Search results for: query processing

Authors: Bülent Kantar, Numan Ünaldı

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This paper presents a new method which includes robust and invisible digital watermarking on images that is colored. Colored images are used as watermark. Frequency region is used for digital watermarking. Discrete wavelet transform and discrete stationary wavelet transform are used for frequency region transformation. Low, medium and high frequency coefficients are obtained by applying the two-level discrete wavelet transform to the original image. Low frequency coefficients are obtained by applying one level discrete stationary wavelet transform separately to all frequency coefficient of the two-level discrete wavelet transformation of the original image. For every low frequency coefficient obtained from one level discrete stationary wavelet transformation, watermarks are added. Watermarks are added to all frequency coefficients of two-level discrete wavelet transform. Totally, four watermarks are added to original image. In order to get back the watermark, the original and watermarked images are applied with two-level discrete wavelet transform and one level discrete stationary wavelet transform. The watermark is obtained from difference of the discrete stationary wavelet transform of the low frequency coefficients. A total of four watermarks are obtained from all frequency of two-level discrete wavelet transform. Obtained watermark results are compared with real watermark results, and a similarity result is obtained. A watermark is obtained from the highest similarity values. Proposed methods of watermarking are tested against attacks of the geometric and image processing. The results show that proposed watermarking method is robust and invisible. All features of frequencies of two level discrete wavelet transform watermarking are combined to get back the watermark from the watermarked image. Watermarks have been added to the image by converting the binary image. These operations provide us with better results in getting back the watermark from watermarked image by attacking of the geometric and image processing.

Keywords: watermarking, DWT, DSWT, copy right protection, RGB

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Authors: Devesh Motwani, Ranjana S. Baruah

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The prime incentive behind up gradation of heavy oil is to increase its API gravity for ease of transportation to refineries, thus expanding the market access of bitumen-based crude to the refineries. There has always been a demand for an integrated approach that aims at simplifying the upgrading scheme, making it adaptable to the production site in terms of economics, environment, and personnel safety. Recent advances in nanotechnology have facilitated the development of two lines of heavy oil upgrading processes that make use of nano-catalysts for producing upgraded oil: In Situ Upgrading and Field Upgrading. The In-Situ upgrading scheme makes use of Hot Fluid Injection (HFI) technique where heavy fractions separated from produced oil are injected into the formations to reintroduce heat into the reservoir along with suspended nano-catalysts and hydrogen. In the presence of hydrogen, catalytic exothermic hydro-processing reactions occur that produce light gases and volatile hydrocarbons which contribute to increased oil detachment from the rock resulting in enhanced recovery. In this way the process is a combination of enhanced heavy oil recovery along with up gradation that effectively handles the heat load within the reservoirs, reduces hydrocarbon waste generation and minimizes the need for diluents. By eliminating most of the residual oil, the Synthetic Crude Oil (SCO) is much easier to transport and more amenable for processing in refineries. For heavy oil reservoirs seriously impacted by the presence of aquifers, the nano-catalytic technology can still be implemented on field though with some additional investments and reduced synergies; however still significantly serving the purpose of production of transportable oil with substantial benefits with respect to both large scale upgrading, and known commercial field upgrading technologies currently on the market. The paper aims to delve deeper into the technology discussed, and the future compatibility.

Keywords: upgrading, synthetic crude oil, nano-catalytic technology, compatibility

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Authors: Tamrat Tesfaye, Bruce Sithole

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Box-Behnken Response surface methodology was used to determine the optimum processing conditions that give maximum extraction yield and whiteness index from mango seed. The steeping time ranges from 2 to 12 hours and slurring of the steeped seed in sodium metabisulphite solution (0.1 to 0.5 w/v) was carried out. Experiments were designed according to Box-Behnken Design with these three factors and a total of 15 runs experimental variables of were analyzed. At linear level, the concentration of sodium metabisulphite had significant positive influence on percentage yield and whiteness index at p<0.05. At quadratic level, sodium metabisulphite concentration and sodium metabisulphite concentration2 had a significant negative influence on starch yield; sodium metabisulphite concentration and steeping time*temperature had significant (p<0.05) positive influence on whiteness index. The adjusted R2 above 0.8 for starch yield (0.906465) and whiteness index (0.909268) showed a good fit of the model with the experimental data. The optimum sodium metabisulphite concentration, steeping hours, and temperature for starch isolation with maximum starch yield (66.428%) and whiteness index (85%) as set goals for optimization with the desirability of 0.91939 was 0.255w/v concentration, 2hrs and 50 °C respectively. The determined experimental value of each response based on optimal condition was statistically in accordance with predicted levels at p<0.05. The Mango seeds are the by-products obtained during mango processing and possess disposal problem if not handled properly. The substitution of food based sizing agents with mango seed starch can contribute as pertinent resource deployment for value-added product manufacturing and waste utilization which might play significance role of food security in Ethiopia.

