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Cluster Analysis methods are very important, popular data summarization techniques applied in diverse environments. These techniques retrieve the hidden patterns in large datasets in the form of characterized patterns which can be interpreted further in different contexts. Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis...
This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification...
Photonic is the study that includes the use of light, where the fundamental element is photon. The optics is the branch of the physics which deals with the properties of the light. In this paper the sensor is designed to detect the glucose content of human body and the spectrum from the output of the sensor is analyzed by using AdaBoost algorithm. The Adaptive Boosting in short considered to be AdaBoost...
The detection and analysis of retinal vessels in ophthalmology is of great use in the diagnosis and progression monitoring of diabetic retinopathy. Automatic Detection of the vessel network has however been challenging due to noise from uneven contrast and illumination during the retinal image acquisition process. This paper presents a robust segmentation technique that combines phase congruence and...
Palm print is a unique and reliable biometric characteristic with high usability. With the increasing demand of highly accurate and robust palm print authentication system, multispectral imaging has been employed to acquire more discriminative information and increase the anti spoof capability of palm print. The experimentation for this project is done using PolyU palm print Database. Palm print images...
Research in student retention is traditionally survey-based, where researchers use questionnaires to collect student data to analyse and to develop student predictive model. The major issues with survey-based study are the potentially low response rates, time consuming and costly. Nevertheless, a large number of datasets that could inform the questions that students are explicitly asked in surveys...
In this paper we introduce a coarse-to-fine arrhythmia classification technique that can be used for efficient processing of large Electrocardiogram (ECG) records. This technique reduces time-complexity of arrhythmia classification by reducing size of the beats as well as by quantizing the number of beats using Multi-Section Vector Quantization (MSVQ) without compromising on the accuracy of the classification...
The digital analysis of heart sounds has revealed itself as an evolving field of study. In recent years, numerous approaches to create decision support systems were attempted. This paper proposes two novel algorithms: one for the segmentation of heart sounds into heart cycles and another for detecting heart murmurs. The segmentation algorithm, based on the autocorrelation function to find the periodic...
A number of automatic sleep scoring algorithms have been published in the last few years. These can potentially help save time and reduce costs in sleep monitoring. However, the use of both R&K and AASM classification, different databases and varying performance metrics makes it extremely difficult to compare these algorithms. In this paper, we describe some readily available polysomnography databases...
The aim of this work is to identify groups of patients with similar patterns that are related to a higher risk of readmission to an Intensive Care Unit (ICU). Patients readmissions to ICUs are introduced as a problem associated with increased mortality, morbidity and costs, which complicates the performance of a good clinical management and medical diagnosis. To approach the readmissions classification...
A signaling pathway, which is represented as a chain of interacting proteins for a biological process, can be predicted from protein-protein interaction (PPI) networks. However, pathway prediction is computationally challenging because of (1) inefficiency in searching all possible paths from the large-scale PPI networks and (2) unreliability of current PPI data generated by automated high-throughput...
People express their opinion about many things like products, political parties, ideas using the facilities of social media. The analysis of these opinions is a gold mine for marketing experts and for humanities research as well. We introduce a system for opinion mining from the textual content of tweets and discuss the differences between tweet-level and target-oriented opinion mining.
Age related macular degeneration (ARMD) is an eye abnormality due to deposits of drusen on the retina and the disease may cause severe blindness. Early detection of ARMD using a computerized system can save patient's vision. The ophthalmologists can find this system useful for screening of ARMD. This paper presents a novel method for accurate detection of drusen in colored retinal images. The system...
Real-time Obstructive Sleep Apnea (OSA) detection and monitoring are important for the society in terms of improvement in citizens' health conditions and of reduction in mortality and healthcare costs. This paper proposes an easy, cheap, and portable approach for monitoring patients with OSA. It is based on singlechannel ECG data, and on the automatic offline extraction, from a database containing...
This paper proposes the neuro-fuzzy classification model to perform the supervised classification of the data. In the proposed classification model, fuzzy membership matrix is formed by using Gaussian membership function. Membership matrix contains the membership of each feature value to the given classes. This membership matrix is given as an input to the artificial neural network and membership...
Prediction of the translation initiation site is of vital importance in bioinformatics since through this process it is possible to understand the organic formation and metabolic behavior of living organisms. Sequential algorithms are not always a viable solution due to the fact that mRNA databases are normally very large, resulting in long processing times. Applying parallel and distributed computing...
Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of...
Arrhythmia (i.e., irregular cardiac beat) classification in electrocardiogram (ECG) signals is an important issue for heart disease diagnosis due to the non-invasive nature of the ECG exam. In this paper, we analyze and criticize the results of some arrhythmia classification methods presented in the literature in terms of how the samples are chosen for training/testing the classifier and the impact...
Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data include disregarding data, or replacing it with...
Influenza is one of the most important emerging and reemerging infectious diseases, causing high morbidity and mortality in communities (epidemic) and worldwide (pandemic). Here, Classification of human vs. non-human influenza, and subtyping of human influenza viral strains virus is done based on Profile Hidden Markov Models. The classical ways of determining influenza viral subtypes depend mainly...
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