Category Archives: Uncategorized

Authors: Maya Fernandes

Abstract: Blockchain technology has emerged as a groundbreaking innovation with the potential to enhance transparency, security, and trust in various digital ecosystems. Its integration into cloud service auditing represents a significant evolution in how organizations manage and verify cloud-based operations. Cloud services, being foundational to modern IT infrastructure, demand rigorous auditing to ensure compliance, reliability, and security. However, traditional auditing methods often face challenges related to trustworthiness, data integrity, and real-time verification. Blockchain’s decentralized, immutable ledger offers a promising solution to these limitations by providing an auditable, tamper-resistant record of transactions and events within cloud environments. This article explores the multifaceted impact of blockchain integration on transparent cloud service auditing. It begins with an analysis of the existing challenges in cloud auditing and the essential attributes for effective auditing frameworks. Subsequently, it delves into the fundamental principles of blockchain technology and its synergy with cloud services. Through specific use cases and the evaluation of emerging frameworks, the discussion highlights how blockchain can transform cloud auditing by enabling continuous monitoring, enhancing data provenance, and facilitating regulatory compliance. The article also addresses potential concerns, including scalability, privacy, and integration complexities, along with current advancements aimed at overcoming these hurdles. By providing a comprehensive overview, this article underscores blockchain’s transformative potential to establish more transparent, trustworthy, and efficient cloud auditing mechanisms that benefit service providers, auditors, and end-users alike.

DOI: http://doi.org/10.5281/zenodo.17777553

Published by: vikaspatanker

Authors: Akash Gupta

Abstract: Serverless computing has emerged as a transformative paradigm in enterprise IT, fundamentally altering how organizations approach infrastructure management and cost efficiency. This model abstracts away the complexities of server administration, allowing enterprises to focus solely on developing and deploying applications without the burden of maintaining underlying hardware. By leveraging a pay-as-you-go pricing mechanism, serverless computing enables significant cost reductions, as organizations are billed exclusively for the actual resources consumed during code execution rather than for idle infrastructure. Such dynamic resource allocation facilitates optimal usage, particularly beneficial for companies with unpredictable or fluctuating workloads. Furthermore, serverless architectures streamline development workflows by eliminating the need for manual scaling and capacity planning, resulting in improved operational agility and innovation throughput. Nonetheless, enterprises must be mindful of potential challenges, including workload-specific cost unpredictability, cold start latencies, and vendor lock-in. The strategic adoption of serverless models, underpinned by comprehensive cost monitoring and optimizations, can unlock substantial benefits in terms of both direct and indirect cost savings, agility, and overall IT modernization. As this article will demonstrate, serverless computing stands at the intersection of technological advancement and cost optimization—reshaping enterprise architectures for the demands of today’s digital economy.

DOI: https://doi.org/10.5281/zenodo.17776418

Published by: vikaspatanker

Authors: Nisha Choudhury

Abstract: The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is revolutionizing industrial automation ecosystems. This integration is driving the transformation of traditional manufacturing and production processes into smart, connected systems that optimize operations, enhance productivity, and facilitate predictive maintenance. IoT facilitates the collection and communication of vast amounts of real-time data from sensors, devices, and machinery across an industrial environment. Coupling this with AI’s ability to analyze complex data patterns, predict outcomes, and automate decision-making processes creates unprecedented opportunities for efficiency and innovation in industrial automation. The synergy between IoT and AI fosters smarter factories, where machines self-optimize, autonomous robots collaborate with human workers, and supply chain logistics are seamlessly managed. This article explores how this dynamic convergence alters industrial workflow, impacts operational resilience, improves safety standards, and cultivates new business models. It also examines the challenges related to cybersecurity, data privacy, and system integration that accompany this digital evolution. Ultimately, the IoT-AI alliance is not only reshaping industrial automation with smarter, more adaptive ecosystems but also paving the way for Industry 4.0 and beyond, ushering in a new era where intelligent systems drive industrial competitiveness and sustainability.