Keywords: mango, synthetic sizing agent, starch, extraction, textile, sizing

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Authors: Qiong Tian, Lifeng Zhang, Demei Yu, Ajit D. Kelkar

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Low specific weight and high strength is the basic requirement for aerospace materials. Fiber-reinforced epoxy resin composites are attractive materials for this purpose. Boron nitride nanoparticles (BNNPs) have good radiation shielding capacity, which is very important to aerospace materials. Herein a processing route for an advanced hybrid composite material is demonstrated by introducing dispersed BNNPs in standard prepreg manufacturing. The hybrid materials contain three parts: E-fiberglass, an aerospace-grade epoxy resin system, and BNNPs. A vacuum assisted resin transfer molding (VARTM) was utilized in this processing. Two BNNP functionalization approaches are presented in this study: (a) covalent functionalization with 3-aminopropyltriethoxysilane (KH-550); (b) non-covalent functionalization with cetyltrimethylammonium bromide (CTAB). The functionalized BNNPs were characterized by Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction(XRD) and scanning electron microscope (SEM). The results showed that BN powder was successfully functionalized via the covalent and non-covalent approaches without any crystal structure change and big agglomerate particles were broken into platelet-like nanoparticles (BNNPs) after functionalization. Compared to pristine BN powder, surface modified BNNPs could result in significant improvement in mechanical properties such as tensile, flexural and compressive strength and modulus. CTAB functionalized BNNPs (CTAB-BNNPs) showed higher tensile and flexural strength but lower compressive strength than KH-550 functionalized BNNPs (KH550-BNNPs). These reinforcements are mainly attributed to good BNNPs dispersion and interfacial adhesion between epoxy matrix and BNNPs. This study reveals the potential in improving mechanical properties of BNNPs-containing composites laminates through surface functionalization of BNNPs.

Keywords: boron nitride, epoxy, functionalization, prepreg, composite

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

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Authors: I GD Yudha Partama, A. Kanno, Y. Akamatsu, R. Inui, M. Goto, M. Sekine

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The aerial photogrammetry of shallow water bottoms has the potential to be an efficient high-resolution survey technique for shallow water topography, thanks to the advent of convenient UAV and automatic image processing techniques Structure-from-Motion (SfM) and Multi-View Stereo (MVS)). However, it suffers from the systematic overestimation of the bottom elevation, due to the light refraction at the air-water interface. In this study, we present an empirical method to correct for the effect of refraction after the usual SfM-MVS processing, using common software. The presented method utilizes the empirical relation between the measured true depth and the estimated apparent depth to generate an empirical correction factor. Furthermore, this correction factor was utilized to convert the apparent water depth into a refraction-corrected (real-scale) water depth. To examine its effectiveness, we applied the method to two river sites, and compared the RMS errors in the corrected bottom elevations with those obtained by three existing methods. The result shows that the presented method is more effective than the two existing methods: The method without applying correction factor and the method utilizes the refractive index of water (1.34) as correction factor. In comparison with the remaining existing method, which used the additive terms (offset) after calculating correction factor, the presented method performs well in Site 2 and worse in Site 1. However, we found this linear regression method to be unstable when the training data used for calibration are limited. It also suffers from a large negative bias in the correction factor when the apparent water depth estimated is affected by noise, according to our numerical experiment. Overall, the good accuracy of refraction correction method depends on various factors such as the locations, image acquisition, and GPS measurement conditions. The most effective method can be selected by using statistical selection (e.g. leave-one-out cross validation).

Keywords: bottom elevation, MVS, river, SfM

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Authors: Nicolas Bigot, M'hamed Boutaous, Nahiene Hamila, Shihe Xin

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Composite materials with polymer matrices are widely used in most industrial areas, particularly in aeronautical and automotive ones. Thanks to the development of a high-performance thermoplastic semicrystalline polymer matrix, those materials exhibit more and more efficient properties. The polymer matrix in composite materials can manifest a specific crystalline structure characteristic of crystallization in a fibrous medium. In order to guarantee a good mechanical behavior of structures and to optimize their performances, it is necessary to define realistic mechanical constitutive laws of such materials considering their physical structure. The interaction between fibers and matrix is a key factor in the mechanical behavior of composite materials. Transcrystallization phenomena which develops in the matrix around the fibers constitute the interphase which greatly affects and governs the nature of the fiber-matrix interaction. Hence, it becomes fundamental to quantify its impact on the thermo-mechanical behavior of composites material in relationship with processing conditions. In this work, we propose a numerical model coupling the thermal and crystallization kinetics in long fiber-based composite materials, considering both the spherulitic and transcrystalline types of the induced structures. After validation of the model with comparison to results from the literature and noticing a good correlation, a parametric study has been led on the effects of the thermal kinetics, the fibers volume fractions, the deformation, and the pressure on the crystallization rate in the material, under processing conditions. The ratio of the transcrystallinity is highlighted and analyzed with regard to the thermal kinetics and gradients in the material. Experimental results on the process are foreseen and pave the way to establish a mechanical constitutive law describing, with the introduction of the role on the crystallization rates and types on the thermo-mechanical behavior of composites materials.

Keywords: composite materials, crystallization, heat transfer, modeling, transcrystallization

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Authors: Regis Pochon, Nicolas Stefaniak, Veronique Baltazart, Pamela Gobin

