Journal articles: 'Color-vector clustering'

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Qi, Li Ying, and Ke Gang Wang. "Information System in Image Classification Based on SVM and Color Clustering Analysis." Advanced Materials Research 886 (January 2014): 572–75. http://dx.doi.org/10.4028/www.scientific.net/amr.886.572.

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Effective use of the color feature of Content Based Image Retrieval (CBIR) and Image classification is an important basic research, but there are some shortcomings in the color histogram representation method, such as high dimension, pixels spatial information is ignored and so on. Although color feature data can reduce the dimension by quantification, but some useful image color information will be discard. In this paper, the image color information processing in space constrained fuzzy clustering to obtain a lower dimensional color feature data of the image characteristics of domain colors description, and use multi-class support vector machine to classify color images. Experimental results show that the proposed method can better describe image color information than color histogram; image domain color description combined with support vector machine model can achieve the automatic classification of images effectively.

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Putra, I. Ketut Gede Darma, Ni Putu Ayu Oka Wiastini, Kadek Suar Wibawa, and I. Made Suwija Putra. "Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine." International Journal of Machine Learning and Computing 10, no. 4 (July 2020): 542–48. http://dx.doi.org/10.18178/ijmlc.2020.10.4.970.

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Putra, I. Ketut Gede Darma, Ni Putu Ayu Oka Wiastini, Kadek Suar Wibawa, and I. Made Suwija Putra. "Identification of Skin Disease Using K-Means Clustering, Discrete Wavelet Transform, Color Moments and Support Vector Machine." International Journal of Machine Learning and Computing 10, no. 5 (October 5, 2020): 700–706. http://dx.doi.org/10.18178/ijmlc.2020.10.5.993.

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Liu, Jin Mei, and Ji Zhong Li. "Image Retrieval Based on Color-Statistic Feature." Advanced Materials Research 765-767 (September 2013): 1046–49. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1046.

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Color is the most widely used visual feature in content based image retrieval. The visual coherence color space, HSV, is adopted to represent image. Hue component is used to denote color. Hue difference statistic is proposed to extract color change information as supplement to color feature. The image is divided into sub images equally. Color and change information is extracted in each region. After feature vector clustering and coding, image content can be expressed as vector codes. The text based analysis technology is used for image retrieval. Experiments show that the proposed method can realize efficient retrieval for unconstrained scene images.

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Qiao, Yu-Long, Kai-Long Yuan, Chun-Yan Song, and Xue-Zhi Xiang. "Detection of Moving Objects with Fuzzy Color Coherence Vector." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/138065.

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Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.

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Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "An Adaptive Image Segmentation Algorithm Based on AP Clustering." Advanced Materials Research 1078 (December 2014): 405–8. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.405.

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For the shortcomings of classsical clustering algorithms: must assign the number of clusters, the initial cluster center and initial membership degree matrix, ignore the spatial structure information, we propose a novel adaptive image segmentation algorithm based on AP clustering (AAP). We calculate the AP clustering preference parameter of different images adaptively, use the color-texture feature vector to clustering segmentation. Compare to K-Means and FCM, the new algorithm is more accurate and robust.

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Phornphatcharaphong, Wutthichai, and Nawapak Eua-Anant. "Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images." Journal of Imaging 6, no. 7 (July 16, 2020): 72. http://dx.doi.org/10.3390/jimaging6070072.

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This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.

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Lin, Ye, Dan Chen, Shijia Liang, Zhezhuang Xu, Yang Qiu, Jiahao Zhang, and Xinxiang Liu. "Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm." Applied Sciences 10, no. 19 (September 29, 2020): 6816. http://dx.doi.org/10.3390/app10196816.

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Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.

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Xu, Yan, Jiangtao Dong, Zishuo Han, and Peiguang Wang. "Multichannel Correlation Clustering Target Detection." Information Technology And Control 49, no. 3 (September 23, 2020): 335–45. http://dx.doi.org/10.5755/j01.itc.49.3.25507.

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During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is established in this study. Hence the detection of moving targets in a complicated environment is achieved. Experiments indicate that the algorithm has good detection performance when inhibiting ghosts, dynamic background, and shade; thus, the execution efficiency can meet the needs of real-time computing.

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Fang, Lu Ping, Yuan Jie Wei, and Fei Lu. "Detection of Color Indicators under Complex Circumstances." Advanced Materials Research 433-440 (January 2012): 6157–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6157.

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A color indicator detection algorithm under different illumination conditions is proposed. First, based on the similarity between consecutive video frames in channel L of Lab color space, background image can be determined. Differentiation of a frame and the background can identify the motion region, and thus the search area for the color indicator is greatly reduced. Second, the convex hull of motion region is specified and sampling is taken within it. By assigning the weight, seeds can be determined using clustering method. Finally, region growing is implemented by applying Bayesian decision with minimal error ratio. The method is applicable to more different conditions and produces better results compared with traditional color-threshold vector method.

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Juang, Chia-Feng, Shih-Hsuan Chiu, and Shen-Jie Shiu. "Fuzzy System Learned Through Fuzzy Clustering and Support Vector Machine for Human Skin Color Segmentation." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 37, no. 6 (November 2007): 1077–87. http://dx.doi.org/10.1109/tsmca.2007.904579.

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R.V.V, Krishna, and Srinivas Kumar S. "Color Image Segmentation Using Soft Rough Fuzzy-C Means Clustering and SMO Support Vector Machine." Signal & Image Processing : An International Journal 6, no. 5 (June 5, 2015): 49–62. http://dx.doi.org/10.5121/sipij.2015.6504.

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Querini, Marco, and Giuseppe Italiano. "Reliability and data density in high capacity color barcodes." Computer Science and Information Systems 11, no. 4 (2014): 1595–615. http://dx.doi.org/10.2298/csis131218054q.

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2D color barcodes have been introduced to obtain larger storage capabilities than traditional black and white barcodes. Unfortunately, the data density of color barcodes is substantially limited by the redundancy needed for correcting errors, which are due not only to geometric but also to chromatic distortions introduced by the printing and scanning process. The higher the expected error rate, the more redundancy is needed for avoiding failures in barcode reading, and thus, the lower the actual data density. Our work addresses this trade-off between reliability and data density in 2D color barcodes and aims at identifying the most effective algorithms, in terms of byte error rate and computational overhead, for decoding 2D color barcodes. In particular, we perform a thorough experimental study to identify the most suitable color classifiers for converting analog barcode cells to digital bit streams. To accomplish this task, we implemented a prototype capable of decoding 2D color barcodes by using different methods, including clustering algorithms and machine learning classifiers. We show that, even if state-of-art methods for color classification could be successfully used for decoding color barcodes in the desktop scenario, there is an emerging need for new color classification methods in the mobile scenario. In desktop scenarios, our experimental findings show that complex techniques, such as support vector machines, does not seem to pay off, as they do not achieve better accuracy in classifying color barcode cells. The lowest error rates are indeed obtained by means of clustering algorithms and probabilistic classifiers. From the computational viewpoint, classification with clustering seems to be the method of choice. In mobile scenarios, simple and efficient methods (in terms of computational time) such as the Euclidean and the K-means classifiers are not effective (in terms of error rate), while, more complex methods are effective but not efficient. Even if a few color barcode designs have been proposed in recent studies, to the best of our knowledge, there is no previous research that addresses a comparative and experimental analysis of clustering and machine learning methods for color classification in 2D color barcodes.

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Lei, Qi, Jun Liu, Min Wu, and Jie Wang. "Image Clustering Using Active-Constraint Semi-Supervised Affinity Propagation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 7 (December 20, 2016): 1035–43. http://dx.doi.org/10.20965/jaciii.2016.p1035.

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Image clustering is an effective way to discover and analyze large quantities of image data. The HSV color space is particularly advantageous in image feature extraction because of its relatively prominent feature vector. The objective of this study is to develop an image clustering method using the active-constraint semi-supervised affinity propagation (ACSSAP) algorithm. The algorithm adds supervision to the affinity propagation (AP) clustering algorithm with pairwise constraints and uses active learning to guide the AP clustering algorithm. Active learning of pairwise constraints leads to an adjustment of the similarity matrix in AP at each iteration. In the experiments, the advantage of HSV space is analyzed and the ACSSAP algorithm is evaluated for data sets of different sizes in comparison with other algorithms. The result demonstrates that the ACSSAP has better performance.

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Liu, Chengzhao, Mingchao Li, Ye Zhang, Shuai Han, and Yueqin Zhu. "An Enhanced Rock Mineral Recognition Method Integrating a Deep Learning Model and Clustering Algorithm." Minerals 9, no. 9 (August 26, 2019): 516. http://dx.doi.org/10.3390/min9090516.

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Rock mineral recognition is a costly and time-consuming task when using traditional methods, during which physical and chemical properties are tested at micro- and macro-scale in the laboratory. As a solution, a comprehensive recognition model of 12 kinds of rock minerals can be utilized, based upon the deep learning and transfer learning algorithms. In the process, the texture features of images are extracted and a color model for rock mineral identification can also be established by the K-means algorithm. Finally, a comprehensive identification model is made by combining the deep learning model and color model. The test results of the comprehensive model reveal that color and texture are important features in rock mineral identification, and that deep learning methods can effectively improve identification accuracy. To prove that the comprehensive model could extract effective features of mineral images, we also established a support vector machine (SVM) model and a random forest (RF) model based on Histogram of Oriented Gradient (HOG) features. The comparison indicates that the comprehensive model has the best performance of all.

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Arun, Ramaiah, and Shanmugasundaram Singaravelan. "Automated communication system for detection of lung cancer using catastrophe features." Informatologia 53, no. 3-4 (December 30, 2020): 184–90. http://dx.doi.org/10.32914/i.53.3-4.5.

