Search results for: end-user trained information extraction

Authors: Madiha Khan, Sidrah Ramzan, Seemab Khan, Shahzad Hassan, Kamran Saeed

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Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information.

Keywords: Graphviz representation, semantic relatedness, similarity measurement, WordNet similarity

Procedia PDF Downloads 237

Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

Procedia PDF Downloads 425

Authors: Narayana Murthy Akurathi, Vijaya Lakshmi Marella

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The literature is reviewed with regard to online Super critical fluid extraction (SFE) coupled directly with supercritical fluid chromatography (SFC) -mass spectrometry that have typically more sensitive than conventional LC-MS/MS and GC-MS/MS. It is becoming increasingly interesting to use on-line techniques that combine sample preparation, separation and detection in one analytical set up. This provides less human intervention, uses small amount of sample and organic solvent and yields enhanced analyte enrichment in a shorter time. The sample extraction is performed under light shielding and anaerobic conditions, preventing the degradation of thermo labile analytes. It may be able to analyze compounds over a wide polarity range as SFC generally uses carbon dioxide which was collected as a by-product of other chemical reactions or is collected from the atmosphere as it contributes no new chemicals to the environment. The diffusion of solutes in supercritical fluids is about ten times greater than that in liquids and about three times less than in gases which results in a decrease in resistance to mass transfer in the column and allows for fast high resolution separations. The drawback of SFC when using carbon dioxide as mobile phase is that the direct introduction of water samples poses a series of problems, water must therefore be eliminated before it reaches the analytical column. Hundreds of compounds analysed simultaneously by simple enclosing in an extraction vessel. This is mainly applicable for pharmaceutical industry where it can analyse fatty acids and phospholipids that have many analogues as their UV spectrum is very similar, trace additives in polymers, cleaning validation can be conducted by putting swab sample in an extraction vessel, analysing hundreds of pesticides with good resolution.

Keywords: super critical fluid extraction (SFE), super critical fluid chromatography (SFC), LCMS/MS, GCMS/MS

Procedia PDF Downloads 391

Authors: Andriana Mkrtchyan, Vahe Khlghatyan

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The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

Procedia PDF Downloads 76

Authors: Aliya Kurbanova, Nurlan Akhmetov, Abilmansur Yeshmuratov, Yerzhigit Sugurbekov, Ramiz Zulkharnay, Gulzat Demeuova, Murat Baisariyev, Gulnar Sugurbekova

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Last decades crude oils have tended to become more challenge to process due to increasing amounts of sour and heavy crude oils. Some crude oils contain high vanadium and nickel content, for example Pavlodar LLP crude oil, which contains more than 23.09 g/t nickel and 58.59 g/t vanadium. In this study, we used two types of metal removing methods such as solvent extraction and electrochemical. The present research is conducted for comparative analysis of the deasphalting with organic solvents (cyclohexane, carbon tetrachloride, chloroform) and electrochemical method. Applying the cyclic voltametric analysis (CVA) and Inductively coupled plasma mass spectrometry (ICP MS), these mentioned types of metal extraction methods were compared in this paper. Maximum efficiency of deasphalting, with cyclohexane as the solvent, in Soxhlet extractor was 66.4% for nickel and 51.2% for vanadium content from crude oil. Percentage of Ni extraction reached maximum of approximately 55% by using the electrochemical method in electrolysis cell, which was developed for this research and consists of three sections: oil and protonating agent (EtOH) solution between two conducting membranes which divides it from two capsules of 10% sulfuric acid and two graphite electrodes which cover all three parts in electrical circuit. Ions of metals pass through membranes and remain in acid solutions. The best result was obtained in 60 minutes with ethanol to oil ratio 25% to 75% respectively, current fits into the range from 0.3A to 0.4A, voltage changed from 12.8V to 17.3V.

