Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.

In spite of the adverse effects of the COVID-19 pandemic on the global economy, it has had a positive impact on the environment. To combat the spread of the virus, many countries, including Jordan, imposed lockdown measures that involved the suspension of economic activities such as manufacturing operations, transportation, and construction (with some exceptions for essential services). Consequently, the levels of certain atmospheric pollutants, such as NO2 and CO, decreased by an average of 54% and 7%, respectively, during the lockdown period in Jordan. By conducting a systematic review of the lockdown measures and applying spatiotemporal analysis, this study established a methodology to analyze the influence of pandemic mitigation measures on regional air quality. The findings revealed a significant improvement in air quality throughout the study area during the lockdown period.

Significant research has been conducted into image edge detection, usually focusing on gradient and higher order derivative approaches. Recent development in the field suggests the incorporation of scale factor in filter design; this is to increase robustness against noise and to facilitate the process of generating multilevel edge maps. While multifractal analysis technique uses scale factors, few researchers examine the effectiveness of this technique in image edge map generation due to its poor efficiency. Herein, we propose F-MED, a fast multifractal edge detector for grayscale images based on generating an edge map from different low frequency versions of the original image by means of frequency domain Gaussian low pass filters. The quality of results is evaluated subjectively via visual assessment and quantitatively using a set of full-reference and non-reference measures. Results show that the proposed F-MED can be leveraged to support not only high efficiency, but also quality improvement and high robustness against blurring effect and Rayleigh noise.

Authors : Ahmad Attiq Al-Ogaibi, Ahmad Sharieh, Moh’d Belal Al-Zoubi, and R Bremananth Abstract : In pattern clustering, nearest neighborhood point computation is a challenging issue for many applications in the area of research such as Remote Sensing, Computer Vision, Pattern Recognition and Statistical Imaging. Nearest neighborhood computation is an essential computation for providing sufficient classification among the volume of pixels (voxels) in order to localize the active-region-of-interests (AROI). Furthermore, it is needed to compute spatial metric relationships of diverse area of imaging based on the applications of pattern recognition. In this paper, we propose a new methodology for finding the nearest neighbor point, depending on making a virtually grid of a hexagon cells, then locate every point beneath them. An algorithm is suggested for minimizing the computation and increasing the turnaround time of the process. The nearest neighbor query points are fetched by seeking fashion of hexagon holistic. Seeking will be repeated until an AROI is to be expected. If any point is located then searching starts in the nearest hexagons in a circular way. The First hexagon is considered be level 0 (L0) and the surrounded hexagons is level 1 (L1). If is located in L1, then search starts in the next level (L2) to ensure that is the nearest neighbor for . Based on the result and experimental results, we found that the proposed method has an advantage over the traditional methods in terms of minimizing the time complexity required for searching the neighbors, in turn, efficiency of classification will be improved sufficiently.

Outlier detection is an important task in a wide variety of application areas. In this paper, a proposed method based on fuzzy clustering approaches for outlier detection is presented. We first perform the c-means fuzzy clustering algorithm. Small clusters are then determined and considered as outlier clusters. The rest of outliers (if any) are then detected in the remaining clusters based on temporary removing a point from the data set and re-calculating the objective function. If a noticeable change occurred in the Objective Function (OF), the point is considered an outlier. Experimental results show that our method works well. The test results show that the proposed approach gave good results when applied to different data sets.

In this paper, a new efficient method for outlier detection is proposed. The proposed method is based on fuzzy clustering techniques. The c-means algorithm is first performed, then small clusters are determined and considered as outlier clusters. Other outliers are then determined based on computing differences between objective function values when points are temporarily removed from the data set. If a noticeable change occurred on the objective function values, the points are considered outliers. Test results were performed on different well-known data sets in the data mining literature. The results showed that the proposed method gave good results.

One popular approach for finding the best number of clusters (K) in a data set is through computing the silhouette coefficients. The silhouette coefficients for different values of K, are first found and then the maximum value of these coefficients is chosen. However, computing the silhouette coefficient for different Ks is a very time consuming process. This is due to the amount of CPU time spent on distance calculations. A proposed approach to compute the silhouette coefficient quickly had been presented. The approach was based on decreasing the number of addition operations when computing distances. The results were efficient and more than 50% of the CPU time was achieved when applied to different data sets.

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of distance calculations by checking the membership value for each point and eliminating those points with a membership value smaller than a threshold value. We applied FFCM on several data sets. The experiments demonstrate the efficiency of the proposed algorithm.

In this paper, two novel image filters are presented. These filters, named as Far Distance Filter (FDF) and Near Distance Filter (NDF), are actually based on calculating the distance between image pixels and their neighbors in order to construct arbitrary values used to enhance abnormal pixels (noise). FDF and NDF use (5 × 5) kernel instead of the usual (3 × 3) kernel to produce better image results. The performance of proposed filters and the well-known mean filters is investigated through the measurement of PSNR and MSE. This performance shows clearly the efficiency of the proposed filters.

In this paper, we propose a new technique to answer GIS queries quickly. The technique uses phone zones as main indexes to narrow down the search processes. In this paper, we build modest GIS software system to handle different GIS queries efficiently. Using our new software, many benefits can be gained including search efficiency, simple interface, displaying spatial relationships, showing sales and service territories.

The objective of this work is to build a proper Geographic Information System (GIS) data-model that can be used for school mapping planning, where all schools with attribute data would be available for problem solving and decision-making in Education. Thus, the end product of the GIS will be part of an Educational Decision Support System that provides the user with a map of specific region with focus on the schools locations and all related info to assist decision-makers in either expanding current school or suggesting sites for new schools in Jordan, and also, for student and resources location/allocation. The built GIS-Data-model is composed of the schools layer, large-scale streets, directorates, sub-districts and Governorate layers with population data. IKONOS Geo imagery and more reliable small-scale vector data format are to be integrated with the current GIS-data-model to build a detailed geospatial-database for school mapping planning purposes. However, there are several problems associated with this mainly because of the different projection systems each dataset is based on and due to the scale problems. This paper reviews the original GIS-data-model format, the used projection systems, the management of the geospatial database and aspects of the project to collect data from ground and satellite semi-rectified image sources and combine the data sets together so that a high level of tractability and integrity are offered. A status report of the existing uses of the model is given together with an outlook of expected future uses as an Educational Decision Support System for planning purposes. The paper concludes with the future trend to build an Internet Wep-mapping site to be available to fit various needs for GIS School Mapping applications, which would be perfect for decentralizing the Decision Support System between Educational Directorates for data updates.