Nearest-neighbor Algorithm Research Articles

HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis

AbstractRed blood cells (RBCs) are the more crucial components of a healthy organism. Poikilocytosis is a disorder in which blood cells lose their standard shapes. The human bodies are affected by disorders like anemia and thalassemia because the structure of RBCs shape changes. Hematologists take longer time to detect abnormalities in RBCs. The proposed hyper‐parameter optimizable K nearest neighbor (HPKNN) algorithm detects and classifies numerous types of poikilocytosis quickly and accurately. On the Chula‐PIC‐Lab and Erythrocytes IDB datasets, the proposed HPKNN algorithm was tested. After the preprocessing stage, the Freeman chain code has been utilized to retrieve the exact boundary shape of RBC morphology. On datasets of Erythrocytes IDB 1, 2, 3, and Chula‐PIC‐Lab(Chula‐RBC‐12), the morphological tools and Freeman chain code based segmentation provide 97.44% and 94.45% accuracy, respectively, which is superior than other methods. The proposed HPKNN classifier has an overall sensitivity of 97.9%, precision of 97.9%, and an f1 score of 97.9% on the Chula‐PIC‐Lab (Chula‐RBC‐12) dataset to classify 10 classes, namely acanthocyte, dacrocyte, echinocyte, elliptocyte, normal, overlapped, schistocyte, spherocyte, stomatocyte, and target cell, which is greater than any other current approach. The HPKNN classifier achieves 98.085% precision, 98.085% sensitivity or recall, and 98.085% f1‐score values on the Erythrocytes IDB datasets to classify circular, elongated, and other RBC classes.

Consensus based target tracking against deception jamming in distributed radar networks

AbstractIn this study, an anti‐deception jamming target tracking algorithm based on the data‐level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non‐collaborative and collaborative false targets respectively. For non‐collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti‐deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.

Study vehicle routing problem using Nearest Neighbor Algorithm

Vehicle routing problem (VRP) has a key role in logistics management. VRP plays a role in designing the optimal route used by a number of vehicles placed at the depot to serve a number of customers with known requests. To solve the VRP, the nearest neighbor algorithm used to get the most optimal results. The Nearest Neighbor algorithm is a heuristic method which is done by starting the starting point than looking for the nearest point. In this paper, algorithm nearest neighbour can save the distance of 13,14% and cost 13,17%.

Large-Scale Hyperspectral Image Restoration via a Superpixel Distributed Algorithm Based on Graph Signal Processing

Hyperspectral image (HSI) is often disturbed by various kinds of noise, which brings great challenges to subsequent applications. Many of the existing restoration algorithms do not scale well for HSI with large size. This paper proposes a novel mixed-noise removal method for HSI with large size, by leveraging the superpixel segmentation-based technology and distributed algorithm based on graph signal processing. First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called skeleton graph, is a rough graph constructed by using the modified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation. The skeleton graph can efficiently characterize the inter-correlations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph consisting of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. In each optimization problem, a graph Laplacian regularization is defined and incorporated into a low-rank-based model. Third, a novel distributed algorithm is tailored for the restoration problem, by using the information interaction between the nodes of skeleton graph and subgraphs. Numerical experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of the proposed restoration algorithm compared with existing methods.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has been formed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completely llabelled Telugu vowel data set, and the accuracy is compared with the different models and the fuzzy k nearest neighbor algorithm. The proposed model gives 84.86% accuracy on 50% training data set and 89.35% accuracy on 80% training data set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

IntelEye: An Intelligent Tool for the Detection of Stressful State based on Eye Gaze Data While Watching Video

Technology to monitor mental health is gaining popularity as it helps to improve the cognitive and behavioral performance of an individual. Considering the growing need to monitor mental health, there is subsequent research in continuous and real-time monitoring technologies that can increase the quality of life by reducing the cost of health care. Eye tracking technology has played a significant role in monitoring a person's mental health. An intelligent system can apply several computational procedures to extract meaningful information from the massive physiological data obtained from eye tracking. The proposed model IntelEye is a tool to detect the stressful states of an individual while watching calm and stressful videos. The eye gaze measures based on pupil diameter, fixation, and blink were used for detecting stressful conditions. The data was collected from hospital employees, and the K Nearest Neighbor algorithm could successfully recognize the stressful states and the corresponding gaze location during stressful situations. IntelEye is not only identifying the stressful states but also has the novelty of identifying the scene and gaze location, making them stressful while watching the video.

