Search results for: image registration techniques

Authors: Sandabad Sara, Sayd Tahri Yassine, Hammouch Ahmed

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The development of science has allowed computer scientists to touch the medicine and bring aid to radiologists as we are presenting it in our article. Our work focuses on the detection and localization of tumors areas in the human brain; this will be a completely automatic without any human intervention. In front of the huge volume of MRI to be treated per day, the radiologist can spend hours and hours providing a tremendous effort. This burden has become less heavy with the automation of this step. In this article we present an automatic and effective tumor detection, this work consists of two steps: the first is the image filtering using the filter Nl-means, then applying the expectation maximization algorithm (EM) for retrieving the tumor mask from the brain MRI and extracting the tumor area using the mask obtained from the second step. To prove the effectiveness of this method multiple evaluation criteria will be used, so that we can compare our method to frequently extraction methods used in the literature.

Keywords: MRI, Em algorithm, brain, tumor, Nl-means

Procedia PDF Downloads 318

Authors: Tajinder Pal Singh

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Teaching of mathematics to engineering students is an open ended problem in education. The main goal of mathematics learning for engineering students is the ability of applying a wide range of mathematical techniques and skills in their engineering classes and later in their professional work. Most of the undergraduate engineering students and faculties feels that no efforts and attempts are made to demonstrate the applicability of various topics of mathematics that are taught thus making mathematics unavoidable for some engineering faculty and their students. The lack of understanding of concepts in engineering mathematics may hinder the understanding of other concepts or even subjects. However, for most undergraduate engineering students, mathematics is one of the most difficult courses in their field of study. Most of the engineering students never understood mathematics or they never liked it because it was too abstract for them and they could never relate to it. A right balance of application and concept based teaching can only fulfill the objectives of teaching mathematics to engineering students. It will surely improve and enhance their problem solving and creative thinking skills. In this paper, some practical (informal) ways of making mathematics-teaching application based for the engineering students is discussed. An attempt is made to understand the present state of teaching mathematics in engineering colleges. The weaknesses and strengths of the current teaching approach are elaborated. Some of the causes of unpopularity of mathematics subject are analyzed and a few pragmatic suggestions have been made. Faculty in mathematics courses should spend more time discussing the applications as well as the conceptual underpinnings rather than focus solely on strategies and techniques to solve problems. They should also introduce more ‘word’ problems as these problems are commonly encountered in engineering courses. Overspecialization in engineering education should not occur at the expense of (or by diluting) mathematics and basic sciences. The role of engineering education is to provide the fundamental (basic) knowledge and to teach the students simple methodology of self-learning and self-development. All these issues would be better addressed if mathematics and engineering faculty join hands together to plan and design the learning experiences for the students who take their classes. When faculties stop competing against each other and start competing against the situation, they will perform better. Without creating any administrative hassles these suggestions can be used by any young inexperienced faculty of mathematics to inspire engineering students to learn engineering mathematics effectively.

Keywords: application based learning, conceptual learning, engineering mathematics, word problem

Procedia PDF Downloads 223

Authors: Sukhwinder Singh Jolly

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Metal spinning is one such process in which the stresses are localized to a small area and the material is made to flow or move over the mandrel with the help of spinning tool. Spinning of tubular products can be performed by two techniques, forward spinning and backward spinning. Many researchers have studied the process both experimentally and analytically. An effort has been made to apply the process to the spinning of thin wall, highly precision, small bore long tube in hard-to-work materials such as titanium.

