Search results for: product feature extraction

Authors: He Yuhai, Ahmad Ziad Bin Sulaiman

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Tongkat Ali, or Eurycoma longifolia, is a traditional Malay and Orang Asli herb used as aphrodisiac, general tonic, anti-Malaria, and anti-Pyretic. It has been recognized as a cashcrop by Malaysia due to its high value for the pharmaceutical use. In Tongkat Ali, eurycomanone, a quassinoid is usually chosen as a marker phytochemical as it is the most abundant phytochemical. In this research, ultrasound and enzyme were used to enhance the extraction of Eurycomanone from Tongkat Ali. Ultrasonic assisted extraction (USE) enhances extraction by facilitating the swelling and hydration of the plant material, enlarging the plant pores, breaking the plant cell, reducing the plant particle size and creating cavitation bubbles that enhance mass transfer in both the washing and diffusion phase of extraction. Enzyme hydrolyses the cell wall of the plant, loosening the structure of the cell wall, releasing more phytochemicals from the plant cell, enhancing the productivity of the extraction. Possible effects of ultrasound on the activity of the enzyme during the hydrolysis of the cell wall is under the investigation by this research. The extracts was analysed by high performance liquid chromatography for the yields of Eurycomanone. In this whole process, the conventional water extraction was used as a control of comparing the performance of the ultrasound and enzyme assisted extraction.

Keywords: ultrasound, enzymatic, extraction, Eurycoma longifolia

Procedia PDF Downloads 394

Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

Procedia PDF Downloads 305

Authors: Sandrine Dourdain, Cesar Lopez, Tamir Sukhbaatar, Guilhem Arrachart, Stephane Pellet-Rostaing

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A promising challenge in solvent extraction is to replace the conventional organic solvents, with ionic liquids (IL). Depending on the extraction systems, these new solvents show better efficiency than the conventional ones. Although some assumptions based on ions exchanges have been proposed in the literature, these properties are not predictable because the involved mechanisms are still poorly understood. It is well established that the mechanisms underlying solvent extraction processes are based not only on the molecular chelation of the extractant molecules but also on their ability to form supra-molecular aggregates due to their amphiphilic nature. It is therefore essential to evaluate how IL affects the aggregation properties of the extractant molecules. Our aim is to evaluate the influence of IL structure and polarity on solvent extraction mechanisms, by looking at the aggregation of the extractant molecules in IL. We compare extractant systems that are well characterized in common solvents and show thanks to SAXS and SANS measurements, that in the absence of IL ion exchange mechanisms, extraction properties are related to aggregation.

Keywords: solvent extraction in Ionic liquid, aggregation, Ionic liquids structure, SAXS, SANS

Procedia PDF Downloads 129

Authors: Adithya Subramanian, S. Ananthan, T. Prasanth, S. P. Selvabharathi

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This project involves extraction of grapefruit essential oil from grapefruit peels using various oils like castor oil, gingelly oil, olive oil as carrier oils. The main aim of this project is to extract the oil which has numerous medicinal uses. The extraction can be performed by two methods. Project involves extraction of the oil with various carrier oil in a view to reduce the cost of production and the physical properties of the extracted oil are examined.

Keywords: essential oil, carrier oil, medicinal uses, cost of production

Procedia PDF Downloads 406

Authors: N. W. U. D. Chathurani, Shlomo Geva, Vinod Chandran, Proboda Rajapaksha

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Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches.

