Statistical Analysis with Missing Data

International Journal of Computer and Applications

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Design Of Analog Cmos Integrated Circuits

Texture features for image classification and retrieval.

There are many emerging areas in which highly constrained devices are interconnected and communicated to accomplish some tasks Nowadays, Internet of Things (IoT) enables many low resources and constrained devices to communicate, compute process and make decision in the communication network In the heterogeneous environments for IoT, there are many challenges and issues like power consumption of devices, limited battery, memory space, performance cost, and security in the Information Communication Technology (ICT) network In this paper, we discuss a state-of-art of lightweight cryptographic primitives which include lightweight block ciphers, hash function, stream ciphers, high performance system, and low resources device for IoT environment in details We analyze many lightweight cryptographic algorithms based on their key size, block size, number of rounds, and structures In addition, we discuss the security architecture in IoT for constrained device environment, and focus on research challenges, issues and solutions Finally, a proposed security scheme with a service scenario for an improvement of resource constrained IoT environment and open issues are discussed

https://link.springer.com/content/pdf/10.1007%2Fs12652-017-0494-4.pdf

Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions

Time series analysis and forecasting future values has been a major research focus since years ago Time series analysis and forecasting in time series data finds it significance in many applications such as business, stock market and exchange, weather, electricity demand, cost and usage of products such as fuels, electricity, etc and in any kind of place that has specific seasonal or trendy changes with time The forecasting of time series data provides the organization with useful information that is necessary for making important decisions In this paper, a detailed survey of the various techniques applied for forecasting different types of time series dataset is provided This survey covers the overall forecasting models, the algorithms used within the model and other optimization techniques used for better performance and accuracy The various performance evaluation parameters used for evaluating the forecasting models are also discussed in this paper This study gives the reader an idea about the various researches that take place within forecasting using the time series data

A survey on forecasting of time series data

Data preprocessing plays an important and critical role in the data mining process. Data preprocessing is required in order to improve the efficiency of an algorithm. This paper focuses on missing value estimation and prediction of time series data based on the historical values. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms like KNNimpute (K-Nearest Neighbours imputation), BPCA (Bayesian Principal Component Analysis) and SVDimpute (Singular Value Decomposition imputation) are not able to deal with the situation where a particular time point (column) of the data is missing entirely. This paper focuses on autoregressive-model-based missing value estimation method (ARLSimpute) which is effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Data preprocessing output is given to the input of the prediction techniques namely linear prediction and quadratic prediction. These techniques are used to predict the future values based on the historical values. The performance of the algorithm is measured by performance metrics like precision and recall. Experimental results on real-life datasets demonstrate that the proposed algorithm is effective and efficient to reveal future time series data.

Imputation for the analysis of missing values and prediction of time series data

Problem statement: In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts. Approach: In this study, we present a hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced. SVM and RF are supervised active learning technique used to improve the effectiveness of the retrieval system. Three kinds of liver diseases are identified including cyst, alcoholic cirrhosis and carcinoma. The diagnosis scheme includes four steps: image registration, feature extraction, feature selection and image retrieval. First the ultrasound images are registered in the database based on the modality. Then the features, derived from first order statistics, gray level co-occurrence matrix and fractal geometry, are obtained from the Pathology Bearing Regions (PBRs) among the normal and abnormal ultrasound images. The Correlation Based Feature Selection (CFS) algorithm selects the certain features for the specific diseases and also reduces dimensionality space for classification. Finally, we implement our hybrid approach for retrieval of specific diseases from the database. Results: This hybrid approach can get the query from user and has retrieved both positive and negative samples from the database, by getting feedback in each round from the radiologist is help to improve the retrieval of correct images. Conclusion: The hybrid approach (SVM+RF) comprises several benefits when compared to existing CBIR for medical system by neural network algorithms. Fractal geometry in feature extraction plays crucial role in ultrasound liver image retrieval. CFS also reduce the dimensionality issue during storage. Image registration plays an important role in the retrieval. It reduces the redundancy of retrieval images and increases the response rate. Getting relevance feedback from physician helps to improve the accuracy of retrieval images from the database.

/pdf/content-based-image-retrieval-of-ultrasound-liver-diseases-3xzhho1vn0.pdf

Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Biomedical image registration, or geometric alignment of twodimensional and /or three-dimensional (3-D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, and computer-guided therapies and in biomedical research [1]. Registration is an important problem and a fundamental task in image processing technique. In the medical image processing fields, some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. In recent years, multimodality image registration techniques are proposed in the medical imaging field. Especially, CT and MR imaging of the head for diagnosis and surgical planning indicates that physicians and surgeons gain important information from these modalities. In radiotherapy planning manual registration techniques performed on MR image and CT images of the brain. Now-adays, physicians segment the volume of interest (VOIs) from each set of slices manually. However, manual segmentation of the object area may require several hours for analysis. Furthermore, MDCT images and MR images contain more than 100 slices. Therefore, manual segmentation and registration method cannot apply for clinical application in the head CT and MR images. Many automatic and semiautomatic image registration methods have been proposed [2]. The main techniques of image registration are performed by the manual operation, using Landmark and using voxel information. In this paper, an automatic intensity based registration of head images by computer has been employed by applying maximization of mutual information. The primary objective of this paper is to increase accuracy of the registration and reduce the processing time. Experiments show our algorithm is a robust and efficient method which can yield accurate registration results.

/pdf/intensity-based-image-registration-by-maximization-of-mutual-m9d7pltimy.pdf

Intensity based image registration by maximization of mutual information

This paper presents the fast and area efficient CORDIC (Coordinate Rotation DIgital Computer)algorithm for sine and cosine wave generation. The concepts of pipelining and multiplexer based CORDIC algorithm is used todecrease the critical path delay and reducing the area respectively. A six stage CORDIC is implemented by two schemes followed by four methods, unrolled CORDIC and multiplexer based CORDIC with and without pipelining. The pipelining is included in four stages(excluding first and last stage). An 8-bit CORDIC algorithm for generating sine wave and cosine wave is designed, implementedand compared by all four methods on Xilinx Spartan3E (XC3S250E).

S. Rajaram

Papers

A survey on forecasting of time series data

Imputation for the analysis of missing values and prediction of time series data

Content Based Image Retrieval of Ultrasound Liver Diseases Based on Hybrid Approach

Intensity based image registration by maximization of mutual information

FPGA implementation of fast and area efficient CORDIC algorithm