Detection of uveal melanoma using fuzzy and neural networks classifiers

The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks

Detection of Malignant Tumour in Mammography Images Using Artificial Neural Networks with Fuzzy Rules

Breast cancer is a collection of cancer cells that starts in the breast cells and it expands from tissue of breast. Now a day Mammogram is one technique to detect the breast cancer earlyusing x-ray image of breast and it is used to reduce the deaths of breast cancer. This breast cancer disease is curable if discovered starting stage. This paper studies different methods utilized for the detection of breast cancer using mammogram classification. In this paper, the feature extraction and classification of mammogram image can be done by the artificial neural networks. Different kinds of feature extraction from mammogram image to detecting the bread cancer contains shape, position and surface features etc., this image feature extraction is significant in classification of image. By utilizing the image processing these image features are extracted. Image segmentation is performed for feature extraction of mammogram image, in this process image is partitioned into multiple segments, therefore when change the image representation into something that is more significant and simple to examine. Here the fuzzy rules are introduced to process the related data from cases of breast cancer in mammogram image in order to give the risk diagnosis of breast cancer. The preprocessing method is used to sustain an effectiveness of image by correct and adjusting the mammogram image and also it is used to improve the image quality and create it ready for additional working by reducing the unrelated noise to provide new brightness value in output image it is called as filtration and unwanted parts of background of mammogram image is eliminated. Some techniques are discussed for mammogram image classification to earlier detection of breast cancer

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

COMPARISON OF SEAGRASS COVER CLASSIFICATION BASED-ON SVM AND FUZZY ALGORITHMS USING MULTI-SCALE IMAGERY IN KODINGARENG LOMPO ISLAND

Padang lamun mempunyai peranan ekologi bagi lingkungan laut dangkal yaitu sebagai habitat biota, produsen primer, penangkap sedimen serta berperan sebagai pendaur zat-zat hara. Mengingat pentingnya peranan ekosistem padang lamun maka kelestarian sumber daya alam ini perlu dijaga, oleh karena itu pemetaan dan pemantauan yang terus-menerus terhadap keberadaan padang lamun sangat penting dilakukan. Metode penginderaan jauh merupakan metode yang dapat digunakan untuk memetakan dan memantau kondisi padang lamun. Perkembangan teknologi sensor satelit yang pesat saat ini, khususnya resolusi spasial dan spektral sensor meningkatkan kualitas peta sebaran lamun. Penggunaan metode dan skema klasifikasi yang kurang tepat dalam klasifikasi kondisi lamun dari citra satelit juga termasuk hal yang dapat memengaruhi akurasi peta, sehingga dibutuhkan berbagai alternatif kajian algoritma yang digunakan. Pada penelitian ini digunakan algoritma Support Vector Machine dan Logika Fuzzy menggunakan citra satelit WorldView-2 dan Sentinel-2 di Pulau Kodingareng Lompo dengan empat kelas tutupan lamun yaitu jarang (0-25%), sedang (26-50%), padat (51-75%), dan sangat padat (76-100%). Hasil yang diperoleh adalah algoritma Logika Fuzzy menggunakan citra WorldView-2 memiliki akurasi keseluruhan klasifikasi tutupan lamun yang paling baik sebesar 78,60%.Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the Support Vector Machine and Fuzzy Logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imageries on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the Fuzzy Logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification

Fuzzy metrics and fuzzy logic for colour image filtering

El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido. En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente. En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879Palanci

Understanding and Design of an Arduino-based PID Controller

This thesis presents research and design of a Proportional, Integral, and Derivative (PID) controller that uses a microcontroller (Arduino) platform. The research part discusses the structure of a PID algorithm with some motivating work already performed with the Arduino-based PID controller from various fields. An inexpensive Arduino-based PID controller designed in the laboratory to control the temperature, consists of hardware parts: Arduino UNO, thermoelectric cooler, and electronic components while the software portion includes C/C++ programming. The PID parameters for a particular controller are found manually. The role of different PID parameters is discussed with the subsequent comparison between different modes of PID controllers. The designed system can effectively measure the temperature with an error of ± 0.6℃ while a stable temperature control with only slight deviation from the desired value (setpoint) is achieved. The designed system and concepts learned from the control system serve in pursuing inexpensive and precise ways to control physical parameters within a desired range in our laboratory

Measurements, Models, Systems and Design

531 s.

Pipeline Implementation of Peer Group Filtering in FPGA

In the paper a parallel FPGA implementation of the Peer Group Filtering algorithm is described. Implementation details, results, performance of the design and FPGA logic resources are discussed. The PGF algorithm customized for FPGA is compared with the original one and Vector Median Filtering