Keywords: metal spinning, hard-to-work materials, roller diameter, power consumption

Procedia PDF Downloads 377

Authors: Surini Duthika Fernandopulle, Kalamba Arachchige Pramodya Wijesinghe

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Macrocapsules containing phase change material (PCM) PEG4000 as core and Calcium Alginate as the shell was synthesized by in-situ polymerization process, and their suitability for textile applications was studied. PCM macro-capsules were sandwiched between two polyurethane foams at regular intervals, and the sandwiched foams were subsequently covered with 100% cotton woven fabrics. According to the mathematical modelling and calculations 46 capsules were required to provide cooling for a period of 2 hours at 56ºC, so a panel of 10 cm x 10 cm area with 25 parts (having 5 capsules in each for 9 parts are 16 parts spaced for air permeability) were effectively merged into one textile material without changing the textile's original properties. First, the available cooling techniques related to textiles were considered and the best cooling techniques suiting the Sri Lankan climatic conditions were selected using a survey conducted for Sri Lankan Public based on ASHRAE-55-2010 standard and it consisted of 19 questions under 3 sections categorized as general information, thermal comfort sensation and requirement of Personal Cooling Garments (PCG). The results indicated that during daytime, majority of respondents feel warm and during nighttime also majority have responded as slightly warm. The survey also revealed that around 85% of the respondents are willing to accept a PCG. The developed panels were characterized using Fourier-transform infrared spectroscopy (FTIR) and Thermogravimetric Analysis (TGA) tests and the findings from FTIR showed that the macrocapsules consisted of PEG 4000 as the core material and Calcium Alginate as the shell material and findings from TGA showed that the capsules had the average weight percentage for core with 61,9% and shell with 34,7%. After heating both control samples and samples incorporating PCM panels, it was discovered that only the temperature of the control sample increased after 56ºC, whereas the temperature of the sample incorporating PCM panels began to regulate the temperature at 56ºC, preventing a temperature increase beyond 56ºC.

Keywords: phase change materials, thermal regulation, textiles, macrocapsules

Procedia PDF Downloads 112

Authors: A. N. Roslen, S. A. Syed-Sahil, A. Musavir

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In Malaysia, there are only few fashion designers who are inspired by the work of artists when creating their collections. The researchers confirmed this statement by interviewing fashion experts in Malaysia. The objectives of this study are to: 1. Investigate the acceptance of fashion inspired by the work of art among consumers. 2. Encourage more designers to use work of art as their inspirations. 3. Promote Malaysian Artists through fashion. Thus, the researchers interviewed Malaysian fashion designers, image consultants, and one famous Malaysian Artist (Awang Damit). All of them had agreed that the fashion inspired by the work of art in Malaysia has a long way to go. Therefore, the researchers’ aim is to attract more fashion designers to use the work of local artists in their creations. The researchers had used interview, survey and experimentation as methods of this study. In the experimentation procedure, paintings of local artist, Awang Damit was used as a source of inspiration in creating a design Line. The result of this study had shown that fashion inspired by work of art is acknowledged and accepted by the designers and consumers.

Keywords: art, fashion, inspiration, local artist

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: semantic links, data mining, linked data, SKOS

Procedia PDF Downloads 157

Authors: X. S. Wu, H. Jia, P. H. Qian, Z. Zhang, H. L. Cai, F. M. Zhang

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A bipolar resistance switching behaviour is detected for a Ti/TiO2-x/Au memristor device, which is fabricated by a masked designed magnetic sputtering. The current dependence of voltage indicates the curve changes slowly and continuously. When voltage pulses are applied to the device, the set and reset processes maintains linearity, which is used to simulate the synapses. We argue that the conduction mechanism of the device is from the oxygen vacancy channel model, and the resistance of the device change slowly due to the reaction between the titanium electrode and the intermediate layer and the existence of a large number of oxygen vacancies in the intermediate layer. Then, Hopfield neural network is constructed to simulate the behaviour of neural network in image processing, and the accuracy rate is more than 98%. This shows that titanium dioxide memristor has a broad application prospect in high performance neural network simulation.

