Search results for: mixed dataset

Authors: Sakshi Gupta, Seema Joshi

Abstract:

Thiosemicarbazones (TSCs) have garnered significant attention in coordination chemistry due to their versatile coordination modes and pharmacological properties. Mixed ligand complexes of TSCs represent a promising area of research, offering enhanced antimicrobial activities compared to their parent compounds. This review provides an overview of the synthesis, characterization, and antimicrobial properties of mixed ligand complexes incorporating thiosemicarbazones. The synthesis of mixed ligand complexes typically involves the reaction of a metal salt with TSC ligands and additional ligands, such as nitrogen- or oxygen-based ligands. Various transition metals, including copper, nickel, and cobalt, have been employed to form mixed ligand complexes with TSCs. Characterization techniques such as spectroscopy, X-ray crystallography, and elemental analysis are commonly utilized to confirm the structures of these complexes. One of the key advantages of mixed ligand complexes is their enhanced antimicrobial activity compared to pure TSC compounds. The synergistic effect between the TSC ligands and additional ligands contributes to increased efficacy, possibly through improved metal-ligand interactions or enhanced membrane permeability. Furthermore, mixed ligand complexes offer the potential for selective targeting of microbial species while minimizing toxicity to mammalian cells. This selectivity arises from the specific interactions between the metal center, TSC ligands, and biological targets within microbial cells. Such targeted antimicrobial activity is crucial for developing effective treatments with minimal side effects. Moreover, the versatility of mixed ligand complexes allows for the design of tailored antimicrobial agents with optimized properties. By varying the metal ion, TSC ligands, and additional ligands, researchers can fine-tune the physicochemical properties and biological activities of these complexes. This tunability opens avenues for the development of novel antimicrobial agents with improved efficacy and reduced resistance. In conclusion, mixed ligand complexes of thiosemicarbazones represent a promising class of compounds with potent antimicrobial activities. Further research in this field holds great potential for the development of novel therapeutic agents to combat microbial infections effectively.

Keywords: metal complex, thiosemicarbazones, mixed ligand, selective targeting, antimicrobial activity

Procedia PDF Downloads 60

Authors: Ladaporn Pithuk

Abstract:

This study aims to investigate the marketing mixed factors that affecting on expectations about commercial transactions through social networks. The research method will using quantitative research, data was collected by questionnaires to person have experience access to trading over the internet for 400 sample by purposive sampling method. Data was analyzed by descriptive statistic including percentage, mean, standard deviation and using quality function deployment for hypothesis testing. Finding the most significant interrelationship between marketing mixed factors and commercial transactions expectations through social networks are product and place the relationship of five ties product and place (location) is involved in almost all will make the site a model that meets the needs of the user visit. In terms of price, the promotion, privacy, personalization and providing a process technical. This will make operations more efficient, reduce confusion, duplication, delays in data transmission, including the creation of different elements in products and services.

Keywords: commercial transactions expectations, marketing mixed factors, social networks, consumer behavior

Procedia PDF Downloads 238

Authors: M. Novak, E. Munoz, A. Jana, M. Nelemans

Abstract:

Cities and local governments the world over are interested to employ circular strategies as a means to bring about food security, create employment and increase resilience. The selection and implementation of circular strategies is facilitated by modeling the effects of strategies locally and understanding the impacts such strategies have had in other (comparable) cities and how that would translate locally. Urban areas are heterogeneous because of their geographic, economic, social characteristics, governance, and culture. In order to better understand the effect of circular strategies on urban systems, we create a dataset for over 300 cities around the world designed to facilitate circular strategy scenario modeling. This new dataset integrates data from over 20 prominent global national and urban data sources, such as the Global Human Settlements layer and International Labour Organisation, as well as incorporating employment data from over 150 cities collected bottom up from local departments and data providers. The dataset is made to be reproducible. Various clustering techniques are explored in the paper. The result is sets of clusters of cities, which can be used for further research, analysis, and support comparative, regional, and national policy making on circular cities.

