Earticle

This paper focus on device aware context delivery with middleware based support for heterogeneous client devices. Data networks are increased tremendously to support heterogeneous devices. There are myriad of new portable computing devices that each have different capabilities with regard to communication platform, communication protocol, computing power, display capabilities etc. Middleware based provisioning of various services based on the device context is proposed in this paper. Our work focuses on identifying different type of handheld devices based on the context of the device. Based on the device context (DC), the necessary services are served from the dynamic web server to the clients. The device identifier object provides the necessary information related to the context of the heterogeneous device (clients). This enables the server to provide the required information as per the context of the device. Our approach is generic in nature to support any kind of device context in an efficient way for different kind of application in a heterogeneous platform. The experimental results provide best solution for the forth-coming devices based on its context. This paper overcomes the deficiencies and limitation of existing device-aware computing by providing a suitable system and mechanism for device aware content delivery.

The ever-growing use of the Internet comes with a surging escalation of communication and data access. Most existing intrusion detection systems have assumed the one-size-fits-all solution model. Such IDS is not as economically sustainable for all organizations. Furthermore, studies have found that Recurrent Neural Network out-performs Feed-forward Neural Network, and Elman Network. This paper, therefore, proposes a scalable application-based model for detecting attacks in a communication network using recurrent neural network architecture. Its suitability for online real-time applications and its ability to self-adjust to changes in its input environment cannot be over-emphasized.

In this paper, the researchers describe the results obtained after assessing the software process activities in five small to medium sized Indian software companies. This work demonstrates a cost effective framework for software process appraisal, specifically targeted at Indian software Small-to-Medium-sized Enterprises (SMEs). The framework explicitly focuses on organizations that have little or no experience in software process improvement (SPI) programmes. The companies involved in this assessment have no CMMI experience prior to the work. For Indian software SME’s, it has always been difficult to find the resources, both time and money, which are necessary to engage themselves properly in SPI. To alleviate this, we have developed a low-overhead and relatively non-invasive solution tool to support SMEs in establishing process improvement initiatives. The paper initially describes how the framework was developed and then illustrates how the method is currently being extended to include a questionnaire based approach that may be used by the appraised organization to perform follow-on self-assessments. The results obtained from this study can be used by organizations to achieve the CMMI standards. Finally, the results are discussed for consistency by incorporating a scientific based approach1 to avoid ambiguities which arise while arriving at a result.

This paper presents a framework of intelligent tutorial system to incorporate adaptive learning and assess the relative performance of adaptive learning system over general classroom learning. In conventional classroom teaching and in distance learning the students may be from different backgrounds, their need of study and goal may be different; above all their ability to learn may not be similar. So teaching style and fitting teaching material may differ from student to student. A layered architecture of an Intelligent Tutorial System [ITS] for adaptive learning is proposed that assess the requirement, goal and capability of a student and dynamically sets a path for study, the instruction materials are dynamically selected as per the student’s level of understanding from a given set of instruction materials. The system monitors the student and changes the path of study automatically as per the performance. Formative and summative test are taken by the system for decision making – as to which material is best for student and for assessing student’s understanding level. The system also intelligently helps the user to overcome the misconceptions, as human teacher. Experimental results show the impressive improvement of the performances of students in distance mode learning using ITS following the proposed framework.