Housing fuzzy recommender system: A systematic literature review.

In recent years, significant attention has been paid to fuzzy recommender systems for housing, highlighting their ability to effectively handle the imprecision and uncertainty inherent in the real estate market. With the objective of improving the filtering of recommendations in the real estate sector, the PRISMA 2020 methodology was applied to perform new systematic reviews using its checklist on six academic databases from 1985 to 2024. RawGraph, Orange Data Minig, Jamovi and R software were used for document classification and data visualization. After classification, 1003 articles were obtained, of which 46.36% were in Scopus, and 57.82% were articles. At the end of the type, 50 articles were identified as primary, subjecting them to six research questions. It was found that 65% of the algorithms used fuzzy logic, 60% used spatial data, and 80% evaluated performance. The main difficulties were related to the integration of various sources of information. Although incorporating reclusive methods is anticipated in future systems, the need remains to address challenging areas to improve the overall performance of fuzzy recommender systems. The reviewed articles focus on enhancing fuzzy data-based recommendation systems by proposing flexible and less intrusive techniques. The significance of incorporating contextual information and exploring hybrid approaches is emphasized, along with the evaluation in real world environments, averaging artificial intelligence.

Feature selection reveal peripheral blood parameter's changes between COVID-19 infections patients from Brazil and Ecuador.

The investigation of conventional complete blood-count (CBC) data for classifying the SARS-CoV-2 infection status became a topic of interest, particularly as a complementary laboratory tool in developing and third-world countries that financially struggled to test their population. Although hematological parameters in COVID-19-affected individuals from Asian and USA populations are available, there are no descriptions of comparative analyses of CBC findings between COVID-19 positive and negative cases from Latin American countries. In this sense, machine learning techniques have been employed to examine CBC data and aid in screening patients suspected of SARS-CoV-2 infection. In this work, we used machine learning to compare CBC data between two highly genetically distinguished Latin American countries: Brazil and Ecuador. We notice a clear distribution pattern of positive and negative cases between the two countries. Interestingly, almost all red blood cell count parameters were divergent. For males, neutrophils and lymphocytes are distinct between Brazil and Ecuador, while eosinophils are distinguished for females. Finally, neutrophils, lymphocytes, and monocytes displayed a particular distribution for both genders. Therefore, our findings demonstrate that the same set of CBC features relevant to one population is unlikely to apply to another. This is the first study to compare CBC data from two genetically distinct Latin American countries.

Testicular germ cell tumors type 2 have high RNA expression of LDHB, the gene for lactate dehydrogenase subunit B.

This study analyzed RNA expression of genes for three serum tumor markers, alpha fetoprotein (AFP), human chorionic gonadotropin (hCG), and lactate dehydrogenase (LDH), in patients with testicular germ cell tumors (TGCT) type 2. The gene AFP encodes AFP, the gene for chorionic gonadotropin beta polypeptide 5 (CGB5) encodes a major part of the specific beta subunit of hCG, and the genes for LDH subunit A (LDHA), LDH subunit B (LDHB), and LDH subunit C (LDHC) encode three different subunits of LDH. LDHB encodes the LDHB subunit present as a tetramer in LDH isoenzyme 1 (LDH-1). We examined three datasets with 203 samples of normal testis tissue (NT) and TGCT type 2. Yolk sac tumor (YST) expressed RNA of AFP fourteen thousand times higher than seminoma (SE), embryonal carcinoma (EC), and teratoma (TER) combined (P = 0.00015). In the second microarray, choriocarcinoma (CC) expressed RNA of CGB5 ten times higher than other histologic types of TGCT combined. EC expressed RNA of LDHB twice higher than SE, YST and TER combined (P = 0.000041). EC expressed RNA of LDHB higher than that YST expressed RNA of AFP and that CC expressed RNA of CGB5. In conclusion, TGCT type 2 expressed RNA of LDHB markedly higher than the RNA of 23 other candidate genes for TGCT type 2.

UrbangEnCy: An emergency events dataset based on citizen sensors for monitoring urban scenarios in Ecuador.

