Journal of Artificial Intelligence Research and Applications
The Journal of Artificial Intelligence Research and Applications (JAIRA) is a peer-reviewed, semi-annual open-access journal based in London, UK. Since its inception in 2021, JAIRA has served as a premier academic platform for the dissemination of research in artificial intelligence, focusing on both theoretical advances and practical implementations. The journal welcomes interdisciplinary contributions that push the boundaries of machine intelligence across fields such as autonomous systems, decision support, computer vision, deep learning, and AI ethics. Adopting a double-blind peer review process, JAIRA ensures scientific integrity and academic excellence in every issue.
Multi-Objective Optimization Frameworks for Healthcare Resource Allocation Using Bayesian Decision Models Multi-Objective Optimization Frameworks for Healthcare Resource Allocation Using Bayesian Decision Models
Healthcare faces resource shortages, demand instability, clinical uncertainty, and escalating costs. Bayesian and advanced machine learning distribute hospital and system-wide multi-objective healthcare resources. Under resource allocation uncertainty, stochastic decision-making optimises operational cost, treatment capacity, service-level equity, and patient risk. Bayesian inference assesses latent patient risk, demand, and treatment outcome probability using clinical databases and real-time observations. Probabilistic constraint management and multi-objective Bayesian optimization effectively assign beds, clinical staff, diagnostic equipment, and critical care units. For high-dimensional decision space computational tractability, modern hospitals employ surrogate modeling and approximation inference. Bayesian multi-objective decision models endure, interpret, and adapt better than deterministic and single-objective optimization methods, according to analysis and simulation. A solid, scalable decision-support paradigm facilitates evidence-based healthcare resource management under uncertainty.
Heterogeneous Graph Neural Networks for Drug Reimbursement Forecasting in National Health Systems
Complex, multi-stakeholder, multi-data source, interdependent entity, and financial risk national health system drug reimbursement decision-making. Nonlinear pharmaceutical consumption, pricing negotiations, clinical efficacy, and regulatory limits may be missed by classic econometric and statistical forecasting models. Heterogeneous graph neural network forecasting mimics system-level drug reimbursement probabilities, use patterns, and downstream budgetary effects. The proposed clinical, economic, and administrative learning model treats drugs, patients, providers, manufacturers, regulatory bodies, and therapeutic classes as distinct nodes connected by semantically meaningful relationships. Real-world evidence, claims data, prescription behavior, and policy aspects predict higher-order interactions and temporal propagation effects that influence reimbursement and financial sustainability. Heterogeneous GNN predicts pharma reimbursement approvals, expenditure growth, and risk concentration better than machine learning and graph baselines. Graph-based representation learning may enhance budget impact assessments, evidence-based reimbursement planning, and national drug finance transparency and robustness.
Data Mesh Meets AI: Federated Ownership and ML-Enabled Contracts for Cross-Domain Data Collaboration
In modern data-driven companies, the problem of fragmented data continues to stifle innovation, make it harder to make these decisions, and make it harder for people from different departments to work together. As companies grow, traditional centralized data architectures have trouble growing quickly & meeting the needs of various fields. This has led to the creation of the Data Mesh paradigm, which supports a decentralized approach to data management in which domain teams own, share, and utilize data as a product. However, enabling substantial cross-domain data collaboration within a distributed framework remains a considerable challenge, particularly regarding the establishment of trust, governance & interoperability across many other domains. This essay looks at how federated ownership and AI-driven smart contracts might change the way information is shared and managed in many other different fields. By adding ML to contract logic, businesses can automate access control, usage limitations, compliance checks & predictive alerts. This reduces friction and builds trust between teams. Through case studies and architectural analyses, we show how machine learning-enabled contracts allow for secure, scalable, and auditable data exchange while keeping autonomy and avoiding bottlenecks. Our study shows that adding AI to federated data governance makes data easier to get to and better quality, and it also gives domain teams more flexibility and responsibility to come up with new ideas. The paper offers a paradigm for businesses seeking to implement Data Mesh via intelligent automation, including practical insights into the design, implementation & measurable impacts of this technique in real-world contexts.
