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In the current scenario, cloud computing has emerged widely in companies and enterprises. Cloud enables the organization to utilize hardware, software and applications (e.g. CPU, Memory, Bandwidth, Network, and Storage) based on virtualization and pay-as-per-use principle. But the main challenge before cloud is resource management that is how efficiently and effectively the computer resources should be managed so that no computing device is underutilized or over utilized in the dynamic environment. In this paper, a task scheduling algorithm has been introduced which allocates and executes user's tasks. This proposed algorithm is based on genetic algorithm (GA). The main goal of this algorithm is to reduce completion time and execution cost of different tasks and maximize resource utilization. CloudSim Toolkit is used to evaluate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The Iris Recognition System is very interesting and today it is widely used for the security point of view. Iris recognition gives accurate results. It never takes false value, so it is really very good for security purpose. Iris recognition is amongst the most robust and accurate biometric technologies available in the market today with existing large scale applications supporting databases in excess of millions of people. Daugman is the first one to give an algorithm for iris recognition. . The algorithm was based on Iris Codes. Integro-differential operators are then used to detect the centre and diameter of the iris, then the pupil is also detected using the differential operators, for conversion from Cartesian to polar transform, rectangular representation of the required area is made. Wildes proposed iris recognition based on texture analysis. High quality iris images was captured using silicon intensified target camera coupled with a standard frame grabber and resolution of 512x480 pixels. [ABSTRACT FROM AUTHOR]

In this paper we are going to deal with the surveillance and security system using raspberry pi. We are using VGG face algorithm to process and extract the features in the image that help for facial recognition. We also make use of different type of sensors for creating a security system that will send alerts to the email. [ABSTRACT FROM AUTHOR]

Cloud computing has become buzzword in computer industry as it provides scalable, reliable and economical services to the users. For better performance of cloud computing it is crucial that an efficient task scheduling algorithm is in place. Scheduling algorithms allocates the resources to the user's task i.e. it provides mapping between resources and tasks. Scheduling in cloud is NP-hard problem because of large solution space available. Meta-heuristic algorithms have become the most famous scheduling approaches as they provide near optimal solution in short span of time. This paper surveys the various approaches for cloudlet scheduling. It also classifies the various approaches based on the objectives considered for optimization. [ABSTRACT FROM AUTHOR]

Machine learning incorporates AI, and is used to solve many problems in data science. The machine reads patterns from existing databases, and then inserts them into an unknown database to predict the outcome. Classification can be a powerful machine learning method commonly used for prediction. Some classification algorithms provide satisfactory accuracy, while others provide restricted accuracy. This paper examines a method called ensemble classification, which is often used to improve the accuracy of weak algorithms by combining multiple categories. Tests for this tool are performed using a diabetic database. A comparative analytical approach was performed to find out how the ensemble process is often used to improve diabetes prognosis. The goal of this paper is not only to increase the accuracy of weak classification algorithms, but also to implement an algorithm on a medical database, to demonstrate its ability to detect the disease at an early age. The results of the study indicate that integrated strategies, such as the random forest, are effective in increasing the predictive accuracy of weak classifiers, and have shown satisfactory effectiveness in identifying the risks of diabetes. A seven-point increase in the accuracy of the weak classifiers was achieved with the help of an ensemble classification. [ABSTRACT FROM AUTHOR]

Sorting algorithms are the very important data structure operation. As we have millions or trillions of data stored in our memories, it is very difficult of us to find a specific required data. To sort data is to arrange them in ascending or descending order so as the searching, locating or arranging of data becomes easy. Every sorting has some advantages and some disadvantages, like wise each sorting algorithm takes different time to sort the data. In this research paper we compared various sorting algorithm in respect to their execution time. The efficiency of every algorithm varies with the number of input and we have compared the efficiency of algorithm so that we can could which algorithm is best to use based on the load. The sorting algorithms are evaluated in JAVA. [ABSTRACT FROM AUTHOR]

No reference image quality assessment IQA algorithms are widely used for finding distortions in images without comparing them to references. Image' areas with no details may indicate a brightness saturation change, as well as the presence of noise in the background, which are more visible types of distortions. Thus, the design of such IQA should take into account the human visual conception. This paper proposes a no-reference image quality evaluation algorithm that takes into account the finest edge detection process and entropy deployment in regard to human visual sensitive HVS to quantify brightness saturation variations and noise in pixels. Statistic objective metrics for correlation coefficient CC which person PCC, spearman rank order SROCC, root mean squared error RMSE are used in the objective evaluation in corresponding with the subjective evaluation. The proposed algorithm is tested and significantly has well correlation PCC > 0.87, SROCC > 0.93, RMSE < 0.34. The findings of this research could be used to improve the performance of no-reference HVS-based IQA algorithms currently in use. [ABSTRACT FROM AUTHOR]

