Chance-constrained programming on sugeno measure space

Uncertain programming is a theoretical tool to handle optimization problems under uncertain environment, it is mainly established in probability, possibility, or credibility measure spaces. Sugeno measure space is an interesting and important extension of probability measure space. This motivates us to discuss the uncertain programming based on Sugeno measure space. We have constructed the first type of uncertain programming on Sugeno measure space, i.e. the expected value models of uncertain programming on Sugeno measure space. In this paper, the second type of uncertain programming on Sugeno measure space, i.e. chance-constrained programming on Sugeno measure space, is investigated. Firstly, the definition and the characteristic of α-optimistic value and α-pessimistic value as a ranking measure are provided. Secondly, Sugeno chance-constrained programming (SCCP) is introduced. Lastly, in order to construct an approximate solution to the complex SCCP, the ideas of a Sugeno random number generation and a Sugeno simulation are presented along with a hybrid approach.     

A context-aware adaptive learning system using agents

Evolution of Web technologies has made e-learning a popular common way of education and training. As an outcome, learning content adaptation has been the subject of many research projects lately. This paper suggests a framework for building an adaptive Learning Management System (LMS). The proposed architecture is based upon multi-agent systems and uses both Sharable Content Object Reference Model (SCORM) 2004 and semantic Web ontology for learning content storage, sequencing and adaptation. This system has been implemented upon a well known open-source LMS and its functionalities are demonstrated through the simulation of a scenario mimicing the real life conditions. The result reveals the system effectiveness for which it appears that the proposed approach may be very promising.     

A new approach for estimation of obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS) is a situation where repeatedly upper airway stops off while the respiratory effort continues during sleep at least for 10 s. Apart from polysomnography, many researchers have concentrated on exploring alternative methods for OSAS detection. However, not much work has been done on using non-Gaussian and nonlinear behavior of the electroencephalogram (EEG) signals. Bispectral analysis is an advanced signal processing technique particularly used for exhibiting quadratic phase-coupling that may arise between signal components with different frequencies. From this perspective, in this study, a new technique for recognizing patients with OSAS was introduced using bispectral characteristics of EEG signal and an artificial neural network (ANN). The amount of Quadratic phase coupling (QPC) in each subband of EEG (namely; delta, theta, alpha, beta and gamma) was calculated over bispectral density of EEG. Then, these QPCs were fed to the input of the designed ANN. The neural network was configured with two outputs: one for OSAS and one for estimation of normal situation. With this technique a global accuracy of 96.15% was achieved. The proposed technique could be used in designing automatic OSAS identification systems which will improve medical service.     

An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique

The deployment of wireless sensor networks and mobile ad-hoc networks in applications such as emergency services, warfare and health monitoring poses the threat of various cyber hazards, intrusions and attacks as a consequence of these networks’ openness. Among the most significant research difficulties in such networks safety is intrusion detection, whose target is to distinguish between misuse and abnormal behavior so as to ensure secure, reliable network operations and services. Intrusion detection is best delivered by multi-agent system technologies and advanced computing techniques. To date, diverse soft computing and machine learning techniques in terms of computational intelligence have been utilized to create Intrusion Detection and Prevention Systems (IDPS), yet the literature does not report any state-of-the-art reviews investigating the performance and consequences of such techniques solving wireless environment intrusion recognition issues as they gain entry into cloud computing. The principal contribution of this paper is a review and categorization of existing IDPS schemes in terms of traditional artificial computational intelligence with a multi-agent support. The significance of the techniques and methodologies and their performance and limitations are additionally analyzed in this study, and the limitations are addressed as challenges to obtain a set of requirements for IDPS in establishing a collaborative-based wireless IDPS (Co-WIDPS) architectural design. It amalgamates a fuzzy reinforcement learning knowledge management by creating a far superior technological platform that is far more accurate in detecting attacks. In conclusion, we elaborate on several key future research topics with the potential to accelerate the progress and deployment of computational intelligence based Co-WIDPSs.     

Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach

The main aim of this study is to determine the factors influencing the adoption of Near Field Communication (NFC)-enabled mobile credit card, an innovation in contactless payment for the future generation. Constructs from psychological science, trust-based and behavioral control theories were incorporated into the parsimonious TAM. Using empirical data and Structural Equation Modeling-Artificial Neural Networks approach together with multi group analysis, the effects of social influence, personal innovativeness in information technology, trust, perceived financial cost, perceived usefulness and perceived ease of use were examined. The significance of indirect effects was examined using the bias-corrected percentile with two-tailed significance through bootstrapping. Gender, age, experience and usage were introduced as the moderator variables with industry being the control variable in the research model. The scarcity in studies regarding the moderating effects of these variables warranted the needs to further investigate their impacts. The mediating effect of perceived usefulness was examined using the Baron–Kenny’s technique. The findings of this study have provided invaluable theoretical, methodological and managerial implications and will contribute to the decision making process by CEOs, managers, manufacturers and policy makers from the mobile manufacturing industry, businesses and financial institutions, mobile commerce, mobile telecommunication providers, mobile marketers, private or government practitioners and etc.     

