Search results for: mineral potential classification

Authors: Camilo Perez Lee, Mark Lefsrud, Edris Madadian, Yves Roy

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Biomass availability is one of the most important aspects to consider when determining the proper location of potential bioenergy plants. Since this aspect has a direct impact on biomass transportation and storage, biomass availability greatly influences the operational cost. Biomass availability is more than the quantity available on a specific region; other elements such as biomass accessibility and potential play an important role. Accessibility establishes if the biomass could be extracted and conveyed easily considering factors such as biomass availability, infrastructure condition and other operational issues. On the other hand, biomass potential is defined as the capacity of a specific region to scale the usage of biomass as an energy source, move from another energy source or to switch the type of biomass to increase their biomass availability in the future. This paper defines methodologies and parameters in order to determine the biomass availability within the administrative regions of the province of Quebec; firstly by defining the forestry, agricultural, municipal solid waste and energy crop biomass availability per administrative region, next its infrastructure accessibility and lastly defining the region potential. Thus, these data are processed to create a biomass availability matrix allowing to define the overall biomass availability per region and to determine the most optional candidates for bioenergy plant location.

Keywords: biomass, availability, bioenergy, accessibility, biomass potential

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Authors: Bhupinder Kaur, P. P. Srivastav

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The fruit and vegetable industries are responsible for producing huge amount of waste, which is a problem to environmental safety and should be utilized efficiently. Mango (Mangifera indica L.) is an important commercially grown fruit and referred as the “King of fruits”. In 2015, India was the largest producer (18.506 MT) of mangoes and out of which 9.16 % lost during post-harvest handling. The mango kernel and peel represent approximately 17-22% and 7-22% of the overall mass of fruit respectively and discarded as waste. Hence, an attempt has been made with three mango cultivars (Langra, Dashehari, Fazli) to investigate the effect of cryogenic grinding on various characteristics of mango peel powder (MPP). The cryogenic grinding is an emerging technology which is used for retention of beneficial volatile and bioactive components. The feed rate was highest for Langra followed by Chausa. The samples have 2-4% fat along with significant amount of protein (4-6%) and crude fiber (9-13%). Mango peel is also a good source of minerals such as calcium, potassium, manganese, iron, copper, zinc, and magnesium. Interestingly, the significant amount of essential minerals like phosphorus and chlorine in all the varieties was found with the highest value in Langra (phosphorus 10.83% and chlorine 2.41%) which are not reported earlier. SEM analysis revealed the surface morphology and shape of the particles. Waste utilization is a promising measure from both an environmental and economic point of view. Chemical characterization of the samples indicated its potential to be used for the fortification of food products which in turn reduces hazards due to waste and improve functional quality of the foods.

Keywords: cryogenic grinding, morphological, mineral composition, SEM

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Authors: M. Bodner, M. Scampicchio

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Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA

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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: Sheetla Prasad

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The objective of this study is ‘discovering the real psyche of human beings for prediction of mode, direction and strength of the potential of actions of the individual. The human face was taken as a source of central point to search for the route of real psyche. Analysis of the face architecture (shape and salient features of face) was done by three directional photographs ( 600 left and right and camera facing) of human beings. The shapes and features of the human face were scaled in 177 units on the basis of face–features locations (FFL). The mathematical analysis was done of FFLs by self developed and standardized formula. At this phase, 800 samples were taken from the population of students, teachers, advocates, administrative officers, and common persons. The finding shows that real psyche has two external rings (ER). These ER are itself generator of two independent psyches (manifested and manipulated). Prima-facie, it was proved that micro differences in FFLs have potential to predict the state of art of the human psyche. The potential of psyches was determined by the saving and distribution of mental energy. It was also mathematically proved.