Keywords: memristor fabrication, neuromorphic computing, bionic synaptic application, TiO₂-based

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Authors: Sang-Tae Ji, Jeong-Woo Park, Jun-Ho Park, Kwang-Woo Nam

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This study aims is to suggest the basic factors by considering the priority of intersection in the diffusion project of Smart intersection. Busan Metropolitan City is conducting a smart intersection project for efficient traffic management. The smart intersection project aims to make breakthrough improvement of the intersection congestion by optimizing the signal system using CCTV (closed-circuit television camera) image analysis technology. This study investigated trends of existing researches and analyzed by setting three things of traffic volume, characteristics of intersection road, and whether or not to conduct the main arterial road as factors for selecting new intersection when spreading smart intersection. Using this, we presented the priority of the newly installed intersection through the present situation and analysis for the Busan Metropolitan City which is the main destination of the spreading project of the smart intersection. The results of this study can be used as a consideration in the implementation of smart intersection business.

Keywords: CCTV, GIS, ICT, Smart City, smart intersection

Procedia PDF Downloads 375

Authors: Akhila Vasudev, Himanshu Kaushik, Tadikonda Venkata Bharat

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A large number of mine tailings are produced every year as part of the extraction process of phosphates, gold, copper, and other materials. Mine tailings are high in water content and have very slow dewatering behavior. The efficient design of tailings dam and economical disposal of these slurries requires the knowledge of tailings consolidation behavior. The large-strain consolidation theory closely predicts the self-weight consolidation of these slurries as the theory considers the conservation of mass and momentum conservation and considers the hydraulic conductivity as a function of void ratio. Classical laboratory techniques, such as settling column test, seepage consolidation test, etc., are expensive and time-consuming for the estimation of hydraulic conductivity variation with void ratio. Inverse estimation of the constitutive relationships from the measured settlement versus time curves is explored. In this work, inverse analysis based on metaheuristics techniques will be explored for predicting the hydraulic conductivity parameters for mine tailings from the base excess pore water pressure dissipation curve and the initial conditions of the mine tailings. The proposed inverse model uses particle swarm optimization (PSO) algorithm, which is based on the social behavior of animals searching for food sources. The finite-difference numerical solution of the forward analytical model is integrated with the PSO algorithm to solve the inverse problem. The method is tested on synthetic data of base excess pore pressure dissipation curves generated using the finite difference method. The effectiveness of the method is verified using base excess pore pressure dissipation curve obtained from a settling column experiment and further ensured through comparison with available predicted hydraulic conductivity parameters.

Keywords: base excess pore pressure, hydraulic conductivity, large strain consolidation, mine tailings

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Authors: Mohamed Ben Rabha

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In this paper, we report on the effect of porous silicon (PS) treatment on the surface passivation of monocrystalline silicon (c-Si). PS film with a thickness of 80 nm was deposited by stain etching. It was demonstrated that PS coating is a very interesting solution for surface passivation. The level of surface passivation is determined by techniques based on photoconductance and FTIR. As a results, the effective minority carrier lifetime increase from 2 µs to 7 µs at ∆n=1015 cm-3 and the reflectivity reduce from 28 % to about 7 % after PS coating.

Keywords: porous silicon, effective minority carrier lifetime, reflectivity

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Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Keunho Park, Sunghwan Jeong

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The 4th Industrial Revolution, the next-generation industrial revolution, which is made up of convergence of information and communication technology (ICT), is no exception to the livestock industry, and various studies are being conducted to apply the livestock smart farm. In order to monitor livestock using sensors, it is necessary to drill holes in the organs such as the nose, ears, and even the stomach of the livestock to wear or insert the sensor into the livestock. This increases the stress of livestock, which in turn lowers the quality of livestock products or raises the issue of animal ethics, which has become a major issue in recent years. In this paper, we conducted a study to monitor livestock activity based on vision technology, effectively monitoring livestock activity without increasing animal stress and violating animal ethics. The movement rate was calculated based on the difference images between the frames, and the livestock activity was evaluated. As a result, the average F1-score was 96.67.

