Search results for: artificial microRNA approach

Authors: Fabio Oettl, Sebastian Hoerbrand, Tobias Wittmeir, Johannes Schilp

Abstract:

By combining the additive manufacturing (AM)- process with digital concepts, like the digital twin (DT) or the downsized and basing concept of the digital part file (DPF), the competitiveness of additive manufacturing is enhanced and new use cases like decentral production are enabled. But in literature, one can´t find any quantitative approach for valuing the usage of a DT or DPF in AM. Out of this fact, such an approach will be developed within this paper in order to further promote or dissuade the usage of these concepts. The focus is set on the production as an early lifecycle phase, which means that the AM-production process gets analyzed regarding the potential advantages of using DPF in AM. These advantages are transferred to a monetary value with this approach. By calculating the costs of the DPF, an overall monetary value is a result. Thereon a tool, based on a simulation environment is constructed, where the algorithms are transformed into a program. The results of applying this tool show that an overall value of 20,81 € for the DPF can be realized for one special use case. For the future application of the DPF there is the recommendation to integrate especially sustainable information because out of this, a higher value of the DPF can be expected.

Keywords: additive manufacturing, digital concept costs, digital part file, digital twin, monetary value estimation

Procedia PDF Downloads 201

Authors: M. Sarikanat, E. Akar, I. Şen, Y. Seki, O. C. Yılmaz, B. O. Gürses, L. Cetin, O. Özdemir, K. Sever

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Chitosan is a natural, nontoxic, polyelectrolyte, cheap polymer. In this study, chitosan based electroactive polymer (CBEAP) was fabricated. Electroactive properties of this polymer were investigated at different voltages. It exhibited excellent tip displacement at low voltages (1, 3, 5, 7 V). Tip displacement was increased as the applied voltage increased. Best tip displacement was investigated as 28 mm at 5V. Characterization of CBEAP was investigated by scanning electron microscope, X-ray diffraction and tensile testing. CBEAP exhibited desired electroactive properties at low voltages. It is suitable for using in artificial muscle and various robotic applications.

Keywords: chitosan, electroactive polymer, electroactive properties

Procedia PDF Downloads 512

Authors: Zakir H. Shaik, Punam L. Vartak

Abstract:

Management By Objectives (MBO) is a management technique in which objectives of an organisation are conveyed to the employees to establish the individual goals. These objectives and goals are then monitored and assessed jointly by management and the employee time to time. This tool can be used for planning, monitoring as well as for performance appraisal. The success of an organisation is largely dependent on its’s Vision. Thus, it is of paramount importance to achieve the realm of vision through a mission which is well crafted within the organisation to address the objectives. The success of the mission depends upon how realistic and action oriented philosophical approach, an organisation caters to; and how the individual goals are set to track and meet the objectives. Thus, focused and passionate efforts of the team, assigned for the mission, are an absolute obligation for achieving the vision of any organisation. Any construction site is generally a controlled disorder having huge investments, resources and logistics involved. The Construction progression is time-consuming with many isolated as well as interconnected activities. Traditional MBO approach can be unsuccessful if planning and control is non-realistic and inflexible. Moreover, the Construction Industry is far behind understanding these concepts. It is important to address the employee engagement in defining and creating awareness to achieve the targets. Besides, current economic environment and competitive world demands refined management tools to achieve profit, growth and survival of the business. Therefore, the necessity of rational MBO becomes vital part towards the success of an organisation. This paper details about the philosophical assumptions to develop the grounded theory in lieu of achieving objectives through RATIONAL MBO approach in Construction Contracting Organisations. The goals and objectives of the Construction Contracting Organisations can be achieved efficiently by adopting this RATIONAL MBO approach, as those are based on realistic, logical and balanced assumptions.

Keywords: growth, leadership, management by objectives, Management By Objectives (MBO), profit, rational

Procedia PDF Downloads 153

Authors: Halim Djerroud, Arab Ali Cherif

Abstract:

We assume that conscious functions are implemented automatically. In other words that consciousness as well as the non-consciousness aspect of human thought, planning, and perception, are produced by biologically adaptive algorithms. We propose that the mechanisms of consciousness can be produced using similar adaptive algorithms to those executed by the mechanism. In this paper, we propose a computational model of consciousness, the ”Global Abstraction Workspace” which is an internal environmental modelling perceived as a multi-agent system. This system is able to evolve and generate new data and processes as well as actions in the environment.

