On Board Diagnostics (OBD) Scan Tool to Diagnose Emission Control System

Climate change has become very important issue the world is facing today. To control impact of climate change and improve quality of life, one of the key factor targeted is vehicular emissions. To control emissions very stringent emission norms are introduced by various government agencies across the world. This called for increased use for electronics in the engines and vehicles. This complicates the matter at service and manufacturers. The engine computer (Electronic Control Unit) with international protocol like OBD is used to control electronic parameters in engines. This review paper describes emission compliance requirement with brief introduction of the OBD system along with scan tool to diagnose the system

Hybrid Low Complex near Optimal Detector for Spatial Modulation

In our previous work maximum throughput in multi stream MIMO is analyzed by overcoming the inter antenna interference. To mitigate the Inter antenna interference spatial modulation can be used. Spatial Modulation(SM) aided MIMO systems are the emerging MIMO systems which are low complex and energy efficient. These systems additionally use spatial dimensions for transmitting information. In this paper a low complex detector based on matched filter is proposed for spatial modulation to achieve near maximum likelihood performance while avoiding exhaustive ML search since MF based detector exhibits a considerable reduced complexity since activated transmitting antenna and modulated amplitude phase modulation constellation are estimated separately. Simulation results show the performance of the proposed method with optimal ML detector, MRC and conventional matched filter methods

Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning

Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning

Covid-19 Detection For CT-scan Images Using Transfer Learning Models

COVID-19 is a respiratory illness caused by a virus called SARS-CoV-2 which affected around 455 million people around the world. CT-scan is a medical imaging technique that uses X-rays to create detailed images of the body and which can be used to detect many respiratory diseases. Transfer learning models are a type of machine learning model that are trained on a large dataset of images and which can be used for their already trained ability to extract features from image in other tasks. They can then be used to classify new images with similar features.This paper presents a study of different transfer learning models for the task of classifying chest X-ray images into three classes: COVID-19, pneumonia, and normal. The study was implemented using Python and the dataset used was the COVID-19 Chest X-ray Dataset. The train-test split used was 0.2–0.8. The parameters used to test the models were the precision, recall, accuracy, F1 score, and Matthew’s correlation score. Other than these, different optimizers were also compared such as ADAM, SGD with different learning rates of 0.01, 0.001, and 0.0001.The models used in this study are EfficientNetB0, EfficientNetB7, VGG16, and InceptionV3. Out of these models, the most effective model was the EfficientNetB0 model, which achieved an accuracy of 98.6%. This study provides valuable insights into the use of transfer learning for medical image analysis. The results suggest that transfer learning can be used to develop accurate and efficient models that can be used as a secondary option for the diagnosis of COVID-19 using chest X-ray images

Heart Disease Prediction using Different Machine Learning Algorithms

Identifying a person's potential for developing heart disease is one of the most challenging tasks medical professionals faces today. With nearly one death from heart disease every minute, it is the leading cause of death in the modern era [4]. The database is taken from Kaggle. Various machine learning algorithms are used for prediction of heart disease detection here are Random Forest, XG-Boost, K- Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM). All these algorithms are implemented using Python programming with Google collab.  The performance evaluation parameters used here are Accuracy, precision, recall and Fi-score. Training and testing are implemented for different ratios such as 60:40, 70:30 and 80:20. From the analysis and comparisons of evaluation parameters of all the above algorithms, XG-Boost is having the highest accuracy and recall value. KNN having worst accuracy and recall amongst all. XG-Boost is having a training accuracy of 98.86, 98.74 and 97.68 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. XG-Boost is having a testing accuracy of 95.85, 95.45 and 96.09 for training and testing ratio of 60:40, 70:30 and 80:20 respectively. So, XG-Boost algorithm can be used for obtaining the best prediction for heart disease.  This type of heart disease prediction can be used as a secondary diagnostic tool for doctors, for best and fast prediction. This can help the early prediction of heart disease thus increasing the chances of the saving the life heart patient

NS1 ANTIGEN DETECTION BY ELISA IN EARLY LABORATORY DIAGNOSIS OF DENGUE INFECTION

Introduction: Dengue is a major public health problem in tropical and sub-tropical regions of the world and it is known for serious life threatening complications. Detection of IgM antibodies forms the mainstay for diagnosis of dengue infection. However, IgM antibodies develop after 4-5 days of infection and there is an urgent need for an alternative diagnostic tools that can detect dengue infection earlier. Aim and Objectives: To evaluate the efficacy of NS1 antigen ELISA for early diagnosis of dengue virus infection in a tertiary care hospital Methods- A total of 2106 serum samples from patients with suspected dengue infection were tested for dengue NS1 antigen and IgM antibody detection by ELISA. Results: 765 (36.32%) were positive for dengue NS1 antigen and 857 (40.69%) were positive for dengue IgM antibody. NS1 antigen was detectable in patient sera from day 1 onwards however; dengue IgM antibody was detected from day 3 onwards. Out of 765 NS1 antigen positive samples, 562 (73.46%) were positive in acute phase of illness and 203 (26.54%) were positive in convalescent phase of illness. Out of 857 MAC ELISA positive samples, 312 (36.41%) were from acute phase of illness and 545 (63.59%) were from early convalescent phase of illness. Combination of two tests resulted in increase in the positivity rate to 52.66% as against to independent positivity rate of 36.32% of NS1 ELISA and 40.69% of MAC ELISA. Conclusion: Combined use of NS1 antigen assay with MAC ELISA test could significantly improve diagnostic sensitivity of dengue infectio

