Search results for: sequential extraction

Authors: Saad M. Darwish, Magda M. Madbouly, Murad B. Khorsheed

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Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper, several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD). SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi attribute hand gesture data to feature vectors. SVD optimally exposes the geometric structure of a matrix. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additive constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 86.21% for cluttered hand images.

Keywords: hand gesture recognition, hand detection, type-2 fuzzy logic, hidden Markov Model

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Authors: Kiran P. Chadayamuri, Saransh Bagdi

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Increasing imbalance between energy supply and demand has made nations and companies involved in the energy sector to boost up their research and find suitable solutions. With the high rates at which conventional oil and gas resources are depleting, efficient exploration and exploitation of heavy oil could just be the answer. Heavy oil may be defined as crude oil having API gravity value of less than 20⁰. They are highly viscous, have low hydrogen to carbon ratios and are known to produce high carbon residues. They have high contents of asphaltenes, heavy metals, sulphur and nitrogen in them. Due to these properties extraction, transportation and refining of crude oil have its share of challenges. Lack of suitable technology has hindered its production in the past, but now things are going in a more positive direction. The aim of this paper is to study the various advantages of heavy oil, associated limitations and its feasibility as a fuel of the future.

Keywords: energy, heavy oil, fuel, future

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Sasidhar B., Bhaskar Rao N., Ramesh Babu D. R., Ravi Shankar M.

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Segmentation of lung pleura is a preprocessing step in Computer-Aided Diagnosis (CAD) which helps in reducing false positives in detection of lung cancer. The existing methods fail in extraction of lung regions with the nodules at the pleura of the lungs. In this paper, a new method is proposed which segments lung regions with nodules at the pleura of the lungs based on curvature analysis and morphological operators. The proposed algorithm is tested on 06 patient’s dataset which consists of 60 images of Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 98.3% average overlap measure (AΩ).

Keywords: curvature analysis, image segmentation, morphological operators, thresholding

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Honggang Qu, Rongmei Liu, Bin Wang, Yong Xu, Zhenji Gao

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There is a big gap in the ability and level of the informatization technology innovation of cores and samples compared with developed countries. Under the current background of promoting the technology innovation, how to strengthen the informatization technology innovation of cores and samples for National Cores and Samples Archives, which is a national innovation research center, is an important research topic. The paper summarizes the development status of cores and samples informatization technology, and finds the gaps and deficiencies, and proposes the innovation research directions and content, including data extraction, recognition, processing, integration, application and so on, so as to provide some reference and guidance for the future innovation research of the archives and support better the geological technology innovation in China.

Keywords: cores and samples;, informatization technology;, innovation;, suggestion

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Authors: Fatima Mokeddem

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The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs.

Keywords: phonocardiogram signal, filtering, Envelope, Detection, murmurs, heart sounds

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Authors: Camille Perréard, Yoann Ladner, Fanny D'Orlyé, Stéphanie Descroix, Vélan Taniga, Anne Varenne, Cédric Guyon, Michael. Tatoulian, Frédéric Kanoufi, Cyrine Slim, Sophie Griveau, Fethi Bedioui

