Search results for: spatial classification

Authors: Carlos Tarmeño Noriega, Igor Aguilar Alonso

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Fungal diseases are common in vineyards; they cause a decrease in the quality of the products that can be sold, generating distrust of the customer towards the seller when buying fruit. Currently, technology allows the classification of fruits according to their characteristics thanks to artificial intelligence. This study proposes the implementation of a control system that allows the identification of the main fungal diseases present in the Italia grape, making use of a convolutional neural network (CNN), OpenCV, and TensorFlow. The methodology used was based on a collection of 20 articles referring to the proposed research on quality control, classification, and recognition of fruits through artificial vision techniques.

Keywords: computer vision, convolutional neural networks, quality control, fruit market, OpenCV, TensorFlow

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Authors: Mohammad Tahsin Karimi Nezhad, Zaynab Shadbahr, Ali Gholami

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In recent years many attempts have been made to assess groundwater contamination by nitrates worldwide. The assessment of spatial and temporal variations of physico-chemical parameters of water is necessary to mange water quality. The objectives of the study were to evaluate spatial variability and temporal changes of hydrochemical factors by water sampling from 24 wells in the Shoush City suburb. The analysis was conducted for the whole area and for different land use and geological classes. In addition, nitrate concentration variability with descriptive parameters such as sampling depth, dissolved oxygen, and on ground nitrogen loadings was also investigated The results showed that nitrate concentrations did not exceed the standard limit (50 mg/l). EC of water samples, ranged from 900 to 1200 µs/cm, TDS from 775 to 830 mg/l and pH from 5.6 to 9.

Keywords: groundwater, GIS, water quality, Iran

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Levent Basayigit, Sinan Demir, Burhan Kara, Yusuf Ucar

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Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.

Keywords: Cannabis, drug, remote sensing, object-based classification

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Authors: Lily Ingsrisawang, Tasanee Nacharoen

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Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

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Authors: Danilo Habermann, Aleksander Medella, Carla Cremon, Yusef Caceres

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This paper aims to analyze the ability of 2d point clouds features to classify different models of helicopters using radars. This method does not need to estimate the blade length, the number of blades of helicopters, and the period of their micro-Doppler signatures. It is also not necessary to generate spectrograms (or any other image based on time and frequency domain). This work transforms a radar return signal into a 2D point cloud and extracts features of it. Three classifiers are used to distinguish 9 different helicopter models in order to analyze the performance of the features used in this work. The high accuracy obtained with each of the classifiers demonstrates that the 2D point clouds features are very useful for classifying helicopters from radar signal.

Keywords: helicopter classification, point clouds features, radar, supervised classifiers

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Authors: Oladejo S. O, Tomori W. B, Adebayo A. O

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The main objective of this research is to identify the geochemical and mineralogical characteristics potential of unexplored tailings around the gold deposit region using spatial statistics and map modeling. Some physicochemical parameters such as pH, redox potential, electrical conductivity, cation exchange capacity, total organic carbon, total organic matter, residual humidity, Cation exchange capacity, and particle size were determined from both the mine drains and tailing samples using standard methods. The physicochemical parameters of tailings ranges obtained were pH (6.0 – 7.3), Eh (−16 - 95 Mev), EC (49 - 156 µS/cm), RH (0.20-2.60%), CEC (3.64-6.45 cmol/kg), TOC (3.57-18.62%), TOM (6.15-22.93%). The geochemical oxide composition were identified using Proton Induced X-ray emission and the results indicated that SiO2>Al2O3>Fe2O3>TiO2>K2O>MgO>CaO>Na2O> P2O5>MnO>Cr2O3>SrO>K2O>P2O5. The major mineralogical components in the tailing samples were determined by quantitative X-ray diffraction techniques using the Rietveld method. Geostatistical relationships among the known points were determined using ArcGIS 10.2 software to interpolate mineral concentration with respect to the study area. The Rietveld method gave a general Quartz value of 73.73-92.76%, IImenite as 0.38-4.77%, Kaolinite group as 3.19-20.83%, Muscovite as 0.77-11.70% with a trace of other minerals. The high percentage of quartz is an indication of a sandy environment with a loose binding site.