ML-Powered Data Catalogs and Metadata Inference for Self-Describing Big Data Ecosystems
In today's fast-changing data world, businesses have too much data that is too different and spread out. Standard methods for managing data don't always work well for finding, understanding, and applying data. Modern data catalogues are more than simply static lists of items; they are also dynamic places to store information that provide structure, meaning, and easy access to large data ecosystems. These catalogues can really shine when ML is applied to figure things out smartly, even when the data sets are too messy, not well-documented, or not well-organized. Data catalogues that utilize ML look at these current data trends, user behaviors & contextual signals to automatically tag, categorize & link their data assets. This makes the data self-descriptive and makes governance easier. This solves a big problem: creating information by hand is hard work, prone to their mistakes, and typically doesn't work well with different teams & also technologies. Our methodology utilizes both supervised and unsupervised ML techniques to infer schema details, data lineage, semantic links, and use contexts, therefore reducing dependence on human input while improving their data discoverability & the dependability. This paper suggests a scalable architecture that integrates ML models directly into the cataloguing process, enabling actual time metadata enhancement as data is ingested into the system. Additionally, we have a feedback mechanism that constantly improves the quality of inferences via user interactions & validations. Initial findings indicate significant improvements in data discoverability, accelerated onboarding for data users, and improved compliance readiness, resulting from enhanced metadata completeness and traceability. This work shows how data catalogues that use machine learning can change from simple documentation tools into these smart systems that make self-service analytics easier, promote data democracy & support truly self-describing big data platforms. This makes organizations more agile and focused on getting insights.
Optimizing Assembly Line Layouts with AI for Increased Productivity and Cost Reduction
Optimisation of assembly line layouts enhances industrial efficiency. Heuristics and specialist knowledge may not work in complex, changing production scenarios, therefore traditional layout design may fail. Data-driven and adaptable assembly line design may be possible using AI-based algorithms. AI can redesign production lines using ML, optimisation, and deep learning. This may increase production and lower expenses. This research examines AI-based assembly line layout improvements to increase production, reduce bottlenecks, reduce material handling costs, and optimise space. Genetic algorithms, simulated annealing, and reinforcement learning optimise layouts. These methods allow the system balance resource utilisation, cycle time, and throughput in design configurations. It begins with product, process, and cellular assembly line layouts and their implications on line performance. Next, we study AI optimisation solutions for layout design's high dimensionality and non-linearity. Multiple layout optimisations are problematic. Choices may include cost reduction and production speed. Complex decision-making algorithms allow AI to address this problem. Genetic algorithms (GAs) are intriguing AI algorithms because they emulate natural evolution to optimise combinatorial problems. GAs may optimise assembly lines by crossing and changing layouts. Also prevalent is simulated annealing. Probabilistic methods to escape local optima and locate additional solutions boost global optima discovery. Reinforcement learning (RL) suggests that an agent may learn the best layout configurations by interacting with its environment and executing different tasks, changing its approach based on rewards or penalties. Effective AI methods may increase decision-making and reduce layout design involvement. AI-improved industrial design case studies and algorithmic methods are discussed. These case studies demonstrate how AI may improve products, reduce lead times, and save costs. Technology issues and AI model integration in industrial processes are being studied. Data quality and timeliness are essential. These are necessary for AI model training and use. The research analyses how companies might build data infrastructure and update AI algorithms with production changes. The paper also discusses how AI might increase manufacturing line optimisation flexibility and autonomy. As they evolve, AI will do more than optimise design. They will also respond swiftly to industrial and supply chain developments. AI can adjust assembly line structure in real time, making it more adaptable for customer needs and market conditions. As businesses cut costs and maintain quality, AI may optimise assembly line layouts for long-term competitive benefits. AI algorithms may increase organisational efficiency and cost-effectiveness, says one research. To implement AI technology, consider manufacturing circumstances, production complexity, and how to integrate it with present systems.
