Search results for: ABC multi-criteria inventory classification

Authors: Mohammad Hadi Mahmoodi, Mojtaba Farahani

Abstract:

The present study aimed at investigating whether reflective thinking differentiates Iranian EFL learners regarding language learning strategy use, beliefs about language learning and teaching, and L2 proficiency. To this end, the researcher adopted a mixed method approach. First, 94 EFL learners were asked to complete Reflective Thinking Questionnaire (Kember et al., 2000), Beliefs about Language Learning and Teaching Inventory (Horwitz, 1985), Strategy Inventory for Language Learning (Oxford, 1990), and Oxford Quick Placement Test. The results of three separate one-way ANOVAs indicated that reflective thinking significantly differentiates Iranian EFL learners concerning: (a)language learning strategy use, (b) beliefs about language learning and teaching, and (c) general language proficiency. Furthermore, to see where the differences lay, three separate post-hoc Tukey tests were run the results of which showed that learners with different levels of reflectivity (high, mid, and low) were significantly different from each other in all three dependent variables. Finally, to increase the validity of the findings thirty of the participants were interviewed and the results were analyzed through template organizing style method (Crabtree & Miller, 1999). The results of the interview analysis supported the results of quantitative data analysis.

Keywords: reflective thinking, language learning strategy use, beliefs toward language learning and teaching

Procedia PDF Downloads 651

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 166

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 158

Authors: Ozlem Karabulutlu, Kiymet Yesilcicek Calik, Nazli Akar

Abstract:

Objectives: To examine fear of childbirth according to parity, gestational age, prenatal education, and obstetric history. Methods: The study was performed as a questionnaire design in a State Hospital in Kars, Turkey with 403 unselected pregnant who were recruited from the delivery unit. The data were collected via 3 questionnaires; the first with sociodemographic and obstetric features, the second with Wijma Delivery Expectance/Experience Questionnaire (W-DEQ) scale, and the third with the scale of Beck Anxiety Inventory (BAI). Results: The W-DEQ and BAI scores were higher in nulliparous than multiparous woman (W-DEQ 67.08±28.33, 59.87±26.91, P=0.039<0.05, BAI 18.97±9.5, 16.65±11.83, P=0.0009<0.05 respectively). Moreover, W-DEQ and BAI scores of pregnant whose gestational week was ≤37 / ≥41 and who didn’t receive training and had vaginal delivery was higher than those whose gestational week was 38-40 weeks and who received prenatal training and had cesarean delivery (W-DEQ 67.54±29.20, 56.44±22.59, 69.72±25.53 p<0.05, BAI 21.41±9.07; 15.77±11.20, 18.36±10.57 p<0.05 respectively). Both in nulliparous and multiparous, as W-DEQ score increases BAI score increases too (r=0.256; p=0.000<0.05). Conclusions: Severe fear of childbirth and anxiety was more common in nulliparous women, preterm and post-term pregnancy and who did not receive prenatal training and had vaginal delivery.

Keywords: Beck Anxiety Inventory (BAI), fear of birth, parity, pregnant women, Wijma Delivery Expectance/Experience Questionnaire (W-DEQ)

Procedia PDF Downloads 287

Authors: Keng H. Kong, Louis C. Tan, Wing L. Aw, Kay Y. Tay

Abstract:

