Shivajirao M. Jadhav's research while affiliated with Dr. Babasaheb Ambedkar Technological University and other places

Publications (19)

Article
Full-text available
The human’s temperature is little known and important to the diagnosis of diseases, according to most researchers and health workers.In ancient medicine, doctors may treat patients with wet mud or slurry clay. The part that would dry up first was considered the diseased part when either of these spread over the body. This can be done today with the...
Preprint
The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heteroge...
Preprint
Full-text available
The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heteroge...
Chapter
Full-text available
Cardiac arrhythmia is very harmful heart disease which can cause severe and even potentially deadly symptoms. Early diagnosis of an arrhythmia can save lives. In the modern health care environment, AQ1 the use of Internet of Things (IoT) technologies brings the suitability for medical professionals and patients, because they are useful in vari- ous...
Chapter
The principal objective of this study was to develop a computer-aided Parkinson’s disease (also called PD) diagnosis system based upon vocal representations. Our two-phase approach uses minimum redundancy maximum relevance (mRMR) algorithm for feature selection and a novel ensemble classifier of fuzzy kNN, weighted kNN, and three different kNNs wit...
Preprint
The aim of the work is to use deep neural network models for solving the problem of image recognition. These days, every human being is threatened by a harmful coronavirus disease, also called COVID-19 disease. The spread of coronavirus affects the economy of many countries in the world. To find COVID-19 patients early is very essential to avoid th...
Article
This article aims to establish an accurate and innovative objective framework for classification of cardiac arrhythmia patients by trying to measure the importance of specific factors that are potentially relevant to its diagnosis. Cardiac arrhythmia(CA) is a group of condition related to the irregular heartbeats. It is very essential to prevent a...
Chapter
The speedy development of the amount of medical health information available to the healthcare industry is driving demand for more accurate, precise, and reliable tools for exploiting these data. Real-world experience suggests that combining the predictions of individual machine learning classifiers may allow us to meet all three criteria at a low...
Conference Paper
Full-text available
One of the most common tasks in machine learning is data classification. Machine learning emerges as a key feature to derive information from corporate operating datasets to large databases. Machine Learning in medical health care is evolving as a significant research field for delivering prognosis and a deeper understanding on medical data. Most m...
Conference Paper
Full-text available
—Arrhythmia is typically detected with ECG tests. One of the main tasks for machine learning is evaluating and classifying proper arrhythmia from complicated ECG dataset. The strategy proposed in this paper involves the following phases (stages): (1) Selection of features (Genetic AlgorithmSVM approach) and (2) classification of the decreased (redu...
Article
Full-text available
The accuracy of the various classifiers depends mainly on good hyper-parameter and consequently on the scheme (hyper-parameter tuning algorithm) adopted to estimate these values. Currently, the hyper-parameter tuning for Ensemble classifiers (which involves a number of hyper-parameters) is receiving a lot of attention. This paper aims to analyze th...
Chapter
Cardiac arrhythmia is one of the serious heart disorders. In many cases; it may lead to stroke and heart failure. Therefore timely and accurate diagnosis is very necessary. In this paper, we proposed a novel ECG Arrhythmia classification approach which includes an Elitist-population based Genetic Algorithm to optimally select the important features...
Article
Full-text available
Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning me...
Article
Medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician’s notes, laboratory results, radiologic results and other s...
Article
Full-text available
Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is detected at the beginning stage, it can often be cured. Many researchers proposed numerous methods for early prediction of this Cancer. In this paper, we proposed feature ensemble learning based on Sparse Autoencoders and Softmax Regression for class...
Chapter
Full-text available
Parkinson’s disease detection through proper representation of the vocal and speech datasets remains an important classification problem. For this problem, we proposed a feature ensemble learning method based on sparse autoencoders. The dataset for this purpose was obtained from UCI, an online repository of comprehensive datasets. Some simulations...

Citations

... We randomly sampled 300 slices from the first data set and segmented them using the proposed method and compared to the results in a preprint by Kasar et al. (2021) in Table 2. Figures 8, 9 present graphs for representative images showing how filtering can affect the final segmentation. We use Jaccard indices, Dice similarity coefficients, sensitivity, and specificity as measures of similarity of the segmented ROI to the ground truth. ...
... The execution of a sophisticated system appliance in health service needs a more incorporated scheme [46] that involves information and communication technologies [20]. Smart data and data analysis with calculation is the expertise that makes great awareness in the field of the healthcare system [43]. In the contemporary era, the urbanized countries are leaving for the smart city's idea [6] in which all the needs and process is proscribed through a computer [34]. ...
... For example, Zoabi and collaborators used this type of technique in conjunction with clinical data to predict coronavirus disease [20]. Other researchers have implemented ML models for the prediction of cardiac arrhythmias [21]. ML has also been applied to cancer diagnosis [22], and it has also been recently applied to SIDS. ...
... Recently many studies [42,41,34,32,31,33,35,14,36,13,40,38,39,37]. have used Machine learning and Deep learning in the medical application. ...
... It transforms the data linearly, which means there is still room for reduction in reconstruction error of side information parameters. SSAE has been proven to extract features in image processing tasks [11,24,26,31,42,43] effectively. So we adopt SSAE to dig the nonlinear relationship between side information parameters. ...
... Yadav et al. [6] in 2020, predicted heart disease and then analyzed the treatment for the disease by different algorithms, such as the Naive Bayes, the K-nearest neighbors, and logistic regression. Alongside simple algorithms, the hybrid algorithms were generated in their research paper. ...
... Determining the accurate hyper-parameters for ensemble classifier (or any ML classifier) is also Black box function optimization problem. A hyperparameter is a parameter whose value is determined prior to the start of the learning process and is used to control the learning process [36]. In ML problems, Hyperparameters are very important, because their values influence the actions of training algorithms directly and have a major impact on the output and efficiency of ML models. ...
... Hyperparameters for CART algorithms are the impurity requirements (entropy and Gini), the large number, the required tests to be separated, and the minimum leaf samples. The decision tree algorithm has been extended to several clinical cares, such as improving dermatological treatment efficiency, estimating critical hypertension, detecting and classifying cardiac arrhythmias [20]- [22], [25]. Random Forest (RF) [14] is an ensemble approach built upon many decision tree classifiers. ...
... Data augmentation has very positive effects on accuracy [36]. Therefore, eight data enhancement methods were collected in this study, and one of them was randomly selected for image processing using hyperparametric optimization search during each training process. ...