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The development of micro total analysis systems is of major interest for contaminant and biomarker analysis. As a lab-on-chip integrates all steps of an analysis procedure in a single device, analysis can be performed in an automated format with reduced time and cost, while maintaining performances comparable to those of conventional chromatographic systems. Moreover, these miniaturized systems are either compatible with field work or glovebox manipulations. This work is aimed at developing an analytical microsystem for trace and ultra trace quantitation in complex matrices. The strategy consists in the integration of a sample pretreatment step within the lab-on-chip by a confinement zone where selective ligands are immobilized for target extraction and preconcentration. Aptamers were chosen as selective ligands, because of their high affinity for all types of targets (from small ions to viruses and cells) and their ease of synthesis and functionalization. This integrated target extraction and concentration step will be followed in the microdevice by an electrokinetic separation step and an on-line detection. Polymers consisting of cyclic olefin copolymer (COC) or fluoropolymer (Dyneon THV) were selected as they are easy to mold, transparent in UV-visible and have high resistance towards solvents and extreme pH conditions. However, because of their low chemical reactivity, surface treatments are necessary. For the design of this miniaturized diagnostics, we aimed at modifying the microfluidic system at two scales : (1) on the entire surface of the microsystem to control the surface hydrophobicity (so as to avoid any sample wall adsorption) and the fluid flows during electrokinetic separation, or (2) locally so as to immobilize selective ligands (aptamers) on restricted areas for target extraction and preconcentration. We developed different novel strategies for the surface functionalization of COC and Dyneon, based on plasma, chemical and /or electrochemical approaches. In a first approach, a plasma-induced immobilization of brominated derivatives was performed on the entire surface. Further substitution of the bromine by an azide functional group led to covalent immobilization of ligands through “click” chemistry reaction between azides and terminal alkynes. COC and Dyneon materials were characterized at each step of the surface functionalization procedure by various complementary techniques to evaluate the quality and homogeneity of the functionalization (contact angle, XPS, ATR). With the objective of local (micrometric scale) aptamer immobilization, we developed an original electrochemical strategy on engraved Dyneon THV microchannel. Through local electrochemical carbonization followed by adsorption of azide-bearing diazonium moieties and covalent linkage of alkyne-bearing aptamers through click chemistry reaction, typical dimensions of immobilization zones reached the 50 µm range. Other functionalization strategies, such as sol-gel encapsulation of aptamers, are currently investigated and may also be suitable for the development of the analytical microdevice. The development of these functionalization strategies is the first crucial step in the design of the entire microdevice. These strategies allow the grafting of a large number of molecules for the development of new analytical tools in various domains like environment or healthcare.

Keywords: alkyne-azide click chemistry (CuAAC), electrochemical modification, microsystem, plasma bromination, surface functionalization, thermoplastic polymers

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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: Homayon Ahmad Panahi, Seyed Mehdi Seyed Nejad, Elham Moniri

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A novel bio-adsorbent is fabricated by attaching a cibacron blue to yeast cells. The modified bio-sorbent has been characterized by some techniques like Fourier transform infrared spectroscopy (FT-IR) and elemental analysis (CHN) and applied for the preconcentration and determination of samarium from aqueous water samples. The best pH value for adsorption of the brilliant crecyle blue by yeast cells- cibacron blue was 7. The sorption capacity of modified biosorbent was 18.5 mg. g⁻¹. A recovery of 95.3% was obtained for Sm(III) when eluted with 0.5 M nitric acid. The method was applied for Sm(III) preconcentration and determination in river water sample.

Keywords: samarium, solid phase extraction, yeast cells, water sample, removal

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Authors: Amber Dummett, Efstathia Tzemou

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Narcissism is a personality trait characterized by selfishness, entitlement, and superiority. Narcissism is split into two subtypes, grandiose narcissism (GN) and vulnerable narcissism (VN). Grandiose narcissists are extraverted and arrogant, while vulnerable narcissists are introverted and insecure. This study investigates the psychological mechanisms that lead to differences in academic achievement (AA) between grandiose and vulnerable narcissists, specifically the mediating effects of self-esteem and self-efficacy. While narcissism is considered to be a negative trait, one of the Dark Triads, GN, has been found to have some benefits; therefore, this study considers if better AA is one of them. Moreover, further research into VN is essential to fully compare and contrast it with GN. We hypothesize that grandiose narcissists achieve higher marks due to having high self-esteem and self-efficacy. In comparison, we hypothesize that vulnerable narcissists underperform and achieve lower marks due to having low self-esteem and self-efficacy. Two online surveys were distributed to undergraduate university students. The first was a collection of scales measuring the mentioned dimensions and semester one AA, and the second investigated end of year AA. Sequential mediation analyses were conducted using the gathered data. Our analysis shows that neither self-esteem nor self-efficacy mediates the relationship between GN and AA. GN positively predicts self-esteem but has no relationship with self-efficacy. Self-esteem does not mediate the relationship between VN and AA. VN has a negative indirect effect on AA via self-efficacy, and VN negatively predicts self-esteem. Self-efficacy positively predicts AA. GN does not affect AA through the mediation of self-esteem and then self-efficacy, and neither does VN in this way. Overall, having grandiose or vulnerable narcissistic traits does not affect students’ AA. However, being highly efficacious does lead to academic success; therefore, universities should employ methods to improve the self-efficacy of their students.