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One of the biggest challenges the world face today is the mortality due to Cancer. One in four of all diagnosed cancers involve the lung cancer, where the mortality rate is high, even after so much of technical and medical advances. Most lung cancer cases are diagnosed either in the third or fourth stage, when the disease is not treatable. The main reason for the highest mortality, due to lung cancer is because of non availability of prescreening system which can analyze the cancer cells at early stages. So it is necessary to develop a prescreening system which helps doctors to find and detect lung cancer at early stages. Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate. The reason is mainly attributed to the increased rate of smoking - both active and passive. In the present work, a system for the classification of lung glandular cells for early detection of Cancer using multiple color spaces is developed. For segmentation, various clustering techniques like K-Means clustering and Fuzzy C-Means clustering on various Color spaces such as HSV, CIELAB, CIEXYy and CIELUV are used. Features are Extracted and classified using Support Vector Machine (SVM).

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Aoki, Terumasa, and Van Nguyen. "Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization." Advances in Multimedia 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/1504691.

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Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

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Kumar, Vijay, Jitender Kumar Chhabra, and Dinesh Kumar. "Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation." Journal of Intelligent Systems 25, no. 4 (October 1, 2016): 595–610. http://dx.doi.org/10.1515/jisys-2015-0004.

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AbstractIn this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.

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YOO, HUN-WOO, DONG-SIK JANG, KWANG-KYU SEO, and MYUNG-EUI LEE. "RETRIEVING IMAGES BY COMPARING HOMOGENEOUS COLOR AND TEXTURE OBJECTS IN THE IMAGE." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 06 (September 2004): 1093–110. http://dx.doi.org/10.1142/s0218001404003514.

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An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

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Qiang, He Qun, Chun Hua Qian, and Sheng Rong Gong. "A Graph-Based Image Segmentation Algorithm." Advanced Materials Research 1078 (December 2014): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.401.

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According to the problem that classical graph-based image segmentation algorithms are not robust to segmentation of texture image. We propose a novel segmentation algorithm that GBCTRS, which overcame the shortcoming of existed graph-based segmentation algorithms N-cut and EGBIS. It extract feature vector of blocks using color-texture feature, calculate weight between each block using the neighborhood relationship, use minimum spanning tree method to clustering segmentation. The experimental show that the new algorithm is more efficient and robust to segment texture image and strong edges image.

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Attia, Mohamed, Waleed Nazih, Mohamed Al-Badrashiny, and Hamed Elsimary. "Encoding true-color images with a limited palette via soft vector clustering as an instance of dithering multidimensional signals." Journal of Visual Communication and Image Representation 25, no. 2 (February 2014): 349–60. http://dx.doi.org/10.1016/j.jvcir.2013.10.008.

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Zhang, Runzhe, Eric Maggard, Yousun Bang, Minki Cho, Mark Shaw, and Jan Allebach. "Color Text Fading Detection." Electronic Imaging 2021, no. 16 (January 18, 2021): 253–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.16.color-253.

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The text fading defect is one of the most common defects in electrophotographic printers; and it dramatically affects print quality. It usually appears in a significant symbol Region of Interest (ROI), easily noticed by a user on his or her print. We can detect text fading by the density reduction for the black and white printed symbol ROI. It is difficult to detect the color text fading only by density reduction, because the depleted cartridge may only cause the color distortion without density reduction in the color printed symbol ROI. In our previous work with print quality defects analysis, the text fading detection method only works for black text fading defect detection [1]. Our new text fading method can detect the color text fading defect and predict the depleted cartridge. In this new text fading detection method, we use whole page image registration and the median threshold bitmap (MTB) matching method to align the text characters between the master and test symbol ROIs, because with the aligned text characters, it is easy to extract the difference between the master and the test text characters to detect the text fading defect. We use a support vector machine classifier to assign a rank to the overall quality of the printed page. We also use the gap statistic method with the K-means clustering algorithm to extract the different text characters’ different colors to predict the depleted cartridge.

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Jadhav, Sachin B., Vishwanath R. Udup, and Sanjay B. Patil. "Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4077. http://dx.doi.org/10.11591/ijece.v9i5.pp4077-4091.

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Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work present a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by Blight, Frogeye leaf spot and Brown Spot were acquired by a digital camera. The acquired images are preprocessed using image enhancement techniques. The background of each image was removed by a thresholding method and the Region of Interest (ROI) is obtained. Color-based segmentation technique based on K-means clustering is applied to the region of interest for partitioning the diseased region. The severity of disease is estimated by quantifying a number of pixels in the diseased region and in total leaf region. Different color features of segmented diseased leaf region were extracted using RGB color space and texture features were extracted using Gray Level Co-occurrence Matrix (GLCM) to compose a feature database. Finally, the support vector machine (SVM) and K-Nearest Negbiour (KNN) classifiers are used for classifying the disease. This proposed classifers system is capable to classify the types of blight, brown spot, frogeye leaf spot diseases and Healthy samples with an accuracy of 87.3% and 83.6 % are achieved.

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Fredes, Claudio, Constantino Valero, Belén Diezma, Marco Mora, José Naranjo-Torres, Manuel Wilson, and Gabriel Delgadillo. "A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives." Foods 10, no. 3 (March 13, 2021): 609. http://dx.doi.org/10.3390/foods10030609.

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Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar color and NIR for building EM of oil content obtained by Soxhlet from a cluster of similar olives. The objective was to estimate OC of individual olives, based on clusters of similar color and NIR in two seasons. This study was performed with Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the three color channels of c1c2c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient number of olives to perform the Soxhlet analysis of OC, as reference value of EM. The training of EM was based on Support Vector Machine. The test was performed with Leave One-Out Cross Validation in different training-testing combinations. The best EM predicted the OC with 6 and 13% deviation with respect to the real value when one season was tested with itself and with another season, respectively. The use of clustering in EM is discussed.

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Wulanningrum, Resty, and Bagus Fadzerie Robby. "Learning Vector Quantization Image for Identification Adenium." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (November 1, 2016): 383. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp383-389.

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Information and technology are two things that can not be separated and it has become a necessity for human life. Technology development at this time was not only used for intelligence purposes only, but has penetrated the world of holtikurtura. Adenium is one of the plants are much favored by ornamental plants lovers. Many of cultivation adenium who crosses that appear new varieties that have the color and shape are similar to each other. From this case, then made an application that can identify the type of adenium based on the image of that flower. Learning Vector quantization is one of the algorithm that used for clustering. Based on test scenarios were performed, image identification applications Adenium petals produce an accuracy of 86.66% with a number of training dataset of 135 images and datasets with a test as many as 45 images max epoch 10 and learning rate between 0.01 to 0.05.

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Dubey, Shiv Ram, and Anand Singh Jalal. "Fusing Color and Texture Cues to Identify the Fruit Diseases Using Images." International Journal of Computer Vision and Image Processing 4, no. 2 (July 2014): 52–67. http://dx.doi.org/10.4018/ijcvip.2014040104.

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The economic and production losses in agricultural industry worldwide are due to the presence of diseases in the several kinds of fruits. In this paper, a method for the classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps; in the first step K-Means clustering technique is used for the defect segmentation, in the second step color and textural cues are extracted and fused from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot and normal apples without diseases. The experimentation points out that the proposed fusion scheme can significantly support accurate detection and automatic classification of fruit diseases.

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Wei, Yuchen, Lisheng Wei, Tao Ji, and Huosheng Hu. "A Novel Image Classification Approach for Maize Diseases Recognition." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 3 (May 18, 2020): 331–39. http://dx.doi.org/10.2174/2352096511666181003134208.

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Background: The spot, streak and rust are the most common diseases in maize, all of which require effective methods to recognize, diagnose and handle. This paper presents a novel image classification approach to the high accuracy recognition of these maize diseases. Methods: Firstly, the k-means clustering algorithm is deployed in LAB color space to reduce the influence of image noise and irrelevant background, so that the area of maize diseases could be effectively extracted. Then the statistic pattern recognition method and gray level co-occurrence matrix (GLCM) method are jointly used to segment the maize disease leaf images for accurately obtaining their texture, shape and color features. Finally, Support Vector Machine (SVM) classification method is used to identify three diseases. Results: Numerical results clearly demonstrate the feasibility and effectiveness of the proposed method. Conclusion: Our future work will focus on the investigation of how to use the new classification methods in dimensional and large scale data to improve the recognizing performance and how to use other supervised feature selection methods to improve the accuracy further.

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Wang, Yong, Jianyong Liu, Chengqun Fu, Jie Guo, Qin Yu, and Lijun Xie. "A Hybrid Vehicle Extraction Approach from Low-Quality LiDAR Data Based on Robust One-Class Support Vector Machine." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (January 12, 2017): 1755004. http://dx.doi.org/10.1142/s0218001417550047.

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Vehicle extraction becomes possible as the high-performance airborne light detection and ranging (LiDAR) systems can offer very dense and accurate point cloud, which means the sophisticated objects can be recorded in detail, combined with color information from airborne image, hyperspectral and intensity. However, few studies have investigated in extracting vehicles from LiDAR data only, especially when its quality is low, which is the main difficulty for most LiDAR applications. In this paper, a hybrid approach has been proposed to extract vehicles from low-quality LiDAR data. In order to extract vehicle from low-resolution LiDAR data, a robust one-class support vector machine-minimum covariance determinant (OCSVM-MCD) is proposed based on a multivariate dispersion estimator and weighted strategy. Firstly, the three-dimensional (3D) point dataset is classified into nonterrain and terrain points with progressive morphological filter with a slight improvement. Secondly, nonterrain points are segmented by clustering technique and missing blobs are searched from terrain points. Then, the vehicles are extracted from clustering and searching results by OCSVM-MCD, and a hybrid principle is put forward to improve the extraction result at last. The proposed method has been evaluated with two benchmark datasets from ISPRS, and proved that by the method, most vehicles can be extracted from low-quality LiDAR data with an encouraging result.

