Search results for: conventional learning method

Authors: Jong Woo Kim, Go Bong Choi, Sang Hwan Son, Dae Shik Kim, Jung Chul Suh, Jong Min Lee

Abstract:

The Markov Decision Process (MDP) based methodology is implemented in order to establish the optimal schedule which minimizes the cost. Formulation of MDP problem is presented using the information about the current state of pipe, improvement cost, failure cost and pipe deterioration model. The objective function and detailed algorithm of dynamic programming (DP) are modified due to the difficulty of implementing the conventional DP approaches. The optimal schedule derived from suggested model is compared to several policies via Monte Carlo simulation. Validity of the solution and improvement in computational time are proved.

Keywords: Markov decision processes, dynamic programming, Monte Carlo simulation, periodic replacement, Weibull distribution

Procedia PDF Downloads 412

Authors: Cyril Deroy, Agata Rumianek, David R. Greaves, Peter R. Cook, Edmond J. Walsh

Abstract:

Various microfluidic platforms have been developed in the past couple of decades offering experimental methods for the study of cell migration; however, their implementation in the laboratory has remained limited. Some reasons cited for the lack of uptake include the technical complexity of the devices, high failure rate associated with gas-bubbles, biocompatibility concerns with the use of polydimethylsiloxane (PDMS) and equipment/time/expertise requirements for operation and manufacture. As sample handling remains challenging due to the closed format of microfluidic devices, open microfluidic systems have been developed offering versatility and simplicity of use. Rather than confining fluids by solid walls, samples can be accessed directly over the open platform, by removing at least one of the solid boundaries, such as the cover. In this paper, a method for the fabrication of open fluid-walled microfluidic circuits for cell migration studies is introduced, where only materials commonly used by the life-science community are required; tissue culture dishes and cell media. The simplicity of the method, and ability to retrieve cells of interest are two key features of the method. Both passive and active flow-devices can be created in this way. To demonstrate the versatility of the method a cell migration assay is performed, which requires fabricating circuits for establishing chemical gradients, loading cells and incubating, creating chemical gradients, real time imaging of cell migration and finally retrieval of cells. The open architecture has high fidelity as it eliminates air bubble related failures and enables the precise control of gradients. The ability to fabricate custom microfluidic designs in minutes should make this method suitable for use in a wide range of cell migration studies.

Keywords: chemotaxis, fluid walls, gradient generation, open microfluidics

Procedia PDF Downloads 135

Authors: Dimitrios J. Dimitriou, Maria F. Sartzetaki

Abstract:

Air Transport links markets and individuals, making regions more competitive and promoting social and economic development. The assessment of social contribution is the key objective of this paper, focusing on the definition of the components of social dimension and welfare metrics in the national scale. According to a top-down approach, the key dimensions that affect the social welfare are presented. Conventional wisdom is to provide estimations on added value to social issues caused by the air transport development and present the methodology framework for measuring the contribution of transport development in social value chain. Greece is the case study of this paper, providing results from the contribution of air transport infrastructures in national welfare. The application key findings are essential for managers and decision makers to support actions and plans towards economic recovery of an economy presenting strong seasonal characteristics (because of tourism) and suffering from recession.

Keywords: air transport, social coherence, resilient business development, socioeconomic impact

Procedia PDF Downloads 212

Authors: O. Ekrami, P. Claes, S. Van Dongen

Abstract:

