Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

A Review of Content-Based and Context-Based Recommendation Systems

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests

A Review of Content-Based and Context-Based Recommendation Systems

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests

The Influence of Meloidogyne incognita Density on Susceptible Tomato

Abstract.-The purpose of this study was to assess the effects of increasing initial population density (Pi) of Meloidogyne incognita on nematode reproduction and growth of susceptible tomato (Lycopersicom esculentum cv. Round-41) at 25°C± 2 in growth chamber. 21-days-old tomato seedlings transplanted in13-cm dia. earthen pot was inoculated with Pi including 250, 500, 1000, 1500 eggs of M. incognita. Nematode reproduction was assessed by determining the number of galls, eggs masses, eggs per root system, and reproduction rate values per root system at 60 days after inoculations. Egg-masses on Phloxine B-stained roots were quantified and root systems were rated for galling and egg mass presence on a 0 to 5 scale where 0 = no gall or egg masses, 1 = 1-2, 2 = 3-10, 3 = 11-30, 4 = 31-100, and 5 = >100 galls or egg masses per root system. Nematode reproduction was directly proportional to Pi. Reproduction rate (Pf/Pi, where Pf = final number of eggs / initial egg density) for Pi's of 250, 500, 1000 and 1500 eggs/plant was lowest when Pi = 250 (2.23) but similar when Pi = 500, 1000 or 1500. Shoot and root growth were inversely related to Pi. Strong liner relationship exited between root length, root galling index and foliage growth but very poor between root weight and foliage growth. High Pf = final eggs per root system than at all Pi suggests that Round-41 is a good host for M. incognit

V2X-Based Mobile Localization in 3D Wireless Sensor Network

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies

Association of ABO and Rh blood groups with breast cancer

Objectives: The aim of this study was to determine the association of “ABO” and “Rhesus” blood groups with incidence of breast cancer. Methods: In this study, we identified 70 research documents from data based search engines including “PubMed”, “ISI-Web of Knowledge”, “Embase” and “Google Scholar”. The research papers were selected by using the primary key-terms including “ABO blood type”, “Rhesus” blood type and “breast cancer”. The research documents in which “ABO” and “Rhesus” blood types and breast cancer was debated were included. After screening, we reviewed 32 papers and finally we selected 25 research papers which met the inclusion criteria and remaining documents were excluded. Results: Blood group “A” has high incidence of breast cancer (45.88%), blood group “O” has (31.69%); “B” (16.16%) and blood group “AB” has (6.27%) incidence of breast cancer. Blood group “A” has highest and blood group “AB” has least association with breast cancer. Furthermore, “Rhesus +ve” blood group has high incidence of breast cancer (88.31%) and “Rhesus –ve” blood group has least association with breast cancer (11.68%). Conclusion: Blood group “A” and “Rhesus +ve” have high risk of breast cancer, while blood type “AB” and “Rhesus –ve” are at low peril of breast cancer. Physicians should carefully monitor the females with blood group “A” and “Rh +ve” as these females are more prone to develop breast cancer. To reduce breast cancer incidence and its burden, preventive and screening programs for breast cancer especially in young women are highly recommended