Keywords: artificial consciousness, cognitive architecture, global abstraction workspace, multi-agent system

Procedia PDF Downloads 340

Authors: Revoti Prasad Bora, Nikita Katyal, Saurabh Yadav

Abstract:

Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques.

Keywords: deep-learning, classification, pre-processing, computer vision, image processing, educational data mining

Procedia PDF Downloads 163

Authors: R. K. Yadav, S. Baral, H. R. Paudel, R. Basnet

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The Public-Private Mix (PPM) approach is a strategic initiative that involves engaging all private and public healthcare providers in the fight against tuberculosis using international healthcare standards. For tuberculosis control in Nepal, the PPM approach could be a milestone. This study aimed to explore the barriers to a public-private mix approach in the management of tuberculosis cases in Nepal. A total of 20 respondents participated in the study. Barriers to PPM were identified in the following three themes: 1) Obstacles related to TB case detection, 2) Obstacles related to patients, and 3) Obstacles related to the healthcare system. PPM implementation was challenged by following subthemes that included staff turnover, low private sector participation in workshops, a lack of training, poor recording and reporting, insufficient joint monitoring and supervision, poor financial benefit, lack of coordination and collaboration, and non-supportive TB-related policies and strategies. The study concludes that numerous barriers exist in the way of effective implementation of the PPM approach, including TB cases detection barriers such as knowledge of TB diagnosis and treatment, HW attitude, workload, patient-related barriers such as knowledge of TB, self-medication practice, stigma and discrimination, financial status, and health system-related barriers such as staff turnover and poor engagement of the private sector in workshops, training, recording, and re-evaluation. Government stakeholders must work together with private sector stakeholders to perform joint monitoring and supervision. Private practitioners should receive training and orientation, and presumptive TB patients should be given adequate time and counseling as well as motivation to visit a government health facility.

Keywords: barrier, tuberculosis, case finding, PPM, nepal

Procedia PDF Downloads 110

Authors: Sonam Chorol, Sharvan Kumar, Pritam Mukhopadhyay

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In biology, supramolecular systems require the use of chemical fuels to stay in sustained nonequilibrium steady states termed dissipative self-assembly in contrast to synthetic self-assembly. Biomimicking these natural dynamic systems, some studies have demonstrated artificial self-assembly under nonequilibrium utilizing various forms of energies (fuel) such as chemical, redox, and pH. Naphthalene diimides (NDIs) are well-known organic molecules in supramolecular architectures with high electron affinity and have applications in controlled electron transfer (ET) reactions, etc. Herein, we report the endergonic ET from tetraphenylborate to highly electron-deficient phosphonium NDI²+ dication to generate NDI•+ radical. The formation of radicals was confirmed by UV-Vis-NIR absorption spectroscopy. Electron-donor and electron-acceptor energy levels were calculated from experimental electrochemistry and theoretical DFT analysis. The HOMO of the electron donor locates below the LUMO of the electro-acceptor. This indicates that electron transfer is endergonic (ΔE°ET = negative). The endergonic ET from NaBPh₄ to NDI²+ dication was achieved thermodynamically by the formation of coupled biphenyl product confirmed by GC-MS analysis. NDI molecule bearing octyl phosphonium at the core and H-bond forming imide moieties at the axial position forms a gel. The rheological properties of purified radical ion NDI⦁+ gels were evaluated. The atomic force microscopy studies reveal the formation of large branching-type networks with a maximum height of 70-80 nm. The endergonic ET from NaBPh₄ to NDI²+ dication was used to design the assembly and disassembly redox reaction cycle using reducing (NaBPh₄) and oxidizing agents (Br₂) as chemical fuels. A part of NaBPh₄ is used to drive assembly, while a fraction of the NaBPh₄ is dissipated by forming a useful product. The system goes back to the disassembled NDI²+ dication state with the addition of Br₂. We think bioinspired dissipative self-assembly is the best approach to developing future lifelike materials with autonomous behavior.

