Search results for: structure learning

Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

Abstract:

Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

Procedia PDF Downloads 143

Authors: Rohit Singh Dangi, Ravi Kant Pal, Monica Sundd

Abstract:

Acyl-CoA binding protein (ACBP) is a housekeeping protein which functions as an intracellular carrier of acyl-CoA esters. Given the fact that the amastigote stage (blood stage) of Leishmania depends largely on fatty acids as the energy source, of which a large part is derived from its host, these proteins might have an important role in its survival. In Leishmania major, genome sequencing suggests the presence of six ACBPs, whose function remains largely unknown. For functional and structural characterization, one of the ACBP genes was cloned, and the protein was expressed and purified heterologously. Acyl-CoA ester binding and stoichiometry were analyzed by isothermal titration calorimetry and Dynamic light scattering. Our results shed light on high affinity of ACBP towards longer acyl-CoA esters, such as myristoyl-CoA to arachidonoyl-CoA with single binding site. To understand the binding mechanism & dynamics, Nuclear magnetic resonance assignments of this protein are being done. The protein's crystal structure was determined at 1.5Å resolution and revealed a classical topology for ACBP, containing four alpha-helical bundles. In the binding pocket, the loop between the first and the second helix (16 – 26AA) is four residues longer from other extensively studied ACBPs (PfACBP) and it curls upwards towards the pantothenate moiety of CoA to provide a large tunnel space for long acyl chain insertion.

Keywords: acyl-coa binding protein (ACBP), acyl-coa esters, crystal structure, isothermal titration, calorimetry, Leishmania

Procedia PDF Downloads 446

Authors: Peng Li, Er-xiang Song

Abstract:

Numerical analysis of longitudinal tunnel seismic response due to spatial variation of earthquake ground motion is an important issue that cannot be ignored in the design and safety evaluation of tunnel structures. In this paper, numerical methods for analysis of tunnel longitudinal response under asynchronous seismic wave is extensively studied, including the improvement of the 1D time-domain finite element method, three dimensional numerical simulation technique for the site asynchronous earthquake response as well as the 3-D soil-tunnel structure interaction analysis. The study outcome will be beneficial to aid further research on the nonlinear meticulous numerical analysis and seismic response mechanism of tunnel structures under asynchronous earthquake motion.

Keywords: asynchronous input, longitudinal seismic response, tunnel structure, numerical simulation, traveling wave effect

Procedia PDF Downloads 433

Authors: Yi-Ju Lee

Abstract:

This study identified the hypothesised relationship between creative experience, and revisit intention of handmade oriental parasol umbrella in Kaohsiung, Taiwan. A face-to-face questionnaire survey was administered in Meinong town, Kaohsiung. The components of creative experience were found as “sense of achievement”, “unique learning” and “interaction with instructors” in creative tourism. The result also revealed significant positive relationships between creative experience and revisit intention in handmade activities. This paper provides additional suggestions for enhancing revisit intention and guidance regarding creative tourism.

Keywords: creative tourism, sense of achievement, unique learning, interaction with instructors, folk art

Procedia PDF Downloads 275

Authors: Joel S. Mtebe, Aron Kondoro, Mussa M. Kissaka, Elia Kibga

Abstract:

Sub-Saharan Africa is described as the second fastest growing mobile phone penetration in the world more than in the United States or the European Union. Mobile phones have been used to provide a lot of opportunities to improve people’s lives in the region such as in banking, marketing, entertainment, and paying various bills such as water, TV, and electricity. However, the potential of using mobile phones to enhance teaching and learning has not been explored. This study presents an experience of developing and delivering SMS quizzes questions that were used to assess mastery of the subject content knowledge of science and mathematics secondary school teachers in Tanzania. The SMS quizzes were used as a follow up support mechanism to 500 teachers who participated in a project to upgrade subject content knowledge of science and mathematics subjects. Quizzes of 10-15 questions were sent to teachers each week for 8 weeks and the results were analyzed using SPSS. The results showed that chemistry and biology had better performance compared to mathematics and physics. Teachers reported some challenges that led to poor performance, invalid answers, and non-responses and they are presented. This research has several practical implications for those who are implementing or planning to use mobile phones for teaching and learning especially in rural secondary schools in sub-Saharan Africa.

