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Summary: One of the expanding network topologies that is frequently utilized to improve network development by successfully separating the control plane and data plane is software‐defined networking (SDN). In order to function inside complex sensor networks, the SDWSN system frequently relies on centralized controller logic that pulls global network information. In wireless sensor networks (WSNs), using several SDN controllers is known as a promising strategy due to reliability and performance considerations. However, using numerous controllers increases the synchronization overhead between the controllers. Consequently, it is a difficult research challenge to discover the best placement of SDN controllers to enhance the performance of a WSN, subject to the maximum number of controllers calculated based on the synchronization overhead. This research introduces a novel technique to overcome the controller placement problem (CPP) by optimizing multi‐constraints within the sensor networks. For selecting the optimal controllers and placing them in an optimal location, a novel sailfish optimization (SO) strategy is introduced that can enhance the search space and maintain optimal global values throughout the iteration. Then, node clustering is performed using the fuzzy‐C‐means (FCM) clustering technique, which can reduce energy consumption and path delay within the network. The overall latency obtained by the proposed method is about 0.51 and 0.56 ms, and a total run time of 4 ms for both single sink and multi‐sink, respectively. The proposed method is implemented in the MATLAB platform, and different performance metrics are analyzed and compared with existing techniques. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANET) are self-launching and self-building multichip wireless networks in which the system's construction deviates significantly owing to the mobility of the nodes. Network nodes serve as both hosts and routers, transferring data from other network nodes. MANETs necessitate an efficient routing protocol with a quality of service (QoS) component. They do not consider mobility as a potential restriction for routing concerns. It is challenging to maintain the necessary QoS in the network due to node mobility, which frequently causes connection failures and high error rates. The aim of the proposed study is to develop an effective MANET framework with dynamic routing and enhanced clustering. In this work, Fuzzy C means clustering is utilized to form the clusters identifying communities or groups of nodes with similar characteristics or behavior, whereas sea lion optimization (SLnO) is utilized to attain the best routing path for data transmission. The proposed model is empirically tested and compared with particle swarm optimization (PSO), ant colony optimization (ACO), grey wolf optimization (GWO), and genetic algorithm (GA) in terms of factors such as throughput, packet loss, energy consumed and end to end delay. The findings demonstrate that SLnO algorithm outperforms other protocols in terms of efficient routing generating a throughput of 680Mb/s and a packet delivery ratio (PDR) of 93.6%. [ABSTRACT FROM AUTHOR]

COVID-19 is the world's most serious threat, affecting billions of people worldwide. Medical imaging, such as CT, has a lot of potential as an alternative to RT-PCR approach for significant judgement and disease control. As a result, automatic image segmentation is in high demand as a therapeutic decision aid. According to studies, medical images may be very useful for early screening since certain aspects of the image imply the existence of virus of COVID-19 and hence may be used as an efficient scanning tool. The proposed work presents a hybrid approach for efficient screening of COVID-19 using chest CT images implementing Hybrid Particle Swarm Optimised-Fuzzy C Means Clustering. The proposed method is tested on 15 chest CT images of COVID-19-infected patients and the results have been validated quantitatively by metrices such as entropy, contrast and standard deviation, which clearly outperforms the conventional Fuzzy C Means Clustering. [ABSTRACT FROM AUTHOR]

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio. [ABSTRACT FROM AUTHOR]

Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intra-observer variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.

An optimal approach to anonymization using small data is proposed in this study. Map Reduce is a big data processing framework used across distributed applications. Prior to the development of a map reduce framework, data are distributed and clustered using a hybrid clustering algorithm. The algorithm used for grouping together similar techniques utilises the k-means clustering algorithm, along with the MFCM clustering algorithm. Clustered data is then fed into the map reduce frame work after it has been clustered. In order to guarantee privacy, the optimal k anonymization method is recommended. When using generalisation and randomization, there are two techniques that can be employed: K-anonymity, which is unique to each, depends on the type of the quasi identifier attribute. Our method replaces the standard k anonymization process by employing an optimization algorithm that dynamically determines the optimal k value. This algorithm uses the Modified Grey Wolf Optimization (MGWO) algorithm for optimization. The memory, execution time, accuracy, and error value are used to assess the recommended method's practise. This experiment has shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security. The current technique gets the lowest accuracy and the privacy proposed achieves the maximum accuracy while compared to the current technique. The solution is implemented in Java with Hadoop Map-Reduce, and it is tested and deployed in the cloud on Google Cloud Platform. [ABSTRACT FROM AUTHOR]

