Data weighting method on the basis of binary encoded output to solve multi-class pattern classification problems

Data weighting is of paramount importance with respect to classification performance in pattern recognition applications. In this paper, the output labels of datasets have been encoded using binary codes (numbers) and by this way provided a novel data weighting method called binary encoded output based data weighting (BEOBDW). In the proposed data weighting method, first of all, the output labels of datasets have been encoded with binary codes and then obtained two encoded output labels. Depending to these encoded outputs, the data points in datasets have been weighted using the relationships between features of datasets and two encoded output labels. To generalize the proposed data weighting method, five datasets have been used. These datasets are chain link (2 classes), two spiral (2 classes), iris (3 classes), wine (3 classes), and dermatology (6 classes). After applied BEOBDW to five datasets, the k-NN (nearest neighbor) classifier has been used to classify the weighted datasets. A set of experiments on used real world datasets demonstrated that the proposed data weighting method is a very efficient and has robust discrimination ability in the classification of datasets. BEOBDW method could be confidently used before many classification algorithms.     

Survey of multipath routing protocols for mobile ad hoc networks

This survey investigates multipath routing protocols for mobile ad hoc networks (MANETs). The main objectives of multipath routing protocols are to provide reliable communication and to ensure load balancing as well as to improve quality of service (QoS) of MANETs. These multipath protocols are broadly classified into five categories based on their major goals. The goals are to improve delay, provide reliability, reduce overhead, maximize network life and support hybrid routing. Multipath routing protocols address issues such as multiple paths discovery and maintaining these paths. Issues, objectives, performances, advantages and disadvantages of these protocols are investigated and summarized. A checklist is provided as a guideline so that a network designer can choose an appropriate multipath routing protocol to meet the network's application objectives.     

Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multi-agent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. We show through large-scale planning under uncertainty simulations and flight experiments with state-dependent actuator and fuel-burn- rate uncertainty that our planning approach can outperform planners that do not account for heterogeneity between agents.     

Automatic recognition of vigilance state by using a wavelet-based artificial neural network

In this study, 5-s long sequences of full-spectrum electroencephalogram (EEG) recordings were used for classifying alert versus drowsy states in an arbitrary subject. EEG signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron (MLP), was used for the classification of EEG signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the MLPNN inputs, spectral analysis of EEG signals was performed using the discrete wavelet transform (DWT) technique. The MLPNN was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3% alert, 96.6% drowsy, and 90% sleep. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective for discriminating the vigilance state of the subject.     

Ultrasonic absorption and visco-relaxation study of poly(vinyl alcohol) aqueous solution. (A method for the determination of molecular weight)

Ultrasonic absorption and velocity measurements are reported on aqueous solutions of poly(vinyl alcohol) (PV-OH) of different molecular weights and concentrations obtained using a pulsed ultrasonic apparatus operating at 2 MHz and 303 K. The variations in these parameters as a function of concentration are interpreted in terms of changes in the magnitude of the contribution due to relaxation of the backbone and of the side chain acetyl groups. The results show an increase in velocity, density and viscosity with increase in molecular weight and concentration of PV-OH. In contrast the attenuation values decrease with increase in molecular weight. This suggests that interaction is occurring between PV-OH and water molecules. Deviation of the variation of the attenuation with concentration from the ‘ideal’ dilute solution behaviour at high concentration is ascribed to the effects of polymer-polymer interaction. The magnitude of the volume change associated with polymer-solvent interactions is estimated and discussed. A mathematical equation correlating the relaxation amplitude and the molecular weight of the polymer is suggested. This approach was applied to the calculation of the molecular weight of four unknown samples of PV-OH from their measured relaxation amplitudes. The results obtained agree well with those obtained from osmometry.     

HPLC-DAD Analysis to Identify the Phenolic Profile of Rhododendron Honeys Collected from Different Regions in Turkey

In this study, phenolic compounds of Rhododendron honey (also known as mad honey) samples collected from the Black Sea Region were identified using high performance liquid chromatography-diode array detector system. The major phenolic substances in Rhododendron honeys were found to be chlorogenic and coumaric acids with the amounts of 0.11–191.54 mg/kg and 0–82.83 mg/kg, respectively. Gallic and ferulic acids were detected in the most honey samples. Additionally, significant correlations were determined between the phenolic substances. The present study showed that Rhododendron honeys contained higher quantities of phenolic acids than flavonoids. Chlorogenic and coumaric acids were the dominant phenolic substances detected in honey samples.     

Effect of Messaging Model on the Reliable Data Transfer Latency in a Fog System

Journal of Network and Systems Management - IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure...     

Experimental Investigation of Oblique Impact Behavior of Adhesively Bonded Composite Single-Lap Joints

Applied Composite Materials - Determining the impact behavior of adhesive joints allows the designing of high-strength joints. Therefore, the dynamic behavior of adhesive joints has recently become...     

Retraction Note: Tolerance rough set firefly-based quick reduct

Neural Computing and Applications -