Journal of Computer Science & Systems Biology

Motifs are repeated patterns of short sequences usually of varying lengths between 6 to 20 bases. Within Deoxyribonucleic Acid (DNA) sequences, these motifs constitute the conserved region of most common signatures for recognizing protein domains that are relevant in it evolution, function and interaction. The Gibbs sampling is a Markov Chain Monte Carlo (MCMC) algorithm which has been applied in the past to discover motifs in DNA sequences. A problem with this technique is the profusion of iterative operations in the sampling process because it progressively chooses new possible motif positions from a continuous randomize sampling in DNA sequences. We applied an Improved Gibbs (iGibbs) sampling algorithm on Breast Cancer (brca) human disease DNA sequences obtained from https://www.ncbi.nlm.nih.gov/nuccore to overcome this unwieldy iteration by altering the processes to obtain a reduced runtime and also achieve an accurate satisfactory motif result. The methodology applied in iGibbs algorithm takes an input of fasta or gbk DNA file and creates a list of all nucleotides to predict a random sampling starting position. It applies motif length, lesser iterative value and further computes the probability and position ranking scores using Position Weight Matrix (PWM). The algorithm was implemented using Python, Python(x,y) and Biopython. The iGibbs algorithm was evaluated using varying motif lengths of 12, 18 and 24 on different base lengths of 5,000, 10,000 and 15,000 with different iteration levels. The result showed that the iGibbs returned a better average runtime of 7, 10 and 23 seconds respectively compared to 12, 32 and 60 seconds respectively in the existing Gibbs sampling algorithm found at http://ccmbweb.ccv.brown.edu/gibbs/gibbs.html. The accuracy of the motif result was checked using the hamming distance for finding the contiguous string and minimum edit distance into consensus sequences.

In August of 2017, companies Google and Facebook announced to invest $150 million dollars into a Toronto based AI institute, making it one of the largest investments in Artificial Intelligence. With companies, and even governments, investing millions into Artificial Intelligence, it is no doubt that the growth of AI ought to be exponential. As companies get closer to introducing their autonomous capable semi-trucks, it is clear there exists a lack of regulations that can potentially regulate the extent to which companies can use Artificial Intelligence to affect the job outlook in the transportation industry. In this study, the major companies involved in the development of Autonomous technologies were researched and analyzed. With a set criterion for the choice to ensure credibility and viability, companies’ (Tesla, Google, GM and Mercedes Benz) autonomous programs were selected as part of secondary data analysis research. The variables research and development budgets and production units set the stage for the quantitative data while, the risks, tech innovation, and industry trends accounted for the qualitative form of data. Upon the collection and analysis of the data from the companies’ annual reports, it was noted that Tesla and Mercedes Benz would be the future industry leaders of autonomous technologies on the roads, with budgets of $ 3.8 billion and $ 4.6 billion respectively. Google and GM do seem behind in terms of budgets and production due to the fact that their department is currently focused on different parts of the transportation industry. With this data mind, it would be in the best interests of the lawmakers to focus on finding solutions towards reducing the number of units being produced and the corresponding budgets of the companies to maximize efficiency in transportation while also maintaining ethical standards.

This paper is about a current research based on fingerprints recognition system. In this paper, we discuss the previous paper’s research on fingerprints recognition system. Paper is a review of security accuracy efficiency and recognition of fingerprints. Fingerprints recognition System widely used in identification tool and biometrics applications. Some fundamental factors also affecting the fingerprints like age and gender. Some human body parts are used for recognition like retina, face recognition, signature, DNA, iris and more. Fingerprints consist of two stages. Data collection and other one concentration on design and implementations. Fingerprints recognition system consists of four steps 1 capture the fingerprints image 2 pre-processing in pre-processing remove the noise and unwanted data 3 feature extraction method to use the different techniques 4 four stage is matching or identification and verification. Security attack occurs in fingerprints system due to some major issue or poor quality. For this purpose, different techniques and algorithms are used. Result and discussion indicate that fingerprints recognition is good for accuracy.

