Search results for: artificial intelligence (AI)

Authors: Jekaterina Novikova, Leon Watts

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In the emerging world of human-robot interaction, people and robots will interact socially in real-world situations. This paper presents the results of an experimental study probing the interaction between situational context and emotional body language in robots. 34 people rated video clips of robots performing expressive behaviours in different situational contexts both for emotional expressivity on Valence-Arousal-Dominance dimensions and by selecting a specific emotional term from a list of suggestions. Results showed that a contextual information enhanced a recognition of emotional body language of a robot, although it did not override emotional signals provided by robot expressions. Results are discussed in terms of design guidelines on how an emotional body language of a robot can be used by roboticists developing social robots.

Keywords: social robotics, non-verbal communication, situational context, artificial emotions, body language

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Authors: Sezer Kefeli, Sertaç Arslan

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Supercavitation is a pressure dependent process which gives opportunity to eliminate the wetted surface effects on the underwater vehicle due to the differences of viscosity and velocity effects between liquid (freestream) and gas phase. Cavitation process occurs depending on rapid pressure drop or temperature rising in liquid phase. In this paper, pressure based cavitation is investigated due to the fact that is encountered in the underwater world, generally. Basically, this vapor-filled pressure based cavities are unstable and harmful for any underwater vehicle because these cavities (bubbles or voids) lead to intense shock waves while collapsing. On the other hand, supercavitation is a desired and stabilized phenomena than general pressure based cavitation. Supercavitation phenomena offers the idea of minimizing form drag, and thus supercavitating vehicles are revived. When proper circumstances are set up, which are either increasing the operating speed of the underwater vehicle or decreasing the pressure difference between free stream and artificial pressure, the continuity of the supercavitation is obtainable. There are 2 types of supercavitation to obtain stable and continuous supercavitation, and these are called as natural and artificial supercavitation. In order to generate natural supercavitation, various mechanical structures are discovered, which are called as cavitators. In literature, a lot of cavitator types are studied either experimentally or numerically on a CFD platforms with intent to observe natural supercavitation since the 1900s. In this paper, firstly, experimental results are obtained, and trend lines are generated based on supercavitation parameters in terms of cavitation number (), form drag coefficientC_D, dimensionless cavity diameter (d_m/d_c), and length (L_c/d_c). After that, natural cavitation verification studies are carried out for disk and cone shape cavitators. In addition, supercavitation parameters are numerically analyzed at different operating conditions, and CFD results are fitted into trend lines of experimental results. The aims of this paper are to generate one generally accepted drag coefficient equation for disk and cone cavitators at different cavitator half angle and investigation of the supercavitation parameters with respect to cavitation number. Moreover, 165 CFD analysis are performed at different cavitation numbers on FLUENT version 21R2. Five different cavitator types are modeled on SCDM with respect tocavitator’s half angles. After that, CFD database is generated depending on numerical results, and new trend lines are generated based on supercavitation parameters. These trend lines are compared with experimental results. Finally, the generally accepted drag coefficient equation and equations of supercavitation parameters are generated.

Keywords: cavity envelope, CFD, high speed underwater vehicles, supercavitation, supercavitating flows, supercavitation parameters, drag reduction, viscous force elimination, natural cavitation verification

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Authors: Ramazan Bakir, Gizem Kayar

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It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.

Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification

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Authors: Huy Q. Tran, Bac V. Nguyen, Choonghyun Kang, Jungwon Huh

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Jumbo container crane is an important part of port structures that needs to be designed properly, even when the port locates in low seismicity zone such as in Korea. In this paper, 30 artificial ground motions derived from the elastic response spectra of Korean Building Code (2005) are used for time history analysis. It is found that the uplift might not occur in this analysis when the crane locates in the low seismic zone. Therefore, a selection of a pinned or a gap element for base supporting has not much effect on the determination of the total base shear. The relationships between the total base shear and peak ground acceleration (PGA) and the relationships between the portal drift and the PGA are proposed in this study.