Predictive Analytics for QA Metrics: Leveraging AI to Forecast Testing Outcomes and Resource Needs
Predictive analytics in QA testing has transformed software development testing and resource use. Traditional reactive QA techniques fail to fulfil corporate needs as software systems become more sophisticated and the requirement for high-quality things on time grows. Predictive analytics tracks QA progress, forecasts testing results, and identifies development lifecycle bottlenecks using AI and ML. This study analyses how predictive analytics and advanced forecasting may increase QA testing accuracy, efficiency, and scalability. Paper covers regression models, time series analysis, classification techniques, and deep learning. Their capacity to predict testing results, defect identification, and resource use is evaluated. QA testing is hard since you can't forecast or use resources effectively. Growing use of Agile and iterative software development requires more adaptable testing methods. Predictive analytics predicts testing delays, resource restrictions, and trouble zones. The article describes how supervised learning algorithms, unsupervised learning techniques, and ensemble methods may forecast how many faults will be detected, how long they will take to correct, and how many resources each testing level would need using prior QA data. This research must also establish the best testing resource use. Predictive models may help QA managers discover resource-intensive testing stages and adjust workforce, tools, and methodologies. The QA team may prioritise key jobs, saving time and boosting productivity. Teams use predictive analytics to predict testing delays. Teams may decrease risks and fulfil deadlines without sacrificing program quality. Big data is used in predictive analytics. Modern QA methods create a lot of data that may be examined for patterns and trends. Hadoop, Spark, and NoSQL allow huge dataset predictive analytics. Improves forecasts. The report also investigates how QA testing tools are leveraging NLP and deep learning. This improves testing system prediction. Using NLP to examine bug reports, user comments, and code reviews helps predictive models find qualitative insights and patterns. The study also highlights industry leaders' predictive analytics-powered QA methods. These case studies show how predictive methods may enhance real-world problem identification, resource optimisation, and test coverage. These organisations use predictive analytics to minimise testing cycle times, increase product quality, and improve development-QA communication. Although promising, predictive analytics in QA testing is tricky. Data quality and availability are important challenges. Predictive models need plenty of data to predict accurately. Noisey or inadequate data may be less useful. Installing these technologies in QA procedures requires data science and QA knowledge. Companies need qualified workers and advanced technology to train and deploy predictive models. Another problem is model interpretability. QA personnel may not trust deep learning models or other complicated machine learning techniques. The article analyses ways to explain predictive analytics models to enhance decision-making. This study also examines how artificial neural networks, reinforcement learning, and sophisticated optimisation algorithms might improve QA predictive analytics. These technologies may predict results and improve testing in real time. An autonomous QA environment would enhance live data. The paper concludes that this study must continue. Predictive analytics in QA testing may help developers work faster, simplify testing, and provide better products.
Dynamic Knowledge Graphs for Integrating Multimodal Life Sciences Data in Translational Research
Translational research is tough because it needs multimodal data from various biological disciplines. Multiple biological data analyses help apply basic scientific insights to therapy. AI-powered knowledge graphs (KGs) may help. Dynamic representation and integration of genomes, proteomics, clinical, and medical imaging data. Understanding complex biological systems, disease processes, and treatment strategies is easier using knowledge graphs. Nodes represent genes, proteins, pathways, diseases, and medicines, while edges demonstrate their relationships in life sciences knowledge graphs. These photos include new data and scientific breakthroughs, making them suitable for translational study. AI technologies like ML, DL, and NLP may add semantic information to these networks. Easy to mix facts, find new information, and form hypotheses. New biomarkers, pharmaceutical repurposing, and tailored therapy improve patient outcomes. Benefits of data-driven dynamic knowledge graphs for multimodal life sciences. Instead of keeping biological data distinct from other data in various research groups or locations, this prevents data fragmentation. Fragmentation hinders biological system understanding and discovery. Knowledge graphs use molecular, cellular, and clinical data to simplify disease and treatment. Knowledge graphs may reveal complex biological and causal links that data analysis ignores. Science improves