Search results for: wound classification

Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

Abstract:

This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

Procedia PDF Downloads 121

Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

Procedia PDF Downloads 389

Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

Procedia PDF Downloads 123

Authors: Harsha B. R., K. S. Anil Kumar

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Land characterization, classification, and soil suitability evaluation of grapes-growing pedons were assessed at fifteen taluks covering four agro climatic zones of Karnataka. Study on problems and potentials of grapes cultivation in selected agro-climatic zones was carried out along with the plant sample analysis. Twenty soil profiles were excavated as study site based on the dominance of area falling under grapes production and existing spatial variability of soils. The detailed information of profiles and horizon wise soil samples were collected to study the morphological, physical, chemical, and fertility characteristics. Climatic analysis and water retention characteristics of soils of major grapes-growing areas were also done. Based on the characterisation and classification study, it was revealed that soils of Doddaballapur (Bangalore Blue and Wine grapes), Bangalore North (GKVK Farm, Rajankunte, and IIHR Farm), Devanahalli, Magadi, Hoskote, Chikkaballapur (Dilkush and Red globe), Yelaburga, Hagari Bommanahalli, Bagalkot (UHS farm) and Indi fall under the soil order Alfisol. Vijaypur pedon of northern dry zone was keyed out as Vertisols whereas, Jamkhandi and Athani as Inceptisols. Properties of Aridisols were observed in B. Bagewadi (Manikchaman and Thompson Seedless) and Afzalpur. Soil fertility status and its mapping using GIS technique revealed that all the nutrients were found to be in adequate range except nitrogen, potassium, zinc, iron, and boron, which indicated the need for application along with organic matter to improve the SOC status. Varieties differed among themselves in yield and plant nutrient composition depending on their age, climatic, soil, and management requirements. Bangalore North (GKVK farm) and Jamkhandi are having medium soil organic carbon stocks of 6.21 and 6.55 kg m⁻³, respectively. Soils of Bangalore North (Rajankunte) were highly suitable (S1) for grapes cultivation. Under northern Karnataka, Vijayapura, B. Bagewadi, Indi, and Afzalpur vineyards were good performers despite the limitations of fertility and free lime content.

Keywords: land characterization, suitability, soil orders, soil organic carbon stock

Procedia PDF Downloads 106

Authors: Nassima Noufail, Sara Bouhali

Abstract:

In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

Procedia PDF Downloads 69

Authors: K. Petcharaporn

Abstract:

The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

Procedia PDF Downloads 359

Authors: Muhammad Al Rizqi Dharma Fauzi, Dwi Yulian Fahruddin Shah, Andre Pratama, Ari Hasna Widyapuspa, Ganden Supriyanto

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Diabetes mellitus is still known as one of diseases which give a big amount of death in the world. From 2012 to 2014, diabetes is estimated to have resulted in 1.5 to 4.9 million deaths each year. In this paper, we present our research in the analysis and dose determination of Tenebrio molitor (Mealworm) extract as an anti-diabetic agent which is believed by Indonesian people as a traditional treatment to prevent and treat diabetes. We found that Tenebrio molitor extract has a potential as an anti-diabetic agent by in vitro test to Mus musculus which were divided into six group of treatment. Our dose determination analysis gave a conclusion that at 2,5 g/mL of concentration of the extract would give the optimal result in healing a wound given to Mus musculus which were induced by aloxane monohydrate. These results show that Tenebrio molitor extract is potential to be used as an Anti-Diabetic agent.

Keywords: diabetes, extraction, Tenebrio molitor, traditional medicine

Procedia PDF Downloads 386

Authors: Iannello G., Coppa F., Pennisi S., Giuffrida G., Lo Faro R., Cartelli S., Ferruggia G., Brundo M. V.

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Skin is the largest multifunctional human organ and possesses a complex, multilayered structure with the ability to regenerate and renew. The key role in skin regeneration is played by fibroblasts, which also occupy an important role in the wound healing process. Different methods, including dynamic light scattering, scanning electron microscopy, ELISA, and MTT assay, were employed to evaluate on fibroblasts the in vitro effects of plant-derived nanovesicles and cord blood stem cells‐derived exosomes. We compared the results with those of cells exposed to a technology called AMPLEX PLUS, containing a mixture of 20 different biologically active factors (GF20) and exosomes isolated and purified from bovine colostrum. AMPLEX PLUS was able to significantly enhance the cell proliferation status of cells at both 24 and 48 hours compared to untreated cells (control). The obtained results suggest how AMPLEX PLUS could be potentially effective in treating skin rejuvenation.