Keywords: mobile learning, elearning, educational technolgies, SMS, secondary education, assessment

Procedia PDF Downloads 279

Authors: Rujia Chen, Ajit Narayanan

Abstract:

Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

Procedia PDF Downloads 182

Authors: Yesim Tumsek, Erkan Celebi

Abstract:

In order to simulate the infinite soil medium for soil-foundation interaction problem, the essential geotechnical parameter on which the foundation stiffness depends, is the value of soil shear modulus. This parameter directly affects the site and structural response of the considered model under earthquake ground motions. Strain-dependent shear modulus under cycling loads makes difficult to estimate the accurate value in computation of foundation stiffness for the successful dynamic soil-structure interaction analysis. The aim of this study is to discuss in detail how to use the appropriate value of soil shear modulus in the computational analyses and to evaluate the effect of the variation in shear modulus with strain on the impedance functions used in the sub-structure method for idealizing the soil-foundation interaction problem. Herein, the impedance functions compose of springs and dashpots to represent the frequency-dependent stiffness and damping characteristics at the soil-foundation interface. Earthquake-induced vibration energy is dissipated into soil by both radiation and hysteretic damping. Therefore, flexible-base system damping, as well as the variability in shear strengths, should be considered in the calculation of impedance functions for achievement a more realistic dynamic soil-foundation interaction model. In this study, it has been written a Matlab code for addressing these purposes. The case-study example chosen for the analysis is considered as a 4-story reinforced concrete building structure located in Istanbul consisting of shear walls and moment resisting frames with a total height of 12m from the basement level. The foundation system composes of two different sized strip footings on clayey soil with different plasticity (Herein, PI=13 and 16). In the first stage of this study, the shear modulus reduction factor was not considered in the MATLAB algorithm. The static stiffness, dynamic stiffness modifiers and embedment correction factors of two rigid rectangular foundations measuring 2m wide by 17m long below the moment frames and 7m wide by 17m long below the shear walls are obtained for translation and rocking vibrational modes. Afterwards, the dynamic impedance functions of those have been calculated for reduced shear modulus through the developed Matlab code. The embedment effect of the foundation is also considered in these analyses. It can easy to see from the analysis results that the strain induced in soil will depend on the extent of the earthquake demand. It is clearly observed that when the strain range increases, the dynamic stiffness of the foundation medium decreases dramatically. The overall response of the structure can be affected considerably because of the degradation in soil stiffness even for a moderate earthquake. Therefore, it is very important to arrive at the corrected dynamic shear modulus for earthquake analysis including soil-structure interaction.

Keywords: clay soil, impedance functions, soil-foundation interaction, sub-structure approach, reduced shear modulus

Procedia PDF Downloads 263

Authors: Alejandra Yanez, Teresa Benavides, Zita Lopez

Abstract:

Even before COVID-19, one of the most important challenges that Higher Education faces today is the need for innovative educational methodologies and flexibility. We could all agree that one of the objectives of Higher Education is to provide students with a variety of intellectual and practical skills that, at the same time, will help them develop competitive advantages such as adaptation and critical thinking. Among the strategic objectives of Universidad de Monterrey (UDEM) has been to provide flexibility and satisfaction to students in the delivery modes of the academic offer. UDEM implemented a methodology that combines face to face with synchronous and asynchronous as delivery modes. UDEM goal, in this case, was to implement new technologies and different teaching methodologies that will improve the students learning experience. In this study, the experience of students during courses implemented in new delivery mode was compared with students in courses with traditional delivery modes. Students chose openly either way freely. After everything students around the world lived in 2020 and 2021, one can think that the face to face (traditional) delivery mode would be the one chosen by students. The results obtained in this study reveal that both delivery modes satisfy students and favor their learning process. We will show how the combination of delivery modes provides flexibility, so the proposal is that universities can include them in their academic offer as a response to the current student's learning interests and needs.

