Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized by Artificial Bee Colony Algorithm

The article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM)was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method

Iris recognition based on 2D Gabor filter

Iris recognition is a type of biometrics technology that is based on physiological features of the human body. The objective of this research is to recognize and identify iris among many irises that are stored in a visual database. This study employed a left and right iris biometric framework for inclusion decision processing by combining image processing and artificial bee colony. The proposed approach was evaluated on a visual database of 280 colored iris pictures. The database was then divided into 28 clusters. Images were preprocessed and texture features were extracted based Gabor filters to capture both local and global details within an iris. The technique begins by comparing the attributes of the online-obtained iris picture with those of the visual database. This technique either generates a reject or approve message. The consequences of the intended work reflect the output’s accuracy and integrity. This is due to the careful selection of attributes, as well as the deployment of an artificial bee colony and data clustering, which decreased complexity and eventually increased identification rate to 100%. We demonstrate that the proposed method achieves state-of-the-art performance and that our recommended procedures outperform existing iris recognition systems

Brain Tumor Segmentation Techniques: A Review

Image processing is used widely in solving a variety of problems. The important and complex phase of image processing is image segmentation. This paper provides a brief description on some of the segmentation algorithms specifically on brain tumor MR Images. Later in this paper, simple comparisons are made between the listed algorithms. This work helps in understanding some of the existing brain MR Image segmentation algorithms better

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

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

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

Automatic assessment of honey bee cells using deep learning

Temporal assessment of honey bee colony strength is required for different applications in many research projects, which often involves counting the number of comb cells with brood and food reserves multiple times a year. There are thousands of cells in each comb, which makes manual counting a time-consuming, tedious and thereby an error-prone task. Therefore, the automation of this task using modern imaging processing techniques represents a major advance. Herein, we developed a software capable of (i) detecting each cell from comb images, (ii) classifying its content and (iii) display the results to the researcher in a simple way. The cells’ contents typically display a high variation of patterns which make their classification by software a challenging endeavour. To address this challenge, we used Deep Neural Networks (DNNs). DNNs are known for achieving the state of art in many fields of study including image classification, because they can learn features that best describe the content being classified by themselves. Our DNN model was trained with over 70,000 manually labelled cell images whose cells were separated into seven classes. Our contribution is an end-to-end software capable of doing automatic background removal, cell detection, and classification of cell content based on an input comb image. With this software, colony assessment achieves an average accuracy of 94% across the seven classes in our dataset, representing a substantial progress regarding the approximation methods (e.g. Lieberfeld) currently used by honey bee researchers and previous techniques based on machine learning that used handmade features like colour and texture.A análise temporal sobre a qualidade e força de colônias de abelha melífera (Apis mellifera L.) é necessária em muitos projetos de pesquisa. Ela pode ser realizada contando alvéolos com alimento (pólen e néctar) e criação. É comum que ela seja feita diversas vezes ao ano. A grande quantidade de alvéolos em cada favo torna a tarefa demorada e tediosa ao pesquisador. Assim, frequentemente essa contagem é feita forma aproximada usando métodos como o de Lieberfeld. Automatizar este processo usando técnicas modernas de processamento de imagem representa um grande avanço, pois resultados mais precisos e padronizados poderão ser obtidos em menos tempo. O objetivo deste trabalho é construir de um software capaz de detectar, classificar e contar alvéolos a partir de uma imagem. Após, ele deverá apresentar os dados de forma simplificada ao usuário. Para tratar da alta variação de padrões como textura, cor e iluminação presente nas alvéolos, usaremos Deep Neural Network (DNN), que são modelos computacionais conhecidos por terem alcançado o estado da arte em várias tarefas relacionadas a processamento de sinais e imagens. Para o treinamento desses modelos utilizamos mais de 70.000 alvéolos anotadas por um apicultor experiente, separadas em sete classes. Entre nossas contribuições estão métodos de préprocessamento que garantem uma alta taxa de detecção de alvéolos, aliados a modelos de segmentação baseados em DNNs que asseguram uma baixa taxa de falsos positivos. Com nossos classificadores conseguimos uma acurácia média de 94% em nosso dataset e obtivemos resultados superiores a outros métodos baseados em contagens aproximadas e técnicas de análise por imagem que não utilizam DNNs.This research was conducted in the framework of the project BEEHOPE, funded through the 2013-2014 BiodivERsA/FACCE-JPI Joint call for research proposals, with the national founders FCT(Portugal), CNRS(France), and MEC(Spain)

A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems

This chapter presents an overview of optimization techniques followed by a brief survey on several swarm-based natural inspired algorithms which were introduced in the last decade. These techniques were inspired by the natural processes of plants, foraging behaviors of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems