Search results for: deep learning algorithms

Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

Procedia PDF Downloads 134

Authors: Nurul Nadrah

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As a result of the COVID-19 pandemic, a movement control order was implemented, leading to the adoption of online learning as a substitute for conventional classroom instruction. Thus, this study aims to determine the differences in skill performance between online learning and conventional methods among nursing students. We employed a quasi-experimental design with purposive sampling, involving a total of 59 nursing students, and used online learning as the intervention. As a result, the study found there was a significant difference in student skill performance between online learning and conventional methods. As a conclusion, in times of hardship, it is necessary to implement alternative pedagogical approaches, especially in critical fields like nursing, to ensure the uninterrupted progression of educational programs. This study suggests that online learning can be effectively employed as a means of imparting knowledge to nursing students during their training.

Keywords: nursing education, online learning, skill performance, conventional learning method

Procedia PDF Downloads 33

Authors: Sumitra Nuanmeesri

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The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: blended learning, new media, infrastructure and computer network, tele-education, online learning

Procedia PDF Downloads 398

Authors: Sandhyarani Mahananda

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This study investigates the preferences of corporate employees for knowledge updates on the e-learning platform. The researchers explore the factors influencing their platform choices through a survey administered to employees across diverse industries and job roles. The survey examines preferences for specific platforms (e.g., Coursera, Udemy, LinkedIn Learning). It assesses the importance of content relevance, platform usability, mobile accessibility, and integration with workplace learning management systems. Preliminary findings indicate a preference for platforms that offer curated, job-relevant content, personalized learning paths, and seamless integration with employer-provided learning resources. This research provides valuable insights for organizations seeking to optimize their investment in e-learning and enhance employee knowledge development.

Keywords: corporate training, e-learning platforms, employee preferences, knowledge updation, professional development

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Authors: Seyyed Hassan Seyyedrezaei

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The advent of computer and ICT thereafter has introduced many irrevocable changes in learning and teaching. There is substantially growing need for the use of IT and ICT in language learning and teaching. In other words, the integration of Information Technology (IT) into online teaching is of vital importance for education and assessment. Considering the fact that the image of education is undergone drastic changes by the advent of technology, education systems and teachers move beyond the walls of traditional classes and methods in order to join with other educational centers to revitalize education. Given the advent of distance learning, online courses and virtual universities, e-assessment has taken a prominent place in effective teaching and meeting the learners' educational needs. The purpose of this paper is twofold: first, scrutinizing e-learning, it discusses how and why e-assessment is becoming widely used by educationalists and administrators worldwide. As a second purpose, a couple of effective strategies for online assessment will be enumerated.

Keywords: e-assessment, e learning, ICT, online assessment

Procedia PDF Downloads 564

Authors: A. Boudjemai, A. Zafrane, R. Hocine

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Honeycomb sandwich panels are increasingly used in the construction of space vehicles because of their outstanding strength, stiffness and light weight properties. However, the use of honeycomb sandwich plates comes with difficulties in the design process as a result of the large number of design variables involved, including composite material design, shape and geometry. Hence, this work deals with the presentation of an optimal design of hexagonal honeycomb sandwich structures subjected to space environment. The optimization process is performed using a set of algorithms including the gravitational search algorithm (GSA). Numerical results are obtained and presented for a set of algorithms. The results obtained by the GSA algorithm are much better compared to other algorithms used in this study.

Keywords: optimization, gravitational search algorithm, genetic algorithm, honeycomb plate

Procedia PDF Downloads 372

Authors: Zachary Blanks, Solomon Sonya

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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

Procedia PDF Downloads 286

Authors: Weiwei Xie, Yichao Li

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The initial installation of the umbilical can affect the subsequent installation process and final installation. Meanwhile, the design of both ends of the ultra-deep water dynamic umbilical (UDWDU), as well as the design of the surface unit and the subsea production system connected by UDWDU,], varies in different oil and gas fields. To optimize the installation process of UDWDU, on the basis of the summary and analysis of the surface-end and the subsea-end design of UDWDU and the mainstream construction resources, the method of initial installation from the surface unit side or the subsea production system side of UDWDU is studied, and each initiation installation method is pointed out if some difficulties that may be encountered.

Keywords: dynamic umbilical, ultra-deep-water, initial installation, installation process

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Authors: Hani Mustafa, Cristina Gonzalez Ruiz, Estelle Bech

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The growing influence of digital technologies has made learning and interaction more accessible, resulting in effective collaboration if properly managed. Technology enabled learning has become an important conduit for learning, including collaborative learning. The use of wiki technology, for example, has opened a new learning platform that enables the integration of social, linguistic, and cognitive processes of language learning. It encourages students to collaborate in the construction, analysis, and understanding of knowledge. But to what extent is the use of wikis effective in promoting collaborative learning among students. In addition, how do students perceive this technology in enhancing their language learning? In this study, students were be given a wiki project to complete collaboratively with their group members. Students had to write collaboratively to produce and present a seven-day travel plan in which they had to describe places to visit and things to do to explore the best historical and cultural aspects of the country. The study involves students learning French, Malay, and Spanish as a foreign language. In completing this wiki project, students will move from passive learning of language to real engagement with classmates, requiring them to collaborate and negotiate effectively with one another. The objective of the study is to ascertain to what extent does wiki technology helped in promoting collaborative learning and improving language skills from students’ perception. It is found that while there was improvement in students language skills, the overall experience was less positive due to unfamiliarity with a new learning tool.

Keywords: collaborative learning, foreign language, wiki, teaching

Procedia PDF Downloads 134

Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

Procedia PDF Downloads 129

Authors: Moung Young Lee, Chul Gyu Song

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Photoacoustic imaging is the imaging technology that combines the optical imaging and ultrasound. This provides the high contrast and resolution due to optical imaging and ultrasound imaging, respectively. We developed the real-time photoacoustic tomography (PAT) system using linear-ultrasound transducer and digital acquisition (DAQ) board. There are two types of algorithm for reconstructing the photoacoustic signal. One is back-projection algorithm, the other is FFT algorithm. Especially, we used the non-uniform FFT algorithm. To evaluate the performance of our system and algorithms, we monitored two wires that stands at interval of 2.89 mm and 0.87 mm. Then, we compared the images reconstructed by algorithms. Finally, we monitored the two hairs crossed and compared between these algorithms.

Keywords: back-projection, image comparison, non-uniform FFT, photoacoustic tomography

Procedia PDF Downloads 429

Authors: Winnie Wing Mui So, Yu Chen

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This study investigated primary school teachers’ and students’ perceptions of science learning in an e-learning environment. This study used a multiple case study design and involved eight science teachers and their students from four Hong Kong primary schools. The science topics taught included ‘season and weather’ ‘force and movement’, ‘solar and lunar eclipse’ and ‘living things and habitats’. Data were collected through lesson observations, interviews with teachers, and interviews with students. Results revealed some differences between the teachers’ and the students’ perceptions regarding the usefulness of e-learning resources, the organization of student-centred activities, and the impact on engagement and interactions in lessons. The findings have implications for the more effective creation of e-learning environments for science teaching and learning in primary schools.

Keywords: e-learning, science education, teacher' and students' perceptions, primary schools

Procedia PDF Downloads 196

Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 254

Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

Procedia PDF Downloads 171

Authors: Eman W. Boghdady, A. R. Shehata, M. A. Azem

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Database (DB) security demands permitting authorized users and prohibiting non-authorized users and intruders actions on the DB and the objects inside it. Organizations that are running successfully demand the confidentiality of their DBs. They do not allow the unauthorized access to their data/information. They also demand the assurance that their data is protected against any malicious or accidental modification. DB protection and confidentiality are the security concerns. There are four types of controls to obtain the DB protection, those include: access control, information flow control, inference control, and cryptographic. The cryptographic control is considered as the backbone for DB security, it secures the DB by encryption during storage and communications. Current cryptographic techniques are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, etc.) and chaos cryptography using continuous (Chau, Rossler, Lorenz, etc.) or discreet (Logistics, Henon, etc.) algorithms. The important characteristics of chaos are its extreme sensitivity to initial conditions of the system. In this paper, DB-security systems based on chaotic algorithms are described. The Pseudo Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented using Matlab and their statistical properties are evaluated using NIST and other statistical test-suits. Then, these algorithms are used to secure conventional DB (plaintext), where the statistical properties of the ciphertext are also tested. To increase the complexity of the PRNGs and to let pass all the NIST statistical tests, we propose two hybrid PRNGs: one based on two chaotic Logistic maps and another based on two chaotic Henon maps, where each chaotic algorithm is running side-by-side and starting from random independent initial conditions and parameters (encryption keys). The resulted hybrid PRNGs passed the NIST statistical test suit.

Keywords: algorithms and data structure, DB security, encryption, chaotic algorithms, Matlab, NIST

Procedia PDF Downloads 263

Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: Shao-Di Feng, Von-Wun Soo

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To generate symbolic music with specific emotions is a challenging task due to symbolic music datasets that have emotion labels are scarce and incomplete. This research aims to generate more refined emotions based on the training datasets that are only labeled with four quadrants in Russel’s 2D emotion model. We focus on the theory of Music Fadernet and map arousal and valence to the low-level attributes, and build a symbolic music generation model by combining transformer and GM-VAE. We adopt an in-attention mechanism for the model and improve it by allowing modulation by conditional information. And we show the music generation model could control the generation of music according to the emotions specified by users in terms of high-level linguistic expression and by manipulating their corresponding low-level musical attributes. Finally, we evaluate the model performance using a pre-trained emotion classifier against a pop piano midi dataset called EMOPIA, and by subjective listening evaluation, we demonstrate that the model could generate music with more refined emotions correctly.

Keywords: music generation, music emotion controlling, deep learning, semi-supervised learning

Procedia PDF Downloads 81

Authors: Ying Zhao, Xingyan Bin

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Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data.

Keywords: semi-supervised clustering, hierarchical agglomerative clustering, reference trees, distance constraints

Procedia PDF Downloads 541

Authors: Wafa Slaibi Alsharafat

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Genetic algorithms (GA) are random algorithms as random numbers that are generated during the operation of the algorithm determine what happens. This means that if GA is applied twice to optimize exactly the same problem it might produces two different answers. In this project, we propose an evolutionary algorithm and Genetic Algorithm (GA) to be implemented in symmetric encryption and decryption. Here, user's message and user secret information (key) which represent plain text to be transferred into cipher text.

Keywords: GA, encryption, decryption, crossover

Procedia PDF Downloads 440

Authors: Ilana Lavy, Rami Rashkovits

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In this study, we describe Information Systems students' learning model which was applied by students in order to cope with professional knowledge gaps in the context of their final project. The students needed to implement a software system according to specifications and design they have made beforehand. They had to select certain technologies and use them. Most of them decided to use programming environments that were learned during their academic studies. The students had to cope with various levels of knowledge gaps. For that matter they used learning strategies that were organized by us as a learning model which includes two phases each suitable for different learning tasks. We analyze the learning model, describing advantages and shortcomings as perceived by the students, and provide excerpts to support our findings.

Keywords: knowledge gaps, independent learner skills, self-regulated learning, final project

Procedia PDF Downloads 474

Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

Procedia PDF Downloads 273

Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

Procedia PDF Downloads 113

Authors: Adamu Zubairu Mohammed, Fransis Oforum Arimoro, Salihu Maikudi Ibrahim, Y. I. Auta, T. I. Arowosegbe, Y. Abdullahi

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The present study was conducted to evaluate the effect of depth on the distribution of zooplankton and some physicochemical parameters in Tungan Kawo Lake (Wushishi dam). Water and zooplankton samples were collected from the surface, 3.0 meters deep and 6.0 meters deep, for a period of 24 hours for six months. Standard procedures were adopted for the determination of physicochemical parameters. Results have shown significant differences in the pH, DO, BOD Hardness, Na, and Mg. A total of 1764 zooplankton were recorded, comprising 35 species, with cladocera having 18 species (58%), 14 species of copepoda (41%), 3 species of diptera (1.0%). Results show that more of the zooplankton were recorded in the 3.0 meters-deep region compared to the two other depts and a significant difference was observed in the distribution of Ceriodaphnia dubia, Daphnia laevis, and Leptodiaptomus coloradensis. Though the most abundant zooplankton was recorded in the 3.0 meters deep, Leptodiaptomus coloradesnsis, which was observed in the 6.0 meters deep as the most individual observed, this was followed by Daphnia laevis. Canonical correspondence analysis between physicochemical parameters and the zooplankton indicated a good relationship in the Lake. Ceriodaphnia dubia was found to have a good association with oxygen, sodium, and potassium, while Daphnia laevis and Leptodiaptomus coloradensis are in good relationship with magnesium and phosphorus. It was generally observed that this depth does not have much influence on the distribution of zooplankton in Wushishi Lake.

