I BELONG TO THIS WORLD! A TEACHER PRACTICE FOR DEVELOPING RELATEDNESS IN THE SCHOOL ENVIRONMENT

The need for relatedness is seen as the need for human connection. Relatedness refers to the social nature of human beings and the connectedness with others. It can be stated that the person meets the needs of being related with sensitivity, warmth, emotional sensitivity and social acceptance. People need to interact with other people socially. In the self-determination theory, this need is considered necessary for people to feel mentally healthier. The aim of this study is to support development of basic relatedness skills in students, who think that they have difficulties in building relationships. The second aim of the study is to determine students’ thoughts about relatedness and sense of belonging after the developed practices. In this study, qualitative research method, one of the scientific research methods, was used. It is an action research based on practice and mutual cooperation. The study group of the research consisted of 21 students from Department of Primary Education at a state university in Turkey. In this study, criterion sampling method was used. The criterion used in the study is that students emphasize that they have communication problems. The research data were obtained from (1) the semi-structured interview questions developed by the researcher, (2) the evaluation questions to answer at the end of each practice, and (3) the diaries written by students at the end of each practice. In order to collect the data of this study, a total of 7 weeks of practice was performed, six of which were the relatedness practices and one was the preparatory practice. Practices were made at the same time every week. Results showed that the students have positive thoughts about relatedness and sense of belonging after practices. According to the students, when "getting to know yourself, caring and respect, getting to know others, having a sense of belonging and developing social skills" come together, relatedness improves positively.  Article visualizations

Estimación de áreas quemadas en incendios forestales utilizando redes neurales artificiales

Introduction: This article is the product of the research “Developing an Artificial Neural Network Based Model for Estimating Burned Areas in Forest Fires”, developed at Karadeniz Technical University in the year 2020. Problem: Forest Fires are an issue that greatly affect human life and the ecological order, leaving long-term issues. It should be estimated because it is not known when, where and how much the fire will be in the area. Objective: The objective of the research is to use artificial neural networks to estimate the burned areas in forest fires. Methodology: A feed-forward backpropagation neural network model was used for estimating the burned areas. Results: We performed a performance evaluation over the proposed model by considering Regression values, Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The results show that the model is efficient in terms of its estimation of burnt areas. Conclusions: The proposed artificial neural network model has low error rate and high estimation accuracy. It is more effective than traditional methods for estimating burned areas in forests. Originality: To the best of our knowledge, this is the first time that this real, unique data has been used for building and testing the model’s estimations and the improvements that have been made in producing results faster and more accurately than with traditional methods. Limitations: Since there are regional differences over different forest areas, effective criteria need to be analysed regarding the target regions.  Introducción: Este artículo es el producto de la investigación "Desarrollo de un modelo basado en redes neuronales artificiales para estimar áreas quemadas en incendios forestales", desarrollado en la Universidad Técnica de Karadeniz en el año 2020. Problema: los incendios forestales son un problema que afecta en gran medida la vida humana y el orden ecológico, dejando problemas a largo plazo. Debe estimarse porque no se sabe cuándo, dónde y cuánto será el incendio en el área. Objetivo: El objetivo de la investigación es utilizar redes neuronales artificiales para estimar las áreas quemadas en incendios forestales. Metodología: Se usó un modelo de red neuronal de propagación hacia atrás para estimar las áreas quemadas. Resultados: Realizamos una evaluación de desempeño sobre el modelo propuesto considerando los valores de regresión, el error de porcentaje absoluto medio (MAPE) y el error de cuadrado medio (MSE). Los resultados muestran que el modelo es eficiente en términos de su estimación de áreas quemadas. Conclusiones: El modelo de red neuronal artificial propuesto tiene una baja tasa de error y una alta precisión de estimación. Es más efectivo que los métodos tradicionales para estimar áreas quemadas en los bosques. Originalidad: según nuestro conocimiento, esta es la primera vez que esta información real y única se ha utilizado para construir y probar las estimaciones del modelo y las mejoras que se han realizado para producir resultados más rápido y con mayor precisión que con los métodos tradicionales. Limitaciones: Dado que existen diferencias regionales sobre las diferentes áreas forestales, es necesario analizar criterios efectivos con respecto a las regiones objetivo

Predicción del cáncer de mama utilizando algoritmos de aprendizaje automático en diferentes conjuntos de datos

Breast cancer is a disease that is becoming more and more common day by day, causing emotional and behavioral reactions and having fatal consequences if not detected early. At this point, traditional methods are insufficient, especially in early diagnosis. In this context, this study aimed to predict breast cancer by using machine learning (ML) algorithms on different datasets and to demonstrate the applicability of these algorithms. Algorithm performances were compared on balanced and unbalanced datasets, taking into account the performance metrics obtained in applications on different datasets. In addition, a model based on the Borda Voting method was developed by including the results obtained from four different algorithms (NB, KNN, DT, and RF) in the process. The prediction values obtained from each algorithm were written in different columns on the same excel file and the most repetitive value was accepted as the final result value. The developed model was tested on real data consisting of 60 records and the results were analyzed. When the results were examined, it was seen that higher performance was obtained with the proposed RF model compared to similar studies in the literature. Finally, the prediction results obtained with the developed model revealed the applicability of ML algorithms in the diagnosis of breast cancer.Introducción: El trabajo de investigación “Predicción del cáncer de mama utilizando algoritmos de aprendizaje automático en diferentes conjuntos de datos”, se desarrolló en la Universidad Técnica de Karadeniz en el año 2022. Problema: El cáncer de mama es una enfermedad cada vez más común, día a día, provocando reacciones emocionales y conductuales y con consecuencias fatales si no se detecta a tiempo. En este punto, los métodos tradicionales son insuficientes, sobre todo en el diagnóstico precoz. Este estudio tiene como objetivo predecir el cáncer de mama mediante el uso de algoritmos de aprendizaje automático (ML) en diferentes conjuntos de datos y demuestra la aplicabilidad de estos algoritmos. Metodología: se compararon los rendimientos de los algoritmos en conjuntos de datos equilibrados y no equilibrados, teniendo en cuenta las métricas de rendimiento obtenidas en aplicaciones en diferentes conjuntos de datos. Además, se desarrolló un modelo basado en el método Borda Voting al incluir en el proceso los resultados obtenidos de cuatro algoritmos diferentes (NB, KNN, DT y RF). Originalidad y Limitaciones de la Investigación: En el modelo desarrollado en el marco del estudio se combinaron los valores de los resultados obtenidos de diferentes algoritmos como NB, KNN, DT y RF; el objetivo es aumentar el rendimiento del modelo con este proceso, que se basa en el método Borda Voting. Resultados: Los valores de predicción obtenidos de cada algoritmo se escribieron en diferentes columnas en la misma hoja de cálculo y se aceptó el valor más repetitivo como valor final del resultado. El modelo desarrollado se probó en datos reales que constaban de 60 registros y se analizaron los resultados. Conclusión: Cuando se examinaron los resultados, se observó que se obtuvo un mayor rendimiento con el modelo de RF propuesto en comparación con estudios similares en la literatura.