Table of Contents - Volume 7 Number 3

In Semantic web, domain model is an abstract image of a small part of the world. It serves to capture the common understanding of the domain to create a basis for clear communication. This paper performs a study clarifying the different domain modeling types to help the individual interested by this topic to gain new insights and gudlines.  In this paper, after a general introduction about the basis of ontologies and its components, a description of the most common domain modeling schema and a comparison between them has given.

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Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. A framework to guide a phone business is discussed using data mining tools (decision Tree) to predict future trends and behaviors of their customers, thus, allowing their businesses to make proactive, knowledge-driven decisions. The impact of integrating data mining with acquisition marketing campaign management is also explained.

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Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning.[1]Clustering is one of the most important data mining techniques that partitions data according to some similarity criterion. The problems of clustering categorical data have attracted much attention from the data mining research community recently [2].The original k-means algorithm [3] or known as Lloyd's algorithm, is designed to work primarily on numeric data sets. This prohibits the algorithm from being applied to definite  data clustering, which is an integral part of data mining and has attracted much attention recently In this paper delineates increase to the k-modes algorithm for clustering definite data. By modifying a simple corresponding Variation measure for definite entities, a heuristic approach was developed in [4, 12], which allows the use of the k-modes paradigm to obtain a cluster with strong intra-similarity, and to efficiently cluster large definite data sets. The main aim of this paper is to derive severely the updating formula of the k-modes clustering algorithm with the new Variation measure, and the convergence of the algorithm under the optimization framework.

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Creation of natural human sources to communicate with the computer is currently one of the greatest challenges of modern science. The speech input facility is the most user-friendly way, adopted by development of speech recognition based on sophisticated technologies. Scientists began the selection of informative signs, describing the voice signal, afterwards the task of classification of speech signals as a set of informative signs. The development of methods of signal processing in the absence of sufficient models lead to questions about the processes of generation signals using artificial neural networks, As a result; when building a signal processing system, the structure of the network should be selected; according to parameters of the signals and training network using an algorithm to maximize the use of the information contained in the data of the experiment.

This article proposes the Application of wavelet transform for reduction of the value of artificial neural networks for speech recognition tasks this method a present Study a new modification of neural networks a neural network with inverse Wavelet Decomposition of the signal. For example, the speech recognition task, the analysis of the proposed method. The effectiveness of the method is proved by the results of computer simulations.

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Understanding decision making activities in static and dynamic decision tasks are reserving a large area of the field of research. This research focuses on how to support dynamic decision making in real-time environments with interdependent decisions. Tracing decision making efforts in different fields are deployed to identify how models, information streams, values and assumptions constrain in decision making.  The study of DMM is experimented in the laboratories using computer to test the impact of computer support on overcoming cognitive limitations. As a result new rules, methodologies and tools are rising up and the technological base is expanded in complex decision making situation. This research investigates the effect of the change of the environment on dynamic decision making. An experiment is represented to study the effects of the change of the environment on decisions in respond to time and the surrounding environment.

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Intrusion Detection System (IDS) serves as an important tool in preventing, detecting and defending against network attack. Due to increasing incidence of cyber-attacks, building an effective hybrid intrusion detection system is essential for prevention of any attack, protecting information system, monitoring networks against attacks or intrusion, and reporting these attacks to the appropriate centre for immediate action. In this paper, a hybrid intrusion detection system, integrating the strengths of the misuse detection system and the anomaly detection system were used to reduce the chances or occurrence of attacks on the network to a minimal level. This system works as an alert device in the event of attacks directed at an entire network and it also helps in reducing the number of false positive as well as false negative alarm.

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Camtasia Studio (CS) used to enhance, supplement, or replace a traditional educational curriculum. As computer technology has become more accessible, inexpensive, and powerful, the demand has increased, leading to more frequent use of resources within classroom. This application is a professional quality video production software package designed to capture, edit, interpret and organize moving action of computer screen into a computer video file. It is the ideal piece of software for creating, editing and distributing interactive multimedia video files for any other range of computer applications. The component of Camtasia is adding caption to recorded lectures &power point presentation, visual training materials. Thus by using Camtasiateacher will have an opportunity to create and edit short video snips. Camtasia helps in creating training material or demonstrating a software feature on-line and allows editing newly created video, also to save it in multiple formats. It is one of the desktop recording applications that adapt the work flow by keeping track of on-screen action. It makes the final editing process much easier and quicker. 

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Steganography is the science of hiding the fact in the information which we are sending. The goal of steganography is to embed a secret message inside a piece of unsusceptible information. The result of steganography depends on the secrecy of the cover carrier. After the steganographic carrier is disclosed, the security depends on the robustness of the algorithm and the cryptographic methods used. In order, to achieve secrecy, either the carrier must be made more robust against steganalysis or new and better carriers must be discovered. The main intention behind this paper is to discuss a new steganography technique for indexed images. 

