Assigning a written document to one or more classes or groups is referred to as this task. We will go over a number of fundamental supervised text classification techniques, such as Decision Trees (DT), Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and k Nearest Neighbor (kNN).
Author(s) Details
Adebola K. Ojo
Department of Computer Science, University of Ibadan, Ibadan,
Nigeria.
Please see the book here:- https://doi.org/10.9734/bpi/mono/978-81-972870-5-3/CH11
The number of financial crimes has increased in modern times. Financial fraud must be identified to ensure that transactions remain secure and to maintain public trust. Machine Learning algorithms like Logistic Regression, Decision Trees, and Random Forests help us achieve this. The straight-forward method of Logistic Regression is used to identify linear relationships. Decision Trees, on the other hand, are more suitable to handle complex fraud patterns as they can capture non-linear relationships. Random Forests use numerous decision trees making them best suited for datasets which are at risk of overfitting.
Algorithms get evaluated by performance criteria such as F1 score,
accuracy and precision. Out of all the transactions labelled as fraud,
precision is the proportion of correctly identified fraud transactions. The
proportion of accurately classified transactions is known as accuracy. The
harmonic mean of precision and recall gives us the F1 score and a normalized
score of the model's performance is obtained by balancing.
Statistical hypothesis tests are applied for an accurate
comparison is used to compare. When three or more algorithms are to be compared
to check for a statistically significant difference between their means, we use
Analysis of Variance (ANOVA). This study aims to understand the effectiveness
of the ML algorithms in fraud detection by performing ANOVA tests on the
selected performance metrics. Here synthetic data set is generated and applied
statistical techniques for evaluation.
Author
(s) Details
W. Grace Shanthi
Kakatiya Institute of Technology and Science, Warangal, India.
Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-10-5/CH5
Heart disease persists to be a major all-encompassing public health issue, contributing to infinite deaths and disabilities. Effective deterrent interventions and individualized situation programs depend on timely and exact risk prediction of myocardial infarction. Significant advancements in the field of cardiac affliction prediction have been created thanks to the development of machine intelligence techniques. This book chapter offers a all-encompassing analysis of the strengths, imperfections, and overall effectiveness of the various machine intelligence models used for heart disease indicator.
Author(s) Details:
Sunanda Budihal,
Department
of Computer Science, Shah Sogamal Peeraji Oswal Government First Grade College,
Muddebhihal, Vijayapura, Karnataka, India.
Sheetalrani
Rukmaji Kawale,
Department
of Computer Science, Karnataka State Akkamahadevi Women University, Vijayapura,
Karnataka, India.
Aparna Atul Junnarkar,
Department of Information Technology, Vishwakarma Institute of
Information Technology (VIIT), Pune, Maharashtra, India.
H. Faritha Begam,
Seethalakshmi Achi College for Women, Pallathur 630 107, Tamilnadu,
India.
Girish M.,
Department
of Computer Science and Engineering, Gopalan College of Engineering and
Management, Bangalore, Karnataka, India.
Please see the link here: https://stm.bookpi.org/ACST-V2/article/view/11926
The aim concerning this chapter is an overview of current knowledge about Glutamate dehydrogenase (GLDH) in dispassionate applicability and possible hints for further search. GLDH is applicable as a marker of addiction, maintenance of abstinence and repetition of alcohol use in daily practice, nevertheless allure role as an indirect flag in determining neurodegenerative processes in the mind offers limited help.In comparison to other indicators of alcoholism is GLDH equally correct. Time aspect deliberate serum GLDH activity is an ideal gravestone of alcoholism since it is exalted in alcohol abuse but its venture declines promptly subsequently the last alcohol intake in intoxicating dependent subjects and the change ends long enough to be smart to be evaluated. Combinations of intoxicating markers increase sensitivity and belittle specificity.The kinetics of blood corpuscle GLDH activity is a particular marker of alcohol dependence in addition to of the cessation of drinking intoxicating. Alcohol consumption reduces GLDH project to some extent and consecutively it take care of lead to increased protein result and strengthen of belittled leukocyte protective skill.The gradually decreased GLDH venture over life may have few contribution when aging the same as the GLDH endeavor in leukocytes diminishes with growing up. The decrease in GLDH activity in the cerebrospinal fluid at neurodegenerative disorders concede possibility be one of the reasons for the neuroexcitotoxic glutamate effect.
Author(s) Details:
Matej Kravos,
ZPA
Dr. Kravos, Trg Svobode 26, 2310 Slovenska Bistrica, Slovenia and The Faculty
of Mathematics, Natural Sciences and Information Technologies, University of
Primorska, Glagoljaška ulica 8, 6000 Koper, Slovenia.
Please see the link here: https://stm.bookpi.org/NAMMS-V7/article/view/11040
