AHP and multichoice goal programming integration for course planning

Course planning is one of the important problems in the education systems of universities. The processes cannot continue efficiently without planning, and various interruptions can appear in the system. This way decisions concerning which courses, when, how, and for what purposes should be answered by considering the available resources and stakeholders’ preferences. Besides, universities aiming to be in the European Higher Education Area (EHEA) have to adapt their systems to the Bologna process in order to create a lifelong student‐centered, learning‐oriented area based on quality assurance. In this study, an integrated approach based on the analytic hierarchy process (AHP) and multichoice goal programming (MCGP) model was proposed to construct an efficient course plan following the Bologna process. The proposed approach was applied in an industrial engineering department.     

Fusion of smartphone sensor data for classification of daily user activities

Multimedia Tools and Applications - New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users...     

Influence of Atomizing Gases on the Oxide-Film Morphology and Thickness of Aluminum Powders

Fine powders of aluminum were produced in a pilot-plant, inert-gas atomizerwith a confined-design nozzle, which operated vertically upward. Argonand helium at 1.85 MPa and nitrogen at 1.56 MPa were used as the atomizingagent. The morphology of the powder particles was examined by SEM. Powderswere sieved dry and wet. The Sauter mean diameter of the powders varied from20.70 to 10.25 m depending on the atomizing gas. The distribution ofsizes was bimodal. The mean thickness of oxide on the surface of the powderwas calculated from the total oxygen contents of powder samples (determinedby a Leco analyzer). In addition, ESCA measurements and BET tests werecarried out for surface-oxide thickness and area measurements,respectively. The finest powder produced under helium incorporated thinnersurface-oxide layers than the coarser ones produced under argon andnitrogen. This was due to differences in physical properties (such asdensity, thermal conductivity) and flow properties (such as gasvelocity and relative velocity) of the atomizing gases used, i.e., helium,argon, and nitrogen. The oxide was very irregular in thickness in thecoarse-size range of the Al powders produced under argon and nitrogen. Thiswas presumably because of the high- and low-temperature oxidation ofaluminum droplets during the atomization and subsequent solidification andcooling periods leading to the rough surfaces observed with SEMinvestigation in the present work.     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

Electrical and Dielectric Properties of Y<Superscript>3+</Superscript>-Substituted Barium Hexaferrites

In this study, Y³⁺ ion-substituted M-type barium hexaferrites (BaM; BaFe₁₂O₁₉) were fabricated via facile ceramic route. As-prepared powders were characterized by X-ray powder diffractometry (XRD), Fourier transform infrared (FT-IR) spectroscopy, and impedance spectroscopy. XRD (Rietveld) analyses confirmed the presence of a single characterization of all samples (except x = 0.0 and 0.1 samples). The crystallite sizes of products are found in the range of 47.2–63.2 nm. Spectral analysis (FT-IR) also presented the formation of spinel structure for all products. The ac conductivity of the substituted samples was found to initially decrease slightly with increase in Y³⁺ compared with unsubstituted, and then variation tendency changes at the medium substitution ranges are observed with a different attitude against temperature. In the end, the lower conductivity for high substitutions is recorded and increases as functions of frequency while it also increases with the elevation of temperature. It was observed that ac conductivities of products increased by increasing frequency which indicate that observed ac conductivity is due to both electronic and polaron hopping mechanism.     

Noise analysis of robot manipulator using neural networks

Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.     

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.     

Pseudo Bond Graph modelling and on-line estimation of unknown kinetics for a wastewater biodegradation process

This paper deals with the problem of modelling and on-line estimation of kinetics for a biomethanation process. This bioprocess is in fact a wastewater biodegradation process with production of methane gas, which takes place inside a Continuous Stirred Tank Bioreactor. The reaction scheme and the analysis of biochemical phenomena inside the bioreactor are used in order to obtain a nonlinear dynamic model of the bioprocess, by means of the pseudo Bond Graph method. Two nonlinear estimation strategies are developed for the identification of unknown kinetics of the bioprocess. First, an estimator is developed by using a state observer based technique. Second, an observer based on high-gain approach is designed and implemented. Several numerical simulations are performed in order to analyse and compare the behaviour and the performance of the proposed estimators.