Preliminary Filter Research Articles

Prospects of Identifying Hierarchical Triple Mergers for the Third-generation Ground-based Detectors

A hierarchical triple merger (HTM) constitutes a type of event in which two successive black hole (BH) mergers occur sequentially within the observational window of gravitational-wave (GW) detectors, which has an important role in testing general relativity and studying BH population. In this work, we conduct an analysis to determine the feasibility of identifying HTMs from a large GW event catalog using third-generation ground-based GW detectors. By comparing the Bhattacharyya coefficient that measures the overlap between the posterior distributions of the remnant and progenitor BH parameters, we find that the overlap between the event pair can serve as a preliminary filter, which balances between computational demand and the probability of false alarms. Following this initial, time-efficient, yet less accurate screening, a subset of potential HTM candidates will be retained. These candidates will subsequently be subjected to a more precise, albeit time-intensive, method of joint parameter estimation for verification. Ultimately, this process will enable us to robustly identify HTMs.

3D orientation field transform

Vascular structure enhancement is very useful in image processing and computer vision. The enhancement of the presence of the structures like tubular networks in given images can improve image-dependent diagnostics and can also facilitate tasks like segmentation. The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in 3D images due to the extremely complicated orientation in 3D against 2D. Given the rising demand and interest in handling 3D images, we experiment with modularising the concept and generalise the algorithm to 3D curves. In this work, we propose a 3D orientation field transform. It is a vascular structure enhancement algorithm that can cleanly enhance images having very low signal-to-noise ratio, and push the limits of 3D image quality that can be enhanced computationally. This work also utilises the benefits of modularity and offers several combinative options that each yield moderately better enhancement results in different scenarios. In principle, the proposed 3D orientation field transform can naturally tackle any number of dimensions. As a special case, it is also ideal for 2D images, owning a simpler methodology compared to the previous 2D orientation field transform. The concise structure of the proposed 3D orientation field transform also allows it to be mixed with other enhancement algorithms, and as a preliminary filter to other tasks like segmentation and detection. The effectiveness of the proposed method is demonstrated with synthetic 3D images and real-world transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones. Extensive experiments and comparisons with existing related methods also demonstrate the excellent performance of the proposed 3D orientation field transform.

REDUCCIÓN DE RIESGO DE FALLOS EN IMPRESORA 3D MEDIANTE EL USO SECUENCIAL DE DFMEA, ARBOL DE FALLOS Y REDES BAYESIANAS

The need to manufacture reliable devices, which include features that guarantee good performance and do not cause problems for the end user, is of paramount importance for manufacturers. To meet this objective, it is necessary to perform a thorough analysis of the devices to identify potential failure events in order to be able to take actions to reduce their risk of occurrence and increase the reliability and quality of the device. This research paper presents an effective tool for the detection from the design of possible failures in the devices, which allows actions to be taken for their correction in early stages. This analysis methodology combines several advanced fault analysis techniques, such as DFMEA, Fault Tree, and Bayesian networks, making the process of analyzing, detecting and correcting potential device failures more efficient. This methodology is applied to the analysis of a commercial 3D printer that uses fused filament deposition technology model Anet A8, making a preliminary filter using a DFMEA for subsequent analysis fault tree and Bayesian network managing to determine the probability of occurrence of 3D printing failures, this allows to take actions and establish priorities of corrective actions focused on reducing the risk of failure based on its probability of occurrence. Keywords: DFMEA, Fault tree, Bayesian Network, 3D printing, Fault probability

DAILY FLOWS, URBAN POLYCENTRICITY, AND TRANSIT COMMUTING PATTERNS: A STUDY OF SHANGHAI, CHINA

ABSTRACT Employment center locations, the jobs-housing relationship, and commuting patterns are inextricably connected in a megacity that heavily relies on the urban transit system to shuttle commuters. Using a transit smartcard dataset, a machine learning method is employed as a preliminary filter to sift through commuter flows and isolate key data, then kernel density and topological analysis methods are implemented to delve into the primary research questions, providing a detailed look at how these patterns unfold. The results show that transit commuting behavior in Shanghai is a dispersal activity based on multi-level employment centers characterized as hierarchical, boundary-clear, and functionally oriented. Further analysis illustrates that the intensity of commuting linkages correlates with the employment center level, shaping a hybrid pattern that couples a core-periphery pattern with a spoke-hub pattern. The commuting network connections present corridor, neighborhood, capture, and replacement features, highlighting the importance of employment centers in shaping commuting patterns. From a daily flow perspective, these findings echo the central place theory and verify that the employment center distribution in Shanghai is also multi-layered and well-nested, forming the basis for commuting links. Policy implications are provided for polycentric megacities with progressively sophisticated urban transit systems.

