Context-based methods represent an important part of toolkit used to build intelligent systems. In the article existent definitions of context for a system with a single decision making agent were discussed. Available formal models for context data representation and processing were compared. The approaches for different forms of reasoning within context were analyzed. Also the application of context awareness in systems with situation awareness is discussed. In the article unresolved problems and tasks in the domain of context aware computing are delineated.

An iris recognition system for identifying human identity using two feature extraction methods is proposed and implemented. The first approach is the Fourier descriptors, which is based on transforming the uniqueness iris texture to the frequency domain. The new frequency domain features could be represented in iris-signature graph. The low spectrums define the general description of iris pattern while the fine detail of iris is represented as high spectrum coefficients. The principle component analysis is used here to reduce the feature dimensionality as a second feature extraction and comparative method. The biometric system performance is evaluated by comparing the recognition results for fifty persons using the two methods. Three classifiers have been considered to evaluate the system performance for each approach separately. The classification results for Fourier descriptors on three classifiers satisfied 86% 94%, and 96%, versus 80%, 92%, and 94% for principle component analysis when Cosine, Euclidean, and Manhattan classifiers were applied respectively. These results approve that Fourier descriptors method as feature extractor has better accuracy rate than principle component analysis.

Cloud computing is emerging as a popular paradigm that provides significant advances and utility-oriented services over shared virtualized resources. Despite the advantage of the cloud services, the majority of cloud users are reluctant to access the cloud due to unprecedented security threats in the cloud environment. The increasing cloud vulnerability incidences show the significance of cloud forensic techniques for the criminal investigation. It is challenging to gather the evidence from the abundant cloud data and identifying the source of the attack from the crime scene. Moreover, the Cloud Service Provider (CSP) confines the investigator to carry out the forensic investigation due to the prime concerns in the multi-tenant cloud infrastructure. To cope up with these constraints, this paper presents INSPECT, an investigation model that accomplishes adaptive evidence acquisition with adequate support for dynamic Chain of Custody presentation. By utilizing the VM log files, the INSPECT approach forensically acquires the corresponding evidence from the cloud data storage based on the location of malicious activity. It enhances the evidence acquisition and analysis process by optimally selecting and exploiting the required forensic fields alone instead of analyzing the entire log information. The INSPECT applies the Modified Fuzzy C-Means (M-FCM) clustering with contextual initialization method on the acquired evidence to recognize the source of the attack and improves the trustworthiness of the evidence through the submission of the chain of custody. By analyzing the Service Level Agreement (SLA) of the cloud users, it facilitates the source of attack identification from the clustered data. Furthermore, it isolates the evidence to avert deliberate modification by an adversary in the multi-tenant cloud. Eventually, INSPECT presents the evidence along with the chain of custody information regarding the crime scene. It enables the law enforcement authority to explore the evidence through the chain of custody information and to reconstruct the crime scene using the VM snapshots associated with timestamp data. The experimental results reveal that the INSPECT approach accomplishes a high level of accuracy in the investigation with the improved trustworthiness over the multi-tenant cloud infrastructure.

Literature show that there are limited factors for existing models in e-learning systems’ adoption. This has raised an increasing sensible debate about factors affecting successful adoption of e-leaning systems in universities in developing world particularly in Tanzania. This preliminary study aimed at exploring multiple factors for successful adoption of e-learning systems in universities in learner perspective, using DeLone and McLean (2003) IS success model as a base model. This study was conducted by collecting data randomly, using the questionnaire from students of Open Universities of Tanzania (OUT) with response rate of 0.83 in a cross-sectional study and later analyzed through content validity, reliability, and criterion-based predictive validity. The preliminary analysis shows that there are twelve distinctive factors affecting e-learning systems’ adoption in universities in Tanzania. This finding suggests more empirical research studies to follow it up, to cement and generalize this case and validate the proposed model in large scale. The novelty of this research lies on the number and uniqueness of factors found.

The Fast query engine is a requirement as a supporting tool for the semantic web technology application such as Electronic Commerce environ. As the large data is represented using the effective data representation called RDF. The focus of this paper is to optimize the specific type of the query called Cyclic query and star query on main-memory RDF data model using ARQ query engine of Jena. For the considered problem, we ruminate a Jaya algorithm for rearrangement of the order of triple pattern and also compare the results with an already proposed approach in the literature. The evaluation result shows that Jaya performs better in terms of execution time in comparison to Ant Colony Optimization.

Accurate Software effort estimation is an ongoing challenge for the modern software engineers in computer science engineering since last 30 years due to the dynamic behavior of the software [1] [2][14]. This is only because of the time and cost estimation during the early stage of the software development is quite difficult and erroneous. So many algorithmic and non algorithmic techniques are used such as SLIM (Software life cycle management), Halstead Model, Bailey-Basil Model, COCOMO model and Function point analysis, etc, but does not estimate all kinds of software accurately. Nowadays these traditional techniques are not acceptable. This research work proposes a new fuzzy model to achieve higher accuracy by multiplying a fuzzy factor with the effort equation predicted empirically. As comparison to both model based and equation based, Model based estimation focused on specific models where as equation based techniques are based on traditional equations. Fuzzy logic is more suitable and flexible to meet the realistic challenges of today’s software estimation process.

In this paper a novel flexible planning strategy based on the teaching-learning-based optimization (TLBO) algorithm and pattern search algorithm (PS) is proposed to improve the security optimal power flow (SOPF) by minimizing the total fuel cost, total power loss and total voltage deviation considering critical load growth. The main particularity of the proposed hybrid method is that TLBO algorithm is adapted and coordinated dynamically with a local search algorithm (PS). In order validate the efficiency of the proposed strategy, it has been demonstrated on the Algerian 59-bus power system and the IEEE 118-bus for different objectives considering the integration of multi SVC devices. Considering the interactivity of the proposed combined method and the quality of the obtained results compared to the standard TLBO and to recent methods reported in the literature, the proposed method proves its ability for solving practical planning problems related to large power systems.