Investigating the Tribological Characteristics of Burnished Polyoxymethylene—ANFIS and FE Modeling

ABSTRACT

Polymers are utilized in numerous tribological applications because of their excellent characteristics; for example, accommodating shock loading and shaft misalignment. A high surface finish is required to ensure consistently good performance and extended service life of manufactured polymeric components. Burnishing is the best choice as a finishing process for this study due to its ability to increase hardness, fatigue strength, and wear resistance and also introduce compressive residual stress on the burnished workpiece. Due to the complexity and uncertainty of the machining processes, soft computing techniques are preferred for anticipating the performance of the machining processes. In this study, ANFIS as an adaptive neuro-fuzzy inference system was applied to anticipate the workpiece hardness and surface roughness after the roller burnishing process. Five burnishing variables, including burnishing depth, feed rate, speed, roller width, and lubrication mode, were analyzed. A Gauss membership function was used for the training process in this study. The predicted surface roughness and hardness data were compared with experimental results and indicated that the Gauss membership function in ANFIS has satisfying accuracy as high as 97% for surface roughness and 96% for hardness. Furthermore, the generated compressive residual stress on the burnished surface was studied by a 2D finite element model (FEM). The simulated results of residual stress were validated with the experimental results obtained from X-ray diffraction (XRD) tests.     

Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling

Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical control (CNC) end milling. Spindle speed, feed rate, and depth of cut were the predictor variables. Experimental validation runs were conducted to validate the ANFIS model. The predicted surface roughness was compared with measured data, and the maximum prediction error for surface roughness was 6.25 %, while the average prediction error was 2.75 %.     

Electric field enhancements in In2O3-coated single-walled carbon nanotubes

In₂O₃ nanoparticles are coated on the surfaces of single-walled carbon nanotubes (SWCNTs) by a successive ionic layer adsorption and reaction process. The thickness of the In₂O₃ nanoparticle film is tuned by controlling the number of coating cycles. The electric field around the In₂O₃-coated SWCNTs is compared with that of pristine SWCNTs. Field enhancement of the In₂O₃-coated SWCNTs is confirmed by conductive atomic force microscopy at low electric field (contact mode: 1 V to −1 V) and also field emission (FE) analysis at high electric field (0–4.2 V/μm). The uniformity and emission stability are also measured via FE analysis. Near infrared and X-ray photoemission spectroscopy data are suggested to explain the charge transfer, bandgap change between the In₂O₃ nanoparticles and SWCNTs, and the electric field enhancements in the In₂O₃-coated SWCNTs at both low and high electric field.     

Early detection of human actions—A hybrid approach

Early detection of human actions is essential in a wide spectrum of applications ranging from video surveillance to health-care. While human action recognition has been extensively studied, little attention is paid to the problem of detecting ongoing human action early, i.e. detecting an action as soon as it begins, but before it finishes. This study aims at training a detector to be capable of recognizing a human action when only partial action sample is seen. To do so, a hybrid technique is proposed in this work which combines the benefits of computer vision as well as fuzzy set theory based on the fuzzy Bandler and Kohout's sub-triangle product (BK subproduct). The novelty lies in the construction of a frame-by-frame membership function for each kind of possible movement. Detection is triggered when a pre-defined threshold is reached in a suitable way. Experimental results on a publicly available dataset demonstrate the benefits and effectiveness of the proposed method.     

In vitro behaviour of sol-gel interconnected porous scaffolds of doped wollastonite

Using the sol-gel method, two different three-dimensional (3D) porous interconnected scaffolds were prepared, whose compositions were MgO-K₂O-CaO-SiO₂ (MgO-K₂O-wollastonite) and Na₂O-K₂O-CaO-SiO₂ (Na₂O-K₂O-wollastonite). Scaffold sintering was performed at 950 °C for 8 h. The scaffolds were obtained and soaked in simulated body fluid for different times (6 h, 3 d, 7 d and 14 d) to study their in vitro behaviour. Scanning electron microscopy and energy-dispersive X-ray spectroscopy revealed the presence of both a hydroxyapatite-like microstructure and a nanostructure on the surface of 3D scaffolds. The presence of Na and K in the scaffolds resulted in the precipitation of a hierarchical hydroxyapatite-like layer composed of nanorods, approximately 200–400 nm in size. The presence of Mg and K ions in the composition caused the precipitation of particles with a nanorod morphology, approximately 50–100 nm in size. The addition of Na, K and Mg, K to the wollastonite resulted in scaffolds with mechanical strengths of 0.03 and 0.02 MPa, respectively.