Keywords: jumbo container crane, portal drift, time history analysis, total base shear

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Authors: Saad Al-Baddai, Karema Al-Subari, Elmar Lang, Bernd Ludwig

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Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.

Keywords: empirical mode decomposition (EMD), mode mixing, sifting process, over-sifting

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Authors: N. Belas, O. Belaribi, S. Aggoun, K. Bendani, N. Bouhamou, A. Mebrouki

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This experimental work is a part of a long research on the valorization of the dam dredged sediments issued from Fergoug Dam (Mascara-West Algeria). These sediments have to be subjected to thermal treatment to become reactive with the cement and thus to obtain an artificial pozzolana. It is therefore a question of developing the calcined mud as substitutable material in part to the cement used in the composition of self compacting concrete. The objective of the present work is to highlight its influence on the behavior of self compacting concrete compared to that of the natural pozzolana and this, in fresh and hardened states. The study is being conducted on three SCC, the first using 20% in volume of natural pozzolana, the second with 20 % of calcined mud and the third for the sake of comparison is made with cement only. The first results showed the possibility of obtaining SCC with calcined mud complying with the AFGC recommendations having a good mechanical behavior which makes interesting its development as construction materials.

Keywords: dam, fresh state, hardened state mud, sediments, self compacting concrete, valorization

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Authors: Tahar Abid, Haoues Ghouss, Abdelhamid Boubertakh

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This present work is focused on the hardening precipitation in two AlMgSi(Cu) automotive body sheets. The effect of pre-aging, aging treatment and 0.10 wt % copper addition on the hardening response was investigated using scanning calorimetry (DSC), transmission electron microscopy (TEM), and Vickers microhardness measurements (Hv). The results reveal the apparition of α-AlFeSi, α-AlFe(Mn)Si type precipitates frequently present and witch remain stable at high temperature in Al-Mg-Si alloys. Indeed, the hardening response in both sheets is certainly due to the predominance of very fine typical phases β' and β'' as rods and needles developed during aging with and without pre-aging. The effect of pre ageing just after homogenization and quenching is to correct the undesirable effect of aging at ambient temperature by making faster alloy hardening during artificial aging.The addition of 0.10 wt % copper has allowed to refine and to enhance the precipitation hardening after quenching.

Keywords: AlMgSi alloys, precipitation, hardening, activation energy

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Authors: André Smith, Amr Abdel-Dayem

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The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.

Keywords: colour data, local stereo matching, stereo correspondence, disparity map

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Muhammad Farooq, Ahtasham Gul

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To meet the desired standard, it is important to monitor and analyze different engineering processes to get desired output. The bivariate distributions got a lot of attention in recent years to describe the randomness of natural as well as artificial mechanisms. In this article, a bivariate model is constructed using two independent models developed by the nesting approach to study the effect of each component on reliability for better understanding. Further, the Bayes analysis of system availability is studied by considering prior parametric variations in the failure time and repair time distributions. Basic statistical characteristics of marginal distribution, like mean median and quantile function, are discussed. We use inverse Gamma prior to study its frequentist properties by conducting Monte Carlo Markov Chain (MCMC) sampling scheme.

Keywords: reliability, system availability Weibull, inverse Lomax, Monte Carlo Markov Chain, Bayesian

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Authors: Rabia Bashir, Sajjad Akbar

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There is an inevitable demand of web mining due to rapid increase of huge information on the Internet, but the striking variety of web structures has made required content retrieval a difficult task. To counter this issue, Web Content Mining (WCM) emerges as a potential candidate which extracts and integrates suitable resources of data to users. In past few years, research has been done on several extraction techniques for WCM i.e. agent-based, template-based, assumption-based, statistic-based, wrapper-based and machine learning. However, it is still unclear that either these approaches are efficiently tackling the significant challenges of WCM or not. To answer this question, this paper identifies these challenges such as language independency, structure flexibility, performance, automation, dynamicity, redundancy handling, intelligence, relevant content retrieval, and privacy. Further, mapping of these challenges is done with existing extraction mechanisms which helps to adopt the most suitable WCM approach, given some conditions and characteristics at hand.