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Chen, Yajun, Zhangnan Wu, Bo Zhao, Caixia Fan, and Shuwei Shi. "Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine." Sensors 21, no. 1 (December 31, 2020): 212. http://dx.doi.org/10.3390/s21010212.

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Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.

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Jasim, Wala’a, and Rana Mohammed. "A Survey on Segmentation Techniques for Image Processing." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (August 16, 2021): 73–93. http://dx.doi.org/10.37917/ijeee.17.2.10.

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The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.

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Syarief, Mohammad, Novi Prastiti, and Wahyudi Setiawan. "Maize Leaf Disease Image Classification Using Bag of Features." JURNAL INFOTEL 11, no. 2 (June 30, 2019): 48. http://dx.doi.org/10.20895/infotel.v11i2.428.

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Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%.

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MR. SANJAY SHITOLE, MRS RUPALI KALE,. "Analysis of Crop disease detection with SVM, KNN and Random forest classification." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 1, 2021): 364–72. http://dx.doi.org/10.17762/itii.v9i1.140.

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Due to an uneven climatic condition crops are being affected which leads to decrease in agriculture yield. It greatly affects global agricultural economy. However, the condition becomes more worse when diseases are identified in crops. Agriculture plays a vital role in every country’s economy. Thus, there is a need to identify the crop disease before it is visible on a crop so that disease can be avoided by using appropriate measures. The traditional way of identifying a crop disease is through observation by naked eyes. But as it requires large number of experts and continuous monitoring of crop it will be costly for large fields. Hence, an automatic system is required which can not only examine the crops to detect disease but also can classify the type of disease on crops. The proposed system determines disease from an input image. The input image has to go through following stages: Image Acquisition, Image pre-processing, Image segmentation, Feature Extraction, and Classification in order to determine diseased crop and accordingly provides remedy for that disease. Infected crop image is taken as input in Image Acquisition stage. In Image pre-processing stage noise is removed from the input image by applying gaussian blur filter and non-local means denoising algorithm. Also, the background of image is eliminated using Thresholding algorithm. To extract Region of Interest (ROI) i.e. infected region from input image K-means Clustering algorithm is used. Then color, texture and shape features are extracted from ROI and features are further send to the classification stage. Three different classification algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest are implemented for classification out of which Support Vector Machine Algorithm is found to be best in terms of accuracy. Hence, classification is carried out by using Multivariate Support Vector Machine algorithm which detect disease present in crop accurately. In this way, the proposed system detects a disease from the given input image.

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Fiser, Karel, Tomas Sieger, Angela Schumich, Julie Irving, Michael N. Dworzak, and Josef Vormoor. "MRD Monitoring of Childhood ALL Using Hierarchical Clustering and Support Vector Machine Learning of Complex Multi-Parameter Flow Cytometry Data." Blood 112, no. 11 (November 16, 2008): 1508. http://dx.doi.org/10.1182/blood.v112.11.1508.1508.

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Abstract Flow cytometry is an important tool both for research and diagnostics of hematologic malignancies - including monitoring of minimal residual disease (MRD). Recent progress and more widespread availability of 6 and higher color flow cytometry leads to complex, information-rich datasets which are very challenging to analyze. Here, we validate a novel approach to multi-parameter flow data analysis in MRD flow data sets from 39 ALL patients (including 23 patients from the I-BFM list mode data (LMD) ring trial). The approach combines hierarchical clustering (HCA) using a newly developed algorithm and support vector machine (SVM) learning. The algorithm employs a scale-invariant Mahalanobis distance measurement for merging clusters. This reflects the extended ellipsoid shape of the populations and is better suited for flow cytometric data compared with standard HCA metrics. The resulting hierarchical tree, combined with the heatmap of the CD marker expression allows visualization of hierarchically clustered data of all analyzed parameters displayed in a single plot. The clusters from HCA (representing the ALL blast population at diagnosis) were used to train SVM classifiers which were then applied to test for presence of a matching population in the test sample (follow-up sample). All work was carried out in MATLAB (MathWorks, Inc.). Using HCA, we have been able to detect the leukemic blast population in diagnostic and follow-up datasets (n=81) from three centers. The correlation (Pearson correlation coefficient = 0.98) between HCA and the standard gating approach was highly comparable to inter-laboratory comparisons within the I-BFM LMD ring trial (Dworzak MN et al; Cytometry B Clin Cytom. 2008 Jun 11.). To further improve sensitivity and exact quantification of low MRD levels and to automate MRD detection, we combined HCA with SVM learning. We have analyzed 21 samples from 5 patients with MRD levels between 0.004 to 57.54%. HCA plus SVM correlated better with standard gating results than HCA alone in particular in samples with low MRD levels (<10−3). In summary, HCA in combination with SVM proved to be a strong analytical tool for flow cytometry with the potential for automated MRD detection. We validated this approach for use in ALL diagnostics and MRD monitoring by comparison with expert-based gating analyses of I-BFM LMD ring trial data.

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Bezborodov, Mikhail A., Mikhail A. Eremin, Vitaly V. Korolev, Ilya G. Kovalenko, and Elena V. Zhukova. "Visualization of the Anisotropy of the Velocity Dispersion and Characteristics of the Multi-Velocity Continuum in the Regions of Multi-Stream Flows of Gas-Dust Media with Polydisperse Dust." Journal of Imaging 6, no. 9 (September 17, 2020): 98. http://dx.doi.org/10.3390/jimaging6090098.

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Collisionless media devoid of intrinsic stresses, for example, a dispersed phase in a multiphase medium, have a much wider variety of space-time structures and features formed in them than collisional media, for example, a carrier, gas, or liquid phase. This is a consequence of the fact that evolution in such media occurs in phase space, i.e., in a space of greater dimensions than the usual coordinate space. As a consequence, the process of the formation of features in collisionless media (clustering or vice versa, a loss of continuity) can occur primarily in the velocity space, which, in contrast to the features in the coordinate space (folds, caustics, or voids), is poorly observed directly. To identify such features, it is necessary to use visualization methods that allow us to consider, in detail, the evolution of the medium in the velocity space. This article is devoted to the development of techniques that allow visualizing the degree of anisotropy of the velocity fields of collisionless interpenetrating media. Simultaneously tracking the behavior of different fractions in such media is important, as their behavior can be significantly different. We propose three different techniques for visualizing the anisotropy of velocity fields using the example of two- and three-continuum dispersed media models. We proposed the construction of spatial distributions of eccentricity fields (scalar fields), or fields of principal directions of the velocity dispersion tensor (tensor fields). In the first case, we used some simple eccentricity functions for dispersion tensors for two fractions simultaneously, which we call surrogate entropy. In the second case, to visualize the anisotropy of the velocity fields of three fractions simultaneously, we used an ordered array (3-vector) of eccentricities for the color representation through decomposition in three basic colors. In the case of a multi-stream flow, we used cluster analysis methods to identify sections of a multi-stream flow (beams) and used glyphs to visualize the entire set of beams (vector-tensor fields).

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Fišer, Karel, Tomáš Sieger, and Josef H. Vormoor. "Identifying Candidate Normal and Leukemic B Cell Progenitor Populations with Hierarchical Clustering of 6-Color Flow Cytometry Data - A Better View." Blood 110, no. 11 (November 16, 2007): 1428. http://dx.doi.org/10.1182/blood.v110.11.1428.1428.

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Abstract 6-color flow cytometry allows multiparameter analysis of high numbers of single cells. It is an excellent tool for the characterization of a wide range of hematopoietic populations and for monitoring minimal residual disease. However, analysis of complex flow data is challenging. Gating populations on 28 two-parameter plots is extremely tedious and does not reflect the multidimensionality of the data. Here, we describe a novel approach, employing hierarchical clustering (HCA) and support vector machine (SVM) learning in analyzing flow data. This approach provides a new perspective for looking at flow data and promises better identification of rare and novel subpopulations that escape classic analysis. Our aim was to identify normal and leukemic B cell progenitor/stem cell populations in normal (n=6) and ALL (n=10) bone marrow. Samples were labelled with fluorochrome-conjugated antibodies to 6 CD markers (CD 10, 19, 22, 34, 38, 117) and 104 to 106 events were acquired (FACSCanto, BD Biosciences). To analyze flow data with HCA we developed a new algorithm, better suited for the ellipsoid nature of cell populations than other current HCA metrics. Data exported from DiVa software were externally compensated and Hyperlog transformed to achieve a logarithmic-like scale that displayed zero and negative values. Normalized data were then subjected to HCA employing a scale-invariant Mahalanobis distance measurement for merging clusters. This reflects the extended ellipsoid shape of the populations (here: 8 dimensional ellipsoids). We developed a new adaptive linkage algorithm that smoothly shifts from the Euclidean distance (when clusters are too small to compute Mahalanobis distance) to Mahalanobis distance measurement. This allowed us to build the hierarchy from single events, yet to retain the advantage of Mahalanobis measurement for larger clusters. To build classifiers we used SVM employing polynomial kernel. All work was carried out in MATLAB (MathWorks, Inc.). The resulting hierarchical tree combined with the heatmap of the CD marker expression allows visualization of hierarchically clustered data with all 8 parameters displayed in a single plot (!) as compared to 28 traditional two-parameter plots. HCA has big advantage of providing populations homogenous in their expression pattern of all parameters (without the need for complex sub or back gating). We were able to identify populations corresponding to the different stages of B-cell development. In a normal control bone marrow we could detect the following candidate B-lineage progenitor populations: CD34+117+38+10−22−19− (0.94% of total) progenitor/stem cells, CD34+117−38+10+22+19med (0.26% of total) pro-B cells, CD34−117−38+10+22+19+ (2.77% of total) small pre-B cells (lower FCS values), CD34−117−38+10+22+19+ (1.09% of total) large pre-B cells (higher FCS values) and CD34−117−38lo10−22+19+ (5.94% of total) (immature) B cells. In 10 diagnostic or relapse samples HCA clearly identified the main leukemic population. HCA is able to visualize otherwise “hidden” populations. This was exemplified by a distinct CD38+B-lin− population that overlapped with other populations in all 28 two-parameter plots (most likely T cells). We have built a classifier able to find established populations across samples and in large datasets (106 events) for which HCA would be computationally too demanding. In summary, we show the advantages of using hierarchical clustering analysis for large complex multiparameter flow cytometry datasets.