AI-powered knowledge graphs. Research may expand knowledge graphs. They can learn and offer data. Real-world graphs reflect the latest scientific advancements thanks to continuous learning. Flexible dynamic knowledge graphs may be utilised in pharmaceutical development, personalised medicine, clinical decision-making, and epidemiology. Knowledge graphs driven by AI can combine data, promising medicinal development. Complex biological linkages reveal novel therapeutic targets and predict safety and effectiveness. The graph shows drug-target interactions, explaining drug activity and off-target consequences. Knowledge graphs disclose new medical uses, aiding translational research. They demonstrate how drugs alter new biomarkers or disease pathways. Personalised medicine, another translational research topic, benefits from dynamic knowledge graphs with several data sources. Genomic, transcriptomic, proteomic, and clinical data may be used to create knowledge graphs of patients' illnesses. Individualised therapy may improve therapies since each patient is physiologically distinct. To update treatment plans, these visualisations are updated live as patient data arrives. Dynamic knowledge graphs have difficulties but are useful in translational research. Combining data from many sources with different formats, sizes, and quality is difficult. Researchers are standardising protocols and data formats for integration. AI models that evaluate knowledge graph data must be validated for pharmaceutical development and clinical choices. Scalability is another knowledge graph issue. As life sciences generate more data, we need better techniques to store, organise, and understand large graphs. Biologists, medics, data scientists, and engineers must collaborate on AI-powered knowledge graph translational research. Biology and AI/graph expertise are needed to incorporate life sciences data to dynamic knowledge graphs. Multiple professions must collaborate to build scientifically correct and technically strong knowledge graphs. This ensures translational research success.
Generative Adversarial Networks for Synthetic Data Creation in Manufacturing Process Simulations
Generational Adversarial Networks (GANs) produce unique data for manufacturing and other businesses. Quality, realistic GAN data aids AI model training and industrial process modelling. Traditional simulations are costly, lack real-world data, and are difficult to replicate. These constraints make training AI models to improve industrial processes, product quality, and operations difficult. GANs may replace traditional methods by creating statistically equivalent datasets. Using synthetic data, simulate reality. This article claims GANs can spoof industrial simulation data. They research industrial AI model training, process optimisation, and predictive analytics improvements. GAN generation and discrimination. The discriminator examines bogus and real data and offers output enhancements. Adversarial training gives GANs diverse, accurate datasets. AI models may be taught without data. GANs may indicate production line modifications, equipment failure, and supply chain issues. In simulations, AI models may optimise processes, discover defects, and increase quality. GANs simulate industry without real data using generated data. GANs simulate industrial system failures and supply chain disruptions using a sample dataset. This expands AI model training datasets. Flexible GANs may generate industrial data. This lets AI models be trained on numerous operational situations, including edge cases that are hard to discover in datasets. Synthetic training data from GANs improves industrial AI model accuracy and efficiency. GAN-generated synthetic datasets may cut AI model training and data collection expenses. Simulating these models may improve accuracy. Improves and contextualises. GANs may generate false data for controlled AI model testing and verification before industrial usage. Reduces untested model hazards. The technical hurdles of adding GAN-generated synthetic data to manufacturing simulations are investigated. Data quality and accuracy are hard. GANs are strong, but phoney data may misrepresent industrial complexity. This may influence AI model predictions. GAN models must be calibrated and tested for industrial and synthetic data. GAN training for large-scale simulations may need a lot of processing resources for smaller industrial enterprises without powerful computers. Some studies imply GANs with reinforcement learning and deep learning may produce synthetic data for industrial process modelling. GAN training may benefit from reinforcement learning. These strategies teach AI models better practices via trial and error. AI models evolve, so autonomous production systems like them. Deep learning feature extraction may aid GAN. Simulate complex industrial processes better. Modelling industrial processes using false data using GANs provides predictive analytics. Manufacturers use synthetic data to estimate production, find bottlenecks, and optimise resources. Production, downtime, and operations may improve using predictive models. Industrial simulations may show and correct production difficulties.
