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e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[4165]
AI-DRIVEN INNOVATIONS IN CLOUD COMPUTING: TRANSFORMING
SCALABILITY, RESOURCE MANAGEMENT, AND PREDICTIVE ANALYTICS
IN DISTRIBUTED SYSTEMS
Prathyusha Nama*1, Suprit Pattanayak*2, Harika Sree Meka*3
*1,2,3Independent Researcher, USA.
DOI : https://www.doi.org/10.56726/IRJMETS47900
ABSTRACT
This research article examines the transformative role of artificial intelligence (AI) in cloud computing, focusing
on its impact on scalability, resource management, and predictive analytics within distributed systems. As
organizations increasingly rely on cloud infrastructure, AI technologies have emerged as essential tools for
optimizing performance and efficiency. This study highlights how AI enhances scalability through dynamic
resource allocation and auto-scaling mechanisms, enabling systems to adapt to fluctuating demands seamlessly.
It also explores AI-driven resource management techniques that improve operational efficiency and reduce
costs by leveraging machine learning algorithms for predictive maintenance and anomaly detection.
Furthermore, the article delves into predictive analytics applications, demonstrating how AI can analyze vast
datasets to inform decision-making and enhance system reliability.
Keywords: Artificial Intelligence (AI), Cloud Computing, Deep Neural Networks, Machine Learning, Predictive
Analytics.
I. INTRODUCTION
1.1 Background on Cloud Computing
Cloud computing, a term synonymous with the digital age, has a rich history that dates back to the 1950s and
1960s. Mainframes and time-sharing systems were introduced, laying the groundwork for shared computing
resources. The development of ARPANET further shaped the history of cloud computing by enabling users to
access information and applications from remote computers. As technology advances, new fields such as
quantum computing are emerging, building upon the foundations laid by cloud computing.
Fast-forward to the 2000s, and cloud-based software, infrastructure, and platforms emerged as the three pillars
of cloud computing. Salesforce blazed the trail by offering business applications via its website, setting the stage
for software-as-a-service (SaaS) offerings. Amazon Web Services (AWS) entered the scene in 2006, marking a
significant milestone in the availability of cloud infrastructure services.
Today, cloud computing mirrors the historical time-sharing model, sharing computing resources among many
users, thereby reducing costs and improving resource utilization. Scalability and simple accessibility are
fundamental factors that have led to widespread cloud adoption. Efficient data and storage management are
achieved by allowing different devices and applications to communicate and share resources over the Internet.
Figure 1: Cloud computing with AI
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[4166]
1.2 Importance of AI in Modern Technology
With deep neural networks, AI can accomplish remarkably precise tasks that were previously thought to be
impossible. For instance, continuous advancements in deep learning have led to more accurate interactions
with Alexa and Google Search. AI methods in the medical domain can precisely identify cancer cells on MRIs at a
level that is on par with highly skilled radiologists. Artificial intelligence (AI) systems can reliably complete
numerous complex and computer-generated jobs. Human skills are necessary for system configuration and
question formulation to optimize the efficacy of these systems. AI improves on current technology rather than
being marketed as stand-alone items. For example, Apple's Siri feature has revolutionized user contact with
devices.
Furthermore, massive volumes of data are utilized by AI-powered chatbots, automation tools, and smart
gadgets to enhance functionality in home and office environments. A few years ago, constructing effective fraud
detection systems would have been impossible. Still, it is achievable because it combines huge data and
superior computer power. Deep learning models need a lot of data to be trained, and the more data accessible,
the more accurate the models become. By using AI, businesses may get insightful information from their data.
Data is more important than ever in today's competitive environment; having a strong data system can give you
a considerable competitive advantage because the greatest data will eventually produce the best results.
