The Journal of Science & Technology (JST) is a peer-reviewed, open-access journal that publishes original research articles, reviews, and short communications in all areas of science and technology. The journal welcomes submissions from all researchers, regardless of their geographic location or institutional affiliation. JST is committed to publishing high-quality research that advances the frontiers of science and technology. The journal's editorial board is composed of leading experts in their respective fields, and all submissions are carefully reviewed by multiple experts before publication. JST is a valuable resource for researchers, students, and professionals who are interested in the latest developments in science and technology. The journal's articles are indexed in major bibliographic databases, and they are also available to read online. Here are some of the key features of the Journal of Science & Technology: Peer-reviewed: All submissions are reviewed by multiple experts in the field before publication. Open access: All articles are freely available to read online. International: The journal welcomes submissions from all researchers, regardless of their geographic location or institutional affiliation. Multidisciplinary: The journal publishes research in all areas of science and technology. High quality: The journal's editorial board is composed of leading experts in their respective fields. If you are a researcher who is interested in publishing your work in the Journal of Science & Technology, please visit the journal's website for more information on how to submit your manuscript.
Data Analytics and Engineering in Automobile Data Systems - Journal of Science & Technology
Data analytics and engineering are transforming vehicle design, production, and customer experience in the auto industry. As the sector faces enormous problems including sustainability, changing customer needs, and disruptive technology, data-driven methods are key to innovation. This study examines how data analytics and engineering improve production processes, product quality, and predictive maintenance in the automotive industry. Analytical applications in market segmentation, tailored marketing, and post-sales services are central to this research. Automakers are adapting solutions to individual demands and enhancing user experience by researching consumer preferences and driving patterns. Data analytics-powered connected car networks and OTA updates allow manufacturers to continuously enhance vehicle software, improving functionality and customer happiness.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network
Rapid Development of IoT devices automates and connects numerous sectors. Growing ecosystems threaten security. IoT devices are susceptible to unauthorized access, data leakage, and crucial functionality modifications due to resource limits and large attack surfaces. This vulnerability might disrupt operations, compromise data, and threaten people.
Copyright (c) 2020 https://creativecommons.org/licenses/by-nc-sa/4.0
Fortifying the Expanding Internet of Things Landscape: A Zero Trust Network Architecture Approach for Enhanced Security and Mitigating Resource Constraints
The exponential growth of IoT threatens security. For perimeter-based security, IoT ecosystems are too dynamic and fragmented. Low processor power, memory, and OS render resource-constrained devices susceptible. The article proposes Zero Trust IoT security. Zero trust uses least privilege access control and "never trust, always verify" security. Since the network distrusts all devices and users, every interaction must be validated. No more castle-and-moat network security with Zero Trust. Instead, it divides the network and limits vital resources, assuming a breach.
Enhancing User Privacy in Decentralized Identity Management: A Comparative Analysis of Zero-Knowledge Proofs and Anonymization Techniques on Blockchain Infrastructures
Rising digital identity management requires strong privacy and security. Online interactions need centralised identity management systems, but their flaws and lack of user control over personal data pose security threats. Common data breaches allow centralized authority to modify or exploit IDs. Blockchain's unchangeable ledger and universal consensus enable self-sovereign identification. These systems safeguard identities and provide dependents optional verification. BC-IMS privacy issues remain. A research examines ZKPs and anonymity. BC-IMS weighs these approaches' pros and cons.
Energy-Efficient Consensus Mechanisms for Sustainable Blockchain Networks
Environmental concerns are driving energy-efficient blockchain consensus methods. Energy- and environment-intensive PoW consensus methodologies are questioned. Energy-efficient consensus research to lessen blockchain's environmental effect. Bitcoin miners use complex cryptographic algorithms to validate transactions and maintain PoW consensus. PoW secures and decentralizes blockchain networks, but regulators and environmentalists hate its energy use. We investigate PoW's environmental impacts and consensus possibilities. A common PoW alternative is PoS. Financially trustworthy validators "stake" to save blockchain energy by decreasing computations. Energy efficiency, network security, and decentralization are assessed for Ethereum 2.0 and Cardano PoS.