Keywords: Ionic-gel, redox-cycle, self-assembly, useful product

Procedia PDF Downloads 84

Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

Abstract:

The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

Procedia PDF Downloads 339

Authors: Marden Silva, Danielle Guerra

Abstract:

Some studies have indicated that pronunciation teaching hasn’t been paid enough attention by teachers regarding EFL contexts. In particular, segmental and suprasegmental features through genre-based approach may be an opportunity on how to integrate pronunciation into a more meaningful learning practice. Therefore, the aim of this project was to carry out a survey on some aspects related to English pronunciation that Brazilian students consider more difficult to learn, thus enabling the discussion of strategies that can facilitate the development of oral skills in English classes by integrating the teaching of phonetic-phonological aspects into the genre-based approach. Notions of intelligibility, fluency and accuracy were proposed by some authors as an ideal didactic sequence. According to their proposals, basic learners should be exposed to activities focused on the notion of intelligibility as well as intermediate students to the notion of fluency, and finally more advanced ones to accuracy practices. In order to test this hypothesis, data collection was conducted during three high school English classes at Federal Center for Technological Education of Minas Gerais (CEFET-MG), in Brazil, through questionnaires and didactic activities, which were recorded and transcribed for further analysis. The genre debate was chosen to facilitate the oral expression of the participants in a freer way, making them answering questions and giving their opinion about a previously selected topic. The findings indicated that basic students demonstrated more difficulty with aspects of English pronunciation than the others. Many of the intelligibility aspects analyzed had to be listened more than once for a better understanding. For intermediate students, the speeches recorded were considerably easier to understand, but nevertheless they found it more difficult to pronounce the words fluently, often interrupting their speech to think about what they were going to say and how they would talk. Lastly, more advanced learners seemed to express their ideas more fluently, but still subtle errors related to accuracy were perceptible in speech, thereby confirming the proposed hypothesis. It was also seen that using genre-based approach to promote oral communication in English classes might be a relevant method, considering the socio-communicative function inherent in the suggested approach.

Keywords: EFL, genre-based approach, oral skills, pronunciation

Procedia PDF Downloads 130

Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

Abstract:

In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

Procedia PDF Downloads 626

Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

Procedia PDF Downloads 231

Authors: Seyedeh Afsoon Mohseni

Abstract:

The importance of the approach to the poverty definition is revealed regarding to it’s effect on the nature of planning poverty alleviation programs. This research employs the capability deprivation approach to alleviate rural poverty and seeks to develop herb’s market to alleviate capability poverty with an NGO’s intervene, Komak charity foundation. This research has employed qualitative approach; the data were collected through field observations, review of documents and interviews. Subsequently they were analyses by thematic analysis method. According to the findings, Komak charity can provide the least sustenance of the rural poor and alleviate capability poverty emergence through Herb’s market development of the village. Employing the themes, the market development is planned in two phases of empirical production and product development. Komak charity can intervene as a facilitator by providing micro credits, cooperative and supervising. Furthermore, planning on education and raising participation are prerequisites for the efficiency of the plan.

Keywords: capability poverty, Herb's market development, NGO, Komak charity foundation

Procedia PDF Downloads 440

Authors: Shubham Jaiswal

Abstract:

During modelling of many physical problems and engineering processes, fractional calculus plays an important role. Those are greatly described by fractional differential equations (FDEs). So a reliable and efficient technique to solve such types of FDEs is needed. In this article, a numerical solution of a class of fractional differential equations namely space fractional order reaction-advection dispersion equations subject to initial and boundary conditions is derived. In the proposed approach shifted Jacobi polynomials are used to approximate the solutions together with shifted Jacobi operational matrix of fractional order and spectral collocation method. The main advantage of this approach is that it converts such problems in the systems of algebraic equations which are easier to be solved. The proposed approach is effective to solve the linear as well as non-linear FDEs. To show the reliability, validity and high accuracy of proposed approach, the numerical results of some illustrative examples are reported, which are compared with the existing analytical results already reported in the literature. The error analysis for each case exhibited through graphs and tables confirms the exponential convergence rate of the proposed method.