1.3 Purpose and Scope of the Article
The primary purpose of this article is to explore the transformative impact of artificial intelligence (AI) on cloud
computing, specifically in the realms of scalability, resource management, and predictive analytics. As cloud
computing becomes increasingly integral to modern enterprise operations, understanding how AI can enhance
its capabilities is essential for organizations seeking to leverage these technologies effectively. This article aims
to provide a comprehensive overview of how AI innovations are reshaping cloud infrastructures, enabling
businesses to adapt to fluctuating demands and optimize resource utilization. By examining real-world
applications and case studies, the article seeks to illustrate the practical benefits of AI in improving operational
efficiency and decision-making processes.
The scope of the article encompasses an analysis of how AI facilitates dynamic resource allocation and auto-
scaling mechanisms that allow cloud systems to automatically adjust to real-time workload demands, delving
into methods that enhance system responsiveness and performance reliability. It investigates AI-driven
techniques for optimizing resource allocation and utilization, exploring machine learning algorithms that
predict resource needs, manage costs, and improve overall system performance. Additionally, the article
discusses the role of AI in enabling advanced data analysis within cloud environments, covering how predictive
analytics can lead to better forecasting, improved operational insights, and enhanced decision-making
capabilities.
Furthermore, it addresses potential challenges related to data privacy, security, and algorithmic bias that may
arise from integrating AI into cloud computing. It provides a balanced view of the benefits and risks associated
with these technologies. Lastly, it highlights emerging trends and potential future developments in AI and cloud
computing, offering insights into how organizations can prepare for and adapt to ongoing changes in the
technological landscape. By focusing on these areas, the article aspires to equip readers—from IT professionals
to decision-makers—with a deeper understanding of the synergistic relationship between AI and cloud
computing, ultimately guiding them in making informed decisions about technology investments and strategic
planning.
1.4 Structure of the Article
1. Infrastructure Layer: The hardware and infrastructure layer is the most critical part of AI due to its high
compute and bandwidth demand, reliance, and reliability requirements. The processing power of CPU and
GPUs, network bandwidth, storage performance, and power efficiency of the whole system play important
roles in the success of an AI cloud. For example, as a business, you don't want to screw up a distributed
training job that is running on hundreds of expensive GPUs at the last moment or not fully utilizing its
potential; it could lead to a massive waste of compute resources involved here.
2. Computer & Network: One can build the stack using bare metal servers or virtual machines (VMs) on-prem
or in the cloud. Bare metal is more performance, though. The compute nodes are connected with high-
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[4167]
performance networks such as RDMA and InfiniBand for efficient data transfer and high-speed
communication.
3. GPUs: They are the heart of AI and the major differentiator. Depending on performance and capabilities, a
pool of various GPUs is required. Furthermore, additional GPU scheduling and optimization strategies
through orchestration layers and drivers are required. Please note that some features, such as enabling
fractional GPUs may require additional licenses from the vendor.
II. OVERVIEW OF CLOUD COMPUTING
Many organizations are adopting cloud computing as a key strategy. The cloud's significant business and
technical advantages are changing how many companies and corporations operate.
Cloud computing is a remote virtual pool of on-demand shared resources offering compute, storage, and
network services that can be rapidly deployed at scale. Cloud computing technology is based on virtualization.
Virtualization allows multiple virtual machines running a separate operating system and applications installed
on one physical server to run simultaneously without being aware of each other's existence while sharing the
server's underlying hardware resources.
There are obvious benefits of virtualization, including reduced capital expenditure. You don't need to purchase
as much physical hardware because you can have multiple VMs installed on one physical host. Less hardware
means a smaller footprint for your data center or server farm and lower costs for power and cooling. In a cloud
environment, optimizing resourcing and equipment means that everyone who uses the infrastructure—
vendors and consumers can benefit from this approach.
2.1 Types of Cloud Services
1. SaaS
SaaS is a software delivery model in which the cloud provider hosts the customer's applications at the cloud
provider's location. The customer accesses those applications over the Internet. Rather than paying for and
maintaining their computing infrastructure, SaaS customers take advantage of subscriptions to the service on a
pay-as-you-go basis.
