Recent Trends in Artificial Intelligence & it's Applications(e-ISSN:2583-4819)

Used Car Price Prediction Using Artificial Neural Networks and Machine Learning Techniques

2023-07-28T11:56:18+0530

Regardless of the substantial rise in car use,
newly constructed motor vehicles fail to
attract potential clients due to a variety of
reasons such as prohibitive costs, shortages
of vehicles, budgetary constraints, along
with other variables. As such, the market
for second-hand automobiles is expanding
quickly everywhere, except for the nation of
India, where it remains very young and
strongly controlled by unorganized
vendors. This raises the possibility of fraud
when buying a vehicle that is used. Asking
choosing the consumer and the
merchandiser in order to determine the
price of a previously owned automobile, a
highly precise estimate is required. This
framework creates two models: a
Computerized Random Walden and an
Oversaw learning-based Artificial Neural
Network framework every one of which can
take advantage of the specified transport
data collection.
A wide range of unique features is taken
into account for accurate projections. The
results obtained beat examples that depend
on simple linear models as well as are
consistent with theoretical predictions.
Artificial neural networks (ANNs). The
collection of data for automobiles is used
for evaluating these methods. The results of
the experiments show that the random
forests approach, which achieves a median
absolute error of 1.0970472 and an R2
mistake that is 0.772584, generates the least
amount of failures compared to every other
approach.
Every day, the world and everyone’s
expectations are expanding. Out of all
the anticipation, one of them will buy the
motor vehicle. As no one is able, the cost of
another one at all times, everybody will
eventually buy a used one. However, a
newcomer is unfamiliar with the asking
price of the wanted automobile on the used
car marketplace. Whenever the purchase
price of new cars increased due to increased
technological costs, the economic case for
older cars became more powerful

A Second User Automotive Value Prediction System for Consumer’s Purchasing Using Machine Learning Approach

2023-09-22T13:24:41+0530

Determining the value of a used automobile
can be a challenging task due to the multitude
of factors influencing its market worth. The
objective of this project is to create machine
learning models capable of accurately
predicting the value of a secondhand vehicle
based on its various attributes, facilitating
informed purchasing decisions. The global
trend of rising interest in buying and selling
used cars, underscores the urgent need for a
reliable Secondhand Vehicle Value Prediction
system that can efficiently determine the
precise value of a car by considering a range
of features. Historically, this challenge was
addressed through arbitrary pricing decisions
by sellers, leaving buyers and sellers in the
dark regarding the actual value of the vehicle
in the current market. Sellers often lacked
knowledge about the vehicle's true worth and
what price to list it for. The project's
approach involves evaluating the
performance of different regression
algorithms, including linear regression, Ridge
Regression, and Lasso Regression. The
primary objective is to provide a reliable tool
that benefits both buyers and sellers by
bringing transparency and accuracy to the
valuation of secondhand vehicles, ultimately
aiding in more informed purchasing
decisions. In essence, this project seeks to
bring transparency and precision to the
valuation process.

Smart Cam: An Empowering Communication with Flask-Powered Image-to-Text Conversion

2023-09-22T14:43:53+0530

Today, many of us go outside of the state or
country for jobs or holidays. Each location or
area may have their regional communication
language because of which a communication
gap may occur. Due to communication gaps,
people often are not able to communicate with
native people or perceive the written notes or
sign boards and typically don't seem to be
ready to do their work. This becomes a big
hurdle for many people who need a quick
solution. However, with the advancement of
technology, it is possible to use some
techniques to fill this communication gap by
translating images into text. The concept of
text-to-image offer user-friendly and efficient
communication between human and
computer (machine) that too in natural
languages. This paper proposed a web
application that translates the image into a
native language as per the user’s need. The
web-based application is developed with a
flask with numerous libraries such as
PyTesseract, OpenCV, Numpy, and textblob
libraries. The proposed work is based on
Tesseract. Tesseract is an OCR engine that
relies on Unicode and can recognize more
than 100 languages. The proposed work helps
the employee or tourist to visit foreign
countries or other places whose language is
not known or understandable to the person.