Background: Fatigue is a common problem in patients with Parkinson's disease, with reported prevalence of up to 70%. Fatigue can be disabling and has adverse effects on patients' quality of life. There is currently no satisfactory treatment of fatigue. Acupuncture is effective in the treatment of fatigue, especially that related to cancer. Its role in Parkinson's disease-related fatigue is uncertain. Aims: To evaluate the clinical efficacy of acupuncture treatment in Parkinson's disease-related fatigue. Hypothesis: We hypothesize that acupuncture is effective in alleviating Parkinson's disease-related fatigue. Design: A single center, randomized, controlled study with two parallel arms. Participants: Forty participants with idiopathic Parkinson's disease will be enrolled. Interventions: Participants will be randomized to receive verum (real) acupuncture or placebo acupuncture. The retractable non-invasive sham needle will be used in the placebo group. The intervention will be administered twice a week for five weeks. Main outcome measures: The primary outcome will be the change in general fatigue score of the multidimensional fatigue inventory at week 5. Secondary outcome measures include other subscales of the multidimensional fatigue inventory, movement disorders society-unified Parkinson's disease rating scale, Parkinson's disease questionnaire-39 and geriatric depression scale. All outcome measures will be assessed at baseline (week 0), completion of intervention (week 5) and 4 weeks after completion of intervention (week 9). Results: To date, 23 participants have been recruited and nine have completed the study. The mean age is 63.5±14.2 years, mean duration of Parkinson’s disease is 6.4±1.8 years and mean MDS-UPDRS score is 8.3±2.8. The mean general fatigue score of the multidimensional fatigue inventory is 13.5±4.6. No significant adverse event related to acupuncture is noted. Potential significance: If the results are as expected, this study will provide preliminary scientific evidence for the efficacy of acupuncture in Parkinson's Disease-related fatigue, and opens the door for a larger multicentre trial to be performed. In the longer term, it may lead to the integration of acupuncture in the care of patients with Parkinson's disease.

Keywords: acupuncture, fatigue, Parkinson's disease, trial

Procedia PDF Downloads 304

Authors: Samira Taleb, Sakina Aoun, Slimane Ziani, Zoheir Mentouri, Adel Boudiaf

Abstract:

Rolling is a pressure treatment technique that modifies the shape of steel ingots or billets between rotating rollers. During this process, defects may form on the surface of the rolled sheets and are likely to affect the performance and quality of the finished product. In our study, we developed a method for detecting surface hole defects using a pixel classification approach. This work includes several steps. First, we performed image preprocessing to delimit areas with and without hole defects on the sheet image. Then, we developed the histograms of each area to generate the gray level membership intervals of the pixels that characterize each area. As we noticed an intersection between the characteristics of the gray level intervals of the images of the two areas, we finally performed a learning step based on a series of detection tests to refine the membership intervals of each area, and to choose the defect detection criterion in order to optimize the recognition of the surface hole.

Keywords: classification, defect, surface, detection, hole

Procedia PDF Downloads 14

Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

Abstract:

In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

Procedia PDF Downloads 213

Authors: Jae Ni Jang, Young Uk Kim

Abstract:

Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

Procedia PDF Downloads 46

Authors: Eva Tsouparopoulou, Maria Symeonaki

Abstract:

The internationally documented declining survey response rates of the last two decades are mainly attributed to refusals. In fieldwork, a refusal may be obtained not only from the respondent himself/herself, but from other sources on the respondent’s behalf, such as other household members, apartment building residents or administrator(s), and neighborhood residents. In this paper, we investigate how the composition of the demographic profile of survey refusals changes when different classifications are implemented and the classification issues arising from that. The analysis is based on the 2002-2018 European Social Survey (ESS) datasets for Belgium, Germany, and United Kingdom. For these three countries, the size of selected sample units coded as a type of refusal for all nine under investigation rounds was large enough to meet the purposes of the analysis. The results indicate the existence of four different possible classifications that can be implemented and the significance of choosing the one that strengthens the contrasts of the different types of respondents' demographic profiles. Since the foundation of social quantitative research lies in the triptych of definition, classification, and measurement, this study aims to identify the multiplicity of the definition of survey refusals as a methodological tool for the continually growing research on non-response.

Keywords: non-response, refusals, European social survey, classification

Procedia PDF Downloads 84

Authors: Benslimane Marwa, Benabbas-Sahki Ilham

Abstract:

Spiders play an important role in agroecosystems due to their great abundance. They are considered a valuable group of invertebrates in agricultural land. They are predators of insects harmful to crops, but their use in biological control requires in-depth research on their ecology. During our study, we counted a total of 768 spiders, which we were able to identify and classify into 14 families over a period between March 2021 and October of the same year. This study aims to compare a station subjected to agricultural practices, including the spreading of herbicides, with another station subjected to the same practices but without the use of phytosanitary products. The inventory shows a strong dominance of the Gnaphosidae family (75.8%). This result affirms that the proliferation of this family is very favorable to the knowledge of the fruits by limiting the populations of aphids infesting the plot, which can therefore be proposed for biological control. The comparative study of the populations of spiders in the stations studied shows the negative effect of agricultural practices on the species richness and abundance of these species; as for the diversity, this one is only slightly affected. Finally, we can note that the effects of herbicides did not cause a significant imbalance in this agroecosystem, unlike plowing, which showed harmful consequences on spiders.