Keywords: AMPLEX PLUS, cell vitality, colostrum, nanovesicles

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

Procedia PDF Downloads 300

Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

Procedia PDF Downloads 54

Authors: K. Petcharaporn, N. Prathengjit

Abstract:

The external characteristics of tomatoes, such as freshness, color and size are typically used in quality control processes for tomatoes sorting. However, the internal mold infection of intact tomato cannot be sorted based on a visible assessment and destructive method alone. In this study, a non-destructive technique was used to predict the internal mold infection of intact tomatoes by using transmittance visible and near infrared (VIS-NIR) spectroscopy. Spectra for 200 samples contained 100 samples for normal tomatoes and 100 samples for mold infected tomatoes were acquired in the wavelength range between 665-955 nm. This data was used in conjunction with partial least squares-discriminant analysis (PLS-DA) method to generate a classification model for tomato quality between groups of internal mold infection of intact tomato samples. For this task, the data was split into two groups, 140 samples were used for a training set and 60 samples were used for a test set. The spectra of both normal and internally mold infected tomatoes showed different features in the visible wavelength range. Combined spectral pretreatments of standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) gave the optimal calibration model in training set, 85.0% (63 out of 71 for the normal samples and 56 out of 69 for the internal mold samples). The classification accuracy of the best model on the test set was 91.7% (29 out of 29 for the normal samples and 26 out of 31 for the internal mold tomato samples). The results from this experiment showed that transmittance VIS-NIR spectroscopy can be used as a non-destructive technique to predict the internal mold infection of intact tomatoes.

Keywords: tomato, mold, quality, prediction, transmittance

Procedia PDF Downloads 515

Authors: T. Garba, Y. Y. Sabo A. Babanyara, K. G. Ilellah, A. K. Mutari

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This study used the Normalized Difference Vegetation Index (NDVI) and maps compiled from the classification of Landsat TM and Landsat ETM images of 1986 and 1999 respectively and Nigeria sat 1 images of 2007 to quantify changes in land use and land cover in selected areas of Nigeria covering 143,609 hectares that are threatened by the encroaching Sahara desert. The results of this investigation revealed a decrease in natural vegetation over the three time slices (1986, 1999 and 2007) which was characterised by an increase in high positive pixel values from 0.04 in 1986 to 0.22 and 0.32 in 1999 and 2007 respectively and, a decrease in natural vegetation from 74,411.60ha in 1986 to 28,591.93ha and 21,819.19ha in 1999 and 2007 respectively. The same results also revealed a periodic trend in which there was progressive increase in the cultivated area from 60,191.87ha in 1986 to 104,376.07ha in 1999 and a terminal decrease to 88,868.31ha in 2007. These findings point to expansion of vegetated and cultivated areas in in the initial period between 1988 and 1996 and reversal of these increases in the terminal period between 1988 and 1996. The study also revealed progressive expansion of built-up areas from 1, 681.68ha in 1986 to 2,661.82ha in 1999 and to 3,765.35ha in 2007. These results argue for the urgent need to protect and conserve the depleting natural vegetation by adopting sustainable human resource use practices i.e. intensive farming in order to minimize persistent depletion of natural vegetation.

Keywords: changes, classification, desertification, vegetation changes

Procedia PDF Downloads 378

Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Yeliz Karaca, Carlo Cattani

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This study focuses on the local Holder exponent as a measure of the function regularity for time series related to finance data. In this study, the attributes of the finance dataset belonging to 13 countries (India, China, Japan, Sweden, France, Germany, Italy, Australia, Mexico, United Kingdom, Argentina, Brazil, USA) located in 5 different continents (Asia, Europe, Australia, North America and South America) have been examined.These countries are the ones mostly affected by the attributes with regard to financial development, covering a period from 2012 to 2017. Our study is concerned with the most important attributes that have impact on the development of finance for the countries identified. Our method is comprised of the following stages: (a) among the multi fractal methods and Brownian motion Holder regularity functions (polynomial, exponential), significant and self-similar attributes have been identified (b) The significant and self-similar attributes have been applied to the Artificial Neuronal Network (ANN) algorithms (Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP)) (c) the outcomes of classification accuracy have been compared concerning the attributes that have impact on the attributes which affect the countries’ financial development. This study has enabled to reveal, through the application of ANN algorithms, how the most significant attributes are identified within the relevant dataset via the Holder functions (polynomial and exponential function).

