Artificial intelligence (AI) with It's Applications
The book "Artificial Intelligence (AI) with It's Applications" provides a comprehensive insight into the field of AI, exploring its fundamental principles, modern applications, and future potential. It serves as a valuable resource for students, researchers, and professionals looking to understand AI’s role in shaping industries and everyday life. The book begins with an introduction to Artificial Intelligence , cov…
Licence
OPEN
CC-BY-4.0
Authors
Dr. Dipikaben Umakant Thakar, Mrs. PL. Natchiammai, Dr. R. J. Kavitha…
From the above heat map, we can see that there are only one highly correlated features which we will remove before training the model on this data as highly correlated features do not help in learning useful patterns in the data.
Model Training
Now we will separate the features and target variables and split them into training and the testing data by using which we will select the model which is performing best on the validation data.
features = df.drop(removal + ['Class/ASD'], axis=1)
target = df['Class/ASD']
Let’s split the data into training and validation data. Also, the data was imbalanced earlier now we will balance it using the Random Over Sampler in this method we sample some
points from the minority class and repeat it multiple times so, that the two classes get balanced.
DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection.
the standard ↗
The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.
The book "Artificial Intelligence (AI) with It's Applications" provides a comprehensive insight into the field of AI, exploring its fundamental principles, modern applications, and future potential. It serves as a valuable resource for students, researchers, and professionals looking to understand AI’s role in shaping industries and everyday life. The book begins with an introduction to Artificial Intelligence , covering its history, evolution, and impact on technology. It explains key AI concepts, including machine learning, neural networks, and deep learning, providing a strong foundation for readers. Moving forward, the book delves into AI algorithms and models , discussing supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP). It emphasizes the significance of data in training AI systems and the methodologies used to improve AI accuracy and efficiency. A significant portion of the book is dedicated to AI applications across industries , such as healthcare, finance, robotics, and autonomous systems. It highlights real-world use cases, demonstrating how AI is revolutionizing various sectors. Additionally, the book explores ethical considerations and challenges in AI development , addressing concerns like bias, transparency, and the impact of automation on employment. It encourages discussions on responsible AI deployment. The final sections cover emerging trends and the future of AI , including quantum computing, AI in cybersecurity, and AI-driven decision-making systems. It provides a forward-looking perspective on how AI will continue to evolve. Through a mix of theoretical explanations and practical insights, this book is an essential guide for anyone interested in learning about Artificial Intelligence, its potential, and its transformative role in the modern world .
The abstract, and the depositor's additional notes after it when the source has a field for them.
Subjectdcterms:subject
none found — the source did not declare it
Declared keywords and taxonomy terms, each carrying its scheme.
Languagedcterms:language
language gate
read the declared field
en
The language of the content as the language gate read it.
Audiencedcterms:audience
mapped
learning_resource_type/Book
none found — the source did not declare it
Imported when the source declares an intended audience. Never inferred.
Education leveldcterms:educationLevel
mapped
learning_resource_type/Book
none found — the source did not declare it
Imported when the source declares an educational context.
Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.
Retrieved from Zenodo on 2026-10-09 in response to the search string “("artificial intelligence" OR "machine learning" OR "generative AI" OR "deep learning" OR "reinforcement learning" OR "large language model" OR "AI") AND ("AI concepts" OR "types of AI" OR "AI fundamentals" OR "recognizing AI" OR "recognising AI" OR "general versus narrow AI" OR "narrow AI" OR "general AI" OR "machine intelligence" OR "AI strengths and weaknesses" OR "traditional software" OR "rule-based systems" OR "introduction to AI" OR "introduction to artificial intelligence" OR "artificial intelligence introduction" OR "AI primer" OR "foundations of artificial intelligence" OR "overview of AI" OR "understanding AI" OR "history of AI" OR "AI essentials" OR "AI terminology" OR "metaphors for AI" OR "AI fundamental concepts" OR "AI key concepts" OR "philosophy of AI" OR "critical AI literacy")”. Zenodo served the resource and is not asserted to be its publisher or author.
Text extracted from pdf to Markdown by pdf-inspector; the original is retained unchanged beside it.
Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.
IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other.
the standard ↗
The book "Artificial Intelligence (AI) with It's Applications" provides a comprehensive insight into the field of AI, exploring its fundamental principles, modern applications, and future potential. It serves as a valuable resource for students, researchers, and professionals looking to understand AI’s role in shaping industries and everyday life. The book begins with an introduction to Artificial Intelligence , covering its history, evolution, and impact on technology. It explains key AI concepts, including machine learning, neural networks, and deep learning, providing a strong foundation for readers. Moving forward, the book delves into AI algorithms and models , discussing supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP). It emphasizes the significance of data in training AI systems and the methodologies used to improve AI accuracy and efficiency. A significant portion of the book is dedicated to AI applications across industries , such as healthcare, finance, robotics, and autonomous systems. It highlights real-world use cases, demonstrating how AI is revolutionizing various sectors. Additionally, the book explores ethical considerations and challenges in AI development , addressing concerns like bias, transparency, and the impact of automation on employment. It encourages discussions on responsible AI deployment. The final sections cover emerging trends and the future of AI , including quantum computing, AI in cybersecurity, and AI-driven decision-making systems. It provides a forward-looking perspective on how AI will continue to evolve. Through a mix of theoretical explanations and practical insights, this book is an essential guide for anyone interested in learning about Artificial Intelligence, its potential, and its transformative role in the modern world .
The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.
Keyword1.5
none found — the source did not declare it
Coverage1.6
not collected — this library does not fill it
The time, place or culture the resource applies to. No source declares it.
Structure1.7
not collected — this library does not fill it
Not declared by any of the three sources, and not estimated here.
Aggregation level1.8
not collected — this library does not fill it
Not declared by any of the three sources, and not estimated here.
Dr. Dipikaben Umakant Thakar (Shri C. J. Patel College of Computer Studies (BCA)) — author
Mrs. PL. Natchiammai (Mookambigai College of Engineering) — author
Dr. R. J. Kavitha (University College of Engineering Panruti) — author
Mrs. P. Anandavalli (University College of Engineering Panruti) — author
Mr. D. Yobu (J.J College of Engineering and Technology) — author
Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.
3 Meta-metadata
4/4
Identifier3.1
this engine
resource_id
URI: tag:aim-pro.eu,2026:oer/b9c036e3727d/record
The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute3.2
this engine
source
AIM-PRO WP3 OER harvester (Zenodo) — creator
Who generated this record and when — a statement about the record, not about the resource.
Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
Markdown extracted from the original by pdf-inspector
The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.
8 Annotation
0/3
Entity8.1
not collected — this library does not fill it
Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.
Date8.2
not collected — this library does not fill it
Description8.3
not collected — this library does not fill it
9 Classification
0/4
Purpose9.1
not collected — this library does not fill it
Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.
Taxon path9.2
not collected — this library does not fill it
Where the competency framework goes. Empty in the record for the reason above.
Description9.3
not collected — this library does not fill it
Keyword9.4
not collected — this library does not fill it
What could not be established4
Where the source's metadata could not be carried
over as it was — missing, contradictory, with no matching term in the
standard, restructured, or taken from the repository — and what was done
instead. Without these notes, an empty element would look like something
the harvester missed.
Status
Field
Why
Not available
subjects
the source published no keywords
Not available
rights_holder
no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available
publisher
the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available
educational
the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them