Keywords: data integration, urban innovation, cluster analysis, circular economy, city profiles, scenario modelling

Procedia PDF Downloads 180

Authors: Chaitanya Varma, Arpan Mehar

Abstract:

The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

Procedia PDF Downloads 460

Authors: Arian Ebneyamini, Hoda Azimi, Jules Thibaults, F. Handan Tezel

Abstract:

In this study, a modification of Hennepe model for pervaporation separation of butanol from aqueous solutions using Polydimethylsiloxane (PDMS) mixed matrix membranes has been introduced and validated by experimental data. The model was compared to the original Hennepe model and few other models which are applicable for membrane gas separation processes such as Maxwell, Lewis Nielson and Pal. Theoretical modifications for non-ideal interface morphology have been offered to predict the permeability in case of interface void, interface rigidification and pore-blockage. The model was in a good agreement with experimental data.

Keywords: butanol, PDMS, modeling, pervaporation, mixed matrix membranes

Procedia PDF Downloads 221

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

Procedia PDF Downloads 42

Authors: Brock Buckingham, Zhe Lin, Wenxuan Guo

Abstract:

Accurate elevation data is critical in deriving topographic attributes for the precision management of crop inputs, especially water and nutrients. Traditional ground-based elevation data acquisition is time consuming, labor intensive, and often inconvenient at the field scale. Various unmanned aerial systems (UAS) provide the capability of generating digital elevation data from high-resolution images. The objective of this study was to compare the performance of two UAS with different global positioning system (GPS) receivers in determining elevation at the field scale. A DJI Phantom 4 Pro and a DJI Phantom 4 RTK(real-time kinematic) were applied to acquire images at three heights, including 40m, 80m, and 120m above ground. Forty ground control panels were placed in the field, and their geographic coordinates were determined using an RTK GPS survey unit. For each image acquisition using a UAS at a particular height, two elevation datasets were generated using the Pix4D stitching software: a calibrated dataset using the surveyed coordinates of the ground control panels and an uncalibrated dataset without using the surveyed coordinates of the ground control panels. Elevation values for each panel derived from the elevation model of each dataset were compared to the corresponding coordinates of the ground control panels. The coefficient of the determination (R²) and the root mean squared error (RMSE) were used as evaluation metrics to assess the performance of each image acquisition scenario. RMSE values for the uncalibrated elevation dataset were 26.613 m, 31.141 m, and 25.135 m for images acquired at 120 m, 80 m, and 40 m, respectively, using the Phantom 4 Pro UAS. With calibration for the same UAS, the accuracies were significantly improved with RMSE values of 0.161 m, 0.165, and 0.030 m, respectively. The best results showed an RMSE of 0.032 m and an R² of 0.998 for calibrated dataset generated using the Phantom 4 RTK UAS at 40m height. The accuracy of elevation determination decreased as the flight height increased for both UAS, with RMSE values greater than 0.160 m for the datasets acquired at 80 m and 160 m. The results of this study show that calibration with ground control panels improves the accuracy of elevation determination, especially for the UAS with a regular GPS receiver. The Phantom 4 Pro provides accurate elevation data with substantial surveyed ground control panels for the 40 m dataset. The Phantom 4 Pro RTK UAS provides accurate elevation at 40 m without calibration for practical precision agriculture applications. This study provides valuable information on selecting appropriate UAS and flight heights in determining elevation for precision agriculture applications.