Keywords: tailings, geochemical, mineralogy, spatial

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Authors: Kamal, Adil Ahmad

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Earthquakes are among the most versatile natural and dynamic processes, and hence a fractal model is considered to be the best representative of the same. We present a novel method to process and analyse information hidden in earthquake sequences using Fractal Dimensions and Iterative Function Systems (IFS). Spatial and temporal variations in the fractal dimensions of seismicity observed around the Indian peninsula in last 30 years are studied. This was used as a possible precursor before large earthquakes in the region. IFS images for observed seismicity in the Himalayan belt were also obtained. We scan the whole data set and coarse grain of a selected window to reduce it to four bins. A critical analysis of four-cornered chaos-game clearly shows that the spatial variation in earthquake occurrences in Himalayan range is not random. Two subzones of Himalaya have a tendency to follow each other in time.

Keywords: earthquakes, fractals, Himalaya, iterated function systems

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Arif Ali, Divya Raj Sapkota

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In this paper, we explain gravitational attraction as the consequence of the dynamics of four-dimensional bodies and the consequent distortion of space. This approach provides an alternative direction to understand various physical phenomena based on the existence of the fourth spatial dimension. For this interpretation, we formulate the acceleration due to gravity and orbital velocity based on the accelerating expansion of three-dimensional symmetric bodies. It is also shown how distortion in space caused by the dynamics of four-dimensional bodies counterbalances the effect of expansion. We find that the motion of four-dimensional bodies through four-dimensional space leads to gravitational attraction, and the expansion of bodies leads to surface gravity. Thus, dynamics in the fourth spatial dimension provide an alternative explanation to gravity.

Keywords: dimensions, four, gravity, voluceleration

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Authors: Ruan Yi-Chen, Li Wang-Ming, Fang Yuan

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With the rapid growth of urbanization in China in recent years, public services are in short supply because of expanding population and limitation of financial support, which makes delicate configuration of public service facilities to become a trend in urban planning. Besides, the facility configuration standard implemented in China is equal to the whole the urban area without considering internal differences in it. Therefore, this article focuses on population Spatial disparities analysis in order to optimize facility configuration in communities of main city district. The used data, including population of 93 communities during 2010 to 2015, comes from GongShu district, Hangzhou city, PRC. Through the analysis of population data, especially the age structure of those communities, the communities finally divided into 3 types. Obviously, urban public service facilities allocation situation directly affect the quality of residents common lives, which turns out that deferent kinds of communities with deferent groups of citizens will have divergences in facility demanding. So in the end of the article, strategies of facility configuration will be proposed based on the population analysis in order to optimize the quantity and location of facilities with delicacy.

Keywords: delicacy, facility configuration, population spatial disparities, urban area

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Authors: A. A. Vasin, N. V. Klassen, A. M. Likhter

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Water suspensions of in-organic (metals and oxides) and organic nano-objects (chitozan and collagen) were subjected to the treatment of direct and alternative electrical fields. In addition to quasi-periodical spatial patterning resonance-like performance of spatial distributions of these suspensions has been found at low frequencies of alternating electrical field. These resonances are explained as the result of creation of equilibrium states of groups of charged nano-objects with opposite signs of charges at the interparticle distances where the forces of Coulomb attraction are compensated by the repulsion forces induced by relatively negative polarization of hydrated regions surrounding the nanoparticles with respect to pure water. The low frequencies of these resonances are explained by comparatively big distances between the particles and their big masses with t\respect to masses of atoms constituting molecules with high resonance frequencies. These new resonances open a new approach to detailed modeling and understanding of mechanisms of the influence of electrical fields on the functioning of internal organs of living organisms at the level of cells and neurons.