Keywords: academic achievement, grandiose narcissism, self-efficacy, self-esteem, vulnerable narcissism

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Authors: Lady Salvador Purganan, Jojo M. Villamin, Noel L. Lansang

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Tourism is a multifaceted but interdependent industry. This industry is composed of four major players, the public sector, the private sector, the local community, and the tourists. Each player has a vital role in the success of delivering high-quality tourism products and activities. There are various manifestations of positive economic outcomes that benefit the local community. Pro-poor tourism development approach has a great potential to serve as an avenue for capacity building leading to economic independence since natural attractions and cultural resources are assets that can be capitalized on, especially by the poor, because it is more accessible to them compared to financial resources. In the National Tourism Development Plan 2016-2022, specific mechanisms are not reflected to combat and lower poverty incidence through tourism. The researcher used the multidimensional poverty theory and sustainable tourism theory to formulate indicators in the research instrument and social exchange theory. The expected output of the study is to unlock opportunities, specifically in Brgy. Sta. Lucia, Guimba, Nueva Ecija, by crafting policies taking into utmost consideration local community involvement and participation in the process of tourism development which is essential in attaining inclusive growth and sustainability. This study will apply the sequential explanatory design mixed-method approach.

Keywords: pro-poor tourism, poverty alleviation, livelihood opportunities, tourism development plan

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Authors: S. Tehsin, S. Kausar

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The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: S. Nagheli, N. Samani, D. A. Barry

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In this paper, the velocity potential and stream function of capture zone for a well field in an aquifer bounded by two parallel streams with or without a uniform regional flow of any directions are presented. The well field includes any number of extraction or injection wells or a combination of both types with any pumping rates. To delineate the capture envelope, the potential and streamlines equations are derived by conformal mapping method. This method can help us to release constrains of other methods. The equations can be applied as useful tools to design in-situ groundwater remediation systems, to evaluate the surface–subsurface water interaction and to manage the water resources.

Keywords: complex potential, conformal mapping, image well theory, Laplace’s equation, superposition principle

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Authors: Violina Angelova, Krasimir Ivanov, Stefan Krustev, Dimitar Dimitrov

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Availability of a metal is characterised by its quantity transgressing from soil into different extractants or by its content in plants. In literature, the terms 'available forms of compounds' and 'mobile' are often considered as equivalents of the term 'accessible' to plants. Rapid and a sufficiently reliable method for defining the accessible for plants forms turns out to be their extraction through different extractants, imitating the functioning of the root system. As a criterion for the pertinence of the extractant to this purpose usually serves the significant statistic correlation between the extracted quantities of the element from soil and its content in plants. The aim of this work was to evaluate the effectiveness of various extractions (DTPA-TEA, AB-DTPA, Mehlich 3, 0.01 M CaCl₂, 1M NH₄NO₃) for the determination of bioavailability of heavy metals in industrially polluted soils from the metallurgical activity near Plovdiv and Kardjali, Bulgaria. Quantity measurements for contents of heavy metals were performed with ICP-OES. The results showed that extraction capacity was as follows: Mehlich 3>ABDTPA>DTPA-TEA>CaCl₂>NaNO₃. The content of the mobile form of heavy metals depends on the nature of metal ion, the nature of extractant and pH. The obtained results show that CaCl₂ extracts a greater quantity of mobile forms of heavy metals than NH₄NO₃. DTPA-TEA and AB-DTPA are capable of extracting from the soil not only the heavy metals participating in the exchange processes but also the heavy metals bound in carbonates and organic complexes, as well as bound and occluded in oxide and secondary clay minerals. AB-DTPA extracts a bit more heavy metals than DTPA-TEA. The darker color of the solutions obtained with AB-DTPA indicates that considerable quantities organic matter are being destructed. A comparison of the mobile forms of heavy metals extracted from clean and highly polluted soils has revealed that in the polluted soils the greater portion of heavy metals exists in a mobile form. High correlation coefficients are obtained between the metals extracted with different extractants and their total content in soil (r=0.9). A positive correlation between the pH, soil organic matter and the extracted quantities of heavy metals has been found. The results of correlation analysis revealed that the heavy metals extracted by DTPA-TEA, AB-DTPA, Mehlich 3, CaCl₂ and NaNO₃ correlated significantly with plant uptake. Significant correlation was found between DTPA-TEA, AB-DTPA, and CaCl₂ with heavy metals concentration in plants. Application of extracting methods contains chelating agents would be recommended in the future research onthe availabilityof heavy metals in polluted soils.