Tourist tram routing problem in Lamphun city, Thailand

This research aims to study the tourist tram routing problem in Lamphun City, Thailand. Considered an application of the Travelling Salesman Problem (TSP), the distance data along all possible routes were gathered from Google Maps and Google Earth, including additional information provided by the city. First, the shortest distance between each pair of the tourist spots is determined. Then, the linear programming (LP) model for TSP is employed so as to find the best tourist tram route traversing through all planned tourist spots with minimum total distance. The solution obtained from this model is then compared with those from the nearest neighbor and the savings algorithms for TSP. The comparison shows that the optimal tram routes from the LP model and the savings algorithm are, though different, tied at the minimum total distance of 9,770 meters, reduced by 9.36% from the current 10,779-meter route. This optimal route is also better than the of 10,050-meter route obtained from the nearest neighbor algorithm.

Inferring drug-disease associations by a deep analysis on drug and disease networks.

Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.

Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

A WiFi Indoor Location Tracking Algorithm Based on Improved Weighted K Nearest Neighbors and Kalman Filter

The Weighted K Nearest Neighbor (WKNN) algorithm is a widely adopted lightweight methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it suffers from the disadvantage of a fixed K value and susceptibility to incorrect reference point matching. To address this issue, we present a novel algorithm in this paper, referred to as Static Continuous Statistical Characteristics-Soft Range Limited-Self-Adaptive WKNN (SCSC-SRL-SAWKNN). Our algorithm not only takes into account location tracking in the motion state but also exploits the continuous statistical features of extended periods of inactivity to enhance localization. In the motion state, we initially employ the adaptive WKNN (SAWKNN) algorithm to determine the optimal K value, followed by the employment of the Soft Range Limited KNN (SR-KNN) algorithm to identify the correct reference point and ultimately estimate the position. When a prolonged stationary state is detected, we first utilize the moving window method to obtain a more stable position fingerprint (SCSC), and then proceed with the positioning process in the same motion state. Ultimately, we use Kalman filter to generate the location trajectory. Our experimental findings demonstrate that the proposed SCSC-SRL-SAWKNN algorithm outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory. Specifically, the localization accuracy of our algorithm is 56.7% and 36.6% higher than that of traditional WKNN in the static state and overall situation, respectively.

A global optimization generation method of stitching dental panorama with anti-perspective transformation.

To address the limitation of narrow field-of-view in local oral cavity images that fail to capture large-area targets at once, this paper designs a method for generating natural dental panoramas based on oral endoscopic imaging that consists of two main stages: the anti-perspective transformation feature extraction and the coarse-to-fine global optimization matching. In the first stage, we increase the number of matched pairs and improve the robustness of the algorithm to viewpoint transformation by normalizing the anti-affine transformation region extracted from the Gaussian scale space and using log-polar coordinates to compute the gradient histogram of the octagonal region to obtain the set of perspective transformation resistant feature points. In the second stage, we design a coarse-to-fine global optimization matching strategy. Initially, we incorporate motion smoothing constraints and improve the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm by utilizing neighborhood information for coarse matching. Then, we eliminate mismatches via homography-guided Random Sample Consensus (RANSAC) and further refine the matching using the Levenberg-Marquardt (L-M) algorithm to reduce cumulative errors and achieve global optimization. Finally, multi-band blending is used to eliminate the ghosting due to unalignment and make the image transition more natural. Experiments show that the visual effect of dental panoramas generated by the proposed method is significantly better than that of other methods, addressing the problems of sparse splicing discontinuities caused by sparse keypoints, ghosting due to parallax, and distortion caused by the accumulation of errors in multi-image splicing in oral endoscopic image stitching.

A Method of Rainfall Detection From X-Band Marine Radar Image Based on the Principal Component Feature Extracted

Since it is difficult to filter out the rainfall interference directly from the X-band marine radar image, it is necessary to detect whether the collected radar image contains rainfall interference to control the quality of the radar image. Aiming at the problem of rainfall recognition in X-band marine radar images, a new rainfall detection method is proposed by using principal component analysis (PCA) technology to reduce dimensions and extract features from radar images. Based on the calculated distance between the features to be tested and the known features, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithm is utilized to determine the classification task of the radar image and recognize the rainfall radar image. The experimental result illuminates that the detection accuracy of the proposed method reaches 99.3% and is 2.0% higher than that of the support vector machine (SVM)-based method. Meanwhile, the proposed method shows good classification performance for the rain-contaminated images under different rainfall intensities.