Recently, the use of the citizen-sensors (people generating and sharing real data by social media) for detecting and disseminating emergency events in real-time have shown a considerable increase because people at the place of the event, as well as elsewhere, can quickly post relevant information on this type of alerts. Here, we present an emergency events dataset called UrbangEnCy. The dataset contains over 25500 texts in Spanish posted on Twitter from January 19th to August 19th, 2020, with emergencies and non-emergencies related content in Ecuador. We obtained, cleaned and, filtered these tweets and, then we selected the location and temporal data as well as tweet content. Besides, the data set includes annotations regarding the type of tweet (emergency / non-emergency) as well as additional nomenclature used to describe emergencies in the Center for immediate response service to emergencies (ECU 911) of Ecuador and international emergency services agencies (ESAs). UrbangEnCy dataset facilitates evaluating data science performance, machine learning, and natural language processing algorithms used with supervised and unsupervised problems re- related to text mining and pattern recognition. The dataset is freely and publicly available at https://doi.org/10.17632/4x37zz82k8.

Influence of the go-based semantic similarity measures in multi-objective gene clustering algorithm performance.

Using a prior biological knowledge of relationships and genetic functions for gene similarity, from repository such as the Gene Ontology (GO), has shown good results in multi-objective gene clustering algorithms. In this scenario and to obtain useful clustering results, it would be helpful to know which measure of biological similarity between genes should be employed to yield meaningful clusters that have both similar expression patterns (co-expression) and biological homogeneity. In this paper, we studied the influence of the four most used GO-based semantic similarity measures in the performance of a multi-objective gene clustering algorithm. We used four publicly available datasets and carried out comparative studies based on performance metrics for the multi-objective optimization field and clustering performance indexes. In most of the cases, using Jiang-Conrath and Wang similarities stand in terms of multi-objective metrics. In clustering properties, Resnik similarity allows to achieve the best values of compactness and separation and therefore of co-expression of groups of genes. Meanwhile, in biological homogeneity, the Wang similarity reports greater number of significant GO terms. However, statistical, visual, and biological significance tests showed that none of the GO-based semantic similarity measures stand out above the rest in order to significantly improve the performance of the multi-objective gene clustering algorithm.

Meta-Analysis of Gene Expressions in Testicular Germ Cell Tumor Histologies.

There is no consensus as to how a precursor lesion, germ cell neoplasia in situ (GCNIS), develops into the histologic types of testicular germ cell tumor type II (TGCT). The present meta-analysis examined RNA expressions of 24 candidate genes in three datasets. They included 203 samples of normal testis (NT) and histologic types of TGCT. The Fisher's test for combined p values was used for meta-analysis of the RNA expressions in the three datasets. The histologic types differed in RNA expression of PRAME, KIT, SOX17, NANOG, KLF4, POU5F1, RB1, DNMT3B, and LIN28A (p < 0.01). The histologic types had concordant differences in RNA expression of the genes in the three datasets. Eight genes had overlap with a high RNA expression in at least two histologic types. In contrast, only seminoma (SE) had a high RNA expression of KLF4 and only embryonal carcinoma (EC) had a high RNA expression of DNMT3B. In conclusion, the meta-analysis showed that the development of the histologic types of TGCT was driven by changes in RNA expression of candidate genes. According to the RNA expressions of the ten genes, TGCT develops from NT over GCNIS, SE, EC, to the differentiated types of TGCT.

A data set for electric power consumption forecasting based on socio-demographic features: Data from an area of southern Colombia.

In this article, we introduce a data set concerning electric-power consumption-related features registered in seven main municipalities of Nariño, Colombia, from December 2010 to May 2016. The data set consists of 4427 socio-demographic characteristics, and 7 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDENAR) according to the client consumption records. Power consumption data collection was carried following a manual procedure wherein company workers are in charge of manually registering the readings (measured in kWh) reported by the electric energy meters installed at each housing/building. Released data set is aimed at providing researchers a suitable input for designing and assessing the performance of forecasting, modelling, simulation and optimization approaches applied to electric power consumption prediction and characterization problems. The data set, so-named in shorthand PCSTCOL, is freely and publicly available at https://doi.org/10.17632/xbt7scz5ny.3.

RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition.

In this article we introduce a robusta coffee leaf images dataset called RoCoLe. The dataset contains 1560 leaf images with visible red mites and spots (denoting coffee leaf rust presence) for infection cases and images without such structures for healthy cases. In addition, the data set includes annotations regarding objects (leaves), state (healthy and unhealthy) and the severity of disease (leaf area with spots). Images were all obtained in real-world conditions in the same coffee plants field using a smartphone camera. RoCoLe data set facilitates the evaluation of the performance of machine learning algorithms used in image segmentation and classification problems related to plant diseases recognition. The current dataset is freely and publicly available at https://doi.org/10.17632/c5yvn32dzg.2.

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies.

BACKGROUND: Biologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence. METHOD: We propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process. RESULTS: The effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm. CONCLUSIONS: Integrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.