Machine Learning and Its Application in Monitoring Diabetes Mellitus

Data science is a fast-growing area that deals with data from its origin to the knowledge exploration. It comprises of two main subdomains, data analytics for preparing data, and machine learning to probe into this data for hidden patterns. Machine learning (ML) endows powerful algorithms for the automatic pattern recognition and producing prediction models for the structured and unstructured data. The available historical data has patterns having high predictive value used for the future success of an industry. These algorithms also help to obtain accurate prediction, classification, and simulation models by eliminating insignificant and faulty patterns. Machine learning provides major advancement in the healthcare industry by assisting doctors to diagnose chronic diseases correctly. Diabetes is one of the most common chronic disease that occurs when the pancreas cells are damaged and do not secrete sufficient amount of insulin required by the human body. Machine learning algorithms can help in early diagnosis of this chronic disease by studying its predictor parameter values.

DIGITAL IMAGING BASED ON FRACTAL THEORY AND ITS SPATIAL DIMENSIONALITY

Due to the complexity of digital imaging targets and imaging conditions, fractal theory techniques in existing digital imaging systems still have various shortcomings. In this paper, a digital imaging processing method based on fractal theory is proposed for the first time. For X-ray images, the rapid calculation method of H-parameters is derived based on the fractional Brownian random field model. The H-parameters of X-ray images are calculated point by point. After that, all the singular points are connected, which is the edge of the defect in the image. We apply this method to analyze and process the X-ray images with defects such as missing joints, skins and hollows. Secondly, by means of fractal geometry, the contour slice measurement of the digital imaging space of this fractal is studied. The approximate index value is the digital imaging section profile dimension (D1 dimension) and the section shadow dimension (D2 dimension), so that the dimension determines the complexity of the form and detail of digital imaging. Finally, it can be seen from the experimental results that this method is effective and explores a new way for the development of digital imaging technology. At the same time, it is of great significance to the automatic pattern recognition of the application.

Automatic calcareous nannofossil biostratigraphy using the latest version of SYRACO

<p>SYRACO (SYstème de Reconnaissance Automatique de COccolithes) is a software that pilots an automatic microscope and a digital camera in order to automatically recognize coccolith species and measure their morphological characteristic based on artificial neural networks. The first version was displayed in 1996 (Dollfus and Beaufort, 1996; 1999) and was scientifically used for the first time in 2001 (Beaufort et al., 2001). SYRACO evolved during the last 20 years in many aspects such as the architecture of the neural networks, the image scanning and pre-treatments. Twenty years ago, SYRACO was dedicated to quaternary paleoceanographic studies, because it was able to recognize morphological classes. With all the developments, it is now able to be used in biostratigraphy as it is able to determine coccolith species. The latest version of SYRACO will be described, and an example of application to a south Pacific core will be given. <span> </span></p><p> </p><p>Beaufort, L., de Garidel Thoron , T., Mix, A. C., and Pisias, N. G.: ENSO-like forcing on Oceanic Primary Production during the late Pleistocene, Science, 293, 2440-2444, 2001.</p><p>Dollfus, D., and Beaufort, L.: Automatic pattern recognition of calcareous nannoplankton, Neural Network and their Applications : NEURAP 96, Marseille, France, 1996, 306-311,<span> </span></p><p>Dollfus, D., and Beaufort, L.: Fat neural network for recognition of position-normalised objects, Neural Networks, 12, 553-560, 1999.</p>

Multi-Font Arabic Isolated Character Recognition Using Combining Machine Learning Classifiers

Nowadays, optical character recognition is one of the most successful automatic pattern recognition applications. Many works have been done regarding the identification of Latin and Chinese characters. However, the reason for having few investigations for the recognition of Arabic characters is the complexity and difficulty of Arabic characters identification compared to the others. In the current work, we investigate combining multiple machine learning algorithms for multi-font Arabic isolated characters recognition, where imperfect and dimensionally variable input charactersare faced. To the best of our knowledge, there is no such work yet available in this regard. Experimental results show that combined multiple classifiers can outperform each individual classifier produces by itself. The current findings are encouraging and opens the door for further research tasks in this direction.

Methods and approaches to the development of a automated analysis’s system of the changes’ dynamics in the area of forest plantations based on the methods of automatic pattern recognition

The development of image recognition technologies has allowed to automate many activities, previously largely based not on the capabilities of technical means and mathematical apparatus, and the experience and skills of people involved in this activity. The subject of this work is the application of image recognition technologies in the processing of photographs of the earth's surface, namely, forests.

Seasonal Variation of Captive Meagre Acoustic Signalling: A Manual and Automatic Recognition Approach.

Many species rely on acoustic communication to fulfil several functions such as advertisement and mediation of social interactions (e.g., agonistic, mating). Therefore, fish calls can be an important source of information, e.g., to recognize reproductive periods or to assess fish welfare, and should be considered a potential non-intrusive tool in aquaculture management. Assessing fish acoustic activity, however, often requires long sound recordings. To analyse these long recordings automatic methods are invaluable tools to detect and extract the relevant biological information. Here we present a study to characterize meagre (Argyrosomus regius) acoustic activity during social contexts in captivity using an automatic pattern-recognition methodology based on the Hidden Markov Model. Calls produced by meagre during the breading season showed a richer repertoire than previously reported. Besides the dense choruses composed by grunts already known for this species, meagre emitted successive series of isolated pulses, audible as ‘knocks’. Grunts with a variable number of pulses were also registered. The overall acoustic activity was concurrent with the number of spawning events. A diel call rhythms exhibit peak of calling activity from 15:00 to midnight. In addition, grunt acoustic parameters varied significantly along the reproduction season. These results open the possibility to use the meagre vocal activity to predict breeding and approaching spawning periods in aquaculture management.

[Chromatographic auxanometry and GC-MS-auxanometry in forest plant species vegetation phenological monitoring based on flavor and gas chemistry principles with automatic pattern recognition (climatic, meteorological, taxonomic and phenospectral)]

Gradov O. V. Chromatographic auxanometry and GC-MS-auxanometry in forest plant species vegetation phenological monitoring based on flavor and gas chemistry principles with automatic pattern recognition (climatic, meteorological, taxonomic and phenospectral) // Optimization and Protection of Ecosystems. Simferopol: TNU, 2014. Iss. 10. P. 30–45. [Градов О. В. Хромато-ауксанометрія і хромато-мас-ауксанометрія у фенологічному стадійному моніторингу лісових порід на основі флейво- та газохімічних принципів з автоматичною динамічною ідентифікацією патернів (таксономічних, метеоролого-кліматичних і феноспектральних // Экосистемы, их оптимизация и охрана. — 2014. — Т. 10, № 29. — С. 30–45].