Keywords: availability, chemical extractants, heavy metals, mobile forms

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Authors: Albert Kwabena Osei Konadu

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One of the endemic forest species of the savannah transition zones enlisted by the Convention of International Treaty for Endangered Species (CITES) in Appendix II is the Rosewood, also known as Pterocarpus erinaceus or Krayie. Its economic viability has made it increasingly popular and in high demand. Ghana’s forest resource management regime for these ecozones is mainly on conservation and very little on resource utilization. Consequently, commercial logging management standards are at teething stage and not fully developed, leading to a deficiency in the monitoring of logging operations and quantification of harvested trees volumes. Tree information form (TIF); a volume estimation and tracking regime, has proven to be an effective sustainable management tool for regulating timber resource extraction in the high forest zones of the country. This work aims to generate TIF that can track and capture requisite parameters to accurately estimate the volume of harvested rosewood within forest savannah transition zones. Tree information forms were created on three scenarios of individual billets, stacked billets and conveying vessel basis. The study was limited by the usage of regulators assigned volume as benchmark and also fraught with potential volume measurement error in the stacked billet scenario due to the existence of spaces within packed billets. These TIFs were field-tested to deduce the most viable option for the tracking and estimation of harvested volumes of rosewood using the smallian and cubic volume estimation formula. Overall, four districts were covered with individual billets, stacked billets and conveying vessel scenarios registering mean volumes of 25.83m3,45.08m3 and 32.6m3, respectively. These adduced volumes were validated by benchmarking to assigned volumes of the Forestry Commission of Ghana and known standard volumes of conveying vessels. The results did indicate an underestimation of extracted volumes under the quotas regime, a situation that could lead to unintended overexploitation of the species. The research revealed conveying vessels route is the most viable volume estimation and tracking regime for the sustainable management of the Pterocarpous erinaceus species as it provided a more practical volume estimate and data extraction protocol.

Keywords: cubic volume formula, smallian volume formula, pterocarpus erinaceus, tree information form, forest transition and savannah zones, harvested tree volume

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Authors: Isa Moazen, Ali Nahvi

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Driver drowsiness is a major cause of vehicle accidents, and facial images are highly valuable to detect drowsiness. In this paper, we perform our research via a thermal camera to record drivers' facial images on a driving simulator. A robust real-time algorithm extracts the features using horizontal and vertical integration projection, contours, contour orientations, and cropping tools. The features are included four target areas on the cheeks and forehead. Qt compiler and OpenCV are used with two cameras with different resolutions. A high-resolution thermal camera is used for fifteen subjects, and a low-resolution one is used for a person. The results are investigated by four temperature plots and evaluated by observer rating of drowsiness.

Keywords: advanced driver assistance systems, thermal imaging, driver drowsiness detection, feature extraction

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Authors: Supriya H. S., Chandrakala B. M.