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Pande, Sandeep D., and Manna S. R. Chetty. "Linear Bezier Curve Geometrical Feature Descriptor for Image Recognition." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 930–41. http://dx.doi.org/10.2174/2213275912666190617155154.

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Background: Image retrieval has a significant role in present and upcoming usage for different image processing applications where images within a desired range of similarity are retrieved for a query image. Representation of image feature, accuracy of feature selection, optimal storage size of feature vector and efficient methods for obtaining features plays a vital role in Image retrieval, where features are represented based on the content of an image such as color, texture or shape. In this work an optimal feature vector based on control points of a Bezier curve is proposed which is computation and storage efficient. Aim: To develop an effective and storage, computation efficient framework model for retrieval and classification of plant leaves. Objective: The primary objective of this work is developing a new algorithm for control point extraction based on the global monitoring of edge region. This observation will bring a minimization in false feature extraction. Further, computing a sub clustering feature value in finer and details component to enhance the classification performance. Finally, developing a new search mechanism using inter and intra mapping of feature value in selecting optimal feature values in the estimation process. Methods: The work starts with the pre-processing stage that outputs the boundary coordinates of shape present in the input image. Gray scale input image is first converted into binary image using binarization then, the curvature coding is applied to extract the boundary of the leaf image. Gaussian Smoothening is then applied to the extracted boundary to remove the noise and false feature reduction. Further interpolation method is used to extract the control points of the boundary. From the extracted control points the Bezier curve points are estimated and then Fast Fourier Transform (FFT) is applied on the curve points to get the feature vector. Finally, the K-NN classifier is used to classify and retrieve the leaf images. Results: The performance of proposed approach is compared with the existing state-of-the-artmethods (Contour and Curve based) using the evaluation parameters viz. accuracy, sensitivity, specificity, recall rate, and processing time. Proposed method has high accuracy with acceptable specificity and sensitivity. Other methods fall short in comparison to proposed method. In case of sensitivity and specificity Contour method out performs proposed method. But in case accuracy and specificity proposed method outperforms the state-of-the-art methods. Conclusion: This work proposed a linear coding of Bezier curve control point computation for image retrieval. This approach minimizes the processing overhead and search delay by reducing feature vectors using a threshold-based selection approach. The proposed approach has an advantage of distortion suppression and dominant feature extraction simultaneously, minimizing the effort of additional filtration process. The accuracy of retrieval for the developed approach is observed to be improved as compared to the tangential Bezier curve method and conventional edge and contour-based coding. The approach signifies an advantage in low resource overhead in computing shape feature.

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Kong, Jun, Olcay Sertel, Amy Gewirtz, Arwa Shana’ah, Frederick Racke, John Zhao, Kim Boyer, Umit Catalyurek, Metin Gurcan, and Gerard Lozanski. "Development of Computer Based System To Aid Pathologists in Histological Grading of Follicular Lymphomas." Blood 110, no. 11 (November 16, 2007): 3318. http://dx.doi.org/10.1182/blood.v110.11.3318.3318.

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Abstract Currently risk stratification and subsequent choice of therapy for follicular lymphoma (FL) relies on histological grading that is based on the number of centroblasts per average high power microscopic field (HPF). Centroblasts are counted manually in ten random HPFs and are expressed as an average number of centroblasts per HPF. Manual centroblast counting is difficult, labor intensive and prone to an individual pathologist’s bias. The resulting poor reproducibility of FL grading has been well documented in the literature. In this abstract, we report development of a computer based image analysis program that will assist pathologists with reliable and reproducible morphological grading of FL. The inputs to the computerized system are immunohistochemical (IHC) and hematoxylin and eosin (H&E) stained tissue sections digitized at 40x magnification with a whole-slide scanner. The system consists of successive stages, including detection of follicles, registration of IHC and H&E-stained images, segmentation of cells and classification of cells as centroblasts and non-centroblasts. We extracted both color and texture features to detect the follicles from HIS-stained images followed by a clustering step, which groups the pixels as background, inter-follicular region and follicles, using the K-means clustering algorithm. This is followed by a morphologic post-processing step to remove the noisy regions and smooth the boundaries of the follicles. A manual registration step is performed to superimpose the detected follicle boundaries on H&E image. Within the identified follicle regions, we have trained the computer to differentiate the centroblasts from non-centroblast cells out of a pool of cells marked by an experienced pathologist. A vector of features combining clinical characteristics and statistical descriptors are then constructed from the given cells. A linear unsupervised statistical method of principle components analysis (PCA) is used to reduce the dimensionality of the feature data distribution. Then, the lower-dimensional data are grouped into centroblast and non-centroblast classes. The performance of the follicle detection system and centroblast differentiation systems are evaluated independently in terms of sensitivity and specificity. Follicle detection step was evaluated by comparing the computer generated results with results of manual microscopy. Based on 53 test images, representing FL cases and follicular hyperplasia, sensitivity and specificity values for the follicles detection were 86.1±10.4% and 92.9±5.1%, respectively. Evaluation of computer recognition of centroblasts was based on 100 cells marked by pathologist as centroblasts (41) and non-centroblast (59) in 11 designated follicular regions of H&E stained sections of four FL cases. The resulting sensitivity and specificity of centroblast identification by the computer was 92.6% and 91.38%, respectively. The high sensitivity and specificity of the developed modules of the system are promising for the further development of a computer-aided follicular lymphoma grading system.

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Long, Shengchun, Xiaoxiao Huang, Zhiqing Chen, Shahina Pardhan, and Dingchang Zheng. "Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation." BioMed Research International 2019 (January 23, 2019): 1–13. http://dx.doi.org/10.1155/2019/3926930.

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Diabetic retinopathy (DR) is one of the most common causes of visual impairment. Automatic detection of hard exudates (HE) from retinal photographs is an important step for detection of DR. However, most of existing algorithms for HE detection are complex and inefficient. We have developed and evaluated an automatic retinal image processing algorithm for HE detection using dynamic threshold and fuzzy C-means clustering (FCM) followed by support vector machine (SVM) for classification. The proposed algorithm consisted of four main stages: (i) imaging preprocessing; (ii) localization of optic disc (OD); (iii) determination of candidate HE using dynamic threshold in combination with global threshold based on FCM; and (iv) extraction of eight texture features from the candidate HE region, which were then fed into an SVM classifier for automatic HE classification. The proposed algorithm was trained and cross-validated (10 fold) on a publicly available e-ophtha EX database (47 images) on pixel-level, achieving the overall average sensitivity, PPV, and F-score of 76.5%, 82.7%, and 76.7%. It was tested on another independent DIARETDB1 database (89 images) with the overall average sensitivity, specificity, and accuracy of 97.5%, 97.8%, and 97.7%, respectively. In summary, the satisfactory evaluation results on both retinal imaging databases demonstrated the effectiveness of our proposed algorithm for automatic HE detection, by using dynamic threshold and FCM followed by an SVM for classification.