Keywords: face architecture, psyche, potential, face functional ratio, external rings

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Authors: Berkan Ural, Arif Eser, Sinan Apaydin

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Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

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Authors: M. Zielina, W. Dabrowski, E. Radziszewska-Zielina

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Several different cements have been tested to evaluate their potential to leach calcium, chromium and aluminum ions in soft water environment. The research allows comparing some different cements in order to the potential risk of water contamination. This can be done only in the same environment. To reach the results in reasonable short time intervals and to make heavy metals measurements with high accuracy, demineralized water was used. In this case the conditions of experiments are far away from the water supply practice, but short time experiments and measurably high concentrations of elements in the water solution are an important advantage. Moreover leaching mechanisms can be recognized, our experiments reported here refer to this kind of cements evaluation.

Keywords: concrete corrosion, hydrogen sulfide, odors, reinforced concrete sewers, sewerage

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Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

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Authors: Seyedeh Mandan Kazzazi, Elena Kranioti

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In the field of human osteology, sex estimation is an important step in developing biological profile. There are a number of methods that can be used to estimate the sex of human remains varying from visual assessments to metric analysis of sexually dimorphic traits. Teeth are one of the most durable physical elements in human body that can be used for this purpose. The present study investigated the utility of cervical measurements for sex estimation through discriminant analysis. The permanent molar teeth of 75 skeletons (28 females and 52 males) from Hasanlu site in North-western Iran were studied. Cervical mesiodistal and buccolingual measurements were taken from both maxillary and mandibular first and second molars. Discriminant analysis was used to evaluate the accuracy of each diameter in assessing sex. The results showed that males had statistically larger teeth than females for maxillary and mandibular molars and both measurements (P < 0.05). The range of classification rate was from (75.7% to 85.5%) for the original and cross-validated data. The most dimorphic teeth were maxillary and mandibular second molars providing 85.5% and 83.3% correct classification rate respectively. The data generated from the present study suggested that cervical mesiodistal and buccolingual measurements of the molar teeth can be useful and reliable for sex estimation in Iranian archaeological populations.

Keywords: cervical measurements, Hasanlu, premolars, sex estimation

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Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Aleksandra Tepic-Horecki, Zdravko Sumic

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In this paper, the influence of six different phytochemical content (phenols, carotenoids, chlorophyll a, chlorophyll b, chlorophyll a + b and vitamin C) on antioxidant potential of Murai and Levistro lettuce varieties was evaluated. Variable selection was made by generalized pair correlation method (GPCM) as a novel ranking method. This method is used for the discrimination between two variables that almost equal correlate to a dependent variable. Fisher’s conditional exact and McNemar’s test were carried out. Established multiple linear (MLR) models were statistically evaluated. As the best phytochemicals for the antioxidant potential prediction, chlorophyll a, chlorophyll a + b and total carotenoids content stand out. This was confirmed through both GPCM and MLR, predictive ability of obtained MLR can be used for antioxidant potential estimation for similar lettuce samples. This article is based upon work from the project of the Provincial Secretariat for Science and Technological Development of Vojvodina (No. 114-451-347/2015-02).

Keywords: antioxidant activity, generalized pair correlation method, lettuce, regression analysis

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Authors: Nyamela M. ‘Masekhohola, Khanare P. Fumane

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The Lesotho downward trend in mathematics attainment at all levels is compounded by the absence of innovative approaches to teaching and learning in Early Childhood. However, studies have shown that play pedagogy can be used to mitigate the challenges of mathematics education. Despite the benefits of play pedagogy to rural learners, its full potential has not been realized in early childhood care and education classrooms to improve children’s performance in mathematics because the adoption of play pedagogy depends on a strength-based approach. The study explores the potential of play pedagogy to improve mathematics education in early childhood care and education in Lesotho. Strength-based approach is known for its advocacy of recognizing and utilizing children’s strengths, capacities and interests. However, this approach and its promisingattributes is not well-known in Lesotho. In particular, little is known about the attributes of play pedagogy that are essential to improve mathematic education in ECCE programs in Lesotho. To identify such attributes and strengthen mathematics education, this systematic review examines evidence published on the strengths of play pedagogy that supports the teaching and learning of mathematics education in ECCE. The purpose of this review is, therefore, to identify and define the strengths of play pedagogy that supports mathematics education. Moreover, the study intends to understand the rural teachers’ perceived intention of using play in ECCE math classrooms through a strength-based approach. Eight key strengths were found (cues for reflection, edutainment, mathematics language development, creativity and imagination, cognitive promotion, exploration, classification, and skills development). This study is the first to identify and define the strength-based attributes of play pedagogy to improve the teaching and learning of mathematics in ECCE centers in Lesotho. The findings reveal which opportunities teachers find important for improving the teaching of mathematics as early as in ECCE programs. We conclude by discussing the implications of the literature for stimulating dialogues towards formulating strength-based approaches to teaching mathematics, as well as reflecting on the broader contributions of play pedagogy as an asset to improve mathematics in Lesotho and beyond.