Keywords: demetallization, deasphalting, electrochemical removal, heavy metals, petroleum engineering, solvent extraction

Procedia PDF Downloads 324

Authors: M. Budakoglu, M. Karaman, D. Kiran, Z. Doner, B. Zeytuncu, B. Tanç, M. Kumral

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Lake Acıgöl is one of the large saline lacustrine environment in Turkey. Eleven trace metals (Cr, Mn, Fe, Al, Co, Ni, Cu, Zn, Cd, Pb and As) in 9 surface and subsurface sediment samples from the Lake Acıgöl were analyzed with the bulk and sequential extraction analysis methods by ICP-MS to obtain the metal distribution patterns in this extreme environment. Five stepped sequential extraction technique (1- exchangeable, 2- bond to carbonates, 3- bond to iron and manganese oxides/hydroxides, 4- bond to organic matter and sulphides, and 5- residual fraction incorporated into clay and silicate mineral lattices) was used to characterize the various forms of metals in the <63μ size sediments. The metal contents (ppm) and their percentages for each extraction step were reported and compared with the results obtained from the total digestion. Results indicate that sum of the four fraction are in good agreement with the total digestion results of Ni, Cd, As, Zn, Cu and Fe with the satisfactory recoveries (94.04–109.0%) and the method used is reliable and repeatable for these elements. It was found that there were high correlations between Fe vs. Ni loads in the fraction of F2 and F4 with R2= 0,91 and 0,81, respectively. Comparison of totally 135 chemical analysis results in three sampling location and for 5 fraction between Fe-Co, Co-Ni and Fe-Ni element couples were presented elevated correlations with R2=0,98, 0,92 and 0,91, respectively.

Keywords: Lake Acigol, sequancial extraction, recent lake sediment, geochemical speciation of heavy metals

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

Procedia PDF Downloads 175

Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

Procedia PDF Downloads 286

Authors: Parvaneh Hajeb, Shahram Shakibazadeh, Md. Zaidul Islam Sarker

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High-quality fish oil for human consumption requires low levels of toxic elements. The aim of this study was to develop a method to extract oil from fish wastes with the least toxic elements contamination. Supercritical fluid extraction (SFE) was applied to detoxify fish oils from toxic elements. The SFE unit used consisted of an intelligent HPLC pump equipped with a cooling jacket to deliver CO2. The freeze-dried fish waste sample was extracted by heating in a column oven. Under supercritical conditions, the oil dissolved in CO2 was separated from the supercritical phase using pressure reduction. The SFE parameters (pressure, temperature, CO2 flow rate, and extraction time) were optimized using response surface methodology (RSM) to extract the highest levels of toxic elements. The results showed that toxic elements in fish oil can be reduced using supercritical CO2 at optimum pressure 40 MPa, temperature 61 ºC, CO2 flow rate 3.8 MPa, and extraction time 4.25 hr. There were significant reductions in the mercury (98.2%), cadmium (98.9%), arsenic (96%), and lead contents (99.2%) of the fish oil. The fish oil extracted using this method contained elements at levels that were much lower than the accepted limits of 0.1 μg/g. The reduction of toxic elements using the SFE method was more efficient than that of the conventional methods due to the high selectivity of supercritical CO2 for non-polar compounds.

Keywords: food safety, toxic elements, fish oil, supercritical carbon dioxide

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Authors: Rammah Abdlbagi, Similoluwa Biyi, Aakash Pai

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Introduction: Short-term ureteric stents are commonly placed after ureteroscopy procedures. The removal usually entails having a flexible cystoscopy, which entails a further invasive procedure. There are often delays in removing the stent as departments have limited cystoscopy availability. However, if stents with extraction strings are used, the patient or a clinician can remove them. The aim of the study is to assess the safety and effectiveness of the use of a stent with a string. Method: A retrospective, single-institution study was conducted over a three-month period. Twenty consecutive patients had ureteric stents with string insertion. Ten of the patients had a stent removal procedure previously with flexible cystoscopy. A validated questionnaire was used to assess outcomes. Primary outcomes included: dysuria, hematuria, urinary frequency, and disturbance of the patient’s daily activities. Secondary outcomes included pain experience during the stent removal. Result: Fifteen patients (75%) experienced hematuria and frequency. Two patients experienced pain and discomfort during the stent removal (10%). Two patients had experienced a disturbance in their daily activity (10%). All patients who had stent removal before using flexible cystoscopy preferred the removal of the stent using a string. None of the patients had stent displacement. The median stent dwell time was five days. Conclusion: Patient reported outcomes measures for the indwelling period of a stent with extraction string are equivalent to the published data on stents. Extraction strings mean that the stent dwell time can be reduced. The removal of the stent on extraction strings is more tolerable than the conventional stent.