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Abdelhedi, Fatma, and Nabil Derbel. "Volume 2, Issue 3, Special issue on Recent Advances in Engineering Systems (Published Papers) Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los Ángeles, Diván, Mario José Adv. Sci. Technol. Eng. Syst. J. 2(3), 12-23 (2017); View Description Low Probability of Interception Beampattern Using Frequency Diverse Array Antenna Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 24-29 (2017); View Description Zero Trust Cloud Networks using Transport Access Control and High Availability Optical Bypass Switching Casimer DeCusatis, Piradon Liengtiraphan, Anthony Sager Adv. Sci. Technol. Eng. Syst. J. 2(3), 30-35 (2017); View Description A Derived Metrics as a Measurement to Support Efficient Requirements Analysis and Release Management Indranil Nath Adv. Sci. Technol. Eng. Syst. J. 2(3), 36-40 (2017); View Description Feedback device of temperature sensation for a myoelectric prosthetic hand Yuki Ueda, Chiharu Ishii Adv. Sci. Technol. Eng. Syst. J. 2(3), 41-40 (2017); View Description Deep venous thrombus characterization: ultrasonography, elastography and scattering operator Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier Adv. Sci. Technol. Eng. Syst. J. 2(3), 48-59 (2017); View Description Improving customs’ border control by creating a reference database of cargo inspection X-ray images Selina Kolokytha, Alexander Flisch, Thomas Lüthi, Mathieu Plamondon, Adrian Schwaninger, Wicher Vasser, Diana Hardmeier, Marius Costin, Caroline Vienne, Frank Sukowski, Ulf Hassler, Irène Dorion, Najib Gadi, Serge Maitrejean, Abraham Marciano, Andrea Canonica, Eric Rochat, Ger Koomen, Micha Slegt Adv. Sci. Technol. Eng. Syst. J. 2(3), 60-66 (2017); View Description Aviation Navigation with Use of Polarimetric Technologies Arsen Klochan, Ali Al-Ammouri, Viktor Romanenko, Vladimir Tronko Adv. Sci. Technol. Eng. Syst. J. 2(3), 67-72 (2017); View Description Optimization of Multi-standard Transmitter Architecture Using Single-Double Conversion Technique Used for Rescue Operations Riadh Essaadali, Said Aliouane, Chokri Jebali and Ammar Kouki Adv. Sci. Technol. Eng. Syst. J. 2(3), 73-81 (2017); View Description Singular Integral Equations in Electromagnetic Waves Reflection Modeling A. S. Ilinskiy, T. N. Galishnikova Adv. Sci. Technol. Eng. Syst. J. 2(3), 82-87 (2017); View Description Methodology for Management of Information Security in Industrial Control Systems: A Proof of Concept aligned with Enterprise Objectives. Fabian Bustamante, Walter Fuertes, Paul Diaz, Theofilos Toulqueridis Adv. Sci. Technol. Eng. Syst. J. 2(3), 88-99 (2017); View Description Dependence-Based Segmentation Approach for Detecting Morpheme Boundaries Ahmed Khorsi, Abeer Alsheddi Adv. Sci. Technol. Eng. Syst. J. 2(3), 100-110 (2017); View Description Paper Improving Rule Based Stemmers to Solve Some Special Cases of Arabic Language Soufiane Farrah, Hanane El Manssouri, Ziyati Elhoussaine, Mohamed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 111-115 (2017); View Description Medical imbalanced data classification Sara Belarouci, Mohammed Amine Chikh Adv. Sci. Technol. Eng. Syst. J. 2(3), 116-124 (2017); View Description ADOxx Modelling Method Conceptualization Environment Nesat Efendioglu, Robert Woitsch, Wilfrid Utz, Damiano Falcioni Adv. Sci. Technol. Eng. Syst. J. 2(3), 125-136 (2017); View Description GPSR+Predict: An Enhancement for GPSR to Make Smart Routing Decision by Anticipating Movement of Vehicles in VANETs Zineb Squalli Houssaini, Imane Zaimi, Mohammed Oumsis, Saïd El Alaoui Ouatik Adv. Sci. Technol. Eng. Syst. J. 2(3), 137-146 (2017); View Description Optimal Synthesis of Universal Space Vector Digital Algorithm for Matrix Converters Adrian Popovici, Mircea Băbăiţă, Petru Papazian Adv. Sci. Technol. Eng. Syst. J. 2(3), 147-152 (2017); View Description Control design for axial flux permanent magnet synchronous motor which operates above the nominal speed Xuan Minh Tran, Nhu Hien Nguyen, Quoc Tuan Duong Adv. Sci. Technol. Eng. Syst. J. 2(3), 153-159 (2017); View Description A synchronizing second order sliding mode control applied to decentralized time delayed multi−agent robotic systems: Stability Proof Marwa Fathallah, Fatma Abdelhedi, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 160-170 (2017); View Description Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor Martin F. Pico, Eduardo J. Adam Adv. Sci. Technol. Eng. Syst. J. 2(3), 171-181 (2017); View Description Development and Validation of a Heat Pump System Model Using Artificial Neural Network Nabil Nassif, Jordan Gooden Adv. Sci. Technol. Eng. Syst. J. 2(3), 182-185 (2017); View Description Assessment of the usefulness and appeal of stigma-stop by psychology students: a serious game designed to reduce the stigma of mental illness Adolfo J. Cangas, Noelia Navarro, Juan J. Ojeda, Diego Cangas, Jose A. Piedra, José Gallego Adv. Sci. Technol. Eng. Syst. J. 2(3), 186-190 (2017); View Description Kinect-Based Moving Human Tracking System with Obstacle Avoidance Abdel Mehsen Ahmad, Zouhair Bazzal, Hiba Al Youssef Adv. Sci. Technol. Eng. Syst. J. 2(3), 191-197 (2017); View Description A security approach based on honeypots: Protecting Online Social network from malicious profiles Fatna Elmendili, Nisrine Maqran, Younes El Bouzekri El Idrissi, Habiba Chaoui Adv. Sci. Technol. Eng. Syst. J. 2(3), 198-204 (2017); View Description Pulse Generator for Ultrasonic Piezoelectric Transducer Arrays Based on a Programmable System-on-Chip (PSoC) Pedro Acevedo, Martín Fuentes, Joel Durán, Mónica Vázquez, Carlos Díaz Adv. Sci. Technol. Eng. Syst. J. 2(3), 205-209 (2017); View Description Enabling Toy Vehicles Interaction With Visible Light Communication (VLC) M. A. Ilyas, M. B. Othman, S. M. Shah, Mas Fawzi Adv. Sci. Technol. Eng. Syst. J. 2(3), 210-216 (2017); View Description Analysis of Fractional-Order 2xn RLC Networks by Transmission Matrices Mahmut Ün, Manolya Ün Adv. Sci. Technol. Eng. Syst. J. 2(3), 217-220 (2017); View Description Fire extinguishing system in large underground garages Ivan Antonov, Rositsa Velichkova, Svetlin Antonov, Kamen Grozdanov, Milka Uzunova, Ikram El Abbassi Adv. Sci. Technol. Eng. Syst. J. 2(3), 221-226 (2017); View Description Directional Antenna Modulation Technique using A Two-Element Frequency Diverse Array Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 227-232 (2017); View Description Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks Estefanía D. Avalos-Rivera, Alberto de J. Pastrana-Palma Adv. Sci. Technol. Eng. Syst. J. 2(3), 233-240 (2017); View Description Magnetically Levitated and Guided Systems Florian Puci, Miroslav Husak Adv. Sci. Technol. Eng. Syst. J. 2(3), 241-244 (2017); View Description Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks Minh T. Nguyen Adv. Sci. Technol. Eng. Syst. J. 2(3), 245-253 (2017); View Description Validity and efficiency of conformal anomaly detection on big distributed data Ilia Nouretdinov Adv. Sci. Technol. Eng. Syst. J. 2(3), 254-267 (2017); View Description S-Parameters Optimization in both Segmented and Unsegmented Insulated TSV upto 40GHz Frequency Juma Mary Atieno, Xuliang Zhang, HE Song Bai Adv. Sci. Technol. Eng. Syst. J. 2(3), 268-276 (2017); View Description Synthesis of Important Design Criteria for Future Vehicle Electric System Lisa Braun, Eric Sax Adv. Sci. Technol. Eng. Syst. J. 2(3), 277-283 (2017); View Description Gestural Interaction for Virtual Reality Environments through Data Gloves G. Rodriguez, N. Jofre, Y. Alvarado, J. Fernández, R. Guerrero Adv. Sci. Technol. Eng. Syst. J. 2(3), 284-290 (2017); View Description Solving the Capacitated Network Design Problem in Two Steps Meriem Khelifi, Mohand Yazid Saidi, Saadi Boudjit Adv. Sci. Technol. Eng. Syst. J. 2(3), 291-301 (2017); View Description A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks Mohammad Nurul Afsar Shaon, Ken Ferens Adv. Sci. Technol. Eng. Syst. J. 2(3), 302-320 (2017); View Description Real Time Advanced Clustering System Giuseppe Spampinato, Arcangelo Ranieri Bruna, Salvatore Curti, Viviana D’Alto Adv. Sci. Technol. Eng. Syst. J. 2(3), 321-326 (2017); View Description Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors Khalid Al-Mutib, Foudil Abdessemed Adv. Sci. Technol. Eng. Syst. J. 2(3), 327-337 (2017); View Description Validity of Mind Monitoring System as a Mental Health Indicator using Voice Naoki Hagiwara, Yasuhiro Omiya, Shuji Shinohara, Mitsuteru Nakamura, Masakazu Higuchi, Shunji Mitsuyoshi, Hideo Yasunaga, Shinichi Tokuno Adv. Sci. Technol. Eng. Syst. J. 2(3), 338-344 (2017); View Description The Model of Adaptive Learning Objects for virtual environments instanced by the competencies Carlos Guevara, Jose Aguilar, Alexandra González-Eras Adv. Sci. Technol. Eng. Syst. J. 2(3), 345-355 (2017); View Description An Overview of Traceability: Towards a general multi-domain model Kamal Souali, Othmane Rahmaoui, Mohammed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 356-361 (2017); View Description L-Band SiGe HBT Active Differential Equalizers with Variable, Positive or Negative Gain Slopes Using Dual-Resonant RLC Circuits Yasushi Itoh, Hiroaki Takagi Adv. Sci. Technol. Eng. Syst. J. 2(3), 362-368 (2017); View Description Moving Towards Reliability-Centred Management of Energy, Power and Transportation Assets Kang Seng Seow, Loc K. Nguyen, Kelvin Tan, Kees-Jan Van Oeveren Adv. Sci. Technol. Eng. Syst. J. 2(3), 369-375 (2017); View Description Secure Path Selection under Random Fading Furqan Jameel, Faisal, M Asif Ali Haider, Amir Aziz Butt Adv. Sci. Technol. Eng. Syst. J. 2(3), 376-383 (2017); View Description Security in SWIPT with Power Splitting Eavesdropper Furqan Jameel, Faisal, M Asif Ali Haider, Amir Aziz Butt Adv. Sci. Technol. Eng. Syst. J. 2(3), 384-388 (2017); View Description Performance Analysis of Phased Array and Frequency Diverse Array Radar Ambiguity Functions Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 389-394 (2017); View Description Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems Hanene Medhaffar, Moez Feki, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 395-400 (2017); View Description Fault Tolerant Inverter Topology for the Sustainable Drive of an Electrical Helicopter Igor Bolvashenkov, Jörg Kammermann, Taha Lahlou, Hans-Georg Herzog Adv. Sci. Technol. Eng. Syst. J. 2(3), 401-411 (2017); View Description Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid Turgay Yalcin, Muammer Ozdemir Adv. Sci. Technol. Eng. Syst. J. 2(3), 412-419 (2017); View Description A Highly-Secured Arithmetic Hiding cum Look-Up Table (AHLUT) based S-Box for AES-128 Implementation Ali Akbar Pammu, Kwen-Siong Chong, Bah-Hwee Gwee Adv. Sci. Technol. Eng. Syst. J. 2(3), 420-426 (2017); View Description Service Productivity and Complexity in Medical Rescue Services Markus Harlacher, Andreas Petz, Philipp Przybysz, Olivia Chaillié, Susanne Mütze-Niewöhner Adv. Sci. Technol. Eng. Syst. J. 2(3), 427-434 (2017); View Description Principal Component Analysis Application on Flavonoids Characterization Che Hafizah Che Noh, Nor Fadhillah Mohamed Azmin, Azura Amid Adv. Sci. Technol. Eng. Syst. J. 2(3), 435-440 (2017); View Description A Reconfigurable Metal-Plasma Yagi-Yuda Antenna for Microwave Applications Giulia Mansutti, Davide Melazzi, Antonio-Daniele Capobianco Adv. Sci. Technol. Eng. Syst. J. 2(3), 441-448 (2017); View Description Verifying the Detection Results of Impersonation Attacks in Service Clouds Sarra Alqahtani, Rose Gamble Adv. Sci. Technol. Eng. Syst. J. 2(3), 449-459 (2017); View Description Image Segmentation Using Fuzzy Inference System on YCbCr Color Model Alvaro Anzueto-Rios, Jose Antonio Moreno-Cadenas, Felipe Gómez-Castañeda, Sergio Garduza-Gonzalez Adv. Sci. Technol. Eng. Syst. J. 2(3), 460-468 (2017); View Description Segmented and Detailed Visualization of Anatomical Structures based on Augmented Reality for Health Education and Knowledge Discovery Isabel Cristina Siqueira da Silva, Gerson Klein, Denise Munchen Brandão Adv. Sci. Technol. Eng. Syst. J. 2(3), 469-478 (2017); View Description Intrusion detection in cloud computing based attack patterns and risk assessment Ben Charhi Youssef, Mannane Nada, Bendriss Elmehdi, Regragui Boubker Adv. Sci. Technol. Eng. Syst. J. 2(3), 479-484 (2017); View Description Optimal Sizing and Control Strategy of renewable hybrid systems PV-Diesel Generator-Battery: application to the case of Djanet city of Algeria Adel Yahiaoui, Khelifa Benmansour, Mohamed Tadjine Adv. Sci. Technol. Eng. Syst. J. 2(3), 485-491 (2017); View Description RFID Antenna Near-field Characterization Using a New 3D Magnetic Field Probe Kassem Jomaa, Fabien Ndagijimana, Hussam Ayad, Majida Fadlallah, Jalal Jomaah Adv. Sci. Technol. Eng. Syst. J. 2(3), 492-497 (2017); View Description Design, Fabrication and Testing of a Dual-Range XY Micro-Motion Stage Driven by Voice Coil Actuators Xavier Herpe, Matthew Dunnigan, Xianwen Kong Adv. Sci. Technol. Eng. Syst. J. 2(3), 498-504 (2017); View Description Self-Organizing Map based Feature Learning in Bio-Signal Processing Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily Adv. Sci. Technol. Eng. Syst. J. 2(3), 505-512 (2017); View Description A delay-dependent distributed SMC for stabilization of a networked robotic system exposed to external disturbances." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (June 2016): 513–19. http://dx.doi.org/10.25046/aj020366.

