Search results for: batch machine learning

Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Hamidreza Akbari

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In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

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Authors: Baoliang Xu

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The future of education should focus on creating meaningful learning for learners. Play is a basic form and an important means of carrying out kindergarten educational activities, which promotes the creation and development of meaningful learning and is of great importance in the harmonious physical and mental development of young children. Through literature research and case studies, this paper finds that: meaningful learning has the characteristics of contextuality, interaction and constructiveness; teachers should pay great attention to the guidance of children's games, fully respect children's autonomy and create a prepared game environment; children's meaningful learning exists in games and hidden in things that interest them, and "the generation of questions The "generation of questions" fuels the depth of children's meaningful learning, and teachers' professional support helps children's meaningful learning to develop continuously. In short, teachers' guidance of young children's play should be emphasized to effectively provide scaffolding instruction to promote meaningful learning in a holistic manner.

Keywords: meaningful learning, young childhood, game, case study

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Authors: Alison Power

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Games-based learning aims to enhance students’ engagement with and enjoyment of learning opportunities using games-related principles to create a fun yet productive learning environment. Motivating students to learn in an online setting can be particularly challenging, so a cross-Faculty synchronous online session provided students with the opportunity to engage with ‘GAMING’: an interactive, flexible and scalable e-resource for students to work synchronously in groups to complete a series of e-tivities designed to enhance their skills of leadership, collaboration and negotiation. Findings from a post-session online survey found the majority of students had a positive learning experience, finding 'GAMING' to be an innovative and engaging e-resource which motivated their group to learn.

Keywords: collaboration, games-based learning, groupwork, synchronous online learning, teamwork

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Authors: Jeelka Ray

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Survey of the papers revealed that there is no practical design available for packaging machine based on Embedded system, so the need arose for the development of the prototype model. In this paper, author has worked on the development of an ARM7 based Embedded Controller for controlling the sequence of packaging machine. The unit is made user friendly with TFT and Touch Screen implementing human machine interface (HMI). The different system components are briefly discussed, followed by a description of the overall design. The major functions which involve bag forming, sealing temperature control, fault detection, alarm, animated view on the home screen when the machine is working as per different parameters set makes the machine performance more successful. LPC2478 ARM 7 Embedded Microcontroller controls the coordination of individual control function modules. In back gone days, these machines were manufactured with mechanical fittings. Later on, the electronic system replaced them. With the help of ongoing technologies, these mechanical systems were controlled electronically using Microprocessors. These became the backbone of the system which became a cause for the updating technologies in which the control was handed over to the Microcontrollers with Servo drives for accurate positioning of the material. This helped to maintain the quality of the products. Including all, RS 485 MODBUS Communication technology is used for synchronizing AC Drive & Servo Drive. These all concepts are operated either manually or through a Graphical User Interface. Automatic tuning of heaters, sealers and their temperature is controlled using Proportional, Integral and Derivation loops. In the upcoming latest technological world, the practical implementation of the above mentioned concepts is really important to be in the user friendly environment. Real time model is implemented and tested on the actual machine and received fruitful results.

Keywords: packaging machine, embedded system, ARM 7, micro controller, HMI, TFT, touch screen, PID

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Mimi Mohaffyza Mohamad, Yee Mei Heong, Nurfirdawati Muhammad Hanafi, Tee Tze Kiong

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This study is conducted to investigate the disparity of between learning styles and cognitive abilities specifically in Vocational Education. Felder and Silverman Learning Styles Model (FSLSM) was applied to measure the students’ learning styles while the content in Building Construction Subject consists; knowledge, skills and problem solving were taken into account in constructing the elements of cognitive abilities. There are four dimension of learning styles proposed by Felder and Silverman intended to capture student learning preferences with regards to processing either active or reflective, perception based on sensing or intuitive, input of information used visual or verbal and understanding information represent with sequential or global learner. The study discovered that students are tending to be visual learners and each type of learner having significant difference whereas cognitive abilities. The finding may help teachers to facilitate students more effectively and to boost the student’s cognitive abilities.