Prediction of Cardiovascular Disease using DeepLearning Algorithm

The leading cause of death, which affects millions of individuals globally is the cardiovascular disease. Heart problems are a major issue in health care, particularly in the field of cardiology. Due to a number of risk factors, including diabetes, high blood pressure, high cholesterol, an irregular pulse rate, obesity, and smoking, cardiac illness is difficult to detect. Due to these limitations, researchers are now using Data Mining and Deep Learning Algorithms to predict heart related disorders. The Cardio Vascular Disease (CVD) is as complicated as it sounds if left untreated. So, the early prediction of this could save millions of people from silent attacks, myocardial infarction etc. Many machine learning algorithms like Naïve Bayes, K-Nearest Neighbor Algorithm (KNN), Decision Trees (DT), Genetic algorithm (GA) are used for cardiovascular disease prediction using text datasets and their efficiencies tend to differ. Generally, convolutional neural network (CNN) algorithm is mostly used for prediction using images. But our concept is to switch over this and predict heart disease using the CNN algorithm for Cleveland dataset which consists of numerical. In this dataset we consider 14 attributes and used K Nearest Neighbor and CNN algorithm. In terms of accuracy, CNN beats KNN, proving that deep learning algorithms may support decision-making and prediction-making based on vast volumes of data supplied by the healthcare sector.

Finding Similar Historical Scenarios for Better Understanding Aircraft Taxi Time: A Deep Metric Learning Approach

Accurate prediction of aircraft taxi time is crucial to optimizing route scheduling and improving airport efficiency. However, studies usually give only a single deterministic taxi time predicted by models, which lacks interpretability and affects controllers’ trust. To solve this problem, using the idea of auxiliary decision-making based on similar scenarios, a deep metric learning model is proposed to learn the similarity among historical situations, consisting of flight properties, surface traffic, and meteorological conditions. Combining the model with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest-neighbor regression algorithm, a set of historical circumstances similar to a reference scenario can be found and used to predict the taxi time. Experimental verification using Shanghai Pudong International Airport data shows that the model achieves better performance than the state-of-the-art variants for taxi-out time prediction accuracy. More importantly, similar historical scenarios can provide insights into the uncertainty quantification of aircraft taxi time and serve as a basis for controllers to make decisions. The methodology enriches decision support tools and enhances airports’ refined management capabilities.

Design of a hybridization between Tabu search and PAES algorithms to solve a multi-depot, multi-product green vehicle routing problem

Vehicle routing problem (VRP) is a classic problem studied in logistic. One of the most important variations within this problem is called Green Vehicle Routing Problem (GVRP), in which environmental aspects are considered when designing product delivery routes. This variant arises due to the high levels of pollution produced by transport vehicles, so it is a variation whose study represents a vital impact nowadays. This project will consider a GVRP and will be developed considering the characteristics of multi-depot (MDVRP) and multi-product (MPVRP) to minimize the costs of assignation of vehicles and CO2 emissions. To solve the problem, this project proposes a hybridization between the classic tabu search (TS) metaheuristic and the PAES algorithm (TS+PAES) to generate the Pareto frontier of both objectives. An integer mixed linear programming model is formulated and developed for each objective function separately to have an optimal point of comparison for the efficiency of the proposed algorithm. Also, the TS+PAES algorithm is compared to the nearest neighbor algorithm for large instances. Two computational experiments were carried out, one for small and the other one for large instances. The experiment for small instances showed that the GAP of each extreme of the frontier compared to the MILP model is on average 0.73%. For large instances, the metaheuristic improves in 0.1% the results presented by the MILP model showing that the metaheuristic provides closer near-optimal solutions in less computational time. Besides, the metaheuristic, in comparison with the nearest neighborhood heuristic, improves in 44.21% the results of emissions and in 3.88% the costs. All these results demonstrate the effectiveness of the metaheuristic.

Identification of glucose levels in urine based on classification usingk-nearest neighbor algorithm method

AbstractGlucose monitoring carried out through the urine testing to make it easier for patients to check their blood sugar without having to physically injure themselves and to prevent external bacteria from entering the body, which happens while using needles. This study aims to classify glucose-containing urine specimens based on diabetes levels by using the K-nearest neighbor method. Classification of urine specimens is achieved by using the Benedict method to produce the color of the urine specimen and the AS7262 sensor to detect the color produced by the specimen. The results showed that the classification of data on urine specimens has an accuracy of 96.33%. Previous studies conducted this experiment using a photodiode sensor and a TCS sensor, which produced red, green, and blue (RGB) colors. For identifying the color of a specimen, the AS7262 sensor can produce six colors (red, green, blue, yellow, violet, and orange) to identify the glucose level.