Automated tuning for the parameters of linear solvers

Robust iterative methods for solving large sparse systems of linear algebraic equations often suffer from the problem of optimizing the corresponding tuning parameters. To improve the performance of the problem of interest, specific parameter tuning is required, which in practice can be a time-consuming and tedious task. This paper proposes an optimization algorithm for tuning the numerical method parameters. The algorithm combines the evolution strategy with the pre-trained neural network used to filter the individuals when constructing the new generation. The proposed coupling of two optimization approaches allows to integrate the adaptivity properties of the evolution strategy with a priori knowledge realized by the neural network. The use of the neural network as a preliminary filter allows for significant weakening of the prediction accuracy requirements and reusing the pre-trained network with a wide range of linear systems.The detailed algorithm efficiency evaluation is performed for a set of model linear systems, including the ones from the SuiteSparse Matrix Collection and the systems from the turbulent flow simulations. The obtained results show that the pre-trained neural network can be effectively reused to optimize parameters for various linear systems, and a significant speedup in the calculations can be achieved at the cost of about 100 trial solves. The hybrid evolution strategy decreases the calculation time by more than 6 times for the black box matrices from the SuiteSparse Matrix Collection and by a factor of 1.4–2 for the sequence of linear systems when modeling turbulent flows. This results in a speedup of up to 1.8 times for the turbulent flow simulations performed in the paper.

Prediction model for suicide based on back propagation neural network and multilayer perceptron.

The aim was to explore the neural network prediction model for suicide based on back propagation (BP) and multilayer perceptron, in order to establish the popular, non-invasive, brief and more precise prediction model of suicide. Data were collected by psychological autopsy (PA) in 16 rural counties from three provinces in China. The questionnaire was designed to investigate factors for suicide. Univariate statistical methods were used to preliminary filter factors, and BP neural network and multilayer perceptron were employed to establish the prediction model of suicide. The overall percentage correct of samples was 80.9% in logistic regression model. The total coincidence rate for all samples was 82.9% and the area under ROC curve was about 82.0% in the Back Propagation Neural Network (BPNN) prediction model. The AUC of the optimal multilayer perceptron prediction model was above 90% in multilayer perceptron model. The discrimination efficiency of the multilayer perceptron model was superior to BPNN model. The neural network prediction models have greater accuracy than traditional methods. The multilayer perceptron is the best prediction model of suicide. The neural network prediction model has significance for clinical diagnosis and developing an artificial intelligence (AI) auxiliary clinical system.

An Assessment of the GEDI Lasers’ Capabilities in Detecting Canopy Tops and Their Penetration in a Densely Vegetated, Tropical Area

The Global Ecosystem Dynamics Investigation (GEDI), specifically designed to measure vertical forest structures, has acquired, since April 2019, more than 35 billion waveforms of Earth’s surface on a nearly global scale. GEDI is equipped with 3 identical 1064 nm lasers with a power of 10 mJ per shot, where 1 laser is split into 2 lasers, resulting in two 5 mJ coverage lasers and two 10 mJ full-power lasers. In this study, we evaluate the potential of GEDI’s four lasers to penetrate through canopies and detect the ground, and their capabilities to detect the top of the canopies over a tropical forest (in French Guiana) characterized by a dense canopy cover and tall trees. The accurate detection of both of these surfaces is the first step in characterizing vertical forest structures. The SRTM Digital Elevation Model (DEM) is used as a reference point for elevations while a canopy height model (CHM), derived from airborne and spaceborne LiDAR data, is used as a reference for canopy heights. In addition, the ground and canopy-top elevations estimated from NASA’s Land, Vegetation, and Ice Sensor (LVIS, 1064 nm full-waveform LiDAR, 5 mJ per shot, ~8 km altitude) are used as a benchmark for comparison with GEDI’s lasers. Results indicate that GEDI’s coverage and full-power lasers, even after the application of a preliminary filter that removes around 50% of acquisitions, tend to underestimate tree heights in densely vegetated, tall forests. Moreover, GEDI’s coverage lasers also exhibited a lower level of performance in comparison to both the full-power lasers and LVIS. Overall, the average estimated maximum canopy heights (RH100) for a CHM greater than 30 m was 24.4 m with the coverage lasers, 32.1 m with the full-power lasers, and 36.7 m with LVIS. The analysis of shots with high-beam sensitivity (sensitivity ≥ 98%) showed that they tend to have a better probability of reaching the ground and have better detection of canopy tops for both GEDI laser types. Nonetheless, GEDI’s coverage lasers still showed an underestimation of canopy heights with an average RH100 of 29.8 m, while for GEDI’s full-power lasers and LVIS, the average RH100 was 35.2 m and 37.7 m, respectively. Finally, the assessment of the acquisition time on the detection of the ground return and the top of the canopies showed that, for the coverage lasers, solar noise could affect the detection of the ground return as acquisitions made during early mornings or late afternoons have more penetration than shots acquired between 8 a.m. and 4 p.m. The effect of acquisition time on the detection of the tops of canopies showed that solar noise slightly affected the coverage lasers. Regarding the full-power lasers, the acquisition time of the shots seem to affect neither the penetration of the lasers, nor the detection of the tops of canopies.

