Sharif Digital Repository

Given a set P of n points in the plane, the all nearest neighbors problem asks for finding the closest point in P for each point in the set. The following folklore algorithm is used for the problem in practice: Pick a line in a random direction, project all points onto the line, and then search for the nearest neighbor of each point in a small vicinity of that point on the line. It is widely believed that the expected number of points needed to be checked by the algorithm in the vicinity of each point is O(pn) on average. We confirm this conjecture in affirmative by providing a careful analysis on the expected number of comparisons made by the algorithm. We also present a matching lower... 

Given a set P of n points in the plane, the all nearest neighbors problem asks for finding the closest point in P for each point in the set. The following folklore algorithm is used for the problem in practice: Pick a line in a random direction, project all points onto the line, and then search for the nearest neighbor of each point in a small vicinity of that point on the line. It is widely believed that the expected number of points needed to be checked by the algorithm in the vicinity of each point is O(pn) on average. We confirm this conjecture in affirmative by providing a careful analysis on the expected number of comparisons made by the algorithm. We also present a matching lower... 

The problem of finding nearest neighbors in a certain distance is well defined in machine learning area. There are well-known and exact solutions for it. However, in real world problems, especially in machine vision area, where we have a moving sensor and we want to know which objects of the scene are in the measurement range of it, two issues are important. First, usually in these problems, the time cost is more important than accuracy. It means they can tolerate some measurements error if they can do the process in real-time. Second, the location of an object can be described in the three-dimensional space and does not require the higher dimensions. According to these issues, we introduced... 

Nowadays, the number of digital images has increased so that the management of this volume of data needs an efficient system for browsing, categorising and searching. Automatic image annotation is designed for assigning tags to images for more accurate retrieval. Non-negative matrix factorisation (NMF) is a traditional machine learning technique for decomposing a matrix into a set of basis and coefficients under the non-negative constraints. In this study, the authors propose a two-step algorithm for designing an automatic image annotation system that employs the NMF framework for its first step and a variant of K-nearest neighbourhood as its second step. In the first step, a new multimodal... 

This paper introduces a new supergraph, called the k-Semi-Yao graph (k-SYG), of the k-nearest neighbor graph (k-NNG) of a set P of points in Rd. We provide a kinetic data structure (KDS) to maintain the k-SYG on moving points, where the trajectory of each point is a polynomial function whose degree is bounded by some constant. Our technique gives the first KDS for the theta graph (i.e., 1-SYG) in Rd. It generalizes and improves on previous work on maintaining the theta graph in R2. As an application, we use the kinetic k-SYG to provide the first KDS for maintenance of all the k-nearest neighbors in Rd, for any k≥1. Previous works considered the k=1 case only. Our KDS for all the 1-nearest... 

The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem.... 

Density peaks clustering (DPC) is as an efficient clustering algorithm due for using a non-iterative process. However, DPC and most of its improvements suffer from the following shortcomings: (1) highly sensitive to its cutoff distance parameter, (2) ignoring the local structure of data in computing local densities, (3) using a crisp kernel to calculate local densities, and (4) suffering from the cause of chain reaction. To address these issues, in this paper a new method called DPC-DBFN is proposed. The proposed method uses a fuzzy kernel for improving separability of clusters and reducing the impact of outliers. DPC-DBFN uses a density-based kNN graph for labeling backbones. This strategy... 

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier pace, is employed for diagnosing the stage of patients’ disease. To improve the accuracy of the proposed algorithm, the best value for k is determined using an experimental procedure. The implementation of the proposed approach on a lung cancer database reveals 100% accuracy. This implies that one... 

The k-nearest neighbor (k-NN) is a widely used classification technique and has significant applications in various domains. The most challenging issues in the k-nearest neighbor algorithm are high dimensional data, the reasonable accuracy of results and suitable computation time. Nowadays, using parallel processing and deploying many-core platforms like GPUs is considered as one of the popular approaches to improving these issues. In this paper, we present a novel and accurate parallel implementation of k-NN based on Mahalanobis distance metric in GPU platform. We design and implement k-NN for GPU architecture and utilize mathematic and algorithmic techniques to eliminate repetitive... 