Keywords: space fractional order linear/nonlinear reaction-advection diffusion equation, shifted Jacobi polynomials, operational matrix, collocation method, Caputo derivative

Procedia PDF Downloads 445

Authors: Nadeem Akhtar, Hira Javed

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An important aspect of research is literature survey. Availability of a large amount of literature across different domains triggers the need for optimized systems which provide relevant literature to researchers. We propose a search system based on keywords for text documents. This experimental approach provides a hierarchical structure to the document corpus. The documents are labelled with keywords using KEA (Keyword Extraction Algorithm) and are automatically organized in a lattice structure using Formal Concept Analysis (FCA). This groups the semantically related documents together. The hierarchical structure, based on keywords gives out only those documents which precisely contain them. This approach open doors for multi-domain research. The documents across multiple domains which are indexed by similar keywords are grouped together. A hierarchical relationship between keywords is obtained. To signify the effectiveness of the approach, we have carried out the experiment and evaluation on Semeval-2010 Dataset. Results depict that the presented method is considerably successful in indexing of scientific papers.

Keywords: formal concept analysis, keyword extraction algorithm, scientific documents, lattice

Procedia PDF Downloads 332

Authors: Jagdish S. Patel, Piyush Chudasama

Abstract:

Context: Over-expression of P-glycoprotein (P-gp) plays critical role in absorption of many drug candidates which results into lower bioavailability of the drug. P-glycoprotein also over expresses in many pathological conditions like diabetes, affecting the drug therapy. Modulation of P-gp expression using inhibitors can help in designing novel formulation enhancing the bioavailability of the drug in question. Objectives: The main focus of the study was to develop advanced glycation end products (AGEs) induced P-gp over expression in Caco-2 cells. Curcumin, piperine and epigallocatechin gallate were used to evaluate their P-gp inhibitory action using combinatorial approach. Materials and methods: Methylglyoxal (MG) induced P-gp over expression was checked in Caco-2 cells using real time PCR. P-gp inhibitory effects of the phytochemicals were measured after induction with MG alone and in combination of any two compounds. Cytotoxicity of each of the phytochemical was evaluated using MTT assay. Results: Induction with MG (100mM) significantly induced the over expression of P-glycoprotein in Caco-2 cells after 24 hr. Curcumin, piperine and epigallocatechin gallate alone significantly reduced the level of P-gp within 6 hr of treatment period monitored by real time PCR. The combination of any two phytochemical also down regulated the expression of P-gp in cells. Combinations of Curcumin and epigallocatechin gallate have shown significant down regulation when compared with other two combinations. Conclusions: Combinatorial approach for down regulating the expression of P-gp, in pathological conditions like diabetes, has demonstrated promising approach for therapeutic purpose.

Keywords: p-glycoprotein, curcumin, piperine, epigallocatechin gallate, p-gp inhibition

Procedia PDF Downloads 334

Authors: H. Ashrafi, S. Ebrahimi, H. Kamalzadeh

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With the development of business competition, it is important for healthcare providers to improve their service qualities. In order to improve service quality of a clinic, four important dimensions are defined: tangibles, responsiveness, empathy, and reliability. Moreover, there are several service stages in hospitals such as financial screening and examination. One of the most challenging limitations for improving service quality is budget which impressively affects the service quality. In this paper, we present an approach to address budget uncertainty and provide guidelines for service resource allocation. In this paper, a service quality improvement approach is proposed which can be adopted to multistage service processes to improve service quality, while controlling the costs. A multi-objective function based on the importance of each area and dimension is defined to link operational variables to service quality dimensions. The results demonstrate that our approach is not ultra-conservative and it shows the actual condition very well. Moreover, it is shown that different strategies can affect the number of employees in different stages.

