Linear Classification Function Research Articles

An analytical identification model for ancient glass based on support vector machine and cluster analysis

Glass artifacts are easily weathered by environmental influences during burial, and the internal chemical composition is exchanged with the external environment, resulting in changes in its composition ratio. For archaeological interrogation, it is of great significance to study the chemical composition of glass before and after weathering and related issues. In this paper, a support vector machine model is used to reasonably separate the data points of high potassium glass and lead-barium glass, and a linear classification function is derived, i.e., the classification law of the two types of glass is obtained. Then, this paper performs R-type cluster analysis on the chemical composition indexes first, and reduces the dimensionality of several indexes; then performs Q-type cluster analysis on the sample detection points, and performs a one-way ANOVA test on the classification scheme of different numbers of subclasses obtained, and finally divides them into four subclasses. On this basis, the data of the unknown class of glass artifacts are substituted into the trained support vector machine model, and the type to which they belong can be determined by calculating the value of the judgment linear classification function.

Predicting the results of rehabilitation treatment of patients with acute myocardial infarction who underwent coronary artery stenting: focus on the rehabilitation potential

To develop a method for assessing RP in patients in the acute period of myocardial infarction and to evaluate its role in predicting the effectiveness of therapeutic measures in the early recovery period. The study consisted of two parts. In the first part, a method for assessing the RP of patients with AMI was created using mathematical modeling methods. For this purpose, the analysis of discharge epicrisis of 137 patients (training sample) with AMI aged from 34 to 85 years (average age 59.4±2.1 years) was carried out. In the second part of the study, the results of rehabilitation measures were analyzed in these patients who, after the intensive care unit and intensive care unit, were transferred to the cardiology department of Angara Clinical Resort JSC. At the end of the II stage of rehabilitation, a multidisciplinary team evaluated the effectiveness of treatment of patients using integral indicators characterizing the clinical condition of a patient who has undergone acute coronary syndrome and stenting. In the first part of the study, devoted to the development of a mathematical model for assessing the RP of patients with AMI, an algorithm of methodology was created: a formalized patient map was formed, the evidence base was represented by 109 indicators; certain indicators were assigned the values of coefficients of linear classification functions, depending on which the patient fell into one of three groups: group 1 - high RP; group 2 - medium RP; group 3 - low RP. The second part of the study was devoted to determining the value of RP for predicting the effectiveness of therapeutic measures in the early recovery period (stage II of medical rehabilitation). When testing patients at the end of treatment at the resort, it was found that the greatest effect was observed in group 1 patients with high RP. A lesser effect was observed in patients of group 2 and, especially, group 3. The use of a method for assessing RP based on mathematical modeling in AMI patients who have undergone stenting allows predicting the results of medical rehabilitation of patients at stage II in the conditions of the resort.

Mathematical model for forecasting the desadaptation of military personnel of the navy according to the results of psychological testing: retrospective study