Keywords: spiders, predator, species richness, herbicides, agricultural practices

Procedia PDF Downloads 90

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

Procedia PDF Downloads 331

Authors: Yusuf Jundi Sado

Abstract:

Changes in land use are mostly credited to human actions that result in negative impacts on biodiversity and ecosystem functions. This study aims to analyze the dynamics of land use and land cover changes for sustainable natural resources planning and management. Chilalo-Galama Mountain Range, Ethiopia. This study used Thematic Mapper 05 (TM) for 1986, 2001 and Landsat 8 (OLI) data 2017. Additionally, data from the Central Statistics Agency on human population growth were analyzed. Semi-Automatic classification plugin (SCP) in QGIS 3.2.3 software was used for image classification. Global positioning system, field observations and focus group discussions were used for ground verification. Land Use Land Cover (LU/LC) change analysis was using maximum likelihood supervised classification and changes were calculated for the 1986–2001 and the 2001–2017 and 1986-2017 periods. The results show that agricultural land increased from 27.85% (1986) to 44.43% and 51.32% in 2001 and 2017, respectively with the overall accuracies of 92% (1986), 90.36% (2001), and 88% (2017). On the other hand, forests decreased from 8.51% (1986) to 7.64 (2001) and 4.46% (2017), and grassland decreased from 37.47% (1986) to 15.22%, and 15.01% in 2001 and 2017, respectively. It indicates for the years 1986–2017 the largest area cover gain of agricultural land was obtained from grassland. The matrix also shows that shrubland gained land from agricultural land, afro-alpine, and forest land. Population dynamics is found to be one of the major driving forces for the LU/LU changes in the study area.

Keywords: Landsat, LU/LC change, Semi-Automatic classification plugin, population dynamics, Ethiopia

Procedia PDF Downloads 83

Authors: Jonghae Park, Daesung Kim

Abstract:

The impact of aggressive and conservative working capital‘s strategies on the value and profitability of the firms has been evaluated by applying the panel data regression analysis. The control variables used in the regression models are natural log of firm size, sales growth, and debt. We collected a panel of 13,988 companies listed on the Korea stock market covering the period 2000-2016. The major findings of this study are as follow: 1) We find a significant negative correlation between firm profitability and the number of days inventory (INV) and days accounts payable (AP). The firm’s profitability can also be improved by reducing the number of days of inventory and days accounts payable. 2) We also find a significant positive correlation between firm profitability and the number of days accounts receivable (AR) and cash ratios (CR). In other words, the cash is associated with high corporate profitability. 3) Tobin's analysis showed that only the number of days accounts receivable (AR) and cash ratios (CR) had a significant relationship. In conclusion, companies can increase profitability by reducing INV and increasing AP, but INV and AP did not affect corporate value. In particular, it is necessary to increase CA and decrease AR in order to increase Firm’s profitability and value.

Keywords: working capital, working capital management, firm value, profitability

Procedia PDF Downloads 188

Authors: Ravinder Bahl, Jamini Sharma

Abstract:

The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

Procedia PDF Downloads 357

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

Due to increasing cost pressure in global markets, artificial intelligence is becoming a technology that is decisive for competition. Predictive quality enables machinery and plant manufacturers to ensure product quality by using data-driven forecasts via machine learning models as a decision-making basis for test results. The use of cross-process Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: predictive quality, hydraulics, machine learning, classification, supervised learning

Procedia PDF Downloads 228

Authors: Ke Ren, Huiruo Shi, Linsen Li, Baoshuai Wang, Yu Zhou

Abstract:

Since some discriminative features are required for ground moving targets classification, we propose a new feature extraction method based on micro-Doppler signature. Firstly, the time-frequency analysis of measured data indicates that the time-frequency spectrograms of the three kinds of ground moving targets, i.e., single walking person, two people walking and a moving wheeled vehicle, are discriminative. Then, a three-dimensional time-frequency feature vector is extracted from the time-frequency spectrograms to depict these differences. At last, a Support Vector Machine (SVM) classifier is trained with the proposed three-dimensional feature vector. The classification accuracy to categorize ground moving targets into the three kinds of the measured data is found to be over 96%, which demonstrates the good discriminative ability of the proposed micro-Doppler feature.

Keywords: micro-doppler, time-frequency analysis, feature extraction, radar target classification

Procedia PDF Downloads 403

Authors: Salah Ghamari

Abstract:

In this study, the ability of self organizing map artificial (SOM) neural networks in clustering the wheat seeds varieties according to morphological properties of them was considered. The SOM is one type of unsupervised competitive learning. Experimentally, five morphological features of 300 seeds (including three varieties: gaskozhen, Md and sardari) were obtained using image processing technique. The results show that the artificial neural network has a good performance (90.33% accuracy) in classification of the wheat varieties despite of high similarity in them. The highest classification accuracy (100%) was achieved for sardari.

Keywords: artificial neural networks, clustering, self organizing map, wheat variety

Procedia PDF Downloads 655

Authors: S. Hamid Mirmohammadi, Shahrazad Tamjidzad

Abstract:

This paper is concerned with the single-item continuous review inventory system in which demand is stochastic and discrete. The budget consumed for purchasing the ordered items is not restricted but it incurs extra cost when exceeding specific value. The unit purchasing price depends on the quantity ordered under the all-units discounts cost structure. In many actual systems, the budget as a resource which is occupied by the purchased items is limited and the system is able to confront the resource shortage by charging more costs. Thus, considering the resource shortage costs as a part of system costs, especially when the amount of resource occupied by the purchased item is influenced by quantity discounts, is well motivated by practical concerns. In this paper, an optimization problem is formulated for finding the optimal (R, Q) policy, when the system is influenced by the budget limitation and a discount pricing simultaneously. Properties of the cost function are investigated and then an algorithm based on a one-dimensional search procedure is proposed for finding an optimal (R, Q) policy which minimizes the expected system costs .

Keywords: (R, Q) policy, stochastic demand, backorders, limited resource, quantity discounts

Procedia PDF Downloads 640

Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

Abstract:

A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

Procedia PDF Downloads 124

Authors: Shoaib Yousaf

Abstract:

The main purpose of this research is to the empirical investigation of the bullwhip effect under sensitivity analysis in the two-tier supply chain. The simulation modeling technique has been applied in this research as a research methodology to see the sensitivity analysis of the bullwhip effect in the rice industry of Pakistan. The research comprises two case studies that have been chosen as a sample. The results of this research have confirmed that reduction in production delay reduces the bullwhip effect, which conforms to the time compressing paradigm and the significance of the reduction in production delay to lessen demand amplification. The result of this research also indicates that by increasing the value of time to adjust inventory decreases the bullwhip effect. Furthermore, by decreasing the value of alpha increases the damping effect of the exponential smoother, it is not surprising that it also reduces the bullwhip effect. Moreover, by reducing the value of time to work in progress also reduces the bullwhip effect. This research will help practitioners and operation managers to reduces the major costs of their products in three ways. They can reduce their i) inventory levels, ii) better utilize their capacity and iii) improve their forecasting techniques. However, this study is based on two tier supply chain, while in reality the supply chain has got many tiers. Hence, future work will be extended across more than two-tier supply chains.

