scholarly journals A New Strategy for Canine Visceral Leishmaniasis Diagnosis Based on Molecular Spectroscopy and Machine Learning

2021 ◽  
Author(s):  
Gustavo Larios ◽  
Matheus Ribeiro ◽  
Carla Arruda ◽  
Samuel L. Oliveira ◽  
Thalita Canassa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Visceral Leishmaniasis ◽  
Early Diagnosis ◽  
Clinical Symptoms ◽  
Molecular Spectroscopy ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Disease Stage ◽  
Canine Visceral Leishmaniasis ◽  
Serological Tests

Abstract Visceral leishmaniasis is a neglected disease caused by protozoan parasites of the genus Leishmania. The successful control of the disease depends on its accurate and early diagnosis, which is usually made by combining clinical symptoms with laboratory tests such as serological, parasitological, and molecular tests. However, early diagnosis based on serological tests may exhibit low accuracy due to lack of specificity caused by cross-reactivities with other pathogens, and sensitivity issues related, among other reasons, to disease stage, leading to misdiagnosis. In this work was investigated the use of mid-infrared spectroscopy and multivariate analysis to perform a fast, accurate and easy canine visceral leishmaniasis diagnosis. Canine blood sera of non-infected, Leishmania infantum, and Trypanosoma evansi infected groups were studied. The data demonstrate that principal component analysis with machine learning algorithms achieved an overall accuracy above 85% in the diagnosis.

scholarly journals Occurrence of Lutzomyia longipalpis (Phlebotominae) and canine visceral leishmaniasis in a rural area of Ilha Solteira, SP, Brazil

2014 ◽  
Vol 23 (4) ◽  
pp. 456-462 ◽  
Author(s):  
Julio Cesar Pereira Spada ◽  
Diogo Tiago da Silva ◽  
Kennya Rozy Real Martins ◽  
Lílian Aparecida Colebrusco Rodas ◽  
Maria Luana Alves ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Rural Area ◽  
Antibody Test ◽  
Lutzomyia Longipalpis ◽  
Climatic Data ◽  
Canine Visceral Leishmaniasis ◽  
Serological Tests ◽  
Relative Air Humidity ◽  
Control And Prevention ◽  
One Year

This study aimed to investigate the occurrence of Lutzomyia longipalpis and also the canine visceral leishmaniasis (CVL) in a rural area of Ilha Solteira, state of São Paulo. Blood samples were collected from 32 dogs from different rural properties (small farms) and were analyzed by ELISA and the indirect immunofluorescence antibody test (IFAT) in order to diagnose CVL. From these serological tests, 31.25% of the dogs were positive for CVL and these were distributed in 66.7% (8/12) of the rural properties, which were positive for L. longipalpis. CDC (Center for Disease Control and Prevention) light traps were installed in 12 properties (one per property) and insects were caught on three consecutive days per month for one year. L. longipalpis was present on 100% of the rural properties visited, at least once during the twelve-month interval, totaling 64 males and 25 females. The insects were more numerous after the peak of the rain, but the association between prevalence of peridomestic vectors and the climatic data (precipitation, relative air humidity and temperature) and the occurrences of CVL among dogs on each rural property were not statistical significant (p <0.05). However, the occurrence of CVL cases in dogs and the presence of L. longipalpis indicate that more attention is necessairy for the control of this disease in the rural area studied.

scholarly journals THE SERO-CONVERSION AND EVALUATION OF RENAL ALTERATIONS IN DOGS INFECTED BY Leishmania (Infantum) chagasi

2013 ◽  
Vol 55 (2) ◽  
pp. 105-112 ◽  
Author(s):  
Georgia Brenda Barros Alves ◽  
Lucilene dos Santos Silva ◽  
Joilson Ferreira Batista ◽  
Ângela Piauilino Campos ◽  
Maria das Graças Prianti ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Post Infection ◽  
Serum Proteins ◽  
Clinical Symptoms ◽  
Periodic Acid ◽  
Biochemical Tests ◽  
Serological Tests ◽  
Periodic Acid Schiff ◽  
Infection Period ◽  
Conversion Period