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Biran, Yahav, George Collins, Borky John M, and Joel Dubow. "Volume 2, Issue 3, Special issue on Recent Advances in Engineering Systems (Published Papers) Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los Ángeles, Diván, Mario José Adv. Sci. Technol. Eng. Syst. J. 2(3), 12-23 (2017); View Description Low Probability of Interception Beampattern Using Frequency Diverse Array Antenna Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 24-29 (2017); View Description Zero Trust Cloud Networks using Transport Access Control and High Availability Optical Bypass Switching Casimer DeCusatis, Piradon Liengtiraphan, Anthony Sager Adv. Sci. Technol. Eng. Syst. J. 2(3), 30-35 (2017); View Description A Derived Metrics as a Measurement to Support Efficient Requirements Analysis and Release Management Indranil Nath Adv. Sci. Technol. Eng. Syst. J. 2(3), 36-40 (2017); View Description Feedback device of temperature sensation for a myoelectric prosthetic hand Yuki Ueda, Chiharu Ishii Adv. Sci. Technol. Eng. Syst. J. 2(3), 41-40 (2017); View Description Deep venous thrombus characterization: ultrasonography, elastography and scattering operator Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier Adv. Sci. Technol. Eng. Syst. J. 2(3), 48-59 (2017); View Description Improving customs’ border control by creating a reference database of cargo inspection X-ray images Selina Kolokytha, Alexander Flisch, Thomas Lüthi, Mathieu Plamondon, Adrian Schwaninger, Wicher Vasser, Diana Hardmeier, Marius Costin, Caroline Vienne, Frank Sukowski, Ulf Hassler, Irène Dorion, Najib Gadi, Serge Maitrejean, Abraham Marciano, Andrea Canonica, Eric Rochat, Ger Koomen, Micha Slegt Adv. Sci. Technol. Eng. Syst. J. 2(3), 60-66 (2017); View Description Aviation Navigation with Use of Polarimetric Technologies Arsen Klochan, Ali Al-Ammouri, Viktor Romanenko, Vladimir Tronko Adv. Sci. Technol. Eng. Syst. J. 2(3), 67-72 (2017); View Description Optimization of Multi-standard Transmitter Architecture Using Single-Double Conversion Technique Used for Rescue Operations Riadh Essaadali, Said Aliouane, Chokri Jebali and Ammar Kouki Adv. Sci. Technol. Eng. Syst. J. 2(3), 73-81 (2017); View Description Singular Integral Equations in Electromagnetic Waves Reflection Modeling A. S. Ilinskiy, T. N. Galishnikova Adv. Sci. Technol. Eng. Syst. J. 2(3), 82-87 (2017); View Description Methodology for Management of Information Security in Industrial Control Systems: A Proof of Concept aligned with Enterprise Objectives. Fabian Bustamante, Walter Fuertes, Paul Diaz, Theofilos Toulqueridis Adv. Sci. Technol. Eng. Syst. J. 2(3), 88-99 (2017); View Description Dependence-Based Segmentation Approach for Detecting Morpheme Boundaries Ahmed Khorsi, Abeer Alsheddi Adv. Sci. Technol. Eng. Syst. J. 2(3), 100-110 (2017); View Description Paper Improving Rule Based Stemmers to Solve Some Special Cases of Arabic Language Soufiane Farrah, Hanane El Manssouri, Ziyati Elhoussaine, Mohamed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 111-115 (2017); View Description Medical imbalanced data classification Sara Belarouci, Mohammed Amine Chikh Adv. Sci. Technol. Eng. Syst. J. 2(3), 116-124 (2017); View Description ADOxx Modelling Method Conceptualization Environment Nesat Efendioglu, Robert Woitsch, Wilfrid Utz, Damiano Falcioni Adv. Sci. Technol. Eng. Syst. J. 2(3), 125-136 (2017); View Description GPSR+Predict: An Enhancement for GPSR to Make Smart Routing Decision by Anticipating Movement of Vehicles in VANETs Zineb Squalli Houssaini, Imane Zaimi, Mohammed Oumsis, Saïd El Alaoui Ouatik Adv. Sci. Technol. Eng. Syst. J. 2(3), 137-146 (2017); View Description Optimal Synthesis of Universal Space Vector Digital Algorithm for Matrix Converters Adrian Popovici, Mircea Băbăiţă, Petru Papazian Adv. Sci. Technol. Eng. Syst. J. 2(3), 147-152 (2017); View Description Control design for axial flux permanent magnet synchronous motor which operates above the nominal speed Xuan Minh Tran, Nhu Hien Nguyen, Quoc Tuan Duong Adv. Sci. Technol. Eng. Syst. J. 2(3), 153-159 (2017); View Description A synchronizing second order sliding mode control applied to decentralized time delayed multi−agent robotic systems: Stability Proof Marwa Fathallah, Fatma Abdelhedi, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 160-170 (2017); View Description Fault Diagnosis and Tolerant Control Using Observer Banks Applied to Continuous Stirred Tank Reactor Martin F. Pico, Eduardo J. Adam Adv. Sci. Technol. Eng. Syst. J. 2(3), 171-181 (2017); View Description Development and Validation of a Heat Pump System Model Using Artificial Neural Network Nabil Nassif, Jordan Gooden Adv. Sci. Technol. Eng. Syst. J. 2(3), 182-185 (2017); View Description Assessment of the usefulness and appeal of stigma-stop by psychology students: a serious game designed to reduce the stigma of mental illness Adolfo J. Cangas, Noelia Navarro, Juan J. Ojeda, Diego Cangas, Jose A. Piedra, José Gallego Adv. Sci. Technol. Eng. Syst. J. 2(3), 186-190 (2017); View Description Kinect-Based Moving Human Tracking System with Obstacle Avoidance Abdel Mehsen Ahmad, Zouhair Bazzal, Hiba Al Youssef Adv. Sci. Technol. Eng. Syst. J. 2(3), 191-197 (2017); View Description A security approach based on honeypots: Protecting Online Social network from malicious profiles Fatna Elmendili, Nisrine Maqran, Younes El Bouzekri El Idrissi, Habiba Chaoui Adv. Sci. Technol. Eng. Syst. J. 2(3), 198-204 (2017); View Description Pulse Generator for Ultrasonic Piezoelectric Transducer Arrays Based on a Programmable System-on-Chip (PSoC) Pedro Acevedo, Martín Fuentes, Joel Durán, Mónica Vázquez, Carlos Díaz Adv. Sci. Technol. Eng. Syst. J. 2(3), 205-209 (2017); View Description Enabling Toy Vehicles Interaction With Visible Light Communication (VLC) M. A. Ilyas, M. B. Othman, S. M. Shah, Mas Fawzi Adv. Sci. Technol. Eng. Syst. J. 2(3), 210-216 (2017); View Description Analysis of Fractional-Order 2xn RLC Networks by Transmission Matrices Mahmut Ün, Manolya Ün Adv. Sci. Technol. Eng. Syst. J. 2(3), 217-220 (2017); View Description Fire extinguishing system in large underground garages Ivan Antonov, Rositsa Velichkova, Svetlin Antonov, Kamen Grozdanov, Milka Uzunova, Ikram El Abbassi Adv. Sci. Technol. Eng. Syst. J. 2(3), 221-226 (2017); View Description Directional Antenna Modulation Technique using A Two-Element Frequency Diverse Array Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 227-232 (2017); View Description Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks Estefanía D. Avalos-Rivera, Alberto de J. Pastrana-Palma Adv. Sci. Technol. Eng. Syst. J. 2(3), 233-240 (2017); View Description Magnetically Levitated and Guided Systems Florian Puci, Miroslav Husak Adv. Sci. Technol. Eng. Syst. J. 2(3), 241-244 (2017); View Description Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks Minh T. Nguyen Adv. Sci. Technol. Eng. Syst. J. 2(3), 245-253 (2017); View Description Validity and efficiency of conformal anomaly detection on big distributed data Ilia Nouretdinov Adv. Sci. Technol. Eng. Syst. J. 2(3), 254-267 (2017); View Description S-Parameters Optimization in both Segmented and Unsegmented Insulated TSV upto 40GHz Frequency Juma Mary Atieno, Xuliang