Aiming at the problem of complex method and low efficiency of fuzzy numbers in classification processing, a parallel Fuzzy CMeans (FCM) clustering method based on cut set is proposed. Firstly, according to the decomposition theorem, the fuzzy numbers are divided horizontally into the form of the union of interval numbers, and then the interval numbers are transformed into the determined "real" data, and the parallel FCM clustering algorithm is used to classify the fuzzy numbers. The theoretical analysis and application show that the method has good classification accuracy and efficiency for fuzzy data clustering. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast. OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial. METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor. RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization. CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging. [ABSTRACT FROM AUTHOR]

The paper presents histogram-based initialzation of Fuzzy C Means (FCM) clustering algorithm for remote sensing image analysis. The drawback of well known FCM clustering is sensitive to the choice of initial cluster centers. In order to overcome this drawback, the proposed algorithm, first, determines the optimal initial cluster centers by maximizing the histogram-based weight function. By using these initial cluster centers, the given image is segmented using fuzzy clustering. The major contribution of the proposed method is the automatic initialization of the cluster centers and hence, the clustering performance is enhanced. Also, it is empirically free of experimentally set parameters. Experiments are performed on remote sensing images and cluster validity indices Davies-Bouldin, Partition index, Xie-Beni, Partition Coefficient and Partition Entropy are computed and compared with prominent methods such as FCM, K-Means, and automatic histogram based FCM. The experimental outcomes show that the proposed method is competent for remote sensing image segmentation.

INTRODUCTION: Pancreatic cancer is highly lethal as it grows, spreads rapidly and difficult to diagnose at its early stages. It can be identified through scan images. The tumorous images obtained from imaging techniques suffer from the drawback of cryptic data due to presence of unwanted noise and poor contrast.OBJECTIVE: To reduce the risk of pancreatic cancer, its detection and diagnosis at an early stage becomes crucial. METHODS: The proposed work encompasses the processing of CT scans of pancreatic tumor using classical and artificial intelligence based optimized edge detection techniques for optimization and detection of tumor.RESULTS: The simulation results are highly encouraging as evident from the far improved visibility of resultant images with Particle Swarm Optimization.CONCLUSION: The output image with PSO shows the quality enhanced CT images which helps in accurate detection and diagnosis of the pancreatic tumor at an early stage providing an aid in medical imaging.

Wireless sensor networks (WSN's) are preferred for industrial applications due to progressive increase of sensor electronics. One such application is deployment of WSN's in smart grids. Smart Grid integrates information and communication techniques with electricity network. Smart grids utilize sophisticated control and monitoring devices for improving the efficiency of the grid. For energy efficient, low cost monitoring and control in smart grid WSN's is treated as a promising technology. Advanced Metering Infrastructure (AMI) is the key technology in the distribution networks of Smart Grid. The AMI is composed of various sensors for metering purpose. The meter data is also useful for the distribution operators to manage the demand response. The network involves smart meters, smart electric gas and water meters along with digital network management appliances for optimizing the electric network with real time data management. The smart sensors are limited in terms of battery, operational power and memory. These sensors communicate with the base station in restricted range. The communication between smart grid nodes and base station (sink) is multi-hop in nature. The communication takes place within limited range of communication so the security concerns that are involved in the network are to be handled by the routing protocols. So as to make the bidirectional communication efficient between the smart sensors and utility an effective routing scheme is required for these energy limited devices to handle the heavy network traffic in smart grids. Here energy efficient routing for WSN's in NAN networks to attain load balancing is proposed through density based Fuzzy C means clustering (DFCM). The obtained simulation results show that DFCM can provide a satisfactory performance for enhancing the network life span. [ABSTRACT FROM AUTHOR]