Keywords: flexibility, new delivery modes, student satisfaction, academic offer

Procedia PDF Downloads 94

Authors: Vikrant Gupta, Amrit Goswami

Abstract:

The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

Procedia PDF Downloads 135

Authors: Laila Akbarali

Abstract:

In the small scale study, an attempt is made to explore the structure and administrative relationships adopted by Multi Campus Universities [MCU] in UK and Pakistan and how these universities deal with some selected issues with respect to student related functions. For this study, literature on multi-site, divisionalized and other complex organizations related to business and Industry was consulted and an attempt was made to empirically test the normative models in the literature with respect to centralized , deconcentrated and decentralized structures. A questionnaire was used to gather data for this study. Purposive sampling was used. The findings of this study are somewhat different for UK and Pakistan. Contrary to a substantial body of organization theory, the results show that deconcentrated and decentralized universities in the UK are prone to delays in decision making and tend not to sensitive to local needs. In Pakistan on the other hand, deconcentrated and decentralized universities are more sensitive to local needs and there are less delays in decision making. The findings suggest that distance and reporting relationships could perhaps be responsible for the contradiction. The results also suggest that there is better coordination when the subsidiary campus sub-registrar reports to the registrar. The findings also highlight, that in both contexts, leadership at the campus level remains an issue. The results suggest that there may be factors other than structure that allow universities to keep their identity intact. The study highlights that MCU are inclined to use Information Technology and develop broad policies within which they allow their campuses to operate.

Keywords: administrative relationships, Multi-Campus, organization structure, registrar

Procedia PDF Downloads 318

Authors: Krishnaiah Arkanti, Ramulu Malothu

Abstract:

The experiments have been conducted to study the mechanical properties of commercially pure copper processing at room temperature by severe plastic deformation using equal channel angular extrusion (ECAE) through a die of 90^oangle up to 3 passes by route B_C i.e. rotating the sample in the same direction by 90^o after each pass. ECAE is used to produce from existing coarse grains to ultra-fine, equiaxed grains structure with high angle grain boundaries in submicron level by introducing a large amount of shear strain in the presence of hydrostatic pressure into the material without changing billet shape or dimension. Mechanical testing plays an important role in evaluating fundamental properties of engineering materials as well as in developing new materials and in controlling the quality of materials for use in design and construction. Yield stress, ultimate tensile stress and ductility are structure sensitive properties and vary with the structure of the material. Microhardness and tensile tests were carried out to evaluate the hardness, strength and ductility of the ECAE processed materials. The results reveal that the strength and hardness of commercially pure copper samples improved significantly without losing much ductility after each pass.

Keywords: equal channel angular extrusion, severe plastic deformation, copper, mechanical properties

Procedia PDF Downloads 185

Authors: Ewelina Nowak, Anna Wisla-Swider, Krzysztof Danel

Abstract:

Aqueous suspensions of DNA were illuminated with linearly polarized visible light and ultraviolet for 5, 15, 20 and 40 h. In order to check the nature of modification, DNA interactions were characterized by FTIR spectroscopy. For each illuminated sample, weight average molecular weight and hydrodynamic radius were measured by high pressure size exclusion chromatography. Resulting optical changes for illuminated DNA were investigated using UV-Vis spectra and photoluminescent. Optical properties show potential application in sensors based on modified DNA. Then selected DNA-surfactant complexes were illuminated with electromagnetic radiation for 5h. Molecular structure, optical characteristic were examinated for obtained complexes. Illumination led to changes of complexes physicochemical properties as compared with native DNA. Observed changes were induced by rearrangement of the molecular structure of DNA chains.