Fluctuating asymmetry (FA) has been studied for many years as an indicator of developmental stability or ‘genetic quality’ based on the assumption that perfect symmetry is ideally the expected outcome for a bilateral organism. Further studies have also investigated the possible link between FA and attractiveness or levels of masculinity or femininity. These hypotheses have been mostly examined using 2D images, and the structure of interest is usually presented using a limited number of landmarks. Such methods have the downside of simplifying and reducing the dimensionality of the structure, which will in return increase the error of the analysis. In an attempt to reach more conclusive and accurate results, in this study we have used high-resolution 3D scans of human faces and have developed an algorithm to measure and localize FA, taking a spatially-dense approach. A symmetric spatially dense anthropometric mask with paired vertices is non-rigidly mapped on target faces using an Iterative Closest Point (ICP) registration algorithm. A set of 19 manually indicated landmarks were used to examine the precision of our mapping step. The protocol’s accuracy in measurement and localizing FA is assessed using simulated faces with known amounts of asymmetry added to them. The results of validation of our approach show that the algorithm is perfectly capable of locating and measuring FA in 3D simulated faces. With the use of such algorithm, the additional captured information on asymmetry can be used to improve the studies of FA as an indicator of fitness or attractiveness. This algorithm can especially be of great benefit in studies of high number of subjects due to its automated and time-efficient nature. Additionally, taking a spatially dense approach provides us with information about the locality of FA, which is impossible to obtain using conventional methods. It also enables us to analyze the asymmetry of a morphological structures in a multivariate manner; This can be achieved by using methods such as Principal Components Analysis (PCA) or Factor Analysis, which can be a step towards understanding the underlying processes of asymmetry. This method can also be used in combination with genome wide association studies to help unravel the genetic bases of FA. To conclude, we introduced an algorithm to study and analyze asymmetry in human faces, with the possibility of extending the application to other morphological structures, in an automated, accurate and multi-variate framework.

Keywords: developmental stability, fluctuating asymmetry, morphometrics, 3D image processing

Procedia PDF Downloads 129

Authors: P. Vanichkobchinda

Abstract:

The Logistics Transportation techniques, Open Vehicle Routing (OVR) is an approach toward transportation cost reduction, especially for long distance pickup and delivery nodes. The outstanding characteristic of OVR is that the route starting node and ending node are not necessary the same as in typical vehicle routing problems. This advantage enables the routing to flow continuously and the vehicle does not always return to its home base. This research aims to develop a heuristic for the open vehicle routing problem with pickup and delivery under time window and loading capacity constraints to minimize the total distance. The proposed heuristic is developed based on the Insertion method, which is a simple method and suitable for the rapid calculation that allows insertion of the new additional transportation requirements along the original paths. According to the heuristic analysis, cost comparisons between the proposed heuristic and companies are using method, nearest neighbor method show that the insertion heuristic. Moreover, the proposed heuristic gave superior solutions in all types of test problems. In conclusion, the proposed heuristic can effectively and efficiently solve the open vehicle routing. The research indicates that the improvement of new transport's calculation and the open vehicle routing with "Insertion Heuristic" represent a better outcome with 34.3 percent in average. in cost savings. Moreover, the proposed heuristic gave superior solutions in all types of test problems. In conclusion, the proposed heuristic can effectively and efficiently solve the open vehicle routing.

Keywords: business competitiveness, cost reduction, SMEs, logistics transportation, VRP

Procedia PDF Downloads 673

Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

Procedia PDF Downloads 375

Authors: Omer Oral, Y. Emre Yilmaz

Abstract:

Topology optimization is an approach that optimizes material distribution within a given design space for a certain load and boundary conditions by providing performance goals. It uses various restrictions such as boundary conditions, set of loads, and constraints to maximize the performance of the system. It is different than size and shape optimization methods, but it reserves some features of both methods. In this study, interior structures of the parts were optimized by using SIMP (Solid Isotropic Material with Penalization) method. The volume of the part was preassigned parameter and minimum deflection was the objective function. The basic idea behind the theory was considered, and different methods were discussed. Rhinoceros 3D design tool was used with Grasshopper and TopOpt plugins to create and optimize parts. A Grasshopper algorithm was designed and tested for different beams, set of arbitrary located forces and support types such as pinned, fixed, etc. Finally, 2.5D shapes were obtained and verified by observing the changes in density function.

Keywords: Grasshopper, lattice structure, microstructures, Rhinoceros, solid isotropic material with penalization method, TopOpt, topology optimization

Procedia PDF Downloads 118

Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

Abstract:

Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

Procedia PDF Downloads 111

Authors: Medjram Sofiane, Babahenini Mohamed Chaouki, Mohamed Benali Yamina

Abstract:

Skin detection is an important task for computer vision systems. A good method for skin detection means a good and successful result of the system. The colour is a good descriptor that allows us to detect skin colour in the images, but because of lightings effects and objects that have a similar colour skin, skin detection becomes difficult. In this paper, we proposed a method using the YCbCr colour space for skin detection and lighting effects elimination, then we use the information of texture to eliminate the false regions detected by the YCbCr colour skin model.