Keywords: feature fusion, image retrieval, membership function, normalization

Procedia PDF Downloads 323

Authors: Mariam Dianat Sabet Gilani, Lars M. Blank, Birgitta E. Ebert

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Plant-derived lupane-type, pentacyclic triterpenoids are biologically active compounds that are highly interesting for applications in medical, pharmaceutical, and cosmetic industries. Due to the low abundance of these valuable compounds in their natural sources, and the environmentally harmful downstream process, alternative production methods, such as microbial cell factories, are investigated. Engineered Saccharomyces cerevisiae strains, harboring the heterologous genes for betulinic acid synthesis, can produce up to 2 g L-1 triterpenoids, showing high potential for large-scale production of triterpenoids. One limitation of the microbial synthesis is the intracellular product accumulation. It not only makes cell disruption a necessary step in the downstream processing but also limits productivity and product yield per cell. To overcome these restrictions, the aim of this study is to develop an in-situ extraction method, which extracts triterpenoids into a second organic phase. Such a continuous or sequential product removal from the biomass keeps the cells in an active state and enables extended production time or biomass recycling. After screening of twelve different solvents, selected based on product solubility, biocompatibility, as well as environmental and health impact, isopropyl myristate (IPM) was chosen as a suitable solvent for in-situ product removal from S. cerevisiae. Impedance-based single-cell analysis and off-gas measurement of carbon dioxide emission showed that cell viability and physiology were not affected by the presence of IPM. Initial experiments demonstrated that after the addition of 20 vol % IPM to cultures in the stationary phase, 40 % of the total produced triterpenoids were extracted from the cells into the organic phase. In future experiments, the application of IPM in a repeated batch process will be tested, where IPM is added at the end of each batch run to remove triterpenoids from the cells, allowing the same biocatalysts to be used in several sequential batch steps. Due to its high biocompatibility, the amount of IPM added to the culture can also be increased to more than 20 vol % to extract more than 40 % triterpenoids in the organic phase, allowing the cells to produce more triterpenoids. This highlights the potential for the development of a continuous large-scale process, which allows biocatalysts to produce intracellular products continuously without the necessity of cell disruption and without limitation of the cell capacity.

Keywords: betulinic acid, biocompatible solvent, in-situ extraction, isopropyl myristate, process development, secondary metabolites, triterpenoids, yeast

Procedia PDF Downloads 119

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 159

Authors: Michele C. Mesomo, Madeline de Souza Correa, Roberta L. Kruger, Luis R. S. Kanda, Marcos L. Corazza

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Natural extracts of plants have been used for many years for different purposes and recently they have been screened for their potential use as alternative remedies and food preservatives. Inflorescences of M. paradisiaca L., also known as the heart of the banana, have great economic interest due to its fruit. All parts of the banana are used for many different purposes, including use in folk medicine. The use of extraction via supercritical technology has grown in recent years, though it is still necessary to obtain experimental information for the construction of industrial plants. This work reports the extraction of Musa paradisiaca L. using compressed propane as solvent. The effects of the supercritical extraction conditions, pressure and temperature on the yield were evaluated. The raw material, inflorescences banana, was dried at 313.15 K and milled. The particle size used for the packaging of the extraction cell was 12 mesh (23.5%), 16 mesh (23.5%), 32 mesh (34.5%), 48 mesh (18.5%). The extractions were performed in a laboratory scale unit at pressures of 3.0 MPa, 6.5 MPa and 10.0 MPa and at 308.15 K, 323.15 K and 338.15 K. The operating conditions tested achieved a maximum yield of 2.94 wt% for the CO2 extraction at 10.0 MPa and 338.15 K, higher pressure and temperature. The lower yield, 2.29 wt%, was obtained in the condition of lower pressure and higher temperature. Temperature presented significant and positive effect on the extraction yield with supercritical CO2, while pressure had no effect on the yield. The overall extraction curves showed typical behavior obtained for the supercritical extraction procedure and and reached a constant extraction rate of about 80 to 100 min. The largest amount of extract was obtained at the beginning of the process, within 10 to 60 min.

Keywords: banana, natural products, supercritical extraction, temperature

Procedia PDF Downloads 578

Authors: Yani Shi, Jiaqi Yan

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A new product is important for companies to extend consumers and manifest competitiveness. New product often involves new features that consumers might not be familiar with while it may also have a competitive advantage to attract consumers compared to established products. However, although most online retailers employ recommendation agents (RA) to influence consumers’ product choice decision, recommended new products are not accepted and chosen as expected. We argue that it might also be caused by providing a new product recommendation in the wrong way at the wrong time. This study seeks to discuss how new product evaluations sourced from third parties could be employed in RAs as evidence of the superiority for the new product and how the new product recommendation could be provided to a consumer at the right time so that it can be accepted and finally chosen during the consumer’s decision-making process. A 2*2 controlled laboratory experiment was conducted to understand the selection of new product recommendation sources and recommendation timing. Human subjects were randomly assigned to one of the four treatments to minimize the effects of individual differences on the results. Participants were told to make purchase choices from our product categories. We find that a new product recommended right after a similar existing product and with the source of the expert review will be more likely to be accepted. Based on this study, both theoretical and practical contributions are provided regarding new product recommendation.