Keywords: ureteric stent, string flexible cystoscopy, stent symptoms, validated questionnaire

Procedia PDF Downloads 94

Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

Procedia PDF Downloads 74

Authors: Vanessa De Carvalho, Chad MacPherson, Julien Tremblay, Julie Champagne, Stephanie-Anne Girard

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Background: The interactions between bacteria and fungi in the human vaginal microbiome are fundamental to the concept of health and disease. The means by which the microbiota and mycobiota interact is still poorly understood and further studies are necessary to properly characterize this complex ecosystem. The aim of this study was to select a DNA extraction method capable of recovering high qualities of fungal and bacterial DNA from a single vaginal swab. Methods: 11 female volunteers ( ≥ 20 to < 55 years old) self-collected vaginal swabs in triplicates. Three commercial extraction kits: Masterpure Yeast Purification kit (Epicenter), PureLink™ Microbiome DNA Purification kit (Invitrogen), and Quick-DNA™ Fecal/Soil Microbe Miniprep kit (Zymo) were evaluated on the ability to recover fungal and bacterial DNA simultaneously. The extraction kits were compared on the basis of recovery, yield, purity, and the community richness of bacterial (16S rRNA - V3-V4 region) and fungal (ITS1) microbiota composition by Illumina MiSeq amplicon sequencing. Results: Recovery of bacterial DNA was achieved with all three kits while fungal DNA was only consistently recovered with Masterpure Yeast Purification kit (yield and purity). Overall, all kits displayed similar microbiota profiles for the top 20 OTUs; however, Quick-DNA™ Fecal/Soil Microbe Miniprep kit (Zymo) showed more species richness than the other two kits. Conclusion: In the present study, Masterpure Yeast purification kit proved to be a good candidate for purification of high quality fungal and bacterial DNA simultaneously. These findings have potential benefits that could be applied in future vaginal microbiome research. Whilst the use of a single extraction method would lessen the burden of multiple swab sampling, decrease laboratory workload and off-set costs associated with multiple DNA extractions, thoughtful consideration must be taken when selecting an extraction kit depending on the desired downstream application.

Keywords: bacterial vaginosis, DNA extraction, microbiota, mycobiota, vagina, vulvovaginal candidiasis, women’s health

Procedia PDF Downloads 201

Authors: Orose Rugchati, Khumthong Mahawongwiriya

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Dendrobium hybrid cv. Ear-Sakul has become one of the important commercial commodities in Thailand agricultural industry worldwide, either as potted plants or as cut flowers due to the attractive color produced in flower petals. Anthocyanins are the main flower pigments and responsible for the natural attractive display of petal colors. These pigments play an important role in functionality, such as to attract animal pollinators, classification, and grading of these orchids. Dendrobium hybrid cv. Ear-Sakul has been collected from local area farm in different stage flowers (F1, F2-F5, and F6). Anthocyanins pigment were extracted from the fresh flower by solvent extraction (MeOH–TFA 99.5:0.5v/v at 4ºC) and purification with ethyl acetate. The main anthocyanins components are cyanidin, pelargonidin, and delphinidin. Pure anthocyanin contents were analysis by UV-Visible spectroscopy technique at λ max 535, 520 and 546 nm respectively. The anthocyanins contents were converted in term of monomeric anthocyanins pigment (mg/L). The anthocyanins contents of all sample were compared with standard pigments cyanidin, pelargonidin and delphinidin. From this experiment is a simple extraction and analysis anthocyanins content in different stage of flowers results shown that monomeric anthocyanins pigment contents of different stage flowers (F1, F2-F5 and F6 ): cyanidin – 3 – glucoside (mg/l) are 0.85+0.08, 24.22+0.12 and 62.12+0.6; Pelargonidin 3,5-di- glucoside(mg/l) 10.37+0.12, 31.06+0.8 and 81.58+ 0.5; Delphinidin (mg/l) 6.34+0.17, 18.98+0.56 and 49.87+0.7; and the appearance of extraction pure anthocyanins in L(a, b): 2.71(1.38, -0.48), 1.06(0.39,-0.66) and 2.64(2.71,-3.61) respectively. Dendrobium Hybrid cv. Ear-Sakul could be used as a source of anthocyanins by simple solvent extraction and stage of flowers as a guideline for the prediction amount of main anthocyanins components are cyanidin, pelargonidin, and delphinidin could be application and development in quantities, and qualities with the advantage for food pharmaceutical and cosmetic industries.