DOI: https://doi.org/10.5281/zenodo.17776309

 

Published by: vikaspatanker

Authors: Priyanka Sharma

Abstract: Hybrid storage systems have increasingly become a pivotal architecture in the realm of large-scale data analytics, addressing the ever-growing demand for managing vast volumes of diverse data with speed and efficiency. By integrating multiple types of storage media, typically solid-state drives (SSDs) and hard disk drives (HDDs), hybrid storage optimizes data accessibility and throughput by leveraging the performance benefits of faster storage technologies alongside the cost-effectiveness and capacity of traditional drives. This synergy is particularly crucial in large-scale data analytics, where substantial datasets must be rapidly processed to derive actionable insights, impacting industries such as finance, healthcare, telecommunications, and scientific research. The influence of hybrid storage systems transcends mere data warehousing, affecting the efficiency of data retrieval, latency, system throughput, and computing cost. These systems support the flexible caching of hot data in faster tiers, while colder, less frequently accessed data remains in slower storage, thereby creating a dynamic environment that can adapt to workload variations. Furthermore, the architecture of hybrid systems is conducive to scalability and fault tolerance, essential features when dealing with petabyte-scale analytics clusters and distributed frameworks like Apache Hadoop and Spark. This article explores the architecture of hybrid storage systems, the performance implications they bear on large-scale data analytics, and the cost-performance balance they offer. Additionally, it examines case studies demonstrating improvements in real-world analytics applications, the challenges in managing hybrid storage environments, and future trends in storage technologies impacting analytics performance. By understanding these aspects, enterprises can better architect their storage infrastructure to meet the demanding requirements of data-intensive analytics workloads.

DOI: https://doi.org/10.5281/zenodo.17776151

Published by: vikaspatanker

Authors: Manoj Patil

Abstract: Digital twins are revolutionizing the realm of predictive infrastructure management by offering enhanced capabilities for monitoring, analysis, and maintenance planning in complex infrastructure systems. By creating virtual replicas of physical assets, digital twins enable real-time data integration, simulation, and predictive analytics, facilitating timely decision-making for infrastructure performance optimization and risk mitigation. The adoption of digital twin technology addresses many challenges faced by traditional infrastructure management, including aging assets, dynamic environmental influences, and the need for sustainable operations. This article explores the multifaceted impact of digital twins on predictive infrastructure management, highlighting their role in predictive maintenance, asset lifecycle management, risk assessment, and system optimization. The integration of advanced sensor networks, Internet of Things (IoT) devices, and artificial intelligence (AI) with digital twins further enhances their predictive power, enabling proactive responses to emerging infrastructure issues. The article also discusses the implementation challenges, data security concerns, and the future outlook of digital twin technology in infrastructure sectors such as utilities, transportation, and smart cities. Through a comprehensive examination of current trends, use cases, and technological advancements, this article provides a detailed understanding of how digital twins are shaping the evolution of infrastructure management practices, ultimately contributing to enhanced resilience, cost efficiency, and sustainability.

DOI: https://doi.org/10.5281/zenodo.17776010

Published by: vikaspatanker

Authors: Dr. Priyanka

 

Abstract: Corporations today operate in environments marked by financial volatility, sustainability pressures, rising workplace safety expectations, and increased scrutiny from regulators and stakeholders. Traditional risk-management frameworks—which focus mainly on financial indicators—are no longer sufficient. This study proposes a comprehensive Integrated Risk Model (IRM) that merges Financial Risk, Occupational Safety & Health (OSH) Risk, and Environmental, Social & Governance (ESG) Risk into a single evaluative structure. Using comparative analysis, survey data, secondary financial records, and regression modelling, the paper examines how OSH and ESG performance influence financial resilience and overall corporate stability. Findings suggest that firms with strong OSH and ESG systems consistently experience fewer disruptions, lower costs, enhanced productivity, and significantly higher financial resilience scores. The research demonstrates that integrating OSH and ESG risk indicators into financial risk management leads to more accurate predictions of long-term corporate performance.

