{"title":"Fractional Anisotropic Diffusion For Image Denoising","authors":"S. K. Chandra, Manish Kumar Bajpai","doi":"10.1109/IADCC.2018.8692094","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692094","url":null,"abstract":"An image denoising plays an important role in wide variety of applications. It is one of critical operation in image processing. Image denoising without losing important features is very difficult and challenging task. Many of the techniques have been proposed for image denoising. But, most of the techniques fail to preserve fine details in the image. In this work, a fractional anisotropic model is being presented which not only removes noise but also preserve fine details present in the image. Qualitative and quantitative analysis has been performed. It has been found that the proposed method is superior in image de-noising.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130598676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Performance Investigation Of CPFSK integrated with OFDM in Inter-satellite OWC System","authors":"Simran Bhogal, Sandeep Singh Gill, Kuldeepak Singh Saini","doi":"10.1109/IADCC.2018.8692101","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692101","url":null,"abstract":"Inter-satellite optical wireless communication (IsOWC) uses light technology which makes it feasible to achieve long haul communication at high data rate. In this paper, the proposed work is aimed to achieve a high speed long haul communication by using continuous phase frequency shift keying modulation technique in combination with orthogonal frequency division multiplexing in IsOWC. The system’s performance is investigated in terms of received power and Q-factor for different transmission ranges and bit rate values. It is observed that the system can successfully transmit the 10 Gbps data stream over a range of 8000 km with high value of received power and an acceptable value of Q-factor.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121226238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hard Problems on Layered Graphs: Parallel Algorithms and Improvements","authors":"Bhadrachalam Chitturi, T. Srinath","doi":"10.1109/IADCC.2018.8692129","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692129","url":null,"abstract":"Layered graph G = (V, E) is defined as a graph containing several subgraphs also called as layers: G<inf>1</inf> = (V<inf>1</inf>, E<inf>1</inf>), G<inf>2</inf> = (V<inf>2</inf>, E<inf>2</inf>), …G<inf>q</inf> = (V<inf>q</inf>, E<inf>q</inf>) where the edges incident on the V<inf>i</inf> are restricted to the vertices from V<inf>i−1</inf>∪ V<inf>i</inf> ∪ V<inf>i+1</inf>. Layered graphs have applications in computational molecular biology and social networks. Several hard graph theoretic problems such as Maximum Independent Set (MIS), Minimum Vertex Cover (MVC) and Minimum Dominating Set (MDS) are shown to be computationally tractable on layered graphs when the corresponding upper bound is imposed on the number of vertices that a layer can have. We present algorithmic improvements and design parallel algorithms for the computing these measures.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134282061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"HUPM: Efficient High Utility Pattern Mining Algorithm for E-Business","authors":"M. M. Bala, Rohit Dandamudi","doi":"10.1109/IADCC.2018.8691944","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8691944","url":null,"abstract":"Utility pattern mining addresses the current common challenges of E-business by analysis of market behavior and customer trends of transactional data. However, it has some important limitations when it comes to analyzing customer transactions in any business as buying quantities are not considered into account. Thus, it leads to misappropriate analysis due to consideration of an item may only appear once or zero times in a transaction data and a weight of all item have given same importance. To address the above said confines, the problem of identification of frequent set of items as patterns has been defined in E business as High Utility Pattern Mining (HUPM). The focus of this paper is finding high utility patterns by using weighted utilization value of each product. This is implemented in two modules finding top k high utility patterns by constructing UP growth tree and TKU algorithm and finding top-k utilities in one phase approach with TKO algorithm to mine HUPs without any assumptions of minimum utility threshold. Experimental results show that the proposed algorithms take a smaller amount of computational cost, thus it shows more efficiency once compared with other present methods on standard data sets.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115262716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Pulmonary Lesion Detection and Staging from CT Images Using Watershed Algorithm","authors":"Mehak Khatri, Munish Kumar, Abhilash Jain","doi":"10.1109/IADCC.2018.8692125","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692125","url":null,"abstract":"Nowadays, various image processing methods are broadly being used as a part of the biomedical zones. It is crucial to diagnose the disease and to classify the specific stage for the radiologists to give reasonable remedial to the patients. Lung cancer is the most widely recognized known cancer among individuals, which can be delegated little cell and non-little cell. In this paper, we have proposed a model for the detection of pulmonary lesions at the initial and advanced stages of lung disease on CT (Computed Tomography) images. The proposed framework consists of four stages; change of RGB to grey scale image, smoothing will be performed using median filter to lessen the effect of noise from images, segmentation will be performed using thresholding and watershed techniques and after that the features are extracted for processed image. A framework has been tested with 12,645 images, a dataset of 50 patients. We have noticed that the proposed model perform better than already existing techniques and performance of this model is zero false positive acceptances.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"27 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115365663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. H. Zunjani, Souvik Sen, Himanshu Shekhar, Aditya Powale, Debadutta Godnaik, G. Nandi