Keywords: learning styles, cognitive abilities, dimension of learning styles, learning preferences

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Noawanit Songkram

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This paper is a report on the findings of a study conducted on e–learning system in virtual learning environment to develop problem solving ability and team learning for learners in higher education. The methodology of this study was R&D research. The subjects were 18 undergraduate students in Faculty of Education, Chulalongkorn University in the academic year of 2013. The research instruments were a problem solving ability assessment, a team learning evaluation form, and an attitude questionnaire. The data was statistically analyzed using mean, standard deviation, one way repeated measure ANOVA and t–test. The research findings discovered the e –learning system in virtual learning environment to develop problem solving ability and team learning for learners in higher education consisted of five components:(1) online collaborative tools, (2) active learning activities, (3) creative thinking, (4) knowledge sharing process, (5) evaluation and nine processes which were (1) preparing in group working, (2) identifying interested topic, (3) analysing interested topic, (4) collecting data, (5) concluding idea (6) proposing idea, (7) creating workings, (8) workings evaluation, (9) sharing knowledge from empirical experience.

Keywords: e-learning system, problem solving ability, team leaning, virtual learning environment

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Authors: Nabila Daly, Hamdi Ellouzi, Hela Ltifi

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In last year’s, several recent researches’ that focus on e-learning approaches having as goal to improve pedagogy and student’s academy level assessment. E-learning-related works have become an important research file nowadays due to several problems that make it impossible for students join classrooms, especially in last year’s. Among those problems, we note the current epidemic problems in the word case of Covid-19. For those reasons, several e-learning-related works based on Artificial Intelligence techniques are proposed to improve distant education targets. In the current paper, we will present a short survey of the most relevant e-learning based on Artificial Intelligence techniques giving birth to newly developed e-learning tools that rely on new technologies.

Keywords: artificial intelligence techniques, decision, e-learning, support system, survey

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Authors: Jibril Mohammed, Usman Dadum Hamza, Muhammad Idris Misau, Baba Yahya Danjuma, Yusuf Bode Raji, Abdulsalam Surajudeen

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Volatile organic compounds (VOCs) have been reported to be responsible for many acute and chronic health effects and environmental degradations such as global warming. In this study, a renewable and low-cost coconut shell activated carbon (PHAC) was synthesized and treated with ammonia (PHAC-AM) to improve its hydrophobicity and affinity towards VOCs. Removal efficiencies and adsorption capacities of the ammonia treated activated carbon (PHAC-AM) for benzene and toluene were carried out through batch and fixed-bed studies respectively. Langmuir, Freundlich and Tempkin adsorption isotherms were tested for the adsorption process and the experimental data were best fitted by Langmuir model and least fitted by Tempkin model; the favourability and suitability of fitness were validated by equilibrium parameter (RL) and the root square mean deviation (RSMD). Judging by the deviation of the predicted values from the experimental values, pseudo-second-order kinetic model best described the adsorption kinetics than the pseudo-first-order kinetic model for the two VOCs on PHAC and PHAC-AM. In the fixed-bed study, the effect of initial VOC concentration, bed height and flow rate on benzene and toluene adsorption were studied. The highest bed capacities of 77.30 and 69.40 mg/g were recorded for benzene and toluene respectively; at 250 mg/l initial VOC concentration, 2.5 cm bed height and 4.5 ml/min flow rate. The results of this study revealed that ammonia treated activate carbon (PHAC-AM) is a sustainable adsorbent for treatment of VOCs in polluted waters.

Keywords: volatile organic compounds, equilibrium and kinetics studies, batch and fixed bed study, bio-based activated carbon

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Authors: Amira Khelil, Habib Affes

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The XXIst century is characterized by promoting teamwork as one of the main drivers of firms` performance. Collective competence is becoming crucial in developing and maintaining a firm’s competitive advantage, as well as its contributions to organizational innovation. In other words, the improvement of collective competence for a firm is no longer a choice, but rather an obligation. Learning capabilities of a firm in the context of knowledge management are assumed to be the main drivers of collective competence. Although there are some efforts to consider these concepts together; they are mostly discussed separately in the management theory. Thus, this paper aims to offer a holistic approach for development collective competence on the basis of Knowledge Management and Organizational Learning Capabilities. A theoretical model that defines a relationship between knowledge management, organizational learning and collective competence is presented at the end of this paper.