Keywords: new product recommendation, recommendation timing, recommendation source, recommendation agents

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Authors: Khet Raj Dahal, Dhruva Dhital, Chhatra Mani Sharma

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A study was conducted during 2012 to 2013 in the selected reach of Tinau River, Nepal. The main objective of the study was to quantify employment and income generation from the extraction of construction materials from the river. A 10 km stretch of the river was selected for the study. Sample survey with a semi-structured questionnaire and field observation were the main tools used during field investigation. Extraction of riverbed materials from the banks, beds and floodplain areas of the river has provided many kinds of job opportunities for the people living in the vicinity of the river. It has also generated an adequate amount of revenues. The collected revenue has been invested for many kinds of social and infrastructures development for years. Though extraction of riverbed materials is beneficial for income and employment generation, it has also negative environmental impacts in and around the river. Furthermore, the study concluded that river bed extraction should be continued with special monitoring and evaluation in the areas where there is still room for extraction.

Keywords: extraction, crusher plants, economic activities, Tinau River

Procedia PDF Downloads 672

Authors: I. F. Ejim, F. L. Kamen

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Aims: In this experiment, algae oil extraction with a combination of n-hexane and ethanol was investigated. The effects of extraction solvent concentration, extraction time and temperature on the yield and quality of oil were studied using Response Surface Methodology (RSM). Experimental Design: Optimization of algae oil extraction using Box-Behnken design was used to generate 17 experimental runs in a three-factor-three-level design where oil yield, specific gravity, acid value and saponification value were evaluated as the response. Result: In this result, a minimum oil yield of 17% and maximum of 44% was realized. The optimum values for yield, specific gravity, acid value and saponification value from the overlay plot were 40.79%, 0.8788, 0.5056 mg KOH/g and 180.78 mg KOH/g respectively with desirability of 0.801. The maximum point prediction was yield 40.79% at solvent concentration 66.68 n-hexane, temperature of 40.0°C and extraction time of 4 hrs. Analysis of Variance (ANOVA) results showed that the linear and quadratic coefficient were all significant at p<0.05. The experiment was validated and results obtained were with the predicted values. Conclusion: Algae oil extraction was successfully optimized using RSM and its quality indicated it is suitable for many industrial uses.

Keywords: algae oil, response surface methodology, optimization, Box-Bohnken, extraction

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Authors: Mohita Gangwar Sharma

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Different factors are compelling manufacturers to move towards the phenomenon of servitization i.e. when firms go beyond giving support to the customers in operating the equipment. The challenges that are being faced in this transition by the manufacturing firms from being a product provider to a product- service provider are multipronged. Product-Service Systems (PSS) lies in between the pure-product and pure-service continuum. Through this study, we wish to understand the dimensions of ‘PSS-quality’. We draw upon the quality literature for both the product and services and through an expert survey for a specific transportation sector using analytical hierarchical process (AHP) derive a conceptual model that can be used as a comprehensive measurement tool for PSS offerings.

Keywords: servitisation, quality, product-service system, AHP

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Authors: Yousuf Alkhezi

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Since most of the literature on algebra does not make much deal with the special case of mapping cone. This paper is an alternative way to examine the special tensor product and mapping cone. Also, we show that the isomorphism that implies the mapping cone commutes with the tensor product for the ordinary tensor product no longer holds for the pinched tensor product. However, we show there is a morphism. We will introduce an alternative way of mapping cone. We are looking for more properties which is our future project. Also, we want to apply these new properties in some application. Many results and examples with classical algorithms will be provided.