Keywords: artificial neural networks, finance data, Holder regularity, multifractals

Procedia PDF Downloads 240

Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Zegrar Ahmed, Ghabi Mohamed

Abstract:

The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

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Authors: Syazwani Ramli, Norhidayu Muhamad Zain

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Nowadays, the use of gelatin as biomaterials in tissue engineering are evolving especially in skin graft and wound dressing applications. Towards year 2018, Malaysia is in the way of planning to get the halal certification for biomedical device in order to cater the needs of Muslims and non-Muslims in Malaysia. However, the use of gelatins in tissue engineering are mostly derived from non-halal sources. Currently, gelatin production mostly comes from mammalian gelatin sources. Moreover, within these past years, just a few studies of the uses of gelatin in tissue engineering from halal perspective has been studied. Thus, this paper aims to give overview of the use of gelatin from different sources from halal perspectives. This review also discussing the current status of halal for the emerging biomedical devices. In addition, the different sources of gelatin used in tissue engineering are being identified and provides better alternatives for halal gelatin. Cold- water fish skin gelatin could be an effective alternative to substitute the mammalian sources. Therefore, this review is important because the information about the halal biomedical devices will delighted Muslim consumers and give better insight of halal gelatin in tissue engineering application.

Keywords: biomedical device, gelatin, halal, skin graft, tissue engineering

Procedia PDF Downloads 265

Authors: Nino Kupatadze, Mzevinar Bedinashvili, Tamar Memanishvili, Manana Gurielidze, David Tugushi, Ramaz Katsarava

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Bacteria easily colonize the surfaces of tissues, surgical devices (implants, orthopedics, catheters, etc.), and instruments causing surgical device related infections. Therefore, the battle against bacteria and the prevention of surgical devices from biofilm formation is one of the main challenges of biomedicine today. Our strategy to the solution of this problem consists in using antimicrobial polymeric coatings as effective “shields” to protect surfaces from bacteria’s colonization and biofilm formation. As one of the most promising approaches look be the use of antimicrobial bioerodible polymeric nanocomposites containing silver nanoparticles (AgNPs). We assume that the combination of an erodible polymer with a strong bactericide should put obstacles to bacteria to occupy the surface and to form biofilm. It has to be noted that this kind of nanocomposites are also promising as wound dressing materials to treat infected superficial wounds. Various synthetic and natural polymers were used for creating biocomposites containing AgNPs as both particles' stabilizers and matrices forming elastic films at surfaces. One of the most effective systems to fabricate AgNPs is an ethanol solution of polyvinylpyrrolidone(PVP) with dissolved AgNO3–ethanol serves as a AgNO3 reductant and PVP as AgNPs stabilizer (through the interaction of nanoparticles with nitrogen atom of the amide group). Though PVP is biocompatible and film-forming polymer, it is not a good candidate to design either "biofilm shield" or wound dressing material because of a high solubility in water – though the solubility of PVP provides the desirable release of AgNPs from the matrix, but the coating is easily washable away from the surfaces. More promising as matrices look water insoluble but bioerodible polymers that can provide the release of AgNPs and form long-lasting coatings at the surfaces. For creating bioerodible water-insoluble antimicrobial coatings containing AgNPs, we selected amino acid based biodegradable polymers(AABBPs)–poly(ester amide)s, poly(ester urea)s, their copolymers containing amide and related groups capable to stabilize AgNPs. Among a huge variety of AABBPs reported we selected the polymers soluble in ethanol. For preparing AgNPs containing nanocompositions AABBPs and AgNO3 were dissolved in ethanol and subjected to photochemical reduction using daylight-irradiation. The formation of AgNPs was observed visually by coloring the solutions in brownish-red. The obtained AgNPs were characterized by UV-spectroscopy, transmission electron microscopy(TEM), and dynamic light scattering(DLS). According to the UV and TEM data, the photochemical reduction resulted presumably in spherical AgNPs with rather high contribution of the particles below 10 nm that are known as responsible for the antimicrobial activity. DLS study showed that average size of nanoparticles formed after photo-reduction in ethanol solution ranged within 50 nm. The in vitro antimicrobial activity study of the new nanocomposite material is in progress now.