In the present paper cosine similarity is used for similarity measurement in sketch to image retrieval with focus on image matching. Similarity measurement is very challenging task in sketches due to its variable dimensions in the edges. Median filters are used to minimize the noises occur in the images due to unknown reasons. After pre-processing of the images in the benchmark database, the features are extracted using Histogram of oriented gradients (HOG). Canny edge detection algorithm is discussed to detect the image edges in several directions. Later the sketch to image matching is calculated by similarity measurement algorithm cosine similarity. A Benchmark sketch image database is considered to prove the proposed algorithms is efficient and works effectively when compared with many existing techniques. It is very important to measure accurate similarity measurement in high dimensional space for information retrieving. This paper presents efficient cosine similarity measurement algorithm. In the present exertion extensive experimental work is carried out inorder to prove the state of art in association with Sketch Based Image Retrieval (SBIR). [ABSTRACT FROM AUTHOR]

The improvement of machine learning household tasks such as classification, clustering and association has exposed the call for machine learning algorithms to be applied on huge amount of data. In this paper we present the evaluation of diverse classification techniques and find the suitable finest classification algorithm for taken dataset, using Waikato Environment for Knowledge Analysis API or in short, WEKA. WEKA is an open source which consists of a group of machine learning algorithms for performing data mining tasks. The aim of this paper is to investigate the performance of different classification methods and come across the finest classification algorithm for given set of large data and we also propose the implication rules for Bankruptcy using Apriori. The actual evaluation of learning by example is done with help of confusion matrix and Receiver Operating Characteristics curve. At this time classification algorithms experienced are Bayesnet, Naivebayesclassifier, ConjuctiveRules, DecisionTable/Naivebayes, DecisionTable, Nearestneighbors, and OneR. In this paper I had chosen Bankruptcy dataset for performing data mining tasks. [ABSTRACT FROM AUTHOR]

This paper explores the idea of keyword extraction from conversations, the goal of using these keywords to retrieve, for each short conversation text file, a small number of possibly relevant documents, which can be recommended to the participants. However, even a short conversation contains different types of words, which are absolutely related to several topics; Therefore, it is difficult to infer precisely the information needs of the conversation participants. The existing system proposed a diverse keyword extraction technique which extracts the keyword from the meeting conversation transcripts and recommends the document to the participants. So, in this paper we first propose an algorithm to extract keywords from the output of preprocessing process where string is processed to its basic meaning by following the basic four activities. Then, we propose a feature extraction method to extract multiple topically differentiated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation to participants. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the conversation text file. The results shows that our system improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations. [ABSTRACT FROM AUTHOR]

Software engineering research in general and software defects' prediction research in particular are facing serious challenges to their reliability and validity. The major reason is that many of the published research outcomes contradict each other. This phenomenon is mainly caused by the lack of research standards as it exists in many well-established scientific and engineering disciplines. The scope of this paper is to focus on fair benchmarking of the defects' prediction models. By experimenting three prediction algorithms, we found that the quality of the resultant predictions would significantly fluctuate as parameters' values changed. Therefore, any published research results not based on optimized prediction algorithms methods can cause inaccurate and misleading benchmarking and recommendations. Thus, we propose optimizing parameters as an essential research standard to conduct reliable and valid benchmarking. We believe if this standard were approved by interested software quality practitioners and research communities, it will present a vital role in soothing the severity of this phenomenon. The three prediction algorithms we used in our analysis were Support Vector Machine SVM, Multilayer Perceptron MLP, and Naïve Bayesian NB. We used KNIME as a data mining platform to design and run all optimization loops on open source Eclipse 2.0 data set. [ABSTRACT FROM AUTHOR]

This paper describes a new color image segmentation method based on low-level features including color, texture and spatial information. The mean-shift algorithm with color and spatial information in color image segmentation is in general successful, however, in some cases, the color and spatial information are not sufficient for superior segmentation. The method uses wavelet frames that provide translation invariant texture analysis. The method integrates additional texture feature to the color and spatial space of standard mean shift segmentation algorithm. The new algorithm with high dimensional extended feature space provides better results than standard mean shift segmentation algorithm as shown in experimental results. [ABSTRACT FROM AUTHOR]