INTRODUCTION: Disadaptation to the conditions of military service is manifested in young recruits in the first 1– 2 months of service, in graduates of training military units — within 2–4 weeks after arrival, and in contract military personnel — during the first three months of service. The effectiveness of the prevention of these risks primarily depends on the quality of the study of military personnel, the adoption of the necessary measures for the early identification of persons with unstable mentality, deviations in physical development and mental health.OBJECTIVE: to develop a mathematical model for predicting the maladjustment of the military personnel of the navy.MATERIALS AND METHODS: 307 men, conscripts aged 18–23 years old in the joint training center of the Navy were examined. 14 of them with objective signs of maladjustment. The following methods were used: a questionnaire of volitional self-control, a technique for diagnosing a dominant mental state, a test «Communicative and organizational inclinations», a brief orientation test, a multifactorial personality questionnaire «Adaptiveness», a questionnaire for character traits of an adult, a test for determining a tendency to deviant behavior, a test «Forecast-2».RESULTS: When performing discriminant analysis using the «forward step by step» method, a highly informative model was obtained: Lambda Wilks: 0.82590 approx. F(7.299)=9.0041 p<0.0001. Linear classification function «Norm»=–47.1631+2.6336׫Personal adaptive potential»+1.1769׫Self-control»+2.3582׫Tendency to overcome norms and rules»+0.1035׫Behavioral regulation»+0.6821׫Social desirability»–0.1497׫Emotional stability»+7.8663׫Number of solved intelligence tasks». Linear classification function «Disadaptation»=–44.6142+1.6254׫Personal adaptive potential»+0.5151׫Self-control»+1.9143׫Tendency to overcome norms and rules»+0.5895׫Behavioral regulation+0.1439׫Social Desirability»+0.1439׫Emotional Stability»+8.5341׫Number of Solved Intelligence Tasks». DISCUSSION: Predictors of maladjustment in the navy is a combination of indicators: personal adaptive potential and behavioral regulation of the Adaptability method, the self-control subscale of the volitional self-control questionnaire, the indicator of the propensity to overcome the norms and rules of the test for determining the propensity for deviant behavior, the scale of emotional stability and the scale of orientation toward social the desirability of a methodology for diagnosing a dominant mental state, the number of completed tasks of a short orientation test. >< 0.0001. Linear classification function «Norm»=–47.1631+2.6336׫Personal adaptive potential»+1.1769׫Self-control»+2.3582׫Tendency to overcome norms and rules»+0.1035׫Behavioral regulation»+0.6821׫Social desirability»–0.1497׫Emotional stability»+7.8663׫Number of solved intelligence tasks». Linear classification function «Disadaptation»=–44.6142+1.6254׫Personal adaptive potential»+0.5151׫Self-control»+1.9143׫Tendency to overcome norms and rules»+0.5895׫Behavioral regulation+0.1439׫Social Desirability»+0.1439׫Emotional Stability»+8.5341׫Number of Solved Intelligence Tasks».DISCUSSION: Predictors of maladjustment in the navy is a combination of indicators: personal adaptive potential and behavioral regulation of the Adaptability method, the self-control subscale of the volitional self-control questionnaire, the indicator of the propensity to overcome the norms and rules of the test for determining the propensity for deviant behavior, the scale of emotional stability and the scale of orientation toward social the desirability of a methodology for diagnosing a dominant mental state, the number of completed tasks of a short orientation test.CONCLUSION: The developed mathematical model for predicting maladjustment of military personnel of the navy is expedient to use when conducting medical and psychological support for conscripts.

Hybrid Fine-Tuning Strategy for Few-Shot Classification.

Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks.

Differential Diagnosis of the Ability to Develop the Skill of Relaxation in the Drivers of Locomotive Crews

The effectiveness of biofeedback trainings to develop relaxation skills was investigated using the NeuroDog hardware and software complex. Discriminant analysis (a method of multivariate statistical analysis) was carried out, which made it possible to assess the quality and accuracy of the grouping of drivers and assistant drivers of locomotive crews into groups with varying degrees of successful development of the relaxation skill. Highlighted the most informative signs of dividing drivers into groups. The accuracy of the presented groups was checked using linear qualification functions, the informativeness of the features was assessed by Fisher's F-criterion. The critical level of significance when testing statistical hypotheses is p < 0.05. In the course of discriminant analysis, a number of characteristics necessary for the interpretation of groups and their differences were calculated, such as: linear classification functions, canonical discriminant functions, and factor load of canonical discriminant functions. Informative differential diagnostic signs for diagnostics of locomotive crew drivers for the development of relaxation skills were shown. Formulas of linear qualifying functions are given. The diagnostic accuracy was 84.91 % on average.

Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis.

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine (SVM) algorithm based on travel consumer sentiment analysis and builds an Hadoop Distributed File System (HDFS) system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification.

Revealing of propensity to development of stress-induced somatic diseases in military university cadets based on psychophysiological, psychological and psychosocial characteristics

It was found that the frequency of stress-induced somatic diseases in cadets during 45 training years increased from 0 in the I cours to 10% in the VVI courses. The first place in frequency is occupied by diseases of the digestive system (K00K93 according to the International Statistical Classification of Diseases and Related Health Problem 10th revision), during the training their level increased from 0 to 7.3%. Diseases of the circulatory system (I00I99) increased to 2.7%. Based on the psychophysiological, psychological and psychosocial indicators of the surveyed cadets, a discriminant analysis was performed and a linear classification function model for predicting the propensity to stress-induced somatic diseases was obtained. The model is statistically significant, the percentage of correct classifications is 95.8%, that indicates a good separation ability of this model. The stability of the model indicators during the entire training period is shown, so that makes it possible to use it as a prognostic model and apply to identify propensity of the junior cadet to developing stress-induced somatic diseases. Timely identification of cadets who are prone to developing stress-induced somatic diseases and personalized approach to psychological correction will contribute to the prevention of somatic health disorders, the increasing of efficiency and reliability of activity and the professional longevity of future military specialists. The indicators included in the model shows that the propensity to stress-induced somatic diseases is determined by both innate features (the speed of nervous processes and the ratio of inhibition and stimulation in the central nervous system, typological properties of the personality), and acquired skills of social behavior (coping models and psychosocial characteristics of the personality), as well as stress-induced neurotic manifestations which accumulated in the process of personal development. Psychological correction should be aimed at awareness and self-disclosure of their personal characteristics while developing the desired behavioral and social interaction skills, as well as training in mental self-regulation skills.