Keywords: biopolymers, deoxyribonucleic acid, ionic liquids, linearly polarized visible light, ultraviolet

Procedia PDF Downloads 206

Authors: Sophio Kalmakhelidze, Eka Shekiladze, Tamar Sanikidze, Mikheil Gogebashvili, Nazi Ivanishvili

Abstract:

Radiation-induced various degrees of brain injury and cognitive impairment have been described after cranial radiotherapy of brain tumors. High doses of ionizing radiation have a severe impact on the central nervous system, resulting in morphological and behavioral impairments. Structures of the limbic system are especially sensitive to radiation exposure. Hence, compounds or drugs that can reduce radiation-induced impairments can be used as promising antioxidants or radioprotectors. In our study Mice whole-body irradiation with 137Cs was performed at a dose rate of 1,1 Gy/min for a total dose of 5 Gy with a “Gamma-capsule-2”. Irradiated mice were treated with Herniarin (20 mg/kg) for five days before irradiation and the same dose was administrated after one hour of irradiation. The immediate and delayed effects of ionizing radiation, as well as, protective effect of Herniarin was evaluated during early and late post-irradiation periods. The results reveal that ionizing radiation (5 Gy) alters the structure of the hippocampus in adult mice during the late post-irradiation period resulting in the decline of memory formation and learning process. Furthermore, Simple Coumarin-Herniarin reveals a radiosensitizing effect reducing morphological and behavioral alterations.

Keywords: ionizing radiation, cognitive impairments, hippocampus, limbic system, Herniarin

Procedia PDF Downloads 70

Authors: Zohreh Derikvand

Abstract:

One new 2D metal-organic coordination polymer of Ni(II) namely [Ni2(ndc)2(DMSO)4(H2O)]n, where ndc = naphthalene-1,4-dicarboxylic acid and DMSO= dimethyl sulfoxide has been synthesized and characterized by elemental analysis, spectral (IR, UV-Vis), thermal (TG/DTG) analysis and single crystal X-ray diffraction. Compound 1 possesses a 2D layer structure constructed from dinuclear nickel(II) building blocks in which two crystallographically independent Ni2+ ions are bridged by ndc2– ligands and water molecule. The ndc2– ligands adopt μ3 bridging modes, linking the metal centers into a two-dimensional coordination framework. The two independent NiII cations are surrounded by dimethyl sulfoxide and naphthalene-1,4-dicarboxylate molecules in distorted octahedron geometry. In the crystal structures of 1 there are non-classical hydrogen bonding arrangements and C-H–π stacking interactions. Electrochemical behavior of [Ni2(ndc)2(DMSO)4(H2O)]n, (Ni-NDA) on the surface of carbon nanotube (CNTs) glassy carbon electrode (GCE) was described. The surface structure and composition of the sensor were characterized by scanning electron microscopy (SEM). Oxidation of fructose on the surface of modified electrode was investigated with cyclic voltammetry and electrochemical impedance spectroscopy (EIS) and the results showed that the Ni-NDA/CNTs film displays excellent electrochemical catalytic activities towards fructose oxidation.

Keywords: naphthalene-1, 4-dicarboxylic acid, crystal structure, coordination polymer, electrocatalysis, impedance spectroscopy

Procedia PDF Downloads 328

Authors: Johanna Gamboa-Kroesen

Abstract:

School connectedness, or the sense of belonging at school, is a critical factor in adolescent health, academic achievement, and socioemotional well-being. In educational research, the construct of the psychological sense of school membership is often referred to as school engagement, school bonding, or school attachment. While current research recognizes school connectedness as integral to a child’s mental health and academic success, many schools have yet to develop adequate interventions to promote a child’s overall sense of belonging at school. However, prior researches in music education indicates that, among other benefits, music classrooms may provide an environment where students feel they belong. While studies indicates that music learning environments, specifically performing ensemble learning environments, instill a sense of school connectedness and, more broadly, contribute to a student’s socio-emotional development, there has been inadequate research on how the actions of music teachers contribute to this phenomenon. The purpose of this study was to examine the relationship between school connectedness and music learning environments with middle school music students enrolled in a school-based music ensemble. In addition, the study aimed to provide a descriptive analysis of the instructional practices that music teachers use to promote an inclusive environment in their classrooms and an overall sense of belonging in their students. Using 191 student surveys of school membership, student reflective writings, 5 teacher interviews, and 10 classroom observations, this study examined the relationship between 7th and 8th-grade student-reported levels of connectedness within their school-based music ensemble and teacher instructional practice. The study found that students reported high levels of positive school membership within their music classes. Students who participate in school-based orchestra ensembles reported a positive change in emotional state during music instruction. In addition, evidence in this study found that music teachers use instructional practices to build connectedness through de-emphasizing competition and strengthening a student’s sense of relational value within their music learning experience. The findings offer implications for future music teacher instruction to create environments of inclusion, strengthen student-teacher relationships, and promote strategies that enhance student connection to school.