Keywords: collective competence, exploitation learning, exploration learning, knowledge management, organizational learning capabilities

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Authors: Mari A. Virtanen, Elina Haavisto, Eeva Liikanen, Maria Kääriäinen

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Ubiquitous learning and the use of ubiquitous learning environments herald a new era in higher education. Ubiquitous environments fuse together authentic learning situations and digital learning spaces where students can seamlessly immerse themselves into the learning process. Definitions of ubiquitous learning are wide and vary in the previous literature and learning environments are not systemically described. The aim of this scoping review was to identify the criteria and the use of ubiquitous learning environments in higher education contexts. The objective was to provide a clear scope and a wide view for this research area. The original studies were collected from nine electronic databases. Seven publications in total were defined as eligible and included in the final review. An inductive content analysis was used for the data analysis. The reviewed publications described the use of ubiquitous learning environments (ULE) in higher education. Components, contents and outcomes varied between studies, but there were also many similarities. In these studies, the concept of ubiquitousness was defined as context-awareness, embeddedness, content-personalization, location-based, interactivity and flexibility and these were supported by using smart devices, wireless networks and sensing technologies. Contents varied between studies and were customized to specific uses. Measured outcomes in these studies were focused on multiple aspects as learning effectiveness, cost-effectiveness, satisfaction, and usefulness. This study provides a clear scope for ULE used in higher education. It also raises the need for transparent development and publication processes, and for practical implications of ubiquitous learning environments.

Keywords: higher education, learning environment, scoping review, ubiquitous learning, u-learning

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Authors: A. Meydanlioglu, F. Arikan

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In recent years, thanks to the development of information and communication technologies, the computer and internet have been used widely in higher education. Internet-based education is impacting traditional higher education as online components increasingly become integrated into face-to-face (FTF) courses. The goal of combined internet-based and traditional education is to take full advantage of the benefits of each platform in order to provide an educational opportunity that can promote student learning better than can either platform alone. Research results show that the use of hybrid learning is more effective than online or FTF models in higher education. Due to the potential benefits, an increasing number of institutions are interested in developing hybrid courses, programs, and degrees. Future research should evaluate the effectiveness of hybrid learning. This paper is designed to determine the impact of hybrid learning on higher education.

Keywords: e-learning, higher education, hybrid learning, online education

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Authors: Tolulope A. Olukunle

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Plastic waste management has emerged as one of the greatest challenges facing developing countries. This paper describes the design of various components of a plastic shredder. This machine is widely used in industries and recycling plants. The introduction of plastic shredder machine will promote reduction of post-consumer plastic waste accumulation and serves as a system for wealth creation and empowerment through conversion of waste into economically viable products. In this design research, a 10 kW electric motor with a rotational speed of 500 rpm was chosen to drive the shredder. A pulley size of 400 mm is mounted on the electric motor at a distance of 1000 mm away from the shredder pulley. The shredder rotational speed is 300 rpm.

Keywords: design, machine, plastic waste, recycling

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Authors: A. Thakur, P. S. Panesar, M. S. Saini

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Extraction of lactic acid by emulsion liquid membrane technology (ELM) using n-trioctyl amine (TOA) in n-heptane as carrier within the organic membrane along with sodium carbonate as acceptor phase was optimized by using response surface methodology (RSM). A three level Box-Behnken design was employed for experimental design, analysis of the results and to depict the combined effect of five independent variables, vizlactic acid concentration in aqueous phase (cl), sodium carbonate concentration in stripping phase (cs), carrier concentration in membrane phase (ψ), treat ratio (φ), and batch extraction time (τ) with equal volume of organic and external aqueous phase on lactic acid extraction efficiency. The maximum lactic acid extraction efficiency (ηext) of 98.21%from aqueous phase in a batch reactor using ELM was found at the optimized values for test variables, cl, cs,, ψ, φ and τ as 0.06 [M], 0.18 [M], 4.72 (%,v/v), 1.98 (v/v) and 13.36 min respectively.

