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Grape constitutes one of the most widely grown fruit crop in the India. Manual observation of experts is used in practice for detection of leaf diseases, which takes more time for further control action. Without accurate disease diagnosis, proper control actions cannot be taken at appropriate time. This is where modern agriculture technique is required to detect and prevent the leaf from different...
Parkinson disease has become a serious problem in the old people. There is no precise method to diagnosis Parkinson disease now. Considering the significance and difficulty of recognizing the Parkinson disease, the measurement of samples' voices is regard as one of the best non-invasive ways to find the real patient. Support Vector Machine is one of the most effective tools to classify in machine...
In the recent years, many new big data systems have been invented to improve different issues related to process a large amount of data. Nevertheless, the use of these systems to solve particular problems, especially in domains sensitive to safety, privacy and accuracy, is still limited due to unfriendly interface designs, complicated configurations and poor security management mechanism. Thus, we...
Chronic kidney disease (CKD), also known as chronic renal disease, which is progressive loss in kidney function over a period of months or years. It is defined by the presence of kidney damage or decreased glomerular filtration rate (GFR). The estimated prevalence of CKD is about 9–13% in the general adult population. Individuals with CKD have a far greater likelihood of cardiovascular death than...
Agriculture industry is one of the main economic activities in Asean countries. The activities involved a lot of crop planting and yield production in paddy, rubber, oil palm and so forth. Meanwhile in Malaysia, paddy is the third most widely planted crop after oil palm and rubber. Rice, produced by paddy, is considered to be one of Malaysia and Asean staple food and cereal crops. Due to its importance,...
The Paper work presents an approaches to classify chilli class from their bulk sample chilli images using RGB and HSI and L∗a∗b Model colour features. A rule based algorithm is implemented taking into account, best RGB, HSI and L∗a∗b colour features, 9 colour features were computed for R-(red), G-(green), B-(Blue), H-(hue), S-(saturation), I-(intensity), L-(brightness), a-(chromaticity layer red&green),...
Recent studies show that in the case of coronary artery disease (CAD) patients, the diastolic part of cardiac cycle contains very weak murmurs caused by turbulent flow in the narrowed coronary arteries. This paper aims to analyze the diastolic period of heart sounds recorded with electronic stethoscope for detection of coronary artery disease. 3M Littmann 3200 model is used for recording of heart...
Development of an automated system for identifying and classifying different diseases of the contaminated plants is an emerging research area in precision agriculture. Identification of the diseases is the key to prevent qualitative and quantitative loss of agricultural yields. Rice (Oryza Sative) is one of the essential crops in India and losses due to the diseases badly impact the economy. Manual...
Emergence of digital news provides new opportunities in information extraction. Proper characterization of unstructured news can help identify signals that may drive variations in many observable phenomena, such as disease outbreaks. In this paper, we propose a method to extract such signals from a large corpus of news events and identify a subset of signals that are closely related to the observed...
Reliable automatic system for Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of systemic autoimmune diseases. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to address the HEp-2 specimen classification problem. The FCN in the proposed framework was adapted from VGG-16, which was trained with ICPR 2016 dataset...
Chronic respiratory diseases, such as asthma, are very common around the world and have been shown to have a significant effect on the quality of life of patients. A crucial component for the effective management of asthma is the adherence of patients to their medication prescription, which can be separated into two distinct and equally important components, i) the adherence of patients to the time...
In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here, the disease incidence and disease...
The advent of Optical Coherence Tomography (OCT) imaging has engendered a quantum leap in ophthalmological disease diagnosis. Specifically, in relation to various retinal disorders, OCT has facilitated visualization of minute structural changes in retinal and choroid layers. However, due to dearth of ophthalmologists, and time and effort required in manual analysis, a large number of patients fail...
Detection of the changes in pattern of disease spread over a population network, Meme-tracking and opinion spread on the Twitter network and product-rating-cascade over a social network are a few among the many embodiments of graph sequence segmentation problem with labeled nodes. Most of the previous approaches to network sequence segmentation are on plain graphs without considerations for the dynamics...
The recent advent of Metagenome-Wide Association Studies (MGWAS) has allowed for increased accuracy in the prediction of patient phenotype (disease), but has also presented big data challenges. Meanwhile, Multiple Instance Learning (MIL) is useful in the domain of bioinformatics because, in addition to classifying patient phenotype, it can also identify individual parts of the microbiome that are...
In this paper, we present a deep learning based disease named entity recognition architecture. First, the word-level embedding, character-level embedding and lexicon feature embedding are concatenated as input. Then multiple convolutional layers are stacked over the input to extract useful features automatically. Finally, multiple label strategy, which is firstly introduced, is applied to the output...
Clinical data records a patient's health status, where multi-label type of data exists. For example, a patient suffering from cough and fever should be associated with both two disease labels in the clinical records. Specifically, due to the redundant or irrelevant features in clinical data, the performance of multi-label classification will be limited, therefore selecting effective features from...
Traditional Chinese Medicine (TCM) is a holistic integrative medical approach. Exploring the relations between the herbal formulae and the symptoms is a crucial problem in researches of TCM. Unlike existing researches, we treat it as a both multi-instance learning and multi-label learning problem. In this paper, we propose a novel approach, which named Weighted Sampling based on Similar Herbs MIML...
A method is proposed to distinguish patients with schizophrenia from healthy controls based on data measured by functional near-infrared spectroscopy (fNIRS) during a cognitive task, which combines principal component analysis (PCA) and support vector machine (SVM). Firstly, a data reduction technique is applied prior to PCA, and then PCA is used to extract features on oxygenated hemoglobin (oxy-Hb)...
In this work, four well known convolutional neural networks (CNNs) that were pretrained on the ImageNet database are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. The images are classified using three different transfer learning strategies and a experimental setup specifically adapted for the classification of endoscopic imagery. The CNNs...
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