Keywords: allocation, budget uncertainty, healthcare resource, service quality assessment, robust optimization

Procedia PDF Downloads 184

Authors: Emmanuel Carlos De Mata Castrejón

Abstract:

Music education has shown to be connected to many areas of sciences and arts, it has also been associated with several facets of human life. The many aspects around the study of music and education, make very difficult for the music educator to find a way through, even though there are lots of methods of teaching music to young children, they are different between one another and so are the students. For the music to help improve children’s development, it is necessary for the children to explore their musicality as they explore their creativity; it must be a challenging, playful, and enjoyable activity. The purpose of this investigation is to focus the music education not in the music, nor the teaching, but the children to be guided through their own musicality. The first approach to this kind of music education comes from the Active learning methods during the nineteenth century, most of which are still used around the world, sometimes with modifications to fit a certain place or type of students. This approach on children’s musicality requires some knowledge of music, pedagogy, and developmental psychology at least, but more important than the theory or the method used for music education, the focus should be on developing the student’s musicality, considering the complexity of this concept. To get this, it is needed, indeed, far more research in the topic, so this is a call for collaborative research and for interdisciplinary teams to emerge. This is a review of authors and methods in music education trying to trace a line pointing to transdisciplinary work and pursuing the development of children’s musicality.

Keywords: children, methods, music education, musicality

Procedia PDF Downloads 332

Authors: C. E. Nugraheni, L. Abednego

Abstract:

This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as meta-heuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported.

Keywords: hyper-heuristics, evolutionary algorithms, production scheduling, meta-heuristic

Procedia PDF Downloads 381

Authors: Eisa M. Al-Balhan, Mamdouh M. Soliman

Abstract:

This study introduced learning styles techniques into mathematics teaching to improve mathematics achievement and motivation among Kuwaiti fourth- and fifth-grade low achievers. The study consisted of two groups. The control group (N = 212) received traditional math tutoring based on a textbook and the tutor’s knowledge of math. The experimental group (N = 209) received math tutoring from instructors trained in the Learning Style™ approach. Three instruments were used: Motivation Scale towards Mathematics; Achievement in Mathematics Test; and the manual of learning style approach indicating the individual’s preferred learning style: AKV, AVK, KAV, KVA, VAK, or VKA. The participating teachers taught to the detected learning style of each student or group. The findings show significant improvement in achievement and motivation towards mathematics in the experimental group. The outcome offers information to variables affecting achievement and motivation towards mathematics and demonstrates the leading role of Kuwait in education within the region.

Keywords: elementary school, learning style, math low achievers, SmartWired™, math instruction, motivation

Procedia PDF Downloads 110

Authors: Bahadir Aydin

Abstract:

Today it is difficult to reach accurate knowledge because of mass data. This huge data makes the environment more and more caotic. Data is a main piller of intelligence. There is a close tie between knowledge and intelligence. Information gathered from different sources can be modified, interpreted and classified by using knowledge development process. This process is applied in order to attain intelligence. Within this process the effect of knowledge is crucial. Knowledge is classified as explicit and tacit knowledge. Tacit knowledge can be seen as "only the tip of the iceberg”. This tacit knowledge accounts for much more than we guess in all intelligence cycle. If the concept of intelligence scrutinized, it can be seen that it contains risks, threats as well as success. The main purpose for all organization is to be succesful by eliminating risks and threats. Therefore, there is a need to connect or fuse existing information and the processes which can be used to develop it. By the help of process the decision-maker can be presented with a clear holistic understanding, as early as possible in the decision making process. Planning, execution and assessments are the key functions that connects to information to knowledge. Altering from the current traditional reactive approach to a proactive knowledge development approach would reduce extensive duplication of work in the organization. By new approach to this process, knowledge can be used more effectively.

Keywords: knowledge, intelligence cycle, tacit knowledge, KIDW

Procedia PDF Downloads 519

Authors: R.M. Asyraf, A. Norazah, S.M. Khairuddin, B. Noraziah

Abstract:

Designing a system at present requires a vital, challenging task; to ensure the design philosophy is maintained in economical ways. This paper perceived the value engineering (VE) approach applied in infrastructure works, namely stormwater drainage. This method is adopted in line as consultants have completed the detailed design. Function Analysis System Technique (FAST) diagram and VE job plan, information, function analysis, creative judgement, development, and recommendation phase are used to scrutinize the initial design of stormwater drainage. An estimated cost reduction using the VE approach of 2% over the initial proposal was obtained. This cost reduction is obtained from the design optimization of the drainage foundation and structural system, where the pile design and drainage base structure are optimized. Likewise, the design of the on-site detention tank (OSD) pump was revised and contribute to the cost reduction obtained. This case study shows that the VE approach can be an important tool in optimizing the design to reduce costs.