Keywords: early childhood education, mathematics education, lesotho, play pedagogy, strength-based approach.

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Authors: Aniket Alam, Sravya Gurram

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This paper proposes a categorization of sport that is based on the system of rules that a sport must adhere to. We focus on these systems of rules to examine how a winner is produced in different sports. The rules of a sport dictate the game play and the direction it takes. We propose to break down the game play into events. At this junction, we observe two kinds of events that constitute the game play of a sport –ones that follow sequential logic and ones that do not. Our focus is pertained to sports that are comprised of sequential events. To examine these events further, to understand how a winner emerges, we take the help of finite-state automaton from the theory of computation (Automata theory). We showcase how sequential sports are eligible to be represented as finite state machines. We depict these finite state machines as state diagrams. We examine these state diagrams to observe how a team/player reaches the final states of the sport, with a special focus on one final state –the final state which determines the winner. This exercise has been carried out for the following sports: Hurdles, Track, Shot Put, Long Jump, Bowling, Badminton, Pacman and Weightlifting (Snatch). Based on our observations of how this final state of winning is achieved, we propose a categorization of sports.

Keywords: sport classification, sport modelling, ontology, automata theory

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: C. Pallis

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Induced-Gravity inflation is a model of chaotic inflation where the inflaton is identified with a Higgs-like modulus whose the vacuum expectation value controls the gravitational strength. Thanks to a strong enough coupling between the inflaton and the Ricci scalar curvature, inflation is attained even for subplanckian values of the inflaton with the corresponding effective theory being valid up to the Planck scale. In its simplest realization, induced-gravity inflation is based on a quatric potential and a quadratic non-minimal coupling and the inflationary observables turn out to be in agreement with the Planck data. Its supersymmetrization can be formulated within no-scale Supergravity employing two gauge singlet chiral superfields and applying a continuous $R$ and a discrete Zn symmetry to the proposed superpotential and Kahler potential. Modifying slightly the non-minimal coupling to Gravity, the model can account for the recent results of BICEP2. These modifications can be also accommodated beyond the no-scale SUGRA considering the fourth order term of the Kahler potential which mixes the inflaton with the accompanying non-inflaton field and small deviations from the prefactor $-3$ encountered in the adopted Kahler potential.

Keywords: cosmology, supersymmetric models, supergravity, modified gravity

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Authors: Katerina Koliou, Scott Parr, Evangelos Kaisar