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Anxiety is known to have an impact on working memory. In reasoning or memory tasks, individuals with anxiety tend to show longer response times and poorer performance. Furthermore, there is a memory bias for negative information in anxiety. Given the crucial role of working memory in lexical learning, anxious students may encounter more difficulties in learning to read and spell. Anxiety could even affect an earlier learning, that is the activation of phonological representations, which are decisive for the learning of reading and writing. The aim of this study is to compare the access to phonological representations of beginning readers and writers according to their level of anxiety, using an auditory lexical decision task. Eighty students of 6- to 9-years-old completed the French version of the Revised Children's Manifest Anxiety Scale and were then divided into four anxiety groups according to their total score (Low, Median-Low, Median-High and High). Two set of eighty-one stimuli (words and non-words) have been auditory presented to these students by means of a laptop computer. Stimuli words were selected according to their emotional valence (positive, negative, neutral). Students had to decide as quickly and accurately as possible whether the presented stimulus was a real word or not (lexical decision). Response times and accuracy were recorded automatically on each trial. It was anticipated a) longer response times for the Median-High and High anxiety groups in comparison with the two others groups, b) faster response times for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups, c) lower response accuracy for Median-High and High anxiety groups in comparison with the two others groups, d) better response accuracy for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups. Concerning the response times, our results showed no difference between the four groups. Furthermore, inside each group, the average response times was very close regardless the emotional valence. Otherwise, group differences appear when considering the error rates. Median-High and High anxiety groups made significantly more errors in lexical decision than Median-Low and Low groups. Better response accuracy, however, is not found for negative-valence words in comparison with positive and neutral-valence words in the Median-High and High anxiety groups. Thus, these results showed a lower response accuracy for above-median anxiety groups than below-median groups but without specificity for the negative-valence words. This study suggests that anxiety can negatively impact the lexical processing in young students. Although the lexical processing speed seems preserved, the accuracy of this processing may be altered in students with moderate or high level of anxiety. This finding has important implication for the prevention of reading and spelling difficulties. Indeed, during these learnings, if anxiety affects the access to phonological representations, anxious students could be disturbed when they have to match phonological representations with new orthographic representations, because of less efficient lexical representations. This study should be continued in order to precise the impact of anxiety on basic school learning.

Keywords: anxiety, emotional valence, childhood, lexical access

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Authors: Marissa Dallara, Amalia Ardeljan, Lexi Frankel, Nadia Obaed, Naureen Rashid, Omar Rashid

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Human Cytomegalovirus (HCMV) is a ubiquitous virus that remains latent in approximately 60% of individuals in developed countries. Viral load is kept at a minimum due to a robust immune response that is produced in most individuals who remain asymptomatic. HCMV has been recently implicated in cancer research because it may impose oncomodulatory effects on tumor cells of which it infects, which could have an impact on the progression of cancer. HCMV has been implicated in increased pathogenicity of certain cancers such as gliomas, but in contrast, it can also exhibit anti-tumor activity. HCMV seropositivity has been recorded in tumor cells, but this may also have implications in decreased pathogenesis of certain forms of cancer such as leukemia as well as increased pathogenesis in others. This study aimed to investigate the correlation between cytomegalovirus and the incidence of breast cancer. Methods The data used in this project was extracted from a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to analyze the patients infected versus patients not infection with cytomegalovirus using ICD-10, ICD-9 codes. Permission to utilize the database was given by Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Data analysis was conducted using standard statistical methods. Results The query was analyzed for dates ranging from January 2010 to December 2019, which resulted in 14,309 patients in both the infected and control groups, respectively. The two groups were matched by age range and CCI score. The incidence of breast cancer was 1.642% and 235 patients in the cytomegalovirus group compared to 4.752% and 680 patients in the control group. The difference was statistically significant by a p-value of less than 2.2x 10^-16 with an odds ratio of 0.43 (0.4 to 0.48) with a 95% confidence interval. Investigation into the effects of HCMV treatment modalities, including Valganciclovir, Cidofovir, and Foscarnet, on breast cancer in both groups was conducted, but the numbers were insufficient to yield any statistically significant correlations. Conclusion This study demonstrates a statistically significant correlation between cytomegalovirus and a reduced incidence of breast cancer. If HCMV can exert anti-tumor effects on breast cancer and inhibit growth, it could potentially be used to formulate immunotherapy that targets various types of breast cancer. Further evaluation is warranted to assess the implications of cytomegalovirus in reducing the incidence of breast cancer.

Keywords: human cytomegalovirus, breast cancer, immunotherapy, anti-tumor

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Authors: Mariana Lima, Rodrigo Silva, Victor Stange, Teodiano Bastos

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Due to the high reliability reached by DNA tests, since the 1980s this kind of test has allowed the identification of a growing number of criminal cases, including old cases that were unsolved, now having a chance to be solved with this technology. Currently, the use of genetic profiling databases is a typical method to increase the scope of genetic comparison. Forensic laboratories must process, analyze, and generate genetic profiles of a growing number of samples, which require time and great storage capacity. Therefore, it is essential to develop methodologies capable to organize and minimize the spent time for both biological sample processing and analysis of genetic profiles, using software tools. Thus, the present work aims the development of a software system solution for laboratories of forensics genetics, which allows sample, criminal case and local database management, minimizing the time spent in the workflow and helps to compare genetic profiles. For the development of this software system, all data related to the storage and processing of samples, workflows and requirements that incorporate the system have been considered. The system uses the following software languages: HTML, CSS, and JavaScript in Web technology, with NodeJS platform as server, which has great efficiency in the input and output of data. In addition, the data are stored in a relational database (MySQL), which is free, allowing a better acceptance for users. The software system here developed allows more agility to the workflow and analysis of samples, contributing to the rapid insertion of the genetic profiles in the national database and to increase resolution of crimes. The next step of this research is its validation, in order to operate in accordance with current Brazilian national legislation.