Early stage of diagnosis of eye diseases through automatic analysis in the retinal image is the emerging technology in the area of retinopathy. Glaucoma is the primary reason for the loss of visibility in people around the world. The separation of the disc and the cup in the optic region is the technique used to identify glaucoma in the human retinal image. In this paper, superpixel segmentation, followed by Modified Kernel Fuzzy C‐Means (MKFCM) algorithm is used to segment the optic disc and optic cup. The proposed segmentation method achieves a maximum average of F‐score as 0.979, an average boundary distance as 10.016 pixels, and an average correlation coefficient of 0.949. To train convolutional neural networks (CNN), the segmented images obtained by the MKFCM segmentation algorithm is given as the input for the identification of glaucoma. This CNN uses the gray level co‐occurrence matrix features calculated from the segmented image. The experiment used for this study demonstrates that CNN gives superior categorization correctness and requires fewer figures of knowledge iterations than the original CNN. The accuracy obtained by this proposed method is 94.2%. The model will help to identify the proper class of severity of glaucoma in retinal images. [ABSTRACT FROM AUTHOR]

To assist an investigation process, forensic experts compare and analyze audio recordings. Speech utterances are compared by humans and/or machines for use in court for investigation. Scientific research community insists for specific automatic or human-based approach to identify uniquxy2e audio features from identical twins group. Filters can be employed to enhance an audio recording for improving clarity. This may entail removal of unnecessary noise to enrich the intelligibility of speech. Forensic audio experts can examine a variety of characteristics of the audio recording to decide the possibility of alterations in the collected evidences. This includes confirming the integrity and authenticating that the content is what it purports to be. Thiswork named as FIST(Forensic Investigation for Twin Identification from Speech: Perceptual and Gamma-tone Features and Models) proposes an automated system to identify a twin from identical twin pairs by the use of gamma-tone features and perceptual features.The proposed features are excerpted from the set of training speeches and templates are created for each twin based on vector quantisation (VQ), Fuzzy C means clustering (FCM) and multivariate hidden Markov modelling (MHMM) techniques. For testing, features are extracted from the set of test utterances and worked out to the templates for classification. Based on the type of classifier used, classification of twin is carried out with minimum distance and maximum loglikelihood value. The proposed features are examinedfor sub-optimal and true success rates as key performance metrics to assess the system and also a comparative analysis is made across the proposed features. Among the inspected features, Gammatone energy features expose better performance in comparison to perceptual features by attaining the overall sub-optimal success rate and true success rate as97.8375% and 92.75% for Gammatone energy features with VQ based modelling technique. This work FIST has also been analysed by inducing disturbance in the form of speech interference from their own twin pairs and Gamma-tone energy feature with VQ based modelling technique performs better for twin identification. A high claim of 99.625% and 95.0625% accuracy has been achieved by employing decision level fusion classifier. [ABSTRACT FROM AUTHOR]

Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from the segmented liver, extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features. In this work, the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering (FCM) which is one of the algorithms to segment the images. The dataset used in this method has been collected from various repositories and scan centers. The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency. It gives better results when compared with other existing algorithms. Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly. [ABSTRACT FROM AUTHOR]

The paper presents histogram-based initialzation of Fuzzy C Means (FCM) clustering algorithm for remote sensing image analysis. The drawback of well known FCM clustering is sensitive to the choice of initial cluster centers. In order to overcome this drawback, the proposed algorithm, first, determines the optimal initial cluster centers by maximizing the histogram-based weight function. By using these initial cluster centers, the given image is segmented using fuzzy clustering. The major contribution of the proposed method is the automatic initialization of the cluster centers and hence, the clustering performance is enhanced. Also, it is empirically free of experimentally set parameters. Experiments are performed on remote sensing images and cluster validity indices Davies-Bouldin, Partition index, Xie-Beni, Partition Coefficient and Partition Entropy are computed and compared with prominent methods such as FCM, K-Means, and automatic histogram based FCM. The experimental outcomes show that the proposed method is competent for remote sensing image segmentation. [ABSTRACT FROM AUTHOR]