Offshore ship recognition based on center frequency projection of improved EMD and KNN algorithm

Ship-radiated underwater noises are essential in marine target identification. However, it is very difficult to extract valuable target information from ocean ambient noises. Here, a combined classification method is proposed based on the improved empirical mode decomposition to process the underwater signals of offshore ships. The signals are separated into a series of samples with a signal duration of 100 ms and a set of intrinsic mode functions (IMFs) is generated for each sample. The feature extraction of each IMF is realized by defining and calculating its central frequency and energy intensity. Then, the central frequency of each IMF is projected into a three-dimensional space according to the three top-ranked IMFs based on energy intensity, and the K nearest neighbor (KNN) algorithm is employed to identify the different offshore ships. It was observed that the proposed method can efficiently distinguish the three ships when 80 % of the samples are randomly selected as the training set and the remaining 20 % are used as the test set. The overall recognition rate after 1000 random tests was greater than 95 % for the short-time signals of approximately 100 ms and completely unknown types of ships, which indicates that the proposed method is applicable to offshore ship classification and identification.

A dataset of benthic material distribution of coastal coral reefs in Hainan during 2018–2020

Based on the Sentinel-2 high resolution remote sensing image data, we adopted the object-oriented multi-scale segmentation technology and the nearest neighbor algorithm to classify benthic materials of offshore coral reefs in Wenchang City, Danzhou City, Sanya City of Hainan Province during 2018-2020. Then, combining with the remote sensing images of Gaofen-2 satellite and field validation data we verified the accuracy of the classification results. The dataset can be used to analyze the temporal and spatial changes of benthic materials in coastal coral reefs and assess the health status of coral reefs. Moreover, it can serve as a data foundation for the management and protection of coral reefs in Hainan Island.

Optimizing the Hub-and-Spoke Network with Drone-Based Traveling Salesman Problem

The hub-and-spoke network (HSN) design generally assumes direct transportation between a spoke node and its assigned hub, while the spoke’s demand may be far less than a truckload. Therefore, the total number of trucks on the network increases unnecessarily. We form a drone-based traveling salesman problem (TSP-D) for the cluster of spokes assigned to a hub. A truck starts from the hub, visiting each spoke node of the hub in turn and finally returning to the hub. We propose a three-stage decomposition model to solve the HSN with TSPD (HSNTSP-D). The corresponding three-stage decomposition algorithm is developed, including cooperation among variable neighborhood search (VNA) heuristics and nearest neighbor algorithm (NNA), and then the spoke-to-hub assignment algorithm through the reassignment strategy (RA) method. The performance of the three-stage decomposition algorithm is tested and compared on standard datasets (CAB, AP, and TR). The numerical analysis of the scenarios shows that whether it is trunk hub-level transportation or drone spoke-level transportation, it integrates resources to form a scale effect, which can reduce transport devices significantly, as well as decreasing the investment and operating costs.

Nonlinear Model Order Selection: A GMM Clustering Approach Based on a Genetic Version of EM Algorithm

Choosing an accurate model order is one of the important sections in system identification. Traditionally, the model order selection of a nonlinear system depends on a predetermined model. However, it requires excess calculation and is impossible to get rid of the trouble of the structural design of the model, once the specific model was determined. A false nearest neighbor (FNN) algorithm that only relies on input-output data to estimate the model order is proposed here. Due to the FNN algorithm is sensitive to its own threshold which is a crucial constant for evaluating the model structure, Gaussian mixture model (GMM) clustering based on a genetic version of the expectation-maximization (EM) algorithm and minimum description length (MDL) criterion is developed in this paper, where the order can be determined without relying on a specific model. The GMM clustering is proposed to calculate the threshold of the FNN. Then, the genetic algorithm and MDL criteria are embedded to optimize the calculation of the EM algorithm as reduce the influence of initial values and not prone to fall into local extreme values as well. Three examples are given here to indicate the superiority of this technique: simulation of a strongly nonlinear system, isothermal polymerization process, and Van der Vusse reaction in relevant reference. Finally, some typical modeling methods are conducted to confirm the validity of this approach.