There are many evidences about unique emotions evoked by music in different individuals and that, the saccadic eye movements (saccades) and pupil size are sensitive to different emotions. The experiment presented in this article concerns both issues, as we investigated possible changes in parameters of saccades, caused by emotions induced by music listening. It was assumed, that found correlations will help to determine subject’s age and mood dependently on recorded saccade parameters. The purpose of this experiment was to find ability to distinguish and classify saccade parameters to one of defined groups of age (young/old) or mood (energetic/relaxed) induced by music.

We have measured saccades of two groups of 6 subjects in age below 30 and over 60, during musical sessions with energetic and relaxing music. Collected data were analyzed in search of possible correlations between characteristics of respondents and saccade parameters, using combination of different types of filters and classifiers from WEKA. Classifications showed statistical changes between age groups, in the latency (23.6% of difference) and in the pupil’s size (16, 6% of difference), both found extremely significant (P>0.0001). In case of Mood, results showed changes in the group of younger adults, in the latency (P=0.4532) and very significant for the amplitude (P=0.0001) and for the average velocity (P=0.0048). The best classification results were obtained for Age and Mood groups. Prediction of age group showed the accuracy of 91.4%. In case of Mood groups, obtained percentage of correctly classified instances was between 96.6 and 97%. For both types of groups, best predictions were obtained by Random Forest and Multilayer Perceptron. The results of classifications allowed to build the confusion matrix and decision trees based on values of saccades parameters and data of subjects. It showed differences in saccade processing between particular groups. Article tries to explain main differences in obtained results by SAT and LATER models, exemplifying the computational nature of human brain processing.

The comparison of predictions made on the basis of the obtained results, showed acceptable statistics for examined subjects, which may suggest further researches at the intersection of machine learning, human age, mood, and eye moves. The results of this experiment suggest usefulness of the Eye Tracking and Eye Movement parameters classifications in machine learning driven detection of human features.

Word sense in the field of natural language processing (NLP) is a corner stone for appropriate word selection. A word can contain more than one sense, but machine can’t extract the actual sense of the given particular content. Implication of this situation is mismatch between the user requirements and result generates through the machine. e.g., User wants to search a query “What is word sensing?” The machine can’t find the relation between these two words “word”, “sensing”. Relationship between words cannot be extracted by the machine and more results corresponding to sensing is displayed and user requirements corresponding to “Word Sensing” as a whole are rejected. primary reason for this mismatch is due to static dictionary possessed by web servers. Techniques we are analysis different types of techniques and algorithms for the word sense. The major techniques are which used to word sense are knowledge based approaches are based on different knowledge sources as machine readable dictionaries to extract the sense like thesauri, Word net are machine readable dictionaries to find the word sense, Supervised learning technique is a manually extract the sense from the data. In this process trained the target words through the labelling, unsupervised learning technique in this process words are no needs to be trained target data are based on the clustering, Semi-Supervised learning technique is a hybrid approach of the supervised and unsupervised. In this process target words are based on the particular content. Tools for building word database to be accessed by the web applications including Word Net, Image Net and Babel Net are discussed in this literature. Our Contribution we conduct comprehensive review of knowledge based, supervised, unsupervised and semisupervised learning techniques used in the field of word sensing and detect the best word sensing mechanism for fetching only relevant material from the web while decreasing the execution time for content retrieval.

The role of information technology and the advancement of the cloud computing have increased the use of the data in everyday life. Data analyzation and data storage become a challenging task during the processing of this large data. In the online applications, the data stream varies with rapid speed and has a larger volume. The recurring concept drift in the data stream makes the classification process to be complicated. Various algorithms discussed in the previous research works have not effectively addressed the problem of detecting the recurring concept drift in the data. The selection of the high-performing classifier model is also a challenging research goal. This research introduces two classification models for classifying the data with the recurring concept drift in the real time environment.

Networking enabled us to be connected to each other and devices thereby making it easy to interact with devices and people but it also can be used into compromising the privacy of its user and of the people connected to it. Network security is one of the major aspect to be considered also it is one of the most invested factor in an organization. With introduction of new technologies like internet of things, AI etc. besides making our life easier can also result in compromising it if not monitored and protected in a proper manner.