Keywords: value engineering, function analysis system technique, stormwater drainage, cost reduction

Procedia PDF Downloads 145

Authors: Berkan Ural, Arif Eser, Sinan Apaydin

Abstract:

Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

Procedia PDF Downloads 418

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 127

Authors: Przemysław Tomczyk, Dagmara Plata-Alf, Piotr Kwiatek

Abstract:

Literature review is indispensable in research. One of the key techniques used in it is bibliometric analysis, where one of the methods is science mapping. The classic approach that dominates today in this area consists of mapping areas, keywords, terms, authors, or citations. This approach is also used in relation to the review of literature in the field of marketing. The development of technology has resulted in the fact that researchers and practitioners use the capabilities of software available on the market for this purpose. The use of science mapping software tools (e.g., VOSviewer, SciMAT, Pajek) in recent publications involves the implementation of a literature review, and it is useful in areas with a relatively high number of publications. Despite this well-grounded science mapping approach having been applied in the literature reviews, performing them is a painstaking task, especially if authors would like to draw precise conclusions about the studied literature and uncover potential research gaps. The aim of this article is to identify to what extent a new approach to science mapping, variable mapping, takes advantage of the classic science mapping approach in terms of research problem formulation and content/thematic analysis for literature reviews. To perform the analysis, a set of 5 articles on customer ideation was chosen. Next, the analysis of key words mapping results in VOSviewer science mapping software was performed and compared with the variable map prepared manually on the same articles. Seven independent expert judges (management scientists on different levels of expertise) assessed the usability of both the stage of formulating, the research problem, and content/thematic analysis. The results show the advantage of variable mapping in the formulation of the research problem and thematic/content analysis. First, the ability to identify a research gap is clearly visible due to the transparent and comprehensive analysis of the relationships between the variables, not only keywords. Second, the analysis of relationships between variables enables the creation of a story with an indication of the directions of relationships between variables. Demonstrating the advantage of the new approach over the classic one may be a significant step towards developing a new approach to the synthesis of literature and its reviews. Variable mapping seems to allow scientists to build clear and effective models presenting the scientific achievements of a chosen research area in one simple map. Additionally, the development of the software enabling the automation of the variable mapping process on large data sets may be a breakthrough change in the field of conducting literature research.

Keywords: bibliometrics, literature review, science mapping, variable mapping

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Authors: Gamze Atay, Altay Colak

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It is a fact that today's technology shapes the change and development of architectural forms by creating different perspectives. The research is an experimental study that explores the integration of architectural forms in this process of change/development into design education through traditional design tools. An examination of the practices in the studio environment shows that the students who just started architectural education have difficulty accessing the form. The main objective of this study has been to enable students to use and interpret different disciplines in the design process to improve their perception of form. In this sense, the origami, which is defined as "the art of paper folding", and isomorphous (equally formed) approaches have been used with design studio students at the beginning stage as methods in the process of 3-dimensional thinking and creating the form. These two methods were examined with students in three stages: analysis, creation, and outcome. As a result of the study, it was seen that the use of different disciplines as a method during form creation gave the designs of the student originality, freedom, and dynamism.

Keywords: architectural form, design education, isomorphic approach, origamic approach

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Authors: Farhan J. M. Al-Behadili, Vineeta Bilgi, Miyuki Taniguchi, Junxi Li, Wei Xu