Keywords: emulsion liquid membrane, extraction, lactic acid, n-trioctylamine, response surface methodology

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Authors: Saleh Elawgali

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Current spectrums of a four pole-pair, 400 kW induction machine were calculated for the cases of full symmetry and static eccentricity. The calculations involve integration of 93 electrical plus four mechanical ordinary differential equations. Electrical equations account for variable inductances affected by slotting and eccentricities. The calculations were followed by Fourier analysis of the stator currents in steady state operation. Zooms of the current spectrums, around the 50 Hz fundamental harmonic as well as of the main slot harmonic zone, were included. The spectrums included refer to both calculated and measured currents.

Keywords: diagnostic, harmonic, induction machine, spectrum

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Authors: Aleksandra Ristic

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The Coronavirus Disease – 2019 hit Europe in February 2020, and infections took place in four waves. It left consequences and demanded changes for the future. More than half of European countries responded quickly by declaring a state of emergency and introducing various containment measures that have had a major impact on individuals’ lives in recent years. Closing public lives was largely achieved by limited access and/or closing public institutions and services, including the closure of educational institutions. Teaching in classrooms converted to distance learning. In the research, we used a quantitative study to analyze various factors of distance learning that influenced pupils in different segments: teachers’ availability, family support, entire online conference learning, successful distance learning, time for themselves, reliable sources, teachers’ feedback, successful distance learning, online participation classes, motivation and teachers’ communication and theoretical review of the importance of digital skills, e-learning Index, World comparison of e-learning in the past, digital education plans for the field of Europe. We have gathered recommendations and distance learning solutions to improve the learning process by strengthening teachers and creating more tiered strategies for setting and achieving learning goals by the children.

Keywords: availability, digital skills, distance learning, resources

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Authors: G. Götz, P. Stich, J. Backhaus, G. Reinhart

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The rising demand for format-flexible packaging machines is caused by current market changes. Increasing the formatflexibility is a new goal for the packaging machine manufacturers’ product development process. There are no methodical or designorientated tools for a comprehensive consideration of this target. This paper defines the term format-flexibility in the context of packaging machines and shows the state-of-the-art for improving the changeover of production machines. The requirements for a new approach and the concept itself will be introduced, and the method elements will be explained. Finally, the use of the concept and the result of the development of a format-flexible packaging machine will be shown.

Keywords: packaging machine, format-flexibility, changeover, design method

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Authors: Mingxi Xiao

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Children’s experiences in the early years build and shape the brain. The early years learning environment plays a significantly important role in children’s development. A well-constructed environment will facilitate children’s physical and mental well-being. This case study used an early learning centre in Australia called SDN Hurstville as an example, describing the learning environment in the centre, as well as analyzing the functions of the affordances. In addition, this report talks about the sustainability of learning in the centre, and how the environment supports cultural diversity and indigenous learning. The early years for children are significant. Different elements in the early childhood centre should work together to help children develop better. This case study found that the natural environment and the artificial environment are both critical to children; only when they work together can children have better development in physical and mental well-being and have a sense of belonging when playing and learning in the centre.

