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.

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.

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

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

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.

386 Published By: Blue Eyes Intelligence Engineering and Sciences Publication © Copyright: All rights reserved. Retrieval Number: 100.1/ijeat.E28430610521 DOI:10.35940/ijeat.E2843.0610521 Journal Website: www.ijeat.org Abstract: Humans are generally exposed to a variety of pollutions present in the air they breathe, the food they eat or in the water they drink. Some of the most dangerous pollutions are metals and heavy metals. These are naturally occurring substances which are harmless when present in the environment at low levels. However, due to many pollutants such as industry processes or war activities, the heavy metal concentration can exceed the limit of tolerance and become very toxic for the natural environment and living organisms in it, including humans. Unlike organic pollutants, the heavy metals (as ions and as particulate matter) once introduced into the environment cannot be biodegraded and remain there indefinitely. By rainfall these pollutants can be partially transferred from air or soil into the rivers and drinking water sources, where they accumulate in even higher toxic levels. The high concentrations of heavy metals in contaminated natural water reservoirs have an impact on the microbial community composition which resides there. This type of water pollution can cause the changes in life cycles of natural bacterial populations, influencing their metabolic processes and proliferation. The presence of pathogens in water is normally indirectly determined by the testing for “indicator organism” such as coliform bacteria. Coliforms are usually present in larger numbers in contaminated water and at the same time they are indicators of whether other pathogenic bacteria are present, too. In crisis situations, like war or some natural disasters, where trusted sources of drinking water are not available anymore, the military and residents of affected areas are forced to use some alternative water resources that cannot be tested for their microbial or metal contamination properly. Therefore, the existence of some fast test that would detect not only dangerous bacterial pathogens in water, but also the presence of metals and heavy metals as well, would be of great help and importance for the human health. Even though the number of pathogens can be drastically reduced by the boiling of water, the heavy metals are not destroyed by high temperature. Hence the main objective of our work was to optimize the biosensor chip for microbial detection in contaminated water that would serve at the same time as an indicator for the chemical composition of the water,

Allelopathy can have an important applicaiton in areas of agriculture, especially in integrated protection from weeds, by using of allelopathic crops in different ways. In this research allelopathic effects of invasive species acacia (Robinia pseudoacacia L.)  and white goosefood (Chenopodium album L.) are explored on germination of   tomato (Solanum lycopersicum L.). Water extracts of dry leaves of white goosefoot and acacia are prepared according tothe  method : Norsworthy (2003). Experiment has been made in controlled laboratory conditions. Results of this research show that acacia and white goosefood have negative allelopathy potential and they act inhibitory on germination of tomatoes. Research of allelopathy and allelopathic relationships of weed species and agricultural cultures represents a big challenge for those people who are working in food production, and at the same time can be an instrument of ecologically sustainable agriculture.

The subject of this article will be the analysis of the application of two modern linguistic approaches to the ancient text. It is about M. Halliday's systemic functional linguistics (SFL) and critical discourse analysis (CDA) through whose patterns we will analyze Suetonius' account of two Roman emperors, Augustus and Nero. Since the language is a strong link between SFL as a linguistic approach and CDA, a movement that unites several different disciplines, including linguistic ones, focused on social change, this article will try to shed light on the role, connection and effectiveness of SFL and CDA in a biographical presentation of a personalities. Critical discourse analysis defines language as a social practice, an essential component of creating social relations and changing them; therefore, it focuses on the language in use - discourse, and analyzes it within the broader social, political, historical, cultural and any other context in which it is realized.

The paper aims to identify patterns and country-specific determinants of intra-industry trade (IIT) in agri-food products between Bosnia and Herzegovina (BiH) and other CEFTA 2006 parties in the period 2008-2018. The purpose of the paper is to contribute to filling the gap in the empirical literature on IIT of the South East European countries, especially in regard to non-manufacturing sectors. To investigate IIT intensity and structure the analysis employed Grubel- Lloyd indices and GHM methodology based on relative unit values. In order to examine the impact of various determinants on IIT in agri-food products, a random-effects Heckman selection model was estimated, following a sector-level approach in the analysis. The analysis indicates a lower level of IIT than expected and a strong dominance of its vertical type in all BiH bilateral relations within CEFTA 2006. The empirical results also suggest that the major determinants positively affecting IIT in agri-food products include the size of the trading economies, the similarity in their ethnic structure, membership in the common regional trade agreement, and common borders. By contrast, the results indicate that IIT is negatively affected by differences between the trading economies in terms of productivity and gross domestic product per capita.