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In wireless sensor-actor networks, the sensors sense the surroundings and transmit the sensed data to the actors. The actor nodes respond collectively to achieve their purpose. Since the actors and sensors have to communicate at all times, a strong network topology has to be established. A failure of an actor may cause the network to be broken into two. The solution can be provided by moving actor node thus restoring connectivity. Current recovery schemes consider only single node failure. This paper overcomes this shortcoming by recovering from multiple node failures through Least-Disruptive topology Repair (LeDiR) algorithm. LeDiR algorithm depends on the local view of each node about its neighbor to find the recovery plan.

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This paper continues the earlier thought of evolutionary study of sorting problem and sorting algorithms (Root to Fruit (1): An Evolutionary Study of Sorting Problem) [1]and concluded with the chronological list of early pioneers of sorting problem or algorithms. Latter in the study graphical method has been used to present an evolution of sorting problem and sorting algorithm on the time line.

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Agriculture is different from industry and plays a significant role in the economic development of a nation. India’s prosperity depends upon the agricultural prosperity. There are many kinds of agricultural products produced in India and the marketing of all these farm products generally tends to be a complex process. Agricultural marketing involves many operations and processes through which the food and raw materials move from the cultivated farm to the final consumers. The conventional approach of extension services have not been able to resolve the challenges posed by various factors in Indian Agriculture marketing. The paper at length discusses about the challenges and the opportunities for ICT mediated services for agricultural marketing.

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Cyclomatic complexity is software metric used in structural testing. The purpose of the paper is to describe the analysis on Cyclomatic complexity with an example. The Cyclomatic complexity is computed using the flow graph of the program: the nodes of the graph correspond to one or more code statement and the edges connect two nodes. Based on the flow graph how to find Cyclomatic complexity is described here.

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A finite state automata is the most important type of graphs ,which is called conceptual graphs, while the expansion of using the graphs in the process of data mining, the use of FSM is still limited because of the difficulty in processing in databases, therefore in order to find methods that make it easier to deal with large groups of machines, as a database, is encourage to  use of this type of representation in this paper of graph mining .  This paper present a method  using vectors of features for find machines matching, which is one task of mining graph data , which are frequently found in a single environment or similar environments, thereby reducing the number of records and increase efficiency of mining tasks.

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Always has the potential to improve the quality of agricultural products and production using information technology that requires efficiency and information in all sectors of agriculture. Emerging view of a non-regularity in agriculture, thanks to the World Trade Organization [WTO], to make a great need and urgency to bring in it’s an integral part of decision making by the Indian Farming Community. Information Technology (IT) has a major role to play in all facets of India Agriculture. In addition to facilitating and improving the efficiency of farmers productivity in agriculture and allied activities, bringing the potential of IT about qualitative improvement in the overall quality of life by providing timely and data inputs for decision making. Who work for the welfare of employees Indian farmers such as extension workers, do not have access to the latest hinders their ability to serve the farming community information effective. This manuscript focusses on the opportunity for people living in the e-powering in India, as well as those peoples who work for their welfare. Latest developments changing patterns of IT in rural India that facilitate effective IT penetration information requirements & IT role, the post-WTO necessarily systems environment, with possible bottlenecks in rural India, e-powering solutions are examined.

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Technical Analysis is widely used by traders for trading decisions in the stock market especially intraday trading. Various technical indicators like the moving averages or momentum indicators assist the traders in decision making. One such trend indicator is the Moving Average Convergence and Divergence (MACD) indicator. This paper analyses the profit or loss generated on application of this indicator on selected five stocks from the Bombay Stock Exchange (BSE). It has been observed after application on a daily basis for over a year that all the decisions based on MACD have generated a profit. Certain precautionary measures have also been suggested for successful implementation of the indicator.

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Active learning has received considerable attention over the past several years. Often presented or perceived as a radical change from traditional instruction, the topic frequently polarizes faculty. Active learning has attracted strong advocates among faculty looking for alternatives to traditional teaching methods, while skeptical faculty regard active learning as another in a long line of educational fads. This study examines the evidence for the effectiveness of active learning. It defines the common forms of active learning most relevant for engineering faculty and critically examines the core element of each method. It is found that there is broad but uneven support for the core elements of active, collaborative, cooperative and problem-based learning.

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Directive Access control system provides protected information with storage facility. This information can be shared among other connected or connectionless system. It is designed for the automation of time and attendance of authenticated or unauthenticated employee/visitor using RFID technology. Each Employee has RFID tag which carries his/her personnel recognition information. When these tags pass through the reader mounted at the specific division’s magnetic door, it generates interrogation field, they transmit information back to the Reader, thereby identifying them, attendance is recorded and access gates are opened, so the system monitor the movement of employee and record their real time data in a Time-stamp, which provides real time employee’s record like attendance, authorized and unauthorized entry in division, and visitor’s database with their purpose. The proposed RFID system’s objectives are to make automatic effective and efficient system so the administration time can be effectively reduced.