Keywords: barn monitoring, livestock, machine vision, smart farm

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Authors: Zahide Tepeli Temiz, Itir Tari Comert

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Attachment patterns based on early emotional interactions between infant and primary caregiver continue to be influential in adult life, in terms of mental health and behaviors of individuals. Several studies reveal that infant-caregiver relationships have impressed the affect regulation, coping with stressful and negative situations, general satisfaction of life, and self image in adulthood, besides the attachment styles. The present study aims to examine the relationships between university students’ attachment style and their self-esteem, alexithymic features, satisfaction of life, and level of resilience. In line with this aim, the hypothesis of the prediction of attachment styles (anxious and avoidant) over life satisfaction, self-esteem, alexithymia, and psychological resilience was tested. Additionally, in this study Structural Equational Modeling was conducted to investigate the mediational role of self-esteem in the relationship between attachment styles and alexithymia, life satisfaction, and resilience. This model was examined with path analysis. The sample of the research consists of 425 university students who take education from several region of Turkey. The participants who sign the informed consent completed the Demographic Information Form, Experiences in Close Relationships-Revised, Rosenberg Self-Esteem Scale, The Satisfaction with Life Scale, Toronto Alexithymia Scale, and Resilience Scale for Adults. According to results, anxious, and avoidant dimensions of insecure attachment predicted the self-esteem score and alexithymia in positive direction. On the other hand, these dimensions of attachment predicted life satisfaction in negative direction. The results of linear regression analysis indicated that anxious and avoidant attachment styles didn’t predict the resilience. This result doesn’t support the theory and research indicating the relationship between attachment style and psychological resilience. The results of path analysis revealed the mediational role self esteem in the relation between anxious, and avoidant attachment styles and life satisfaction. In addition, SEM analysis indicated the indirect effect of attachment styles over alexithymia and resilience besides their direct effect. These findings support the hypothesis of this research relation to mediating role of self-esteem. Attachment theorists suggest that early attachment experiences, including supportive and responsive family interactions, have an effect on resilience to harmful situations in adult life, ability to identify, describe, and regulate emotions and also general satisfaction with life. Several studies examining the relationship between attachment styles and life satisfaction, alexithymia, and psychological resilience draw attention to mediational role of self-esteem. Results of this study support the theory of attachment patterns with the mediation of self-image influence the emotional, cognitive, and behavioral regulation of person throughout the adulthood. Therefore, it is thought that any intervention intended for recovery in attachment relationship will increase the self-esteem, life satisfaction, and resilience level, on the one side, decrease the alexithymic features, on the other side.

Keywords: alexithymia, anxious attachment, avoidant attachment, life satisfaction, path analysis, resilience, self-esteem, structural equation

Procedia PDF Downloads 179

Authors: Abdou Elhendy

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Numerous study have shown the efficacy of the percutaneous intervention (PCI) and coronary stenting in improving left ventricular function and relieving exertional angina. Furthermore, PCI remains the main line of therapy in acute myocardial infarction. Improvement of procedural techniques and new devices have resulted in an increased number of PCI in those with difficult and extensive lesions, multivessel disease as well as total occlusion. Immediate and late outcome may be compromised by acute thrombosis or the development of fibro-intimal hyperplasia. In addition, progression of coronary artery disease proximal or distal to the stent as well as in non-stented arteries is not uncommon. As a result, complications can occur, such as acute myocardial infarction, worsened heart failure or recurrence of angina. In a stent, restenosis can occur without symptoms or with atypical complaints rendering the clinical diagnosis difficult. Routine invasive angiography is not appropriate as a follow up tool due to associated risk and cost and the limited functional assessment. Exercise and pharmacologic stress testing are increasingly used to evaluate the myocardial function, perfusion and adequacy of revascularization. Information obtained by these techniques provide important clues regarding presence and severity of compromise in myocardial blood flow. Stress echocardiography can be performed in conjunction with exercise or dobutamine infusion. The diagnostic accuracy has been moderate, but the results provide excellent prognostic stratification. Adding myocardial contrast agents can improve imaging quality and allows assessment of both function and perfusion. Stress radionuclide myocardial perfusion imaging is an alternative to evaluate these patients. The extent and severity of wall motion and perfusion abnormalities observed during exercise or pharmacologic stress are predictors of survival and risk of cardiac events. According to current guidelines, stress echocardiography and radionuclide imaging are considered to have appropriate indication among patients after PCI who have cardiac symptoms and those who underwent incomplete revascularization. Stress testing is not recommended in asymptomatic patients, particularly early after revascularization, Coronary CT angiography is increasingly used and provides high sensitive for the diagnosis of coronary artery stenosis. Average sensitivity and specificity for the diagnosis of in stent stenosis in pooled data are 79% and 81%, respectively. Limitations include blooming artifacts and low feasibility in patients with small stents or thick struts. Anatomical and functional cardiac imaging modalities are corner stone for the assessment of patients after PCI and provide salient diagnostic and prognostic information. Current imaging techniques cans serve as gate keeper for coronary angiography, thus limiting the risk of invasive procedures to those who are likely to benefit from subsequent revascularization. The determination of which modality to apply requires careful identification of merits and limitation of each technique as well as the unique characteristic of each individual patient.