Keywords: music education, belonging, instructional practice, school connectedness

Procedia PDF Downloads 60

Authors: Shawndra Bowers, Allie Brandriet, Betsy Gilbertson

Abstract:

This paper will highlight how Auburn Online’s instructional designers leveraged student and faculty data to update and improve online course design and instructional materials. When designing and revising online courses, it can be difficult for faculty to know what strategies are most likely to engage learners and improve educational outcomes in a specific discipline. It can also be difficult to identify which metrics are most useful for understanding and improving teaching, learning, and course design. At Auburn Online, the instructional designers use a suite of data based student’s performance, participation, satisfaction, and engagement, as well as faculty perceptions, to inform sound learning and design principles that guide growth-mindset consultations with faculty. The consultations allow the instructional designer, along with the faculty member, to co-create an actionable course improvement plan. Auburn Online gathers learning analytics from a variety of sources that any instructor or instructional design team may have access to at their own institutions. Participation and performance data, such as page: views, assignment submissions, and aggregate grade distributions, are collected from the learning management system. Engagement data is pulled from the video hosting platform, which includes unique viewers, views and downloads, the minutes delivered, and the average duration each video is viewed. Student satisfaction is also obtained through a short survey that is embedded at the end of each instructional module. This survey is included in each course every time it is taught. The survey data is then analyzed by an instructional designer for trends and pain points in order to identify areas that can be modified, such as course content and instructional strategies, to better support student learning. This analysis, along with the instructional designer’s recommendations, is presented in a comprehensive report to instructors in an hour-long consultation where instructional designers collaborate with the faculty member on how and when to implement improvements. Auburn Online has developed a triage strategy of priority 1 or 2 level changes that will be implemented in future course iterations. This data-informed decision-making process helps instructors focus on what will best work in their teaching environment while addressing which areas need additional attention. As a student-centered process, it has created improved learning environments for students and has been well received by faculty. It has also shown to be effective in addressing the need for improvement while removing the feeling the faculty’s teaching is being personally attacked. The process that Auburn Online uses is laid out, along with the three-tier maintenance and revision guide that will be used over a three-year implementation plan. This information can help others determine what components of the maintenance and revision plan they want to utilize, as well as guide them on how to create a similar approach. The data will be used to analyze, revise, and improve courses by providing recommendations and models of good practices through determining and disseminating best practices that demonstrate an impact on student success.

Keywords: data-driven, improvement, online courses, faculty development, analytics, course design

Procedia PDF Downloads 55

Authors: Grace karimi Muriithi, Behnaz Papari, Ali Arsalan, Christopher Shannon Edrington

Abstract:

Complexity and nonlinearity of the control system design is increasing for DC microgrid applications when the cyber concept associated with the technology constraints will added to the picture. Controllers’ functionality during the critical operation mode is required to guaranteed specifically for a high profile applications such as NAVY DC ship power system (SPS) as an small-scaled DC microgrid. Thus, SPS is susceptible to cyber-attacks and, accordingly, can provide the disastrous effects. In this study, a machine learning (ML) approach is demonstrated to offer the promising performance of SPS for developing an effective and robust functionality over attacks time. Simulation results analysis demonstrate that the proposed method can improve the controllability successfully.

Keywords: controlability, cyber attacks, distribute control, machine learning

Procedia PDF Downloads 109

Authors: Dimitrios Vlachopoulos, George Tsokkas

Abstract:

Although it is not intended to identify distance education students as a homogeneous group, recent research has demonstrated that there are some demographic and personality common traits among most of them that provide the basis for the description of a typical distance learning student. The purpose of this paper is to describe these common traits and to facilitate their learning journey within a distance education program. The described research is an initiative of the Distance Education Unit at the European University Cyprus (Laureate International Universities) in the context of its action for the improvement of the students’ performance.