Keywords: nanocomposites, silver nanoparticles, polymer, biodegradable

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Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

Procedia PDF Downloads 59

Authors: Anitha Muralidhara, Victor Engelen, Christophe Len, Pascal Pandard, Guy Marlair

Abstract:

Production of bio-based chemicals and materials from lignocellulosic biomass is gaining tremendous importance in advanced bio-refineries while aiming towards progressive replacement of petroleum based chemicals in transportation fuels and commodity polymers. One such attempt has resulted in the production of key furan derivatives (FD) such as furfural, HMF, MMF etc., via acid catalyzed dehydration (ACD) of C6 and C5 sugars, which are further converted into key chemicals or intermediates (such as Furandicarboxylic acid, Furfuryl alcohol etc.,). In subsequent processes, many high potential FD are produced, that can be converted into high added value polymers or high energy density biofuels. During ACD, an unavoidable polyfuranic byproduct is generated which is called humins. The family of FD is very large with varying chemical structures and diverse physicochemical properties. Accordingly, the associated risk profiles may largely vary. Hazardous Material (Haz-mat) classification systems such as GHS (CLP in the EU) and the UN TDG Model Regulations for transport of dangerous goods are one of the preliminary requirements for all chemicals for their appropriate classification, labelling, packaging, safe storage, and transportation. Considering the growing application routes of FD, it becomes important to notice the limited access to safety related information (safety data sheets available only for famous compounds such as HMF, furfural etc.,) in these internationally recognized haz-mat classification systems. However, these classifications do not necessarily provide information about the extent of risk involved when the chemical is used in any specific application. Factors such as thermal stability, speed of combustion, chemical incompatibilities, etc., can equally influence the safety profile of a compound, that are clearly out of the scope of any haz-mat classification system. Irrespective of the bio-based origin, FD has so far received inconsistent remarks concerning their toxicity profiles. With such inconsistencies, there is a fear that, a large family of FD may also follow extreme judgmental scenarios like ionic liquids, by ranking some compounds as extremely thermally stable, non-flammable, etc., Unless clarified, these messages could lead to misleading judgements while ranking the chemical based on its hazard rating. Safety is a key aspect in any sustainable biorefinery operation/facility, which is often underscored or neglected. To fill up these existing data gaps and to address ambiguities and discrepancies, the current study focuses on giving preliminary insights on safety assessment of FD and their potential targeted by-products. With the available information in the literature and obtained experimental results, physicochemical safety, environmental safety as well as (a scenario based) fire safety profiles of key FD, as well as side streams such as humins and levulinic acid, will be considered. With this, the study focuses on defining patterns and trends that gives coherent safety related information for existing and newly synthesized FD in the market for better functionality and sustainable applications.

Keywords: furanics, humins, safety, thermal and fire hazard, toxicity

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Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

Abstract:

Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

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Authors: W. Chatan, W. Promprom

Abstract:

Phu Langka National Park is located in Nakhon Phanom Province, the Northeast of Thailand. It contains about 50 km2 of one mountain and three types of forest including deciduous dipterocarp, mixed deciduous and dry evergreen forests. It was interesting area because of that there were some local ethnic groups living around the national park and most people use plants in this area for their life. The objective of this research is to preliminary survey of the use of medicinal plants from this area by local ethnic groups living around the national park. Colour photographs of each species were prepared. In addition, ecology, distribution in the study area, utilization and vernacular names were provided. The result showed that sixteen species of medicinal plant species were found and most plants were used for digestive system and wound. The voucher specimens were deposited in the Forest Herbarium, Department of National Parks, Wildlife and Plant Conservation (BKF), Thailand.

Keywords: diversity, ethnobotany, ethnophamacology, taxonomy, utilization

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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

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

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Authors: Kajal Zarei

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Supply chain management is becoming an increasingly important issue in many businesses today. In such circumstances, a number of reasons such as management deficiency in different segments of the supply chain, lack of streamlined processes, resistance to change the current systems and technologies, and lack of advanced information system have paved the ground to ask for innovative research studies. To this end, information technology (IT) is becoming a major driver to overcome the supply chain limitations and deficiencies. The emergence of IT has provided an excellent opportunity for redefining the supply chain to be more effective and competitive. This paper has investigated the IT impact on two-digit industry codes in the International Standard Industrial Classification (ISIC) that are operating in four groups of the supply chains. Firstly, the primary fields of the supply chain were investigated, and then paired comparisons of different industry parts were accomplished. Using experts' ideas and Analytical Hierarchy Process (AHP), the status of industrial activities in Kurdistan Province in Iran was determined. The results revealed that manufacturing and inventory fields have been more important compared to other fields of the supply chain. In addition, IT has had greater impact on food and beverage industry, chemical industry, wood industry, wood products, and production of basic metals. The results indicated the need to IT awareness in supply chain management; in other words, IT applications needed to be developed for the identified industries.

