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Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

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One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance the ML method. This work proposes variants of the Voting and Stacking (VC and SC) ensemble strategies based on diverse auto-tuning supervised machine learning techniques to increase the efficacy of traditional baseline classifiers for the automatic diagnosis of vertebral column orthopedic illnesses. The ensemble strategies are created by first combining a complete set of auto-tuned baseline classifiers based on different processes, such as geometric, probabilistic, logic, and optimization. Next, the three most promising classifiers are selected among k-Nearest Neighbors (kNN), Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Tree (DT). The grid-search K-Fold cross-validation strategy is applied to auto-tune the baseline classifier hyperparameters. The performances of the proposed ensemble strategies are independently compared with the auto-tuned baseline classifiers. A concise analysis evaluates accuracy, precision, recall, F1-score, and ROC-ACU metrics. The analysis also examines the misclassified disease elements to find the most and least reliable classifiers for this specific medical problem. The results show that the VC ensemble strategy provides an improvement comparable to that of the best baseline classifier (the kNN). Meanwhile, when all baseline classifiers are included in the SC ensemble, this strategy surpasses 95% in all the evaluated metrics, standing out as the most suitable option for classifying vertebral column diseases.

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This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone's GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model's ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model's capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin-destination (OD) matrix to better understand how people move within the city.

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Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual's level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting position when improper movement occurs. Therefore, the computerized analysis of movement patterns might assist people in detecting changes in limb position during exercises with different loads or enhance the analysis of an observer with expertise in weightlifting exercises. In this study, hidden Markov models (HMMs) were employed to automate the detection of joint position and barbell trajectory during back squat exercises. Ten volunteers performed three lift movements each with a 0, 50, and 75% load based on body weight. A smartphone was used to record the movements in the sagittal plane, providing information for the analysis of variance and identifying significant position changes by video analysis (p < 0.05). Data from individuals performing the same movements with no added weight load were used to train the HMMs to identify changes in the pattern. A comparison of HMMs and human experts revealed between 40% and 90% agreement, indicating the reliability of HMMs for identifying changes in the control of movements with added weight load. In addition, the results highlighted that HMMs can detect changes imperceptible to the human visual analysis.

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Mayaro virus (MAYV), the etiological agent of Mayaro fever (MAYF), is an emergent arbovirus pathogen belonging to Togaviridae family. MAYF is characterized by high inflammatory component that can cause long-lasting arthralgia that persists for months. Macrophages are viral targets and reservoirs, key components of innate immunity and host response. Given the importance of this pathogen, our aim was to determine the inflammatory and antiviral response of human monocyte-derived macrophages (MDMs) infected with MAYV. First, we established the replication kinetics of the virus. Thereafter, we determined the expression of pattern recognition receptors, NF-ĸB complex, interferons (IFNs), two interleukin 27 (IL27) subunits, IFN-stimulated genes (ISGs), and the production of cytokines/chemokines. We found that human MDMs are susceptible to MAYV infection in vitro, with a peak of viral particles released between 24- and 48-hours post-infection (h.p.i) at MOI 0.5, and between 12 and 24 h.p.i at MOI 1. Interestingly, we observed a significant decline in the production of infectious viral particles at 72 h.p.i that was associated with the induction of antiviral response and high cytotoxic effect of MAYV infection in MDMs. We observed modulation of several genes after MAYV infection, as well, we noted the activation of antiviral detection and response pathways (Toll-like receptors, RIG-I/MDA5, and PKR) at 48 h.p.i but not at 6 h.p.i. Furthermore, MAYV-infected macrophages express high levels of the three types of IFNs and the two IL27 subunits at 48 h.p.i. Moreover, we found higher production of IL6, IL1ß, CXCL8/IL8, CCL2, and CCL5 at 48 h.p.i as compared to 6 h.p.i. A robust antiviral response (ISG15, APOBEC3A, IFITM1, and MX2) was observed at 48 but not at 6 h.p.i. The innate and antiviral responses of MAYV-infected MDMs differ at 6 and 48 h.p.i. We conclude that MAYV infection induces robust pro-inflammatory and antiviral responses in human primary macrophages.