Transfer Learning Techniques for Domain-Specific Quality Assurance Testing
QA professionals like AI because it produces software rapidly and reliably. Traditional software testing increases costs and delays product introduction due to time and resource restrictions. Transfer learning lets pre-trained AI models evaluate domain-specific tasks. This boosts software and product launches. This study examines domain-specific quality assurance testing transfer learning theoretically and practically. It emphasises how different methods may speed up testing, use resources better, and cover more tests. Transfer learning lets robots use knowledge from similar but distinct fields. Customise general-purpose dataset models for specific software environments or use cases using software testing. With pre-trained AI models for each testing assignment, companies save time and effort. This strategy generalises AI systems across software domains to lower model generation costs and enhance testing. Quality assurance teams may use transfer learning to detect issues, build test cases, and assess performance. Domain-specific QA annotation may benefit from transfer learning. Labelling big, specialist datasets takes time and manpower. With some new tagged data, transfer learning adjusts pre-trained models to the target domain after huge data training. Faster training and model release minimise testing cycles without sacrificing outcomes. We also highlight transfer learning's software QA restrictions. Similar source and target domains aid transfer learning. If the source and destination domains don't match, the transferred model may need target domain fine-tuning or new models. With antiquated systems and technology, QA may have trouble testing transfer learning models. These difficulties show the need of a robust basis for transfer learning software testing. Also vital for software testing is transfer learning. Domain adaptation, fine-tuning, and feature extraction are used. Each has quality assurance pros and cons. Fine-tuned pre-trained model weights match domain. Feature extraction employs pre-trained model learning features without restructuring. Data transmission requires domain adaptation to match source and destination domains. This study explains how these tactics may improve quality assurance. Transfer learning-based domain-specific QA testing case studies are also offered. These cases demonstrate how transfer learning aids banking, healthcare, and software testing. Transfer learning always lowered testing time, detected more issues, and increased product dependability. These examples demonstrate how quality assurance professionals and organisations employ transfer learning. The paper recommends further transfer learning research in software testing notwithstanding these encouraging case studies. Future research should improve scalability, performance in changing software environments, and tool automation to assess transfer learning models. AI testing raises fairness and data privacy problems, the report claims. If AI-based testing becomes popular, consider it.
Using Time Series Analysis and Machine Learning for Catastrophe Modeling in Property Insurance
Time series analysis and ML help property insurers control disaster modelling risk. Community and asset damage is evaluated via natural disaster simulations. This aids insurance loss prediction and pricing. Severe weather increases with climate change. This emphasises the need for fast, accurate catastrophic risk assessments. Natural disaster patterns' intricate and sudden alterations may challenge traditional actuarial methods. This work uses ML-enhanced time series analysis to forecast catastrophic risk. It excels in precision, data management, and real-time risk assessment. Machine learning and time series analysis may help computers identify complex patterns, non-linear correlations, and adapt to new data. Improves forecasts. The little but substantial modifications in disaster modelling may help insurers forecast claims. RNNs, LSTMs, and gradient boosting models may show historical data temporal links. Improved prediction algorithms adjust live. Train, evaluate, and deploy catastrophic risk time series forecasting ML models. Time series analysis integrates outside factors into catastrophe modelling estimates. ML algorithms evaluate risk using climate change, economics, and prior losses. ML frameworks manage many data sources, missing data, normalisation, and environmental event uncertainty. This research compares ARIMA, GLM, and ML-enhanced time series models. They manage volatile, irregular data. An examination of data-driven approaches reveals they can improve property insurance choices. It advocates using ML to demonstrate catastrophes' spatial and temporal links to improve loss forecasts. Ensemble learning combines model predictions to decrease bias and enhance reliability. Ensemble methods manage catastrophic risk complexity better by using several models for a more reliable conclusion. Insurance time series analysis using machine learning is problematic. Insurers struggle with algorithm development, operations, and regulations. Ethics, data privacy, and algorithmic decision-making transparency are covered. Ethical AI and explainable models build trust and obey rules. ML, real-time data streaming, and decision-support systems may enable insurance firms construct models that swiftly respond to new disasters. Case studies and research demonstrate how ML-based time series modelling may predict hurricanes, floods, wildfires, and other natural disasters. New and old modelling approaches are compared to assess how well they predict and operate. Research examines topic futures. It suggests hybrid models that combine the best of physical and statistical models with machine learning may yield new discoveries. Satellite images and IoT devices improve disaster modelling data collecting and inputs.
