Non-implementation of property rating practice, any impact on community healthcare in Bauchi Metropolis Nigeria?

The practice of rating real estate is essentially an internal revenue source, synonymous to tenement tax levied on the owner/occupier. Property rating in Nigeria is bedevilled by many factors that impeded its smooth implementation and operation, thus, this form of taxation yields zero revenue in Bauchi, due to failure of implementation. This study is aimed at measuring the impact of non-implementation of property rating on community healthcare in Bauchi metropolis of Nigeria. Two hundred and fifty (250) closed-ended questionnaires composed in five-level Likert scale were distributed to professionals in the field of real estate and facilities management, in the academia and estate firms, and two hundred and twenty one questionnaires (221) were mailed back for analysis. The Structural Equation Modelling (SEM) in IBM version of SPSS with AMOS was used to establish relationship between the variables. Findings from this study reveals that PRP does not command direct impact on community healthcare services, however, the services financed by property rating in the area of sanitation and sewage cleaning has the tendencies to curb the occurrence of diseases like cholera and malaria. Thus, it can be understood that a fully institutionalized practice of property rating could avert the outbreak of diseases

Non-implementation of property rating practice, any impact on community healthcare in Bauchi Metropolis Nigeria?

The practice of rating real estate is essentially an internal revenue source, synonymous to tenement tax levied on the owner/occupier. Property rating in Nigeria is bedevilled by many factors that impeded its smooth implementation and operation, thus, this form of taxation yields zero revenue in Bauchi, due to failure of implementation. This study is aimed at measuring the impact of non-implementation of property rating on community healthcare in Bauchi metropolis of Nigeria. Two hundred and fifty (250) closed-ended questionnaires composed in five-level Likert scale were distributed to professionals in the field of real estate and facilities management, in the academia and estate firms, and two hundred and twenty one questionnaires (221) were mailed back for analysis. The Structural Equation Modelling (SEM) in IBM version of SPSS with AMOS was used to establish relationship between the variables. Findings from this study reveals that PRP does not command direct impact on community healthcare services, however, the services financed by property rating in the area of sanitation and sewage cleaning has the tendencies to curb the occurrence of diseases like cholera and malaria. Thus, it can be understood that a fully institutionalized practice of property rating could avert the outbreak of diseases

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis

Computer Vision for Marine Environmental Monitoring

Osterloff J. Computer Vision for Marine Environmental Monitoring. Bielefeld: Universität Bielefeld; 2018.Ocean exploration using imaging techniques has recently become very popular as camera systems became affordable and technique developed further. Marine imaging provides a unique opportunity to monitor the marine environment. The visual exploration using images allows to explore the variety of fauna, flora and geological structures of the marine environment. This monitoring creates a bottleneck as a manual evaluation of the large amounts of underwater image data is very time consuming. Information encapsulated in the images need to be extracted so that they can be included in statistical analyzes. Objects of interest (OOI) have to be localized and identified in the recorded images. In order to overcome the bottleneck, computer vision (CV) is applied in this thesis to extract the image information (semi-) automatically. A pre-evaluation of the images by marking OOIs manually, i.e. the manual annotation process, is necessary to provide examples for the applied CV methods. Five major challenges are identified in this thesis to apply of CV for marine environmental monitoring. The challenges can be grouped into challenges caused by underwater image acquisition and by the use of manual annotations for machine learning (ML). The image acquisition challenges are the optical properties challenge, e.g. a wavelength dependent attenuation underwater, and the dynamics of these properties, as different amount of matter in the water column affect colors and illumination in the images. The manual annotation challenges for applying ML for underwater images are, the low number of available manual annotations, the quality of the annotations in terms of correctness and reproducibility and the spatial uncertainty of them. The latter is caused by allowing a spatial uncertainty to speed up the manual annotation process e.g. using point annotations instead of fully outlining OOIs on a pixel level. The challenges are resolved individually in four different new CV approaches. The individual CV approaches allow to extract new biologically relevant information from time-series images recorded underwater. Manual annotations provide the ground truth for the CV systems and therefore for the included ML. Placing annotations manually in underwater images is a challenging task. In order to assess the quality in terms of correctness and reproducibility a detailed quality assessment for manual annotations is presented. This includes the computation of a gold standard to increase the quality of the ground truth for the ML. In the individually tailored CV systems, different ML algorithms are applied and adapted for marine environmental monitoring purposes. Applied ML algorithms cover a broad variety from unsupervised to supervised methods, including deep learning algorithms. Depending on the biologically motivated research question, systems are evaluated individually. The first two CV systems are developed for the _in-situ_ monitoring of the sessile species _Lophelia pertusa_. Visual information of the cold-water coral is extracted automatically from time-series images recorded by a fixed underwater observatory (FUO) located at 260 m depth and 22 km off the Norwegian coast. Color change of a cold water coral reef over time is quantified and the polyp activity of the imaged coral is estimated (semi-) automatically. The systems allow for the first time to document an _in-situ_ change of color of a _Lophelia pertusa_ coral reef and to estimate the polyp activity for half a year with a temporal resolution of one hour. The third CV system presented in this thesis allows to monitor the mobile species shrimp _in-situ_. Shrimp are semitransparent creating additional challenges for localization and identification in images using CV. Shrimp are localized and identified in time-series images recorded by the same FUO. Spatial distribution and temporal occurrence changes are observed by comparing two different time periods. The last CV system presented in this thesis is developed to quantify the impact of sedimentation on calcareous algae samples in a _wet-lab_ experiment. The size and color change of the imaged samples over time can be quantified using a consumer camera and a color reference plate placed in the field of view for each recorded image. Extracting biologically relevant information from underwater images is only the first step for marine environmental monitoring. The extracted image information, like behavior or color change, needs to be related to other environmental parameters. Therefore, also data science methods are applied in this thesis to unveil some of the relations between individual species' information extracted semi-automatically from underwater images and other environmental parameters