Keywords: bio-polymers, chitosan, collagen, nanoparticles, coulomb attraction, polarization repulsion, periodical patterning, electrical low frequency resonances, transformations

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: Mustafa Alhamdi

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

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

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Camilla Simoncelli

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The connection between language and cognition has been one of the main interests of linguistics from several years. According to the Sapir-Whorf Linguistic Relativity Theory, the way we perceive reality depends on the language we speak which in turn has a central role in the human cognition. This paper is in line with this research work with the aim of analyzing how language structures reflect on our cognitive abilities even in the description of space, which is generally considered as a human natural and universal domain. The main objective is to identify the differences in the encoding of spatial inclusion relationships in French and Italian speakers to make evidence that a significant variation exists at various levels even in two similar systems. Starting from the constitution a corpora, the first step of the study has been to establish the relevant complex prepositions marking an inclusion relation in French and Italian: au centre de, au cœur de, au milieu de, au sein de, à l'intérieur de and the opposition entre/parmi in French; al centro di, al cuore di, nel mezzo di, in seno a, all'interno di and the fra/tra contrast in Italian. These prepositions had been classified on the base of the type of Noun following them (e.g. mass nouns, concrete nouns, abstract nouns, body-parts noun, etc.) following the Collostructional Analysis of lexemes with the purpose of analyzing the preferred construction of each preposition comparing the relations construed. Comparing the Italian and the French results it has been possible to define the degree of representativeness of each target Noun for the chosen preposition studied. Lexicostatistics and Statistical Association Measures showed the values of attraction or repulsion between lexemes and a given preposition, highlighting which words are over-represented or under-represented in a specific context compared to the expected results. For instance, a Noun as Dibattiti has a negative value for the Italian Al cuore di (-1,91), but it has a strong positive representativeness for the corresponding French Au cœur de (+677,76). The value, positive or negative, is the result of a hypergeometric distribution law which displays the current use of some relevant nouns in relations of spatial inclusion by French and Italian speakers. Differences on the kind of location conceptualization denote syntactic and semantic constraints based on spatial features as well as on linguistic peculiarity, too. The aim of this paper is to demonstrate that the domain of spatial relations is basic to human experience and is linked to universally shared perceptual mechanisms which create mental representations depending on the language use. Therefore, linguistic coding strongly correlates with the way spatial distinctions are conceptualized for non-verbal tasks even in close language systems, like Italian and French.

Keywords: cognitive semantics, cross-linguistic variations, locational terms, non-verbal spatial representations

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Authors: Peter Boyes, Sarah Sharples, Paul Tennent, Gary Priestnall, Jeremy Morley

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Significant long-term investment projects can involve complex decisions. These are often described as capital projects, and the factors that contribute to their complexity include budgets, motivating reasons for investment, stakeholder involvement, interdependent projects, and the delivery phases required. The complexity of these projects often requires management groups to be established involving stakeholder representatives; these teams are inherently multidisciplinary. This study uses two university campus capital projects as case studies for this type of management group. Due to the interaction of projects with wider campus infrastructure and users, decisions are made at varying spatial granularity throughout the project lifespan. This spatial-related context brings complexity to the group decisions. Sensemaking is the process used to achieve group situational awareness of a complex situation, enabling the team to arrive at a consensus and make a decision. The purpose of this study is to understand the role of people and data in the complex spatial related long-term decision and sensemaking processes. The paper aims to identify and present issues experienced in practical settings of these types of decision. A series of exploratory semi-structured interviews with members of the two projects elicit an understanding of their operation. From two stages of thematic analysis, inductive and deductive, emergent themes are identified around the group structure, the data usage, and the decision making within these groups. When data were made available to the group, there were commonly issues with the perception of veracity and validity of the data presented; this impacted the ability of group to reach consensus and, therefore, for decisions to be made. Similarly, there were different responses to forecasted or modelled data, shaped by the experience and occupation of the individuals within the multidisciplinary management group. This paper provides an understanding of further support required for team sensemaking and decision making in complex capital projects. The paper also discusses the barriers found to effective decision making in this setting and suggests opportunities to develop decision support systems in this team strategic decision-making process. Recommendations are made for further research into the sensemaking and decision-making process of this complex spatial-related setting.

Keywords: decision making, decisions under uncertainty, real decisions, sensemaking, spatial, team decision making

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Authors: Xiaoqi Jia, Qing Zhu, Hao Zhang, Su Yang

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A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets.