Data classification in large scale systems, such as peer-to-peer networks, can be very communication-expensive and impractical due to the huge amount of available data and lack of central control. Frequent data updates pose even more difficulties when applying existing classification techniques in peer-to-peer networks. We propose a distributed, scalable and robust classification algorithm based on k-nearest neighbor estimation. Our algorithm is asynchronous, considers data updates and imposes low communication overhead. The proposed method uses a content based overlay structure to organize data and moderate the number of query messages propagated in the network. Simulation results show that... 

Herniation in the lumbar area is one of the most common diseases which results in lower back pain (LBP) causing discomfort and inconvenience in the patients' daily lives. A computer aided diagnosis (CAD) system can be of immense benefit as it generates diagnostic results within a short time while increasing precision of diagnosis and eliminating human errors. We have proposed a new method for automatic diagnosis of lumbar disc herniation based on clinical MRI data. We use T2-W sagittal and myelograph images. The presented method has been applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. We employ Otsu thresholding method to extract the... 

The building sector is a major source of energy consumption and greenhouse gas emissions in urban regions. Several studies have explored energy consumption prediction, and the value of the knowledge extracted is directly related to the quality of the data used. The massive growth in the scale of data affects data quality and poses a challenge to traditional data mining methods, as these methods have difficulties coping with such large amounts of data. Expanded algorithms need to be utilized to improve prediction performance considering the ever-increasing large data sets. In this paper, a preprocessing method to remove noisy features is coupled with predication methods to improve the... 

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by... 

Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and... 

The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was... 

This paper adopts a holistic approach to stress detection issues in software and hardware phases and aims to develop and evaluate a specific low-power and low-cost sensor using physiological signals. First, a stress detection model is presented using a public data set, where four types of signals, temperature, respiration, electrocardiogram (ECG), and electrodermal activity (EDA), are processed to extract 65 features. Using Kruskal-Wallis analysis, it is shown that 43 out of 65 features demonstrate a significant difference between stress and relaxed states. K nearest neighbor (KNN) algorithm is implemented to distinguish these states, which yields a classification accuracy of 96.0 ± 2.4%. It... 

This article reports the development of a new smart electrocardiogram (ECG) monitoring system, consisting of the related hardware, firmware, and Internet of Things (IoT)-based web service for artificial intelligence (AI)-assisted arrhythmia detection and a complementary Android application for data streaming. The hardware aspect of this article proposes an ultralow power patch sampling ECG data at 256 samples/s with 16-bit resolution. The battery life of the device is two weeks per charging, which alongside the flexible and slim (193.7 mm times62.4 mm times8.6 mm) and lightweight (43 g) allows the user to continue real-life activities while the real-time monitoring is being done without... 

Although being expensive and time-consuming, petroleum industry still is highly reliant on well logging for data acquisition. However, with advancements in data science and AI, methods are being sought to reduce such dependency. In this study, several important well logs, CNL, FDC and PEF from ten wells are predicted based on ML models such as multilinear regression, DNN, DT, RT, GBoost, k-NN, and XGBoost. Before applying these models, depth matching, bad hole correction, de-spiking, and preprocessing of the data, including normalization, are carried out. Three statistical metrics, R2, RMSE, and PAP, are applied to evaluate the models' performance. Results showed that RF, k-NN, and XGBoost... 

Atherosclerosis is a major cause of coronary artery disease leading to morbidity and mortality worldwide. Currently, coronary angiography is considered to be the most accurate technique to diagnose coronary artery disease (CAD). However, this technique is an invasive and expensive procedure with risks of serious complications. Since the symptoms of CAD are not noticed until advanced stages of the disease, early and effective diagnosis of CAD is considered a pertinent measure. In this paper, a non-invasive optical signal, the finger photoplethysmogram (PPG) obtained before and after reactive hyperemia is investigated to discriminate between subjects with different CAD conditions. To this end,...