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The surface transportation of freight is particularly vulnerable to storm and hurricane disasters, while at the same time, it is the primary transportation mode for delivering medical supplies, fuel, water, and other essential goods. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The research investigation used Florida's statewide continuous-count station traffic volumes, where then compared between years, to identify locations where traffic was moving differently during the evacuation. The data was then used to identify days on which traffic was significantly different between years. While the literature on auto-based evacuations is extensive, the consideration of freight travel is lacking. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The goal of this research was to investigate the movement of vehicles by classification, with an emphasis on freight during two major evacuation events: hurricanes Irma (2017) and Michael (2018). The methodology of the research was divided into three phases: data collection and management, spatial analysis, and temporal comparisons. Data collection and management obtained continuous-co station data from the state of Florida for both 2017 and 2018 by vehicle classification. The data was then processed into a manageable format. The second phase used geographic information systems (GIS) to display where and when traffic varied across the state. The third and final phase was a quantitative investigation into which vehicle classifications were statistically different and on which dates statewide. This phase used a two-sample, two-tailed t-test to compare sensor volume by classification on similar days between years. Overall, increases in freight movement between years prevented a more precise paired analysis. This research sought to identify where and when different classes of vehicles were traveling leading up to hurricane landfall and post-storm reentry. Of the more significant findings, the research results showed that commercial-use vehicles may have underutilized rest areas during the evacuation, or perhaps these rest areas were closed. This may suggest that truckers are driving longer distances and possibly longer hours before hurricanes. Another significant finding of this research was that changes in traffic patterns for commercial-use vehicles occurred earlier and lasted longer than changes for personal-use vehicles. This finding suggests that commercial vehicles are perhaps evacuating in a fashion different from personal use vehicles. This paper may serve as the foundation for future research into commercial travel during evacuations and explore additional factors that may influence freight movements during evacuations.

Keywords: evacuation, freight, travel time, evacuation

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Authors: Seongwon Im, Jimin Kim, Kyeongcheol Kim, Dong-Hoon Kim

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Cattle manure (CM) produced from farmhas been utilized to soils for increasing crop production owing to high nutrients content and effective microorganisms. Some cities with the concentrated activity of livestock industry have suffered from environmental problems, such as odorous gas emissions and soil and water pollution, caused by excessive use of compost. As an alternative option, the anaerobic digestion (AD) process can be utilized, which can reduce the volume of organic waste but also produce energy. According to Korea-Ministry of Trade, Industry, and Energy (KMTIE), the energy potential of CM via biogas production was estimated to be 0.8 million TOE per year, which is higher than that of other organic wastes. However, limited energy is recovered since useful organic matter, capable of converting to biogas, may be degraded during the long storage period (1-6 months).In this study, the effect of storage period on biogas potential of CM was investigated. Compared to fresh CM (VS 14±1 g/L, COD 205±5 g/L, TKN 7.4±0.8 g/L, NH4+-N 1.5±0.1), old CM has higher organic (35-37%) and nitrogen content (50-100%) due to the drying process during storage. After stabilization period, biogas potential of 0.09 L CH4/g VS was obtained in R1 (old CM supplement) at HRT of 150-100 d, and it was decreased further to 0.06 L CH4/g VS at HRT of 80 d. The drop of pH and organic acids accumulation were not observed during the whole operation of R1. Ammonia stripping and pretreatment of CM were found to be not effective to increase CH4 yield. On the other hand, a sudden increase of biogas potential to 0.19-0.22 L CH4/g VS was achieved in R2 after changing feedstock to fresh CM. The expected reason for the low biogas potential of old CM might be related with the composition of organic matters in CM. Easily biodegradable organic matters in the fresh CM were contained in high concentration, butthey were removed by microorganisms during storing CM in a farm, resulting low biogas yield. This study implies that fresh storage is important to make AD process applicable for CM.

Keywords: storage period, cattle manure, biogas potential, microbial analysis

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

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Authors: H. Boukhatem, L. Djouadi, H. Khalaf, R. M. Navarro, F. V. Ganzalez

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Synthetic organic dyes are used in various industries, such as textile industry, leather tanning industry, paper production, hair dye production, etc. Wastewaters containing these dyes may be harmful to the environment and living organisms. Therefore, it is very important to remove or degrade these dyes before discharging them into the environment. In addition to standard technologies for the degradation and/or removal of dyes, several new specific technologies, the so-called advanced oxidation processes (AOPs), have been developed to eliminate dangerous compounds from polluted waters. AOPs are all characterized by the same chemical feature: production of radicals (•OH) through a multistep process, although different reaction systems are used. These radicals show little selectivity of attack and are able to oxidize various organic pollutants due to their high oxidative capacity (reduction potential of HO• Eo = 2.8 V). Heterogeneous photocatalysis, as one of the AOPs, could be effective in the oxidation/degradation of organic dyes. A major advantage of using heterogeneous photocatalysis for this purpose is the total mineralization of organic dyes, which results in CO2, H2O and corresponding mineral acids. In this study, nanomaterials based on montmorillonite and CuxCd1-xS with different Cu concentration (0.3 < x < 0.7) were utilized for the degradation of the commercial cationic textile dye Methylene blue (MB), used as a model pollutant. The synthesized nanomaterials were characterized by fourier transform infrared (FTIR) and thermogravimetric-differential thermal analysis (TG–DTA). Test results of photocatalysis of methylene blue under UV-Visible irradiation show that the photoactivity of nanomaterials montmorillonite/ CuxCd1-xS increases with the increasing of Cu concentration. The kinetics of the degradation of the MB dye was described with the Langmuir–Hinshelwood (L–H) kinetic model.

