Liver is a needful body organ that forms an important barrier between the gastrointestinal blood, which contains large amounts of toxins, and antigens. Liver diseases contain hepatitis B and hepatitis C virus infections, alcoholic liver disease, nonalcoholic fatty liver disease and associated cirrhosis, liver failure and hepatocellular carcinoma are primary causes of death. The main purpose of this study is to investigate which attributes are important for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods. In order to generate more accuracy, dataset was balanced by utilizing the Random Under-Sampling method. Important ones in all attributes were detected by utilizing the Stability Selection method which was performed on sub-datasets, which were obtained with 5 fold cross-validation technique. By sending these datasets to the Random Forest algorithm, the performance of the proposed approach was evaluated within the frame of accuracy and sensitive metrics. The experimental results clearly show that the Random Under-Sampling method can potentially improve the performance of the combination of Stability Selection and Random Forest methods in machine learning. And, the combination of these methods provides new perspectives for the diagnosis of this disease and other medical diseases.

Temporal clustering (segmentation) for video streams has revolutionized the world of multimedia. Detected shots are principle units of consecutive sets of images for semantic structuring. Evaluation of time series similarity is based on Dynamic Time Warping and provides various solutions for Content Based Video Information Retrieval. Time series clustering in terms of the iterative Dynamic Time Warping and time series reduction are discussed in the paper.

E-reputation management has become an important challenge for firms that try to improve their notoriety across the web and more specifically in social media. Indeed, the power of online communities to impact a brand’s image is undeniable and companies need a powerful system to measure their reputation as perceived by connected society. Moreover, they need to follow its variation and forecast its evolution to anticipate any impacting change. For this purpose we have implemented an Intelligent Reputation Measuring System (IRMS) that assesses reputation in online social networks on the basis of members’ activity and popularity. In this paper, we add a predictive module to IRMS that forecasts the evolution of reputation score using influence propagation algorithms.

The research has implemented document summarizing system uses TextRank algorithms and Semantic Networks and Corpus Statistics. The use of TextRank allows extraction of the main phrases of a document that used as a sentence in the summary output. The TextRank consists of several processes, namely tokenization sentence, the establishment of a graph, the edge value calculation algorithms using Semantic Networks and Corpus Statistics, vertex value calculation, sorting vertex value, and the creation of a summary. Testing has done by calculating the recall, precision, and F-Score of the summary using methods ROUGE-N to measure the quality of the system output. The quality of the summaries influenced by the style of writing, the selection of words and symbols in the document, as well as the length of the summary output of the system. The largest value of the F-Score is 10% of the length ta of the document with the F-Score 0.1635 and 150 words with the F-Score 0.1623.

Opinions are used to express views and reviews are used to provide information about how a product is perceived. People contributions lie in posting text messages in the form their opinions and emotions which may be based on different topics such as movie, book, product, and politics and so on. The reviews available online can be available in thousands, so making the right decision to select a product becomes a very tedious task. Several research works has been proposed in the past but they were limited to certain issues discussed in this paper. The reviews are collected which periodically updates itself using crawler discussed in our previous work. Further after applying certain pre-processing tasks in order to filter reviews and remove unwanted tokens, the sentiments are classified according to the novel unsupervised algorithm proposed. Our algorithm does not require annotated training data and is adequate to sufficiently classify the raw text into each domain and it is applicable enough to categorize complex cases of reviews as well. Therefore, we propose a novel unsupervised algorithm for categorizing sentiments into positive, negative and neutral category. The accuracy of the designed algorithm is evaluated using the standard datasets like IRIS, MTCARS, and HAR.

The subject matter of the article is developing information and communication network (ICN) for critical infrastructure systems (CIS). The aim of the work is to provide high-quality information and telecommunication processes by developing the optimal version of distributing CIS functional tasks and ICN processes to the network nodes. The article deals with following problems: developing a model for mapping the information and technical ICN structures, developing a method for variant synthesis of ITS structural models, a formalized representation of the problem of selecting CIS optimal structure. The methods used are: the system method, the set-theoretic and graphic analytic approaches, methods of hierarchic structures synthesis, optimization methods. The following results were obtained: the use of system approach for formalizing the information processing process in CIS was justified; mapping the ICS functional system into the information and technical one was presented as multilevel graph chain; the generalized representation of graph structures hierarchy was developed for the set of data transmitting tasks; this approach enabled formal representing alternative variants that consider the main links, sequencing, the amount and flows of the processed information among the different structure levels; the scheme of variant synthesis method of ICN models according to graph structures mapping was developed; the problem of selecting optimal ICN structures was formally presented; a complex efficiency criterion for solving problems of optimizing variant synthesis of structures; the problem of optimal synthesis of the structure of the given level factored in resource constraints was formulated. Conclusions. The article deals with such novelty aspects as improving the model of problem of selecting the optimal ICN structure by set-theoretic formalization factored in the criterion of maximum intensity of computational resource application, which enabled determining structural links among the major elements considering the decomposition of the model up to the basic elements such as "node" and "task" and the development of a new method of optimal ICN structuring which unlike the existing ones involves the variant synthesis of structures hierarchy and formalizing selection problems on the basis of set-theoretic models, which enables providing the efficiency of application of information and technical net resources.

This paper proposed a self-initialization process to K-Means method for automatic segmentation of human brain Magnetic Resonance Image (MRI) scans. K-Means clustering method is an iterative approach and the initialization process is usually done either manually or randomly. In this work, a method has been proposed to make use of the histogram of the gray scale MRI brain images to automatically initialize the K-means clustering algorithm. This is done by taking the number of main peaks as well as their values as number of clusters and their initial centroids respectively. This makes the algorithm faster by reducing the number of iterations in segmenting the MRI image. The proposed method is named as Histogram Based Self Initializing K-Means (HBSIKM) method. Experiments were done with the MRI brain volumes available from Internet Brain Segmentation Repository (IBSR). Similarity validation was done by Dice coefficient with the available gold standards from the IBSR website. The performance of the proposed method is compared with the traditional K-Means method. For the IBSR volumes, the proposed method yields 3 to 4 times faster results and higher dice value than traditional K-Means method.

The first fact of sequence analysis is sequence alignment for the study of structural and functional analysis of the molecular sequence. Owing to the increase in biological data, there is a trade-off between accuracy and the computation of sequence alignment process. Sequences can be aligned both in locally and globally to gives vital information for biologists. Focusing these issues, in this work the local and global alignment are focused on aligning multiple molecular sequences by applying a wavelet transform. Here, the sequence is converted into numerical values using the electron-ion interaction potential model. This is decomposed using a type of wavelet transform and the similarity between the sequences is found using the cross- correlation measure. The significance of the similarity is evaluated using two scoring function namely Position Specific Matrix and a new function called Count score. The work is compared with Fast Fourier Transform based approach and the result shows that the proposed method improves the alignment.