This study investigated the sero-conversion period in which dogs from endemic areas test positive for visceral leishmaniasis (VL) as well as the early post-infection period in which renal alterations are observed. Dogs that were initially negative for Canine Visceral Leishmaniasis (CVL) were clinically evaluated every three months by serological, parasitological and biochemical tests until sero-conversion was confirmed, and six months later a subsequent evaluation was performed. Samples of kidney tissues were processed and stained with Hematoxylin and Eosin (H&E), Periodic Acid Schiff (PAS) and Masson’s trichrome stain and lesions were classified based on the WHO criteria. Of the 40 dogs that initially tested negative for VL, 25 (62.5%) exhibited positive serological tests during the study period. Of these 25 dogs, 15 (60%) tested positive within three months, five (20%) tested positive within six months and five (20%) tested positive within nine months. The dogs exhibited antibody titers between 1:40 and 1:80 and 72% of the dogs exhibited clinical symptoms. The Leishmania antigen was present in the kidneys of recently infected dogs. We found higher levels of total protein and globulin as well as lower levels of albumin in the infected dogs when compared to the control dogs. Additionally, infected dogs presented levels of urea and creatinine that were higher than those of the uninfected dogs. Glomerulonephritis was detected in some of the dogs examined in this study. These data suggest that in Teresina, the sero-conversion for VL occurs quickly and showed that the infected dogs presented abnormal serum proteins, as well as structural and functional alterations in the kidneys during the early post-infection period.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

2020 ◽  
pp. 150-175
Author(s):  
Amudha P. ◽  
Sivakumari S.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

scholarly journals Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

2020 ◽  
Vol 9 (9) ◽  
pp. 507
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Stuart Phinn
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forest Height ◽  
Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

scholarly journals Canine visceral leishmaniasis in Londrina, Paraná - investigation and case report

2018 ◽  
Vol 39 (3) ◽  
pp. 1371
Author(s):  
Eloiza Teles Caldart ◽  
Cínthia Peres Camilo ◽  
Jéssica Regina Moreira ◽  
Andressa Maria Rorato Nascimento de Matos ◽  
Fernanda Pinto Ferreira ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Visceral Leishmaniasis ◽  
Present Report ◽  
Northern Region ◽  
Lutzomyia Longipalpis ◽  
Canine Visceral Leishmaniasis ◽  
Serological Tests ◽  
Mild Anemia ◽  
Severe Clinical Picture ◽  
Search For Information

Dogs are considered the main reservoirs of visceral leishmaniasis for humans, which also present a chronic and severe clinical picture when affected. The objective of the present report was to describe a canine visceral leishmaniasis case diagnosed in Londrina, an indene city, and its investigation. A street animal with extensive dermatological lesions, onychogryphosis, mild anemia and leukopenia was attended at a veterinary hospital in Londrina, where positivity was reported for Leishmania spp. in serological tests. Cytology was positive in bone marrow, PCR and parasite culture were positive in skin, spleen, liver, lymph node and bone marrow, and DNA sequencing confirmed the species of the parasite as L. (L.) infantum. The official diagnosis was made by the Central Laboratory of Paraná (LACEN), and through an official report, an investigation of the case was started for the confirmation of autochthony. An active search for the vector and other canine cases in the neighborhood was carried out along with a search for information on the origin of the animal in question. However, the species, Lutzomyia longipalpis, new canine cases, or origin of the sick animal were not identified. Although, the present case cannot be confirmed as autochthonous, we suggest that it is necessary to disseminate the present report to serve as a warning to veterinarians and other public health professionals in the northern region of Paraná to be attentive to suspicious cases and to not fail to investigate these cases to the end.

scholarly journals IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2556
Author(s):  
Liyang Wang ◽  
Yao Mu ◽  
Jing Zhao ◽  
Xiaoya Wang ◽  
Huilian Che
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Test Set ◽  
Non Invasive ◽  
Deep Learning Model