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Tuberculosis (TB) is an infectious disease displaying a multifactorial pathology. The immunomodulatory role attributed to steroid hormones, such as vitamin D3 (VD3) and 17ß-estradiol (E2), highlighted the importance of these hormones against Mycobacterium tuberculosis (Mtb) infection. In order to understand their influence upon gene expression of immune and inflammatory responsive genes against Mtb we tested it in vitro using peripheral blood mononuclear cells (PBMCs). Cells were pretreated with VD3 (50 ng/mL) or E2 (100 nM/mL) and co-cultured with H37Rv Mtb or stimulated with lipopolysaccharide from Escherichia coli (LPS). After 24 h and 72 h of co-culture the Mtb viability in macrophages test was performed, as well the total RNA isolation for gene expression analysis by RT-qPCR of the following target genes: NLRP3, DC-SIGN, IL-1ß, and IL-10. We also measured IL-10, TNF, IFN-γ, IL-4, IL-6, and IL-2 supernatant levels. As the main results, we found that VD3 and E2 downregulated the expression of inflammatory genes NLRP3, IL-1ß, and IL-10 expression in Mtb co-cultured cells. Finally, VD3 treatment increased the release of the cytokine IFN-γ in Mtb-infected cells, while E2 treatment inhibited the release of IL-10, TNF, IFN-γ, and IL-6. Therefore, we report an immunogenetic influence of VD3 and E2 upon Mtb co-culture.

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Reducing inflammation by diet is a major goal for prevention or lowering symptoms of a variety of diseases, such as auto-immune reactions and cancers. Natural polysaccharides are increasingly gaining attention due to their potential immunomodulating capacity. Structures of those molecules are highly important for their effects on the innate immune system, cytokine production and secretion, and enzymes in immune cells. Such polysaccharides include ß-glucans, pectins, fucoidans, and fructans. To better understand the potential of these immunomodulatory molecules, it is crucial to enhance dedicated research in the area. A bibliometric analysis was performed to set a starting observation point. Major pillars of inflammation, such as pattern recognition receptors (PRRs), enzymatic production of inflammatory molecules, and involvement in specific pathways such as Nuclear-factor kappa-B (NF-kB), involved in cell transcription, survival, and cytokine production, and mitogen-activated protein kinase (MAPK), a regulator of genetic expression, mitosis, and cell differentiation. Therefore, the outcomes from polysaccharide applications in those scenarios are discussed.

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Morris water maze (MWM) test is widely used to evaluate the learning and memory deficits in rodents. Image processing and pattern recognition can be used to analyse videos and recognize automatically the tracking in MWM. There are several commercial and free access software that allows analyzing the behavioral tasks although they also have limitations such as automation, cost, user intervention among other things. The aim of this paper was to develop a new image processing technique to automatically analyse the track of the rat in the MWM, which we called RatsTrack. The MWM test was performed with an animal model for Alzheimer, and the videos were recorded to measure the distance, time, and speed. The segmentation method based on the projection of the video frames was made for pool identification, eliminating the rat, while conserving the shape of the pool. Then, the Hough transformation was used to recognize the position and radius of the pool. Finally, the frame in which the rat is released into the pool was established automatically using mathematical morphology techniques and added as a plugin on free access ImageJ software. The new image processing technique, RatsTrack, successfully detected and located the pool and rat without user intervention, significantly decreasing operational time and providing results for distance, time, speed, and acceleration parameters of the MWM test. Alzheimer's rats compared with the control group presented significant data measured with the RatsTrack. RatsTrack is a plugin of ImageJ software and will be made freely available for public use.

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Hypericum perforatum L. (St. John's wort) is one of the world's most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John's wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS-MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid-liquid extraction. For the first time, PS-MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs' quality control in Brazil.

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Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS gestures, a database with 14 selected gestures for SIS was recorded from 10 volunteers, with 30 repetitions per user. Segmentation, feature extraction, feature selection, and classification were performed, and several parameters were evaluated. These steps were performed by taking into account a wearable application, for which the complexity of pattern recognition algorithms is crucial. The system was tested offline and verified as to its contribution for all databases and each volunteer individually. An automatic segmentation algorithm was applied to identify the muscle activation; thus, 13 feature sets and 6 classifiers were tested. Moreover, 2 ensemble techniques aided in separating the sEMG signals into the 14 SIS gestures. Accuracy of 76% was obtained for the Support Vector Machine classifier for all databases and 88% for analyzing the volunteers individually. The system was demonstrated to be suitable for SIS gesture recognition using sEMG signals for wearable applications.