Keywords: supply chain, information technology, analytical hierarchy process, two-digit codes, international standard industrial classification

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Authors: Kamakshaiah Kolli, R. Seshadri

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Water is a key resource in all economic activities ranging from agriculture to industry. Only a tiny fraction of the planet's abundant water is available to us as fresh water. Assessment of water quality has always been paramount in the field of environmental quality management. It is the foundation for health, hygiene, progress and prosperity. With ever increasing pressure of human population, there is severe stress on water resources. Therefore efficient water management is essential to civil society for betterment of quality of life. The present study emphasizes on the groundwater quality, sources of ground water contamination, variation of groundwater quality and its spatial distribution. The bases for groundwater quality assessment are groundwater bodies and representative monitoring network enabling determination of chemical status of groundwater body. For this study, water samples were collected from various areas of the entire corporation area of Guntur. Water is required for all living organisms of which 1.7% is available as ground water. Water has no calories or any nutrients, but essential for various metabolic activities in our body. Chemical and physical parameters can be tested for identifying the portability of ground water. Electrical conductivity, pH, alkalinity, Total Alkalinity, TDS, Calcium, Magnesium, Sodium, Potassium, Chloride, and Sulphate of the ground water from Guntur district: Different areas of the District were analyzed. Our aim is to check, if the ground water from the above areas are potable or not. As multivariate are present, Data mining technique using JRIP rules was employed for classifying the ground water.

Keywords: groundwater, water quality standards, potability, data mining, JRIP, PCA, classification

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Authors: Manal Hassan Abdel Aziz, Fatma Mohamed Magdy Badr El Dine, Nourhan Mohamed Mohamed Saeed

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Establishing association between lip print patterns and those of fingerprints as well as blood groups is of fundamental importance in the forensic identification domain. The first aim of the current study was to determine the prevalent types of ABO blood groups, lip prints and fingerprints patterns in both studied populations. Secondly, to analyze any relation found between the different print patterns and the blood groups, which would be valuable in identification purposes. The present study was conducted on 60 healthy volunteers, (30 males and 30 females) from each of the studied population. Lip prints and fingerprints were obtained and classified according to Tsuchihashi's classification and Michael Kuchen’s classification, respectively. The results show that the ulnar loop was the most frequent among both populations. Blood group A was the most frequent among Egyptians, while blood groups O and B were the predominant among Malaysians. Significant relations were observed between lip print patterns and fingerprint (in the second quadrant for Egyptian males and the first one for Malaysian). For Malaysian females, a statistically significant association was proved in the fourth quadrant. Regarding the blood groups, 89.5% of ulnar loops were significantly related to blood group A among Egyptian males. The results proved an association between the fingerprint pattern and the lip prints, as well as between the ABO blood group and the pattern of fingerprints. However, further researches with larger sample sizes need to be directed to approve the current results.

Keywords: ABO, cheiloscopy, dactylography, Egyptians, Malaysians

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Authors: Iliana Georganta, Clare Deehan, Marysia Thomson, Miriam McDonald, Kerrie McNulty, Anna Strachan, Elizabeth Anderson, Alyaa Mostafa