DevOps Project Management: Aligning Development and Operations Teams
Aligned development-operations teams increase contemporary software delivery and performance. We study DevOps project management, which integrates development and operations to improve software engineering. CI/CD, IaC, and automated testing link development and operations. CI and CD automate code creation, testing, and deployment in DevOps. Daily human-free CI/CD processes push code changes to production. This iterative technique reduces integration and deployment mistakes and accelerates deployments. Infrastructure-as-Code DevOps project management. IaC provided machine-readable computer infrastructure specification files instead of hardware or interactive tools. Improved infrastructure management scalability, consistency, and efficiency speed up development and operations infrastructure resource supply and setup.
Blockchain's Role in Enhancing Transparency and Security in Digital Transformation
Blockchain technology is enhancing trust, security, and openness of digital ecosystems. Security, openness, and efficiency are especially important when businesses migrate customer interactions, data management, and operations online. Digital transformation data integrity and privacy may be impacted by distributed blockchain, unchangeable ledger, and cryptographic techniques. This paper argues that real-time, tamper-proof blockchain records might increase stakeholder trust and accountability. Decentralized blockchain removes single points of failure, therefore lowering cyberattacks and illegal access. Blockchain consensus systems like PoW, PoS, and others confirm transactions and records tamper-resistively, thereby enhancing the dependability of digital transformation information systems. Research, practical applications, and theoretical models reveal blockchain enhances operational transparency and data security. Cases in banking, healthcare, supply chain management, and public administration all exhibit blockchain secure identity verification, audit trails, and transaction transparency. The immutable audit trails of blockchain for financial services transactions and regulatory compliance help to reduce fraud and safeguard data integrity. Blockchain provides complete, tamper-resistant medical records that boost patient trust and enable proper treatment as well as protects private patient data across systems.
Time Complexity Analysis of Graph Algorithms in Big Data: Evaluating the Performance of PageRank and Shortest Path Algorithms for Large-Scale Networks
We investigate temporal complexity of PageRank and shortest path methods in large-scale big data networks. Speed depends on an awareness of the computational difficulty of graph-based structural algorithms for large-scale networks. Web page assessment and node route finding have to be evaluated for accuracy, scalability, and efficiency as network traffic increases fast. PageRank ranks network nodes in connection recursively. Graph nodes, edges, and convergence conditions determine temporal complexity of an algorithm. To derive PageRank's iterative and temporal complexity, we investigate network size, convergence tolerance, and damping factor. Investigate how distributed and parallel processing impact large network PageRank. We evaluate the method on dynamic and stationary networks with evolving topology. Although algorithmic and infrastructural improvements may lower PageRank's computational complexity, network scale increases it.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
The Role of Infrastructure as Code (IaC) in Platform Engineering for Enterprise Cloud Deployments
Security, optimization, commercial cloud scalability all call for complex solutions. By means of codes, IaC automates and controls cloud infrastructure, thereby modifying platform design. In corporate cloud systems needing scalable, adaptable, consistent infrastructure, IaC is very vital in platform development. IaC automatically controls infrastructure, therefore enhancing consistency, mistake avoidance, and deployment. Since cloud computing and platform engineering first emerged, research has concentrated on managing intricate cloud infrastructues. IaC foundations in version control, repeatability, and scalability are then discussed in turn. Businesses evaluate CI/CD pipeline support, DevOps cooperation, and unchanging infrastructure of IaC. The paper discusses full acceptance of business clouds. Among them are misconfiguration security risks, high IaC tool learning curves, and infrastructure scalability issues. To help to solve these problems, the study advises modular infrastructure codes, extensive testing and validation, and restricted access policies. Furthermore looked at is IaC interaction with many CSPs and how it affects hybrid and multi-cloud systems. Studies of business cases indicate how IaC improves cloud infrastructure, affordability, and agility.
Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
Streamlining Operations: A Comparative Analysis of Enterprise Integration Strategies in the Insurance and Retail Industries
Efficiency is crucial for modern firms, particularly in difficult industries like retail and insurance. Business integration solutions in these two crucial areas are thoroughly examined in this research. Strategies, techniques, and technology boost operational performance, according to the research. Businesses require enterprise integration for agility, responsiveness, and scalability, which encompasses different system-to-system communication and data flow methods. Despite their different frameworks, insurance and retail use integration improves operations, customer experiences, and innovation.
Securing AI/ML Operations in Multi-Cloud Environments: Best Practices for Data Privacy, Model Integrity, and Regulatory Compliance
Operations in multi-cloud artificial intelligence and machine learning depend on data security, model correctness, and regulatory compliance. Adopting AI/ML models across different cloud platforms runs businesses to risk data loss, model design problems, and regulatory non-compliance for scalability, flexibility, and processing. Multi-cloud AI/ML data privacy, model integrity, and regulatory compliance are examined in this paper. We explore in multi-cloud artificial intelligence and machine learning systems the advantages and drawbacks of cross-cloud data transfers, shared infrastructure, and cloud service provider security. Several cloud platforms collecting and processing data makes data privacy challenging. Businesses have to abide by policies on data residency, access, and sharing. For data privacy several cloud platforms advise homomorphic encryption and secure multi-party computing. Safe enclaves, federated learning, and differential privacy provide data privacy without compromising model performance. These systems protect private information from leaks.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time IoT Data Analytics for Smart Manufacturing: Leveraging Machine Learning for Predictive Analytics and Process Optimization in Industrial Systems
As the Internet of Things develops, smart manufacturing uses real-time data for process optimization and predictive analytics. This research analyzes how IoT and ML might boost industrial production and predictive maintenance. Massive volumes of data from industrial IoT devices may improve production, cost, and real-time decision-making. Machine learning is essential to manage, analyze, and comprehend this massive data set. Smart manufacturing uses real-time IoT data analytics, supervised, unsupervised, and reinforcement learning. Also debated are IoT data stream management and analysis technologies. Machine learning and real-time analytics identify production issues and monitor KPIs to prevent costly downtimes and mistakes. Smart manufacturing predictive maintenance uses IoT data and machine learning models to forecast equipment failures and maintenance needs, reducing downtime, asset consumption, and costs.
Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach
Platform engineering streamlines corporate cloud CI and software development. This article examines platform engineering methodologies using corporate cloud CI cases. Platform engineering's scalability, assurance, and development team cooperation are highlighted in CI integration architectural and operational concepts. Case examples from various sectors with diverse difficulties and needs demonstrate platform engineering's versatility. We explore case studies' architectural frameworks for CI pipeline integration with cloud-native services. Microservices, containerization, and infrastructure as code provide these frameworks flexibility, scalability, and consistency across development environments. High-quality software delivery involves CI automation for testing, deployment, and monitoring.
Optimizing Talent Management in Cloud-Based HCM Systems: Leveraging Machine Learning for Personalized Employee Development Programs
Cloud HCM allows dynamic, data-driven HR. This work uses machine learning to adapt cloud-based HCM staff development. It examines how machine learning may improve training, find skill shortages, and assess vast staff data. Predictive analytics and artificial intelligence in cloud-based HCM systems support staff development, skill evaluation, and career planning. Modern cloud-based HCM solutions enable data-driven decisions made by HR departments of large corporations. With machine learning, these systems evaluate employee performance data, regulate KPIs, and provide actionable insights for individualized learning and development initiatives. Using personnel data, deep learning algorithms, random forests, and decision trees, career path prediction, training, and leadership identification is modeled. The research stresses employee engagement and real-time performance monitoring in order to adjust development to changing corporate objectives and employee expectations.