Keywords: database, forensic genetics, genetic analysis, sample management, software solution

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Authors: Sinead Morrison, Ann Swillen, Therese Van Amelsvoort, Samuel Chawner, Elfi Vergaelen, Michael Owen, Marianne Van Den Bree

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22q11.2 Deletion Syndrome (22q11.2DS) is caused by the deletion of approximately 60 genes on chromosome 22 and is associated with high rates of neurodevelopmental disorders such as Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD). The presentation of these disorders in 22q11.2DS is reported to be comparable to idiopathic forms and therefore presents a valuable model for understanding mechanisms of neurodevelopmental disorders. Cognitive deficits are thought to be a core feature of neurodevelopmental disorders, and possibly manifest in behavioural and emotional problems. There have been mixed findings in 22q11.2DS on whether the presence of ADHD or ASD is associated with greater cognitive deficits. Furthermore, the influence of developmental stage has never been taken into account. The aim was therefore to examine whether the presence of ADHD or ASD was associated with cognitive deficits in childhood and/or adolescence in 22q11.2DS. We conducted the largest study to date of this kind in 22q11.2DS. The same battery of tasks measuring processing speed, attention and spatial working memory were completed by 135 participants with 22q11.2DS. Wechsler IQ tests were completed, yielding Full Scale (FSIQ), Verbal (VIQ) and Performance IQ (PIQ). Age-standardised difference scores were produced for each participant. Developmental stages were defined as children (6-10 years) and adolescents (10-18 years). ADHD diagnosis was ascertained from a semi-structured interview with a parent. ASD status was ascertained from a questionnaire completed by a parent. Interaction and main effects of cognitive performance of those with or without a diagnosis of ADHD or ASD in childhood or adolescence were conducted with 2x2 ANOVA. Significant interactions were followed up with t-tests of simple effects. Adolescents with ASD displayed greater deficits in all measures (processing speed, p = 0.022; sustained attention, p = 0.016; working memory, p = 0.006) than adolescents without ASD; there was no difference between children with and without ASD. There were no significant differences on IQ measures. Both children and adolescents with ADHD displayed greater deficits on sustained attention (p = 0.002) than those without ADHD. There were no significant differences on any other measures for ADHD. Magnitude of cognitive deficit in individuals with 22q11.2DS varied by cognitive domain, developmental stage and presence of neurodevelopmental disorder. Adolescents with 22q11.2DS and ASD showed greater deficits on all measures, which suggests there may be a sensitive period in childhood to acquire these domains, or reflect increasing social and academic demands in adolescence. The finding of poorer sustained attention in children and adolescents with ADHD supports previous research and suggests a specific deficit which can be separated from processing speed and working memory. This research provides unique insights into the association of ASD and ADHD with cognitive deficits in a group at high genomic risk of neurodevelopmental disorders.

Keywords: 22q11.2 deletion syndrome, attention deficit hyperactivity disorder, autism spectrum disorder, cognitive development

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Authors: Bara M Yousef, Naresh B Raj, Nadiah W Arfah, Brightlin N Dhas

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Background: Executive function (EF) impairment is common in children with autism spectrum disorder (ASD). EF strategies are considered effective in improving the therapeutic outcomes of children with ASD. Aims: This study primarily aims to explore whether integrating EF strategies combined with regular occupational therapy intervention is more effective in improving daily life skills (DLS) and sensory integration/processing (SI/SP) skills than regular occupational therapy alone in children with ASD and secondarily aims to assess treatment outcomes on improving visual motor integration (VMI) skills. Procedures: A total of 92 children with ASD will be recruited and, following baseline assessments, randomly assigned to the treatment group (45-min once weekly individual occupational therapy plus EF strategies) and control group (45-min once weekly individual therapy sessions alone). Results and Outcomes: All children will be evaluated systematically by assessing SI/SP, DLS, and VMI, skills at baseline, 7 weeks, and 14 weeks of treatment. Data will be analyzed using ANCOVA and T-test. Conclusions and Implications: This single-blind, randomized controlled trial will provide empirical evidence for the effectiveness of EF strategies when combined with regular occupational therapy programs. Based on trial results, EF strategies could be recommended in multidisciplinary programs for children with ASD. Trial Registration: The trial has been registered in the clinicaltrail.gov for a registry, protocol ID: MRC-01-22-509 ClinicalTrials.gov Identifier: NCT05829577, registered 25th April 2023

Keywords: autism spectrum disorder, executive function strategies, daily life skills, sensory integration/processing, visual motor integration, occupational therapy, effectiveness

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Authors: V. Suganya, V. Balasuriya

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The functioning of Micro and Small Scale (MSS) businesses in the northern part of Sri Lanka was vulnerable due to three decades of internal conflict and the subsequent post-war economic openings has resulted new market prospects for MSS businesses. MSS businesses survive and operate with limited resources and struggle to access finance, raw material, markets, and technology. This study attempts to identify the manner in which entrepreneurial orientation puts into practice by the business operators to overcome these business challenges. Business operators in the traditional food processing sector are taken for this study as this sub-sector of the food industry is developing at a rapid pace. A review of the literature was done to recognize the concepts of entrepreneurial orientation, defining MMS businesses and the manner in which business performance is measured. Direct interview method supported by a structured questionnaire is used to collect data from 80 respondents; based on a fixed interval random sampling technique. This study reveals that more than half of the business operators have opted to commence their business ventures as a result of identifying a market opportunity. 41 per cent of the business operators are highly entrepreneurial oriented in a scale of 1 to 5. Entrepreneurial orientation shows significant relationship and strongly correlated with business performance. Pro-activeness, innovativeness and competitive aggressiveness shows a significant relationship with business performance while risk taking is negative and autonomy is not significantly related to business performance. It is evident that entrepreneurial oriented business practices contribute to better business performance even though 70 per cent prefer the ideas/views of the support agencies than the stakeholders when making business decisions. It is recommended that appropriate training should be introduced to develop entrepreneurial skills focusing to improve business networks so that new business opportunities and innovative business practices are identified.

