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Requirements Engineering A Good Practice Guide

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules’ evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitus.

An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis

This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.

https://www.beei.org/index.php/EEI/article/download/2362/1735

Generation of high dynamic range for enhancing the panorama environment

Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adelie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response.

Multidrone aerial surveys of penguin colonies in Antarctica

The construction of a high-resolution panoramic image from a sequence of input overlapping images of the same scene is called image stitching/mosaicing. It is considered as an important, challenging topic in computer vision, multimedia, and computer graphics. The quality of the mosaic image and the time cost are the two primary parameters for measuring the stitching performance. Therefore, the main objective of this paper is to introduce a high-quality image stitching system with least computation time. First, we compare many different features detectors. We test Harris corner detector, SIFT, SURF, FAST, GoodFeaturesToTrack, MSER, and ORB techniques to measure the detection rate of the corrected keypoints and processing time. Second, we manipulate the implementation of different common categories of image blending methods to increase the quality of the stitching process. From experimental results, we conclude that ORB algorithm is the fastest, more accurate, and with higher performance. In addition, Exposure Compensation is the highest stitching quality blending method. Finally, we have generated an image stitching system based on ORB using Exposure Compensation blending method.

/pdf/image-stitching-system-based-on-orb-feature-based-technique-40l7sd8b3z.pdf

Image Stitching System Based on ORB Feature-Based Technique and Compensation Blending

stitching (Mosaicing) is considered as an active research area in computer vision and computer graphics. Image stitching is concerned with combining two or more images of the same scene into one high resolution image which is called panoramic image. Image stitching techniques can be categorized into two general approaches: direct and feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, whereas feature based techniques aim to determine a relationship between the images through distinct features extracted from the processed images. The last approach has the advantage of being more robust against scene movement, faster, and has the ability to automatically discover the overlapping relationships among an unordered set of images. The purpose of this paper is to present a survey about the feature based image stitching. The main components of image stitching will be described. A framework of a complete image stitching system based on feature based approaches will be introduced. Finally, the current challenges of image stitching will be discussed. Keywordsstitching/mosaicing, panoramic image, features based detection, SIFT, SURF, image blending.

/pdf/image-stitching-based-on-feature-extraction-techniques-a-5fwwseecdw.pdf

Image Stitching based on Feature Extraction Techniques: A Survey

Over the past years, the knowledge and data are rising rapidly with the development of communication technology. There is a growing demand for integrating data, especially the medical healthcare da...

Distributed electronic health record based on semantic interoperability using fuzzy ontology: a survey

Image mosaicing/stitching is considered as an active research area in computer vision and computer graphics. Image mosaicing is concerned with combining two or more images of the same scene into one panoramic image with high resolution. There are two main types of techniques used for creating image stitching: direct methods and feature-based methods. The greatest advantages of feature-based methods over the other methods are their speed, robustness, and the availability of creating panoramic image of a non-planar scene with unrestricted camera motion. In this paper, we propose a real time image stitching system based on ORB feature-based technique. We compared the performance of our proposed system with SIFT and SURF feature-based techniques. The experiment results show that the ORB algorithm is the fastest, the highest performance, and it needs very low memory requirements. In addition, we make a comparison between different feature-based detectors. The experimental result shows that SIFT is a robust algorithm but it takes more time for computations. MSER and FAST techniques have better performance with respect to speed and accuracy.

Real time image mosaicing system based on feature extraction techniques

EHR semantic interoperability is urgently needed for systems to improve healthcare quality. However, its achievement has many challenges and barriers. The primary purpose of this chapter is to attract attention to the importance of semantic interoperability in the healthcare industry. We intensely surveyed the current literature for aspects of semantic interoperability, including its main definitions, standards, schemas, models, terminologies, barriers, and future challenges. We depended on the existing databases, including ScienceDirect, IEEE Xplore, PubMed, ELSEVIER, MEDLINE, Cochranelibrary, Informit, and Springer. The results include a comprehensive survey of healthcare semantic interoperability. We noted that the most intuitive EHR semantic interoperability approach is based on standards, but they have some challenges. The medical domain is characterized by its vagueness and uncertainty. All parties in the healthcare domain use imprecise concepts to describe their ideas. As a result, we recommend fuzzy ontology to achieve the target goal of global interoperability.

Ebtsam Adel

Papers

Image Stitching based on Feature Extraction Techniques: A Survey

Image Stitching System Based on ORB Feature-Based Technique and Compensation Blending

Distributed electronic health record based on semantic interoperability using fuzzy ontology: a survey

Real time image mosaicing system based on feature extraction techniques

Ontology-based electronic health record semantic interoperability: A survey