Keywords: analysis, anthocyanins contents, different stage flowers, Dendrobium Hybrid cv. Ear-Sakul

Procedia PDF Downloads 150

Authors: X. Wang

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Timely and accurate information on urban building damage caused by earthquake is important basis for disaster assessment and emergency relief. Very high resolution (VHR) remotely sensed imagery containing abundant fine-scale information offers a large quantity of data for detecting and assessing urban building damage in the aftermath of earthquake disasters. However, the accuracy obtained using spectral features alone is comparatively low, since building damage, intact buildings and pavements are spectrally similar. Therefore, it is of great significance to detect urban building damage effectively using multi-source data. Considering that in general height or geometric structure of buildings change dramatically in the devastated areas, a novel multi-stage urban building damage detection method, using bi-temporal spectral, height and corner information, was proposed in this study. The pre-event height information was generated using stereo VHR images acquired from two different satellites, while the post-event height information was produced from airborne LiDAR data. The corner information was extracted from pre- and post-event panchromatic images. The proposed method can be summarized as follows. To reduce the classification errors caused by spectral similarity and errors in extracting height information, ground surface, shadows, and vegetation were first extracted using the post-event VHR image and height data and were masked out. Two different types of building damage were then extracted from the remaining areas: the height difference between pre- and post-event was used for detecting building damage showing significant height change; the difference in the density of corners between pre- and post-event was used for extracting building damage showing drastic change in geometric structure. The initial building damage result was generated by combining above two building damage results. Finally, a post-processing procedure was adopted to refine the obtained initial result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010, using pre-event GeoEye-1 image, pre-event WorldView-2 image, post-event QuickBird image and post-event LiDAR data. The results showed that the method proposed in this study significantly outperformed the two comparative methods in terms of urban building damage extraction accuracy. The proposed method provides a fast and reliable method to detect urban building collapse, which is also applicable to relevant applications.

Keywords: building damage, corner, earthquake, height, very high resolution (VHR)

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

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Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

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Authors: Huang Bai-Cheng

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When deploying the Oriented Fast and Rotated Binary Robust Independent Elementary Feature (BRIEF) (ORB) extraction algorithm on field-programmable gate arrays (FPGA), the access of global storage for 31×31 pixel patches of the features has become the bottleneck of the system efficiency. Therefore, a feature zone strategy has been proposed. Zones are searched as features are detected. Pixels around the feature zones are extracted from global memory and distributed into patches corresponding to feature coordinates. The proposed FPGA structure is targeted on a Xilinx FPGA development board of Zynq UltraScale+ series, and multiple datasets are tested. Compared with the streaming pixel patch extraction method, the proposed architecture obtains at least two times acceleration consuming extra 3.82% Flip-Flops (FFs) and 7.78% Look-Up Tables (LUTs). Compared with the non-streaming one, the proposed architecture saves 22.3% LUT and 1.82% FF, causing a latency of only 0.2ms and a drop in frame rate for 1. Compared with the related works, the proposed strategy and hardware architecture have the superiority of keeping a balance between FPGA resources and performance.

Keywords: feature extraction, real-time, ORB, FPGA implementation

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Authors: Sudip Ghosh, Dipanwita Roy, Dipan Chatterjee, Paramita Bhattacharjee, Satadal Das

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Highest yield of eugenol-rich fractions from Cinnamomum tamala (bay leaf) leaves were obtained by supercritical carbon dioxide (SC-CO2), compared to hydro-distillation, organic solvents, liquid CO2 and subcritical CO2 extractions. Optimization of SC-CO2 extraction parameters was carried out to obtain an extract with maximum eugenol content. This was achieved using a sample size of 10 g at 55°C, 512 bar after 60 min at a flow rate of 25.0 cm3/sof gaseous CO2. This extract has the best combination of phytochemical properties such as phenolic content (1.77 mg gallic acid/g dry bay leaf), reducing power (0.80 mg BHT/g dry bay leaf), antioxidant activity (IC50 of 0.20 mg/ml) and anti-inflammatory potency (IC50 of 1.89 mg/ml). Identification of compounds in this extract was performed by GC-MS analysis and its antimicrobial potency was also evaluated. The MIC values against E. coli, P. aeruginosa and S. aureus were 0.5, 0.25 and 0.5 mg/ml, respectively.