In this paper we are analyzing and developing algorithms for the connectivity and complexity of point to point network. For the development of communication network algorithms we will convert the point to point network into its connectivity matrix. These connectivity matrix relations will be depending on the Processors to Processors relation or processor to edge relation or edge to edge relations. The logical operations will be performed between the vectors of nodes or edges of the connectivity matrix. These operations will be used to explore the topological properties of the architecture for information flow. [ABSTRACT FROM AUTHOR]

Noise is a random variation of image intensity and visible as grains in the image. Gaussian noise is one of the noise that can be found in gray as well as colored images. Gaussian noise is a noise that has a random and normal distribution of instantaneous amplitudes over time. Gaussian noise is statistical noise having a probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution. A lot of algorithms and techniques have been developed to remove the Gaussian noise from the image (both gray scale and colored).In this paper, we compare the Bilateral Filter, Block-matching and 3D filtering, Gaussian smoothing Filter, Median Filter and Spatial gradient Bilateral Filter for gray scale images and, Adaptive Bilateral Filter and Sparse 3-D transform-domain collaborative Filter for colored images. A comparative study based on Peak Signal to Noise Ratio and Mean Absolute Error of these algorithms has been provided in this paper. [ABSTRACT FROM AUTHOR]

The WWW (World Wide Web) contain a huge amount of data that is rising in both dimension and volume day by day. Data mining process has been in use in almost every field of business. Nowadays, various data mining processes use web mining techniques for discovering the valid, novel, understandable and useful data. Web Mining can be classified into three major categories including the web content mining, web structure mining and web usage mining. Web usage mining is an effective approach for discovering the relevant and useful information through data preprocessing, pattern discovery and pattern analysis. There are various web mining techniques available but suffer from many privacy issues. In this paper, we will explore the various web usage mining algorithm used in data mining. The review of web mining research will help for the further research in the same field. [ABSTRACT FROM AUTHOR]

This paper represents that lane coloration has become popular in real time vehicular adhoc system. Lane detection is normally helpful to localize path limits. Determine undesired lane variations and to enable approximation of the upcoming geometry of the road. There are different types of methods that are used for detecting lines i.e. Hough transform, clustering and curve fitting. The paper represents that by using these methods working efficiently but problem is that they fail or not give efficient results when there are curved lane road images. The objective of this paper is to improve lane coloration algorithm by modifying the Hough transform i.e. additive Hough transform with different performance metrics to improve the accuracy. [ABSTRACT FROM AUTHOR]

In this modern era, cloud computing has become one of the fastest emerging technology. Cloud computing offers services as a request- response model, where a customer can request for computing resource in a pay-as-you-use manner. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under the different administrative domain. Due to the increasing demands of the users for cloud services or resources, it becomes a complex problem of finding the best match of resource-workload combination and scheduling of suitable resources to cloud workloads depends on the QoS requirements of cloud applications. Researchers still face difficulties to select the efficient and suitable resource scheduling algorithm for a specific workload from the standing literature of resource scheduling algorithms. This paper gives an overview of the existing scheduling policies in cloud computing systems. The literature concerning to fifteen types of scheduling algorithms has been stated. Systematic analysis of this research work will help researchers to find the important characteristics of scheduling policies in cloud computing. [ABSTRACT FROM AUTHOR]

This paper proposes effective approach to overcome the problem of finding initial number of clusters for Supervised Data mining algorithms. We present critical review of various approaches that finds the optimal number of clusters for clustering algorithms. In this paper we have used Dbscan algorithms to obtain initial seeds for basic k-means algorithm. To evaluate the proposed approach we have used iris data set, liver disorder dataset and seed dataset. [ABSTRACT FROM AUTHOR]

The online social media is the great area of research which is explored by the researchers now days. There are number of areas in which overlapping community detection works. In this paper comparison of various algorithms used to detect the overlapping will be made. The disjoint clusters can be identified by the use of algorithm. The framework for evaluating various algorithms will be described which will help disclosing the person's membership in multiple clusters. The cluster will be collection of number of distinct users belonging to only one group. This paper evaluates all the algorithms which are used for overlapping community membership detection. [ABSTRACT FROM AUTHOR]