Keywords: coronary artery disease, stress testing, cardiac imaging, restenosis

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammad Hossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: facial image, segmentation, PCM, FCM, skin error, facial surgery

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Authors: Lawrence Williams

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As the mechanism which converts domains to internet protocol (IP) addresses, Domain Name System (DNS) is an essential part of internet usage. It was not designed securely and can be subject to attacks. DNS attacks have become more frequent and sophisticated and the need for detecting and preventing them becomes more important for the modern network. DNS tunnelling attacks are one type of attack that are primarily used for distributed denial-of-service (DDoS) attacks and data exfiltration. Discussion of different techniques to detect and prevent DNS tunneling attacks is done. The methods, models, experiments, and data for each technique are discussed. A proposal about feasibility is made. Future research on these topics is proposed.

Keywords: DNS, tunneling, exfiltration, botnet

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Authors: M. O. Olayiwola

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Numerical solutions of the generalized Burger-Fisher are obtained using a Modified Variational Iteration Method (MVIM) with minimal computational efforts. The computed results with this technique have been compared with other results. The present method is seen to be a very reliable alternative method to some existing techniques for such nonlinear problems.

Keywords: burger-fisher, modified variational iteration method, lagrange multiplier, Taylor’s series, partial differential equation

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Authors: Gulce Kanturer Yasar, Hayriye Esbah Tuncay

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Cultivated food production systems have coexisted harmoniously with nature for thousands of years through ancient techniques. Based on this experience, experimentation, and discovery, these culturally embedded methods have evolved to sustain food production, restore ecosystems, and harmoniously adapt to nature. In this era, as we seek solutions to food security challenges, enhancing and repairing our food production systems is crucial, making them more resilient to future disasters without harming the ecosystem. Instead of unsustainable conventional systems with ongoing destructive effects, we must investigate innovative and restorative production systems that integrate ancient wisdom and technology. Whether we consider agricultural fields, pastures, forests, coastal wetland ecosystems, or lagoons, it is crucial to harness the potential of these natural resources in addressing future global challenges, fostering both socio-economic resilience and ecological sustainability through strategic organization for food production. When thoughtfully designed and managed, marine-based food production has the potential to function as a living infrastructure system that addresses social and environmental challenges despite its known adverse impacts on the environment and local economies. These areas are also stages of daily life, vibrant hubs where local culture is produced and shared, contributing to the distinctive rural character of coastal settlements and exhibiting numerous spatial expressions of public nature. When we consider the history of humanity, indigenous communities have engaged in these sustainable production practices that provide goods for food, trade, culture, and the environment for many ages. Ecosystem restoration and socio-economic resilience can be achieved by combining production techniques based on ecological knowledge developed by indigenous societies with modern technologies. Coastal lagoons are highly productive coastal features that provide various natural services and societal values. They are especially vulnerable to severe physical, ecological, and social impacts of changing, challenging global conditions because of their placement within the coastal landscape. Coastal lagoons are crucial in sustaining fisheries productivity, providing storm protection, supporting tourism, and offering other natural services that hold significant value for society. Although there is considerable literature on the physical and ecological dimensions of lagoons, much less literature focuses on their economic and social values. This study will discuss the possibilities of coastal lagoons to achieve both ecologically sustainable and socio-economically resilient while maintaining their productivity by combining local techniques and modern technologies. The case study will present Turkey’s traditional aquaculture method, "Dalyans," predominantly operated by small-scale farmers in coastal lagoons. Due to human, ecological, and economic factors, dalyans are losing their landscape characteristics and efficiency. These 1000-year-old ancient techniques, rooted in centuries of traditional and agroecological knowledge, are under threat of tourism, urbanization, and unsustainable agricultural practices. Thus, Dalyans have diminished from 29 to approximately 4-5 active Dalyans. To deal with the adverse socio-economic and ecological consequences on Turkey's coastal areas, conserving Dalyans by protecting their indigenous practices while incorporating contemporary methods is essential. This study seeks to generate scenarios that envision the potential ways protection and development can manifest within case study areas.