Optimization of CI/CD Pipelines in Cloud-Native Enterprise Environments: A Comparative Analysis of Deployment Strategies
Companies have quickly embraced cloud-native technologies, needing robust CI/CD pipelines. Pipelines are needed to scale application deployment, stability, and delivery cycles. To optimize CI/CD pipelines for scalability, reliability, and speed in cloud-native enterprises, this research examines deployment methods. The research starts with cloud-native CI/CD basics and focuses on major enterprises' demands. As organizations adopt cloud-native designs, serverless computing, containerization, and microservices have replaced monolithic software deployment. This technique offers advantages but also challenges that must be addressed to optimize CI/CD operations.
Machine Learning Applications in Actuarial Product Development: Enhancing Pricing and Risk Assessment
Risk assessment and competitive pricing are essential to insurance. These goals were achieved by actuaries using statistical models and historical data. The digital age's data volume and complexity provide challenges and opportunities for actuarial science. Machine learning (ML) may use this data stream to enhance pricing accuracy and risk assessment in actuarial product creation. This study explores ML's impact on actuarial pricing and risk assessment. We discuss actuarial pricing and risk assessment and emphasize the limitations of previous methods in a changing risk environment. We then discuss actuarial-relevant machine learning algorithms.
Legacy System Integration in the Insurance Sector: Challenges and Solutions
Complex legacy system integration challenges may hinder modern insurance's operational efficiency, data integrity, and organizational agility. Effective integration solutions control data flow and functional coherence between historical systems and current digital infrastructure. The whole collection of solutions to integrating insurance systems is provided in this paper. Technology, operations, and organization legacy system integration are highlighted in the paper. Incoherent data, incompatible systems, and outdated software are technical challenges. Professional data mapping, transformation, and middleware may be needed to overcome technological constraints. Modern operations and procedures are difficult to integrate with rigid archaic system design. Resistance to change, reskilling, and stakeholder expectations are organizational issues.
Integrating AI/ML Workloads with Serverless Cloud Computing: Optimizing Cost and Performance for Dynamic, Event-Driven Applications
Event-driven applications may benefit from artificial intelligence, machine learning, serverless clouds. Serverless cloud computing might reduce performance and AI/ML workload costs. Real-time analytics, tailored solutions, and intelligent automation abound in IoT, e-commerce, and banking serverless AI/ML workloads. Serverless computing answers cost, complexity, and scalability as developers build code and application logic. Serverless artificial intelligence and machine learning workload difficulties include cold starts, stateful executions, memory and compute optimization, and real-time application low-latency solutions. Scholars study. Container-based services protect data and enable long-running operations; asynchronous processing models and event-driven designs lower cold start latencies; serverless function memory and CPU resource tuning balance cost and performance. Function composition, data location optimization, and intelligent workload distribution help to offset serverless computing's financial effect on AI/ML workloads. Major cloud providers' cost comparison.
Implementing Continuous Integration and Continuous Deployment Pipelines in Hybrid Cloud Environments: Challenges and Solutions
Enterprise hybrid clouds provide CI and CD pipeline problems. Agility, scalability, and operational effectiveness need on-premise and cloud CI/CD pipelines in modern IT systems. Technology and integration challenges are covered here. Unique security, compliance, and orchestration for hybrid cloud networks. The need to integrate systems, maintain performance, and meet strict security and regulatory requirements compounds these issues. Starting with CI/CD pipelines' basics, the research stresses their role in automating software development for fast, dependable application delivery. These pipelines must be deployed in hybrid clouds with significant knowledge of on-premise and cloud systems and interactions. CI/CD pipelines in hybrid clouds must be flexible to meet their dynamic nature. Article examines architectural aspects. Network latency, data synchronization, and multi-environment management are challenging hybrid cloud CI/CD deployment issues. These concerns affect CI/CD pipeline reliability, performance, and scalability. Best practices in orchestration, automation, and cloud management may fix these challenges.