Keywords: skin detection, YCbCr, GLCM, texture, human skin

Procedia PDF Downloads 440

Authors: T. J. Hasanova, C. N. Imamalieva

Abstract:

The problem about the movement of the rigid cylinder keeping the vertical position under the influence of running superficial waves in a liquid is considered. The indignation of a falling wave caused by the presence of the cylinder which moves is thus considered. Special decomposition on a falling harmonious wave is used. The problem dares an operational method. For a finding of the original decision, Considering that the image denominator represents a tabular function, Voltaire's integrated equation of the first sort which dares a numerical method is used. Cylinder movement in the continuous environment under the influence of waves is considered in work. Problems are solved by an operational method, thus originals of required functions are looked for by the numerical definition of poles of combinations of transcendental functions and calculation of not own integrals. Using specificity of a task below, Decisions are under construction the numerical solution of the integrated equation of Volter of the first sort that does not create computing problems of the complex roots of transcendental functions connected with search.

Keywords: rigid cylinder, linear interpolation, fluctuations, Voltaire's integrated equation, harmonious wave

Procedia PDF Downloads 311

Authors: Gurbanova J., Majidova N., Ali-Zade S., Hasanova A., Mikailzade P.

Abstract:

Currently, the problem of oncogynecological diseases is widespread and remains relevant in terms of quantitative growth. It is known that due to the increase in the number of benign diseases of the cervix, the development of precancerous conditions occurs. Benign diseases of the cervix represent the most common gynecological problem, which are often precursors of malignant neoplasms, especially cervical cancer. According to statistics, benign diseases of the cervix cover 25-45% of all gynecological diseases. Among women's oncogynecological diseases, cervical cancer ranks second in the world after breast cancer and ranks first in the mortality rate among oncological diseases in economically underdeveloped countries. We performed a comprehensive clinical and laboratory examination of 130 women aged 18 to 73 with benign cervical diseases. 59 (38.5%) women of reproductive age, as well as 39 (30%) premenopausal and 41 (31.5%) menopausal patients, participated in the study. Detailed anamnesis was collected from all patients, objective and gynecological examination was performed, laboratory and instrumental examinations (USM, IPV DNA, smear microscopy, and PCR bacteriological examination of sexually transmitted infections), simple and extended colposcopy, liquid-based РАР-smear smear and РАР-classic smear examinations were conducted. As a result of the research, the following nosological forms were found in women with benign diseases of the cervix: non-specific vaginitis in 10 (7.7%) cases; ectopia, endocervicitis - 60(46.2%); cervical ectropion - 7(5.4%); cervical polyp - 9(6.9%); cervical leukoplakia - 15(11.5%); atrophic vaginitis - 7(5.4%); condyloma - 12(9.2%); cervical stenosis - 2(1.5%); endometriosis of the cervix - was noted in 8 (6.2%) cases (p<0.001), respectively. Characteristics of the menstrual cycle among the examined women: normal cycle in 97 (74.6%) cases; oligomenorrhea – 23 (17.7%); polymenorrhea – 4(3.1%); algomenorrhea – noted in 6 (4.6%) cases (p<0.001). Cytological examination showed that: the specificity of liquid-based cytology was 76.2%, and the traditional PAP test was set at 70.6%. The overall diagnostic value was calculated to be 86% in liquid-based cytology and 78.5% in conventional PAP tests. Treatment of women with benign diseases of the cervix was carried out by diathermocoagulation method and "FOTEK EA 141M" device. It should be noted that 6 months after the treatment, after treatment with the "FOTEK EA 141M" device, there was no relapse in any patient. Recurrence was found in 23.7% of patients after diathermoelectrocoagulation. Thus, it is clear from the above that the study of cervical pathologies, the determination of optimal examinations, and effective treatment methods is one of the urgent problems facing obstetrics and gynecology.