Keywords: unmanned aerial system, elevation, precision agriculture, real-time kinematic (RTK)

Procedia PDF Downloads 165

Authors: Ulrich Wake, Eniman Syamsuddin

Abstract:

The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights

Keywords: ​ One Cycle Policy, ResNet34, ResNet50, Test-Time Agumentation

Procedia PDF Downloads 209

Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

Abstract:

Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

Procedia PDF Downloads 208

Authors: Shamm Kidane, Ibrahim Abdella, Fitsum Gaim, Simon Mulugeta, Sirak Asmerom, Natnael Ambasager, Yoel Ghebrihiwot

Abstract:

The call for natural language processing (NLP) systems for low-resource languages has become more apparent than ever in the past few years, with the arduous challenges still present in preparing such systems. This paper presents an improved dataset version of the Nagaoka Tigrinya Corpus for Parts-of-Speech (POS) classification system in the Tigrinya language. The size of the initial Nagaoka dataset was incremented, totaling the new tagged corpus to 118K tokens, which comprised the 12 basic POS annotations used previously. The additional content was also annotated manually in a stringent manner, followed similar rules to the former dataset and was formatted in CONLL format. The system made use of the novel approach in NLP tasks and use of the monolingually pre-trained TiELECTRA, TiBERT and TiRoBERTa transformer models. The highest achieved score is an impressive weighted F1-score of 94.2%, which surpassed the previous systems by a significant measure. The system will prove useful in the progress of NLP-related tasks for Tigrinya and similarly related low-resource languages with room for cross-referencing higher-resource languages.

Keywords: Tigrinya POS corpus, TiBERT, TiRoBERTa, conditional random fields

Procedia PDF Downloads 103

Authors: Hector Valverde Martínez

Abstract:

In this text, the application possibilities of developments in MR are explored as an element within the museographic space that affects the visitor-museum relationship to satisfy the needs of knowledge and recreation that visitors have to improve the experience. The emphasis points out the way in which it is thinking from the digital to understand the possibilities in the design of museum experiences, and are analyzed the strategies used inside and outside the museum space are exemplified from the use of MR and their impact on the visitors' experience to reach different levels of depth of knowledge in an exhibition; the exploration of limits in the creation of atmospheres that allow visitors to feel immersed in a completely different reality from the one they live to better understand the topics addressed in the exhibition, and strategies that are used to encourage museum audiences to actively participate and extend the experience of the museum beyond its walls.

Keywords: mixed realities, experience, visitor, museums

Procedia PDF Downloads 186

Authors: Saba Ebrahimi, Hedieh Ashrafi

Abstract:

Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 217

Authors: Ahmed Alkarimi, Kevin Welham

Abstract:

Organic mixed mode monolithic columns were fabricated from; glycidyl methacrylate-co-ethylene dimethacrylate-co-stearyl methacrylate, using glycidyl methacrylate and stearyl methacrylate as co monomers representing 30% and 70% respectively of the liquid volume with ethylene dimethacrylate crosslinker and 2,2-dimethoxy-2-phenylacetophenone as the free radical initiator. The monomers were mixed with a binary porogenic solvent, comprising propan-1-ol, and methanol (0.825 mL each). The monolith was formed by photo polymerization (365 nm) inside a borosilicate glass tube (1.5 mm ID and 3 mm OD x 50 mm length). The monolith was observed to have formed correctly by optical examination and generated reasonable backpressure, approximately 650 psi at a flow rate of 0.2 mL min⁻¹ 50:50 acetonitrile: water. The morphological properties of the monolithic columns were investigated using scanning electron microscopy images, and Brunauer-Emmett-Teller analysis, the results showed that the monolith was formed properly with 19.98 ± 0.01 mm² surface area, 0.0205 ± 0.01 cm³ g⁻¹ pore volume and 6.93 ± 0.01 nm average pore size. The polymer monolith formed was further investigated using proton nuclear magnetic resonance, and Fourier transform infrared spectroscopy. The monolithic columns were investigated using high-performance liquid chromatography to test their ability to separate different samples with a range of properties. The columns displayed both hydrophobic/hydrophilic and hydrophobic/ion exchange interactions with the compounds tested indicating that true mixed mode separations. The mixed mode monolithic columns exhibited significant separation of proteins.