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Context: A meta-analysis of randomized controlled trials (RCTs) comparing hysterectomy versus endometrial ablation and resection in the management of heavy menstrual bleeding. Objective: To evaluate the clinical efficacy, satisfaction rates and adverse events of hysterectomy compared to more minimally invasive techniques in the treatment of HMB. Evidence Acquisition: A literature search was performed for all RCTs and quasi-RCTs comparing hysterectomy with either endometrial ablation endometrial resection of both. The search had no language restrictions and was last updated in June 2020 using MEDLINE, EMBASE, Cochrane Central Register of Clinical Trials, PubMed, Google Scholar, PsycINFO, Clinicaltrials.gov and Clinical trials. EU. In addition, a manual search of the abstract databases of the European Haemophilia Conference on women's health was performed and further studies were identified from references of acquired papers. The primary outcomes were patient-reported and objective reduction in heavy menstrual bleeding up to 2 years and after 2 years. Secondary outcomes included satisfaction rates, pain, adverse events short and long term, quality of life and sexual function, further surgery, duration of surgery and hospital stay and time to return to work and normal activities. Data were analysed using RevMan software. Evidence synthesis: 12 studies and a total of 2028 women were included (hysterectomy: n = 977 women vs endometrial ablation or resection: n = 1051 women). Hysterectomy was compared with endometrial ablation only in five studies (Lin, Dickersin, Sesti, Jain, Cooper) and endometrial resection only in five studies (Gannon, Schulpher, O’Connor, Crosignani, Zupi) and a mixture of the Ablation and Resection in two studies (Elmantwe, Pinion). Of the 1² studies, 10 reported women’s perception of bleeding symptoms as improved. Meta-analysis showed that women in the hysterectomy group were more likely to show improvement in bleeding symptoms when compared with endometrial ablation or resection up to 2-year follow-up (RR 0.75, 95% CI 0.71 to 0.79, I² = 95%). Objective outcomes of improvement in bleeding also favored hysterectomy. Patient satisfaction was higher after hysterectomy within the 2 years follow-up (RR: 0.90, 95%CI: 0.86 to 0.94, I²:58%), however, there was no significant difference between the two groups at more than 2 years follow up. Sepsis (RR: 0.03, 95% CI 0.002 to 0.56; 1 study), wound infection (RR: 0.05, 95% CI: 0.01 to 0.28, I²: 0%, 3 studies) and Urinary tract infection (UTI) (RR: 0.20, 95% CI: 0.10 to 0.42, I²: 0%, 4 studies) all favoured hysteroscopic techniques. Fluid overload (RR: 7.80, 95% CI: 2.16 to 28.16, I² :0%, 4 studies) and perforation (RR: 5.42, 95% CI: 1.25 to 23.45, I²: 0%, 4 studies) however favoured hysterectomy in the short term. Conclusions: This meta-analysis has demonstrated that endometrial ablation and endometrial resection are both viable options when compared with hysterectomy for the treatment of heavy menstrual bleeding. Hysteroscopic procedures had better outcomes in the short term with fewer adverse events including wound infection, UTI and sepsis. The hysterectomy performed better when measuring more long-term impacts such as recurrence of symptoms, overall satisfaction at two years and the need for further treatment or surgery.

Keywords: menorrhagia, hysterectomy, ablation, resection

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Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

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The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

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Authors: A. N. Ukwuani, M. G. Abubakar, S. W. Hassan

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Grewia crenata is used locally in the treatment of fractured bones, wound healing and inflammatory conditions. The anti-inflammatory activity of hydromethanolic extract of G. crenata leaves was investigated using egg albumin induced-hind paw oedema model in albino rat. The extract produced a time-dependent inhibition of egg albumin induced-hind paw oedema at 30th minutes up to 150th minutes in all the groups compared to the control. Significant reduction (p<0.05) of hind paw oedema was observed 150 minutes after egg albumin injection. The percentage inhibition produced by the extract at 200 mg/kg (22.1%) was comparable to that produced by 10 mg/kg indomethacin (24.9%) at the 150th minutes of post-egg albumin injection. Preliminary qualitative phytochemical analysis revealed the presence of saponins, steroids, flavonoids, anthraquinones and glycosides. The results obtained in this study suggest that Grewia crenata can be a potential source of anti-inflammatory agent and validates its use in the treatment of inflammatory conditions.

Keywords: Grewia crenata, anti-inflammatory, hind paw, oedema

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Authors: Jairson Barbosa Rodrigues, Paulo Romero Martins Maciel, Germano Crispim Vasconcelos

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This paper presents an investigation of the performance impacts regarding the variation of five factors (input data size, node number, cores, memory, and disks) when applying a distributed implementation of Naïve Bayes for text classification of a large Corpus on the Spark big data processing framework. Problem: The algorithm's performance depends on multiple factors, and knowing before-hand the effects of each factor becomes especially critical as hardware is priced by time slice in cloud environments. Objectives: To explain the functional relationship between factors and performance and to develop linear predictor models for time and cost. Methods: the solid statistical principles of Design of Experiments (DoE), particularly the randomized two-level fractional factorial design with replications. This research involved 48 real clusters with different hardware arrangements. The metrics were analyzed using linear models for screening, ranking, and measurement of each factor's impact. Results: Our findings include prediction models and show some non-intuitive results about the small influence of cores and the neutrality of memory and disks on total execution time, and the non-significant impact of data input scale on costs, although notably impacts the execution time.

Keywords: big data, design of experiments, distributed machine learning, natural language processing, spark

Procedia PDF Downloads 110