NS1 ANTIGEN DETECTION BY ELISA IN EARLY LABORATORY DIAGNOSIS OF DENGUE INFECTION

Introduction: Dengue is a major public health problem in tropical and sub-tropical regions of the world and it is known for serious life threatening complications. Detection of IgM antibodies forms the mainstay for diagnosis of dengue infection. However, IgM antibodies develop after 4-5 days of infection and there is an urgent need for an alternative diagnostic tools that can detect dengue infection earlier. Aim and Objectives: To evaluate the efficacy of NS1 antigen ELISA for early diagnosis of dengue virus infection in a tertiary care hospital Methods- A total of 2106 serum samples from patients with suspected dengue infection were tested for dengue NS1 antigen and IgM antibody detection by ELISA. Results: 765 (36.32%) were positive for dengue NS1 antigen and 857 (40.69%) were positive for dengue IgM antibody. NS1 antigen was detectable in patient sera from day 1 onwards however; dengue IgM antibody was detected from day 3 onwards. Out of 765 NS1 antigen positive samples, 562 (73.46%) were positive in acute phase of illness and 203 (26.54%) were positive in convalescent phase of illness. Out of 857 MAC ELISA positive samples, 312 (36.41%) were from acute phase of illness and 545 (63.59%) were from early convalescent phase of illness. Combination of two tests resulted in increase in the positivity rate to 52.66% as against to independent positivity rate of 36.32% of NS1 ELISA and 40.69% of MAC ELISA. Conclusion: Combined use of NS1 antigen assay with MAC ELISA test could significantly improve diagnostic sensitivity of dengue infectio

Rapid identification and susceptibility pattern of various Candida isolates from different clinical specimens in a tertiary care hospital in Western Uttar Pradesh

Background: Candida species are component of normal flora of human beings. Candidiasis is the commonest fungal disease affecting mucosa, skin, nails and internal organs. A variety of predisposing factors are known to cause candidiasis either by altering balance of normal microbial flora of the body or by lowering the host defence.Methods: A total of 90 specimens submitted in the department of microbiology were included in this study.  Identification of Candida species as well as antifungal sensitivity testing was performed with Vitek®2 compact (Biomerieux France) using Vitek2 cards for identification of yeast and yeast like organisms (ID-YST cards). Antifungal susceptibility testing was performed using Vitek2 fungal susceptibility card (AST YS01) kits respectively.Results: The distribution of the clinical samples were urine 53 (58.9%), sputum 14 (15.5%), blood 10 (11.1%), nail 6 (6.7%) and high vaginal swab 7 (7.8%). Among 90 clinical isolates, species obtained were C. tropicalis 53 (59%), C. albicans 23 (25.5%), C. glabrata 6 (6.7%), C. parapsilosis 4 (4.4%), C. krusei 2 (2.2%), C. pelliculosa 1 (1.1%), C. famata 1 (1.1%).Conclusions: Infections caused by non-candida albicans species have increased. Identification of Candida species and their antifungal susceptibility are important for the treatment of hospitalized patients with serious underlying disease

Comparison of BACTEC MGIT with conventional methods for detection of Mycobacteria in clinically suspected patients of extra pulmonary tuberculosis in a tertiary care hospital

Background: Tuberculosis is an important public health problem in India and globally.  Extra pulmonary tuberculosis (EPTB) constitutes for approximately 15 to 20 per cent of all cases of tuberculosis in immunocompetent patients and accounts for more than 50 per cent of the cases in HIV- positive individuals. Main problem with the extra-pulmonary tuberculosis is the paucibacillary nature of the specimen, which makes the diagnosis difficult and delay the treatment. With this in background, this study aimed at the isolation of Mycobacteria from clinical specimens of patients suspected of extra pulmonary tuberculosis using BACTEC MGIT, Lowenstein Jensen (LJ) media and direct acid-fast bacilli smear examination.Methods: A total of 66 samples were processed for direct AFB smear examination, and culture on MGIT and LJ media. Acid fast staining of the specimens was done using the Ziehl-Neelsen method.Results: Among 66 specimens, MGIT gave a higher yield of mycobacteria (46.9%), lower contamination rate (3%) and shorter time to positive culture as compared to LJ media.Conclusions: MGIT gives higher yield and faster results