Keywords: coastal foodscape, lagoon aquaculture, regenerative food systems, watershed food networks

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Authors: Ibtissem Talbi

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During the last decade, a variety of chaos-based cryptosystems have been investigated. Most of them are based on the structure of Fridrich, which is based on the traditional confusion-diffusion architecture proposed by Shannon. Compared with traditional cryptosystems (DES, 3DES, AES, etc.), the chaos-based cryptosystems are more flexible, more modular and easier to be implemented, which make them suitable for large scale-data encyption, such as images and videos. The heart of any chaos-based cryptosystem is the chaotic generator and so, a part of the efficiency (robustness, speed) of the system depends greatly on it. In this talk, we give an overview of the state of the art of chaos-based block ciphers and we describe some of our schemes already proposed. Also we will focus on the essential characteristics of the digital chaotic generator, The needed performance of a chaos-based block cipher in terms of security level and speed of calculus depends on the considered application. There is a compromise between the security and the speed of the calculation. The security of these block block ciphers will be analyzed.

Keywords: chaos-based cryptosystems, chaotic generator, security analysis, structure of Fridrich

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Authors: Siti Khairunniza-Bejo, Yusnida Yusoff, Nik Salwani Nik Yusoff, Idris Abu Seman, Mohamad Izzuddin Anuar

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Most of the oil palm plantations have been threatened by Basal Stem Rot (BSR) disease which causes serious economic impact. This study was conducted to identify the healthy and BSR-infected oil palm tree using thirteen color indices. Multispectral and thermal camera was used to capture 216 images of the leaves taken from frond number 1, 9 and 17. Indices of normalized difference vegetation index (NDVI), red (R), green (G), blue (B), near infrared (NIR), green – blue (GB), green/blue (G/B), green – red (GR), green/red (G/R), hue (H), saturation (S), intensity (I) and thermal index (T) were used. From this study, it can be concluded that G index taken from frond number 9 is the best index to differentiate between the healthy and BSR-infected oil palm trees. It not only gave high value of correlation coefficient (R=-0.962), but also high value of separation between healthy and BSR-infected oil palm tree. Furthermore, power and S model developed using G index gave the highest R2 value which is 0.985.

Keywords: oil palm, image processing, disease, leaves

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Authors: Daniel Gebreslassie Mekonnen