Zhang, HE Song Bai Adv. Sci. Technol. Eng. Syst. J. 2(3), 268-276 (2017); View Description Synthesis of Important Design Criteria for Future Vehicle Electric System Lisa Braun, Eric Sax Adv. Sci. Technol. Eng. Syst. J. 2(3), 277-283 (2017); View Description Gestural Interaction for Virtual Reality Environments through Data Gloves G. Rodriguez, N. Jofre, Y. Alvarado, J. Fernández, R. Guerrero Adv. Sci. Technol. Eng. Syst. J. 2(3), 284-290 (2017); View Description Solving the Capacitated Network Design Problem in Two Steps Meriem Khelifi, Mohand Yazid Saidi, Saadi Boudjit Adv. Sci. Technol. Eng. Syst. J. 2(3), 291-301 (2017); View Description A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks Mohammad Nurul Afsar Shaon, Ken Ferens Adv. Sci. Technol. Eng. Syst. J. 2(3), 302-320 (2017); View Description Real Time Advanced Clustering System Giuseppe Spampinato, Arcangelo Ranieri Bruna, Salvatore Curti, Viviana D’Alto Adv. Sci. Technol. Eng. Syst. J. 2(3), 321-326 (2017); View Description Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors Khalid Al-Mutib, Foudil Abdessemed Adv. Sci. Technol. Eng. Syst. J. 2(3), 327-337 (2017); View Description Validity of Mind Monitoring System as a Mental Health Indicator using Voice Naoki Hagiwara, Yasuhiro Omiya, Shuji Shinohara, Mitsuteru Nakamura, Masakazu Higuchi, Shunji Mitsuyoshi, Hideo Yasunaga, Shinichi Tokuno Adv. Sci. Technol. Eng. Syst. J. 2(3), 338-344 (2017); View Description The Model of Adaptive Learning Objects for virtual environments instanced by the competencies Carlos Guevara, Jose Aguilar, Alexandra González-Eras Adv. Sci. Technol. Eng. Syst. J. 2(3), 345-355 (2017); View Description An Overview of Traceability: Towards a general multi-domain model Kamal Souali, Othmane Rahmaoui, Mohammed Ouzzif Adv. Sci. Technol. Eng. Syst. J. 2(3), 356-361 (2017); View Description L-Band SiGe HBT Active Differential Equalizers with Variable, Positive or Negative Gain Slopes Using Dual-Resonant RLC Circuits Yasushi Itoh, Hiroaki Takagi Adv. Sci. Technol. Eng. Syst. J. 2(3), 362-368 (2017); View Description Moving Towards Reliability-Centred Management of Energy, Power and Transportation Assets Kang Seng Seow, Loc K. Nguyen, Kelvin Tan, Kees-Jan Van Oeveren Adv. Sci. Technol. Eng. Syst. J. 2(3), 369-375 (2017); View Description Secure Path Selection under Random Fading Furqan Jameel, Faisal, M Asif Ali Haider, Amir Aziz Butt Adv. Sci. Technol. Eng. Syst. J. 2(3), 376-383 (2017); View Description Security in SWIPT with Power Splitting Eavesdropper Furqan Jameel, Faisal, M Asif Ali Haider, Amir Aziz Butt Adv. Sci. Technol. Eng. Syst. J. 2(3), 384-388 (2017); View Description Performance Analysis of Phased Array and Frequency Diverse Array Radar Ambiguity Functions Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 389-394 (2017); View Description Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems Hanene Medhaffar, Moez Feki, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 395-400 (2017); View Description Fault Tolerant Inverter Topology for the Sustainable Drive of an Electrical Helicopter Igor Bolvashenkov, Jörg Kammermann, Taha Lahlou, Hans-Georg Herzog Adv. Sci. Technol. Eng. Syst. J. 2(3), 401-411 (2017); View Description Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid Turgay Yalcin, Muammer Ozdemir Adv. Sci. Technol. Eng. Syst. J. 2(3), 412-419 (2017); View Description A Highly-Secured Arithmetic Hiding cum Look-Up Table (AHLUT) based S-Box for AES-128 Implementation Ali Akbar Pammu, Kwen-Siong Chong, Bah-Hwee Gwee Adv. Sci. Technol. Eng. Syst. J. 2(3), 420-426 (2017); View Description Service Productivity and Complexity in Medical Rescue Services Markus Harlacher, Andreas Petz, Philipp Przybysz, Olivia Chaillié, Susanne Mütze-Niewöhner Adv. Sci. Technol. Eng. Syst. J. 2(3), 427-434 (2017); View Description Principal Component Analysis Application on Flavonoids Characterization Che Hafizah Che Noh, Nor Fadhillah Mohamed Azmin, Azura Amid Adv. Sci. Technol. Eng. Syst. J. 2(3), 435-440 (2017); View Description A Reconfigurable Metal-Plasma Yagi-Yuda Antenna for Microwave Applications Giulia Mansutti, Davide Melazzi, Antonio-Daniele Capobianco Adv. Sci. Technol. Eng. Syst. J. 2(3), 441-448 (2017); View Description Verifying the Detection Results of Impersonation Attacks in Service Clouds Sarra Alqahtani, Rose Gamble Adv. Sci. Technol. Eng. Syst. J. 2(3), 449-459 (2017); View Description Image Segmentation Using Fuzzy Inference System on YCbCr Color Model Alvaro Anzueto-Rios, Jose Antonio Moreno-Cadenas, Felipe Gómez-Castañeda, Sergio Garduza-Gonzalez Adv. Sci. Technol. Eng. Syst. J. 2(3), 460-468 (2017); View Description Segmented and Detailed Visualization of Anatomical Structures based on Augmented Reality for Health Education and Knowledge Discovery Isabel Cristina Siqueira da Silva, Gerson Klein, Denise Munchen Brandão Adv. Sci. Technol. Eng. Syst. J. 2(3), 469-478 (2017); View Description Intrusion detection in cloud computing based attack patterns and risk assessment Ben Charhi Youssef, Mannane Nada, Bendriss Elmehdi, Regragui Boubker Adv. Sci. Technol. Eng. Syst. J. 2(3), 479-484 (2017); View Description Optimal Sizing and Control Strategy of renewable hybrid systems PV-Diesel Generator-Battery: application to the case of Djanet city of Algeria Adel Yahiaoui, Khelifa Benmansour, Mohamed Tadjine Adv. Sci. Technol. Eng. Syst. J. 2(3), 485-491 (2017); View Description RFID Antenna Near-field Characterization Using a New 3D Magnetic Field Probe Kassem Jomaa, Fabien Ndagijimana, Hussam Ayad, Majida Fadlallah, Jalal Jomaah Adv. Sci. Technol. Eng. Syst. J. 2(3), 492-497 (2017); View Description Design, Fabrication and Testing of a Dual-Range XY Micro-Motion Stage Driven by Voice Coil Actuators Xavier Herpe, Matthew Dunnigan, Xianwen Kong Adv. Sci. Technol. Eng. Syst. J. 2(3), 498-504 (2017); View Description Self-Organizing Map based Feature Learning in Bio-Signal Processing Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily Adv. Sci. Technol. Eng. Syst. J. 2(3), 505-512 (2017); View Description A delay-dependent distributed SMC for stabilization of a networked robotic system exposed to external disturbances Fatma Abdelhedi, Nabil Derbel Adv. Sci. Technol. Eng. Syst. J. 2(3), 513-519 (2017); View Description Modelization of cognition, activity and motivation as indicators for Interactive Learning Environment Asmaa Darouich, Faddoul Khoukhi, Khadija Douzi Adv. Sci. Technol. Eng. Syst. J. 2(3), 520-531 (2017); View Description Homemade array of surface coils implementation for small animal magnetic resonance imaging Fernando Yepes-Calderon, Olivier Beuf Adv. Sci. Technol. Eng. Syst. J. 2(3), 532-539 (2017); View Description An Encryption Key for Secure Authentication: The Dynamic Solution Zubayr Khalid, Pritam Paul, Khabbab Zakaria, Himadri Nath Saha Adv. Sci. Technol. Eng. Syst. J. 2(3), 540-544 (2017); View Description Multi-Domain Virtual Network Embedding with Coordinated Link Mapping Shuopeng Li, Mohand Yazid Saidi, Ken Chen Adv. Sci. Technol. Eng. Syst. J. 2(3), 545-552 (2017); View Description Semantic-less Breach Detection of Polymorphic Malware in Federated Cloud." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (June 2017): 553–61. http://dx.doi.org/10.25046/aj020371.