Keywords: cervical cancer, cytological examination, PAP-smear, non-specific vaginitis

Procedia PDF Downloads 90

Authors: Nicole Merkle, Stefan Zander

Abstract:

Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

Procedia PDF Downloads 267

Authors: Tianqi Wu, Min Wang

Abstract:

Research investigating language acquisition from a constructionist perspective has demonstrated that language is learned as constructions at various linguistic levels, which is related to factors of frequency, semantic prototypicality, and form-meaning contingency. However, previous research on construction learning tended to focus on clause-level constructions such as verb argument constructions but few attempts were made to study morpheme-level constructions such as the progressive construction, which is regarded as a source of acquisition problems for English learners from diverse L1 backgrounds, especially for those whose L1 do not have an equivalent construction such as German and Chinese. To trace the developmental trajectory of Chinese EFL learners’ use of the progressive with respect to verb frequency, verb-progressive contingency, and verbal prototypicality and generality, a learner corpus consisting of three sub-corpora representing three different English proficiency levels was extracted from the Chinese Learners of English Corpora (CLEC). As the reference point, a native speakers’ corpus extracted from the Louvain Corpus of Native English Essays was also established. All the texts were annotated with C7 tagset by part-of-speech tagging software. After annotation all valid progressive hits were retrieved with AntConc 3.4.3 followed by a manual check. Frequency-related data showed that from the lowest to the highest proficiency level, (1) the type token ratio increased steadily from 23.5% to 35.6%, getting closer to 36.4% in the native speakers’ corpus, indicating a wider use of verbs in the progressive; (2) the normalized entropy value rose from 0.776 to 0.876, working towards the target score of 0.886 in native speakers’ corpus, revealing that upper-intermediate learners exhibited a more even distribution and more productive use of verbs in the progressive; (3) activity verbs (i.e., verbs with prototypical progressive meanings like running and singing) dropped from 59% to 34% but non-prototypical verbs such as state verbs (e.g., being and living) and achievement verbs (e.g., dying and finishing) were increasingly used in the progressive. Apart from raw frequency analyses, collostructional analyses were conducted to quantify verb-progressive contingency and to determine what verbs were distinctively associated with the progressive construction. Results were in line with raw frequency findings, which showed that contingency between the progressive and non-prototypical verbs represented by light verbs (e.g., going, doing, making, and coming) increased as English proficiency proceeded. These findings altogether suggested that beginning Chinese EFL learners were less productive in using the progressive construction: they were constrained by a small set of verbs which had concrete and typical progressive meanings (e.g., the activity verbs). But with English proficiency increasing, their use of the progressive began to spread to marginal members such as the light verbs.

Keywords: Construction learning, Corpus-based, Progressives, Prototype

Procedia PDF Downloads 118

Authors: Nada Mouchfiq, Ahmed Habbani, Chaimae Benjbara

Abstract:

The security aspect of the IoT occupies a place of great importance especially after the evolution that has known this field lastly because it must take into account the transformations and the new applications .Blockchain is a new technology dedicated to the data sharing. However, this does not work the same way in the different systems with different operating principles. This article will discuss network security using the Blockchain to facilitate the sending of messages and information, enabling the use of new processes and enabling autonomous coordination of devices. To do this, we will discuss proposed solutions to ensure a high level of security in these networks in the work of other researchers. Finally, our article will propose a method of security more adapted to our needs as a team working in the ad hoc networks, this method is based on the principle of the Blockchain and that we named ”MPR Blockchain”.

Keywords: Ad hocs networks, blockchain, MPR, security

Procedia PDF Downloads 165

Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

Abstract:

This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

Procedia PDF Downloads 47

Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

Abstract:

Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

Procedia PDF Downloads 121

Authors: Noriyuki Suyama

Abstract:

The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

Procedia PDF Downloads 83

Authors: Li-Zhi Liao

Abstract:

The central path has played the key role in the interior point method. However, the convergence of the central path may not be true even in some convex programming problems with linear constraints. In this paper, the generalized central paths are introduced for convex programming. One advantage of the generalized central paths is that the paths will always converge to some optimal solutions of the convex programming problem for any initial interior point. Some additional theoretical properties for the generalized central paths will be also reported.