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Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach.

Keywords: MLHN, network traffic prediction

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Authors: Xiangming Ge, Bing Pan, Wei He, Hao Chen, Yong Zhou, Jiayao Wu, Weijiang Chu

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How the extraction and penetration of spudcan affect the performance of the adjacent pile foundation supporting the jacket platform was studied in the program FLAC3D depending on a wind farm project in Bohai sea. The simulations were conducted at the end of the spudcan penetration, which induced a pockmark in the seabed. The effects of the distance between the pile foundation and the pockmark were studied. The displacement at the mudline arose when the pockmark was closer. The bearing capacity of this jacket platform with deep pile foundations has been less influenced by the process of spudcan penetration, which can induce severe stresses on the pile foundation. The induced rotation was also satisfied with the rotation-controlling criteria.

Keywords: offshore foundation, pile-soil interaction, spudcan penetration, FLAC3D

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Authors: Vishwanath Pethri Kamath, Jayantha Gowda Sarapanahalli, Vishal Mishra, Siddhesh Balwant Bandgar

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Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions.

Keywords: text emotion recognition, bidirectional LSTM, multi-head attention, multi-level classification, google word2vec word embeddings

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Authors: Supriyono, Sumardiyono, Puti Pertiwi

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Crude palm oil (CPO) is a vegetable oil that came from a palm tree bunch. Palm oil tree was known as highest vegetable oil yield. It was grown across Equatorial County, especially in Malaysia and Indonesia. The greenish red color on CPO was came from carotenoid antioxidant, which could be extracted and use separately as functional food and other purposes as antioxidant source. Another antioxidant that also found in CPO is tocopherol. The aim of the research work is to find antioxidant activity on CPO comparing to the synthetic antioxidant that available in a market. On this research work, antioxidant was extracted by using a mixture of acetone and n. hexane, while activity of the antioxidant extract was determine by DPPH method. The extracted matter was shown that their antioxidant activity was about 45% compare to pure tocopherol and beta carotene.

Keywords: antioxidant, , beta carotene, , crude palm oil, , DPPH, , tocopherol

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Authors: W. Pungchompoo, A. Richardson, L. Brindle

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Background: The developing of a culturally acceptable end of life survey (the VOICES-ESRD/Thai questionnaire) is an essential instrument for evaluation health services provision of older persons with ESRD in Thailand. The focus of the questionnaire was on symptoms, symptom control and the health care needs of older people with ESRD who are managed without dialysis. Objective: The objective of this study was to develop and adapt VOICES to make it suitable for use in a population survey in Thailand. Methods: The mixed methods exploratory sequential design was focussed on modifying an instrument. Data collection: A cognitive interviewing technique was implemented, using two cycles of data collection with a sample of 10 bereaved carers and a prototype of the Thai VOICES questionnaire. Qualitative study was used to modify the developing a culturally acceptable end of life survey (the VOICES-ESRD/Thai questionnaire). Data analysis: The data were analysed by using content analysis. Results: The revisions to the prototype questionnaire were made. The results were used to adapt the VOICES questionnaire for use in a population-based survey with older ESRD patients in Thailand. Conclusions: A culturally specific questionnaire was generated during this second phase and issues with questionnaire design were rectified.

Keywords: VOICES-ESRD/Thai questionnaire, cognitive interviewing, end of life survey, health services provision, older persons with ESRD

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 44

Authors: F. Garousi, S. Veres, É. Bódi, S. Várallyay, B. Kovács

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Extracting and determining chlorophyll pigments (chlorophyll a and b) in green leaves are the procedures based on the solvent extraction of pigments in samples using N,N-dimethylformamide as the extractant. In this study, two species of soluble inorganic selenium forms, selenite (Se( IV)) and selenate (Se( VI)) at different concentrations were investigated on maize plants that were growing in nutrient solutions during 2 weeks and at the end of the experiment, amounts of chlorophyll a and b for first and second leaves of maize were measured. In accordance with the results we observed that our regarded Se concentrations in both forms of Se( IV) and Se( VI) were not effective on maize plants’ chlorophyll a and b significantly although high level of 3 mg.kg-1 Se( IV) had negative affect on growth of the samples that had been treated by it but about Se( VI) samples we did not observe this state and our different considered Se( VI) concentrations were not toxic for maize plants.