Generative AI for Retail CRM Systems: Revolutionizing Customer Engagement and Satisfaction Through Data-Driven Personalization
Generative AI may make retail CRM appealing. Personalization and generative AI interaction optimization may improve retail CRM customer experiences. Generative AI models can learn consumer preferences, habits, and spending patterns from huge customer data, but CRM systems cannot. In this project, GANs, VAEs, and transformer-based models anticipate client traits and update content. Generative AI may make retail CRM proactive and customer-focused. For wide marketing without client targeting, traditional CRM leverages history and rule-based algorithms. Generative AI algorithms provide hyper-personalized ideas, dynamic content, and customer-specific engagement using real-time data and strong machine learning. Gen AI anticipates customer marketing reactions, personalizing content and brand loyalty. We study how generative AI enhances sentiment analysis to help CRM systems react fast and relevantly to modest sentiment changes across digital interactions to boost customer satisfaction.
Enterprise Architecture and Project Management Synergy: Optimizing Post-M&A Integration for Large-Scale Enterprises
Large company post-mergers and acquisition integration projects call for EA and PM integration. This paper suggests that post-M&A integration benefits from EA-PM synergy. M&A integrations are challenging; hence, EA and PM concepts have to be matched to lowered complexity, risk, and efficiency. Enterprise architecture tries to mix IT infrastructure with business strategy while considering operations, information systems, and technology of a corporation. EA concepts might help to link business divisions with technologies. Knowing present systems exposes integration duplicity and inefficiencies.
Cross-Industry Enterprise Integration: Best Practices from Insurance and Retail
For operational performance and customer satisfaction today's economy depends on cross-sector corporate integration. This paper examines best practices in insurance and retail company integration, which despite varied clients provide both challenges and opportunities. Through research on effective integration techniques, new technology, and cross-industry comparisons, we help companies integrate. Given their complexity and evolving technologies, insurance and retail are suitable subjects for business integration study. For the systems and operations of both industries, data sharing and operational synergy call for sophisticated frameworks. Good integration calls for APIs, so-called SOAs, ESBS. Scalability, efficiency, and operational flexibility abound in this paradigm.
Cloud Adoption in Large Enterprises: A Case Study on Implementing Enterprise Architecture Frameworks for Seamless Integration
Big businesses want scalability, efficiency, and competitive advantage by means of cloud integration into IT infrastructures. Adoption of clouds is challenging when modern technologies are incorporated with present systems and corporate goals. The importance of structured frameworks for managing complexity, compatibility, and governance is shown by a case study on how large companies integrate clouds under using enterprise architecture (EA) frameworks. Using TOGAF, Zachman Framework, and FEAF, companies design, build, and oversee cloud environments to meet company objectives and infrastructues. These frameworks are very vital in big businesses as cloud use affects data security, operational agility, and cost control. Studies indicate that adoption of clouds calls for integrated planning and management of the technical and strategic aspects of corporate architectural frameworks. Following a significant company through cloud integration analysis, planning, implementation, optimization, and governance, the case study To demonstrate how EA models could address industry standard compliance, application compatibility, and data migration. Combining corporate design with cloud operations increases data security, regulatory compliance, and hazards of data protection. The research exposes how TOGAF's ADM (Architecture Development Method) arranges stages of cloud integration and points out key IT assets needing rearranging and cloud-based service integration. This approach guarantees cloud adoption modularity, scalability, and reusability, therefore supporting the company's aims for digital transformation.
Architecting Predictive Analytics-Based Dynamic Scaling Solutions for Multi-Tenant Cloud Platforms
Scalability is needed for multi-tenant cloud workload fluctuations. Cloud platforms must distribute resources to tenants for optimum performance and cost as enterprises grow. This research uses sophisticated auto-scaling methods, predictive models, and shared infrastructure cost management to deliver predictive analytics-based dynamic scaling solutions for multi-tenant cloud environments. Under shifting demand, multi-tenant cloud expansion requires technological and economic cloud resource management. This study estimates resource requirements using predictive analytics and cloud platform dynamic scaling. Previous consumption, tenant behavior, and workload may anticipate resource demands. Projection may improve resource allocation. Machine learning, time-series forecasting, and hybrid approaches estimate multi-tenant resource use.