Keywords: complex, tensor product, pinched tensore product, mapping cone

Procedia PDF Downloads 101

Authors: C. Lopez, S. Dourdain, G. Arrachart, S. Pellet-Rostaing

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Ionic liquids (ILs) are considered a good alternative for organic solvents in extractive processes; however, the higher or lower extraction efficiency in ILs remains difficult to predict because a lack of understanding of the extraction mechanisms in this class of diluents, making their application difficult to generalize. We have studied the extraction behavior of La(III) and Eu(III) from aqueous solution into n-dodecane and two ionic liquids (ILs), 1-ethyl-1-butylpiperidinium bis (trifluoromethylsulfonyl)imide [EBPip⁺] [NTf₂⁻] and 1-ethyl-1-octylpiperidinium bis (trifluoromethylsulfonyl)imide [EOPip⁺] [NTf₂⁻], at room temperature using N,N’- dimethyl- N,N’-dioctylhexylethoxymalonamide (DMDOHEMA) as extractant. Fe(III) was introduced to the aqueous phase in order to study the selectivity toward La(III) and Eu(III) and the effect of variation of PH was investigated by using of several HNO₃ concentrations. We found that the ionic liquid with shorter alkyl chain [EBPip⁺] [NTf₂⁻] showed a higher extraction ability than [EOPip⁺] [NTf₂⁻] and that the use of ILs as organic solvent instead n-dodecane, greatly enhanced the extraction percentage of the target metals with a good selectivity. Cation ([EBPip⁺] or [EOPip⁺]) and anion ([NTf₂⁻]) concentration in the aqueous phase, has been determined in order to elucidate the extraction mechanism.

Keywords: extraction mechanism, ionic liquids, rare earths elements, solvent extraction

Procedia PDF Downloads 91

Authors: M. Bouhoun Ali, A. Y. Badjah Hadj Ahmed, M. Attou, A. Elias, M. A. Didi

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In this paper, the solvent extraction of uranium(VI), plutonium(IV) and thorium(IV) from aqueous solutions using 1-hydroxyhexadecylidene-1,1-diphosphonic acid (HHDPA) in treated kerosene has been investigated. The HHDPA was previously synthesized and characterized by FT-IR, 1H NMR, 31P NMR spectroscopy and elemental analysis. The effects contact time, initial pH, initial metal concentration, aqueous/organic phase ratio, extractant concentration and temperature on the extraction process have been studied. An empirical modelling was performed by using a 25 full factorial design, and regression equation for extraction metals was determined from the data. The conventional log-log analysis of the extraction data reveals that ratios of extractant to extracted U(VI), Pu(IV) and Th(IV) are 1:1, 1:2 and 1:2, respectively. Thermodynamic parameters showed that the extraction process was exothermic heat and spontaneous. The obtained optimal parameters were applied to real effluents containing uranium(VI), plutonium(IV) and thorium(IV) ions.

Keywords: solvent extraction, uranium, plutonium, thorium, 1-hydroxyhexadecylidene-1-1-diphosphonic acid, aqueous solution

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Authors: B. S. Harish, M. B. Revanasiddappa

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Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: Izza Hidaya, Korichi Mourad

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Solvent extraction is an affective method for reduction of aromatic content of lube oil. Frequently with phenol, furfural, NMP(N-methyl pyrrolidone). The solvent power and selectivity can be further increased by using surfactant as additive which facilitate phase separation and to increase raffinate yield. The aromatics in lube oil were extracted at different temperatures (ranging from 333.15 to 343.15K) and different concentration of surfactant (ranging from 0.01 to 0.1% wt).The extraction temperature and the amount of sulfate lauryl éther de sodium In phenoll were investigated systematically in order to determine their optimum values. The amounts of aromatic, paraffinic and naphthenic compounds were determined using ASTM standards by measuring refractive index (RI), viscosity, molecular weight and sulfur content. It was found that using 0,01%wt. surfactant at 343.15K yields the optimum extraction conditions.

Keywords: extraction, lubricating oil, aromatics, hydrocarbons

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Authors: Saimatun Nisa

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Walnut is an important export product from the Union Territory of Jammy and Kashmir. After extraction of the kernel, the walnut shell forms a solid waste that needs to be managed. Pyrolysis is one interesting option for the utilization of this walnut waste. In this study microwave pyrolysis reactor is used to convert the walnut shell biomass into its value-added products. Catalytic and non-catalytic conversion of walnut shell waste to oil, gas and char was evaluated using a Co-based catalyst. The catalyst was characterized using XPS and SEM analysis. Pyrolysis temperature, reaction time, particle size and sweeping gas (N₂) flow rate were set in the ranges of 400–600 °C, 40 min, <0.6mm to < 4.75mm and 300 ml min−1, respectively. The heating rate was fixed at 40 °C min−1. Maximum gas yield was obtained at 600 °C, 40 min, particle size range 1.18-2.36, 0.5 molar catalytic as 45.2%. The liquid product catalytic and non-catalytic was characterized by GC–MS analyses. In addition, the solid product was analyzed by means of FTIR & SEM.