Linear Classification Function Emulated by Pectin‐Based Polysaccharide‐Gated Multiterminal Neuron Transistors

AbstractNeuromorphic computing, which merges learning and memory functions, is a new computing paradigm surpassing traditional von Neumann architecture. Apart from the plasticity of artificial synapses, the simulation of neurons’ multi‐input signal integration is also of great significance to realize efficient neuromorphic computing. Since the structure of transistors and neurons is strikingly similar, capacitively coupled multi‐terminal pectin‐gated oxide electric double layer transistors are proposed here as artificial neurons for classification. In this work, the free logic switching of “AND” and “OR” is realized in the device with triple in‐plane gates. More importantly, the linear classification function on a single neuron transistor is demonstrated experimentally for the first time. All the results obtained in this work indicate that the prepared artificial neuron can improve the efficiency of artificial neural networks and thus will play an important role in neuromorphic computing.

Use of morphometric characters of a fish species to predict its location; a statistical approach

Precise taxonomic identification is the preliminary requirement in a study of an organ- ism/specimen. Correct identification however gives only the identity of the specimen. The value of the correctly identified specimen as a study material becomes low when the habitat/location of its collection is unknown. Knowing the exact place of collection, enables to gather information on the distribution of the organism, possible environmental conditions that the organisms encounter and to describe the variations found in morphological and genetic features of the organism. Present study therefore, aimed on to develop a statistical rule to predict place of collection (river which is unknown) of a given Puntius dorsalis (a freshwater fish species) specimen using its morphometric characters. Fifty-two individuals were collected from four major rivers (Mahaweli, Kelani, Kalu, Nil- wala) in Sri Lanka and 23 morphometric characters were measured from each specimen. Those individuals were categorized into 4 groups according to the river from which they were collected. Measured morphometric characters were used as independent variables of the model to predict unknown group membership (river) of a given Puntius dorsalis specimen. In the case of re-substitution, 82.7% of the Puntius dorsalis specimens were successfully classified or predicted with respect to the place of collection (river) using their posterior probabilities. The process had a hit ratio of 69.2% when generalized, as a valid tool to classify fresh Puntius dorsalis specimen of unknown group membership. It was also discovered that linear classification function could be used to predict unknown place of collection of a fish. The paper concludes with some suggestions to move into nonparametric approach like Classification and Regression Trees (CART) and Neural Networks.

Recognition of culling reasons in Polish dairy cows using data mining methods

Cow longevity and reasons for culling are one of the most important research problems in the contemporary cattle breeding. Therefore, the analysis of the relationship between cow performance and involuntary disposal contributes to taking more informed decisions in herd management. The aim of the present study was to compare the efficiency of artificial neural networks (ANN) and boosted classification trees (BT) with that of linear discriminant analysis (LDA) and classification functions (CF) in recognizing culling reasons of dairy cows in Poland, based on the lifetime performance data, routinely monitored in a herd. The analyses carried out in the present study showed that the accurate recognition of different culling reasons based on predictors included in the above-mentioned models is, in general, impossible. Only BT had limited discrimination abilities, but the results obtained using this method were not much improved compared with ANN and LDA with CF. In order to predict precisely various culling reasons, more specific data are required. They could be obtained from the increasingly popular, technologically advanced, systems of real-time monitoring of animal health status (physical activity, rumination rate, etc.), dependent also on environmental conditions (e.g. temperature–humidity index).