Machine Learning Models for Predicting Disease Progression in Neurodegenerative Disorders
Neurodegenerative diseases like Alzheimer's and Parkinson's are serious. Slowly spreading illnesses may be catastrophic. Accurate disease forecasting improves patient outcomes, optimises treatment regimes, and facilitates early illness modifications. Machine learning (ML) systems can analyse massive quantities of data, uncover patterns physicians may miss, and reveal neurological reasons, making them a popular disease prediction tool. This study examines machine learning models for neurodegenerative disorders including Alzheimer's and Parkinson's, the most frequent. Using clinical, genetic, neuroimaging, and biomarker data, SVM, random forests, DNN, and RNN predict clinical outcomes. Deep learning models may uncover complex non-linear interactions that statistical approaches miss. These algorithms predicted Alzheimer's and Parkinson's cognitive decline, motor dysfunction, and other outcomes, the research revealed. Machine learning for disease progression involves multi-source data integration. MRI/PET shows brain architecture and function changes. However, genetic and biomarker data explain disease biology. Machine learning models with several inputs are needed for accurate projections. Having so many data sources makes data preparation and model interpretation difficult. These issues must be addressed for accurate and adaptive prediction models. Machine learning model development and dataset quality and quantity are covered. Few neurodegenerative disease databases have long-term data. This might overfit prediction models. These issues are addressed via transfer learning, data augmentation, and cross-validation. According to the article, PCA and t-Distributed Stochastic Neighbour Embedding (t-SNE) may improve machine learning models by selecting and reducing dimensions. Before being extensively deployed in clinical settings, machine learning must overcome numerous hurdles to predict disease development. Deep neural network forecasts may be too opaque for doctors. Standardising evaluation methods, scaling clinical machine learning models, and linking them to EHRs are ongoing challenges. Manage sensitive health data according to data privacy rules. Machine learning-based sickness prediction systems function in real life, according to many case studies. Machine learning can predict Alzheimer's cognitive decline using neuroimaging data. Using clinical and demographic data, predictive algorithms may anticipate Parkinson's motor symptoms. Case studies demonstrate how machine learning may improve neurodegenerative disease diagnosis and therapy. Machine learning may predict neurodegenerative disease. Improved algorithms, data collection, and computing capability may predict sickness course more accurately and personally. Future study should combine data, simplify models, and evaluate multi-center prediction models. When developing and applying machine learning models in clinical practice, ethics must be considered.
AI-Driven DevOps Insights: Enhancing Operational Efficiency in Banking with GitLab and ServiceNow AIOps
Operating efficiency & resilience are very vital in the fast digital banking scene of the present day. This work investigates how AI-driven DevOps techniques—especially GitLab CI/CD & ServiceNow AIOps—might be used to revolutionize their banking operations. Including AI into the DevOps cycle helps companies to automate further tedious tasks & also support more intelligent and quick decision-making that enhances their dependability & more compliance. While ServiceNow AIOps improves issue discovery, root cause investigation & proactive remedial action with intelligence, GitLab's complete CI/CD pipelines enable code delivery & infrastructure automation. These tools used together provide a closed-loop system wherein issues are autonomously resolved or highlighted with actionable information, hence reducing Mean Time to Recovery (MTTR) & minimizing their downtime. Moreover, the integration provides auditability & traceability—qualities necessary to maintain their financial sector regulatory compliance. By means of actual world case studies & pragmatic insights, the article shows how the AI-enhanced DevOps approach supports agility, reduces operational risk, and helps engineering & operations teams to focus on their higher-value tasks. Whether in handling complex microservices or enabling perfect customer experiences, the partnership between GitLab & ServiceNow AIOps sets the latest benchmark for innovation & more efficiency in banking IT.