A Search Methodology Based on Industrial Ontology and Machine Learning to Analyze Georeferenced Italian Districts

The subject of the proposed study is a method implementable for a search engine able to provide supply chain information, gaining the company’s knowledge base. The method is based on the construction of specific supply chain ontologies to enrich Machine Learning (ML) algorithm results able to filter and refine the searching process. The search engine is structured into two main search levels. The first one provides a preliminary filter of supply chain attributes based on the hierarchical clustering approach. The second one improves and refines the research by means of an ML classification and web scraping. The goal of the searching method is to identify a georeferenced supply chain district, finalized to optimize production and planning production strategies. Different technologies are proposed as candidates for the implementation of each part of the search engine. A preliminary prototype with limited functions is realized by means of Graphical User Interfaces (GUIs). Finally, a case study of the ice cream supply chain is discussed to explain how the proposed method can be applied to construct a basic ontology model. The results are performed within the framework of the project “Smart District 4.0”.

DESIGN AND MOLECULAR DOCKING OF SULFONAMIDE DERIVATIVES

Objective: Sulfonamides are a sulfa-related group of antibiotics, which are used to treat bacterial infections and some fungal infections. Some sulfonamides are also devoid of antibacterial activity, such as thiazide diuretics, etc. In this study, an effort was made to find out some novel and potent Sulfonamide derivatives as diuretic agents.
 Methods: Here, 30 three-dimensional sulphonamides are designed and docking simulation with PDB ID 1AZM which was downloaded from www. rcsb. org. All the molecules were also screened through a preliminary property filter (Molinspiration Property Calculator).
 Results: Among the 30 different molecules designed, 5 molecules were found to have a very good affinity towards the target protein.
 Conclusion: These molecular properties define if a molecule can be orally active in our body.

PNS46 Application of Text Mining to the Development of a Geographic Search Filter to Facilitate Evidence Retrieval in Ovid Medline

Text mining is a valuable technique for analyzing large unstructured datasets to identify meaningful patterns. Given the increasing volume of published research in bibliographic databases like MEDLINE, efficient retrieval of relevant evidence is crucial and represents an opportunity to integrate text mining tools. This study aimed to develop a geographic search filter for accurately identifying research from the United States (U.S.) in Ovid MEDLINE. U.S. and non-U.S. citations with a valid PUBMED ID were collected from bibliographies of evidence-based reviews by the U.S. Preventive Services Task Force. U.S. citations were defined as having U.S.-based author affiliations; and U.S.-based publishing location and/or grant funding. Citations were partitioned by U.S./non-U.S. status and randomly divided 3:1 to a training set to identify search terms for the filter, and testing set for its validation. Punctuation and commonly occurring words like conjunctions were removed. Using text mining, common one- and two-word terms in title/abstract fields were identified, and frequencies compared between U.S. and non-U.S. citations. Analyses used the tidytext package in R. 21,915 citations were collected; 16,436 were assigned to the training set (N=5,902 U.S.; N=10,534 non-U.S.). Common U.S.-related terms included (expressed as ratio of frequency in U.S. to non-U.S. citations) U.S. populations (“Americans” (15.5), “Medicare beneficiaries” (12.0)), and U.S. geographic terms (“Baltimore” (20.0)). Terms common to non-U.S. citations were non-U.S. geographic terms (“Japan” (0.04), “French” (0.05)). A preliminary search filter was developed by combining terms related to U.S. citations in title/abstract fields. This development of a MEDLINE-based search filter will streamline the systematic identification of evidence from U.S. studies. Periodic updates will be necessary to reflect changes in MEDLINE’s controlled vocabulary. Future work will include validation of the filter using the testing set, refinement to improve sensitivity/specificity, and application of these methods to develop search strategies specific for other jurisdictions.