Advanced Techniques for Storage Optimization in Resource-Constrained Systems Using AI and Machine Learning
Data volume and complexity are rising in resource-constrained systems, making storage management difficult. IoT, edge, and mobile devices use these systems. Their memory, cognition, and energy are limited. Traditional storage management systems waste resources, delay performance, and limit data access. This paper explores advanced AI and ML techniques for resource-constrained storage optimization. We boost storage efficiency and system performance in resource-constrained contexts. Resource-constrained systems struggle with storage. Storage, processing, and energy limits are investigated. We analyze how these limits affect data access latency, retrieval throughput, and system responsiveness. These systems' storage management is next. Caching, prefetching, and compression are examples. Traditional methods are successful, but they lack the flexibility and dynamic decision-making needed to maximize storage under changing data access patterns and system resource variations. Studying AI and ML integration into resource-constrained storage management systems addresses these restrictions. Learning and adapting AI may improve storage and system efficiency. AI/ML storage optimization is in the study. Data compression via machine learning is prevalent. ML systems can dynamically pick compression algorithms by training on data kinds and access patterns. Resource-constrained scenarios need optimum compression and little computational cost. Machine learning is crucial for clever caching. Traditional caching uses static rules or heuristics. Certain solutions may not support dynamic access patterns. Data requests may be predicted by machine learning from prior access patterns. Caching frequently used data using ML reduces wait time and boosts system performance.
Advanced CI/CD Pipelines in Multi-Tenant Cloud Platforms: Strategies for Secure and Efficient Deployment
Scalable, reasonably priced cloud computing depends on multi-tenant architecture by companies. In such systems, however, CI/CD and application deployment complicate security, efficiency, and tenant isolation. This work examines advanced multi-tenant cloud CI/CD processes. Technical issues of these pipelines have to be handled by big companies with complex compliance and regulatory systems. Multi-tenant companies share infrastructure but have to segregate and follow data policies. We investigate architectural issues for CI/CD pipelines devoid of tenant security or data leak. We provide tenant-aware pipeline topologies, safe artifact management, and necessary CI/CD infrastructure tailored for individual workloads.
Advanced Analytics in Actuarial Science: Leveraging Data for Innovative Product Development in Insurance
Actuaries and statisticalians evaluate insurance risk, pricing, and claims. "Big Data," the development in present data amount, variety, and pace could vary. Research suggests that actuarial science analytics could improve the innovation in insurance products. The job begins with estimation of actuarial risk and uncertainty. Survival and GLM actuarial approaches work well. Data and theories restrict these methods. Machine learning and artificial intelligence challenge this convention. Big datasets let machine learning approaches find intricate patterns and relationships not possible with conventional approaches. They may combine social networking with wearable sensors. Modern analytics allows actuaries to develop complex risk models from massive data sets: Broad risk categories and historical averages help to increase standard model pricing accuracy. Gradient boosting and random forests might let actuaries modify consumer pricing in response to minor variations in risk profile. Choice of best-risk enhances insurer profit and policyholder fairness.
Actuarial Data Analytics for Life Insurance Product Development: Techniques, Models, and Real-World Applications
Competition, laws, and consumer expectations change life insurance. Actuarial data analytics helps insurers compete and provide innovative services. This work builds, validates, and executes life insurance models using actuarial data analytics. Historical data and actuarial skills were used to evaluate mortality risk, price policies, and provide features for life insurance products. Data explosion has improved analytics, but this method is still important. Actuarial data analytics uses statistics and machine learning to analyze large datasets. These discoveries improve actuarial methodologies and let insurers create more complex, customer-focused solutions. Predictive modeling uses data analytics. Insurers may use historical mortality data, socio-economic characteristics, anonymised and controlled health data, and lifestyle behaviors to predict death. Risk profiles are analyzed and costs are adjusted for each covered individual. Risk-based pricing favors price transparency and fairness over one-size-fits-all.