In mineral exploration, geophysical inversion is a common mathematical tool to obtain reliable information on subsurface density properties based on gravity measurements. Many inversion algorithms were developed to obtain the density distribution in the Earth's subsurface. Recovered density values are usually lower/higher than the actual density as a consequence of inversion algorithm. This paper presents the use of a fuzzy cross-update inversion (FCUI) procedure to improve the subsurface density model based on a triangular grid. The algorithm is written in MATLAB and uses fuzzy c means clustering to improve the density modeling result per iteration. Two additional input parameters are added, namely the number of geologic units in the model (i.e., number of clusters) and the cluster center values of the geologic units (mean density value of each geologic unit). Inversion results from the FCUI are presented and compared with conventional inversion. The effectiveness of the developed technique is tested for the interpretation of synthetic data and two sets of field data. The FCUI approach shows improvement over conventional inversion approaches in differentiating geologic units. Further, FCUI was performed to reduce ambiguity of interpretation for the delineation of chromite and uranium deposits as the first and second case studies, respectively. We integrated favorable information and show the efficacy of FCUI over conventional inversion for the field datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a PV and wind output power generation forecasting agent for a multi-agent-based energy management system (EMS) in a smart microgrid. The microgrid EMS requires both generation forecast and load forecast to provide effective dispatch strategies. The efficiency of the EMS significantly relies on its forecasting accuracy. Firstly, this paper develops an adaptive neuro-fuzzy inference system (ANFIS)-based forecasting model and then utilise it for the development of wind and PV generation forecasting agent for microgrid energy management. ANFIS adopt the self-learning capability from the neural network and linguistic expression function from fuzzy logic inference and stands at the top of both the technologies in performance. The proposed model has been tested using two data sets, i.e., PV historical data and historical wind data. The fuzzy c means clustering (FCM) with hybrid optimisation algorithm-based ANFIS model shows better forecasting accuracy with both PV and wind forecast, therefore, implemented as PV and wind forecasting agent for microgrid EMS.

Abstract The conventional methods of urban impervious surfaces extraction mainly use the shallow-layer machine learning algorithms based on the medium- or low-resolution remote sensing images, and always provide low accuracy and poor automation level because the potential of multi-source remote sensing data are not fully utilized and the low-level features are not effectively organized. In order to address this problem, a novel method (AEIDLMRS) is proposed to automatically extract impervious surfaces based on deep learning and multi-source remote sensing data. First, the multi-source remote sensing data consisting of LIDAR points cloud data, Landsat8 images and Pléiades-1A images are pre-processed, re-sampled and registered, and then the combined features of spectral, elevation and intensity from the multi-source data are denoised using the minimum noise fraction (MNF) method to generate some representative MNF features. A small number of reliable labelled samples are automatically extracted using the fuzzy C-means (FCM) clustering method based on the MNF features. Secondly, the convolutional neural network (CNN) is used to extract the representative features of the neighborhood windows of each pixel in the fused Pléiades-1A image through multi-layer convolution and pooling operations. Finally, the combined features of MNF features and CNN features are pre-learned via the deep belief network (DBN). The DBN parameters are globally optimized jointly using the Extreme Learning Machine (ELM) classifier on the top level and the small set of labelled samples extracted via FCM, and the urban impervious surfaces are distinguished from others based on the trained ELM classifier and morphological operations. Experiments are performed to compare the proposed method with other three related methods in three different experimental regions respectively. Experimental results demonstrate that AEIDLMRS has better accuracy and automation level than the others under relatively good efficiency, and it is more suitable for the extraction of complex urban impervious surfaces. [ABSTRACT FROM AUTHOR]