Sensitivity analysis for filtering with preliminary processing of measurements

The sensitivity of estimation algorithms to the parameters of the preliminary filter is analyzed, using the problem of gravity anomaly estimation using a damped gravimeter as an example. Two models for preliminary filter are considered, which corresponds the models for gravimeter sensitive element in the example. First is a physically substantiated elastic pendulum model. Second is its simplified version in the form of a first-order system model. The results of the sensitivity analysis of gravity anomaly estimation accuracy to the models are presented.

AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD

Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.

INS/Odometer Land Navigation by Accurate Measurement Modeling and Multiple-Model Adaptive Estimation

Land vehicle navigation based on inertial navigation system (INS) and odometers is a classical autonomous navigation application and has been extensively studied over the past several decades. In this work, we seriously analyze the error characteristics of the odometer (OD) pulses and investigate three types of odometer measurement models in the INS/OD integrated system. Specifically, in the pulse velocity model, a preliminary Kalman filter is designed to obtain accurate vehicle velocity from the accumulated pulses; the pulse increment model is accordingly obtained by integrating the pulse velocity; a new pulse accumulation model is proposed by augmenting the travelled distance into the system state. The three types of measurements, along with the nonhonolomic constraint (NHC), are implemented in the standard extended Kalman filter. In view of the motion-related pulse error characteristics, the multiple model adaptive estimation (MMAE) approach is exploited to further enhance the performance. Simulations and long-distance experiments are conducted to verify the feasibility and effectiveness of the proposed methods. It is shown that the standard pulse velocity measurement achieves the superior performance, whereas the accumulated pulse measurement is most favorable with the MMAE enhancement.

Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development.

BackgroundThe time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms.ObjectiveOur purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework.MethodsWe conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review.ResultsThe search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms.ConclusionsThe review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

The relative impact of looks, income, warmth, and intelligence on female online dating preferences

Abstract Female participants (N ​= ​70) were exposed to a series of simulated online dating profiles and asked to indicate their likelihood of entering into either a short-term or long-term relationship with each. Sets of 16 profiles were constructed to orthogonally vary the physical attractiveness, income, warmth, and intelligence. Results were generally supportive of other work in finding that women tended to be most influenced by the physical appearance of the model. However, the non-physical features were also predictive of short-term mate choices, albeit in a somewhat weaker fashion. For long-term selection, physical attractiveness still appeared to have the greatest impact on choice, and tended to serve as a preliminary filter for mate selection such that the other three factors were taken into consideration primarily for profiles whose models received high ratings in physical appearance, but did not significantly vary otherwise.

Simulation-guided enzyme discovery: A new microbial source of cellobiose 2-epimerase

Cellobiose 2-epimerase (CE) is a promising industrial enzyme that can be utilized in the dairy industry. More thermostable CEs from different microorganisms are still needed for a higher lactulose productivity. This study demonstrated the feasibility to use molecular dynamics (MD) simulation as the preliminary computational filter for thermostable enzymes screening. Sequence information of eleven uncharacterized CEs were chosen to be analyzed by MD simulations. The CE from Dictyoglomus thermophilum (Dith-CE) was determined experimentally to be one of the most thermostable CEs with the highest epimerization (160 ± 6.5 U mg−1) and isomerization activities (3.52 ± 0.23 U mg−1) among all the reported CEs. This enzyme shows the highest isomerization activity at 85 °C and pH 7.0. The kinetic parameters (kcat and Km) of isomerization activity of this CE are 3.98 ± 0.3 s−1 and 235.2 ± 11.2 mM, respectively. These results suggest that the CE from Dith-CE is a promising lactulose-producing enzyme.