Keywords: zooplankton, standard procedures, canonical correspondence analysis, Wushishi, canonical, physicochemical parameter

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Siti Nurjanah, Supahar

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E-learning has become an increasingly popular mode of instruction, particularly in the field of physics education, where it offers opportunities for interactive and engaging learning experiences. This research aims to analyze the trends of research that investigated e-learning in physics education. Data was extracted from Scopus's database using the keywords "physics" and "e-learning". Of the 380 articles obtained based on the search criteria, a trend analysis of the research was carried out with the help of RStudio using the biblioshiny package and VosViewer software. Analysis showed that publications on this topic have increased significantly from 2014 to 2021. The publication was dominated by researchers from the United States. The main journal that publishes articles on this topic is Proceedings Frontiers in Education Conference fie. The most widely cited articles generally focus on the effectiveness of Moodle for physics learning. Overall, this research provides an in-depth understanding of the trends and key findings of research related to e-learning in physics.

Keywords: bibliometric analysis, physics education, biblioshiny, E-learning

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Authors: Mehdi Ameur, Ahmed Essadki, Tamou Nasser

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The purpose of this paper is the evaluation of photovoltaic system with MPPT techniques. This system is developed by combining the models of established solar module and DC-DC converter with the algorithms of perturbing and observing (P&O), incremental conductance (INC) and fuzzy logic controller (FLC). The system is simulated under different climate conditions and MPPT algorithms to determine the influence of these conditions on characteristic power-voltage of PV system. According to the comparisons of the simulation results, the photovoltaic system can extract the maximum power with precision and rapidity using the MPPT algorithms discussed in this paper.

Keywords: fuzzy logic controller, FLC, hill climbing, HC, incremental conductance (INC), perturb and observe (P&O), maximum power point, MPP, maximum power point tracking, MPPT

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Authors: Natarajan Hema, Tamilarasan Karunakaran

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Technology is remodeling the process of teaching and learning. An inflection point is faced where technological interventions are rewiring learning process in formal classrooms. Employment depends on dynamic learning capability. Transforming the functionalities of teaching-learning-assessment through innovation is needed to modify the roles of teacher to enabler and learner to the dynamic learner. This makeover is vital for English language teaching where English is acquired as a skill, exercised as ability and get stabilized as a competence. This reshaping could be achieved through providing autonomy to participants of learning. This paper explores parameters and components aiding such a transformation. The differentiated responsibilities and other critical learning support systems are projected as viable options. New age teaching practices are studied for feasibilities to aid transformation and being put forth an inter-operable teaching-learning system for a learner-centric ELT classrooms. LOTUS model developed by the authors is also studied for its inclusiveness to promote skill acquisition.

Keywords: ELT methodology, communicative competence, skill acquisition , new age teaching

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

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Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

Procedia PDF Downloads 153

Authors: Pia Kastl

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By combining face-to-face sessions with digital selflearning units, the learning process can be enhanced and learning success improved. Potentials of blended learning are the flexibility and possibility to get in touch with lecturers and fellow students face-toface. It also offers the opportunity to individualize and self-regulate the learning process. Aim of this article is to analyse how different learning environments affect students’ learning behavior and how digital tools can be used effectively. The analysis also considers the extent to which the field of study affects the students’ preferences. Semi-structured interviews were conducted with students from different disciplines at two German universities (N= 60). The questions addressed satisfaction and perception of online, faceto-face and blended learning courses. In addition, suggestions for improving learning experience and the use of digital tools in the different learning environments were surveyed. The results show that being present on campus has a positive impact on learning success and online teaching facilitates flexible learning. Blended learning can combine the respective benefits, although one challenge is to keep the time investment within reasonable limits. The use of digital tools differs depending on the subject. Medical students are willing to use digital tools to improve their learning success and voluntarily invest more time. Students of the humanities and social sciences, on the other hand, are reluctant to invest additional time. They do not see extra study material as an additional benefit their learning success. This study illustrates how these heterogenous demands on learning environments can be met. In addition, potential for improvement will be identified in order to foster both learning process and learning success. Learning environments can be meaningfully enriched with digital elements to address student diversity in higher education.

Keywords: blended learning, higher education, diversity, learning styles

Procedia PDF Downloads 63

Authors: Parveen Kumar

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The cope the rapid change of technology and to control the ephemeral rate of information generation, librarians along with their professional colleagues need to equip themselves as per the requirement of the electronic information society. E-learning is purely based on computer and communication technologies. The terminologies like computer based learning. It is the delivery of content via all electronic media through internet, internet, Extranets television broadcast, CD-Rom documents, etc. E-learning poses lot of issues in the transformation of literature or knowledge from the conventional medium to ICT based format and web based services.

Keywords: e-learning, digital libraries, online learning, electronic information society

Procedia PDF Downloads 503