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Effective leadership has a crucial role in excelling in the overall success of a school. Today there is much attention given to school leadership, without which schools can never be successful. Therefore, the study was aimed at investigating the role of principals’ leadership styles and their emotional intelligence on the work motivation and job performance of teachers in Addis Ababa, Ethiopia. The study, thus, first examined the relationship between work motivation and job performance of the teachers in relation to the perceived leadership styles and emotional intelligence of principals. Second, it assessed the mean differences and the interaction effects of the principals’ leadership styles and emotional intelligence on the work motivation and job performance of the teachers. Finally, the study investigated whether principals’ leadership styles and emotional intelligence variables had significantly predicted the work motivation and job performance of teachers. As a means, a quantitative approach and descriptive research design were employed to conduct the study. Three hundred sixteen teachers were selected using multistage sampling techniques as participants of the study from the eight sub-cities in Addis Ababa. The main data-gathering instruments used in this study were the path-goal leadership questionnaire, emotional competence inventory, multidimensional work motivation scale, and job performance appraisal scale. The quantitative data were analyzed by using the statistical techniques of Pearson–product-moment correlation analysis, two-way analysis of variance, and stepwise multiple regression analysis. Major findings of the study have revealed that the work motivation and job performance of the teachers were significantly correlated with the perceived participative leadership style, achievement-oriented leadership style, and emotional intelligence of principals. Moreover, the emotional intelligence of the principals was found to be the best predictor of the teachers’ work motivation, whereas the achievement-oriented leadership style of the principals was identified as the best predictor of the job performance of the teachers. Furthermore, the interaction effects of all four path-goal leadership styles vis-a-vis the emotional intelligence of the principals have shown differential effects on the work motivation and job performance of teachers. Thus, it is reasonable to conclude that emotional intelligence is the sine qua non of effective school leadership. Hence, this study would be useful for policymakers and educational leaders to come up with policies that would enhance the role of emotional intelligence on school leadership effectiveness. Finally, pertinent recommendations were drawn from the findings and the conclusions of the study.

Keywords: emotional intelligence, leadership style, job performance, work motivation

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Chengyuan Zhu, Zhu Wang

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With the strategy of Rural Rejuvenation proposed in China, the rural has become the focus of all works today. In addition to the support of industry and policy, the rural planning and construction which is the space dependence of Rural Rejuvenation are also very crucial. Based on an analysis of the case of Yangdun Village in Deqing County, this paper summarizes village existing resources and construction status quo. It tries to illuminate the micro-activated organic renewal strategies and methods, based on ecological landscape, history context, industry development and living life requirements. It takes advantage of industrial linkage and then asks for the coordination of both spatial and industrial planning, the revival and remodeling of the rural image can be achieved through shaping the of architectural and landscape nodes as well as the activation of street space.

Keywords: rural construction, rural human settlements, micro-activation, organic renewal

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

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Authors: Ashley Sainvil

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The present study explores the relationship and possible effects of religiosity on both adverse childhood experiences and childhood attachment. Furthermore, to explore the idea that adult religiousness may play as a protective role, specifically protecting adults with a past of adverse childhood experiences and an insecure childhood attachment from reporting depression. Analyses are based on 57 participants (N= 57, 32.1% of ages 18-22; 70.2% female, 28.1% male, 1.8% other). In the form of an online Qualtrics survey through questionnaires, childhood attachment, adverse childhood experiences, sense of religiosity, and depression were measured. While not significant at conventional levels, there was no direct relationship between adverse childhood experiences, insecure childhood attachment, and sense of religiosity, and when assessing age for the relationship in later adulthood, there was no significance. Positive childhood experiences of feeling protected, love, and special had a direct relationship with a positive image and sense of closeness to God. Results highlight the importance of positive childhood experiences, secure childhood attachment quality relationship, such as trust, communication for positive health outcomes, such as less depression.