{"title":"Intent-based Object Grasping by a Robot using Deep Learning","authors":"F. H. Zunjani, Souvik Sen, Himanshu Shekhar, Aditya Powale, Debadutta Godnaik, G. Nandi","doi":"10.1109/IADCC.2018.8692134","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692134","url":null,"abstract":"A robot needs to predict an ideal rectangle for optimal object grasping based on the intent for that grasp. Mask Regional - Convolutional Neural Network (Detectron) can be used to generate the object mask and for object detection and a Convolutional Neural Network (CNN) can be used for ideal grasp rectangle prediction according to the supplied intent, as described in this paper. The masked image obtained from Detectron along with the metadata of the intent type has been fed to the Fully-Connected layers of the CNN which would generate the desired optimal rectangle for the specific intent and object. Before settling for a CNN for optimal rectangle prediction, conventional Neural Networks with different hidden layers have been tried and the accuracy achieved was low. A CNN has then been developed and tested with different layers and sizes of pool and strides to settle on the final CNN model that has been discussed here. The optimal predicted rectangle is then fed to a robot, ROS simulation of Baxter robot in this case, to perform the actual grasping of the object at the predicted location.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127435477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Robust Edge Detection using Pseudo Voigt and Lorentzian modulated arctangent kernel","authors":"Diganta Misra","doi":"10.1109/IADCC.2018.8692115","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692115","url":null,"abstract":"Convolution Neural Networks have been the standard neural network architecture for Image Classification and Object Segmentation. Convolutional Neural Network involves a fundamental operation for feature learning on the images which is called as Convolution, where a kernel is convoluted with the corresponding pixel values on the image. Various types of kernels exist which serves different purposes from pixel mapping to edge detection and image blurring. Gaussian-Gabor kernels have been the standard filter for edge detection. This paper presents new robust edge detection filters which produce sharper edge representations as compared to the traditional Gaussian Gabor Filters.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130461089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identifying Gender Specific Interaction Pattern: A Social Network Approach","authors":"Sangita Garg, T. Gandhi, B. K. Panigrahi","doi":"10.1109/IADCC.2018.8692108","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692108","url":null,"abstract":"The objective of analysis is to identify gender specific interaction patterns in primary school children. The social network approach is taken for the purpose. Dyadic relationships formed as a result of face-to-face interaction between two children was analyzed quantitatively in the boundary of social network research. The strength of dyads was a key consideration to measure various temporal interaction behavior patterns such as dyadic churn rate, persistence rate, retention rate, new dyads. The analysis conducted was also a motivation to determine differentiating patterns w.r.t students’ mobility and social collaboration ability with students of same and other gender. The variations in degree centrality measures for each node was also suggestive of the preference for gender and grade specific ties. The outcome of analysis was also fundamental in the phenomenon of social contagion and information diffusion.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131079203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EEG Based Participant Independent Emotion Classification using Gradient Boosting Machines","authors":"Sagar Aggarwal, Luv Aggarwal, Manshubh Singh Rihal, Swati Aggarwal","doi":"10.1109/IADCC.2018.8692106","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692106","url":null,"abstract":"Analysis of EEG (Electroencephalography) signals provides an alternative ingenious approach towards Emotion recognition. Nowadays, Gradient Boosting Machines (GBMs) have emerged as state-of-the-art supervised classification techniques used for robust modeling of various standard machine learning problems. In this paper, two GBM’s (XGBoost and LightGBM) were used for emotion classification on DEAP Dataset. Furthermore, a participant independent model was fabricated by excluding participant number from features. The proposed approach performed well with high accuracies and faster training speed.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132517487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatic Data Extraction from 2D and 3D Pie Chart Images","authors":"Paramita De","doi":"10.1109/IADCC.2018.8692104","DOIUrl":"https://doi.org/10.1109/IADCC.2018.8692104","url":null,"abstract":"Due to the perceptual advantages, pie charts are frequently used in digital documents to represent meaningful numerical data. Automatic extraction of underlying slice data of pie charts is necessary for further processing of chart data. In this paper, a novel technique have been presented for identification of pie charts in document images followed by the extraction of chart data. To identify pie charts in documents, a Region-based Convolutional Neural Network (RCNN) model has been trained with 2D and 3D pie chart images. Then, different slices of a pie chart are analyzed using image gradients as one of the primary feature and compute the values of different slices. The algorithm successfully identifies different 3D structural information of a 3D pie chart which are used only for a 3D representation of such charts and are excluded from processing. To demonstrate the superiority, the algorithm has been tested on a number of 2D and 3D pie chart images.","PeriodicalId":365713,"journal":{"name":"2018 IEEE 8th International Advance Computing Conference (IACC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130121873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}