Keywords: Micro and Small Scale (MMS) businesses, entrepreneurial orientation (EO), food processing, business operators

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Authors: Teodora Vukovic, Christoph Hottiger, Noah Bubenhofer

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The development of VIAN-DH aims at bridging two linguistic approaches: conversation analysis/interactional linguistics (IL), so far a dominantly qualitative field, and computational/corpus linguistics and its quantitative and automated methods. Contemporary IL investigates the systematic organization of conversations and interactions composed of speech, gaze, gestures, and body positioning, among others. These highly integrated multimodal behaviour is analysed based on video data aimed at uncovering so called “multimodal gestalts”, patterns of linguistic and embodied conduct that reoccur in specific sequential positions employed for specific purposes. Multimodal analyses (and other disciplines using videos) are so far dependent on time and resource intensive processes of manual transcription of each component from video materials. Automating these tasks requires advanced programming skills, which is often not in the scope of IL. Moreover, the use of different tools makes the integration and analysis of different formats challenging. Consequently, IL research often deals with relatively small samples of annotated data which are suitable for qualitative analysis but not enough for making generalized empirical claims derived quantitatively. VIAN-DH aims to create a workspace where many annotation layers required for the multimodal analysis of videos can be created, processed, and correlated in one platform. VIAN-DH will provide a graphical interface that operates state-of-the-art tools for automating parts of the data processing. The integration of tools that already exist in computational linguistics and computer vision, facilitates data processing for researchers lacking programming skills, speeds up the overall research process, and enables the processing of large amounts of data. The main features to be introduced are automatic speech recognition for the transcription of language, automatic image recognition for extraction of gestures and other visual cues, as well as grammatical annotation for adding morphological and syntactic information to the verbal content. In the ongoing instance of VIAN-DH, we focus on gesture extraction (pointing gestures, in particular), making use of existing models created for sign language and adapting them for this specific purpose. In order to view and search the data, VIAN-DH will provide a unified format and enable the import of the main existing formats of annotated video data and the export to other formats used in the field, while integrating different data source formats in a way that they can be combined in research. VIAN-DH will adapt querying methods from corpus linguistics to enable parallel search of many annotation levels, combining token-level and chronological search for various types of data. VIAN-DH strives to bring crucial and potentially revolutionary innovation to the field of IL, (that can also extend to other fields using video materials). It will allow the processing of large amounts of data automatically and, the implementation of quantitative analyses, combining it with the qualitative approach. It will facilitate the investigation of correlations between linguistic patterns (lexical or grammatical) with conversational aspects (turn-taking or gestures). Users will be able to automatically transcribe and annotate visual, spoken and grammatical information from videos, and to correlate those different levels and perform queries and analyses.

Keywords: multimodal analysis, corpus linguistics, computational linguistics, image recognition, speech recognition

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Authors: C. Sánchez de Rojas Candela, A. Riquelme, P. Rodrigo, M. D. Escalera-Rodríguez, B. Torres, J. Rams

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Selective laser melting is one of the most commonly used AM techniques. In it, a thin layer of metallic powder is deposited, and a laser is used to melt selected zones. The accumulation of layers, each one molten in the preselected zones, gives rise to the formation of a 3D sample with a nearly arbitrary design. To ensure that the properties of the final parts match those of the powder, all the process is carried out in an inert atmosphere, preferentially Ar, although this gas could be substituted. Ti6Al4V alloy is widely used in multiple industrial applications such as aerospace, maritime transport and biomedical, due to its properties. However, due to the demanding requirements of these applications, greater hardness and wear resistance are necessary, together with a better machining capacity, which currently limits its commercialization. To improve these properties, in this study, Selective Laser Melting (SLM) is used to manufacture Ti/TiN metal matrix composites with in-situ and ex-situ titanium nitride reinforcement where the scanning speed is modified (from 28.5 up to 65 mm/s) to study the influence of the processing parameters in SLM. A one-step method of nitriding the Ti6Al4V alloy is carried out to create in-situ TiN reinforcement in a reactive atmosphere and it is compared with ex-situ composites manufactured by previous mixture of both the titanium alloy powder and the ceramic reinforcement particles. The microstructure and mechanical properties of the different Ti/TiN composite materials have been analyzed. As a result, the existence of a similar matrix has been confirmed in in-situ and ex-situ fabrications and the growth mechanisms of the nitrides have been studied. An increase in the mechanical properties with respect to the initial alloy has been observed in both cases and related to changes in their microstructure. Specifically, a greater improvement (around 30.65%) has been identified in those manufactured by the in-situ method at low speeds although other properties such as porosity must be improved for their future industrial applicability.