Many businesses find SaaS the ideal solution because it enables them to get up and running quickly with the
most innovative technology available. Automatic updates reduce the burden on in-house resources. Customers
can scale services to support fluctuating workloads, adding more services or features they grow. A modern
cloud suite provides complete software for every business need, including customer experience, customer
relationship management, customer service, enterprise resource planning, procurement, financial
management, human capital management, talent management, payroll, supply chain management, enterprise
planning, and more.
2. PaaS
PaaS gives customers the advantage of accessing the developer tools they need to build and manage mobile and
web applications without investing in—or maintaining—the underlying infrastructure. The provider hosts the
infrastructure and middleware components, and the customer accesses those services via a web browser.
PaaS solutions need ready-to-use programming components that allow developers to build new capabilities
into their applications, including innovative technologies such as artificial intelligence (AI), chatbots,
blockchain, and the Internet of Things (IoT). The right PaaS offering should also include solutions for analysts,
end users, and professional IT administrators, including big data analytics, content management, database
management, systems management, and security.
3. IaaS
IaaS enables customers to access infrastructure services on-demand via the Internet. The key advantage is that
the cloud provider hosts the infrastructure components that provide computing, storage, and network capacity
so subscribers can run their workloads in the cloud. The cloud subscriber is usually responsible for installing,
configuring, securing, and maintaining any software on the cloud-native solutions, such as database,
middleware, and application software.
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[4168]
Figure 2: Types of cloud services
III. THE ROLE OF AI IN CLOUD COMPUTING
AI and cloud computing are two of our most significant technological advancements. Both technologies have
transformed how we live and work, and their integration has resulted in even more powerful capabilities and
benefits.
Artificial Intelligence (AI) refers to the ability of machines to simulate human intelligence and perform tasks
that would normally require human intelligence. AI can automate complex tasks, analyze large amounts of data,
and make predictions.
On the other hand, cloud computing refers to the delivery of computing services, including servers, storage,
databases, and software, over the Internet. It enables organizations to access computing resources on demand
and pay only for what they use rather than investing in and maintaining their own IT infrastructure.
The integration of AI in cloud computing has resulted in improved accuracy, speed, and efficiency. AI can
automate complex tasks in cloud computing, optimize system performance, personalize services, enhance
security, and improve user experience.
The future of AI in cloud computing is bright, and the demand for AI professionals is expected to grow in the
coming years. Suppose you are interested in starting a career in AI and cloud computing. In that case,
possessing essential skills and qualifications, building a strong resume, and staying up-to-date with the latest
technological advancements and trends can help you succeed.
In this article, we will explore the basics of AI and cloud computing, their integration, the benefits of their
combination, and the career opportunities available in this field.
IV. TRANSFORMING SCALABILITY WITH AI
4.1.1 Standard software engineering technologies
Organizations can adopt standard software engineering technologies to maximize the value of their AI
investments. Continuous integration/deployment (CI/CD) and automated testing frameworks allow
organizations to automate AI building, testing, and deployment. With these technologies, all ML models follow a
standard deployment pattern set by the organization and are effectively integrated into the broader IT
infrastructure. In addition, fostering a culture of collaboration and shared responsibility through these new
technologies can reduce time to market, minimize errors, and enhance the overall quality of AI applications. For
example, a leading Asian bank implemented new protocols to scale AI and the tooling to enforce them, which
helped reduce the time to impact ML use cases from 18 months to less than five months.
e-ISSN: 2582-5208
International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
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4.1.2 Data and ML best practices
Emphasizing data and ML best practices is paramount to successfully scaling AI applications within an
organization. Organizations can streamline the analytics process by implementing clearly defined protocols.
Such protocols typically define how organizations approach new projects, ingest data, engineer ML features,
and build and test models after a model is deployed; closely monitoring its performance and conducting
maintenance become essential to achieving the best possible performance.
