<p>Modern data collection, storage, and processing rely on diverse techniques to handle various types of information, ranging from structured tables to free-form text. This paper explores the captivating application of Natural Language Processing (NLP) for categorizing titles from Google Forms or any other textual data. The process of training an NLP model will be demonstrated through a specific example. Just as we learn from our past experiences, NLP models need to be fed with relevant data and labels. This ensures accurate and efficient processing even when new titles are introduced. We will conclude with a fascinating demonstration of how NLP algorithms analyze the structure and meaning of titles. By identifying keywords and understanding the context, they can automatically classify titles into relevant categories. This dramatically simplifies data organization and analysis, empowering us to extract valuable insights faster.</p>

In research aimed at determining the level of interest of high school students in enrolling in colleges, predictive analysis models and comparisons are rarely applied during the classification and processing of various data. All of this leads to significant fluctuations in college admissions, where certain schools are unable to admit a large number of students who show interest in a specific field. On the other hand, high school students lose interest in certain schools, leading to the discontinuation of specific directions essential for today's job market needs. Institutions largely fail to conduct a comparison and linkage of teaching and non-teaching activities when analyzing the talents and interests of high school students from different fields. The goal of this paper is to use programming language classifiers to predict student enrollments in colleges based on the results students demonstrate during regular attendance in high schools through participation in innovation fairs.

Early diagnosis and treatment of brain cancer depend on the detection and categorization of brain tumors. Deep learning algorithms have produced amazing results in medical imaging applications including tumor identification. Most of this field's research has concentrated on applying CNN algorithms like VGG16, DNN, and ANN to this problem. This work describes the identification and classification of brain tumors using the Python Imaging Library (PIL) and the VGG16 deep learning algorithm. A dataset of 7000 MRI pictures categorized by tumor type served as the foundation for the research. The main objective of this study was to develop a high-efficiency, high-accuracy model. We suggested utilizing the VGG16 architecture and preprocessing images with PIL to ensure consistent images for training on a sizable dataset of brain magnetic resonance imaging (MRI) images. A novel technique we have used in our work is one that can analyze a single image and predict the presence of a tumor from the results. The research's methods produced robust tumor detection across the dataset with 96, 9% accuracy, indicating the value of the method in helping medical professionals make informed decisions when diagnosing the presence of tumors.

In research to determine the degree of interest in enrolling students in certain high schools, predictive analysis and comparison models are rarely used when classifying and processing different data. All this leads to large fluctuations in enrolment in secondary schools, where certain schools are unable to enrol numerous students who show an interest in a particular field. On the other hand, students lose interest in certain schools, which leads to the discontinuation of certain courses necessary for the needs of today's labour market. Institutions responsible for organizing the educational process do not sufficiently compare and connect teaching and non-teaching activities when analysing the talents and interests of elementary school students from different fields. The goal of this work is to predict the enrolment of students in secondary schools, using the classifiers of programming languages, based on the results that students express during regular classes in elementary schools.The results show that the accuracy of the data during the training of the Random Forest predictor is 52%, while in Wolfram Alpha it is 62%

This paper aims to show how business intelligence can be applied in the credit card approval process. More specifically, the paper investigates how information like an applicant’s age, credit score, debt, income, and prior default can be used in credit card approval prediction.The dataset used for analysis is a publicly available dataset from the UCI machine learning repository. Logistic regression is used to make a prediction model with a reasonable number of attributes for a comprehensible business model. The Chi-square test of independence is used to test the dependence of credit card approval results with attributes. Research uncovers that prior default is supposed to be the most important attribute in the approval process. Finally, the authors propose several visualizations that could help make smarter decisions with effective credit risk assessment.

Optical character recognition represents the mechanical or electronic conversion of handwritten, typed or printed images into coded text. Optical character recognition is widely used as a form of data entry from records that have been printed, and it can include invoices, bank statements, passports and many more. In the research, Optical character recognition reads data from the Re-Captcha dataset of images, converts them into strings, and these strings are used for testing, training and calculating prediction accuracy. The methodologies used are Convolutional neural network and Recurrent neural network. The convolutional neural network consist of neurons that receive data and group them according to similarity. A recurrent neural network cycle can be created between the connections of nodes, allowing the output from nodes to influence the subsequent input to other nodes. For data were used Re-Captcha images, and for the prediction of characters from images was used TensorFlow with Keras. The best results that are produced can be compared between first and last result, where the loss for first result was 20.63 and value loss was 16.45, while last result has loss of 0.56 and value loss of 2.96