Time is the most important feature that counts for an executing process. Nowadays, applications are more focused to reduce the computational time and thus to increase the speed. Therefore, there is a need to redesign the fundamental algorithms especially for searching methods to achieve this objective. Targeting this, approach of memorization is proposed in this paper to optimize the time of searching during execution. It is an optimization technique used to eliminate re-execution time for pure function. A novel approach of memoized Quadratic Search Algorithm is presented in this paper. Results show remarkable improvement in the performance in terms of time leading to achieve the said objective. This will help to optimize the time in other respective areas of computation processes. [ABSTRACT FROM AUTHOR]

In this paper an election algorithm is presented that can be used in distributed systems to elect a coordinator. Many distributed algorithms need one process to act as a leader or coordinator to make synchronization between different processes. There are various algorithms that elect a particular node as coordinator from all the different nodes in the distributed network. This paper will present a modified version of bully election algorithm using a new concept called election commission. This approach will not only reduce the number of messages per election but also minimize total number of elections and hence it will minimize network traffic, redundant elections. [ABSTRACT FROM AUTHOR]

Shellsort is a comparison sort that uses insertion sort at each iteration to make a list of interleaved elements nearly sorted so that at the last iteration the list is almost sorted. The time complexity of Shellsort is dependent upon the method of interleaving (called increment sequence) giving variants of Shellsort. However, the problem of finding proper of interleaving to achieve the minimum time complexity of O(n log n) is still open. In this paper, we have analyzed the performance of variants of Shellsort based on their time complexity. Our measure of time complexity is independent of the machine configuration and considers all the operations of a sorting process. We found that the interleaving method or increment sequence proposed by Sedgewick performs best among the analyzed variants. [ABSTRACT FROM AUTHOR]

Sequential pattern mining is a technique of data mining whose objective is to identify statistically relevant patterns within a database with time-related data. It has a wide range of applications in variety of domains like education, healthcare, bioinformatics, web usage mining, telecommunications, intrusion detection etc. At present, most of the real sequence databases are incremental in nature. So there is a need to explore incremental and distributed pattern mining algorithms. Periodic pattern mining is a technique to discover periodic pattern which may be a pattern that repeats itself after a specific time interval. It has a wide range of applications in weather prediction, stock market analysis, web usage recommendation etc. Moreover, uncertain frequent pattern mining has become a popular research domain among researchers, as many real-life databases at present consist of uncertain and incomplete data. In this paper, a novel attempt is made to incorporate a systematic literature review of state-of-the-art techniques of sequential pattern mining which ranges from incremental pattern mining, periodic pattern mining and uncertain frequent pattern mining. Researchers in the field of pattern mining will find it very useful to get the information about various algorithms of different types of pattern mining. [ABSTRACT FROM AUTHOR]

This paper will provide an Inquiry management service for various hospitals which will be working online to help people from various places. This system, targets every single user in the present Tech world. More specifically, the application will allow the users to enter a query and get the response from the hospital server. This is very useful in places like hospitals. The current system does not possess proper database and the importance for such queries is less. This single response to these queries will benefit them on mining these data after an interval of time. Responses are given by the system staff. Every time when it is done, the status is updated in the backend for the respective query. The system staff can escalate the query if it is not able to reply for the query generated. The key challenge achieved here is that every single query is dealt with properly. For this proper dealing, the system application is made User-friendly. The inquiries are first dealt with matching algorithm from which the member in the bottom of the stack holder list will be able to forward the inquiries easily. Later the prioritization technique to split the inquiries will help the clients to be served as quickly as possible. The data mining is later performed on these pile of queries stored, and a result is produced which is going to benefit the Organization by increasing the clientbase. The application can be operated on various tools like PCs, Laptops, palmtop. The user interface is created with HTML5, CSS and pHP. Apache tomcat is used to connect to the database and the database is accessed by MYSQL. [ABSTRACT FROM AUTHOR]

In today's world we people have stated using the internet for many purposes be it for browsing, internet banking, E-mailing, social media and for many more things. But is our information safe and secure? Network security is very important and there are many algorithms which will help us to keep our data safe from people who hack into our system. In this paper we will study the different cryptographic algorithms which in keeping the data safe. [ABSTRACT FROM AUTHOR]

Rapid expansion of cloud technologies were mainly due to the increased requirements of cloud users. However, increased requests also laden with increased resource requirements especially due to the elastic nature of the cloud. This mandates the need for effective resource provisioning model. This paper presents a Time Window based Auto-Regressive Hybrid PSO (TWARP) model that provides faster and more appropriate resource allocations. The TWARP model is composed of a temporal data grouping model to create training data, an auto-regression model to predict future requirements, a PSO-SA based optimal package selection mechanism and a final request handling mechanism that allocates the actual resource to a user. Experiments indicate low time requirements and effective allocation levels. Comparison with recent literature works also indicates highly effective performances of the proposed model. [ABSTRACT FROM AUTHOR]