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"Integrated SURF and Spatial Augmented Color Feature Based Bovw Model with Svm for Image Classification." International Journal of Engineering and Advanced Technology 8, no. 6 (August 30, 2019): 1875–77. http://dx.doi.org/10.35940/ijeat.f7900.088619.

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In this paper, Bag-of-visual-words (BoVW) model with Speed up robust features (SURF) and spatial augmented color features for image classification is proposed. In BOVW model image is designated as vector of features occurrence count. This model ignores spatial information amongst patches, and SURF Feature descriptor is relevant to gray images only. As spatial layout of the extracted feature is important and color is a vital feature for image recognition, in this paper local color layout feature is augmented with SURF feature. Feature space is quantized using K-means clustering for feature reduction in constructing visual vocabulary. Histogram of visual word occurrence is then obtained which is applied to multiclass SVM classifier. Experimental results show that accuracy is improved with the proposed method.

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"Content based Retrieval Management Systems in Web Engineering." International Journal of Recent Technology and Engineering 8, no. 2S11 (November 2, 2019): 81–93. http://dx.doi.org/10.35940/ijrte.b1014.0982s1119.

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The tremendous upsurge and abundant availability of graphic documents on the internet directed to the high concern in research on Content-Based Image Retrieval (CBIR). It has cemented the attitude for a massive sum of innovative procedures and schemes, and growing curiosity in respective fields to preserve such research. Existing technologies were discussed about various CBIR techniques such as k-means, content-based image, and hybrid clustering, etc. in combination with the feature vector of information images and texture features. In similar cases, it is constricted in stating the user’s semantic knowledge to permit information distribution and reuse. Hence models ought to be managed within repositories, where they might be retrieved upon users’ queries. Due to the lack of adequate tools on incisive/handling for visual content, this research work proposes an Efficient Density-based Clustering Algorithm (EDBC) for CBIR technique that will enhance scalability, reduce the user search time, and lower the maintenance cost. Using the existing CBIR, the color, texture, and shape features of an image is identified by integrating with the proposed EDBC algorithm and thereby, it improves the scalability and user search time. When comparing the feature between the color histograms of RGB and CMYK, it shows better color characteristics in CMYK by using the proposed technique. Also, the grouping of color, shape and texture features based image retrieval improves the accuracy when compared with existing methods

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"Detection and Classification of Ring, Rust and Yellow Sugarcane Leaf Diseases." International Journal of Engineering and Advanced Technology 8, no. 6 (August 30, 2019): 3310–15. http://dx.doi.org/10.35940/ijeat.f9314.088619.

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Agriculture is an important sector in Economic and Social life. Crop disease detection is an emerging field in India. We can minimize the diseases infection on sugarcane leaf by detecting and grading the leaf disease in early stages. In this paper, we are detecting and recognize Sugar cane leaf diseases by using grey scale and color image processing and analyze the efficacy by comparing both. In grey scale processing, we presented Gradient Magnitude, Otsu method, Morphological Operations and Normalization to extract the Region of interest (ROI) i.e., disease part. In color processing initially converted RGB to L*a*b format, later K-means clustering and edge detection operations are applied on L*a*b image format. The features of Grey scale & color processed image are extracted and feed to Support Vector Machine (SVM) classifier which classifies ring, rust & yellow spot sugarcane leaf diseases. The Sugarcane leaf diseases are classified successfully with an average accuracy of 84% & 92% for grey scale & color features respectively.

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"Image Clustering using Multichannel Decoded Local Binary Pattern." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 8117–22. http://dx.doi.org/10.35940/ijrte.d8580.118419.

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CBIR uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. Local Binary Pattern based descriptors have been used for the purpose of image feature description. Local binary pattern (LBP) has widely increased the popularity due to its simplicity and effectiveness in several applications. In this paper, we proposes a novel method for image description with multichannel decoded local binary patterns. Introduce adder and decoder based two schemas for the combination of the LBPs from more than one channel. Finally, uses Fuzzy C-means clustering under semi- supervised framework. The outcomes are processed as far as the normal exactness rate and average recovery rate and improved execution is seen when contrasted and the aftereffects of the current multichannel based methodologies over every database. The component vector is figured for snake and decoder channels utilizing histograms. At long last, the image ordering process is enhanced utilizing information grouping methods for images having a place with a similar class

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"An Integrated Color Image Segmentation with Multi-class SVM followed by SRFCM." International Journal of Recent Technology and Engineering 8, no. 2S8 (September 17, 2019): 1607–10. http://dx.doi.org/10.35940/ijrte.b1114.0882s819.

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In existing the segmentation of a color image is mostly depends on the features color , texture or on both color and texture but proposed method for color image segmentation is based on both color and texture with multi-class SVM (Support Vector Machine).For color feature extraction we used homogeneity model and for textural features we used PLD (Power Law Descriptor). With the help of SR-FCM (Soft Rough Fuzzy-C-Means) clustering. Membership functions based on the fuzzy set are facing the major problem of cluster overlapping. The rough set concepts can help us to get correct data from incomplete data, uncertainty of data. For defining the soft set theory there is no any requirement of parameterization tools. To get improved results of proposed algorithm the combination of aspects of fuzzy sets, rough sets as well as soft sets are used. The feature extraction for textural feature is done by using spatial domain which helps to reduce the run time complexity. Proposed method provides better performance which is compared with all the state of art techniques which is developed and analyzed using MATLAB.

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"A Hybrid Leaf Disease Detection Scheme using Grayco-Occurance Matrix Support Vector Machine Algorithm." International Journal of Recent Technology and Engineering 8, no. 2S11 (November 2, 2019): 300–309. http://dx.doi.org/10.35940/ijrte.b1048.0982s1119.

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An Indian economy depends upon the agriculture up to 70% approximately. Hence, there is a need to Take care of agriculture and its resources. In such aspects, the plant disease and leaf disease is one of the major concerns that affect the overall processing of producing food, feed, fiber and many other favorite products by the cultivation. It is one of the reasons that disease identification and detection in plant adopts a significant job in agro industry area. Due to this reason, appropriate detection methodology consideration is to be taken here. Most of the research focused more on combining image processing and soft computing algorithms to solve this issue. With this motivation, this research utilize Median filter for noise removal in initial stage. Later, Hue-Saturation-Value is used for preprocessing. Further, Fuzzy C-Means Clustering (FCM) considered for clustering image samples at different iteration. Finally, the research considered a hybrid mechanism by combining Gray Co-Occurrence Matrix and Support Vector Machine. Further, the proposed method results better outcome in terms of efficiency as 87.43% K-nearest neighbor (KNN) classifier, Color Transform and Exponential Spider Monkey Optimization.

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Hu, Qiu-Xia, Jie Tian, and Dong-Jian He. "Multi-Channel Mapping Image Segmentation Method Based on LDA." International Journal of Pattern Recognition and Artificial Intelligence, October 22, 2020, 2154012. http://dx.doi.org/10.1142/s0218001421540124.

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In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method’s mapping and K-means’ clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.

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"Leaf Disease Detection Based on Local Gabor Binary Pattern Histogram Sequence and Neural Network." International Journal of Innovative Technology and Exploring Engineering 9, no. 7 (May 10, 2020): 175–80. http://dx.doi.org/10.35940/ijitee.e2864.059720.

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Agriculture forms the main source of food in India, especially in the southern area. The economy of India directly depends on agriculture plants. But due to some major diseases such as blast, brown spot, and bacterial blight, there is a reduction in plant growth which greatly affects agricultural productivity. The farmers add irrelevant pesticides with their limited knowledge which will degrade the quality of the crop but also degrade the soil quality. In the proposed method Machine Vision techniques based on neural networks are used to detect plant health or diseases indicated by leaf anomaly. Image processing algorithms such as K means clustering is used to segment affected areas. From the segmented images of the plant leaf, features are extracted using Color Coherence Vector (CCV) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS). The extracted features are fed as input to a backpropagation neural network to classify the unhealthy leaf

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"TSD-CPI: Traffic Sign Detection Technique Based on Centroid Position Identification in Text Mining." International Journal of Engineering and Advanced Technology 9, no. 2 (December 30, 2019): 1649–53. http://dx.doi.org/10.35940/ijeat.b3056.129219.

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Detecting and Identifying traffic sign is a complicated issue due to the changing variability in cloud conditions. Hence, it is necessary to identify and detect of traffic signs during journey. The traffic text sign identification fails due to noise, blur, distortion and occlusion. In order to identify the text, a technique should be adapted that recognizes the text with improved accuracy. In existing algorithms such as Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) were not detecting the Centroid position. In this paper, the text Centroid of position sign is detected using text color, font and size. During journey, if the text is blurred, this Traffic Sign Detection Technique based on Centroid Position Identification (TSD-CPI) K-means algorithm for clustering is possible to use. As a result, it detects the text that with improved accuracy. Ultimately, it reduces the processing time. The experimental result reveals that using WEKA-3.8 with the proposed technique shows improvement over the existing algorithms in terms of precision and Recall which enhance the accuracy in text mining

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"Segmentation and Detection of Brain Tumor by using Machine Learning." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 2019): 3226–35. http://dx.doi.org/10.35940/ijrte.d8038.118419.

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The segmentation and detection of brain pathologies in medical images is an indispensible step. This helps the radiologist to diagnose a variety of brain deformity and helps in the set up for a suitable treatment. Magnetic Resonance Imaging (MRI) plays a significant character in the research area of neuroscience. The proposed work is a study and probing of different classification techniques used for automated detection and segmentation of brain tumor from MRI in the field of machine learning. This paper try to present the feature extraction from raw MRI and fed the same to four classifier named as, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). This mechanism was done in various stages for Computer Aided Detection System. In the preliminary stage the pre-processing and post-processing of MR image enhancement is done. This was done as the processed image is more likely suitable for the analysis. Then the k-means clustering is used to sectioning the MRI by applied mean gray level method. In the subsequent stage, statistical feature analysis were done, the features were computed using Haralick’s equation for feature based on the Gray Level Co-occurrence Matrix. Feature chosen dependent on tumor region, location, periphery, and color from the sectioned image is then classified by applying the classification techniques. In the third stage SVM, DT, ANN, and KNN classifiers were used for diagnoses. The performances of the classifiers are tested and evaluated successfully.

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Journal articles on the topic 'Color-vector clustering'

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