Mathematical and Data-driven Pattern Representation with Applications in Image Processing, Computer Graphics, and Infinite Dimensional Dynamical Data Mining

Patterns represent the spatial or temporal regularities intrinsic to various phenomena in nature, society, art, and science. From rigid ones with well-defined generative rules to flexible ones implied by unstructured data, patterns can be assigned to a spectrum. On one extreme, patterns are completely described by algebraic systems where each individual pattern is obtained by repeatedly applying simple operations on primitive elements. On the other extreme, patterns are perceived as visual or frequency regularities without any prior knowledge of the underlying mechanisms. In this thesis, we aim at demonstrating some mathematical techniques for representing patterns traversing the aforementioned spectrum, which leads to qualitative analysis of the patterns' properties and quantitative prediction of the modeled behaviors from various perspectives. We investigate lattice patterns from material science, shape patterns from computer graphics, submanifold patterns encountered in point cloud processing, color perception patterns applied in underwater image processing, dynamic patterns from spatial-temporal data, and low-rank patterns exploited in medical image reconstruction. For different patterns and based on their dependence on structured or unstructured data, we present suitable mathematical representations using techniques ranging from group theory to deep neural networks.Ph.D

A review on the use of computer vision and artificial intelligence for fish recognition, monitoring, and management.

Abstract: Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine the best course of action for new studies. In this context, this article characterizes the current state of the art by identifying the main studies on the subject and briefly describing their approach. In contrast with most previous reviews related to technology applied to fish recognition, monitoring, and management, rather than providing a detailed overview of the techniques being proposed, this work focuses heavily on the main challenges and research gaps that still remain. Emphasis is given to prevalent weaknesses that prevent more widespread use of this type of technology in practical operations under real-world conditions. Some possible solutions and potential directions for future research are suggested, as an effort to bring the techniques developed in the academy closer to meeting the requirements found in practice

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Optics And Computer Vision For Biomedical Applications

Bioengineering is at the cross sections of biology, clinical technology, electrical engineering, computer science and many other domains. The smooth translation of domain technologies to clinics is not just about accuracy and practicality of the technology. It also has to take into account the accessibility (cost and portability), the patients’ comfort and the ease to adapt into the workflow of medical professionals. The dissertation will explore three projects, (1) portable and low-cost near infrared florescence imaging system on mobile phone platform, (2) computer aided diagnosis software for diagnosing chronical kidney disease based on optical coherence tomography (OCT) images and (3) the tracking and localization of hand-held medical imaging probe. These projects aim to translate and adapt modern computation hardware, data analysis models and computer vision technologies to solve and refine clinical diagnosis applications. The dissertation will discuss how the translation, tradeoffs and refinement of those technologies can bring a positive impact on the accuracy, ease of conduct, accessibility and patients’ comfort to the clinical applications