Anatomization of Respiratory Diseases Using Machine Learning

2023-10-09T17:29:53+0530

The respiratory system is frequently
susceptible to various diseases, with chronic
obstructive pulmonary disease (COPD)
serving as a notable illustration. COPD is
distinguished by an enduring lung ailment
characterized by a gradual reduction in lung
function over time. Precise prediction of
respiratory diseases is of utmost importance,
as a failure to do so can lead to fatal
consequences. Timely diagnosis plays a
pivotal role in reducing mortality rates. In
this research, raw spirometry data undergoes
a process of feature selection to identify
relevant attributes. These selected
characteristics are subsequently input into a
classification system to distinguish between
normal, obstructive, and restrictive cases. The
study illustrates how the accuracy of
classification algorithms, particularly in the
field of machine learning, can be significantly
improved through feature selection methods.
The suggested study has significantly
enhanced the accuracy of categorization using
a variety of algorithms, such as Naïve Bayes,
Support Vector Machine, Logistic Regression,
and K-Nearest Neighbor. Among these
algorithms, Logistic Regression emerges as
the most accurate classifier in this specific
context. This investigation underscores the
critical importance of early detection and
emphasizes the potential of machine learning
techniques in enhancing the accuracy of
diagnosing respiratory diseases, particularly
COPD, which can have a profound impact on
patient outcomes

Natural Language Processing Techniques Using Machine Learning

2023-10-30T15:12:19+0530

Natural language processing (NLP) is an
artificial intelligence subset. Computers can
talk in people's native languages, and other
language-related jobs are scaled. NLP enables
computers to read text and hear voices,
comprehend it, measure sentiment, and
identify significant components. NLP is a
synthesis of several disciplines, including
computing linguistics, machine learning, deep
learning, and statistics. These technologies
work together to enable computer software to
interpret and understand human language in
the same way that another human would,
including meaning, intent, and sentiment.
Human language can be understood by
machines. It could take the form of speech or
text. It employs ML (Machine Learning) to
achieve the goal of Artificial Intelligence. The
ultimate goal is to bridge the gap between
how humans communicate and what
computers can understand.
When used in conjunction NLP produces
systems that can learn to complete tasks on
their own and improve with practice using
machine learning techniques. Among many
other things, NLP-powered solutions can
assist you in categorizing and recognizing
identifiable entities from commercial
correspondence or assessing mood in social
media posts. In this study, the importance of
machine learning in NLP and its potential
applications are discussed in detail.

Applications of AI in Technology and Management

2023-11-29T16:30:29+0530

Artificial intelligence refers to the emulation
of human cognitive processes via the use of
machines, particularly computer systems. The
objectives of Artificial Intelligence (AI) have
been focused on the advancement of
computers to accomplish activities that were
previously exclusive to human capabilities,
including decision-making, natural language
processing, and intricate problem-solving.
This pursuit has been the driving force
behind AI research from its start. Currently,
the field of artificial intelligence (AI) is
marked by a diverse range of applications,
including several sectors such as healthcare,
banking, and manufacturing. These
applications include self-driving vehicles and
virtual assistants, which have brought about
significant advancements in these areas. The
current state of Artificial Intelligence exhibits
indications of not just automating tasks, but
also possessing the potential to revolutionize
our lifestyles and professional endeavors. In
this discourse, we shall explore the realm of
artificial intelligence, delving into its many
classifications, objectives, and obstacles, while
also examining its evolutionary trajectory.
The primary aim of this article is to highlight
the various applications of artificial
intelligence (AI) within the domains of
technology and management. These
applications encompass automation,
predictive maintenance, and computer vision,
natural language processing (NLP),
recommendation systems, data analytics,
supply chain optimization, and financial
management.