The structural-parametrical method for design of adaptive observers (AO) for nonlinear dynamic sys-tems under uncertainty is proposed. The design of AO is consisting of two stages. The structural stage allowed identifying a class of nonlinearity and its structural pa-rameters. The solution of this task is based on an estima-tion of the system structural identifiability (SI). The method and criteria of the system structural identi-fiability are proposed. Effect of an input on the SI is showed. We believe that the excitation constancy condition is satisfied for system variables. Requirements to the input at stages of structural and parametrical design of AO differ. The parametrical design stage AO uses the results obtained at the first stage of the adaptive observer construction. Two cases of the structural information application are considered. The main attention is focused on the case of the insufficient structural information. Adaptive algorithms for tuning of parameters AO are proposed. The uncertainty estimation procedure is proposed. Stability of the adaptive system is proved. Simulation results confirmed the performance of the proposed approach.

Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.

Processes involved in maintaining a system play a crucial role in enhancing customer satisfaction and longevity of the system. Maintenance engineers are the most critical resources in Software Maintenance. They play a significant role in fixing bugs and ensuring the normal functioning of systems. Software maintenance is a tedious task for novice engineers who are new to the system domain. The lack of up-to-date documentation makes system comprehension more challenging for inexperienced engineers. Assignment of high priority bugs to novice engineers may lead to inappropriate fixes and delay in the revival of an impacted system. Such issues may degrade customer satisfaction and also poor fixes can have a severe impact on the functioning of the system at a later stage. Our research is focussed on identification of engineers with the right level of experience to fix a given bug. We have used the concept of page ranking and graph databases to compute the importance of bugs and assignees in a graph. A newly reported bug will be scored and matched with bugs that have a similar score in the graph database. Assignees who have fixed a bug that closely maps the score of the reported bug will be assigned the task of fixing the bug. We have implemented this methodology using bug reports from QT framework on neo4j graph database. Our results are promising and will definitely pave way for a new bug assignment strategy in software maintenance.

The article deals with the creation of virtual research teams of scientists from various geographically distributed organizations united for joint interdisciplinary researches. Library social institutions are the satellites of virtual research teams and have to implement information and communication support of scientific researches. The use of cloud managers by academic libraries is proposed as platforms to facilitate remote collaborative work of the participants of the virtual research teams. The research of number of free cloud managers and their capabilities was held. The most successful cloud manager for supporting the scientific work of virtual research teams was selected by using hierarchy analysis method.

Word completion and word prediction are two important phenomena in typing that have intense effect on aiding disable people and students while using keyboard or other similar devices. Such auto completion technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. N-gram based language models are proposed for auto completing a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.

Software testing is one of the most arduous and challenging phase which is to be implemented with the intention of finding faults with the execution of minimum number of test cases to increase the overall quality of the product at the time of delivery or during maintenance phase. With the ever increasing demand of web applications and to meet never ending customer expectations, updations are to incorporate which will be validated through testing process. The structure of the web applications (dynamic website) can be modeled using weighted directed graph which consists of numerous paths starting from homepage (index page) of the website. For thorough testing of the website each and every path of the graph should be tested but due to various constraints like time, money and human resources it becomes very much impractical. This scenario ultimately gives rise to the motivation for the development of technique which reduces the number of paths to be tested so that tester community can test only these numbers of path instead of all possible paths so that satisfactory number of faults can be exposed.
In this proposed approach assignment of weights on the edges of the directed graph takes place on the basis of the organization of the website, changes in the structure of the website at page level, experience of the coder and the behaviour of the users who have visited the website earlier. The most fault prone paths are identified using random, greedy, Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) algorithms. Two small size websites and one company’s website, and their two versions, were considered for experimentation. Results obtained through ACO and ABCO are promising in nature. This approach will support testing process to be completed in time and delivery of the updated version within given hard deadlines.

The traditional energy aware routing policies are not capable enough to keep up with dynamic properties of mobile ad-hoc network (e.g., mobility, quick topology changes, link-layer contentions etc.) and do not offer adequate performance in high congested situations. In past decades, authors have expressed their concerns towards smart routing paradigms concerning lesser energy consumption. However, many of these proposals are not able to offer significant performance concerning the quality of service. Consequently, the pattern of interest shifts towards cross-layer energy optimization schemes. These proposals did use of lower layers’ special information and provide significant performance enhancements. Still, many of the issues are associated with these proposals. Moreover, many of the proposals consider idle and sleep power consumption which too causes a considerable amount of energy consumption. Nevertheless, these methods require complex synchronization and efficient coordination which is too inefficient for extremely variable networks (MANETs). To address these issues, we propose an effective fuzzy- based energy efficient load distribution scheme which takes care of energy consumption considering congestion as a parameter. In comparison with some of the existing energy aware routing strategies, proposed method offers substantial improvements in terms of total energy consumption, network lifetime, total number of dead nodes, and average throughput.

The problem in the autonomous navigation of a mobile robot is to define a strategy that allows it to reach the final destination and avoiding obstacles. Fuzzy logic is considered as an important tool to solve this problem. It can mimic reasoning abilities of the human being in navigation tasks. However a major problem of fuzzy systems is obtaining their parameters which are generally specified by human experts. This process can be long and complex. In order to generate optimal parameters of fuzzy controller, this work propose a learning and optimization process based on ant colony algorithm ACO and genetic algorithm operators (crossover and mutation).We present a comparison between inference system for autonomous navigation based on fuzzy logic before and after learning. The simulated results show clearly the impact of the optimization approach improves the fuzzy controller performance mainly in obstacle avoidance and detection of the shortest path.