Data clustering refers to the partition of a dataset into homogeneous subsets where each subset is dissimilar to the rest of the subsets. K-means is a familiar approach for data clustering particularly when all the attributes of the data objects are of numeric type. Though the k-means approach is popular and efficient it is susceptible to misclassify the data due to the noise and outliers that are common in datasets. The aim of this paper is to study the strategies available to overcome the problems like high dimensionality, redundancy, noise and outliers while implementing the k-means algorithm and to propose a better approach to deal with the problem. An iterative attribute reduction procedure based on correlations among attributes was proposed to cluster the given dataset using k-means algorithm in an improved manner. The standard dataset "Iris" was used to test the proposed methodology. The obtained results are reasonably better. [ABSTRACT FROM AUTHOR]

Dealing with the enormous amount of recruiting information on the Internet, a job seeker always spends hours to find useful ones. To reduce this laborious work, we design and implement a recommendation system for online job hunting. In this paper, we contrast user-based and item-based collaborative filtering algorithm to choose a better performed one. We also take background information including students' resumes and details of recruiting information into consideration, bring weights of coapply users (the users who had applied the candidate jobs) and weights of student used liked jobs into their commendation algorithm. At last, the model we proposed is verified through experiments study which is using actual data. The recommended results can achieve higher score of precision and recall, and they are more relevant with users' preferences before. [ABSTRACT FROM AUTHOR]

Till now we have not come across any algorithm that uses graphs to sort any set of numbers (only topological sorts like-Breadth First Search and Depth First Search exist). Our aim in this paper is to sort a set of numbers in either ascending or descending order, using undirected weighted graphs and is based on already existing Kruskal's algorithm. This paper highlights this new application of the algorithm and the fact that it can be extended to sorting numbers as well. The given set of numbers to be sorted are assigned to the vertices of the graph and the edges incident to that ordered pair of vertices are assigned some integer value(weights) based on which the sorting is done. The complexity of the algorithm in this paper is in accordance with the complexities of already existing sorting algorithms. [ABSTRACT FROM AUTHOR]

This research work is concerned with the development of a rule-based stemmer for stemming of adjectives in the Punjabi language. Stemming is a method of deriving the root word from the inflected word. The proposed Punjabi Adjective Stemmer (PAS) uses a rule-based approach for converting the inflected Punjabi adjectives to their root forms. A database containing valid root adjectives occurring in the Punjabi language has been created. This database stores 1,762 Punjabi root adjectives. When an adjective word is fed to PAS as an input, first it compares the input word with the root database to determine whether the input adjective is a root adjective or an inflected one. If the input adjective is a root adjective, then no stemming is required and the input adjective is returned as the output. Otherwise, the inflected input adjective is sent to the suffix-stripping algorithm to get the corresponding root adjective. The suffix-stripping algorithm uses a set of predefined rules. India is a linguistically rich country with 22 languages recognized officially. But the computational resources developed for these languages are very scarce. Most of the stemmers developed for Punjabi language so far concentrated on nouns and proper names. PAS is the only stemmer developed so far for specifically addressing the problem of stemming of Punjabi adjectives. PAS has an overall accuracy of 88.76%. [ABSTRACT FROM AUTHOR]

Content Distribution Networks have been attracted a great deal of attraction in recent years on cloud computing. Replica placement problems (RPPs) as one of the key technologies in the Content Distribution Networks have been widely studied. The internet services are hosted by multiple geographically distributed datacenters. For the increasingly expanded utility of Cloud storage, the improvement of resources management and reduces the storage space, cost is complicated issues on data replication. So in order to reduce the data replication, this paper, proposed the concept of Reliability Assurance algorithm (RA). The RA is reducing the file replication from the server and improves the reliability of the server. The RA algorithm identified the data replication on un-accessing files. The unpredicted files called as replicated files, these files occupy a more space on cloud server. The low ranking prediction algorithm identified the unpredicted files on cloud server based on file accessing. Reduced data replication on cloud server it's allows optimizing the storage space and cost. Using the RA and Ranking algorithm combined to reduce the data replication and storage space. [ABSTRACT FROM AUTHOR]