Keywords: distance education students, successful student performance, European University Cyprus, common traits

Procedia PDF Downloads 483

Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

Abstract:

This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

Procedia PDF Downloads 45

Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

Abstract:

In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

Procedia PDF Downloads 38

Authors: Bahattin Abay

Abstract:

Thin film of polycyclic aromatic hydrocarbon rubrene, C₄₂H₂₈ (5,6,11,12-tetraphenyltetracene), has been surfaced on Moderately Doped (MD) n-InP substrate as an interfacial layer by means of spin coating technique for the electronic modification of Au/MD n-InP structure. Ex situ annealing has been carried out at 150 °C for three minutes under a brisk flow of nitrogen for the better adhesion of the deposited film with the substrate surface. Room temperature electrical characterization has been performed on the C₄₂H₂₈/MD n-InP hybrid junctions by current-voltage (I-V) and capacitance-voltage (C-V) measurement in the dark. It has been seen that the C₄₂H₂₈/MD n-InP structure demonstrated extraordinary rectifying behavior. An effective barrier height (BH) as high as 0.743 eV, along with an ideality factor very close to unity (n=1.203), has been achieved for C₄₂H₂₈/n-InP organic/inorganic device. A thin C₄₂H₂₈ interfacial layer between Au and MD n-InP also reduce the reverse leakage current by almost four orders of magnitude and enhance the BH about 0.278 eV. This good performance of the device is ascribed to the passivation effect of organic interfacial layer between Au and n-InP. By using C-V measurement, in addition, the value of BH of the C₄₂H₂₈/n-InP organic/inorganic hybrid junctions have been obtained as 0.796 eV. It has been seen that both of the BH value (0.743 and 0.796 eV) for the organic/inorganic hybrid junction obtained I-V and C-V measurement, respectively are significantly larger than that of the conventional Au/n-InP structure (0.465 and 0.503 eV). It was also seen that the device had good sensitivity to the light under 100 mW/cm² illumination conditions. The obtained results indicated that modification of the interfacial potential barrier for Metal/n-InP junctions might be attained using polycyclic aromatic hydrocarbon thin interlayer C₄₂H₂₈.

Keywords: I-V and C-V measurements, heterojunction, n-InP, rubrene, surface passivation

Procedia PDF Downloads 159

Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

Abstract:

In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

Procedia PDF Downloads 364

Authors: Hassan Gomar, Shahin Bagheri, Nader Keyvan, Mozhdeh Shirinzadeh

Abstract:

Air-Cooled Condenser (ACC) platform structures are the most complicated and principal structures in power plants and other industrial parts which need to condense the low-pressure steam in the cycle. Providing large spans for this structure has great merit as there would be more space for other subordinate buildings and pertinent equipment. Moreover, applying methods to reduce the overall cost of construction while maintaining its strength against severe seismic loading is of high significance. Tabular spatial structures and composite frames have been widely used in recent years to satisfy the need for higher strength at a reasonable price. In this research program, three different structural systems have been regarded for ACC steel platform using Special Concentrate Braced Frames (SCBF), which is the most common system (first scheme), modular spatial frames (second scheme) and finally, a modified method applying Concrete Filled Steel Tabular (CFST) columns (third scheme). The finite element method using Sap2000 and Etabs software was conducted to investigate the behavior of the structures and make a precise comparison between the models. According to the results, the total weight of the steel structure in the second scheme decreases by 13% compared to the first scheme and applying CFST columns in the third scheme causes a 3% reduction in the total weight of the structure in comparison with the second scheme while all the lateral displacements and P-M interaction ratios are in the admissible limit.