Keywords: antimicrobial potency, Cinnamomum tamala, eugenol, supercritical carbon dioxide extraction

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Birute Bugelyte, Ingrida Jurkute, Vida Vickackaite

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The most complicated step of the determination of volatile compounds in complex matrices is the separation of analytes from the matrix. Traditional analyte separation methods (liquid extraction, Soxhlet extraction) require a lot of time and labour; moreover, there is a risk to lose the volatile analytes. In recent years, headspace gas chromatography has been used to determine volatile compounds. To date, traditional extraction solvents have been used in headspace gas chromatography. As a rule, such solvents are rather volatile; therefore, a large amount of solvent vapour enters into the headspace together with the analyte. Because of that, the determination sensitivity of the analyte is reduced, a huge solvent peak in the chromatogram can overlap with the peaks of the analyts. The sensitivity is also limited by the fact that the sample can’t be heated at a higher temperature than the solvent boiling point. In 2018 it was suggested to replace traditional headspace gas chromatographic solvents with non-volatile, eco-friendly, biodegradable, inexpensive, and easy to prepare deep eutectic solvents (DESs). Generally, deep eutectic solvents have low vapour pressure, a relatively wide liquid range, much lower melting point than that of any of their individual components. Those features make DESs very attractive as matrix media for application in headspace gas chromatography. Also, DESs are polar compounds, so they can be applied for microwave assisted extraction. The aim of this work was to investigate the possibility of applying deep eutectic solvents for microwave assisted extraction and headspace gas chromatographic determination of hexanal in fat-rich food. Hexanal is considered one of the most suitable indicators of lipid oxidation degree as it is the main secondary oxidation product of linoleic acid, which is one of the principal fatty acids of many edible oils. Eight hydrophilic and hydrophobic deep eutectic solvents have been synthesized, and the influence of the temperature and microwaves on their headspace gas chromatographic behaviour has been investigated. Using the most suitable DES, microwave assisted extraction conditions and headspace gas chromatographic conditions have been optimized for the determination of hexanal in potato chips. Under optimized conditions, the quality parameters of the prepared technique have been determined. The suggested technique was applied for the determination of hexanal in potato chips and other fat-rich food.

Keywords: deep eutectic solvents, headspace gas chromatography, hexanal, microwave assisted extraction

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Authors: Georgios Alexandropoulos

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The purpose of this study is to examine the historical text of Alexiad by Anna Comnena using computational tools for the extraction of lexical bundles containing the name of her father, Alexius Comnenus. For this reason, in this research we apply corpus linguistics techniques for the automatic extraction of lexical bundles and through them we will draw conclusions about how these lexical bundles serve her support provided to her father.

Keywords: lexical bundles, computational literature, critical discourse analysis, Alexiad

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Authors: Mohamed Abdullah Ahmed

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In a study of chickpea protein isolate (CPI) preparation, the wet alkaline extraction was carried out. The objectives were to determine the optimal extracting conditions of CPI and apply CPI into a sponge cake recipe to replace egg and make acceptable product. The design used in extraction was a central composite design. The response surface methodology was preferred to graphically express the relationship between extraction time and pH with the output variables of percent yield and protein content of CPI. It was noted that optimal extracting conditions were 60 min and pH 10.5 resulting in 90.07% protein content and 89.15% yield of CPI. The protein isolate (CPI) could be incorporated in cake to 20% without adversely affecting the cake physical properties such as cake hardness and sensory attributes. The higher protein content in cake was corresponding to the amount of CPI added. Therefore, adding CPI can significantly (p<0.05) increase protein content in cake. However, sensory evaluation showed that adding more than 20% of CPI decreased the overall acceptability. The results of this investigation could be used as a basic knowledge of CPI utilization in other food products.

Keywords: chick bean protein isolate, sponge cake, utilization, sponge

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Authors: Muhamad Syafiq Hakimi Kamaruddin, Norhidayah Suleiman

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Ginger extracts (Zingiber officinale Rosc.) have been attributed therapeutic properties primarily as antioxidant, anticancer, and anti-inflammatory properties. Conventional extractions including Soxhlet and maceration are commonly used to extract the bioactive compounds from plant material. Nevertheless, high energy consumption and being non-environmentally friendly are the predominant limitations of the conventional extractions method. Herein, green technology, namely supercritical carbon dioxide (scCO2) extraction, is used to study process variables' effects on extract yields. Herein, green technology, namely supercritical carbon dioxide (scCO2) extraction, is used to study process variables' effects on extract yields. A pressure (10-30 MPa), temperature (40-60 °C), and median particle size (300-600 µm) were conducted at a CO2 flow rate of 0.9 ± 0.2 g/min for 120 mins. The highest overall yield was 4.58% obtained by the scCO2 extraction conditions of 300 bar and 60 °C with 300µm of ginger powder for 120 mins. In comparison, the yield of the extract was increased considerably within a short extraction time. The results show that scCO2 has a remarkable ability over ginger extract and is a promising technology for extracting bioactive compounds from plant material.