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Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one's own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.

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The triggering receptor expressed on myeloid cells-1 (TREM-1) is a pattern recognition receptor heavily investigated in infectious and non-infectious diseases. Because of its role in amplifying inflammation, TREM-1 has been explored as a diagnostic/prognostic biomarker. Further, as the receptor has been implicated in the pathophysiology of several diseases, therapies aiming at modulating its activity represent a promising strategy to constrain uncontrolled inflammatory or infectious diseases. Despite this, several aspects concerning its interaction with ligands and activation process, remain unclear. Although many molecules have been suggested as TREM-1 ligands, only five have been confirmed to interact with the receptor: actin, eCIRP, HMGB1, Hsp70 and PGLYRP1. However, the domains involved in the interaction between the receptor and these proteins are not clarified yet. Therefore, here we used in silico approaches to investigate the putative binding domains in the receptor, using hot spots analysis, molecular docking and molecular dynamics simulations between TREM-1 and its five known ligands. Our results indicated the complementarity-determining regions (CDRs) of the receptor as the main mediators of antigen recognition, especially the CDR3 loop. We believe that our study could be used as structural basis for the elucidation of TREM-1's recognition process, and may be useful for prospective in silico and biological investigations exploring the receptor in different contexts.

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Cationic and amphiphilic peptides can be used as homing devices to accumulate conjugated antibiotics to bacteria-enriched sites and promote efficient microbial killing. However, just as important as tackling bacterial infections, is the modulation of the immune response in this complex microenvironment. In the present report, we designed a peptide chimaera called Chim2, formed by a membrane-active module, an enzyme hydrolysis site and a formyl peptide receptor 2 (FPR2) agonist. This molecule was designed to adsorb onto bacterial membranes, promote their lysis, and upon hydrolysis by local enzymes, release the FPR2 agonist sequence for activation and recruitment of immune cells. We synthesized the isolated peptide modules of Chim2 and characterized their biological activities independently and as a single polypeptide chain. We conducted antimicrobial assays, along with other tests aiming at the analyses of the cellular and immunological responses. In addition, assays using vesicles as models of eukaryotic and prokaryotic membranes were conducted and solution structures of Chim2 were generated by 1H NMR. Chim2 is antimicrobial, adsorbs preferentially to negatively charged vesicles while adopting an α-helix structure and exposes its disorganized tail to the solvent, which facilitates hydrolysis by tryptase-like enzymes, allowing the release of the FPR2 agonist fragment. This fragment was shown to induce accumulation of the cellular activation marker, lipid bodies, in mouse macrophages and the release of immunomodulatory interleukins. In conclusion, these data demonstrate that peptides with antimicrobial and immunomodulatory activities can be considered for further development as drugs.

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Toll-like receptors (TLRs) are a well-characterized family of cell-bound pattern recognition receptors able to identify and respond to conserved structures of external microorganisms or Pathogen Molecular-Associated Pattern (PAMPs). They can also interact with Damage-Associated Molecular Patterns (DAMPs) involved with any infectious and sterile cell stress of tissue injury. Accumulated knowledge about TLRs has revealed that these receptors and intracellular signaling pathways triggered through TLR activation contribute to the physiopathology of different inflammatory diseases, including arthritic conditions. Mostly, the literature focuses on exploring TLRs in rheumatoid and osteoarthritis. However, TLRs also seem to be an essential mediator for monosodium urate (MSU) crystals-induced gouty arthritis, both in animal models and humans. Accordingly, naked MSU crystals have a highly negatively charged surface recognized by TLRs; intracellular adapter protein MyD88 are significant mediators of MSU crystals-induced IL1ß production in mice, and gouty patients demonstrate a robust positive correlation between TLR4 mRNA level and serum IL1ß. Here, we revised the literature evidence regarding the involvement of TLRs in gout arthritis pathogenesis, with particular reference to TLR2 and TLR4, by analyzing the actual literature data.

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Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the "AM-ULA" test, scores of 222 and 144 demonstrated the prosthesis's potential to improve the user's ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability.