Relational Database is interpreted as collection of interrelated data or records, where Database Management System is software of synthesize programs used to manage overall problem arises in the Relational database system. Join Operation is used to combine more than one relation using single query statement. Sometimes, it is impossible to get result from a single relation of the relational database in their case join operation is useful to fetch data from more than one relation of the database. Join is a performed as Cartesian product followed by some specific condition process. In this paper we are going to proposed, an algorithm that reduce the communication cost when the join operation is performed on the distributed database query system. In distributed database system, join operation is performed on different sites of the communication network. This proposed algorithm is automatically selecting the lowest cost communication path to perform the join operation on relational database. [ABSTRACT FROM AUTHOR]

The accumulation rate of data is growing tremendously than ever before. These datasets often described as Big Data as they are quite large and complex and unable to process by traditional applications. Analyzing Big Data computationally reveals many useful and interesting patterns, trends or associations. Extraction of meaningful information from massive amount of data is very much useful in many sectors. In the field of Artificial Intelligence, Machine learning is a prominent area used to uncover the hidden patterns from complex and huge datasets. This paper discussed the role of machine learning techniques in Big Data analytics and how the machine learning algorithms helps to explore massive datasets that leads towards better decision making process and prediction. It also discussed the challenges and technologies available for integrating machine learning with Big Data. [ABSTRACT FROM AUTHOR]

Within the age of the Internet, protecting our data has turn out to be simply as critical as shielding our belongings. Information safety is the exercise of protecting both bodily and digital statistics from destruction or unauthorized get entry to. Numerous strategies have been proposed to preserve safety of statistics so that best the authenticated people have to be able to acquire the message sent through the sender. DNA is a new method evolved for facts protection. It has many advantages over traditional methods along with low electricity consumption, excessive processing pace, lesser time intake and lesser computational overhead. This paper discusses diverse DNA based totally authentication techniques, cryptographic algorithms and their advantages and obstacles. [ABSTRACT FROM AUTHOR]

Feature Reduction is an important step in data mining technique which removes unwanted attributes. The feature selection technique is used in various research fields. In this paper a hybrid algorithm named Radial Basis Function-Particle Swarm Optimization (RBF-PSO) Algorithm is proposed by combining the two existing algorithms, Radial Basis Function and Particle Swarm Optimization Algorithm. The result obtained proves that the proposed algorithm work well for Feature Reduction when comparing with the existing algorithms. [ABSTRACT FROM AUTHOR]

Image stitching is process of stitching different images together so as to get wider field of view as well as retaining as much as information possible. In this paper an attempt is made to stitch the images with different noises. A comparison is done after stitching images with different noises based on simple Image stitching algorithm using SIFT as key point detector. Hence the comparisons are done among different noises to find out how badly images are affected by a particular noise. [ABSTRACT FROM AUTHOR]

Bayesian networks have been at the core of the research pertaining to probabilistic reasoning. Several machine learning algorithms and techniques highly rely on Bayesian networks for their reasoning capabilities. Since several decades Bayesian networks proved to be the essential tool in the hands of researchers working in artificial intelligence domain. Yet Bayesian networks do have certain limitations [1], Bayesian networks need extensions to be more expressive and functional for probabilistic reasoning needs of various domains. Multi Entity Bayesian Networks (MEBN) is a theory combining expressivity of first-order logic principles and probabilistic reasoning of Bayesian networks. The paper thoroughly introduces the MEBN theory with an example modelling for a problem description and discusses various other works which included MEBN theory as a core of their research. The study in this paper concludes with the current highlights and challenges in MEBN and future developments. [ABSTRACT FROM AUTHOR]

Distributed database is very much popular in organizations which are distributed and operating from multiple geographic locations. The two important issues to improve the performance of distributed database systems (DDS) are - grouping of different sites into clusters, and dynamic reallocation of data fragment to these clusters. Clustering network sites and data allocation are still open research problems as their optimal solution is NP-hard. The prime contribution in this field is to develop a near optimal solution. In this paper a new algorithm is proposed for clustering distributed database sites based on the distance between sites to minimise the number of communications and network overhead between sites. Moreover, a Clustered Approach to Dynamic Data Allocation (CADDA) algorithm is proposed to dynamically reallocate data fragments to clusters in redundant and non-redundant distributed database system to reduce the remote data accesses and network overhead. The proposed approach is implemented on a sample distributed database system and compared with other cluster and non-cluster based data allocation algorithms. The comparison results show that proposed approach efficiency is better and it improves the overall performance of the distributed database system. [ABSTRACT FROM AUTHOR]