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

scholarly journals Leishmania (infantum) chagasi in canine urinary sediment

2015 ◽  
Vol 24 (1) ◽  
pp. 92-94 ◽  
Author(s):  
Ivete Lopes de Mendonça ◽  
Joilson Ferreira Batista ◽  
Leucio Camara Alves
Keyword(s):  
Bone Marrow ◽  
Clinical Symptoms ◽  
High Sensitivity ◽  
Enzyme Linked Immunosorbent Assay ◽  
Canine Visceral Leishmaniasis ◽  
Serological Tests ◽  
Urine Sediment ◽  
Urinary Sediment ◽  
Control Center ◽  
Veterinary Hospital

Canine visceral leishmaniasis (CVL) is difficult to diagnosis, mainly due to the presence of asymptomatic animals, the diversity of clinical symptoms and the difficulty in obtaining diagnostic evidence of high sensitivity and specificity. The purpose of this study was to diagnose CVL in urinary sediment of 70 dogs of different breeds, sexes and ages from the veterinary hospital of the Federal University of Piauí and Zoonosis Control Center of Teresina, Brazil. The serological tests were TR DPP® for CVL and enzyme-linked immunosorbent assay (ELISA) for CVL, parasitological exams of bone marrow and lymph nodes and urine sediment cultures. Leishmania was detected in the bone marrow and/or lymph node of 61.0% of the animals (43/70), and urine sediment culture was positive in 9.30% (4/43) of these animals. In the serological exams, 70.0% (49/70) were reactive using the DPP and 78.2% (55/70) were reactive using ELISA. The goal of this study was to diagnose the presence of L. (infantum) chagasi in a culture of urinary sediment.

scholarly journals Predicting Forest Fires using Supervised and Ensemble Machine Learning Algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 3697-3705 ◽  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Forest Fires ◽  
Principal Component ◽  
Climatic Conditions ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Physical Factors

Forest fires have become one of the most frequently occurring disasters in recent years. The effects of forest fires have a lasting impact on the environment as it lead to deforestation and global warming, which is also one of its major cause of occurrence. Forest fires are dealt by collecting the satellite images of forest and if there is any emergency caused by the fires then the authorities are notified to mitigate its effects. By the time the authorities get to know about it, the fires would have already caused a lot of damage. Data mining and machine learning techniques can provide an efficient prevention approach where data associated with forests can be used for predicting the eventuality of forest fires. This paper uses the dataset present in the UCI machine learning repository which consists of physical factors and climatic conditions of the Montesinho park situated in Portugal. Various algorithms like Logistic regression, Support Vector Machine, Random forest, K-Nearest neighbors in addition to Bagging and Boosting predictors are used, both with and without Principal Component Analysis (PCA). Among the models in which PCA was applied, Logistic Regression gave the highest F-1 score of 68.26 and among the models where PCA was absent, Gradient boosting gave the highest score of 68.36.

scholarly journals EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

2020 ◽  
Vol 8 (5) ◽  
pp. 3353-3360
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Standard Deviation ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Android Malware ◽  
Android Malware Detection ◽  
Significant Research

Android is the most popular Operating Systems with over 2.5 billion devices across the globe. The popularity of this OS has unfortunately made the devices and the services they enable, vulnerable to numerous security threats. As a result of this, a significant research is being done in the field of Android Malware Detection employing Machine Learning Algorithms. Our current work emphasizes on the possible use of Machine Learning techniques for the detection of malware on such android devices. The proposed EKMPRFG is applied for the classification of Android Malware after a preprocessing phase involving a hybrid Feature Selection model using proposed Standard Deviation of Standard Deviation of Ranks (SDSDR) and several other builtin Feature Selection algorithms such as Correlation based Feature Selection (CFS), Classifier SubsetEval, Consistency SubsetEval, and Filtered SubsetEval followed by Principal Component Analysis(PCA) for dimensionality reduction. The experimental results obtained on two data sets indicate that EKMPRFG outperforms the existing works in terms of Prediction Accuracy and Weighted F- Measure values.

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