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Electromyography (EMG) processing is a fundamental part of medical research. It offers the possibility of developing new devices and techniques for the diagnosis, treatment, care, and rehabilitation of patients, in most cases non-invasively. However, EMG signals are random, non-stationary, and non-linear, making their classification difficult. Due to this, it is of vital importance to define which factors are helpful for the classification process. In order to improve this process, it is possible to apply algorithms capable of identifying which features are most important in the categorization process. Algorithms based on metaheuristic methods have demonstrated an ability to search for suitable subsets of features for optimization problems. Therefore, this work proposes a methodology based on genetic algorithms for feature selection to find the parameter space that offers the slightest classification error in 250 ms signal segments. For classification, a support vector machine is used. For this work, two databases were used, the first corresponding to the right upper extremity and the second formed by movements of the right lower extremity. For both databases, a feature space reduction of over 65% was obtained, with a higher average classification efficiency of 91% for the best subset of parameters. In addition, particle swarm optimization (PSO) was applied based on right upper extremity data, obtaining an 88% average error and a 46% reduction for the best subset of parameters. Finally, a sensitivity analysis was applied to the characteristics selected by PSO and genetic algorithms for the database of the right upper extremity, obtaining that the parameters determined by the genetic algorithms show greater sensitivity for the classification process.

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Antimicrobial peptides are part of the organism's defense system. They are multifunctional molecules capable of modulating the host's immune system and recognizing molecules present in pathogens such as lipopolysaccharides (LPSs). LPSs are recognized by molecular patterns associated with pathogens known as Toll-like receptors (TLRs) that protect the organism from pathological microorganisms. TLR4 is responsible for LPS recognition, thus inducing an innate immune response. TLR4 hyperstimulation induces the uncontrolled inflammatory process that is observed in many illnesses, including neurodegenerative, autoimmune and psoriasis). Molecules that act on TLR4 can antagonize the exacerbated inflammatory process. In this context, antimicrobial peptides (AMPs) are promising molecules capable of mediating toll-like receptor signaling. Therefore, here we address the AMPs studied so far with the aim of inhibiting the intense inflammatory process. In addition, we aim to explore some of the interactions between exogenous AMPs and TLR4.

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Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

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PURPOSE: Among diverse Pattern Recognition Receptors (PRRs), Toll-like receptor-4 (TLR-4) is a key urothelial trigger for innate immune response impacting urothelial bladder carcinoma (BC). Androgen activation promotes immunotolerance, playing an immunoregulatory role by unknown mechanisms. We explored the castration impact on urothelial TLR-4 modulation in carcinogenesis and immunotherapeutic scenario. METHODS: Intact (SHAM) versus castrated male Fisher-344 rats were evaluated in 2 scenarios: (A) Carcinogenesis: After randomization to SHAM (n = 5) and Castration (n = 5), carcinogenesis was induced by four intravesical doses of 1.5 mg/kg n-methyl-n-nitrosourea (MNU) every 15 days. (B) Treatment: After ultrasonographic confirmed MNU-induced papillary BC on week 8, rats were randomized to SHAM (n = 5) and Castration (n = 5) and offered 6 weekly intravesical treatment of 106 CFU of bacillus Calmette Guerin (BCG) in 0.2 ml saline. After 15 weeks the urinary bladders underwent histopathology. Urothelial cell proliferation was measured by Ki-67 immunohistochemistry (IHC), and TLR-4 expression was quantified by IHC and WB. RESULTS: Castration induced higher TLR-4 urothelial expression (p = 0.007) and anticarcinogenic effect with fewer urothelial tumors (60 vs. 80%) and lower urothelial cell proliferation compared to intact animals (p = 0.008). In the intravesical BCG treatment setting, castration has potentialized the BCG activation of TLR-4 (p = 0.007) with no residual in situ carcinoma compared to intact animals, suggesting the potential to amplify the BCG immune response. CONCLUSION: To our knowledge, this is the first description of TLR-4 urothelial expression hormonal modulation. The described castration-mediated immunomodulation will help to improve the knowledge of urothelial cancer gender diversities and PRRs modulations with treatment implications.

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Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN. Here, the function nearest neighbor and a genetic algorithm were used for the automation of parameters MinPts and Eps. Furthermore, the Factor Analysis (FA) method was defined for pre-processing through a dimensionality reduction of high-dimensional datasets with dimensions greater than two. Finally, the performance of the clustering algorithm called FA+GA-DBSCAN was evaluated using artificial datasets. In addition, the precision and Entropy of the clustering hybrid algorithm were measured, which showed there was less probability of error in clustering the most condensed datasets.