Detection of Stock Price Manipulation Using Discriminant Analysis

This paper seeks to define a model based on discriminant analysis to categorize a stock as manipulated or non-manipulated based on certain key variables that capture the characteristics of the stock. The model helps identify stocks witnessing activities that are indicative of potential manipulation irrespective of the type of manipulation – action-based, information-based or trade-based. The proposed model helps investigators to arrive at a shortlist of securities that are potentially manipulated and which could be subject to further detailed investigation to detect the type and nature of the manipulation, if any. In a market like India, where there are about 5000 plus securities listed on its major exchanges, it becomes extremely difficult to monitor all securities for potential market abuse. Authors who have earlier used discriminant analysis have used the Linear Classification Function without validating the assumption that governs the model. In this paper we have tested the assumption on data from the Indian capital market and have found that the data does not comply with the assumptions that govern the use of the linear classification function. This resulted in us using the Quadratic Classification Function, which is the appropriate technique for instances where the data does not meet the sated assumptions, to categorise stocks into two categories, namely manipulated and non-manipulated.

Support image set machine: Jointly learning representation and classifier for image set classification

In this paper, we investigate the problem of classifying an image set of an object, and develop a novel image set representation and classification algorithm. We propose to represent an image set by a joint representation method using both an affine hull of its image samples and a combination of its reference images, and further classify it by a linear classification function from its representation. A unified objective function is formulated to learn both the representation and classifier parameters. Similar to support vector machine, the hinge losses and the squared ℓ2 norm of the image set classifier are minimized simultaneously in the objective. Moreover, the differences between the two different representations are also minimized. The objective function is optimized with respect to representation and classifier parameters alternately in an iterative algorithm. The proposed algorithm is named as support image set machine (SupISMac) because it takes advantage of support vector machine formulation to learn an image set classifier. The experiments on two different image set classification benchmark databases show that SupISMac not only outperforms the state-of-the-art image set classification methods, but also reduces the running time of test procedures significantly.

On Proper Classification and Placement of Students in Nigerian University Systems Using Discriminant Analysis

This paper attempts to provide more realistic and reliable way of placing Nigerian students seeking admission into Nigerian University system using Discriminant Analysis and also providing quantitative analysis of a Discriminant Analysis approach to prediction of student’s admission scores into a university system. The paper utilizes secondary data sourced from the Admission and Public Relation Department of Cross Rivers University of Technology Calabar, the conditions for predictive and classification discriminant analysis were obtained, and the empirical result yield a discriminant linear classification function results on the various faculties of interest on the method of admission system obtainable in Nigeria university system. The study reveals university mandatory examination (UME) and Aptitude Test score of students in their various faculties. The linear function established a hit ratio of 83% of which successfully predicted the student admission scores. The study had apparent error rate of 17% which explains the probabilities of misclassification.

Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of ‘available’ small molecules (with synthetic feasibility) in our ‘in-house’ library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad—antiprotozoan—spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses.

Combined particle swarm optimization and linear discriminant analysis for landslide image classification: application to a case study in Taiwan

In Taiwan many reservoirs are constructed in mountain areas. Unfortunately, several earthquakes shook the soil, and typhoons brought a huge amount of water to the reservoir zone. In the past studies, remote-sensing image data were used to effectively monitor the landslide near reservoirs. In recent years, linear discriminant analysis (LDA) has become a well-known method for image classification. However, there are few studies to optimize the linear classification function. While the ancillary information has been adopted easily by new methodologies, the ancillary information must be examined by a landslide image classification system. To explore the effects of optimization on the LDA equations, three approaches were compared: (a) conventional LDA; (b) combined discrete rough sets and LDA (DRS + LDA), which identify the core factors and the corresponding thresholds of landslide occurrence; and (c) combined particle swam optimization algorithm and LDA (PSO + LDA), which optimizes the parameters of LDA equation to attain the best classification outcomes. The above methods were applied to a reservoir region in Taiwan, and the following classification results were obtained. The application of DRS + LDA in our case study reduced the number of ancillary attributes from 14 to 5, and resulted in an accuracy rate of 0.83. On the other hand, the application of PSO + LDA resulted in the same accuracy rate as that of DRS + LDA, whereas the accuracy rate of conventional LDA was found to be 0.78.