Keywords: early childhood center, early childhood education, learning environment, Australia

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Authors: Kwaku Damoah

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The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: D. P. Lawrence, T. M. M. D. Ariyarathna, W. N. K. De Silva, M. D. S. C. De Silva, Lasantha Abeysiri, Pradeep Abeygunawardhna

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Safari traveling is one of the most famous hobbies all over the world. In Sri Lanka, 'Yala' is the second-largest national park, which is a better place to go for a safari. Many number of local and foreign travelers are coming to go for a safari in 'Yala'. But 'Yala' does not have a mobile application that is made to facilitate the traveler with some important features that the traveler wants to achieve in the safari experience. To overcome these difficulties, the proposed mobile application by adding those identified features to make travelers, guiders, and administration's works easier. The proposed safari traveling guidance mobile application is called 'SMART SAFARI' for the 'Yala' National Park in Sri Lanka. There are four facilities in this mobile application that provide for travelers as well as the guiders. As the first facility, the guider and traveler can view the created map of the park, and the guider can add temporary locations of animals and special locations on the map. This is a Geographic Information System (GIS) to capture, analyze, and display geographical data. And as the second facility is to generate optimal paths according to the travelers' requirements through the park by using machine learning techniques. In the third part, the traveler can get information about animals using an animal identification system by capturing the animal. As in the other facility, the traveler will be facilitated to add reviews and a rate and view those comments under categorized sections and pre-defined score range. With those facilities, this user-friendly mobile application provides the user to get a better experience in safari traveling, and it will probably help to develop tourism culture in Sri Lanka.

Keywords: animal identification system, geographic information system, machine learning techniques, pre defined score range

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Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Victor Oluwaseyi Olowonisi

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The paper aimed at presenting an overview on the concept of e-learning as it relates to higher education and how it provides opportunities for students, instructors and the government in developing the educational sector. It also touched on the benefits and challenges attached to e-learning as a new medium of reaching more students especially in the Nigerian context. The opportunities attributed to e-learning in the paper includes breaking boundaries barriers, reaching a larger number of students, provision of jobs for ICT experts, etc. In contrary, poor power supply, cost of implementation, poor computer literacy, technophobia (fear of technology), computer crime and system failure were some of the challenges of e-learning discussed in the paper. The paper proffered that the government can help the people gain more from e-learning through its financing. Also, it was stated that instructors/lecturers and students need to undergo training on computer application in order for e-learning to be more effective in developing higher education in Nigeria.

Keywords: e-learning, education, higher education, increasing literacy

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Authors: Pinn Tsin Isabel Yee

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Students are increasingly turning towards online learning materials to supplement their education. One such approach would be the gamified student engagement platforms (GSEPs) to instill a new learning culture. Data was collected from closed-ended questions via content analysis techniques. About 81.8% of college students from the Monash University Foundation Year agreed that GSEPs (Quizizz) was an effective tool for learning. Approximately 85.5% of students disagreed that games were a waste of time. GSEPs were highly effective among students to facilitate the learning process.

Keywords: engagement, gamified, Quizizz, technology

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Authors: Po-Ting Chen, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

Abstract:

Classical molecular dynamics (CMD) simulations are highly efficient for material simulations but have limited accuracy. In contrast, ab initio molecular dynamics (AIMD) provides high precision by solving the Kohn–Sham equations yet requires significant computational resources, restricting the size of systems and time scales that can be simulated. To address these challenges, we employed NequIP, a machine learning model based on an E(3)-equivariant graph neural network, to accelerate molecular dynamics simulations of a 1M LiPF6 in EC/EMC (v/v 3:7) for Li battery applications. AIMD calculations were initially conducted using the Vienna Ab initio Simulation Package (VASP) to generate highly accurate atomic positions, forces, and energies. This data was then used to train the NequIP model, which efficiently learns from the provided data. NequIP achieved AIMD-level accuracy with significantly less training data. After training, NequIP was integrated into the LAMMPS software to enable molecular dynamics simulations of larger systems over longer time scales. This method overcomes the computational limitations of AIMD while improving the accuracy limitations of CMD, providing an efficient and precise computational framework. This study showcases NequIP’s applicability to electrolyte systems, particularly for simulating the dynamics of LiPF6 ionic mixtures. The results demonstrate substantial improvements in both computational efficiency and simulation accuracy, highlighting the potential of machine learning models to enhance molecular dynamics simulations.

Keywords: lithium-ion batteries, electrolyte simulation, molecular dynamics, neural network

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Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

Abstract:

Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

Procedia PDF Downloads 108