Pregnancy is a cycle that invites us to give up on the hidden power behind all life. A stable pregnancy good thing. The likelihood of safe increased by timely and daily fetal treatment. Fetal welfare program controls pregnant woman during her pregnancy. Every should know what result about. day about 800 women die from birth complications. Maternal health have close relation, as nearly three million newborns every year. Therefore, adequate treatment important, including risk evaluations for...

Software defect prediction aims to identify defect-prone modules before testing, reducing costs and duration. Machine learning (ML) techniques are widely used develop predictive models for classifying defective software components. However, high-dimensional training datasets often degrade classification accuracy precision due irrelevant or redundant features. To address this, effective feature selection is crucial, but it poses an NP-hard challenge that can be efficiently tackled using...

Software defect prediction identifies defect-prone modules before testing, reducing costs and development time. Machine learning techniques are widely used, but high-dimensional datasets often degrade classification accuracy due to irrelevant features. To address this, effective feature selection is essential remains an NP-hard challenge best tackled with heuristic algorithms. This study introduces a binary, multi-objective starfish optimizer for optimal selection, balancing reduction...

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), crucial preprocessing step in that seeks out ideal set characteristics with maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers concentrating on variety metaheuristic (or evolutionary) algorithms trying suggest cutting-edge hybrid techniques handle FS issues. use approaches for has thus been subject numerous research...

The objective of this research is to speed up the classification efficiency machine learning (ML) approaches through use preprocessing techniques such as balancing and selection features over imbalanced CTG (Cardiotocography) data. After analyzing class distribution data, it found that dataset imbalanced, most samples belong same class. So, deal with disparity in an over-sampling technique called SMOTE employed boost performance various classifiers. Also, suggested work intends find out...

The Extreme Learning Machine (ELM) has sparked a lot of attention since it can learn fast and be applied to various problems. In this study, convolutional layer-based extreme learning machine (CELM) architecture been designed implemented recognize handwritten characters reduce execution time. Furthermore, validate the robustness approach, are chosen from four different languages, mainly Indian including English. recognition performed on both numerals alphabets. experimental results regarding...

The Handwritten character recognition (HCR) is one of the most promising tasks in field optical (OCR). This paper implements a system from word images an offline manner. In system, pen stroke information not available to system. approach presented here performs several steps accordance get final recognized characters. A unique feature representation technique adapted for handwritten segmented These features are generated by vertically scanning characters using sliding window. Experiments...

Handwritten Number or digit recognition plays an important role in different platforms and applications. Here, a novel technique based on string edit distance algorithm is proposed to recognize offline handwritten images. The system tries build up each digit's then predicts the class by comparing test against existing from training set. lower among two string, greater chances of similarity them. Some databases have evaluated this variety mixed datasets been developed validate system's...

In response to the evolving technology, field of education and remote learning is undergoing a significant shift an online mode, connecting students educators worldwide at press button. A novel virtual study room platform has been developed efficiently handle this eliminate existing barriers. After thorough research reviewing feedback from during COVID19 pandemic, aims include seamless video-audio interface fostering sense community, collaborative document sharing, robust security protocols....

Offline optical character recognition (Offline OCR) is one of the important applications pattern recognition. To achieve a better result, input images must have good quality. That why preprocessing step be-comes essential for any image identification task. Lots research has been performed in numerous jobs towards this literature. Here, an attempt made to summarize different procedures and aspects adopted implementing these techniques. This done hope that may help community gaining knowledge...

A good benchmark dataset is a primary requirement in the offline handwritten character recognition (HCR) process. Only three numerals and alphabet datasets from Odia are publicly accessible for study, although many writers have used several their experiments. In this article, two tasks done to address issue. Those following: First, an extensive survey focused on various provided with methodologies chronological order. The second factor solution lack of available characters numeral datasets....

Recognizing printed characters is a little easier than when compared to handwritten by offline OCR machines. When the images of are taken from natural scene, they become hazy and in various angles. Then harder recognize, even though printed. In this study, an attempt has been made identify that colored different orientations, using deep convolutional neural network known as VGG16. The effect on classification accuracy with respect epochs datasets projected conducting some experiments. Two...

Despite promising results in character recognition techniques, research on handwritten characters from Indian regional scripts is still limited. In fact, most recognizers languages are far less accurate than those built for English alphanumeric characters. Traditional techniques rely extracted characteristics that require extensive knowledge of the chosen script, which never practical. such a circumstance, automatically extracting features may create interest. This study demonstrates how...

The classification of alphanumeric English hand-written characters is a challenging task. This paper presents novel approach for the recognition handwritten using Convolutional Neural Network (CNN). To create comprehensive and diverse dataset, three separate data sets namely MNIST, KAGGLE, EMNIST, were combined, so that proposed model can able to recognize numerals, capital small letters language. resulting dataset contained total 555,249 samples, encompassing wide range characters. Prior...