Keywords: ACC, SCBF frames, spatial structures, CFST frames

Procedia PDF Downloads 192

Authors: Santanu Pakhira, Chandan Mazumdar, R. Ranganathan, Maxim Avdeev

Abstract:

Magnetically frustrated systems are of great interest and one of the most adorable topics for the researcher of condensed matter physics, due to their various interesting properties, viz. ground state degeneracy, finite entropy at zero temperature, lowering of ordering temperature, etc. Ternary intermetallics with the composition RE₂TX₃ (RE = rare-earth element, T= d electron transition metal and X= p electron element) crystallize in hexagonal AlB₂ type crystal structure (space group P6/mmm). In a hexagonal crystal structure with the antiferromagnetic interaction between the moments, the center moment is geometrically frustrated. Magnetic frustration along with disorder arrangements of non-magnetic ions are the building blocks for metastable spin-glass ground state formation for most of the compounds of this stoichiometry. The newly synthesized compound Er₂NiSi₃ compound forms in single phase in AlB₂ type structure with space group P6/mmm. The compound orders antiferromagnetically below 5.4 K and spin freezing of the frustrated magnetic moments occurs below 3 K for the compound. The compound shows magnetic relaxation behavior and magnetic memory effect below its freezing temperature. Neutron diffraction patterns for temperatures below the spin freezing temperature have been analyzed using FULLPROF software package. Diffuse magnetic scattering at low temperatures yields spin glass state formation for the compound.

Keywords: antiferromagnetism, magnetic frustration, spin-glass, neutron diffraction

Procedia PDF Downloads 257

Authors: Ralph Barnes

Abstract:

The internationalization and globalization of education are now a huge, multi-million dollar industry. The movement of international students across the globe has provided a rich vein of revenue for universities and institutions of higher learning to exploit and harvest. A concerted effort has been made by universities worldwide to court students from overseas, with some countries relying up to one-third of student fees, coming from international students. Australian universities and English Language Centres are coming under increased government scrutiny in respect to such areas as the academic progression of international students, management and understanding of student visa requirements and the design of higher education courses and effective assessment regimes. As such, universities and other higher education institutions are restructuring themselves more as service providers rather than as strictly education providers. In this paper, the high-touch, tailored academic model currently followed by some Australian educational institutions to support international students, is examined and challenged. Academic support services offered to international students need to be coordinated, sustained and reviewed regularly, in order to assess their effectiveness. Maintaining the delivery of high-quality educational programs and learning outcomes for this high income-generating student cohort is vital, in order to continue the successful academic and social engagement by international students across the Australian university and higher education landscape.

Keywords: ESL, engagement, tertiary, learning

Procedia PDF Downloads 198

Authors: Alaa Yehia, Ayatollah Yehia, Sherif Yehia

Abstract:

There is a recurring issue of low enrollments across many civil engineering departments in postsecondary institutions. While there have been moments where enrollments begin to increase, civil engineering departments find themselves facing low enrollments at around 60% over the last five years across the Middle East. There are many reasons that could be attributed to this decline, such as low entry-level salaries, over-saturation of civil engineering graduates in the job market, and a lack of construction projects due to the impending or current recession. However, this recurring problem alludes to an intrinsic issue of the curriculum. The societal shift to the usage of high technology such as machine learning (ML) and artificial intelligence (AI) demands individuals who are proficient at utilizing it. Therefore, existing curriculums must adapt to this change in order to provide an education that is suitable for potential and current students. In this paper, In order to provide potential solutions for this issue, the analysis considers two possible implementations of high technology into the civil engineering curriculum. The first approach is to implement a course that introduces applications of high technology in Civil Engineering contexts. While the other approach is to intertwine applications of high technology throughout the degree. Both approaches, however, should meet requirements of accreditation agencies. In addition to the proposed improvement in civil engineering curriculum, a different pedagogical practice must be adapted as well. The passive learning approach might not be appropriate for Gen Z students; current students, now more than ever, need to be introduced to engineering topics and practice following different learning methods to ensure they will have the necessary skills for the job market. Different learning methods that incorporate high technology applications, like AI, must be integrated throughout the curriculum to make the civil engineering degree more attractive to prospective students. Moreover, the paper provides insight on the importance and approach of adapting the Civil Engineering curriculum to address the current low enrollment crisis that civil engineering departments globally, but specifically in the Middle East, are facing.