Keywords: religiosity, childhood trauma, childhood attachment, depression

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Authors: Tamara Matevosyan

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In this globalized world, intercultural communication is becoming essential for understanding communication among people, for developing understanding of cultures, to appreciate the opportunities and challenges that each culture presents to people. Moreover, it plays an important role in developing an ideal personification to understand different behaviors in different cultures. Native speakers assimilate sociolinguistic knowledge in natural conditions, while it is a great problem for language learners, and in this context feature films reveal cultural peculiarities and involve students in real communication. As we know nowadays the key role of language learning is the development of intercultural competence as communicating with someone from a different cultural background can be exciting and scary, frustrating and enlightening. Intercultural competence is important in FL learning classroom and here feature films can perform as essential tools to develop this competence and overcome the intercultural gap that foreign students face. Current proposal attempts to reveal the correlation of the given culture and language through feature films. To ensure qualified, well-organized and practical classes on Intercultural Communication for language learners a number of methods connected with movie watching have been implemented. All the pre-watching, while watching and post-watching methods and techniques are aimed at developing students’ communicative competence. The application of such activities as Climax, Role-play, Interactive Language, Daily Life helps to reveal and overcome mistakes of cultural and pragmatic character. All the above-mentioned activities are directed at the assimilation of the language vocabulary with special reference to the given culture. The study dwells into the essence of culture as one of the core concepts of intercultural communication. Sometimes culture is not a priority in the process of language learning which leads to further misunderstandings in real life communication. The application of various methods and techniques with feature films aims at developing students’ cultural competence, their understanding of norms and values of individual cultures. Thus, feature film activities will enable learners to enlarge their knowledge of the particular culture and develop a fundamental insight into intercultural communication.

Keywords: climax, intercultural competence, interactive language, role-play

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Authors: N. Khanyile, P. Dlamini, M. Masenya

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Background: Most of the population in Africa is still dependent on indigenous medicinal knowledge for treating and managing ailments. Indigenous traditional knowledge owners/practitioners who own this knowledge are consulted by communities, but their knowledge is not known how they get it. The question of how knowledge is acquired and preserved remains one of the biggest challenges in traditional healing and treatment with herbal medicines. It is regrettable that despite the importance and recognition of indigenous medicinal knowledge globally, the details of acquirement, storing and transmission, and preservation techniques are not known. Hence this study intends to unveil the process of acquirement and transmission, and preservation techniques of indigenous medical knowledge by its owners. Objectives: This study aims to assess the process of acquiring and preservation of traditional medicinal knowledge by traditional medicinal knowledge owners/practitioners in uMhlathuze Municipality, in the province of KwaZulu-Natal, South Africa. The study was guided by four research objectives which were to: identify the types of traditional medicinal knowledge owners who possess this knowledge, establish the approach used by indigenous medicinal knowledge owners/healers for acquiring medicinal knowledge, identify the process of preservation of medicinal knowledge by indigenous medicinal knowledge owners/healers, and determine the challenges encountered in transferring the knowledge. Method: The study adopted a qualitative research approach, and a snowball sampling technique was used to identify the study population. Data was collected through semi-structured interviews with indigenous medicinal knowledge owners. Results: The findings suggested that uMhlathuze municipality had different types of indigenous medicinal knowledge owners who possess valuable knowledge. These are diviners (Izangoma), faith healers (Abathandazi), and herbalists (Izinyanga). The study demonstrated that indigenous medicinal knowledge is acquired in many different ways, including visions, dreams, and vigorous training. The study also revealed the acquired knowledge is preserved or shared with specially chosen children and trainees. Conclusion: The study concluded that this knowledge is gotten through vigorous training, which requires the learner to be attentive and eager to learn. It was recommended that a study of this nature be conducted but at a broader level to enhance an informed conclusion and recommendations.

Keywords: preserving, indigenous medicinal knowledge, indigenous knowledge, indigenous medicinal knowledge owners/practitioners, acquiring

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Authors: Yanjie Zhu, André Jesus, Irwanda Laory

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Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

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Authors: Ahlem Bouziri, Chiraz Latiri, Eric Gaussier

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The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. The key feature of our approach is a better trade off between the size of the mining result and the conveyed knowledge. Thus, face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm such as SVM or k-means. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results. The experiments were performed on SDA 95 collection, a data collection for information retrieval. It was found that the results were better in both terms of MAP and NDCG. The main observation is that the hybridization of text mining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.

Keywords: supervised leaning, classification, query expansion, association rules

Procedia PDF Downloads 310