Both small and large companies are facing tough competition in today’s rapidly changing environment. They should be able to adapt continuously to these changes and to exploit them as opportunities of development. Firms should then innovate in order to gain sustainable advantage, by proposing adequate products and services allowing them to increase market shares and sustain their growth. Disruptive technologies are thus adopted in order to conceive innovative products, to develop a new use of existing products/services or to optimize processes. In this article, we describe our theoretical framework and its main constructs. It presents the direct effect of information technology innovation on economic, social and environmental performance. Moreover, our proposed framework focuses on the intermediary role of organizational agility. In addition, the main findings of our quantitative study based on the analysis of survey data from 103 participants are highlighted. After verifying the validity and reliability of our questionnaire results, the framework’s hypotheses are tested using partial least squares path modeling method.

This paper presents the failure and repair simulation for electric power house with N Turbo-alternator, where N may be up to 32. The program employs a pseudo-random number generator for individual power plant that can be described by exponential probability density functions. The resulting sequences of failure and repair events are then combined for the plants to give scenarios for different time horizons. The implementation in Excel®-VBA includes an appropriately designed userform containing the macro Active-X control for input of relevant information. The result shows that as the number of samples increased the behavior of the random events better represented the desired form with a correlation of almost 99% for 25 trials. This corresponds to a confidence interval of better than 95% and hence should be used as the median for practical applications. The results were tested and the distributions of the events were found to be close approximation of the target exponential distributions.

A Cyber-Physical System strongly depends on the sensor data to understand the current condition of the environment and act on that. Due to network faults, insufficient power supply, and rough environment, sensor data become noisy and the system may perform unwanted operations causing severe damage. In this paper, a technique has been proposed to analyze the trustworthiness of a sensor reading before performing operation based on the record. The technique employs regression analysis to select nearby sensors and develops a linear model for a target sensor. Using the linear model, target sensor reading is predicted in a particular time stamp with respect to each nearby sensor’s reading. If the difference between the predicted and actual value is within a given limit, the reading is considered as trustworthy for the corresponding nearby sensor. At last, majority consensus is taken to consider the reading as trustworthy. To evaluate the proposed technique, a data set containing temperature reading of 8 sensors for 24 hours was used where first 90% data was used for nearby sensor selection and linear model construction, and rest 10% for testing. The result analysis shows that the proposed technique detects 19, 69, and 73 trustworthy data from 73 records with respect to 3%, 4% and 5% deviation from actual reading.

Agile software development methodologies gaining the attention in the field of software engineering. There are several methods of agile such as Scrum, Lean, and Kanban. Scrum methodology divides the product into series of sprints. Lean is agile toolkit which has seven principles that facilitate: eliminating the wastes, delivering fast, and improving value for the final customer. Kanban is a visual method that can help in managing the production. To take the advantages of the following methodologies: Lean, Scrum, and Kanban we can integrate them together thus, the result will be a new methodology that can contribute in enhancing and improving the efficiency of the software development process, which is the aim of this thesis. An integrated methodology that integrating Scrum, Kanban, and Lean methodologies to yield a comprehensive agile methodology called L-ScrumBan has been proposed. The validation of the proposed methodology has been done through a survey by using a questionnaire; the survey results confirmed the efficiency of the proposed methodology.

An improvement in detection of alias names of an entity is an important factor in many cases like terrorist and criminal network. Accurately detecting these aliases plays a vital role in various applications. In particular, it is critical to detect the aliases that are intentionally hidden from the real identities, such as those of terrorists and frauds. Alias Detection (AD) as the name suggests, a process undertaken in order to quantify and identify different variants of single name showing up in multiple domains. This process is mainly performed by the inversion of one-to-many and many-to-one mapping. Aliases mainly occur when entities try to hide their actual names or real identities from other entities i.e.; when an object has multiple names and more than one name is used to address a single object. N-gram distance algorithm (N-DIST) have find wide applicability in the process of AD when the same is based upon orthographic and typographic variations. Kondrak approach, a popular N-DIST works well and fulfill the cause, but at the same time we uncover that (N-DIST) suffers from serious inabilities when applied to detect aliases occurring due to the transliteration of Arabic name into English. This is the area were we have tried to hammer in this paper. Effort in the paper has been streamlined in extending the N-gram distance metric measure of the approximate string matching (ASM) algorithm to make the same evolve in order to detect aliases which have their basing on typographic error. Data for our research is of the string form (names & activities from open source web pages). A comparison has been made to show the effectiveness of our adjustment to (N-DIST) by applying both forms of (N-DIST) on the above data set. As expected we come across that adjusted (A-N-DIST) works well in terms of both performance & functional efficiency when it comes to matching names based on transliteration of Arabic into English language from one domain to another.

Current days privacy concern about an individual, an organization and social media etc. plays a vital role. Online business deals with millions of transactions daily, these transactions may leads to privacy issues. Association rule hiding is a solution to these privacy issue, which focuses on hiding the sensitive information produces from online departmental stores ,face book datasets etc..These techniques are used to identify the sensitive rules and provide the privacy to the sensitive rules, so that results the lost rules and ghost rules. Algorithms developed so far are lack in achieving the better outcomes. This paper propose two novel algorithm that uses the properties from genetic algorithm and water marking algorithm for better way of hiding the sensitive association rules.