Keywords: walnut shell, biooil, biochar, microwave pyrolysis

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Authors: Nais Pinta Adetya, H. Hadiyanto

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Microalgae has a potential to be utilized as food and natural colorant. The microalgae components consists of three main parts, these are lipid, protein, and carbohydrate. Crucial step in producing lipid and protein from microalgae is extraction. Microalgae has high water level (70-90%), it causes drying process of biomass needs much more energy and also has potential to distract lipid and protein from microalgae. Extraction of lipid from wet biomass is able to take place efficiently with cell disruption of microalgae by osmotic shock method. In this study, osmotic shock method was going to be integrated with ultrasound to maximalize the extraction yield of lipid and protein from wet biomass Spirulina sp. with osmotic shock method assisted ultrasound. This study consisted of two steps, these were osmotic shock process toward wet biomass and ultrasound extraction assisted. NaCl solution was used as osmotic agent, with the variation of concentrations were 10%, 20%, and 30%. Extraction was conducted in 40°C for 20 minutes with frequency of ultrasound wave was 40kHz. The optimal yield of protein (2.7%) and (lipid 38%) were achieved at 20% osmotic agent concentration.

Keywords: extraction, lipid, osmotic shock, protein, ultrasound

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Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

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Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

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Authors: Sudarat Jiamyangyuen, Tipawan Thongsook, Riantong Singanusong, Chanida Saengtubtim

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Rice is a staple food as well as exported commodity of Thailand. Rice bran, a 10.5% constituent of rice grain, is a by-product of rice milling process. Rice bran is normally used as a raw material for rice bran oil production or sold as feed with a low price. Therefore, this study aimed to increase value of defatted rice bran as obtained after extracting of rice bran oil. Conventionally, the protein in defatted rice bran was extracted using alkaline extraction and acid precipitation, which results in reduction of nutritious components in rice bran. Rice bran protein concentrate is suitable for those who are allergenic of protein from other sources eg. milk, wheat. In addition to its hypoallergenic property, rice bran protein also contains good quantity of lysine. Thus it may act as a suitable ingredient for infant food formulations while adding variety to the restricted diets of children with food allergies. The objectives of this study were to compare properties of rice bran protein concentrate (RBPC) extracted from defatted rice bran using enzymes together with precipitation step using polysaccharides (alginate and carrageenan) to those of a control sample extracted using a conventional method. The results showed that extraction of protein from rice bran using enzymes exhibited the higher protein recovery compared to that extraction with alkaline. The extraction conditions using alcalase 2% (v/w) at 50 C, pH 9.5 gave the highest protein (2.44%) and yield (32.09%) in extracted solution compared to other enzymes. Rice bran protein concentrate powder prepared by a precipitation step using alginate (protein in solution: alginate 1:0.006) exhibited the highest protein (27.55%) and yield (6.62%). Precipitation using alginate was better than that of acid. RBPC extracted with alkaline (ALK) or enzyme alcalase (ALC), then precipitated with alginate (AL) (samples RBP-ALK-AL and RBP-ALC-AL) yielded the precipitation rate of 75% and 91.30%, respectively. Therefore, protein precipitation using alginate was then selected. Amino acid profile of control sample, and sample precipitated with alginate, as compared to casein and soy protein isolated, showed that control sample showed the highest content among all sample. Functional property study of RBP showed that the highest nitrogen solubility occurred in pH 8-10. There was no statically significant between emulsion capacity and emulsion stability of control and sample precipitated by alginate. However, control sample showed a higher of foaming and lower foam stability compared to those of sample precipitated with alginate. The finding was successful in terms of minimizing chemicals used in extraction and precipitation steps in preparation of rice bran protein concentrate. This research involves in a production of value-added product in which the double amount of protein (28%) compared to original amount (14%) contained in rice bran could be beneficial in terms of adding to food products eg. healthy drink with high protein and fiber. In addition, the basic knowledge of functional property of rice bran protein concentrate was obtained, which can be used to appropriately select the application of this value-added product from rice bran.