These best practices must be codified into comprehensive guides that explain the sequence of activities,
important deliverables, and roles of various stakeholders, such as data scientists, engineers, and business
professionals. Organizations that adopt these best practices can scale AI more efficiently and foster a culture of
cross-functional collaboration.
4.1.3 Ethical and legal implications
Finally, as ML models grow in their sophistication and societal reach, they must operate within the bounds of
legal and ethical norms. With clear rules and guidelines, ML models become increasingly easier and more time-
intensive to correct as they develop, limiting their scalability. Understanding applicable rules, compliance
needs, and ethical considerations helps organizations operate within the limits of laws and societal
expectations. Organizations that embrace regulatory compliance and ethical best practices as part of their AI
development process can mitigate risks by requiring that ML models conform to codified compliance guidelines
before release. The reliability of these practices also helps organizations build trust with their stakeholders and
increases the longevity of their AI endeavors.
4.2 Challenges and Limitations
1. Hitting technology roadblocks
While AI has existed since the mid-50s, AI-powered chatbots, face swap apps, and robot dogs only became
viable realities a couple of years ago. Neither businesses nor technology partners have a tried-and-true formula
for developing and deploying AI systems company-wide.
Some of the common AI pitfalls include:
1. Poor architecture choices. Making accurate predictions is not the only thing you should expect from an AI
solution. In multi-tenant AI as a service (AIaaS) applications serving thousands of users, performance,
scalability, and effortless management are equally important. You cannot expect your vendor to write a
Flask service, package it in a Docker container, and deploy your ML model. When the system reaches its
maximum capacity, you’ll be left with an app that is too big and complex to manage effectively.
2. Inaccurate or insufficient training data. AI systems' performance depends on the data quality with which
they have been trained. Companies sometimes struggle to provide quality data (and a substantial volume
thereof!) to train AI algorithms. The situation is not uncommon in healthcare, where patient data like X-ray
images and CT scans is hard to obtain due to privacy reasons. To better identify and understand recurring
patterns in input data, it is also crucial to manually label training datasets using annotation tools like
Supervise.ly. According to Gartner, data-related AI problems were the #1 reason 85% of artificial
intelligence projects delivered erroneous results through 2022.
3. Lack of AI explainability. Explainable artificial intelligence (XAI) is a concept that revolves around providing
enough data to clarify how AI systems come to their decisions. Powered by white-box algorithms, XAI-
compliant solutions deliver results that developers and subject matter experts can interpret. Ensuring AI
explainability is critical across various industries where smart systems are used. For example, a person
operating injection molding machines at a plastic factory should comprehend why the novel predictive
maintenance system recommends running the machine in a certain way — and reverse bad decisions.
Compared to black-box models like neural networks and complicated ensembles, white-box AI models may
lack accuracy and predictive capacity, which somewhat undermines the whole notion of artificial
intelligence.
2. Replicating lab results in real-life situations
An AI-based breast cancer scanning system created by Google Health and Imperial College London reportedly
delivers fewer false-positive results than two certified radiologists.
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( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
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[4170]
In 2016, Oxford and Google DeepMind scientists developed a deep neural network that reads people’s lips with
93% accuracy (compared to just 52% scored by humans).
And now there’s evidence that machine learning models can accurately detect COVID-19 in asymptomatic
patients based on a cellphone-recorded cough!
When fueled by powerful hardware and a wealth of training data, AI algorithms can perform a wide range of
tasks on par with humans—and even outmatch them. The problem with AI is that most companies fail to
replicate the results achieved by Google, Microsoft, and MIT—or the accuracy displayed by their AI
prototypes—outside the laboratory walls.
3. Scaling artificial intelligence
Software scalability issues haunt various IT projects regardless of their technology stack, and AI solutions are
no exception; according to Gartner, just 53% of AI projects successfully transition from prototypes to
production. This statistic indicates a lack of technical expertise, competencies, and resources needed to deploy
intelligent systems at a large scale.