Highlights • First time, Anisotropic Diffusion filter is integrated in FCM Clustering that proves its ability both qualitatively and quantitatively. • ADFCM proved its novelty in the extraction of hottest region. • Uniform numbers of clusters are chosen in ADFCM irrespective of images. • Limitations of breast thermograms and FCM are well addressed by ADFCM. • First time, Hottest Area Index (HAI) is introduced in breast thermogram. Abstract Clustering analysis of medical infrared images is found to be very effective for extracting local information, which helps to identify the abnormalities associated with region of interest. Breast tissue extraction in breast thermograms is challenging due to its low signal-to-noise ratio, low contrast and absence of clear edges. Accurate detection of asymmetrical thermal patterns in breast cancer screening is essential for better diagnosis. Therefore, in this work, the limitations are addressed using Anisotropic Diffusion filter based Fuzzy-C-Means clustering (ADFCM). This technique is employed for precise segmentation of hottest regions in breast tissues. Initially, the ground truth masks are multiplied with raw images for the extraction of non-breast tissues. The corresponding left and right breasts are delineated. Anisotropic diffusion filtering is performed on delineated breast tissues and simple thresholding edge map output is integrated with FCM clustering. For comparison, conventional FCM clustering is employed to segment hottest region. The grey level values of hottest regions are extracted by multiplying the clustered output with input image. The mean area of hottest region gray level values is computed for normal and abnormal images. The performance comparison of FCM and ADFCM has been carried out qualitatively and quantitatively. The results show that ADFCM performs very well compared to FCM method. Further, it is observed that asymmetry analysis of breast tissues using ADFCM based clustering is found to be statistically highly significant (ρ < 0.0001) thereby efficiently discriminate normal and abnormal breast tissues. Hottest Area Index (HAI) is found to be very effective for identifying normal and abnormal conditions in breast cancer diagnosis. Therefore, the proposed pipeline of work could enhance the diagnostic relevance of thermal images and assist in the early diagnosis of breast abnormalities. [ABSTRACT FROM AUTHOR]

Owing to the advancements in information and telecommunication technologies, mobile location-based services are able to use previously collected mobile check-in data. This multi-dimensional data can provide new opportunities for research problems such as establishing new platforms for location-based advertising and location-based personalized recommendations. In other words, location data of potential customers indicate personal interests and visiting preferences. In some cases, customers’ preferences could not be easily determined or predicted while considering visiting patterns of mobile users. In this respect, this study provides a novel retailer segmentation approach based on multi-criteria decision-making (MCDM) combined fuzzy data clustering. The proposed model consists of two phases: (1) an interval type 2 hesitant MCDM approach for the determination of location perceived value and (2) retailer (store) clustering via different product sale prices with fuzzy data-based fuzzy c means (FcM) clustering. Proposed approach enables the simplification of FcM clustering adaptation to non-symmetric fuzzy data using dissimilarity measure. Using this integrated approach, advertisers and recommender system suppliers will be able to manage their product-special offerings to customers considering retailer segments and shopping mall characteristics. Additionally, the proposed approach constitutes the infrastructure of location-based recommender systems under imprecise environment. [ABSTRACT FROM AUTHOR]

A Recommender system (RS) is an information filtering software that helps users with a personalized manner to recommend online products to Users and give suggestions about the products that he or she might like. In e-commerce, collaborative Movie recommender system assist users to select their favorite movies based on their similar neighbor's movie ratings. However due to data sparsity and scalability problems, neighborhood selection is more challenging with the rapid increasing number of users and movies. In this paper, a hybrid Collaborative Movie Recommender system is proposed that combines Fuzzy C Means clustering (FCM) with Bat optimization to reduce the scalability problem and enhance the clustering which improves recommendation quality. Fuzzy c means clustering is used to cluster the users into different groups. Bat Algorithm is used to obtain the initial position of clusters. Lastly, the proposed system creates movie recommendations for target users. The proposed system was evaluated over Movie Lens dataset. Experiment results obtained show that the proposed Algorithm can yield better recommendation results compared to other techniques in terms of Mean Absolute Error (MAE), precision and Recall. [ABSTRACT FROM AUTHOR]

