Safe LLM Orchestration Layers

Sahil Agarwal — 2026-02-14

Distributed artificial intelligence systems, a new model for integrating large language models with enterprise infrastructure, require orchestration layers to coordinate large models across heterogeneous computing environments. These orchestration frameworks address issues such as retrieving context, controlling execution, managing system state, and ensuring observability, improving the overall effectiveness of the deployment. Retrieval-augmented generation (RAG) is a major model for LLMs to complement model output with grounded information to reduce hallucinations, using hybrid retrieval architectures combining lexical and dense retrieval with multi-agent coordination patterns, organising specialised autonomous agents to decompose compositional reasoning problems into subproblems, and enabling efficient pinpointing of semantically relevant documents. Policy-aware execution mechanisms implement security functionalities, such as authorization gates and context sanitization pipelines, that respect zero-trust principles during inference via mutual authentication and encryption protocols. Fault tolerance mechanisms address probabilistic failures unique to language model inference, including token truncation and semantic coherence degradation. Scalability patterns employ horizontal and vertical strategies to maintain performance under variable workloads while preserving tenant isolation boundaries. This article presents architectural patterns, performance benchmarks, and governance frameworks for production-ready language model systems that meet enterprise goals for reliability, security, and regulatory compliance. This work is informed by production deployment patterns and operational metrics observed in large-scale enterprise language model systems, emphasizing practical applicability over purely theoretical analysis.

Numerical Solution of the 2D Cauchy–Riemann System Using Classical and Quantum-Inspired Finite Difference and Crank–Nicolson Schemes

Mitat Uysal — 2026-02-26

The Cauchy–Riemann (CR) equations form the fundamental condition for analyticity in complex analysis and arise in potential theory, fluid mechanics, and electromagnetic field modeling. In this study, the two-dimensional Cauchy–Riemann system is solved numerically under prescribed Dirichlet boundary conditions using four approaches: (i) Finite Difference (FD), (ii) Quantum-Inspired Finite Difference (QI-FD), (iii) Crank–Nicolson (CN), and (iv) Quantum-Inspired Crank–Nicolson (QI-CN). Full mathematical derivations of discretization schemes are provided. The quantum-inspired schemes introduce amplitude-modulated update operators motivated by quantum probability dynamics. Comparative simulations demonstrate convergence behavior, stability properties, and error characteristics. Multiple graphical outputs including surface plots, contour maps, error heatmaps, and convergence curves are presented.

Designing Reliable Event-Driven Enterprise Platforms Using Apache Kafka

Chandramouli Holigi — 2026-02-24

Enterprise platforms in domains such as digital payments, supply chains, and customer engagement increasingly leverage event-driven architectures to achieve real-time data propagation, service decoupling, and horizontal scalability. Apache Kafka has emerged as a foundational element to build high-throughput, fault-tolerant messaging systems that can sustain event streams across distributed architectures. Kafka-based systems require discipline across delivery semantics, partitioning, consumer group coordination, back pressure, and schema evolution. Exactly-once semantics are achieved through idempotent producers and transactional APIs to avoid duplicate processing with throughput that is sufficient for production workloads at enterprise scale. Partition keys that match business rules help keep the order of transactions, while adjusting the number of consumers based on lag and controlling producer access help maintain system stability during different load levels. Schema compatibility enforcement via registry-driven governance keeps producers from accidentally publishing incompatible breaking changes to production topics. Together, these architectural and operational principles provide the durability, correctness, and resilience required from enterprise-grade event processing in the modern system of record when building Kafka-based platforms.

From Sampling to Population Testing: Continuous Audit Analytics for ICFR Effectiveness

Karishma Velisetty — 2026-02-22

Internal control over financial reporting has historically depended on periodic, sample-based testing methods that create measurable coverage gaps across high-volume transaction populations. The transition to continuous audit analytics represents a fundamental shift in assurance architecture—from discrete, interval-driven sampling to automated, population-level control testing executed in real time. This article examines the structural drawbacks of conventional sampling models, proposes a three-layer continuous audit architecture integrating deterministic testing, anomaly detection, and behavioral analytics, and redefines key controls within the context of algorithmic execution and machine learning-driven fraud detection. An implementation pathway progressing through foundation, build, operate, and optimize phases is presented alongside the operational governance metrics required to sustain continuous ICFR effectiveness. The convergence of enterprise resource planning infrastructure, big data analytics, and artificial intelligence has rendered full-population testing operationally deployable, compressing control failure detection timelines and strengthening the reliability of financial reporting assurance in ways that periodic audit cycles are structurally unable to achieve.