Bioinformatics is a promising and inventive research field. Biological Sequence alignment is the inborn part of bioinformatics, which helps to find similarity between biological sequences i.e. DNA and protein. Alignment of biological sequences helps to discover functional and structural similarity of sequences. The biological sequence database has been expanding rapidly due to new sequences being found, which has raised the demand to employ more efficient and fast algorithm. There has been an eruption algorithm in the past few decades to find optimal or nearly-optimal alignments. This paper is focused on the popular sequence alignment algorithms. Different types of alignment method have been discussed on the basis of their optimality and approximate solutions. It has been studied that optimal algorithms, which are based on dynamic programming are giving exact solutions. But these are highly computationally complexed. The stochastic optimization methods has been chosen from literature as the potential candidates for the solution of complex multiple sequence alignments with better speed and care. [ABSTRACT FROM AUTHOR]

Operating System is the resource manager of our computer system. And one of most important resource of the computer system is CPU. In multiprogramming operating system, there are several jobs in the main memory. There must be a proper criteria to serve these jobs, this criteria is called CPU scheduling. There are different algorithms to schedule the CPU but each of them have their advantages and disadvantages. In this paper, we have considered mainly Round Robin (RR) Algorithm. This algorithm has the demerit of having high Average Waiting Time (AWT) and high Average Turnaround Time (ATT). The goal is to propose and analyse a CPU Scheduling algorithm in order to reduce the Average Turnaround Time and Average Waiting Time by combining Round Robin Algorithm with Shortest Job first (SJF) Algorithm. To achieve the goal, we have propose two approaches to modify the existing Round Robin Algorithm. In first approach, we have combined the Round Robin Algorithm with Shortest Job First Algorithm and assigned the time quantum as the lowest burst time of first process in sorted (ascending) ready queue. By doing this we are able to reduce Average Turnaround Time but our aim is to reduce the Average Waiting Time also. Therefore, we proposed second approach, in this we have implemented the combination of Round Robin Algorithm with Shortest Job First Algorithm and assign the time quantum with the average of burst time of all the process of the ready queue. By doing this we are able to reduce Average Turnaround Time as well as Average Waiting Time, which was our ultimate goal. [ABSTRACT FROM AUTHOR]

This paper aims to analysis the performance of different activation functions and various number of hidden neurons applied to an integrated Extreme Learning Machine (ELM) k-Means algorithm for clustering. ELM is an emerging and effective learning algorithm which is used to project the data into higher dimensional feature space, then k-means techniques used for clustering in this feature space. In this work we applying three different activation functions such as sigmoidal, radial basis and triangular basis function in ELM feature space were examined. ELM k means with different activation functions and various number of hidden neurons where considered for this analysis and the performance were compared with traditional k means algorithm. The experimental results proves that ELM k-Means gives better accuracy then k-Means algorithms especially the sigmoidal activation function along with ELM k means returns high accuracy with best number of hidden neurons for all four data sets. [ABSTRACT FROM AUTHOR]

Crime, when someone does any unlawful activity, the intensity level of crime can be from very low to very high. In current society, crime exist everywhere in distinct form, and if we collect all the data related to different crime, that data would be very large in volume which can be managed through data mining techniques. Using various data mining techniques, lot of conclusion can be drawn like rise or fall in particular type of crime, percentage of particular crime, time when crime mostly or less happens, area in which maximum or minimum crime happens etc. In this paper, we use rapid miner data mining tool and naïve Bayes classification algorithm to show the different types of crime. By using crime data on classification algorithm with rapid miner tool we tries to demonstrate that how efficiently naïve Bayes algorithm can manage this data and the accuracy of result. As there is a probability of crime prediction and naïve Bayes algorithm gives result based on probability, thus the help of naïve Bayes classification algorithm the better accuracy of result can be achieved. This research will be very useful in crime control and crime detection but these all depends on the volume and correctness of the data, more data gives the more accurate prediction. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) is a key step for performing various tasks in bioinformatics, such as identification of conserved motifs, 2D and 3D Structure prediction of proteins, etc. Various algorithms are designed to implement MSA and they are evolved too, to give biologically perfect alignment. In this paper, the survey of Multiple Sequence Alignment using Multi Objective Optimization Algorithm and Single Objective Optimization Algorithm which are evolved till now are specified. [ABSTRACT FROM AUTHOR]