Keywords: heterogeneous photocatalysis, methylene blue, montmorillonite, nanomaterial

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Authors: M. Jamil Ahmad

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In this paper, the heating/cooling potential of a passive building (mosque) of Prof. K. A. Nizami center for Quranic studies at AMU Aligarh, has been evaluated on the basis of energy balance under quasi-steady state condition by incorporating the effect of ventilation. The study has been carried out for composite climate of Aligarh. The performance of the above mentioned building has been presented in this study. It is observed that the premises of the mosque are cooler than the outside ambient temperature by an average of 2°C and 4°C during the month of March and April respectively. Provision of excellent ventilation, high amount of thermal mass, high ceilings and circulation of cool natural air helps in maintaining an optimal thermal comfort temperature in the passive building.

Keywords: heating/cooling potential, passive building, ambient temperatures

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Authors: Aya Salama

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Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Asiri Wijesinghe, N. D. Kodikara, Damitha Sandaruwan

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The Diabetic Retinopathy (DR) is a rapidly growing interrogation around the world which can be annotated by abortive metabolism of glucose that causes long-term infection in human retina. This is one of the preliminary reason of visual impairment and blindness of adults. Information on retinal pathological mutation can be recognized using ocular fundus images. In this research, we are mainly focused on resurrecting an automated diagnosis system to detect DR anomalies such as severity level classification of DR patient (Non-proliferative Diabetic Retinopathy approach) and vessel tortuosity measurement of untwisted vessels to assessment of vessel anomalies (Proliferative Diabetic Retinopathy approach). Severity classification method is obtained better results according to the precision, recall, F-measure and accuracy (exceeds 94%) in all formats of cross validation. In ROC (Receiver Operating Characteristic) curves also visualized the higher AUC (Area Under Curve) percentage (exceeds 95%). User level evaluation of severity capturing is obtained higher accuracy (85%) result and fairly better values for each evaluation measurements. Untwisted vessel detection for tortuosity measurement also carried out the good results with respect to the sensitivity (85%), specificity (89%) and accuracy (87%).

Keywords: fundus image, exudates, microaneurisms, hemorrhages, tortuosity, diabetic retinopathy, optic disc, fovea

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Authors: Mohamed El-Bady

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The Northern Egyptian Lagoons are (from east to west) Bardawil Lagoon, Manzala Lagoon, Burullus Lagoon, Edku Lagoons and Mariute Lagoon. These lagoons have been received the bulk of drainage water from the lands of Delta and from the other coastal areas. Where, the heavy metals can occur in Lagoons environments through a variety of sources, including industries, wastewaters and domestic effluents. The potential ecological risk index (RI) calculation of the bottom sediments of the northern lagoons depends on contamination factor (CF), potential ecological risk factor and proposed toxic response factor (Tr). Each lagoon with special indices according to its conditions.

