Goals, Operators, Methods, and Selection rules (GOMS) model is a widely recognised concept in Human-Computer Interaction (HCI). Since the initial idea, several GOMS techniques were developed that were used for analysis, differing in their form defined by the logical structure and prediction power. Through defined operators and methods and following the certain rules, the user can reach a specific goal. This work represents an effort to apply GOMS method in the field of artificial intelligence, specifically on a state-space search problems. Card, Morgan, Newman GOMS (CMN-GOMS) model has been chosen, since it represents ground-floor of the GOMS idea that solves the given task through a sequence of operators. Compared with the informed search algorithms for solving the given task, CMN-GOMS model gave better results. Moreover, it was shown that this model could be used in any other space motion problem in the natural environment. LEGO® MINDSTORMS® EV3 robot was used to demonstrate the application of GOMS model in real world pathfinding problems and as a test-bed for comparing proposed model with well-known search algorithms.

The biometric authentication, which use the characteristic of persons to verify their identity by using their behavioral and physiological characteristics are an important application of the pattern recognition. There are different biometric modalities used to achieve the task of recognition. Among the most popular traits biometric currently used in several applications are the face and the iris. This paper proposes a multi-biometric technique which combines the face modality with the both irises (the left and the right irises) to authenticate the persons. The fusion of these two traits biometrics combines the advantages of the both instances of the iris modality with the face modality. The wavelets are used for the extraction of the biometrics features and the Support Vector Machine is used to obtain scores for fusion. Then, the Min-Max operator is used to normalize these scores. The fusion is operated at score level by the combination of two methods: a combination method and a classification method. So, we used the five rules (Sum, Product, Max, Min, Mean) combined with a classification method for the fusion. The Fusion is tested using the SDUMLA-HMT database. The experimental results show that multi-biometric systems achieve the task of recognition better than the mono-modal systems.

We analyzed the dynamic behavior of the damping system with a two-mass damper pendulum. The equations of motion of nonlinear systems were built. AFC equation systems have been identified in the linear formulation. Proposed and implemented a new numerical method of determining the optimum parameters of optimal settings two-mass damper.

The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners’ collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners’ collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner’s collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners’ collaboration competence level.

Clustering is the process of grouping a set of data objects into multiple groups or clusters with high similarities and dissimilarities. Dissimilarities and Similarities are assessed on the attribute values describing the objects and often involve distance measures. Clustering acts as a data mining tool by having its roots in many application areas such as biology, security, business intelligence, web search etc.
Our Institute is currently using a software application with a name “Merit System”, which evaluates the performance of the staff members regarding their level of teaching by considering various factors. It computes the performance level by collecting feedback from every student. It gives the appraisal result in the form of 30 points earned to every staff member. It acts as a tool for the management of our college to gauge the performance level of the teacher which in turn helps them in assessing annual increments and other promotions.
The main drawback of this system is its inability in grouping of staff members like Group-A, Group-B, Group-C etc. Because, many of the staff members have scored the performance points in the range of 21 to 30 which will creates lot of ambiguities to the management to make clusters of staff members to these groups. This issue is the prime concern of this paper and it was given with an approach to solve this problem by considering possible optimum soft computing technique that includes Feed Forward Neural Network approach.

The intra-uterine development of the fetus depends on various factors, one such critical factor is umbilical blood flow because the quantity of oxygen delivered to the placenta and to the fetus is directly limited by umbilical blood flow rate. Since the measurement of the hemodynamic quantities such as blood pressure and blood flow rate is not possible in utero hence the use of patient-specific mathematical modeling is beneficial for the assessment of feto-maternal well-being. A Markov model based mathematical model of fetal circulation is developed by taking three node concept. The fetus, the umbilical cord, and the placenta represent the 3 nodes of Markov model. A LabVIEW-based virtual instrument is designed to simulate the mathematical model which results in waveform similar to Doppler blood flow velocimetry of umbilical artery. The model is simulated at various degree of conductivity of the umbilical cord to the oxygenated blood. Simulation results show that the umbilical artery blood flow velocity waveform depends on gestation age, fetal heart rate, uterine contraction and placental insufficiency. The Doppler indices calculated from simulation helps in predicting both fetal and maternal abnormalities at various degrees of the conductivity to the blood flow passage. Therefore, integrating patient-specific models along with established medical equipments will be helpful in identifying true intra-uterine growth restricted fetuses from normal fetuses and helps clinicians to take timely interventions.

Cross-Language Information Retrieval (CLIR) is a most demanding research area of Information Retrieval (IR) which deals with retrieval of documents different from query language. In CLIR, translation is an important activity for retrieving relevant results. Its goal is to translate query or document from one language into another language. The correct translation of the query is an essential task of CLIR because incorrect translation may affect the relevancy of retrieved results.
The purpose of this paper is to compute the accuracy of query translation using the back translation for a Hindi-English CLIR system. For experimental analysis, we used FIRE- 2011 dataset to select Hindi queries. Our analysis shows that back translation can be effective in improving the accuracy of query translation of the three translators used for analysis (i.e. Google, Microsoft and Babylon). Google is found best for the purpose.

The Permanent Magnet Synchronous Motor has been applying widely due to it’s high efficiency, high reliability, relatively low cost and low moment of inertia. However, the PMSM drives are easily affected by the uncertain factors such as the variation of motor parameters and load disturbance. In order to realize the control of the PMSM accurately, a novel adaptive chaotic kinetic molecular theory optimization algorithm was implemented for seeking the best parameters of PID controller. In the PMSM vector control system, the speed loop will be adjusted by a CKMTOA PID controller. In modified kinetic molecular theory optimization algorithm, the adaptive inertia weight factors are used to accelerate the convergence speed, and chaotic searching is conducted within the neighbor set of the solutions to avoid the local minima. The model of PMSM and its` space vector control system are set up in the software of MATLAB/Simulink. The effectiveness of the self-tuning CKMTOA PID controller is verified by comparing with the conventional PID and particle swarm optimization algorithm. The extensive simulations and analysis also show the effectiveness of the proposed approach.