Keywords: content mining challenges, web content mining, web content extraction approaches, web information retrieval

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Authors: Mohammed Saad Alanazi

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Satisfaction of travelers with services provided in the airports is a sign of competitiveness and the corporate image of the airport. This study conducted a systematic literature review of recent studies published after 2017 regarding the factors that positively influence travelers’ satisfaction and encourage them to report positive reviews online. This study found variations among the studies found. They used several research methodologies, and datasets and focused on different airports, yet, they commonly categorized airport services into seven categories that should receive high intention because their qualities were found increasing review rate and positivity. It was found that studies targeting travelers’ satisfaction and intention of revisiting tended to use primary sources of data (survey); meanwhile, studies concerned positivity and negativity of comments towards airport services often used online reviews provided by travelers.

Keywords: business Intelligence, airport service quality, passenger satisfaction, thorough analysis

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Authors: Masoud Madadelahi, Amir Shamloo, Seyedeh Sara Salehi

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Previously, some materials like solid metals and their alloys have been used as implants in human’s body. In order to amend fixation of these artificial hard human tissues, some porous structures have been introduced. In this way, tissues in vicinity of the porous structure can be attached more easily to the inserted implant. In particular, the porous bone scaffolds are useful since they can deliver important biomolecules like growth factors and proteins. This study focuses on the properties of the degradable porous hard tissues using a three-dimensional numerical Finite Element Method (FEM). The most important studied properties of these structures are diffusivity flux and concentration of different species like glucose, oxygen, and lactate. The process of cells migration into the scaffold is considered as a diffusion process, and related parameters are studied for different values of production/consumption rates.

Keywords: bone scaffolds, diffusivity, numerical simulation, tissue engineering

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Simon B. N. Thompson

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Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.

Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning

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Authors: Abdelghani Qadem, Zohair Qadem, Mohamed Lasri

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At the level of the Moroccan Sais Plateau, groundwater constitutes strategic natural resources for agricultural, industrial, and domestic use. Today, due to climate change and population growth, the pressure on groundwater has increased considerably. This contribution aims to delineate and map potential areas for groundwater storage in the area in question using GIS and remote sensing. The methodology adopted is based on the identification of the thematic layers used to assess the potential recharge of the aquifer. The mapping of potential areas for groundwater storage is developed through the method of modeling and weighted overlay using the spatial analysis tool on the Geographic Information System. The results obtained can be used for the planning of future artificial recharge projects in the study area in order to ensure the good sustainable use of this underground gift.

Keywords: Morocco, climate change, groundwater, mapping, recharge

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Tukur Husaini Nahuche

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The parent resources play a vital role in the life of the offspring. It help give children basic necessities of life like food, clothing, and housing. In a like manner financial assets allow parents to move into neighborhood with more affluent school systems, to pay school bills, purchase expensive technologies like personal computer, save money for tutoring books, magazines, journals, Newspapers etc. Making of proper provision in the home environment conducive for learning after school hours and creation of other outdoor activities for them are what necessitate in enhancing and accelerating children’s learning opportunities. Indeed, this paper intends to discuss parental investment in education, parent income resources, parental education, occupation, and income as relatively influencing children’s access to quality education. With the hope that families would provide equal opportunities for children irrespective of their sex, intelligence, subject choice,etc.