Machine getting to know is the essence of synthetic intelligence. Machine Learning learns from beyond studies to improve the performances of wise applications. Machine studying; machine builds the mastering version that efficiently "learns" a way to estimate from education facts of given example. IT refers to a set of topics managing the advent and evaluation of algorithms that facilitate pattern popularity, type, and prediction, based on fashions derived from existing information. In this new technology, Machine learning is in general in use to illustrate the promise of producing always accurate estimates. The principal aim and contribution of this review paper is to provide the overview of gadget getting to know and presents system-getting to know techniques. Also paper critiques the deserves and demerits of numerous device getting to know algorithms in unique approaches. [ABSTRACT FROM AUTHOR]

Data storage and DNA banking are receiving tremendous attention recently, due to the explosion of genetic research, biomedical research and forensic sciences. DNA databases when compared to other databases occupy more storage due to the enormous size of each DNA sequence. They are growing exponentially as newer samples are being collected and stored. This poses a major task for storage, transfer, searching and retrieval of data. Hence, Compression of DNA Databases have become relevant. DNA sequences contain repetitions of A, C, T, G,so compression of the sequence involves searching and encoding these repetitions,then decoding them while decompression.This paper explores a few existing DNA sequence compression algorithms which were developed based on certain prevailing generalcompression algorithms. First a few, existing compression techniques are explained and then, five available DNA sequence compression algorithms are evaluated for their suitability in compressing large collections of DNA sequences. [ABSTRACT FROM AUTHOR]

This paper aims at analyzing all-pairs shortest paths algorithm by Floyd to extract optimal path between any pair of vertices of various path length. To form least cost round tour, vertices of an optimal chain are grouped into cluster. These optimal chains are combined by taking an account of both path length as well as magnitude of the path. The chain which has largest path length and optimal distance will be considered first. Tour is constructed by adding up vertices to optimal chains in suitable positions based on nearness. This approach is a mix of both dynamic and greedy strategies. Illustrations of different variations are focus of the study. [ABSTRACT FROM AUTHOR]

Clustering is very central to the concept of data mining applications and data analysis. It is a very desirable capability to be able to identify regions of highly co-related objects as their count becomes very high and at the same time the data sets enlarge and changes in their properties and relationships among the datasets also altered. It is important to note that the concept of clustering is fundamentally a partitioning of some or many objects based on a set of rules. In the literature, a considerable number of clustering algorithms are available which are classified into various categories. In this paper, an attempt is made to perform a comparative analysis of some state-of-the-art clustering algorithms based on different parameters. At the end, some recent advances of clustering algorithms are also highlighted. [ABSTRACT FROM AUTHOR]

The identification and classification of noise sources in the ocean is a challenging task due to a myriad of impediments introduced by the complicated oceanic environment. The underwater target recognition system essentially has to identify underwater targets of interest that are heavily masked by the oceanic noise. The characteristic acoustic signatures of the underwater targets of interest, are patterned by feature recognition algorithms operating on data captured by hydrophone. In this paper, an SVM based classifier is used to distinguish between 4 classes of acoustic targets and attempt is made towards improving the performance of the classifier by automating the selection of kernel and algorithmic parameters of the underlying classifier through Whale Optimization Algorithm (WOA). [ABSTRACT FROM AUTHOR]

In last few years there are major changes and evolution has been done on classification of data. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. This paper has reviewed various natural computing techniques for solving the problem of classification basically based on data streams and skewed method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new system for cryptographic hash function generation with the help of molecular biology and to give light to a sector in computer science called Digital Cell Emulation Technique which can further be used for other purposes than the one which we have include in this paper; here we render the concept of meiotic cell division process, which is the chromosomal cross over undergone during fertilization which is the real reason for dissimilarity of an individual, genetically to another. We consider the latest finding on chaotic advection in micro-scale flow geometries which can be harnessed to greatly enhance the rate of thermally activated biochemical reactions like the polymerase chain reaction, well known as PCR. We use the above concept to emulate PCR during binary meiosis padding in the algorithm and we consider the state of the base pairs in the nucleoplasm and the motion that takes place in the fluid which we implement for crossover emulation by using 3D Navier Stokes equation with maximal convection speed of 0.055 micrometre's during metaphase of meiosis. Even for slow-diffusing species such as lambda-DNA the above process can be considered and so we can generalise it as the emulation of meiosis. Initial encryption is carried out which imitates the cross over and then the hash code generation that imitates the condensed chromosome formation. This algorithm ensures all the requirement standards for a good hash code algorithm and the pseudo code generator. [ABSTRACT FROM AUTHOR]