Keywords: conventional, ginger, non-environmentally, supercritical carbon dioxide, technology

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Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: Duaa Mughal, Syeda Areeba Nadeem, Shakil Ahmed, Ishtiaq Ahmed Khan

Abstract:

The identification of porcine DNA in Halal food items is critical to ensuring compliance with dietary restrictions and religious beliefs. In Islam, Porcine is prohibited as clearly mentioned in Quran (Surah Al-Baqrah, Ayat 173). The purpose of this study was to compare two DNA extraction procedures for detecting 0.001% of porcine DNA in processed Halal food sample mixtures containing chicken, camel, veal, turkey and goat meat using the TaqMan Real-Time PCR technology. In this research, two different commercial kit protocols were compared. The processed sample mixtures were prepared by spiking known concentration of porcine DNA to non-porcine food matrices. Afterwards, TaqMan Real-Time PCR technique was used to target a particular porcine gene from the extracted DNA samples, which was quantified after extraction. The results of the amplification were evaluated for sensitivity, specificity, and reproducibility. The results of the study demonstrated that two DNA extraction techniques can detect 0.01% of porcine DNA in mixture of Halal food samples. However, as compared to the alternative approach, Eurofins| GeneScan GeneSpin DNA Isolation kit showed more effective sensitivity and specificity. Furthermore, the commercial kit-based approach showed great repeatability with minimal variance across repeats. Quantification of DNA was done by using fluorometric assay. In conclusion, the comparison of DNA extraction methods for detecting porcine DNA in Halal food sample mixes using the TaqMan Real-Time PCR technology reveals that the commercial kit-based approach outperforms the other methods in terms of sensitivity, specificity, and repeatability. This research helps to promote the development of reliable and standardized techniques for detecting porcine DNA in Halal food items, religious conformity and assuring nutritional.

Keywords: real time PCR (qPCR), DNA extraction, porcine DNA, halal food authentication, religious conformity

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Authors: Assma Azeroual, Karim Afdel, Mohamed El Hajji, Hassan Douzi

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Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition.

Keywords: FSDWT, key frame extraction, shot detection, singular value decomposition

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Authors: Junnile Romero, Ilhwan Park, Vannie Joy Resabal, Carlito Tabelin, Richard Alorro, Leaniel Silva, Joshua Zoleta, Takunda Mandu, Kosei Aikawa, Mayumi Ito, Naoki Hiroyoshi

Abstract:

Rare earth elements (REE) play a vital role in technological advancement due to their unique physical and chemical properties essential for various renewable energy applications. However, this increasing demand represents a challenging task for sustainability that corresponds to various research interests relating to the development of various extraction techniques, particularly on the extractant being used. In this study, TK221 (a modified polymer resin containing diglycolamide, carbamoyl methyl phosphine oxide (CMPO), and diglycolamide (DGA-N)) has been investigated as a conjugate extractant. FTIR and SEM analysis results confirmed the presence of CMPO and DGA-N being coated onto the PS-DVB support of TK221. Moreover, the kinetic rate law and adsorption isotherm batch test was investigated to understand the corresponding adsorption mechanism. The results show that REEs’ (Nd, Y, Ce, and Er) obtained pseudo-second-order kinetics and Langmuir isotherm, suggesting that the adsorption mechanism undergoes a single monolayer adsorption site via a chemisorption process. The Qmax values of Nd, Ce, Er, Y, and Fe were 45.249 mg/g, 43.103 mg/g, 35.088 mg/g, 15.552 mg/g, and 12.315 mg/g, respectively. This research further suggests that TK221 polymer resin can be used as an alternative absorbent material for an effective REE extraction.

