Earticle

Surface roughness is an index which determines the quality of machined products and is influenced by the cutting parameters. In this study the average surface roughness Ra (value) for Aluminum after ball end milling operation has been measured. 84 experiments have been conducted varying cutter axis inclination angle (φ degree), spindle speed (S rpm), feed rate (fy mm/min), radial depth of cut (feed fx mm), axial depth of cut (t mm) in order to find Ra. This data has been divided into two sets on a random basis; 68 training data set and 16 testing data set. The training data set has been used to train different ANN and ANFIS models for Ra prediction. And testing data set has been used to validate the models. Better ANFIS model has been selected based on the minimum value of Root Mean Square Error (RMSE) which is constructed with three Gaussian membership functions (gaussmf) for each input variables and linear membership function for output. Similarly better ANN model has been selected based on the minimum value of Root Mean Square Error (RMSE) and Mean Absolute Percentage of Error (MAPE). The Selected ANFIS model has been compared with theoretical equation output, ANN and Response Surface Methodology (RSM). This comparison is done based on RMSE and MAPE. The comparison shows that selected ANFIS model gives better result for training and testing data. So, this ANFIS model can be used further for predicting surface roughness of Aluminum for three dimensional end milling operation.

Wireless sensor networks have emerged as a promising technology for a wide range of important applications. Energy conservation and prolonging the network lifetime are significant challenges in the design and implementation of wireless sensor networks. Clustering techniques have emerged as a popular choice for prolonging the lifetime of a wireless sensor network. In this paper, we propose a Two-Level Cluster based Protocol (TLCP) for wireless sensor networks to minimize the energy consumption and maximize the lifetime of wireless sensor networks. TLCP organizes sensor nodes into clusters and improves the data transmission mechanism from the cluster heads to the base station via constructing a cluster among the cluster heads. Simulation results show that our proposed protocol has better performance than LEACH and CBRP in terms of network lifetime, stability period, instability period, energy consumption and network throughput.

In real world computing environment, the information is not complete, precise and certain, making very difficult to derive an actual decision. To deal with processing and modeling of such information, fuzzy techniques are applied to exercise the proper conclusion. This paper focuses on the basics of Fuzzy Logic and its application in Rule Based Systems to make them capable to handle the real world problems. Also, different research issues associated with FRBSs have been discussed. Also, a traffic control system is proposed and evaluated using MATLAB.

Cloud Agile Manufacturing is a new paradigm proposed in this article. The main objective of Cloud Agile Manufacturing is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledge — or at least without having to be experts — in managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.

Educational quality varies in the degree to which they attempt to deal with different completing aspects of quality such as relative importance of various quality attributes of key process areas. With the emerging of information and communication technology, educational quality has drastically changed. This revolution has brought radical change in the way educational data are generated for ease of decision making. It is well established fact that use of information at the right time provides an advantage to educational quality. But the real challenge lies in converting high dimensional data into knowledge. Though present technology helps in creating databases, but most of the data may not be relevant for formulating a quality educational model. In this paper, we propose a new capability maturity decision making model based on rough computing for extracting key process areas and its relevance for the development of quality education. In particular, 769 educational institutions data are considered for validation and the results shows the practical viability of the proposed model.

This paper expands a sliding mode fuzzy controller which sliding surface gain is optimized by Gradient Descent Optimization Algorithm (GDOA). The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear system (robot manipulator) actual and the desired trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The outputs represent torque, respectively. The GDOA is the most prominent iterative method for solving sparse systems of nonlinear equations. The GDOA is a composite of simple, elegant ideas that almost anyone can understand. Pure sliding mode fuzzy controller has difficulty in handling unstructured model uncertainties. To solve this problem applied GDOA to sliding mode fuzzy controller for adjusting the sliding surface gain ( ). Since the sliding surface gain ( ) is adjusted by GDOA, it is nonlinear and continuous. GDOA sliding mode fuzzy controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in GDOA sliding mode fuzzy controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0.1%, rise time=0.6 second, steady state error = 1.1e-9 and RMS error=1.8e-9).

In this paper, a new splitting criterion to build a decision tree is proposed. Splitting criterion specifies the best splitting variable and its threshold for further splitting in a tree. Giving the idea from classical Forward Selection method and its enhanced versions, the variable having the largest absolute correlation with the target value is chosen as the best splitting variable in each node. Then, the idea of maximizing the margin between classes in SVM is used to find the best threshold on the selected variable to classify the data. This procedure will execute recursively in each node, until reaching the leaf nodes. The final decision tree has a comparable shorter height than the previous methods, which effectively reduces more useless variables and the time of classification for future data. Unclassified regions are also generated, which can be interpreted as an advantage or disadvantage for the proposed method. Simulation results demonstrate this improvement in the proposed decision tree.

In this paper, work has been extended to prolong the lifetime and energy proficiency of wireless sensor network. An idea of deterministic HEED has been proposed that take care of cluster head which has not been elected in 1/pi rounds. Here the comparative investigations have been reported to ascertain the performance of HEED, Multilevel Heterogeneous-Hybrid Energy Efficient Distributed Protocol (MH-HEED) and the Deterministic-HEED (D-HEED) for Wireless Sensor Network (WSN) in case of different deployment strategies. The results envisage that there is increase of about 364.3%, 268.7% & 300% comparing D-HEED over H-HEED, whereas this increase is about 25%, 22.9% &15.38% over MH-HEED in case of circular, grid and rectangular deployment strategies respectively.

In the past few years, Saudi universities have boarded to utilize e-learning tools and technologies to expand and look up their educational services. After a few years of carrying out e-learning programs, a discussion took place within the directors, supervisors, executives and managers or decision makers of the e-learning associations regarding which activities are the most impact on the learning development for online students. This research is expected to inspect the impact of a number of e-learning actions on the students’ learning development. The results show that involvement in virtual classroom sessions has the most considerable impact on the students’ final scoring or grade. This paper presents the procedure of applying data mining methods to the web observing records of students’ behaviors in a practical learning environment. The main idea is to rank the learning activities supported on their importance in order to develop students’ performance by focusing on the most important ones.

Offshore software development outsourcing (OSDO) is an important paradigm in global software development. OSDO is a well known business strategy adopted by many organisations in developed countries by outsourcing their software development work to low-wages countries to get high quality software development at low cost. However outsourcing is not a risk free business. Vendor organisations need to address a number of factors for successful outcomes of the outsourcing relationships. Knowledge sharing management (KSM) is one of the key areas to be addressed by vendor organisations for successful outcomes of OSDO relationships. This research seeks to explore KSM in the context of OSDO relationships from vendors’ perspective. The objective of this project is to design Knowledge sharing management model (KSMM) to assist outsourcing vendor organisations in managing and executing knowledge sharing management efficiently. This paper contributes to the design of planned structure for the development of KSMM.