For the thermal error model of computer numerical control (CNC) machine tools based on empirical modeling method, selecting the appropriate temperature-sensitive points is very important. Owing to the existence of multi-collinearity among temperature variables, the accuracy and robustness of the error model are degraded. Therefore, a reconstructed variable regression (RVR) algorithm is presented firstly in this paper. The reconstructed variable is derived from representative temperature points which are selected from the raw temperature data through fuzzy C means (FCM) clustering method. Then, the reconstructed variable is utilized to build the thermal error model based on regression analysis. Meanwhile, a criterion is proposed to find out the optimal cluster result from the whole results of clustering. RVR algorithm was validated through the thermal error experiments of positioning error on a 3-axis machine tool. The number of temperature measuring points could be reduced from 9 to 2. The results of experiments showed that the fitting accuracy of RVR model could reach 92 % and the prediction accuracy could reach about 90 %. It is verified that the RVR model is of high prediction accuracy and strong robustness which is a great choice for thermal error modeling. [ABSTRACT FROM AUTHOR]

In modern agricultural field, pest detection is a major role in plant cultivation. In order to increase the Production rate of agricultural field, the presence of whitefly pests which cause leaf discoloration is the major problem. This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image processing is affected by illumination conditions, random noise and environmental disturbances due to atmospheric pressure or temperature fluctuation. The quality of pest images is directly affected by atmosphere medium, pressure and temperature. The fuzzy c means (FCM) have been proposed to identify accurate location of whitefly pests. The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to pest image analysis, it has important drawbacks (over segmentation, sensitivity to noise). In this paper, pest image segmentation using marker controlled watershed segmentation is presented. Objective of this paper is segmenting the pest image and comparing the results of fuzzy c means algorithm and marker controlled watershed transformation. The performance of an image segmentation algorithms are compared using nonlinear objective assessment or the quantitative measures like structural content, peak signal to noise ratio, normalized correlation coefficient, average difference and normalized absolute error. Out of the above methods the experimental results show that fuzzy c means algorithm performs better than watershed transformation algorithm in processing pest images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale image and remove noise by applying an erode operation. Finally, the brain tumors in the images are detected using a watershed algorithm. The proposed method was compared with two well-known methods namely the conventional K-mean clustering and Fuzzy C Means (FCM) clustering. We verified the precision and the objectivity of our proposed method. The average precision and recall for our proposed method were excellent with values of 0.914052 and 0.995641, respectively. Our method detected more brain tumors than the conventional K-means clustering and FCM clustering methods and was able to provide for an efficient image data processing with reduced file sizes. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is an early manifestation of insulation fault that occurs in electrical equipment, which holds rich feature information used to insulation assessment. In this study, step voltage method was applied to investigate the PD development process of typical protrusion defects in gas‐insulated switchgear. Nine features were extracted to represent the deterioration degree of internal insulation. Fuzzy C means clustering was applied to solve the state centres. PD severity was divided into four states based on the PD developing process, and the harmfulness of each state was illustrated. Finally, adaptive objective weight technology based on the theory of maximising deviation was adopted, and two‐level fuzzy comprehensive evaluation (TL‐FCE) model was established to address the problem of the weight allocation of different features on the evaluation result. The experimental test has been conducted to test the validity and effectiveness of the model. It is proved that TL‐FCE owns good performance to assess PD severity, and it is more suitable to handle fuzzy boundary problems than support vector machine. [ABSTRACT FROM AUTHOR]

Web Usage mining consists of three main steps: Pre-processing, Knowledge Discovery and Pattern Analysis. The information gained from the analysis can then be used by the website administrators for efficient administration and personalization of their websites and thus the specific needs of specific communities of users can be fulfilled and profit can be increased. Also, Web Usage Mining uncovers the hidden patterns underlying the Web Log Data. These patterns represent user browsing behaviours which can be employed in detecting deviations in user browsing behaviour in web based banking and other applications where data privacy and security is of utmost importance. Proposed work pre-process, discovers and analyses the Web Log Data of Dr. T.M.A.PAI polytechnic website. A neuro-fuzzy based hybrid model is employed for Knowledge Discovery from web logs. [ABSTRACT FROM AUTHOR]