Keywords: rare earth element, diglycolamide, characterization, extraction resin

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Authors: Jayprakash Yadav, Nivedita Patra

Abstract:

Polyhydroxybutyrate (PHB) is an interesting material in the field of medical science, pharmaceutical industries, and tissue engineering because of its properties such as biodegradability, biocompatibility, hydrophobicity, and elasticity. PHB is naturally accumulated by several microbes in their cytoplasm during the metabolic process as energy reserve material. PHB can be extracted from cell biomass using halogenated hydrocarbons, chemicals, and enzymes. In this study, a cheaper and non-toxic solvent, acetone, was used for the extraction process. The different parameters like acetone percentage, and solvent pH, process temperature, and incubation periods were optimized using the Response Surface Methodology (RSM). RSM was performed and the determination coefficient (R2) value was found to be 0.8833 from the quadratic regression model with no significant lack of fit. The designed RSM model results indicated that the fitness of the response variable was significant (P-value < 0.0006) and satisfactory to denote the relationship between the responses in terms of PHB recovery and purity with respect to the values of independent variables. Optimum conditions for the maximum PHB recovery and purity were found to be solvent pH 7, extraction temperature - 43 °C, incubation time - 70 minutes, and percentage acetone – 30 % from this study. The maximum predicted PHB recovery was found to be 0.845 g/g biomass dry cell weight and the purity was found to be 97.23 % using the optimized conditions.

Keywords: acetone, PHB, RSM, halogenated hydrocarbons, extraction, bacillus subtilis.

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Authors: Raana Babadi Fathipour

Abstract:

Tea, a beverage widely cherished across the globe, has captured the interest of scholars with its recent acknowledgement for possessing noteworthy health advantages. Of particular significance is its proven ability to ward off ailments such as cancer and cardiovascular afflictions. Moreover, within the realm of culinary creations, lipid oxidation poses a significant challenge for food product development. In light of these aforementioned concerns, this present discourse turns its attention towards exploring diverse methodologies employed in extracting polyphenols from various types of tea leaves and examining their utility within the vast landscape of the ever-evolving food industry. Based on the discoveries unearthed in this comprehensive investigation, it has been determined that the fundamental constituents of tea are polyphenols possessed of intrinsic health-enhancing properties. This includes an assortment of catechins, namely epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate. Moreover, gallic acid, flavonoids, flavonols and theaphlavins have also been detected within this aromatic beverage. Of these myriad components examined vigorously in this study's analysis, catechin emerges as particularly beneficial. Multiple techniques have emerged over time to successfully extract key compounds from tea plants, including solvent-based extraction methodologies, microwave-assisted water extraction approaches and ultrasound-assisted extraction techniques. In particular, consideration is given to microwave-assisted water extraction method as a viable scheme which effectively procures valuable polyphenols from tea extracts. This methodology appears adaptable for implementation within sectors such as dairy production along with meat and oil industries alike.

Keywords: camellia sinensis, extraction, food application, shelf life, tea

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Authors: Meenakshi Verma, Mandeep Singh Bakshi, Kultar Singh

Abstract:

Magnetic nanoparticles have received incredible importance in view of their diverse applications, which arise primarily due to their response to the external magnetic field. The magnetic behaviour of magnetic nanoparticles (NPs) helps them in numerous different ways. The most important amongst them is the ease with which they can be purified and also can be separated from the media in which they are present merely by applying an external magnetic field. This exceptional ease of separation of the magnetic NPs from an aqueous media enables them to use for extracting/removing metal pollutants from complex aqueous medium. Functionalized magnetic NPs can be subjected for the metallic impurities extraction if are favourably adsorbed on the NPs surfaces. We have successfully used the magnetic NPs as vehicles for gold and silver NPs removal from the complex fluids. The NPs loaded with gold and silver NPs pollutant fractions has been easily removed from the aqueous media by using external magnetic field. Similarly, we have used the magnetic NPs for extraction of protein from complex media and then constantly washed with pure water to eliminate the unwanted surface adsorbed components for quantitative estimation. The purified and protein loaded magnetic NPs are best analyzed with SDS Page to not only for characterization but also for separating the protein fractions. A collective review of the results indicates that we have synthesized surfactant coated iron oxide NPs and then functionalized these with selected materials. These surface active magnetic NPs work very well for the extraction of metallic NPs from the aqueous bulk and make the whole process environmentally sustainable. Also, magnetic NPs-Au/Ag/Pd hybrids have excellent protein extracting properties. They are much easier to use in order to extract the magnetic impurities as well as protein fractions under the effect of external magnetic field without any complex conventional purification methods.

Keywords: magnetic nanoparticles, protein, functionalized, extraction

Procedia PDF Downloads 99