Sharif Digital Repository

Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a harmony search (HS) algorithm, and a heuristic algorithm to classify medical datasets intelligently. Two fuzzy approaches, as well as orthogonal and triangular fuzzy sets, are first utilized to define the attributes of data. Then, an HS algorithm is integrated with a heuristic to generate fuzzy rules to select the best rules in the fuzzy rule-based systems. Moreover, to improve the performance of the proposed classification approach, a three-phase parameter tuning approach is... 

Municipal solid waste (MSW) directly impacts community health and environmental degradation; therefore, the management of MSW is crucial. Medical waste is a specific type of MSW which is generally divided into two categories: infectious and non-infectious. Wastes generated by coronavirus disease 2019 (COVID-19) are classified among infectious medical wastes; moreover, these wastes are hazardous because they threaten the environment and living organisms if they are not appropriately managed. This paper develops a bi-objective mixed-integer linear programming model for medical waste management during the COVID-19 outbreak. The proposed model minimizes the total costs and risks, simultaneously,... 

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of ‘weak classifiers’ (i.e., single voxels) in higher dimensions. We propose instead that ‘real’ multivoxel... 

This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate identification of LB patients. 24 healthy individuals and 28 low back pain patients performed trunk motion tasks in five different directions for validation. Four combinations of these motions were selected based on literature, and the corresponding kinematic data was collected. Upon filtering (4th order, low pass Butterworth filter) and normalizing the data, Principal Component Analysis was used for feature extraction, while Support Vector Machine... 

Abnormal operating conditions for the timing belt can lead to cracks, fatigue, sudden rupture and damage to engines. In this study, an intelligent system was developed to detect and classify high-load operating conditions and high-temperature operating conditions for timing belts. To achieve this, vibration signals in normal operating conditions, high-load operating conditions and high-temperature operating conditions were collected. Time-domain signals were transformed to the frequency domain and the time-frequency domain using the fast Fourier transform method and the wavelet transform method respectively. In the data-mining stage, 25 statistical features were extracted from different... 

Haplotype information has become increasingly important in analyzing fine-scale molecular genetics data, Due to the mutated form in human genome; SNPs (Single Nucleotide Polymorphism) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of studies in human genomics. In this paper, a data fusion method based on multiple parallel classifiers for reconstruction of haplotypes from a given sample Single Nucleotide Polymorphism (SNP) is proposed. First, we design a single greedy algorithm for solving haplotype reconstructions. [2] is used as an efficient approach to be combined with first classification method. The... 

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the... 

Melanoma is the deadliest type of human skin cancer. However, it is curable if diagnosed in an early stage. Recently, computer aided diagnosis (CAD) systems have drawn much interests. Segmentation is a crucial step of a CAD system. There are different types of skin lesions having high similarities in terms of color, shape, size and appearance. Most available works focus on a binary segmentation. Due to the huge variety of skin lesions and high similarities between different types of lesions, multi-class segmentation is still a challenging task. Here, we propose a method based on joint dictionary learning for multi-class segmentation of dermoscopic images. The key idea is based on combining... 

Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients’ features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis... 

Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for...