Keywords: central path, convex programming, generalized central path, interior point method

Procedia PDF Downloads 308

Authors: Mahmoud Nabilu

Abstract:

The present study explored the effect of using Mobile listening applications on developing listening skills by Iranian intermediate EFL learners. Fifty male intermediate English learners whose age range was between 15 and 20, participated in the study. The participants were placed in two groups on the basis of their scores on a placement test. Therefore, the participants of the study were homogenized in terms of general proficiency, and groups were assigned as one experimental group and one control group. The experimental group was instructed by the treatment which was using mobile applications to develop their listening skills while the control group received traditional methods. The research data were obtained from the 40-item multiple-choice tests as a pre-test and a post-test. The results of the t-test clearly revealed that the learners in the experimental group performed better in the post-test than the pre-test. This implies that using a mobile application for developing listening skills as a treatment was effective in helping the language learners perform better on post-test. However, a statistically significant difference was found between the post-tests scores of the two groups. The mean of the experimental group was greater compared to the control group. The participants were Iranian and from an Iranian Language Institute, so care should be taken while generalizing the results to the learners of other nationalities. However, in the researcher's view, the findings of this study have valuable implications for teachers and learners, methodologists and syllabus designers, linguists and MALL/CALL (mobile/computer-assisted language learning) experts. Using the result of the present paper is an aim of raising the consciousness of a better technique of developing listening skills in order to make language learning more efficient for the learners.

Keywords: Mobile listening applications, intermediate EFL learners, MALL, CALL

Procedia PDF Downloads 182

Authors: Didem Can

Abstract:

Scheduling problems are one of the most fundamental issues of production systems. Many different approaches and models have been developed according to the production processes of the parts and the main purpose of the problem. In this study, one of the bottleneck stations of a company serving in the aerospace industry is analyzed and considered as a single machine scheduling problem with sequence-dependent setup times. The objective of the problem is assigning a large number of similar parts to the same shift -to reduce chemical waste- while minimizing the number of tardy jobs. The goal programming method will be used to achieve two different objectives simultaneously. The assignment of parts to the shift will be expressed using the integer programming method. Finally, the constraint programming method will be used as it provides a way to find a result in a short time by avoiding worse resulting feasible solutions with the defined variables set. The model to be established will be tested and evaluated with real data in the application part.

Keywords: constraint programming, goal programming, integer programming, sequence-dependent setup, single machine scheduling

Procedia PDF Downloads 218

Authors: Carol S. Binnekade, Deon Scott, Christina C. Shuttleworth, Annelien A. Van Rooyen

Abstract:

Higher education institutions are constantly challenged to deliver skilled graduates into the workplace. Employers expect graduates to have the required technical knowledge as well as various pervasive skills. This also applies to accountants who need to know the technical requirements of financial reporting and be able to communicate with individuals, teams and clients at a high level. Accountants need to develop effective business conversational skills and use these skills to communicate up, down and across organizations, taking into consideration cultural and gender diversity. In addition, they need to master business writing and presentation skills. However, providing students with these skills in a distance-learning environment where interaction between students and instructors is limited, is a challenge for academics. The study on which this paper reports, forms part of a larger body of research, which explored the perceptions of accounting practitioners of the communication skills (or lack thereof) of recently qualified accounting students. Feedback (qualitative and quantitative) was obtained from various accounting practitioners in South Africa. Taking into consideration that distance learners communicate mainly with their instructors via email communication and their assignments are submitted using various word processor software, the researchers were of the opinion that the accounting graduates would be capable of communicating effectively once they entered the workplace. However, the research findings, inter alia, suggested that the accounting graduates lacked communication skills and that training was needed to differentiate between business and social communication once they entered the workplace. Recommendations on how these communication challenges may be addressed by higher education institutions are provided.

Keywords: accounting practitioners, communication skills, distance education, pervasive skills

Procedia PDF Downloads 192

Authors: Alvin Uchenna Ugwu

Abstract:

The United Nation’s 2030 agenda and Global Action Programme (GAP) for implementation of the Sustainable Development Goals (SDGs), has mandated all sectors in the societies, including education, to develop strategies towards actualizing sustainability in all facets of the society, by the year 2030. Education is no doubt a key tool for social change. However, educational institutions in most African nations need a paradigmatic shift to strike a balance between policies (curricular) and practices, with regards to Education for Sustainable Development (ESD). The paradigm shift in this regard is described as whole-institution/school approach. The whole institution approaches advocate action-focused ESD. In other words, ESD policy and curriculum makers, formal and non-formal education institutions, need to ‘practice what they preach’. This paper is developed from an ongoing study carried out by the author and guided by two research questions: -What are the views of intermediate phase science and technology preservice teachers on the ESD content included in the science and technology modules? -What challenges or enable intermediate phase science and technology pre-service teachers to learn about ESD in science and technology modules? The study drew from the views and experiences of preservice science teachers, learning about ESD in a university’s college of education in South Africa. Using qualitative case study research design, the research data were generated via questionnaires and focus group discussions. Analysis of generated data indicates that universities and institutions of higher learning need to demonstrate practical involvement while implementing ESD in societies, rather than just standing as knowledge media. Findings of the study further suggest that natural sciences and technology courses in teacher education programmes and other institutions of higher learning, should be perceived as key transformative tools in shaping the consciousness of students towards integrating and fostering ESD in developing countries such as South Africa. Thus, this paper seeks to promote ‘Whole Institution Involvement’ in teacher education colleges in South Africa, as a measure of improving ESD in higher education settings. The paper suggests that in order to achieve ESD in higher education settings and beyond, policies and practices should be reexamined beyond rhetoric and buzzwords. The paper further argues that implementation of ESD is largely influenced by context, hence two different contexts should be examined empirically.

Keywords: education for sustainable development, higher education institutions, pre-service science teachers, qualitative case study research, whole institution involvement

Procedia PDF Downloads 157

Authors: Fabiola M. Villalobos-Castaldi, Nicolás C. Kemper, Esther Rojas-Krugger, Laura G. Ramírez-Sánchez

Abstract:

This paper presents the classification results about exploring the combination of a motion based approach with a local appearance method to describe the facial motion caused by the muscle contractions and expansions that are presented in facial expressions. The proposed feature extraction method take advantage of the knowledge related to which parts of the face reflects the highest deformations, so we selected 4 specific facial regions at which the appearance descriptor were applied. The most common used approaches for feature extraction are the holistic and the local strategies. In this work we present the results of using a local appearance approach estimating the correlation coefficient to the 4 corresponding landmark-localized facial templates of the expression face related to the neutral face. The results let us to probe how the proposed motion estimation scheme based on the local appearance correlation computation can simply and intuitively measure the motion parameters for some of the most relevant facial regions and how these parameters can be used to recognize facial expressions automatically.

Keywords: facial expression recognition system, feature extraction, local-appearance method, motion-based approach

Procedia PDF Downloads 400

Authors: Ayuko Itsuki, Sachiyo Aburatani

Abstract:

In recent years, pollutions of the environment by toxic substances become a serious problem. While there are many methods of environmental clean-up, the methods by microorganisms are considered to be reasonable and safety for environment. Compost is known that it catabolize the meladorous substancess in its production process, however the mechanism of its catabolizing system is not known yet. In the catabolization process, organic matters turn into inorganic by the released enzymes from lots of microorganisms which live in compost. In other words, the cooperative of activated enzymes in the compost decomposes malodorous substances. Thus, clarifying the interaction among enzymes is important for revealing the catabolizing system of meladorous substance in compost. In this study, we utilized statistical method to infer the interaction among enzymes. We developed a method which combined partial correlation with cross correlation to estimate the relevance between enzymes especially from time series data of few variables. Because of using cross correlation, we can estimate not only the associative structure but also the reaction pathway. We applied the developed method to the enzyme measured data and estimated an interaction among the enzymes in decomposition mechanism of toluene.

Keywords: enzyme activities, comparative analysis, compost, toluene

Procedia PDF Downloads 257

Authors: Ian Marc G. Cabugsa, Kim Ryan A. Won

Abstract:

Fast, reliable and simultaneous HPLC analysis of theobromine and caffeine in cacao and cocoa products was optimized in this study. The samples tested were raw, fermented, and roasted cacao beans as well as commercially available cocoa products. The HPLC analysis was carried out using step gradient solvent system with acetonitrile and water buffered with H3PO4 as the mobile phase. The HPLC system was optimized using 273 nm wavelength at 35 °C for the column temperature with a flow rate of 1.0 mL/min. Using this method, the theobromine percent recovery mean, Limit of Detection (LOD) and Limit of Quantification (LOQ) is 118.68(±3.38)%, 0.727 and 1.05 respectively. The percent recovery mean, LOD and LOQ for caffeine is 105.53(±3.25)%, 2.42 and 3.50 respectively. The inter-day and intra-day precision for theobromine is 4.31% and 4.48% respectively, while 7.02% and 7.03% was for caffeine respectively. Compared to the standard method in AOAC using methanol in isocratic solvent system, the results of the study produced lesser chromatogram noise with emphasis on theobromine and caffeine. The method is readily usable for cacao and cocoa substances analyses using HPLC with step gradient capability.