Keywords: bullwhip effect, rice industry, supply chain dynamics, simulation, sensitivity analysis

Procedia PDF Downloads 142

Authors: Charalambos C. Cleanthous, Ryan Sain, Tabitha Black, Stephen Vera, Suzanne Milton

Abstract:

A concern when administering questionnaires is whether the participant is providing information that is accurate. The results may be invalid because the person is trying to present oneself in an unrealistic positive manner referred to as ‘faking good’, or in an unrealistic negative manner known as ‘faking bad’. The Balanced Inventory of Desirable Responding (BIDR) was used to assess commercial pilots’ responses on the two subscales of the BIDR: impression management (IM) and self-deceptive enhancement (SDE) that result in high or low scores. Thus, the BIDR produces four valid profiles: IM low and SDE low, IM high and SDE low, IM low and SDE high, and IM high and SDE high. The various profiles were used to compare the respondents’ answers to crew resource management (CRM) items developed from the USA Federal Aviation Administration’s (FAA) guidelines for CRM composition and training. Of particular interest were the results on the IM subscale. The comparisons between those scoring high (lying or faking) versus those low on the IM suggest that there were significant differences regarding their views of the various dimensions of CRM. One of the more disconcerting conclusions is that the high IM scores suggest that the pilots were trying to impress rather than honestly answer the questions regarding their CRM training and practice.

Keywords: USA commercial pilots, crew resource management, faking, social desirability

Procedia PDF Downloads 255

Authors: Younghoon Kim, Seoung Bum Kim

Abstract:

One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

Procedia PDF Downloads 173

Authors: Joseph Kimutai Langat

Abstract:

Tea typology is the science of classifying tea. This study was carried out between November 2023 and July 2024, whose main objective was to investigate the typological classification nomenclature of processed tea in the world, narrowing down to Kenya. Centres of origin, historical background, tea growing region, scientific naming system, market, fermentation levels, processing/ oxidation levels and cultural reasons are used to classify tea at present. Of these, the most common typology is by oxidation, and more specifically, by the production methods within the oxidation categories. While the Asian tea producing countries categorises tea products based on the decreasing oxidation levels during the manufacturing process: black tea, green tea, oolong tea and instant tea, Kenya’s tea typology system is based on the degree of fermentation process, i.e. black tea, purple tea, green tea and white tea. Tea is also classified into five categories: black tea, green tea, white tea, oolong tea, and dark tea. Black tea is the main tea processed and exported in Kenya, manufactured mainly by withering, rolling, or by use of cutting-tearing-curling (CTC) method that ensures efficient conversion of leaf herbage to made tea, oxidizing, and drying before being sorted into different grades. It is from these varied typological methods that this review paper concludes that different regions of the world use different classification nomenclature. Therefore, since tea typology is not standardized, it is recommended that a global tea regulator dealing in tea classification be created to standardize tea typology, with domestic in-country regulatory bodies in tea growing countries accredited to implement the global-wide typological agreements and resolutions.

Keywords: classification, fermentation, oxidation, tea, typology

Procedia PDF Downloads 40

Authors: Abdulaziz Alsadhan, Naveed Khan

Abstract:

In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

Procedia PDF Downloads 364

Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

Abstract:

Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

Procedia PDF Downloads 303

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

Abstract:

With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

Procedia PDF Downloads 349

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 407

Authors: Hamidreza Fallah Lajimi , Elham Karami, Fatemeh Ali nasab, Mostafa Mahdavikia

Abstract:

Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering

Procedia PDF Downloads 472

Authors: Bilel Chalghaf, Mathieu Varin

Abstract:

Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

Procedia PDF Downloads 131

Authors: Riffi Mohammed Amine

Abstract:

A cloud is made up of small particles of liquid water or ice suspended in the atmosphere, which generally do not reach the ground. Various methods are used to classify clouds. This article focuses specifically on a technique known as particle swarm optimization (PSO), an AI approach inspired by the collective behaviors of animals living in groups, such as schools of fish and flocks of birds, and a method used to solve complex classification and optimization problems with approximate solutions. The proposed technique was evaluated using a series of second-generation METOSAT images taken by the MSG satellite. The acquired results indicate that the proposed method gave acceptable results.

Keywords: remote sensing, particle swarm optimization, clouds, meteorological image

Procedia PDF Downloads 14