DOI: https://doi.org/10.5281/zenodo.17775670

Published by: vikaspatanker

• DOI
• ISBN
• E-ISSN
• Publication Certificates to Each Author (Digital)
• Attend Speakers
• Global reach of Published Content

Important Dates for Paper/Document Submission & Registration

• Paper status share in 2 to 4 working days, after submission
• Registration starts from 10th December
• Conference Date:14th -15th December- 2025

---

Registration

Online Participants
	Indian (RS)	Non Indian (USD)
Scholar	1000	20$
Academic Professional	1200	25$
Industrial Professional	1200	25$
Register Now	PayNow	PAYPAL

Offline Participants
	Indian (RS)	Non Indian (USD)
Scholar	3500	70
Academic Professional	5500	100
Industrial Professional	6000	110
Register Now	PayNow	PAYPAL

---

 Topics

• Engineering: Civil, Mechanical, Computers, Electronics, Electrical, Marine, Mines
• Science: Physics, Chemistry, Mathematics, Social Analysis
• Management: Marketing Analysis, Production, Resource Management, etc.

Terms and Conditions

Published by: vikaspatanker

Authors: G S P Gunasekara

Abstract: In The Sri Lankan construction industry has a central position in the national development, instead of being one of the worst agents of environmental destruction due to energy consumption and production of waste and carbon dioxide. As response, strategies to reduce the environmental impact of building operations have come into the limelight in the form of green concepts and sustainable building practices in the world. Although sustainability programs have been increased and structures have been designed like the GREENSL Rating System, there is still little uptake of green practices in Sri Lanka. This paper will seek to establish and discuss the challenges to the adoption of green construction concepts in the Sri Lankan context. Based on a qualitative content analysis of twenty academic and institutional sources on local repositories, such as the University of Moratuwa, General Sir John Kotelawala Defence University, and the Green Building Council of Sri Lanka, the research synthesizes research results on the economic, institutional, technical, and cultural levels. Findings indicate that the initial high costs, lax regulatory implementation, low technical capacity and deep-rooted behavioural resistance are all complex and interdependent webs of constraints. The lack of financial incentives, poor awareness and disjointed policy frameworks are the forces that reinforce these barriers. The paper suggests a conceptual framework that demonstrates the relationship between these barriers and provides policy, industry, and research solutions to promote integrated solutions. It concludes that a rational system of national policy, coupled with specific capacity building and financial solutions, is essential in achieving mainstreaming of sustainable construction and long-term environmental resilience in Sri Lanka.

Published by: vikaspatanker

submit research paper for conference publication, how to publish a paper in a conference for free, how to publish a paper in online conference, how to publish a paper in the international conference, publish research paper online conference free

Published by: vikaspatanker

Authors: Ankit Verma, Anmol Soni, Arnav Singh, Adnan Khan, Ahtesham Farooqi

Abstract: This paper presents AI Career Navigator, a com- prehensive AI-driven career guidance platform integrating ten essential modules: career recommendation, resume analysis, job search aggregation, interview preparation, cover letter genera- tion, skill gap analysis, career trend analytics, learning resources, conversational AI chatbot, and LinkedIn profile optimization. The system employs a hybrid methodology combining NLP- based resume intelligence, psychometric profiling using the Big Five Personality Model, supervised machine learning for career prediction, and ATS-based scoring mechanisms. The frontend ar- chitecture leverages React with TypeScript, enhanced by Tailwind CSS, ShadCN UI, and Framer Motion, while the backend imple- ments advanced AI/NLP/ML algorithms for semantic analysis, personality classification, and intelligent career-profile matching. The modular architecture incorporates domain-skill mapping, career-interest correlation, and real-time trend analytics with adaptive feedback loops to generate context-aware recommenda- tions. Comparative evaluation demonstrates that AI Career Navi- gator significantly outperforms conventional systems by providing data-driven insights, holistic skill assessment, and personalized recommendations, thereby enhancing graduate employability and reducing career mismatch rates observed in traditional models.

DOI: http://doi.org/10.5281/zenodo.17761603

Published by: vikaspatanker