There is a growing technological development in intelligent teaching systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that helps students un-derstand the basics of linear programming using Linear Program Solver and Service for Solving Linear Programming Problems, through which students will be able to solve economic problems. It comes down to determining the minimum or maximum value of a linear function, which is called the objective function, according to pre-set limiting conditions expressed by linear equations and inequalities. The goal function and the limiting conditions represent a mathematical model of the observed problem. Working as a professor of mathematics in high school, I felt the need for one such work and dealing with the study of linear programming as an integral part of mathematics. There are a number of papers in this regard, but exclusively related to traditional ways of working, as stated in the introductory part of the paper. The center of work as well as the final part deals with the study of linear programming using programs that deal with this topic.

Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

In recent years IPTV (Internet Protocol Television) platforms are becoming one of the most popular entertainment multimedia services which are used to serve movies, tv-series and other video and audio attractive content using the Internet Protocol. VoD (Video on Demand) is the most popular multimedia IPTV service, which provides content without the need for the old traditional way of using video playback devices. Except that it is necessary to have high-quality VoD content, IPTV platforms must provide the best end-user experience. Moreover, it is imperative to provide new features to attract new customers and keep the existing ones. We confirmed the efficacy of this classifier thru simple trial and error. When we searched for movies that have sequels, our engine recommended those sequels. Since Cosine Similarity Classifier considers multiple factors, such as actor, genre, year, etc. Even if the movie does not have prequels or sequels this algorithm was able to provide us with movies that share other common characteristics.

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much attention, because of Twitter's growth and popularity. The solution for handling enormous amounts of data from social media is a new term called Big data. Big data is not just for having a large amount of data, but also the importance of processing and the usage of the data.

It can be confidently stated that access to education is one of the most prized possessions available to us today. Although there are underlying factors such as the discrepancies in the education being provided worldwide, it is imperative that data scientists and all those interested take advantage of the data publicly available to draw necessary insights into how to better the education sector in our respective countries. The purpose of this research is to showcase various analytical insights into the 2020 New York State (NYS) high school graduation rate data using various advanced database systems techniques, specifically using SQL. With these analyses, further studies and conclusions can be drawn for local governments to implement into their plans to increase the quality of the schooling system, to aim for equality for all without reg

Since the early beginnings of education systems, attendance has always played a crucial role in student success, as well as in the overall interest of the matter. The most productive way of increasing the student attendance rate is to understand why it decreases, try to predict when it is going to happen, and act on causing factors in order to prevent it. Many benefits of predicted and increased attendance rate can be achieved, including better lecture organization (i.e. lecture time and duration, lecture class choice, etc). This paper describes the steps in the extraction of knowledge from the university's student database and making a model that predicts whether the student will attend the class or not. Results show that the attendance patterns are best reflected when employing a decision tree algorithm, a C4.5 model that is interpretable and able to predict the attendance with 0.81 AUC performance measure

Homicide rates are still high in the world and they are the worst crime in human existence. Despite all the technological advances and usage of information by various agencies, the number of homicides is not decreasing. Homicide prediction in certain countries should notably be the number one priority, which can help the government to easily identify the kind of profile they are looking for, or even help them prevent those cases. This paper compares different Machine Learning Techniques classifications of homicide prediction. Random Forest (RF), Random Tree, J48, Naive Bayes and k-Nearest-Neighbor (KNN) were tested to determine which method provides the best results in homicide prediction classification. The results of sample accuracy for all algorithms were around 99%, which clearly shows that all algorithms give great results. However, J48 is the best technique applied on the dataset, as it classified all instances correctly.

K-means and hierarchical clustering algorithms are employed to cluster genes according to the gene expression to determine the harming level of the genes in brain cancer. The gene expression data with a control group from The Cancer Genome Atlas database were used. The optimal cluster number for each clustering technique was obtained using the elbow method and dendrogram for K-means and hierarchical clustering methods respectively. We identified the ideal number of clusters as three and further classified them into seven groups. We observed that the second cluster contains over half the genes in healthy people and the cluster distribution of a healthy patient and a patient who died six months after being diagnosed with brain cancer is similar. Further analysis indicated that of all the time spent by patients after being diagnosed with brain cancer, group 0 has the highest percentage in one month after the diagnosis, while group -2 has the lowest percentage. Most genes shift their clusters when Kmeans and hierarchical clustering techniques we compared with the genes from the control and disease groups. The result of the measure of dissimilarity between the genes expression patterns indicates that the K-means technique outperforms the hierarchical technique with a higher rate of change in the cluster.