Purpose: A new segmentation technique is implemented for automatic lumen area extraction and stent strut detection in intravascular optical coherence tomography (OCT) images for the purpose of quantitative analysis of in-stent restenosis (ISR). In addition, a user-friendly graphical user interface (GUI) is developed based on the employed algorithm toward clinical use. Methods: Four clinical datasets of frequency-domain OCT scans of the human femoral artery were analyzed. First, a segmentation method based on fuzzy C means (FCM) clustering and wavelet transform (WT) was applied toward inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function. Results: The inner lumen contour and the position of stent strut were extracted with high precision. Compared to manual segmentation by an expert physician, the automatic lumen contour delineation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure achieved an overall accuracy of 93.80% and successfully identified 9.57 ± 0.5 struts for every OCT image. Processing time was confined to approximately 2.5 s per OCT frame. Conclusions: A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm integrated in a GUI represents a step forward toward the employment of automated quantitative analysis of ISR in clinical practice. [ABSTRACT FROM AUTHOR]

In this paper, an efficient preprocessing module has been described which focuses on building a trademark database that can be used for developing a trademark retrieval system. The preprocessing module focuses on noise removal from the trademark images using an adaptive filtering technique using Wiener filters, followed by Karhunen-Loève transform that makes the trademark search process rotation invariant by rotating the object along positive y direction. Since the registered trademarks are huge in number and will increase invariantly in the future it will be strenuous for the search system to search for similarity in such huge database. Intention was to reduce the search space hence fuzzy clustering has been applied. All these preprocessing steps make a retrieval system more efficient and reduce computation cost. [ABSTRACT FROM AUTHOR]

Automated detection of welding defects in radiographic images becomes non-trivial when uneven illumination, contrast and noise are present. In this paper, a new surface thresholding method is introduced to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu's thresholding and morphological operations method are utilised to locate the area in which defects might exist. This is followed by the implementation of inverse surface thresholding with partial differential equation to locate isolated areas that represent the defects in the second stage. The proposed method obtained a promising result with high precision. [ABSTRACT FROM PUBLISHER]

Abstract: Automated detection of welding defects in radiographic images becomes nontrivial when uneven illumination, contrast and noise are present. In this paper, a new approach using surface thresholding method is proposed to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu thresholding, and morphological operations method are utilized to locate the area where defects might exist. This is followed by the construction of the inverse thresholding surface and its implementation to locate defects in the identified area. The proposed method was tested on 60 radiographic images and it obtained 94.6% sensitivity. Its performance is compared to that of the watershed segmentation, which obtained 69.6%. [Copyright &y& Elsevier]

Abstract: 3D modelling finds a wide range of applications in robot vision and reverse engineering. However due to the presence of surface scanning noise, accumulative registration errors, and improper data fusion, the reconstructed surfaces from multiple registered range images are often non-smooth and distorted with thick patches, false connections and blurred features. These shortcomings will limit the wide applications of 3D modelling using the latest laser scanning systems. In this paper, the clustering approach, surface segmentation and classification are employed to fuse registered range images to reduce the adverse effect of large accumulative registration errors and heavy scanning noise, minimize the dissimilarity of the fused surface with respect to the original overlapping surfaces, and produce smooth and detailed 3D object computer models. For initialization of the clustering approach, an automatic method is developed, shifting possible corresponding points from different viewpoints toward each other and thus, making sure that the initialization of the cluster centroids are in between the two data sets so that more efficient and effective integration of data can be obtained. Then the principal component analysis (PCA) is employed to segment these centroids into different areas for the representation of the initially fused surface and then the tensor analysis is applied to classify these areas into featured and non-featured ones. In the integration process, the K means and fuzzy c means clustering approaches from the pattern recognition and machine learning literatures are employed to integrate non-featured areas for a smooth fused surface and featured areas for keeping the geometric details. By controlling a parameter, the final integrated surface can be traded off between smoothness and geometric details. Finally the fused point set is triangulated using an improved Delaunay method, guaranteeing a watertight surface. The new method is theoretically guaranteed to converge and minimize the dissimilarity between the final fused surface and original surfaces. A comparative study based on real images shows that the proposed algorithm desirably retains geometric details, produces smooth surface and minimizes the integration error. [Copyright &y& Elsevier]