Keywords: LC separation, proteins separation, monolithic column, mixed mode

Procedia PDF Downloads 163

Authors: M. Babul Hasan

Abstract:

Management sciences (MS), big group of companies and industries or government policies (GP) is affiliated with a huge number of decision ingredients and complicated restrictions. Every factor in MS, every product in Industries or decision in GP is not always bankable in practice. After formulating these models there arises large-scale mixed integer programming (MIP) problem. In this paper, we developed decomposition-based pricing procedure to filter the unnecessary decision ingredients from MIP where the variables in huge number will be abated and the complicacy of restrictions will be elementary. A real life numerical example has been illustrated to demonstrate the methods. We develop the computer techniques for these methods by using a mathematical programming language (AMPL).

Keywords: Lagrangian relaxation, decomposition, sub-problem, master-problem, pricing, mixed IP, AMPL

Procedia PDF Downloads 505

Authors: Aneesh Babu, S. P. Anusha

Abstract:

A dynamic signal control system combines traditional traffic lights with an array of sensors to intelligently control vehicle and pedestrian traffic. The present study focus on evaluating the performance of dynamic signal control systems for mixed traffic conditions. Data collected from four different approaches to a typical four-legged signalized intersection at Trivandrum city in the Kerala state of India is used for the study. Performance of three other dynamic signal control methods, namely (i) Non-sequential method (ii) Webster design for consecutive signal cycle using flow as input, and (iii) dynamic signal control using RFID delay as input, were evaluated. The evaluation of the dynamic signal control systems was carried out using a calibrated VISSIM microsimulation model. Python programming was used to integrate the dynamic signal control algorithm through the COM interface in VISSIM. The intersection delay obtained from different dynamic signal control methods was compared with the delay obtained from fixed signal control. Based on the study results, it was observed that the intersection delay was reduced significantly by using dynamic signal control methods. The dynamic signal control method using delay from RFID sensors resulted in a higher percentage reduction in delay and hence is a suitable choice for implementation under mixed traffic conditions. The developed dynamic signal control strategies can be implemented in ITS applications under mixed traffic conditions.

Keywords: dynamic signal control, intersection delay, mixed traffic conditions, RFID sensors

Procedia PDF Downloads 107

Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

Abstract:

Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

Procedia PDF Downloads 123

Authors: Harshan Baskar, Anirudh S. Chakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout

Abstract:

In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task, particularly for autonomous navigation applications, and we hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.

Keywords: dehazing, image enhancement, nighttime, computer vision

Procedia PDF Downloads 159

Authors: Xiaoci Li, Yonghua Huang, Hui Lin

Abstract:

Optimization of the concentration of components in mixed refrigerants leads to potential improvement of either thermodynamic cycle performance or safety performance of heat pumps and refrigerators. R32+R1234yf and R125+R290 are two promising binary mixed refrigerants for the application of heat pumps working in the cold areas. The p-ρ-T data of these mixtures are one of the fundamental and necessary properties for design and evaluation of the performance of the heat pumps. Although the property data of mixtures can be predicted by the mixing models based on the pure substances incorporated in programs such as the NIST database Refprop, direct property measurement will still be helpful to reveal the true state behaviors and verify the models. Densities of the mixtures of R32+R1234yf an d R125+R290 are measured by an Anton Paar U shape oscillating tube digital densimeter DMA-4500 in the range of temperatures from 0°C to 100 °C and pressures up to 10 MPa. The accuracy of the measurement reaches 0.00005 g/cm³. The experimental data are compared with the predictions by Refprop in the corresponding range of pressure and temperature.

Keywords: mixed refrigerant, density measurement, densimeter, thermodynamic property

Procedia PDF Downloads 297

Authors: June Keeling, Christina Athanasiades, Vaiva Hendrixson, Delyth Wyndham

Abstract:

Recognition and education in violence, abuse, and neglect for medical and healthcare practitioners (REVAMP) is a trans-European project aiming to introduce a training programme that has been specifically developed by partners across seven European countries to meet the needs of medical and healthcare practitioners. Amalgamating the knowledge and experience of clinicians, researchers, and educators from interdisciplinary and multi-professional backgrounds, REVAMP has tackled the under-resourced and underdeveloped area of domestic violence and abuse. The team designed an online training programme to support medical and healthcare practitioners to recognise and respond appropriately to survivors of domestic violence and abuse at their point of contact with a health provider. The REVAMP partner countries include Europe: France, Lithuania, Germany, Greece, Iceland, Norway, and the UK. The training is delivered through a series of interactive online modules, adapting evidence-based pedagogical approaches to learning. Capturing and addressing the complexities of the project impacted the methodological decisions and approaches to evaluation. The challenge was to find an evaluation methodology that captured valid data across all partner languages to demonstrate the extent of the change in knowledge and understanding. Co-development by all team members was a lengthy iterative process, challenged by a lack of consistency in terminology. A mixed methods approach enabled both qualitative and quantitative data to be collected, at the start, during, and at the conclusion of the training for the purposes of evaluation. The module content and evaluation instrument were accessible in each partner country's language. Collecting both types of data provided a high-level snapshot of attainment via the quantitative dataset and an in-depth understanding of the impact of the training from the qualitative dataset. The analysis was mixed methods, with integration at multiple interfaces. The primary focus of the analysis was to support the overall project evaluation for the funding agency. A key project outcome was identifying that the trans-European approach posed several challenges. Firstly, the project partners did not share a first language or a legal or professional approach to domestic abuse and neglect. This was negotiated through complex, systematic, and iterative interaction between team members so that consensus could be achieved. Secondly, the context of the data collection in several different cultural, educational, and healthcare systems across Europe challenged the development of a robust evaluation. The participants in the pilot evaluation shared that the training was contemporary, well-designed, and of great relevance to inform practice. Initial results from the evaluation indicated that the participants were drawn from more than eight partner countries due to the online nature of the training. The primary results indicated a high level of engagement with the content and achievement through the online assessment. The main finding was that the participants perceived the impact of domestic abuse and neglect in very different ways in their individual professional contexts. Most significantly, the participants recognised the need for the training and the gap that existed previously. It is notable that a mixed-methods evaluation of a trans-European project is unusual at this scale.

Keywords: domestic violence, e-learning, health professionals, trans-European

Procedia PDF Downloads 84

Authors: Dana S. Smith, Angela Goins, Shahnaz Savani

Abstract:

Using a mixed-methods approach, this study explored costs to student wellbeing generated by the social work field practicum requirement. The project was conducted by faculty at a medium-sized university in the United States. Social work educators and field practicum instructors participated in interviews. Students and former students completed surveys on the topic. The data analysis revealed emotional burdens as well as threats to student wellbeing in association with the fieldwork required for those in pursuit of a social work degree. The study includes recommendations for anti-oppressive approaches for academic programs and implications for further research.

Keywords: emotional wellbeing, field practicum, mixed-methods, social justice

Procedia PDF Downloads 101

Authors: Dana S. Smith, Angela Goins, Shahnaz Savani

Abstract:

Using a mixed-methods approach, this study explored costs to student wellbeing generated by the social work field practicum requirement. The project was conducted by faculty at a medium sized university in the United States. Social work educators and field practicum instructors participated in interviews. Students and former students completed surveys on the topic. The data analysis revealed emotional burdens as well as threats to student wellbeing in association with the fieldwork required for those in pursuit of a social work degree. The study includes recommendations of anti-oppressive approaches for academic programs and implications for further research.

Keywords: emotional wellbeing, field practicum, mixed-methods, social justice

Procedia PDF Downloads 91

Authors: Mohamed Moustafa Mahmoud Sedky

Abstract:

In line start permanent magnet synchronous motor, eccentricity is a common fault that can make it necessary to remove the motor from the production line. However, because the motor may be inaccessible, diagnosing the fault is not easy. This paper presents an FEM that identifies different models, static eccentricity, dynamic eccentricity, and mixed eccentricity, at no load and full load. The method overcomes the difficulty of applying FEMs to transient behavior. It simulates motor speed, torque and flux density distribution along the air gap for SE, DE, and ME. This paper represents the various effects of different eccentricities types on the transient performance.