Identification and harmfulness analysis of solid particles contained in medical injections and their removal by nuclear track membranes

Summary Two types of cascade nuclear track filters have been developed: (1) cascade filter made of polypropylene (PP) fiber membrane as preliminary filter and nuclear track membrane (NTM) as terminal filter; (2) Nylon tortuous-pore membrane as preliminary filter and NTM as terminal filter. Removal of solid particles by the cascade filters has been investigated. The solid particles studied include glass shards from broken ampoules and rubber fragments from rubber covers (plugs) of medical bottles. When an ampoule containing medicine is broken by emery wheel and by hands, large number of glass shards are produced and fall into the medicine. When a rubber cover is punctured by syringe needle, large number of rubber fragments are produced by friction between rubber and needle. The investigation shows that fiber or tortuous membranes alone usually cannot completely remove the microscopic particles due to existence of large gaps in the structure of the two common membranes. Nuclear track membranes alone and their cascade filters are superior to remove all solid particles bigger than the pore size of the track membrane. Removal efficiency for solid particles and flow rate of injections through the cascade filters have been calibrated and shown that both features above fulfill the requirements of Chinese regulations. The two types of cascade filters have been accepted for use in Chinese hospitals.

Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy.

With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.

A New Approach for Monitoring the Terra Nova Bay Polynya through MODIS Ice Surface Temperature Imagery and Its Validation during 2010 and 2011 Winter Seasons

Polynyas are dynamic stretches of open water surrounded by ice. They typically occur in remote regions of the Arctic and Antarctic, thus remote sensing is essential for monitoring their dynamics. On regional scales, daily passive microwave radiometers provide useful information about their extent because of their independence from cloud coverage and daylight; nonetheless, their coarse resolution often does not allow an accurate discrimination between sea ice and open water. Despite its sensitivity to the presence of clouds, thermal infrared (TIR) Moderate Resolution Imaging Spectroradiometer (MODIS) provides higher-resolution information (typically 1 km) at large swath widths, several times per day, proving to be useful for the retrieval of the size of polynyas. In this study, we deal with Aqua satellite MODIS observations of a frequently occurring coastal polynya in the Terra Nova Bay (TNB), Ross Sea (Antarctica). The potential of a new methodology for estimating the variability of this polynya through MODIS TIR during the 2010 and 2011 freezing season (April to October) is presented and discussed. The polynya is observed in more than 1600 radiance scenes, after a preliminary filter evaluates and discards cloudy and fog-contaminated scenes. This reduces the useful MODIS swaths to about 50% of the available acquisitions, but a revisit time of less than 24 h is kept for about 90% of the study period. As expected, results show a high interannual variability with an opening/closing fluctuation clearly depending on the regime of the katabatic winds recorded by the automatic weather stations Rita and Eneide along the TNB coast. Retrievals are also validated through a comparison with a set of 196 co-located high-resolution ENVISAT ASAR images. Although our estimations slightly underestimate the ASAR derived extents, a good agreement is found, the linear correlation reaching 0.75 and the average relative error being about 6%. Finally, a sensitivity test on the applied thermal thresholds supports the effectiveness of our setting.

Pharmacophore and Docking Guided Virtual Screening Study for Discovery of Type I Inhibitors of VEGFR-2 Kinase.

Kinase domain of VEGFR-2 displays conformational flexibility which leads to existence of two kinds of inhibitors viz. type I and type II inhibitors. They exhibit different binding modes and this necessitates the development of separate pharmacophore models for them. The virtual screening study for discovery of type I inhibitors of VEGFR-2 kinase was done by using combined pharmacophore (generated using PHASE and validated by 3D-QSAR) and docking (Glide) based approach. Validated pharmacophore was used as preliminary filter followed by docking. ADME properties were predicted for retrieved hits using QikProp. ADHRR.94 with statistical parameters r2 test 0.94, r2 training 0.99, SD 0.0766, r2 0.9861, F 283.3, RMSE 0.2605, q2 0.8115 and Pearson's R 0.9723was identified as the best pharmacophore hypothesis for type I inhibitors of VEGFR-2 kinase. Virtual screening study was done for Asinex Elite Libraries comprising of 104400 molecules using ADHRR.94, HTVS docking and XP docking that resulted in twelve hits. Asinex ligand 5686 with docking score of -10.48kcal/mol was top-ranking hit. It made two hydrogen bonding interactions with Cys 919, one as an acceptor and other as a donor, which are characteristic of type I inhibitors. Additional interactions observed were π-cation with Lys 868 and π- πstacking with Phe 1047.Twelve hits had acceptable values for ADME properties. Twelve hits with best obtained docking scores ranging from -10.48 to -7.23 kcal/mol and mimicking characteristic type I inhibitor interactions were identified which could be probable inhibitors of VEGFR-2.