A Combined Anomaly Base Intrusion Detection Using Memetic Algorithm and Bayesian Networks

Anomaly base intrusion detection systems (IDSs) detection rate trend and enjoy relatively numerous false negatives and false positives. In this study, we aim to achieve a linear classification function using Memtic algorithm, to minimize errors of such IDSs and to improve such systems, as well. A combined system is offered in this paper which tries to find the optimum subset for detecting intrusion of any set of four attack classes of Knowledge Discovery in Database 99 (KDD99) by using of both correlation analysis amongst features and information theory. Then proper classification function is measured for each attack class through a Memetic algorithm. Bayesian networks are employed to combine results of any function in order to achieve the final classification. Kdd99 dataset and its refined version, NSL-kdd, were used to estimate the offered system, our findings showed 93.42 detection rate. Likewise, NSL-kdd estimation shows the suggested system for R2L attack class has succeeded to classify 86.60% of records which have not been classified correctly by the previous algorithms.

Image Feature Extraction in the Last Screening Mammograms Prior to Detection of Breast Cancer

Image feature extraction was utilized to retrospectively analyze screening mammograms taken prior to the detection of a malignant mass for early detection of breast cancer. The mammograms of 58 biopsy proven breast cancer patients were collected. In each case, the mammograms taken 10 to 18 months prior to cancer detection were evaluated. For each of the two mammographic projections of the abnormal breast, two regions were marked: 1) region one, which corresponded to the site where the malignant mass subsequently developed and 2) a region which appeared similar to region one on the same mammogram. On each projection of the normal breast a third region which corresponds to region one but on the opposite breast was also marked (mirror-image site). Sixty-two texture and photometric image features were then calculated for all of the marked areas. A stepwise discriminant analysis showed that six of these features could be used to best distinguish between the normal and abnormal regions. The best linear classification function resulted in a 72% average classification. At its current stage, the system can be used by a radiologist to examine any pattern in a mammogram. The regions which are flagged by the system have a 72% chance of developing a malignant mass by the time of the next screening. Therefore, further evaluation of these patients (e.g., a screening examination sooner than the normal one year interval) could result in earlier detection of breast cancer. The ultimate goal is to run the system automatically over the whole mammogram and flag any suspicious area.

Classification of Activated Sludge Settleability Using Linear and Nonlinear Classification Functions

In this paper, two classifiers are proposed to distinguish between bulking and non-bulking situations in an activated sludge wastewater treatment plant, based on available image analysis information. The first classifier consists of a simple linear classification function, while the second classifier uses a highly nonlinear least squares support vector machine (LS-SVM) to distinguish between both situations. It is shown that the nonlinear LS-SVM classification function outperforms the linear classifier. Both exhibit identical misclassification rates, but fewer samples are located in the uncertainty area when using the nonlinear classifier. However, this better classification performance requires the identification of a substantial amount of model parameters, while the linear classifier is, except for the threshold values, parameterless.

A Revised Taxonomy of Patients with Chronic Pain

To examine the taxonomic categories derived from a reorganization of the popular and useful Multidimensional Pain Inventory (MPI)/Multiaxial Assessment of Pain (MAP) system. Cluster-analytic procedures were utilized to analyze data from 976 pain patients who presented for treatment at a pain clinic. Ward's method was utilized followed by seeded and unseeded k-means procedures and a discriminant function analysis. Three clusters were interpreted as: Interpersonally Focused, Stoic, and Adaptively Focused. Discriminant analyses followed by internal and external classification analyses provide evidence for the stability and replicability of the new clusters. Fisher's linear discriminant classification function coefficients were produced for clinical use. Meaningful differences exist between the new MPI clusters and those previously reported. The results provide a contribution toward improvements on the original and popular MPI/MAP system.

New ligand-based approach for the discovery of antitrypanosomal compounds

The antitrypanosomal activity of 10 already synthesized compounds was in silico predicted as well as in vitro and in vivo explored against Trypanosoma cruzi. For the computational study, an approach based on non-stochastic linear fingerprints to the identification of potential antichagasic compounds is introduced. Molecular structures of 66 organic compounds, 28 with antitrypanosomal activity and 38 having other clinical uses, were parameterized by means of the TOMOCOMD-CARDD software. A linear classification function was derived allowing the discrimination between active and inactive compounds with a confidence of 95%. As predicted, seven compounds showed antitrypanosomal activity (%AE > 70) against epimastigotic forms of T. cruzi at a concentration of 100 μg/mL. After an unspecific cytotoxic assay, three compounds were evaluated against amastigote forms of the parasite. An in vivo test was carried out for one of the studied compounds.