Keywords: line start permanent magnet, synchronous machine, static eccentricity, dynamic eccentricity, mixed eccentricity

Procedia PDF Downloads 380

Authors: Pongsawee Supanonth

Abstract:

This study aims to investigate the consumer behavior and marketing mixed factor affect on consumer decision making for independent movies presented in Lido cinema. The research method will use quantitative research, data was collected by questionnaires distributed to the audience in the Lido cinema for 400 sample by accidental sampling technique. Data was analyzed by descriptive statistic including percentage, mean, standard deviation and inferential statistic including independent t-test for hypothesis testing. The results showed that marketing mixed factors affecting consumer decision-making for Independent movies presented in Lido cinema by gender as different as less than the 0.05 significance level, it was found that the kind of movie ,quality of theater ,price of ticket, facility of watching movies, staff services and promotion of Lido cinema respectively had a vital influence on their attention and response which makes the advertisement more attractive is in harmony with the research hypotheses also.

Keywords: consumer behavior, marketing mixed factor, resonance, consumer decision making, Lido cinema

Procedia PDF Downloads 312

Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

Procedia PDF Downloads 425

Authors: Mohammad Farid

Abstract:

In this paper, we introduce and study an explicit iterative method based on hybrid extragradient method to approximate a common solution of generalized mixed equilibrium problem and fixed point problem for a nonexpansive semigroup in Hilbert space. Further, we prove that the sequence generated by the proposed iterative scheme converge strongly to the common solution of generalized mixed equilibrium problem and fixed point problem for a nonexpansive semigroup. This common solution is the unique solution of a variational inequality problem and is the optimality condition for a minimization problem. The results presented in this paper are the supplement, extension and generalization of the previously known results in this area.

Keywords: generalized mixed equilibrium problem, fixed-point problem, nonexpansive semigroup, variational inequality problem, iterative algorithms, hybrid extragradient method

Procedia PDF Downloads 475

Authors: Kemal Polat

Abstract:

In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

Procedia PDF Downloads 249

Authors: Gurukiran Veerapur, Nytik Birudavolu, Seetharam U. N., Chandravva Hebbi, R. Praneeth Reddy

Abstract:

This work presents a robust approach for the recognition of characters in Telugu and Kannada, two South Indian scripts with structural similarities in characters. To recognize the characters exhaustive datasets are required, but there are only a few publicly available datasets. As a result, we decided to create a dataset for one language (source language),train the model with it, and then test it with the target language.Telugu is the target language in this work, whereas Kannada is the source language. The suggested method makes use of Canny edge features to increase character identification accuracy on pictures with noise and different lighting. A dataset of 45,150 images containing printed Kannada characters was created. The Nudi software was used to automatically generate printed Kannada characters with different writing styles and variations. Manual labelling was employed to ensure the accuracy of the character labels. The deep learning models like CNN (Convolutional Neural Network) and Visual Attention neural network (VAN) are used to experiment with the dataset. A Visual Attention neural network (VAN) architecture was adopted, incorporating additional channels for Canny edge features as the results obtained were good with this approach. The model's accuracy on the combined Telugu and Kannada test dataset was an outstanding 97.3%. Performance was better with Canny edge characteristics applied than with a model that solely used the original grayscale images. The accuracy of the model was found to be 80.11% for Telugu characters and 98.01% for Kannada words when it was tested with these languages. This model, which makes use of cutting-edge machine learning techniques, shows excellent accuracy when identifying and categorizing characters from these scripts.