The AI-Augmented DevOps Loop: Automating Java Microservices Delivery with GitHub and AWS
In the modern world of rapid software development, the role of DevOps is of the utmost importance for the successful seamless integration and delivery of applications based on microservices. This piece of writing is dedicated to the role that artificial intelligence is taking in transforming the traditional DevOps loop and thus introducing automation, versatility, and intelligence into every stage of the software delivery lifecycle. More specifically, we are going to demonstrate how Java microservices and certain tools like GitHub for version control and CI/CD workflows, and AWS for scalable infrastructure deployment, can be supercharged by AI to become the development-to-deployment pipeline. Now, with AI, the range encompasses intelligent code reviews, log anomaly detection, through to predictive scaling and automated testing. The AI-driven DevOps is no longer just about speed it is now also about making the process faster, more efficient and reliable. The authors of the paper have dedicated a special section to AI-engined DevOps that not only automates the predictable tasks but also uses the data from the operations side to become more and more proactive in the performance and reliability game. The combination of the sturdy Java language, the collaborative environment in GitHub, and AWS's elasticity paved the way for technology while the AI built a cognitive layer on top of it that makes it possible for static processes to become dynamic, context-aware operations. This document is a pure source of innovations like self-healing deployments, intelligent rollback mechanisms, and context-driven CI/CD pipelines. With the introduction of machine learning models into the DevOps toolset, companies are able to see the future, be prepared to face the issues and refine their delivery strategies continually through the feedback loops. This essay has a focusing goal to suggest the brighter side of the involvement of AI into the DevOps toolchain. Through thoughtful integration, AI can make a traditional DevOps toolchain a more adaptive, autonomous, and efficient system for mass delivering Java microservices. The future of DevOps is beyond automation – intelligent.
From Monolith to AI-Enabled Microservices: A Migration Blueprint for Java Enterprises on AWS
Conventional monolithic architectures are fast becoming obstacles rather than reliable foundations in view of modern software delivery needs for quicker iterations, intelligent systems, and easy scaling. This blueprint offers Java-based companies a clear, realistic road map for updating their systems on AWS from monolithic initiatives to AI-powered microservices. It begins with realizing the obvious problems with monolithic systems: strong coupling, limited scalability, slow reaction to change, and so-called deployment limitations. On the other hand, microservices provide teams a modular and agile approach that lets them build, start, and grow on their own. These services could provide intelligent automation, data-driven decision-making, and dynamic user experiences when combined with artificial intelligence capacity. This course emphasizes not just on breaking down a monolith but also on re-envisioning your business architecture with a creative base. Starting with a system review, we go through the key steps of transformation: planning for decommission, creating the migration strategy to AWS, and painstakingly adding artificial intelligence capabilities into your offerings. The path plan regularly emphasizes AWS-native technologies and best practices improving high availability, security, and economy of cost-effective performance. Emphasizing the challenges faced and the specific benefits obtained, we provide a real-world case study showing how a Java company successfully implemented its move, thereby actualizing these ideas. At the end of this journey, the transformation goes beyond technical considerations incorporating organizational changes: teams show more agility, systems scale naturally, and applications improve their intelligence via artificial intelligence. This goes beyond a simple migration strategy; it provides a stage for creating smart, cloud-native applications that change with your business.
The Role of Machine Learning in Modern Investment Strategies: A Review
Integration of machine learning (ML) techniques in modern investment strategies highlights their transformative impact on portfolio optimization, asset pricing, risk management, and algorithmic trading. The objective of this paper is to synthesize academic lecture and industrial practices and examines the supervised, unsupervised, and reinforcement learning models, including their methodological frameworks, computational challenges, and empirical performance.
AI and ML in Financial Markets: A Holistic Review of Applications in Trading, Risk, and Operations
Artificial intelligence (AI) and machine learning (ML) have transforming roles in trading strategies, risk management, and operational efficiency, so their applications in financial markets center on these aspects. This work aims to showcase the developments in deep learning, reinforcement learning, and natural language processing that support financial organizations in creating improved algorithmic trading, market prediction, credit risk modeling, fraud detection, and back-office automation.
Advancing Financial Inclusion through Machine Learning: Techniques, Applications, and Impact
Financial services are emerged after the integration of machine learning (ML) as a crucial force in advancing financial inclusion, especially in underserved and economically marginalized populations. The objective of this paper is to provide a comprehensive analysis of ML methodologies which includes supervised, unsupervised, and reinforcement learning and also their deployment in credit scoring, fraud detection, customer segmentation, and alternative data utilization.