Keywords: alginate, carrageenan, rice bran, rice bran protein

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Authors: Sanaz Mosadeghsedghi, Mathew Hudder, Mohammad Ali Baghbanzadeh, Charbel Atallah, Seyedeh Laleh Dashtban Kenari, Konstantin Volchek

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Lithium has recently been extensively used in lithium-ion batteries (LIBs) for electric vehicles and portable electronic devices. The conventional evaporative approach to recover and concentrate lithium is extremely slow and may take 10-24 months to concentrate lithium from dilute sources, such as geothermal brines. To response to the increasing industrial lithium demand, alternative extraction and concentration technologies should be developed to recover lithium from brines with low concentrations. In this study, a combination of electrocoagulation (EC) and electrodialysis (ED) was evaluated for the recovery of lithium from geothermal brines. The brine samples in this study, collected in Western Canada, had lithium concentrations of 50-75 mg/L on a background of much higher (over 10,000 times) concentrations of sodium. This very high sodium-to-lithium ratio poses challenges to the conventional direct-lithium extraction processes which employ lithium-selective adsorbents. EC was used to co-precipitate lithium using a sacrificial aluminium electrode. The precipitate was then dissolved, and the leachate was treated using ED to separate and concentrate lithium from other ions. The focus of this paper is on the study of ED, including a two-step ED process that included a mono-valent selective stage to separate lithium from multi-valent cations followed by a bipolar ED stage to convert lithium chloride (LiCl) to LiOH product. Eventually, the ED cell was reconfigured using mono-valent cation exchange with the bipolar membranes to combine the two ED steps in one. Using this process at optimum conditions, over 95% of the co-existing cations were removed and the purity of lithium increased to over 90% in the final product.

Keywords: electrochemical separation, electrocoagulation, electrodialysis, lithium extraction

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Authors: Ferhat Mohamed, Boukhatem Mohamed Nadjib, Chemat Farid

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Simultaneous distillation–extraction (SDE) is routinely used by analysts for sample preparation prior to gas chromatography analysis. In this work, a new process design and operation for microwave assisted simultaneous distillation – solvent extraction (MW-SDE) of volatile compounds was developed. Using the proposed method, isolation, extraction and concentration of volatile compounds can be carried out in a single step. To demonstrate its feasibility, MW-SDE was compared with the conventional technique, Simultaneous distillation–extraction (SDE), for gas chromatography-mass spectrometry (GC-MS) analysis of volatile compounds in a fresh orange juice and a dry spice “carvi seeds”. SDE method required long time (3 h) to isolate the volatile compounds, and large amount of organic solvent (200 mL of hexane) for further extraction, while MW-SDE needed little time (only 30 min) to prepare sample, and less amount of organic solvent (10 mL of hexane). These results show that MW-SDE–GC-MS is a simple, rapid and solvent-less method for determination of volatile compounds from aromatic plants.

Keywords: essential oil, extraction, distillation, carvi seeds

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Anam Alam, Rahim Jamaluddin Kanji

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With the rapid growth of event in everywhere, event extraction has now become an important matter to retrieve the information from the unstructured data. One of the challenging problems is to extract the event from it. An event is an observable occurrence of interaction among entities. The paper investigates the effectiveness of event extraction capabilities of three software tools that are Wandora, Nitro and SPSS. We performed standard text mining techniques of these tools on the data sets of (i) Afghan War Diaries (AWD collection), (ii) MUC4 and (iii) WebKB. Information retrieval measures such as precision and recall which are computed under extensive set of experiments for Event Extraction. The experimental study analyzes the difference between events extracted by the software and human. This approach helps to construct an algorithm that will be applied for different machine learning methods.

Keywords: event extraction, Wandora, nitro, SPSS, event analysis, extraction method, AFG, Afghan War Diaries, MUC4, 4 universities, dataset, algorithm, precision, recall, evaluation

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

Abstract:

In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Kilari Nikhil, Ankur Tibrewal, Srinivas Kruthiventi S. S.

Abstract:

Conventional speaker identification systems often fall short of capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN-based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net-inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.

Keywords: multi-scale feature extraction, squeeze and excitation, VoxCeleb1 speaker identification, mel-spectrograms, USENet

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

Abstract:

Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

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Authors: Issam Ben Jami

Abstract:

Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results.

Keywords: feature matching, historical documents, pattern recognition, word spotting

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