Keywords: base characters, modifiers, guninthalu, aksharas, vattakshara, VAN

Procedia PDF Downloads 53

Authors: Sepehr Moradi, Mehdi Asadi Rad

Abstract:

The aim of this study is to evaluate and compare carcass weight of pure and mixed races Namebar 30-day pigeons to investigate about their sex, race, and some auxiliary variables. In this paper, 68 pieces of pigeons as 34 male and female pairs with equal age are studied randomly. A natural incubation was done from each pair. All produced chickens were slaughtered at 30 days age after 12 hours hunger. Then their carcasses were weighted by a scale with one gram precision. A covariance analysis was used since there were many auxiliary variables and unequal observations. SAS software was used for statistical analysis. Mean weight of carcass in pure race (Namebar-Namebar) with 8 records, 219.5±61.3 gr and mixed races of Kabood-Namebar, Parvazy-Namebar, Tizpar-Namebar, Namebar-Kabood, Namebar-Tizpar, and Namebar-Parvazy with 8, 10, 8, 12, 12, and 10 records were 369.9±54.6, 338.3±52.7, 224.5±73.6, 142.3±67.8, 155.6±56.2, and 170.2±55 gr, respectively.. Difference carcass weight of 30-day of Namebar-Namebar race with Namebar-Kabood, Namebar-Parvazy, Namebar-Tizpar, Parvazy-Namebar and Tizpar-Namebar mixed races was not significant, and was significant in level 5% with Kabood- Namebar (P < 0.05). Effect of sex and age were also significant in 1% level (P < 0.01), but mutual effect of sex and race was not significant. The results showed that most and least weights of carcass belonged to Kabood-Namebar and Namebar-Kabood.

Keywords: squab, Namebar race, 30-day carcass weight, pigeons

Procedia PDF Downloads 180

Authors: Ferry Kurniawan

Abstract:

In this paper, we nowcast quarterly output growth in Indonesia by exploiting higher frequency data (monthly indicators) using a mixed-frequency factor model and exploiting both quarterly and monthly data. Nowcasting quarterly GDP in Indonesia is particularly relevant for the central bank of Indonesia which set the policy rate in the monthly Board of Governors Meeting; whereby one of the important step is the assessment of the current state of the economy. Thus, having an accurate and up-to-date quarterly GDP nowcast every time new monthly information becomes available would clearly be of interest for central bank of Indonesia, for example, as the initial assessment of the current state of the economy -including nowcast- will be used as input for longer term forecast. We consider a small scale mixed-frequency factor model to produce nowcasts. In particular, we specify variables as year-on-year growth rates thus the relation between quarterly and monthly data is expressed in year-on-year growth rates. To assess the performance of the model, we compare the nowcasts with two other approaches: autoregressive model –which is often difficult when forecasting output growth- and Mixed Data Sampling (MIDAS) regression. In particular, both mixed frequency factor model and MIDAS nowcasts are produced by exploiting the same set of monthly indicators. Hence, we compare the nowcasts performance of the two approaches directly. To preview the results, we find that by exploiting monthly indicators using mixed-frequency factor model and MIDAS regression we improve the nowcast accuracy over a benchmark simple autoregressive model that uses only quarterly frequency data. However, it is not clear whether the MIDAS or mixed-frequency factor model is better. Neither set of nowcasts encompasses the other; suggesting that both nowcasts are valuable in nowcasting GDP but neither is sufficient. By combining the two individual nowcasts, we find that the nowcast combination not only increases the accuracy - relative to individual nowcasts- but also lowers the risk of the worst performance of the individual nowcasts.

Keywords: nowcasting, mixed-frequency data, factor model, nowcasts combination

Procedia PDF Downloads 331

Authors: Karan R. Chavan, Srivats Gopalan, Kumudini V. Marathe

Abstract:

In the current work, the comparison of degradation of malathion by single species, Pseudomonas Stutzeri, and Activated Sludge/Mixed Microbial Culture is studied in a Membrane Bioreactor. Various parameters were considered to study the effect of single species degradation compared to degradation by activated sludge. The experimental results revealed 85-90% reduction in the COD of the Malathion containing synthetic wastewater. Complete reduction of malathion was observed within 24 hours in both the cases. The critical flux was 10 LMH for both the systems. Fouling propensity, Cake and Membrane resistances were calculated thus giving an insight regarding the working of Membrane Bioreactor-based on single species and activated sludge.

Keywords: fouling, membrane bioreactor, mixed microbial culture, single species

Procedia PDF Downloads 359