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Mediterranean fruit fly (Ceratitis capitata) is one of the most destructive pests of fruits and vegetables. Medfly originated from Africa and spread in many countries, and is currently an endemic pest in Western Australia. Medfly has been recorded from over 300 plant species including fruits, vegetables, nuts and its main hosts include blueberries, citrus, stone fruit, pome fruits, peppers, tomatoes, and figs. Global trade of fruits and other farm fresh products are suffering from the damages of this pest, which prompted towards the need to develop more effective ways to control these pests. The available quarantine treatment technologies mainly include chemical treatment (e.g., fumigation) and non-chemical treatments (e.g., cold, heat and irradiation). In recent years, with the loss of several chemicals, it has become even more important to rely on non-chemical postharvest control technologies (i.e., heat, cold and irradiation) to control fruit flies. Cold treatment is one of the most potential trends of focus in postharvest treatment because it is free of chemical residues, mitigates or kills the pest population, increases the strength of the fruits, and prolongs storage time. It can also be applied to fruits after packing and ‘in transit’ during lengthy transport by sea during their exports. However, limited systematic study on cold treatment of Medfly stages in artificial diets was reported, which is critical to provide a scientific basis to compare with previous research in plant products and design an effective cold treatment suitable for exported plant products. The overall purpose of this study was to evaluate and understand Medfly responses to cold treatments. Medfly stages were tested. The long-term goal was to optimize current postharvest treatments and develop more environmentally-friendly, cost-effective, and efficient treatments for controlling Medfly. Cold treatment with different exposure times is studied to evaluate cold eradication treatment of Mediterranean fruit fly (Ceratitis capitata), that reared on carrot diet. Mortality is important aspect was studied in this study. On the other hand, study effects of exposure time on mortality means of medfly stages.

Keywords: cold treatment, fruit fly, Ceratitis capitata, carrot diet, temperature effects

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Authors: Nilda San Miguel, Elymar Pascual

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The main focus of this study is exploring the effective approaches in teaching Mathematics that is being applied in public schools, s.y. 2018-2019. This research was written as connected output to the district-wide School Learning Action Cell (DISLAC) on Math teaching approaches which was recently conducted in Victoria, Laguna. Fifty-four math teachers coming from 17 schools in Victoria became the respondents of this study. Qualitative method of doing research was applied. Teachers’ responses to the following concerns were gathered, analyzed and interpreted: (1) evaluation of the recently conducted DISLAC, (2) status of the use of different approaches, (3) perception on the effective use of approaches, (4) preference of approach to explore in classroom sessions, (5) factors affecting the choice of approach, (6) difficulties encountered, (7) and perceived benefit to learners. Results showed that the conduct of DISLAC was very highly satisfactory (mean 4.41). Teachers looked at collaborative approach as very highly effective (mean 4.74). Fifty-two percent of the teachers is using collaborative approach, 17% constructivist, 11% integrative, 11% inquiry-based, and 9% reflective. Reflective approach was chosen to be explored by most of the respondents (29%) in future sessions. The difficulties encountered by teachers in using the different approaches are: (1) learners’ difficulty in following instructions, (2) lack of focus, (3) lack of willingness and cooperation, (4) teachers’ lack of mastery in using different approaches, and (5) lack of time of doing visual aids because of time mismanagement. Teachers deemed the use of various teaching approaches can help the learners to have (1) mastery of competency, (2) increased communication, (3) improved confidence, (4) facility in comprehension, and (5) better academic output. The result obtained from this study can be used as an input for SLACs. Recommendations at the end of the study were given to school/district heads and future researchers.

Keywords: approaches, collaborative, constructivism, inquiry-based, integrative, reflective

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Authors: Zoran Spasovski

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The paper shortly discusses the significance and the benefits of the historical approach in the research of languages by presenting examples of it in the fields of phonetics and phonology, lexicology, morphology, syntax, and even in the onomastics (toponomy and anthroponomy). The examples from the field of phonetics/phonology include insights into animal speech and its evolution into human speech, the evolution of the sounds of human speech from vocals to glides and consonants and from velar consonants to palatal, etc., on well-known examples of former researchers. Those from the field of lexicology show shortly the formation of the lexemes and their evolution; the morphology and syntax are explained by examples of the development of grammar and syntax forms, and the importance of the historical approach in the research of place-names and personal names is briefly outlined through examples of place-names and personal names and surnames, and the conclusions that come from it, in different languages.

Keywords: animal speech, glotogenesis, grammar forms, lexicology, place-names, personal names, surnames, syntax categories

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Authors: Oluwaponmile David Alao

Abstract:

Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

Procedia PDF Downloads 390