4. Overestimating AI’s power
Back in 2020, MIT Sloan Management Review and Boston Consulting Group released a report that provided
insights into why certain companies reap the benefits of AI while others do not. DHL, a postal and logistics
company that delivers 1.5 billion parcels a year, is among the AI winners.
The company uses a computer vision system to determine whether shipping pallets can be stacked together
and optimize space in cargo planes.
Gina Chung, VP of innovation at DHL, says the cyber-physical system performed poorly in its early days. The
results improved dramatically once it learned from human experts with years of experience detecting non-
stackable pallets. In business settings, such a balanced approach to AI implementation is rather an exception
than a rule.
In reality, many companies are influenced by the hype around AI and begin ambitious projects without
adequately assessing their needs, IT capabilities, AI development costs, and the technology's legal and ethical
implications.
5. Dealing with AI ethical issues
Greater adoption of smart applications comes along with several AI ethical challenges, including:
Bias in algorithmic decision-making stems from flawed training data prepared by human engineers and
bears the mark of social and historical inequities. For instance, a facial recognition system deployed by US
law enforcement agencies is more likely to identify a non-white person as a criminal.
Moral implications mainly revolve around some companies' intent to replace human workers with highly
productive, always-on robots. Even though two-thirds of business executives believe AI will eventually
create more jobs than it will kill, 69% of organizations might need different skills to thrive in the digital era.
Limited transparency and explainability are typical of advanced black-box AI solutions. Deep learning
networks fail to explain the reasoning behind their decisions, and it's also challenging to determine
accountability for AI recommendations in case of system errors and harm.
V. ENHANCED RESOURCE MANAGEMENT THROUGH AI
AI in resource management is the application of artificial intelligence and machine learning to analyze real-time
and historical data and continuously learn from that data. This helps reshape how organizations handle
resource allocation, utilization, forecasting, etc.
AI in resource management helps you process massive amounts of data quickly, draw meaningful insights, and
instantly make data-driven decisions instantly make data-driven decisions.
To put this into context, AI is transforming resource planning in the following key ways:
Intelligent resource recommendation: AI algorithms scrutinize your project specifics, required skills, and
resource availability to suggest the best fit intelligently. Drawing on past data and patterns, AI offers
forward-thinking recommendations to guarantee you're making the best use of your resources and
enhancing the success of your projects.
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( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
[4171]
Dynamic skill matching: AI-enabled systems ensure that your projects are always matched with the right
resources with the needed skills. They learn from previous assignments, evaluate employee performance,
and comprehend skill proficiencies to make intelligent suggestions tailored for you. The outcome? Improved
project efficiency and skyrocketing client satisfaction.
Continued learning and optimization: AI continuously learns and refines resource allocation strategies and
suggestions. As a result, it gets progressively smarter at catering to your unique resource management
requirements.
Figure 3: Human resource Management Through AI
In summary, AI applies predictive analytics, simulations, and advanced optimization to develop data-driven
resource plans that get smarter over time. It also supports human resource planners by handling complex
analyses. With AI, you're not just managing resources better today, but it's becoming increasingly effective
daily.
VI. FUTURE TRENDS AND INNOVATIONS
Artificial intelligence (AI) has the potential to be the most powerful and transformative technology the business
world has ever seen, helping us make smarter decisions, automate tasks, and fully realize the value of the data
businesses are generating at an ever-growing rate.
Over the last decade, AI technology has shown revolutionary implications across all industries. Several trends
have evolved in parallel to form a perfect storm. Together, they have brought us to the point where "thinking
machines" – once the domain of science fiction – are a practical reality today and are set to have a revolutionary
impact on our future.
Today, we live in a digitally connected world full of Internet of Things devices, in which collecting and sharing
data has become part of almost everything, and we have increasingly sophisticated analytic technologies and
methods to turn the data into business value. Perhaps most significantly, this includes deep learning neural
network models. Though based on research into machine learning going back to the 1960s, they have become
far more useful since we've been able to plug them into the internet and fuel them with almost unlimited
amounts of data. This has rapidly advanced what is possible with machine learning and provides us with the
"brains" of today's AI applications.