Keywords: gender recognition, beep belief net-works, semi-supervised learning, greedy-layer wise RBMs

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Authors: Shenpu Liu

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Space syntax, a paradigm of the urban research, which manifests people’s intuitive and abstract perception of a material space with a solid mathematical way, explores how space represents its social characteristics. Taking Xiazhuang village and Shijia Village in Huzhou as an example and focusing on inward structure and street space, this article recognizes the connotative significance of the settlement with the aid of space syntax theory and quantitative analysis method from the perspective of spatial configuration to present relevant suggestions for its future planning and provides references for traditional rural settlement protection.

Keywords: Shijia village, space configuration, space syntax, traditional rural settlement, Xiazhuang village

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Milad Kurdi, Hadi Farhadian, Teymour Eslamkish

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This study examined the hydro-geochemistry of Qare-Sou catchment and Gorgan Gulf in order to determine the spatial distribution of major ions. In this regard, six hydrometer stations in the catchment and four stations in Gorgan Gulf were chosen and the samples were collected. Results of spatial and temporal distribution of major ions have shown similar variation trends for calcium, magnesium, and bicarbonate ions. Also, the spatial trend of chloride, sulfate, sodium and potassium ions were same as Electrical Conductivity (EC) and Total Dissolved Solid (TDS). In Nahar Khoran station, the concentrations of ions were more than other stations which may be related to human activities and the role of geology. The Siah Ab station’s ions showed high concentration which is may be related to the station’s close proximity to Gorgan Gulf and the return of water to Qare-Sou River. In order to determine the interaction of water and rock, the Gibbs diagram was used and the results showed that water of the river falls in the rock range and it is affected more by weathering and reaction between water and stone and less by evaporation and crystallization. Assessment of the quality of river water by using graphic methods indicated that the type of water in this area is Ca-HCO₃-Mg. Major ions concentration in Qare-Sou in the universal average was more than but not more than the allowed limit by the World Health Organization and China Standard Organization. A comparison of ions concentration in Gorgan Gulf, seas and oceans showed that the pH in Gorgan Gulf was more than the other seas but in Gorgan Gulf the concentration of anion and cation was less than other seas.

Keywords: hydro-geochemistry, Qare-Sou river, Gorgan gulf, major ions, Gibbs diagram, water quality, graphical methods

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Subhadeep Mandal, Sujoy Bhattacharya, Pabitra Mitra, Diya Guha Roy, Seema Bhattacharya

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E-commerce firms allow customers to evaluate and review the things they buy as a positive or bad experience. The e-commerce transaction processes are made up of a variety of diverse organizations and activities that operate independently but are connected together to complete the transaction (from placing an order to the goods reaching the client). After a negative shopping experience, clients frequently disregard the critical assessment of these businesses and submit their feedback on an all-over basis, which benefits certain enterprises but is tedious for others. In this article, we solely dealt with negative reviews and attempted to distinguish between negative reviews where the e-commerce firm is explicitly blamed by customers for a bad purchasing experience and other negative reviews.

Keywords: e-commerce, online shopping, customer reviews, customer behaviour, text analytics, n-grams classification

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Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei

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Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.

Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, P. L. Goh, Grace H. B. Foo, M. L. Leong

Abstract:

This paper presents the evaluation of various soil testing methods such as the four-probe soil electrical resistivity method and cone penetration test (CPT) that can complement a newly developed novel rapid soil classification scheme using computer vision, to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from the local construction industry are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labor-intensive. Thus, a rapid classification method is needed at the SGs. Four-probe soil electrical resistivity and CPT were evaluated for their feasibility as suitable additions to the computer vision system to further develop this innovative non-destructive and instantaneous classification method. The computer vision technique comprises soil image acquisition using an industrial-grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the following three items were targeted to be added onto the computer vision scheme: the apparent electrical resistivity of soil (ρ) measured using a set of four probes arranged in Wenner’s array, the soil strength measured using a modified mini cone penetrometer, and w measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay,” and a mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay” and are feasible as complementing methods to the computer vision system.

Keywords: computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

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Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

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