Keywords: Northern Lagoons, Nile Delta, ecological risk index, contamination factor

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Authors: Varun Agarwal

Abstract:

Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Osa D Egonwa

Abstract:

In the 21^st century, the disciplinary boundaries of past centuries that we often create through mainstream art historical classification, techniques and sources may have been eroded by visual culture, which seems to provide a more inclusive umbrella for the new ways artists go about the creative process and its resultant commodities. Over the past four decades, artists in Africa have resorted to new materials, techniques and themes which have affected our ways of research on these artists and their art. Frontline artists such as El Anatsui, Yinka Shonibare, Erasmus Onyishi are demonstrating that any material is just suitable for artistic expression. Most of times, these materials come with their own techniques/effects and visual syntax: a combination of materials compounds techniques, formal aesthetic indexes, halo effects, and iconography. This tends to challenge the categories and we lean on to view, think and talk about them. This renders our main stream art historical research methods inadequate, thus suggesting new discursive concepts, terms and theories. This paper proposed the Africanist eclectic methods derived from the dual framework of Masquerade Theory and What Comes Behind Six is More Than Seven. This paper shares thoughts/research on art historical methods, terminological re-alignments on classification/source data, presentational format and interpretation arising from the emergent trends in our subject. The outcome provides useful tools to mediate new thoughts and experiences in recent African art and visual culture.

Keywords: art historical methods, classifications, concepts, re-alignment

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Authors: Alya Harichane, Abderraouf Achour, Abdelbaki Benmounah

Abstract:

The main difficulty encountered in the formulation of high-performance concrete (HPC) consists in choosing the most efficient cement-superplasticizer pair allowing to obtain maximum water reduction, good workability of the concrete in the fresh state, and very good mechanical resistance in the hardened state. The aim of this work is to test the efficiency of three polycarboxylate ether-based superplasticizers (PCE) marketed in Algeria with CEMI 52.5 R cement and to study the effect of chemical structure of PCE on zeta potential, rheological and mechanical properties of cement pastes. The property of the polymers in cement was tested by a Malvern Zetasizer 2000 apparatus and VT 550 viscometer. Results showed that the zeta potential and its rheological properties are related to the molecular weight and the density carboxylic of PCE. The PCE with a moderate molecular weight and the highest carboxylic groups had the best dispersion (high value of zeta potential) and lowest viscosity. The effect of the chemical structure of PCEs on mechanical properties is evaluated by the formulation of cement mortar with these PCEs. The result shows that there is a correlation between the zeta potential of polymer and the compressive strength of cement paste.

Keywords: molecular weight, polycarboxylate-ether superplasticizer, rheology, zeta potential

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Authors: Sh. Hamada

Abstract:

The current work aims to study the rainbow like-structure observed in the elastic scattering of alpha particles on both ¹²C and ¹⁶O nuclei. We reanalyzed the experimental elastic scattering angular distributions data for α+¹²C and α+¹⁶O nuclear systems at different energies using both optical model and double folding potential of different interaction models such as: CDM3Y1, DDM3Y1, CDM3Y6 and BDM3Y1. Potential created by BDM3Y1 interaction model has the shallowest depth which reflects the necessity to use higher renormalization factor (N_r). Both optical model and double folding potential of different interaction models fairly reproduce the experimental data.

Keywords: density distribution, double folding, elastic scattering, nuclear rainbow, optical model

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Authors: A. Vani, M. N. Mamatha

Abstract:

Visual signal processing in human beings occurs in the occipital lobe of the brain. The signals that are generated in the brain are universal for all the human beings and they are called Visual Evoked Potential (VEP). Generally, the visually impaired people lose sight because of severe damage to only the eyes natural photo sensors, but the occipital lobe will still be functioning. In this paper, a technique of artificially generating VEP is proposed to enhance the visual ability of the subject. The system uses the electrical photoreceptors to capture image, process the image, to detect and recognize the subject or object. This voltage is further processed and can transmit wirelessly to a BIOMEMS implanted into occipital lobe of the patient’s brain. The proposed BIOMEMS consists of array of electrodes that generate the neuron potential which is similar to VEP of normal people. Thus, the neurons get the visual data from the BioMEMS which helps in generating partial vision or sight for the visually challenged patient. 

Keywords: BioMEMS, neuro-prosthetic, openvibe, visual evoked potential

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

Abstract:

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

Procedia PDF Downloads 592