A third major development on the road to artificial intelligence is undoubtedly the arrival of cloud computing.
Getting to the point where we can perform tasks that we consider "AI" – identifying objects in images or
understanding natural language – requires crunching a lot of data through many algorithms. This involves
massive amounts of computing power and storage space. Although both commodities constantly fall in price,
the requirements would still be prohibitively expensive for most businesses.
Cloud computing is the solution to this problem that AI technology can potentially create $15.7 trillion of value
within the world economy over the next decade. However, much of that will depend on businesses' ability to
access and analyze the data, technology, and skills to make it possible.
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International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
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Not only that, but there could be untold social, economic, or political consequences if technology was
transformative only in the hands of the most powerful corporations and governments. Cloud's real value is that
it acts as a democratizer of AI, letting virtually anyone create supercomputer-powered apps and services that
can be run from the tiny devices we carry in our pockets. This is one of the biggest trends we see in AI and the
cloud today – companies of all sizes seizing the chance to create AI-powered products and services that
previously only could have been delivered by the biggest and best-resourced enterprises.
Companies of any size can use leading-edge AI analytics to drive results like those seen by Netflix, whose users
make 80 percent of their decisions on what to view next based on the company's prediction. Or German online
retailer Otto, which uses AI to predict with 90 percent accuracy what the company will sell over the next 30
days. This generates huge savings (value) for the company by cutting money spent on the purchase, storage,
distribution, and retailing of products that aren't going to sell.
Another trend is the accelerated pace at which all this is happening. Due to the COVID-19 pandemic, in just a
few months, many of my customers have achieved levels of digital transformation they previously would have
expected to take three or four years. This has sped up migration to the cloud as requirements have arisen for
more widely distributed infrastructure. Cloud tools like Office 365 and Slack have been the backbone of the
work-from-home revolution that has helped many companies keep going. As music, films, and gaming move to
the cloud, so do the business services and productivity tools we use to communicate with customers,
collaborate with colleagues, and manage our day-to-day operations.
Meanwhile, the cloud itself is changing. Options now exist to integrate your infrastructure fully into a public
cloud environment. Increasingly, businesses are looking toward multi-cloud or hybrid-cloud approaches,
allowing them to take more direct control over where their data is stored and how it is guarded. Hybrid models
such as "cloud-on-premises" involve public cloud providers deploying containerized micro-clouds at client
premises that benefit from the public cloud's connective and feature-rich environment. Hence, the client never
has to let the data out of their sight or direct control.
VII. CONCLUSION
In summary, integrating artificial intelligence into cloud computing represents a pivotal advancement that
enhances scalability, resource management, and predictive analytics in distributed systems. By leveraging AI
technologies, organizations can achieve unprecedented efficiency in resource allocation, enabling dynamic
adjustments to meet varying demands. Furthermore, AI-driven analytics empower businesses to harness large
volumes of data for informed decision-making, ultimately leading to improved operational outcomes.
However, successfully implementing these innovations requires careful consideration of challenges such as
data privacy, security concerns, and the potential for algorithmic bias. Addressing these issues will foster trust
and ensure equitable access to AI benefits across different sectors.
As the landscape of cloud computing continues to evolve, ongoing research and development in AI will be
critical. Future advancements should focus on enhancing the robustness and transparency of AI systems,
ensuring they can effectively support the needs of diverse industries. By embracing these innovations,
organizations can optimize their operations and position themselves for sustained growth in an increasingly
competitive digital environment.
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International Research Journal of Modernization in Engineering Technology and Science
( Peer-Reviewed, Open Access, Fully Refereed International Journal )
Volume:05/Issue:12/December-2023 Impact Factor- 7.868 www.irjmets.com
www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science
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International Research Journal of Modernization in Engineering Technology and Science
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