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Time Analytic Processing (RTAP) in this modern world is inducing huge data traffic by everyone knowingly or unknowingly compared to few years back data traffic where only few companies was source of data traffic. This is really a challenge to the technology and needs a solution which can be implemented at the earliest and with an ease. This paper tries to discuss about the solution for handling the Big-Data traffic without downgrading the processing time. However, the analysis of big data can be troublesome because of its heterogeneous nature i.e. Big-data often involves the collection and storage of mixed data based on different patterns or rules. This has made the heterogeneous mixture property of data a very important issue.This paper focuses on applying “Divide and Conquer Technique” to handle the Big-data traffic using parallel processing in Network”

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Day-by-day smartphone network’s structures are improving in an efficient manner; they are becoming ideal users to accessing the any web resources or a service, specifically, Services which are access by Internet. Web services that are used to provide changed kind of services for an app running on smart mobile users suitable and widespread used; still there are some limitations of the current smart phone clients in common manner, like as low processing speed, limited storage capacity, less band-width, latency, and in-adequate memory. This paper gears a platform free architecture for connecting mobile users to the existing Internet based Services. In this architecture includes a cross-platform design of smart mobile users based on client services and a middle ware for acquisitive the communication between mobile users and Internet based Web Services. We have used the architecture for deployed services on cloud platforms, such as “Google App Engine” (GAE) and “Cloud Sim” to enhance the consistency and scalability and reached up to the end-users.

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Support Vector Machine SVM is one of the most effective Machine Learning Algorithms for data classification, which has a significant area of research. Since the training process of large data sets is very computationally intensive, there is a need to improve its efficiency using high performance computing techniques. Initially, SVM was used for binary classification, but most applications have more than two categories, resulting in multiclass classification. Classifying different categories among large data sets has become one of the most important computing problems. Since the complexity of training of non-linear SVMs has been estimated to be quadratic in the number of training examples, which is unreasonable for data sets with tens of thousands of training examples. In this paper, we developed an efficient parallel algorithm which combines Parallel Binary Class with Parallel Multi Class Support Vector Machines for classification, PMC-PBC-SVM, which means two level parallel algorithm. The main idea of this algorithm is how to divide a set of processors into two subsets, one is responsible for the multi class case and the other is responsible for the binary class case. PMC-PBC-SVM algorithm was implemented using C++ programming language and Message passing Interface, MPI, communication routines. The parallel code was executed on an ALBACORE Linux cluster, and then tested with four data sets with difference sizes, Earthworm, Protein, Mnist, and Mnist8m. The results show that the PMC-PBC-SVM implementation can significantly improve the performance of data classification without loss of accuracy. The results also demonstrated a form of proportionality between the size of the data set and the PMC-PBC-SVM efficiency. The larger the data set is the higher efficiency the PMC-PBC-SVM achieves.

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Brain computer interface (BCI) is relatively new technology aiming at assisting, augmenting, or repairing human cognitive or sensory-motor functions. Recent advances in bio-signal processing as well as advances in neuro-imaging techniques have boosted BCI development.  The most important barrier in developing BCI technology is currently the lack of a sensor modality that provides safe, accurate and robust access to brain signals. However, recent advances in biosensor technology, signal processing and new insights into association between EEG-based measures and mental states and advances in high resolution EEG measurements have dramatically revolutionized the BCI development.  The present study reviews the basic principles of BCI, recent advances and future directions of the technology.

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Depression is one of the most common and debilitating diseases worldwide, especially in industrialized countries. Depression is traditionally managed with psychological, pharmacological, or physical interventions alone or in combination forms. However, all of the conventional methods have various limitations, such as medication side effects, frequent relapse, and high economics costs. In addition, a significant portion of the depressive disorders are treatment-refractory that do not respond to the usual medications. These different challenges emphasize the need for effective alternative treatment for depression.

Neurofeedback technique (NFT) is a noninvasive and non-drug treatment through which patients learn to modulate their brain activities using repeated practice to eliminate the disease-specific patterns in electroencephalogram (EEG) of the patient. NFT emphasizes on the correlation between EEG and cognitive and behavioral disorders. There are several clinical protocols for NFT in treatment of Depression. All of these protocols aim to shift the brain activities, EEG waves, from disorder into normal state. Therefore, in developing efficient NFT for depression or any other disorders, determining the EEG based indices which are specifically correlated with different cognitive states is necessary. Several protocols have been developed based on these indices for depressive disorders and some of them are clinically used. Left to the right hemisphere alpha predominance, decreasing Theta/Beta ratio in left prefrontal cortex, decreasing left- or increasing right- hemispheric alpha activity, shifting an asymmetry index toward the right to rebalance activation levels in favor of the left hemisphere are some of the main protocols.  This paper reviews the basic principles of NFT, its procedures and protocols for the treatment of depressive disorders and clinical outcomes.

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