Keywords: maize, sodium selenate, sodium selenite, chlorophyll a and b

Procedia PDF Downloads 403

Authors: A. T. Zhakiyenova, E. E. Zhatkanbaev, Zh. K. Zhatkanbaeva

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Nickel plays an important role in mechanical engineering and creation of military equipment; practically all steel are alloyed by nickel and other metals for receiving more durable, heat-resistant, corrosion-resistant steel and cast iron. There are many ways of processing of nickel in the world. Generally, it is igneous metallurgy methods. In this article, the review of majority existing ways of technologies of processing silicate nickel - cobalt ores is considered. Leaching of ores of a field Bugetkol is investigated by solution of sulfuric acid. We defined a specific consumption of sulfuric acid in relation to the mass of ore and to the mass of metal.

Keywords: cobalt, degree of extraction, hydrometallurgy, igneous metallurgy, leaching, matte, nickel

Procedia PDF Downloads 389

Authors: Kabil Boukhari

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In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on different corpora and gave an interesting result.

Keywords: text preprocessing, segmentation, knowledge extraction, normalization, text generation, information retrieval

Procedia PDF Downloads 94

Authors: C. R. Ríos-Soberanis, V. M. Moo-Huchin, R. J. Estrada-Leon, E. Perez-Pacheco

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Polymers from renewable resources have attracted an increasing amount of attention over the last two decades, predominantly due to two major reasons: firstly environmental concerns, and secondly the realization that our petroleum resources are finite. Finding new uses for agricultural commodities is also an important area of research. Therefore, it is crucial to get new sources of natural materials that can be used in different applications. Ramon tree (Brosimum alicastrum) is a tropical plant that grows freely in Yucatan countryside. This paper focuses on the seeds recollection, processing and starch extraction and characterization in order to find out about its suitability as biomaterial. Results demonstrated that it has a high content of qualities to be used not only as comestible but also as an important component in polymeric blends.

Keywords: biomaterials, characterization techniques, natural resource, starch

Procedia PDF Downloads 326

Authors: Asamaporn Puetpaiboon, Sunisa Chatmongkolchart, Nalinee Kovitwanawong, Osaree Akaraborworn

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Traumatic patients with massive bleeding require intensive resuscitation. The actual cost of anesthesia per case has never been clarified, so our study aimed to quantify the direct cost, and cost-to-charge ratio of anesthetic care in traumatic patients with intraoperative massive bleeding. This study was a prospective, observational, cost analysis study, conducted in Prince of Songkla University hospital, Thailand, with traumatic patients, of any mechanisms being recruited. Massive bleeding was defined as estimated blood loss of at least one blood volume in 24 hours, or a half of blood volume in 3 hours. The cost components were identified by the micro-costing method, and valued by the bottom-up approach. The direct cost was divided into 4 categories: the labor cost, the capital cost, the material cost and the cost of drugs. From September 2017 to August 2018, 10 patients with multiple injuries were included. Seven patients had motorcycle accidents, two patients fell from a height and another one was in a minibus accident. Two patients died on the operating table, and another two died within 48 hours. The median Sequential Organ Failure Assessment (SOFA) score was 8. The median intraoperative blood loss was 3,500 ml. The median direct cost, per case, was 250 United States Dollars (2017 exchange rate), and the cost-to-charge ratio was 0.53. In summary, the direct cost was nearly half of the hospital charge, for these traumatic patients with massive bleeding. However, our study did not analyze the indirect cost.

Keywords: cost, cost-to-charge ratio, micro-costing, trauma

Procedia PDF Downloads 148

Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

Procedia PDF Downloads 354