Designing High-Performance Distributed Systems for In-Memory Secure Data Processing in Cloud Security Analytics

Akhil Karrothu — 2026-02-25

The surge of cloud-based apps and advanced cyber threats has led to a huge demand for high-powered security analytics that can ingest and process enormous amounts of data in real time. Conventional disk-based centralized security analysis systems tend to have high latency, limited scalability and insufficient privacy of sensitive data. To cope with these issues, we introduce in this paper the design of a high-performance distributed in-memory secure data processing system for cloud security analytics. The proposed model employs distributed in-memory computing, parallel processing and secure data management techniques to support us with low-latency threat analysis and real-time analytics. Advanced security features, such as data encryption in memory, secure access management, and isolation across distributed nodes are included to maintain the confidentiality and integrity of data during analytics processing. The system is deployable in a scalable form factor across cloud platforms, and yet also achieves fault tolerance and resource efficiency. Experimental results show large savings in terms of processing, types response and scalability of traditional disk-centric security analytics platforms. The results show in-memory distributed processing can provide a viable platform for next-generation cloud security analytics, leading to faster threat identification, increased operation efficiency, and strengthened data protection in the ever-evolving cloudy world.

AI-Based Predictive Maintenance for General Aviation Aircraft

Sam Suseelan — 2026-02-28

Advances in artificial intelligence have brought many new possibilities into predictive maintenace. this happens especialy on general aviation. Predictive maintenance, which uses artificial intelligence to predict when machinery will break down, is revolutionizing how work needs to be done. It allows us to focus less on reparing things we've already broken and focus more on keeping things up and running smoothly. This paper analyzes how artificial intelligence is integrated into predictive maintenance systems with the goal of doing away with orderly current aircraft, analyzing methodologies that use data analytics and also predictive machine learning to predict the failure of components and schedule maintenance accordingly. This article talks about the large amount of benifits AI has on the aviation inditrly. Such as better sefety, less expence, and it makes everything run smoother. The Essay coves the issue on what challanges thease companies are facening they are facening challanges on trying to get their systems work. One thing that the AI Advanced PdM System does is it introduces future possibilities for technological advancements in PdM including, but not limited to, edge computing, real time data prediction, and autonomous maintanance. This paper delves deep into what the future holds for maintenance in the state of GA aviation with the use of AI.

Leveraging AI for Predictive Technical Debt Management in SAP Development Ecosystems: Case Studies and Future Prospects

Vamsi Krishna Talasila — 2026-02-28

Technical debt (TD) acts as the silent killer in massive, integrated SAP ecosystems and is often the main reason projects crash and burn. We simply can’t afford to be reactive anymore; we need to get ahead of the problem with Predictive Technical Debt Management (PTDM). This paper proposes a PTDM framework that uses Artificial Intelligence (AI) to handle three critical jobs: predicting what will break, prioritizing what to fix, and keeping the deployment line moving. We use a binary classification model (Algorithm 1) to guess the odds of an ABAP object failing, and we apply Natural Language Processing (NLP) to support tickets to figure out which bugs are actually hurting the business (Algorithm 2). By wrapping this in a Continuous PTDM Loop (Algorithm 3), we automate the creation of remediation tasks. Our operational case studies like an S/4HANA migration triage and continuous performance forecasting (Algorithm 4) show that this AI-driven approach speeds up custom code cleanup and stabilizes the system by calculating the "interest rate" of debt before it becomes too expensive to pay off. We wrap up by discussing future research into Deep Learning for semantic debt detection and managing debt in cloud-native SAP landscapes.

Adaptive AI Governance in Regulated Enterprise Data Platforms: A Trust-Calibrated Automation Framework

Suman Reddy Gaddam — 2026-02-14

Artificial intelligence (AI) has become foundational to enterprise data platforms in regulated industries, including financial services, healthcare, and compliance-sensitive digital ecosystems. While AI automation improves spotting unusual patterns, making predictions, and scaling operations, giving more decision-making power to algorithms adds challenges in governance, regulatory risks, and overall system safety. Traditional governance methods that depend on fixed rules or after-the-fact checks are not enough for environments where AI is making decisions, as they fail to account for the dynamic nature of AI systems and the need for real-time oversight and adaptability to changing circumstances, particularly in light of the complex challenges posed by algorithmic bias and regulatory compliance in sectors like healthcare and finance. The Trust-Calibrated Automation (TCA) Framework provides a clear method for handling AI that changes how much automation is used based on the specific risks, rules, and financial importance of different decision-making situations. The framework has various control levels, a method to assess overall risks, systems that focus on important issues based on trust, and elements that make sure the design fixes known problems in AI systems, like algorithmic bias that led to a 50% lower identification of high-need Black patients compared to equally sick White patients in healthcare risk prediction.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8126