BinEHO: a new binary variant based on elephant herding optimization algorithm

One of the new optimization techniques proposed in recent years is elephant herding optimization (EHO) algorithm. Despite its short history, EHO has been used to solve many engineering and real-world problems by attracting researcher attention with its advantages such as efficient global search ability, having fewer control parameters and ease of implementation. However, there is no remarkable binary variant of EHO algorithm in the literature. A new binary approach based on EHO algorithm is proposed in this study. The newer binary variant of EHO named as BinEHO is binarized with preserving the search ability of basic EHO. The main purpose of the study is to present a simple, efficient and robust binary variant which copes with different binary problems. Therefore, the proposed method is tested on three important binary optimization problems, 0–1 knapsack, uncapacitated facility location and wind turbine placement, in order to show its performance and accuracy. In addition, the BinEHO is compared with various binary variants on these problems. Experimental results and comparisons show that the BinEHO algorithm is a robust and efficient tool for binary optimization.

     

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern,dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

     

A comparison of clustering-based privacy-preserving collaborative filtering schemes

Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.In this study, we first propose a content-based profiling (CBP) of users to overcome sparsity issues while performing clustering because the very sparse nature of rating profiles sometimes do not allow strong discrimination. To cope with scalability and accuracy problems of PPCF schemes, we then show how to apply k-means clustering (KMC), fuzzy c-means method (FCM), and self-organizing map (SOM) clustering to CF schemes while preserving users’ confidentiality. After presenting an evaluation of clustering-based methods in terms of privacy and supplementary costs, we carry out real data-based experiments to compare the clustering algorithms within and against traditional CF and PPCF approaches in terms of accuracy. Our empirical outcomes demonstrate that FCM achieves the best low cost performance compared to other methods due to its approximation-based model. The results also show that our privacy-preserving methods are able to offer precise predictions.     

Synthesis and metal ions sorption properties of styrene‐based polymers

Atactic polystyrene (PS) was chemically modified with maleic (MAH), succinic (SAH), and phtalic (PhAH) anhydrides. Styrene was copolymerized with acrylic (AA) and methacrylic acids (MA). Amount of carboxyl groups (%) bound to polymers was determined in the range of 6.9–25.9. Different modified polystyrenes (MPS) and styrene copolymers were used in the experiments to study Cu(II) and Zn(II) ions adsorption probability and their comparison. Sorption capacity of the polymers for the metal ions were investigated in aqueous media containing different amounts of these ions (5–40 mg/L) and at different pH values (2.0–6.0). Adsorption behavior of heavy metal ions could be modeled using both the Langmuir and Freundlich isotherms. It was found that the adsorption capacity is highest at pH value of 6, whereas it decreases as the pH value decreases at temperature 25°C ± 1°C for 240 min. The results obtained from the adsorption capacity experiments for Cu(II) and Zn(II) ions were 3.47–5.45 and 5.42–6.85 mg/g, respectively. The affinity order of polymers for both metal ions was observed as follows: SMAC > SAAC > MPS with MAH > MPS with SAH > MPS with PhAH. The maximum adsorption capacities of SMAC were 6.85mg/g for Zn(II) and 5.45 mg/g for Cu(II). © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Recycling studies of marble processing waste: Composites based on commercial epoxy resin

Marble waste was obtained from marble processing plant wastewater with precipitation using different coagulants, such as sepiolite, zeolite, and pumice in dosages of 0.5–8 g/500 mL and mixed in 20 wt % with commercial epoxy resin. The effects of marble, coagulant type and dosage on the physicomechanical and thermal properties were investigated. The incorporation of marble processing waste particles increases the 10% decomposition temperature of pure epoxy by 5–50°C. Surface hardness, tensile strength, percentage elongation, and stress at maximum load of the composites were higher than those of pure resin, too. The composites reinforced with marble processing waste-pumice showed about 10% increase in elastic modulus, whereas the composite reinforced with marble processing waste-sepiolite or zeolite showed about 76.67–143.33% increase in elastic modulus over the pure epoxy matrix. Scanning electron microscopy (SEM) was used for characterization of surface and cross sections of the composites to verify the results. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Mechanical and Antibacterial Properties of Injection Molded Polypropylene/TiO2 Nano-Composites:Effects of Surface Modification

Polypropylene (PP)/titanium dioxide (TiO2) nano-composites were prepared by melt compounding with a twin screw extruder. Nanoparticles were modified prior to melt mixing with maleic anhydride grafted styrene-ethylene-butylene-styrene (SEBS-g-MA) and silane. The composites were injection molded and mechanical tests were applied to obtain tensile strength, elastic modulus and impact strength. Antibacterial efficiency test was applied on the injection molded composite plaques by viable cell counting technique. The results showed that the composites including SEBS-g-MA and silane coated TiO2 gave better mechanical properties than the composites without SEBS-g-MA. Antibacterial efficiency of the composites varied according to the dispersion and the concentration of the particles and it was observed that composites at low content of TiO2 showed higher antibacterial property due to the better photocatalytic activity of the particles during UV exposure.     

Overview of Amorphous and Nanocrystalline Magnetocaloric Materials Operating Near Room Temperature

The observation of a giant magnetocaloric effect in Gd₅Ge_1.9Si₂Fe_0.1 has stimulated the magnetocaloric research in the last two decades. However, the high price of Gd and its proclivity to corrosion of these compounds have prevented their commercial use. To reduce raw materials cost, transition metal-based alloys are investigated to replace rare earth-based materials. Environmental considerations, substitution for scarce and strategic elements, and cost considerations all speak to potential contributions of these new materials to sustainability. Fe-based soft amorphous alloys are believed to be promising magnetic refrigerants. Efforts in improving the refrigeration capacity (RC) of refrigerants mainly rely on broadening the magnetic entropy change. One promising technique is to couple two phases of magnetic materials with desirable properties. Second is the investigation of nanoparticle synthesis routes, with ball milling being the most widely used one. The motivation for the nanoparticles synthesis is rooted in their inherent tendency to have distributed exchange coupling, which will broaden the magnetic entropy curve. As proven with the cost analysis, the focus is believed to shift from improving the RC of refrigerants toward finding the most economically advantageous magnetic refrigerant with the highest performance.     

Private predictions on hidden Markov models

Hidden Markov models (HMMs) are widely used in practice to make predictions. They are becoming increasingly popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal processing, and so on. However, traditional HMMs do not allow people and model owners to generate predictions without disclosing their private information to each other. To address the increasing needs for privacy, this work identifies and studies the private prediction problem; it is demonstrated with the following scenario: Bob has a private HMM, while Alice has a private input; and she wants to use Bob’s model to make a prediction based on her input. However, Alice does not want to disclose her private input to Bob, while Bob wants to prevent Alice from deriving information about his model. How can Alice and Bob perform HMMs-based predictions without violating their privacy? We propose privacy-preserving protocols to produce predictions on HMMs without greatly exposing Bob’s and Alice’s privacy. We then analyze our schemes in terms of accuracy, privacy, and performance. Since they are conflicting goals, due to privacy concerns, it is expected that accuracy or performance might degrade. However, our schemes make it possible for Bob and Alice to produce the same predictions efficiently while preserving their privacy.