Keywords: in-situ reinforcement, nitriding reaction, selective laser melting, titanium nitride

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Authors: Pulkeria Pascoe, Hawa P. Tundui, Marcia Dutra de Barcellos, Hans de Steur, Xavier Gellynck

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A majority of Micro and Small Enterprises (MSEs) experience low or no growth. Understanding their performance remains unfinished and disjointed as there is no consensus on the factors influencing it, especially in developing countries. Using a Resource-Based View (RBV) as the theoretical background, this cross-sectional study employed four regression models to examine the influence of firm-level factors (firm-specific characteristics, firm resources, manager socio-demographic characteristics, and selected management practices) on the overall performance variations among 442 Tanzanian micro and small agri-food processing firms. Study results confirmed the RBV argument that intangible resources make a larger contribution to overall performance variations among firms than that tangible resources. Firms' tangible and intangible resources explained 34.5% of overall performance variations (intangible resources explained the overall performance variability by 19.4% compared to tangible resources, which accounted for 15.1%), ranking first in explaining the overall performance variance. Firm-specific characteristics ranked second by influencing variations in overall performance by 29.0%. Selected management practices ranked third (6.3%), while the manager's socio-demographic factors were last on the list, as they influenced the overall performance variability among firms by only 5.1%. The study also found that firms that focus on proper utilization of tangible resources (financial and physical), set targets, and undertake better working capital management practices performed higher than their counterparts (low and average performers). Furthermore, accumulation and proper utilization of intangible resources (relational, organizational, and reputational), undertaking performance monitoring practices, age of the manager, and the choice of the firm location and activity were the dominant significant factors influencing the variations among average and high performers, relative to low performers. The entrepreneurial background was a significant factor influencing variations in average and low-performing firms, indicating that entrepreneurial skills are crucial to achieving average levels of performance. Firm age, size, legal status, source of start-up capital, gender, education level, and total business experience of the manager were not statistically significant variables influencing the overall performance variations among the agri-food processors under the study. The study has identified both significant and non-significant factors influencing performance variations among low, average, and high-performing micro and small agri-food processing firms in Tanzania. Therefore, results from this study will help managers, policymakers and researchers to identify areas where more attention should be placed in order to improve overall performance of MSEs in agri-food industry.

Keywords: firm-level factors, micro and small enterprises, performance, regression analysis, resource-based-view

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Ross J. Maestas

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Biofuels are derived from multiple renewable bioenergy feedstocks including animal fats, wood, starchy grains, and oil seeds. Transportation agencies have considered growing the latter two on underutilized and nontraditional lands that they manage, such as in the Right of Way (ROW), abandoned weigh stations, and at maintenance yards. These crops provide the opportunity to generate revenue or supplement fuel once converted and offer a solution to increasing fuel costs and instability by creating a ‘home-grown’ alternative. Biofuels are non-toxic, biodegradable, and emit less Green House Gasses (GHG) than fossil fuels, therefore allowing agencies to meet sustainability goals and regulations. Furthermore, they enable land managers to achieve soil erosion and roadside aesthetic strategies. The research sought to understand if the cultivation of a biofuel feedstock within the Arkansas State Highway Transportation Department’s (AHTD) managed and marginalized lands is feasible by identifying potential land areas and crops. To determine potential plots the parcel data was downloaded from Arkansas’s GIS office. ArcGIS was used to query the data for all variations of the names of property owned by AHTD and a KML file was created that identifies the queried parcel data in Google Earth. Furthermore, biofuel refineries in the state were identified to optimize the harvest to transesterification process. Agricultural data was collected from federal and state agencies and universities to assess various oil seed crops suitable for conversion and suited to grow in Arkansas’s climate and ROW conditions. Research data determined that soybean is the best adapted biofuel feedstock for Arkansas with camelina and canola showing possibilities as well. Agriculture is Arkansas’s largest industry and soybean is grown in over half of the state’s counties. Successful cultivation of a feedstock in the aforementioned areas could potentially offer significant employment opportunity for which the skilled farmers already exist. Based on compiled data, AHTD manages 21,489 acres of marginalized land. The result of the feasibility assessment offer suggestions and guidance should AHTD decide to further investigate this type of initiative.

Keywords: Arkansas highways, biofuels, renewable energy initiative, marginalized lands

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Authors: Archana Rao R., Deepak P., Chayashree P. D., Darshan H. S.

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Cognitive abilities in bilinguals have been widely studied over the last few decades. Bilingualism has been found to extensively facilitate the ability to store and manipulate information in Working Memory (WM). The mechanism of WM includes primary memory, attentional control, and secondary memory, each of which makes a contribution to WM. Many researches have been done in an attempt to measure WM capabilities through both verbal (phonological) and nonverbal tasks (visuospatial). Since there is a lot of speculations regarding the relationship between WM and bilingualism, further investigation is required to understand the nature of WM in bilinguals, i.e., with respect to sequential and simultaneous bilinguals. Hence the present study aimed to highlight the verbal working memory abilities in sequential and simultaneous bilinguals with respect to the processing and recall abilities of nouns and verbs. Two groups of bilinguals aged between 18-30 years were considered for the study. Group 1 consisted of 20 (10 males and 10 females) sequential bilinguals who had acquired L1 (Kannada) before the age of 3 and had exposure to L2 (English) for a period of 8-10 years. Group 2 consisted of 20 (10 males and 10 females) simultaneous bilinguals who have acquired both L1 and L2 before the age of 3. Working memory abilities were assessed using two tasks, and a set of stimuli which was presented in gradation of complexity and the stimuli was inclusive of frequent and infrequent nouns and verbs. The tasks involved the participants to judge the correctness of the sentence and simultaneously remember the last word of each sentence and the participants are instructed to recall the words at the end of each set. The results indicated no significant difference between sequential and simultaneous bilinguals in processing the nouns and verbs, and this could be attributed to the proficiency level of the participants in L1 and the alike cognitive abilities between the groups. And recall of nouns was better compared to verbs, maybe because of the complex argument structure involved in verbs. Similarly, authors found a frequency of occurrence of nouns and verbs also had an effect on WM abilities. The difference was also found across gradation due to the load imposed on the central executive function and phonological loop.

