Asian Journal of Mathematical Sciences(AJMS)

ALGEBRAIC SOLUTION OF FERMAT'S THEOREM (MATHEMATICS, NUMBER THEORY)

2024-06-20T05:46:12+00:00

Fermat's Last Theorem (or Fermat's last theorem) is one of the most popular theorems in mathematics.
Formulated in French mathematician Pierre Fermat in 1637. Despite the simplicity of the formulation,
literally, at the “school” arithmetic level, proof of the theorem sought by many mathematicians for more
than three hundred years. And only in 1994 year the theorem was proven by the English mathematician
Andrew Wilson with colleagues; The proof was published in 1995. [1]-[5] The author of this article has
been searching for his own for a long time. accessible algebraic solution to this problem and believes that
he succeeded,which he presents in this article.

BRIDGING THE KNOWLEDGE GAP: ENHANCING TRACHEOSTOMY CARE SKILLS THROUGH STRUCTURED LEARNING

2024-06-20T05:49:25+00:00

Background-Tracheostomy care is essential for patients with severe respiratory issues, yet traditional
nursing education often inadequately prepares students for this complex procedure. This study explores
the impact of a structured educational program on improving the knowledge and skills of nursing students
in tracheostomy care.Aim-The primary objective of this study is to evaluate the effectiveness of a
structured educational program in enhancing the knowledge, skills, and confidence of nursing students
regarding tracheostomy care.Methods-An evaluative approach with a pre-experimental one-group
pretest-post-test design was employed. The study was conducted at SCPM College of Nursing and
Paramedical Sciences, Gonda, UP, involving 40 second-year GNM students. A structured questionnaire
assessed knowledge levels before and after the intervention and study samples were recruited with the
help of purposive sampling technique. Data collection was performed by six trained BSc nursing tutors
and analysed using SPSS, with a significance level of <0.05 and a 95% confidence interval.Results-The
pretest mean score was 11.42 (SD = 1.9596), while the post-test mean score increased to 17.50 (SD =
2.2870), with a mean difference of 6.08. The standard error was 0.47, and the calculated t-value was
12.76, compared to the tabulated value of 1.685, indicating a significant improvement in knowledge
levels.Conclusion-The structured educational program significantly enhanced the knowledge and
practical skills of nursing students regarding tracheostomy care, demonstrating its effectiveness as a
teaching method.

PREDICTING DIABETES USING DEEP LEARNING TECHNIQUES: A STUDY ON THE PIMA DATASET

2024-06-20T05:52:24+00:00

Diabetes is one of the key reasons of growing death rates around the world. Diabetes is a medical
condition that arises from chronic issues that influence carbohydrate metabolism and raise blood glucose
levels. Scientific research is needed to diagnose diabetes early for prevention and treatment due to the
growing rates of the disease. Researchers have recognized the value of classification models for disease
prediction developed using machine learning and deep learning techniques. This study explores the
efficacy of deep learning techniques Convolutional Neural Network (CNN), Long Short-Term Memory
(LSTM), and Multi-Layer Perceptron (MLP)—in forecasting diabetes using the Pima dataset.
Preprocessing steps encompassed normalization, handling missing values, and outlier removal. The
models were trained and evaluated, yielding noteworthy performance metrics. The CNN exhibited the
highest accuracy of 0.77, while achieving precision, recall, and ROC-AUC scores of 0.69, 0.67, and 0.83,
respectively. The LSTM and MLP models also demonstrated competitive results, achieving accuracies of
0.75 with similar precision, recall, and ROC-AUC values around 0.64-0.67 and 0.80-0.82, respectively.
These findings highlight the potential of deep learning methodologies for predictive diabetes analysis and
emphasize the significance of proper preprocessing techniques in enhancing model performance.