Keywords: cacao, caffeine, HPLC, step gradient solvent system, theobromine

Procedia PDF Downloads 269

Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

Abstract:

The edges of low contrast images are not clearly distinguishable to the human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: low contrast image, fractional order differentiator, Laplacian of Gaussian (LoG) method, chebyshev polynomial

Procedia PDF Downloads 619

Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

Abstract:

Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

Procedia PDF Downloads 102

Authors: Raptis Sotirios

Abstract:

Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 215

Authors: Siti Aisyah binti Jumpaan

Abstract:

21ˢᵗ century skills (PAK-21) integration has made its way into Malaysian curriculum when Ministry of Education introduce its implementation since 2016. This study was conducted to explore pre-service teachers’ readiness in integrating 21st century skills in the classroom via the digital storytelling (DST) method and to find gaps between theory and practice that can be integral towards pre-service teachers’ professional growth. Qualitative research method was used in this research involving six respondents who were selected using a purposive sampling method. Their response from interviews and lesson plan analysis were analysed using narrative analysis. The findings showed that pre-service teachers showed a moderate level of readiness in integrating 21st century skills using DST. Pre-service teachers demonstrated high level of preparedness in writing their lesson plan, but their interview revealed that they faced struggles in implementation due to several factors, such as lack of technology and failure to obtain students’ participation. This study further strengthens the need for specialised curriculum for pre-service teachers in teaching 21st century skills via DST.

Keywords: digital storytelling, 21ˢᵗ century skills, preservice teachers, teacher training

Procedia PDF Downloads 79

Authors: S. B. Bhandare, K. S. Laddha

Abstract:

Podphyllum hexandrum belonging to family berberidaceae has gained attention in phytochemical and pharmacological research as it shows excellent anticancer activity and has been used in treatment of skin diseases, sunburns and radioprotection. Chemically it contains lignans and flavonoids such as kaempferol, quercetin and their glycosides. Objective: To isolate and identify Kaempferol and Quercetin from Podophyllum rhizome. Method: The powdered rhizome of Podophyllum hexandrum was subjected to soxhlet extraction with methanol. This methanolic extract is used to obtain podophyllin. Podohyllin was extracted with ethyl acetate and this extract was then concentrated and subjected to column chromatography to obtain purified kaempferol and quercetin. Result: Isolated kaempferol, quercetin were light yellow and dark yellow in colour respectively. TLC of the isolated compounds was performed using chloroform: methanol (9:1) which showed single band on silica plate at Rf 0.6 and 0.4 for kaempferol and quercetin. UV spectrometric studies showed UV maxima (methanol) at 259, 360 nm and 260, 370 nm which are identical with standard kaempferol and quercetin respectively. Both IR spectra exhibited prominent absorption bands for free phenolic OH at 3277 and 3296.2 cm-1 and for conjugated C=O at 1597 and 1659.7 cm-1 respectively. The mass spectrum of kaempferol and quercetin showed (M+1) peak at m/z 287 and 303.09 respectively. 1H NMR analysis of both isolated compounds exhibited typical four-peak pattern of two doublets at δ 6.86 and δ 8.01 which was assigned to H-3’,5’ and H-2’,6’ respectively. Absence of signals less than δ 6.81 in the 1H NMR spectrum supported the aromatic nature of compound. Kaempferol and Quercetin showed 98.1% and 97% purity by HPLC at UV 370 nm. Conclusion: Easy and simple method for isolation of Kaempferol and Quercetin was developed and their structures were confirmed by UV, IR, NMR and mass studies. Method has shown good reproducibility, yield and purity.

Keywords: flavonoids, kaempferol, podophyllum rhizome, quercetin

Procedia PDF Downloads 294