Keywords: parental investment, children's access, quality education

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Yuriko Takayama, Norihiro Kato

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Quorum sensing (QS) is a cell-to-cell communication system in bacteria to regulate expression of target genes. In gram-negative bacteria, activation on QS is controlled by a concentration increase of N-acylhomoserine lactone (AHL), which can diffuse in and out of the cell. Effective control of QS is expected to avoid virulence factor production in infectious pathogens, biofilm formation, and antibiotic production because various cell functions in gram-negative bacteria are controlled by AHL-mediated QS. In this research, we applied cyclodextrins (CDs) as artificial hosts for the AHL signal to reduce the AHL concentration in the culture broth below its threshold for QS activation. The AHL-receptor complex induced under the high AHL concentration activates transcription of the QS-target gene. Accordingly, artificial reduction of the AHL concentration is one of the effective strategies to inhibit the QS. A hydrophobic cavity of the CD can interact with the acyl-chain of the AHL due to hydrophobic interaction in aqueous media. We studied N-hexanoylhomoserine lactone (C6HSL)-mediated QS in Serratia marcescens; accumulation of C6HSL is responsible for regulation of the expression of pig cluster. Inhibitory effects of added CDs on QS were demonstrated by determination of prodigiosin amount inside cells after reaching stationary phase, because production of prodigiosin depends on the C6HSL-mediated QS. By adding approximately 6 wt% hydroxypropyl-β-CD (HP-β-CD) in Luria-Bertani (LB) medium prior to inoculation of S. maecescens AS-1, the intracellularly accumulated prodigiosin was drastically reduced to 7-10%, which was determined after the extraction of prodigiosin in acidified ethanol. The AHL retention ability of HP-β-CD was also demonstrated by Chromobacterium violacuem CV026 bioassay. The CV026 strain is an AHL-synthase defective mutant that activates QS solely by adding AHLs from outside of cells. A purple pigment violacein is induced by activation of the AHL-mediated QS. We demonstrated that the violacein production was effectively suppressed when the C6HSL standard solution was spotted on a LB agar plate dispersing CV026 cells and HP-β-CD. Physico-chemical analysis was performed to study the affinity between the immobilized CD and added C6HSL using a quartz crystal microbalance (QCM) sensor. The COOH-terminated self-assembled monolayer was prepared on a gold electrode of 27-MHz AT-cut quartz crystal. Mono(6-deoxy-6-N, N-diethylamino)-β-CD was immobilized on the electrode using water-soluble carbodiimide. The C6HSL interaction with the β-CD cavity was studied by injecting the C6HSL solution to a cup-type sensor cell filled with buffer solution. A decrement of resonant frequency (ΔFs) clearly showed the effective C6HSL complexation with immobilized β-CD and its stability constant for MBP-SpnR-C6HSL complex was on the order of 102 M-1. The CD has high potential for engineered control of QS because it is safe for human use.

Keywords: acylhomoserine lactone, cyclodextrin, intracellular signaling, quorum sensing

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: J. L. Liu, L. Wang, B. Zhu, J. Zhou, W. D. Gao

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Fabric textures are very common in our daily life. However, we never explore the representation of fabric textures from neuroscience view. Theoretical studies suggest that primary visual cortex (V1) uses a sparse code to efficiently represent natural images. However, how the simple cells in V1 encode the artificial textures is still a mystery. So, here we will take fabric texture as stimulus to study the response of independent component analysis that is established to model the receptive field of simple cells in V1. Experimental results based on 140 classical fabric images indicate that the receptive fields of simple cells have obvious selectivity in orientation, frequency, and phase when drifting gratings are used to determine their tuning properties. Additionally, the distribution of optimal orientation and frequency shows that the patch size selected from each original fabric image has a significant effect on the frequency selectivity.

