A binary level set method based on k-Means for contour tracking on skin cancer images

A great challenge of research and development activities have recently highlighted in segmenting of the skin cancer images. This paper presents a novel algorithm to improve the segmentation results of level set algorithm with skin cancer images. The major contribution of presented algorithm is to simplify skin cancer images for the computer aided object analysis without loss of significant information and to decrease the required computational cost. The presented algorithm uses k-means clustering technique and explores primitive segmentation to get initial label estimation for level set algorithm. The proposed segmentation method provides better segmentation results as compared to standard level set segmentation technique and modified fuzzy cmeans clustering technique

Some Clustering Methods, Algorithms and their Applications

Clustering is a type of unsupervised learning [15]. When no target values are known, or "supervisors," in an unsupervised learning task, the purpose is to produce training data from the inputs themselves. Data mining and machine learning would be useless without clustering. If you utilize it to categorize your datasets according to their similarities, you'll be able to predict user behavior more accurately. The purpose of this research is to compare and contrast three widely-used data-clustering methods. Clustering techniques include partitioning, hierarchy, density, grid, and fuzzy clustering. Machine learning, data mining, pattern recognition, image analysis, and bioinformatics are just a few of the many fields where clustering is utilized as an analytical technique. In addition to defining the various algorithms, specialized forms of cluster analysis, linking methods, and please offer a review of the clustering techniques used in the big data setting

Group technology: amalgamation with design of organisational structures

Group technology has been studied extensively from an ‘industrial engineering’ perspective (layout, scheduling, workflow, etc.), but less often from an organisational design viewpoint. To study this implication of group technology, the approach of applied systems theory for the design of organisational structures was used as framework for analysis in three empirical cases. To increase the reliability of findings from the analysis of these three empirical cases, five more cases were drawn from archival search. Cluster analysis and product flow analysis were the methods used for forming groups of machines and employees in manufacturing cells, whereas the coding of parts was not employed to this end. Furthermore, the results indicate that the implementation of group technology generally meets shifts in performance requirements caused by competitive pressures, particularly flexibility and responsiveness, albeit the companies considered group technology only when under pressure of ‘poor’ business performance. However, group technology is not always a solution to challenges that companies experience; one empirical case shows that defunctionalisation and scheduling with virtual groups was more beneficial. Nevertheless, when the introduction of group technology is feasible, it also allows firms to consider delegating responsibility for production planning and scheduling to lower levels in the hierarchy and semi-autonomous groups as an alternative to ‘complex’ software applications (a socio-technical approach). Whereas the current study sheds light on the relationship between group technology and design of organisational structures, further research is necessary into the design of these structures and their relationship to group technology

Estimativa do Peso de Corvinas e Deteção de Períodos de Alimentação

O presente trabalho de investigação tem como objetivo explorar a aplicação de modelos de machine learning e deep learning a imagens obtidas em tanques que agregam múltiplos peixes (fish farms). O correto desenvolvimento dos seres vivos presentes nestes tanques envolve processos de controlo minuciosos, não só das condições do meio como também das características dos próprios animais. O peso é uma destas características e o seu controlo fornece informações importantes relativamente ao processo de crescimento e à saúde dos animais. É frequente que os processos de controlo utilizados periodicamente pelas instituições responsáveis pela criação e desenvolvimento de determinados seres vivos sejam realizados de forma manual, o que implica não só um consumo de tempo significativo como também poderá colocar em risco o bem-estar do ser vivo e do individuo responsável. Na tentativa de reduzir a janela temporal necessária para a recolha de dados relativos ao peso de corvinas que habitam as fish farms da empresa SEAentia é proposta a utilização de um procedimento, composto por um modelo YOLOv4, por um script em Python e por um modelo de regressão linear simples, capaz de realizar estimativas de peso para cada ser vivo. Adicionalmente, é proposta também a utilização do mesmo modelo de visão por computador e de um script de pós-processamento para identificação de períodos de alimentação, caracterizados pelo agrupamento das corvinas numa determinada região das fish farms.This research work aims to explore the application of machine learning and deep learning models to images obtained from tanks that aggregate multiple fish (fish farms). The correct development of the living beings present in these tanks involves detailed control processes, not only of the environmental conditions but also of the characteristics of the animals themselves. Weight is one of these characteristics and its control provides important information regarding the growth process and the health of the animals. It is common for monitoring processes used periodically by institutions responsible for the breeding and development of certain living creatures to be done manually, which not only implies a significant consumption of time but may also put at risk the welfare of the living being and the individual responsible. In an attempt to reduce the time window required to collect data on the weight of croakers present in SEAentia's fish farms, it is proposed to use a procedure, composed of a YOLOv4 model, a Python script, and a simple linear regression model, capable of making weight estimates for each living creature. Additionally, it is also proposed to use the same computer vision model and a postprocessing script to identify feeding periods, which are characterized by the existence of groups of meagres in a certain region of the fish farms

A new approach of clustering based machine-learning algorithm

Machine-learning research is to study and apply the computer modeling of learning processes in their multiple manifestations, which facilitate the development of intelligent system. In this paper, we have introduced a clustering based machine-learning algorithm called clustering algorithm system (CAS). The CAS algorithm is tested to evaluate its performance and find fruitful results. We have been presented some heuristics to facilitate machine-learning authors to boost up their research works. The InfoBase of the Ministry of Civil Services is used to analyze the CAS algorithm. The CAS algorithm is compared with other machine-learning algorithms like UNIMEM, COBWEB, and CLASSIT, and was found to have some strong points over them. The proposed algorithm combined advantages of two different approaches to machine learning. The first approach is learning from Examples, CAS supports Single and Multiple Inheritance and Exceptions. CAS also avoids probability assumptions which are well understood in concept formation. The second approach is learning by Observation. CAS applies a set of operators that have proven to be effective in conceptual clustering. We have shown how CAS builds and searches through a clusters hierarchy to incorporate or characterize an object