Keywords: bilinguals, nouns, verbs, working memory

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Authors: Jae-Il Jeong, Hoon-Jae Park, Jung-Woo Cho, Yang-Gon Kim, Jin-Young Park, Joo-Young Oh, Si-Geun Choi, Seock-Sam Kim, Young Tae Cho, Chan Gyu Kim, Jong-Hyoung Kim

Abstract:

Bearing component has strongly required to decrease vibration and wear to achieve high durability and life time. In the industry field, bearing durability is improved by surface treatment on the bearing surface by centrifugal casting or gravity casting production method. However, this manufacturing method has caused problems such as long processing time, defect rate, and health harmful effect. To solve this problem, there is a laser cladding deposition treatment, which provides fast processing and food adhesion. Therefore, optimum conditions of white metal laser deposition should be studied to minimize bearing contact axis wear using laser cladding techniques. In this study, we deposit a soft white metal layer on SCM440, which is mainly used for shaft and bolt. On laser deposition process, the laser power and powder feed rate and laser head speed factors are controlled to find out the optimal conditions. We also measure hardness using micro Vickers, analyze FE-SEM (Field Emission Scanning Electron Microscope) and EDS (Energy Dispersive Spectroscopy) to study the mechanical properties and surface characteristics with various parameters change. Furthermore, this paper suggests the optimum condition of laser cladding deposition to apply in industrial fields. This work was supported by the Industrial Innovation Project of the Korea Evaluation Institute of Industrial Technology (KEIT) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (Research no. 10051653).

Keywords: laser deposition, bearing, white metal, mechanical properties

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Authors: Yonggu Jang, Jisong Ryu, Woosik Lee

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The study aims to address the limitations of existing underground facility exploration equipment in terms of exploration depth range, relative depth measurement, data processing time, and human-centered ground penetrating radar image interpretation. The study proposed the use of 3D absolute positioning technology to develop a precision underground facility exploration system. The aim of this study is to establish a precise exploration system for underground facilities based on 3D absolute positioning technology, which can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The study developed software and hardware technologies to build the precision exploration system. The software technologies developed include absolute positioning technology, ground surface location synchronization technology of GPR exploration equipment, GPR exploration image AI interpretation technology, and integrated underground space map-based composite data processing technology. The hardware systems developed include a vehicle-type exploration system and a cart-type exploration system. The data was collected using the developed exploration system, which employs 3D absolute positioning technology. The GPR exploration images were analyzed using AI technology, and the three-dimensional location information of the explored precise underground facilities was compared to the integrated underground space map. The study successfully developed a precision underground facility exploration system based on 3D absolute positioning technology. The developed exploration system can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The system comprises software technologies that build a 3D precise DEM, synchronize the GPR sensor's ground surface 3D location coordinates, automatically analyze and detect underground facility information in GPR exploration images and improve accuracy through comparative analysis of the three-dimensional location information, and hardware systems, including a vehicle-type exploration system and a cart-type exploration system. The study's findings and technological advancements are essential for underground safety management in Korea. The proposed precision exploration system significantly contributes to establishing precise location information of underground facility information, which is crucial for underground safety management and improves the accuracy and efficiency of exploration. The study addressed the limitations of existing equipment in exploring underground facilities, proposed 3D absolute positioning technology-based precision exploration system, developed software and hardware systems for the exploration system, and contributed to underground safety management by providing precise location information. The developed precision underground facility exploration system based on 3D absolute positioning technology has the potential to provide accurate and efficient exploration of underground facilities up to a depth of 5m. The system's technological advancements contribute to the establishment of precise location information of underground facility information, which is essential for underground safety management in Korea.

Keywords: 3D absolute positioning, AI interpretation of GPR exploration images, complex data processing, integrated underground space maps, precision exploration system for underground facilities

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Mafawez Alharbi, Mahdi Jemmali

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E-learning is a developing area especially in education. E-learning can provide several benefits to learners. An intelligent system to collect all components satisfying user preferences is so important. This research presents an approach that it capable to personalize e-information and give the user their needs following their preferences. This proposal can make some knowledge after more evaluations made by the user. In addition, it can learn from the habit from the user. Finally, we show a walk-through to prove how intelligent process work.

Keywords: artificial intelligence, architecture, e-learning, software engineering, processing

Procedia PDF Downloads 171

Authors: Marianna Maiaru, Gregory M. Odegard

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During the processing of high-performance thermoset polymer matrix composites, chemical reactions occur during elevated pressure and temperature cycles, causing the constituent monomers to crosslink and form a molecular network that gradually can sustain stress. As the crosslinking process progresses, the material naturally experiences a gradual shrinkage due to the increase in covalent bonds in the network. Once the cured composite completes the cure cycle and is brought to room temperature, the thermal expansion mismatch of the fibers and matrix cause additional residual stresses to form. These compounded residual stresses can compromise the reliability of the composite material and affect the composite strength. Composite process modeling is greatly complicated by the multiscale nature of the composite architecture. At the molecular level, the degree of cure controls the local shrinkage and thermal-mechanical properties of the thermoset. At the microscopic level, the local fiber architecture and packing affect the magnitudes and locations of residual stress concentrations. At the macroscopic level, the layup sequence controls the nature of crack initiation and propagation due to residual stresses. The goal of this research is use molecular dynamics (MD) and finite element analysis (FEA) to predict the residual stresses in composite laminates and the corresponding effect on composite failure. MD is used to predict the polymer shrinkage and thermomechanical properties as a function of degree of cure. This information is used as input into FEA to predict the residual stresses on the microscopic level resulting from the complete cure process. Virtual testing is subsequently conducted to predict strength allowables. Experimental characterization is used to validate the modeling.

Keywords: molecular dynamics, finite element analysis, processing modeling, multiscale modeling

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Authors: M. Shahin Alam, Satoru Takahashi, Mariko Itoh, Miyuki Komura, Mayuko Suzuki, Natthanan Sangsriratanakul, Kazuaki Takehara

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Cleaning and disinfection are key components of routine biosecurity in livestock farming and food processing industry. The usage of suitable disinfectants and their proper concentration are important factors for a successful biosecurity program. Disinfectants have optimum bactericidal and virucidal efficacies at temperatures above 20°C, but very few studies on application and effectiveness of disinfectants at low temperatures have been done. In the present study, the bactericidal efficacies of food additive grade calcium hydroxide (FdCa(OH)), quaternary ammonium compound (QAC) and their mixture, were investigated under different conditions, including time, organic materials (fetal bovine serum: FBS) and temperature, either in suspension or in carrier test. Salmonella Infantis and Escherichia coli, which are the most prevalent gram negative bacteria in commercial poultry housing and food processing industry, were used in this study. Initially, we evaluated these disinfectants at two different temperatures (4°C and room temperature (RT) (25°C ± 2°C)) and 7 contact times (0, 5 and 30 sec, 1, 3, 20 and 30 min), with suspension tests either in the presence or absence of 5% FBS. Secondly, we investigated the bactericidal efficacies of these disinfectants by carrier tests (rubber, stainless steel and plastic) at same temperatures and 4 contact times (30 sec, 1, 3, and 5 min). Then, we compared the bactericidal efficacies of each disinfectant within their mixtures, as follows. When QAC was diluted with redistilled water (dW2) at 1: 500 (QACx500) to obtain the final concentration of didecyl-dimethylammonium chloride (DDAC) of 200 ppm, it could inactivate Salmonella Infantis within 5 sec at RT either with or without 5% FBS in suspension test; however, at 4°C it required 30 min in presence of 5% FBS. FdCa(OH)2 solution alone could inactivate bacteria within 1 min both at RT and 4°C even with 5% FBS. While FdCa(OH)2 powder was added at final concentration 0.2% to QACx500 (Mix500), the mixture could inactivate bacteria within 30 sec and 5 sec, respectively, with or without 5% FBS at 4°C. The findings from the suspension test indicated that low temperature inhibited the bactericidal efficacy of QAC, whereas Mix500 was effective, regardless of short contact time and low temperature, even with 5% FBS. In the carrier test, single disinfectant required bit more time to inactivate bacteria on rubber and plastic surfaces than on stainless steel. However, Mix500 could inactivate S. Infantis on rubber, stainless steel and plastic surfaces within 30 sec and 1 min, respectively, at RT and 4°C; but, for E. coli, it required only 30 sec at both temperatures. So, synergistic effects were observed on different carriers at both temperatures. For a successful enhancement of biosecurity during winter, the disinfectants should be selected that could have short contact times with optimum efficacy against the target pathogen. The present study findings help farmers to make proper strategies for application of disinfectants in their livestock farming and food processing industry.

Keywords: carrier, food additive grade calcium hydroxide (FdCa(OH)₂), quaternary ammonium compound, synergistic effects

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Authors: Paula Lopez-Arce, Hector Altamirano, Dimitrios Rovas, James Berry, Bryan Hindle, Steven Hodgson

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Efficient mould remediation and accurate moisture diagnostics leading to condensation and mould growth in dwellings are largely untapped. Number of factors are contributing to the rising trend of excessive moisture in homes mainly linked with modern living, increased levels of occupation and rising fuel costs, as well as making homes more energy efficient. Environmental monitoring by means of data collection though loggers sensors and survey forms has been performed in a range of buildings from different UK regions. Air and surface temperature and relative humidity values of residential areas affected by condensation and/or mould issues were recorded. Additional measurements were taken through different trials changing type, location, and position of loggers. In some instances, IR thermal images and ventilation rates have also been acquired. Results have been interpreted together with environmental key parameters by processing and connecting data from loggers and survey questionnaires, both in buildings with and without moisture issues. Monitoring exercises carried out during Winter and Spring time show the importance of developing and following accurate protocols for guidance to obtain consistent, repeatable and comparable results and to improve the performance of environmental monitoring. A model and a protocol are being developed to build a diagnostic tool with the goal of performing a simple but precise residential atmospheric moisture diagnostics to distinguish the cause entailing condensation and mould generation, i.e., ventilation, insulation or heating systems issue. This research shows the relevance of monitoring and processing environmental data to assign moisture risk levels and determine the origin of condensation or mould when dealing with a building atmospheric moisture excess.

Keywords: environmental monitoring, atmospheric moisture, protocols, mould

Procedia PDF Downloads 123

Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Ozan Kahraman, Hao Feng

Abstract:

Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

Procedia PDF Downloads 348