Keywords: artificial intelligence (AI), civil engineering curriculum, high technology, low enrollment, pedagogy

Procedia PDF Downloads 158

Authors: Marcos Devaner, Marcela Alves, Cledson Braga, Fabiano Alves, Wilton Bezerra

Abstract:

This article discusses the inclusion of people with disabilities in the process of testing an accessible system solution for distance education. The accessible system, team profile, methodologies and techniques covered in the testing process are presented. The testing process shown in this paper was designed from the experience with user. The testing process emerged from lessons learned from past experiences and the end user is present at all stages of the tests. Also, lessons learned are reported and how it was possible the maturing of the team and the methods resulting in a simple, productive and effective process.

Keywords: experience report, accessible systems, software testing, testing process, systems, e-learning

Procedia PDF Downloads 390

Authors: Eyup Ozkan, Ozkan U. Nalbantoglu, Aycan Gundogdu, Mehmet Hora, A. Emre Onuk

Abstract:

The human microbiome has been associated with cardiological conditions and this relationship is becoming to be defined beyond the gastrointestinal track. In this study, we investigate the alteration in circulatory microbiota in the context of Coronary Artery Disease (CAD). We received circulatory blood samples from suspected CAD patients and maintain 16S ribosomal RNA sequencing to identify each patient’s microbiome. It was found that Corynebacterium and Methanobacteria genera show statistically significant differences between healthy and CAD patients. The overall biodiversities between the groups were observed to be different revealed by machine learning classification models. We also achieve and demonstrate the performance of a diagnostic method using circulatory blood microbiome-based estimation.

Keywords: coronary artery disease, blood microbiome, machine learning, angiography, next-generation sequencing

Procedia PDF Downloads 151

Authors: Amina Babou

Abstract:

Gender and language is a moot and miscellaneous arena in the sphere of socio-linguistics, which has been proliferated so widely and rapidly in recent years. The dawn of research on gender and foreign language education was against the feminist researchers who allowed space for the bustling concourse of voices and perspectives in the arena of gender and language differences, in the early to the mid-1970. The objective of this scrutiny is to explore to what extent teaching gender and language in the English as a Foreign Language (EFL) classroom plays a pivotal role in learning language information and skills. Moreover, the gist of this paper is to investigate how EFL students in Algeria conflate their gender identities with the linguistic practices and scholastic expertise. To grapple with the full range of issues about the EFL students’ awareness about the negotiation of meanings in the classroom, we opt for observing, interviewing, and questioning later to check using ‘how-do-you do’ procedure. The analysis of the EFL classroom discourse, from five Algerian universities, reveals that speaking strategies such as the manners students make an abrupt topic shifts, respond spontaneously to the teacher, ask more questions, interrupt others to seize control of conversations and monopolize the speaking floor through denying what others have said, do not sit very lightly on 80.4% of female students’ shoulders. The data indicate that female students display the assertive style as a strategy of learning to subvert the norms of femininity, especially in the speaking module.

Keywords: EFL students, gender identity, linguistic styles, foreign language

Procedia PDF Downloads 459

Authors: Prakash Singh

Abstract:

This study focused on the importance of collegiality to improve the effectiveness of a poorly resourced school (PRS). In an effective school that embraces collegiality as its culture, one can expect to find a teaching staff and a management team that shares responsibilities and accountabilities through the development of a common purpose and vision, regardless of whether the school is considered to be poorly resourced or not. Working together in collegial teams is a more effective way to accomplish tasks and to create a climate for effective learning, even for learners in PRSs from poor communities. The main aim of this study was therefore to determine whether collegiality as a leadership strategy could extract the best from people in a PRS, and consequently create the most effective and efficient educational climate possible. The responses received from the teachers and the principal at the PRS supports the notion that collegiality does have a positive influence on learning, as demonstrated by the improved academic achievement of the learners. The teachers were now more involved in the school. They agreed that this was a positive development. Their descriptions of increased involvement, shared accountability and shared decision-making identified important aspects of collegiality that transformed the school from being dysfunctional. Hence, it is abundantly clear that a collegial leadership style can help extract the best from people because the most effective and efficient educational climate can be created at a school when collegiality is employed. Collegial leadership demonstrates that even in PRSs, there are boundless opportunities to improve teaching and learning.

Keywords: collegiality, collegial leadership, effective educational climate, poorly resourced school

Procedia PDF Downloads 402