Keywords: fabric texture, receptive filed, simple cell, spare coding

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Authors: Mahdi Ghasemi-Varnamkhasti, Ayat Mohammad-Razdari, Seyedeh-Hoda Yoosefian

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Taste is an important sensory property governing acceptance of products for administration through mouth. The advent of artificial sensorial systems as non-destructive tools able to mimic chemical senses such as those known as electronic tongue (ET) has open a variety of practical applications and new possibilities in many fields where the presence of taste is the phenomenon under control. In recent years, electronic tongue technology opened the possibility to exploit information on taste attributes of fruits providing real time information about quality and ripeness. Electronic tongue systems have received considerable attention in the field of sensor technology during the last two decade because of numerous applications in diverse fields of applied sciences. This paper deals with some facets of this technology in the quality monitoring of fruits along with more recent its applications.

Keywords: fruit, electronic tongue, non-destructive, taste machine, horticultural

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Authors: Ali Safaeianpour, Nima Shamandi

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As air traffic expands, many airports have evolved into transit centers for people, information, and commerce, and technology implementation is an absolute part of airport development. Several challenges are in the way of implementing technology in an airport. Airport 4.0 proposes the "Smart Airport" concept, which focuses on using modern technologies such as Big Data, the Internet of Things (IoT), advanced biometric systems, blockchain, and cloud computing to alter and enhance passengers' journeys. Several common IoT concrete topics as partial keys to smart airports are discussed and introduced, ranging from automated check-in systems to exterior tracking processes, with the goal of enlightening more and more insightful ideas and proposals about smart airport solutions. IoT will dramatically alter people's lives by infusing intelligence, boosting the quality of life, and assembling it smarter. This paper reviews the approaches to transforming an airport into a smart airport and describes several enabling components of IoT and challenges that can hinder the implementation of a smart airport's function, which require to be addressed.

Keywords: airport 4.0, digital airport, smart airport, IoT

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Authors: S. A. Naeini, A. Khalili

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Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.

Keywords: settlement, Subway Line, FLAC3D, ANFIS Method

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Authors: H. Kishor, G. Prabhuling, D. S. Ambika, D. P. Prakash

Abstract:

The chemical mutagens have become important tool to enhance agronomic traits of banana crop. It is being used to develop fusarium resistance lines in various susceptible banana cultivars. There are several mutagens like EMS and NaN3 available for banana crop improvement and each mutagen has its own important role as positive or negative effects on growth and development of banana plants. Explants from shoot tip culture were treated with various EMS (0.30, 0.60, 0.90 and 0.12%) and NaN3 (0.01, 0.02 and 0.03%) concentrations. The putative mutants obtained after in vitro rooting were subjected for artificial inoculation of Fusarium oxysporum f.sp. cubense. Screening putative mutants resistance to Panama disease was carried out by using syringe method of inoculation. It was observed that, EMS treated mutants were more susceptible compared to NaN3 treatment. Among the NaN3 doses 0.01% found to produce 3 resistant lines during preliminary screening under greenhouse conditions.

Keywords: Nanjangud rasabale, EMS, NaN3, putative mutants

Procedia PDF Downloads 169

Authors: Mukta Avachat-Shirke

Abstract:

The process of manifestation in art has been studied deeply by various Indian and Western philosophers through times. In the art of painting, ‘Practice’ is always considered as techniques or making and ‘Theory’ is related to intelligence or the ‘conceptual.' The question about the significance of ‘Practice’ in artistic research has been a topic of debate. The aim of this qualitative study is to find the relevance of practice and theory while creating artworks. This study analyzes the thoughts and philosophy of Abhinavgupta, Hegel, and Croce to find a new perspective for looking at practice and theory within artistic research. With the method of grounded theory, the study attempts to establish the importance of both in artistic research. It discusses the issues like stages of creating art, role of tacit knowledge and importance of the decision-making the ability of the artist. This comparative analysis of these three philosophers along with the present systems can be used as a point of reference for further developments in the pedagogy of art research and artists, to understand the psychology and to follow the process of creativity effectively.

Keywords: artistic research, Indian philosophy, practice, Western Philosophy

Procedia PDF Downloads 275

Authors: Aydin Teymourifar, Gurkan Ozturk

Abstract:

In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, neural network

Procedia PDF Downloads 370