Search results for: students with learning disabilities

Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

Procedia PDF Downloads 93

Authors: Seulki Lee, Seoung Bum Kim

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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

Procedia PDF Downloads 303

Authors: Golak Bihari Mahanta, Bibhuti Bhusan Biswal, B. B. V. L. Deepak, Amruta Rout, Gunji Balamurali

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Robots are widely used in the manufacturing industry for rapid production with higher accuracy and precision. With the help of End-of-Arm Tools (EOATs), robots are interacting with the environment. Robotic grippers are such EOATs which help to grasp the object in an automation system for improving the efficiency. As the robotic gripper directly influence the quality of the product due to the contact between the gripper surface and the object to be grasped, it is necessary to design and optimize the gripper mechanism configuration. In this study, geometric and kinematic modeling of the parallel gripper is proposed. Grey wolf optimizer algorithm is introduced for solving the proposed multiobjective gripper optimization problem. Two objective functions developed from the geometric and kinematic modeling along with several nonlinear constraints of the proposed gripper mechanism is used to optimize the design variables of the systems. Finally, the proposed methodology compared with a previously proposed method such as Teaching Learning Based Optimization (TLBO) algorithm, NSGA II, MODE and it was seen that the proposed method is more efficient compared to the earlier proposed methodology.

Keywords: gripper optimization, metaheuristics, , teaching learning based algorithm, multi-objective optimization, optimal gripper design

Procedia PDF Downloads 192

Authors: Daniel Scherman, Daniel Madani, Shanu Gambhir, Marcus Ling Zhixing, Yingda Li

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Objective: This study aims to identify clinical factors that may predict failed endoscopic lumbar spine surgery to guide surgeons with patient selection during the initial learning curve. Methods: This is an Australasian prospective analysis of the first 105 patients to undergo lumbar endoscopic spine decompression by 3 surgeons. Modified MacNab outcomes, Oswestry Disability Index (ODI) and Visual Analogue Score (VAS) scores were utilized to evaluate clinical outcomes at 6 months postoperatively. Descriptive statistics and Anova t-tests were performed to measure statistically significant (p<0.05) associations between variables using GraphPad Prism v10. Results: Patients undergoing endoscopic lumbar surgery via an interlaminar or transforaminal approach have overall good/excellent modified MacNab outcomes and a significant reduction in post-operative VAS and ODI scores. Regardless of the anatomical location of disc herniations, good/excellent modified MacNab outcomes and significant reductions in VAS and ODI were reported post-operatively; however, not in patients with calcified disc herniations. Patients with central and foraminal stenosis overall reported poor/fair modified MacNab outcomes. However, there were significant reductions in VAS and ODI scores post-operatively. Patients with subarticular stenosis or an associated spondylolisthesis reported good/excellent modified MacNab outcomes and significant reductions in VAS and ODI scores post-operatively. Patients with disc herniation and concurrent degenerative stenosis had generally poor/fair modified MacNab outcomes. Conclusion: The outcomes of endoscopic spine surgery are encouraging, with a low complication and reoperation rate. However, patients with calcified disc herniations, central canal stenosis or a disc herniation with concurrent degenerative stenosis present challenges during the initial learning curve and may benefit from traditional open or other minimally invasive techniques.

Keywords: complications, lumbar disc herniation, lumbar endoscopic spine surgery, predictors of failed endoscopic spine surgery

Procedia PDF Downloads 158

Authors: Catarina Cruz, Ana Breda

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In the early years, until the children’s entrance at the elementary school, they are stimulated by their educators, through rich and attractive contexts, to explore and develop skills in different domains, from the socio-emotional to the cognitive. Many of these contexts trigger real or imaginary situations, familiar or not, through resources or pedagogical practices that incite children's curiosity, questioning, expression of ideas or emotions, social interaction, among others. Later, when children enter at the elementary school, their activity at school becomes more focused on developing skills in the cognitive domain, namely acquiring learning from different subject areas, such as Mathematics, Natural Sciences, History, among others. That is, to ensure that children develop the standardized learning recommended in the guiding curriculum documents, they spend part of their time applying formulas, memorizing information, following instructions, and so on, and in this way not much time is left to listen children, to learn about their interests and likes, as well as their perspective and questions about the surround world. In Elementary School, especially in the 1st Cycle, children are naturally curious, however, sometimes this skill is subtly conditioned by adults. Curious children learn more, since they have an intrinsic desire to know more, especially about what is unknown. When children think on subjects or themes that they are interested in or curious about, they attribute more meaning to this learning and retain it for longer. Therefore, it is important to approach subjects in the classroom that seduce or captivate children's attention, trigger them curiosity, and allow to hear their ideas. There are several resources, strategies and pedagogical practices to awaken children's curiosity, to explore their knowledge, to understand their perspectives and their way of thinking, to know a little more about their personality and to provide space for dialogue. The storytelling, its narrative’s exploration and interpretation is one of those pedagogical practices. Children’s literature, about real or imaginary subjects, stimulate children’s insights supported into their experiences, emotions, learnings and personality, and promote opportunities for children express freely their feelings and thoughts. This work focuses on a session developed with children in the 3rd year of schooling, from a Portuguese 1st Cycle Basic School, in which the story "From the Outside In and From the Inside Out" was presented. The story’s presentation was mainly centred on children’s activity, who read excerpts and interpreted/explored them through a dialogue led by one of the authors. The study presented here intends to show an example of how an exploration of a children's story can trigger ideas, thoughts, emotions or attitudes in children in the 3rd year of elementary school. To answer the research question, this work aimed to: identify ideas, thoughts, emotions or attitudes that emerged from the exploration of story; analyse aspects of the story and the orchestration/conduction of dialogue with/between children that facilitated or inhibited the emergence of ideas, thoughts, emotions or attitudes by children,

Keywords: storytelling, children’s perspectives, soft skills, non-formal learning contexts, orchestration

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Authors: Khalida Douibi, Rodrigo Balp, Solène Le Bars

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In the medical field, applications related to human experiments are frequently linked to reduced samples size, which makes the training of machine learning models quite sensitive and therefore not very robust nor generalizable. This is notably the case in Brain-Computer Interface (BCI) studies, where the sample size rarely exceeds 20 subjects or a few number of trials. To address this problem, several resampling approaches are often used during the data preparation phase, which is an overly critical step in a data science analysis process. One of the naive approaches that is usually applied by data scientists consists in the transformation of the entire database before the resampling phase. However, this can cause model’ s performance to be incorrectly estimated when making predictions on unseen data. In this paper, we explored the effect of data leakage observed during our BCI experiments for device control through the real-time classification of SSVEPs (Steady State Visually Evoked Potentials). We also studied potential ways to ensure optimal validation of the classifiers during the calibration phase to avoid overfitting. The results show that the scaling step is crucial for some algorithms, and it should be applied after the resampling phase to avoid data leackage and improve results.

Keywords: data leackage, data science, machine learning, SSVEP, BCI, overfitting

Procedia PDF Downloads 156

Authors: Rolando Barradas, Salviano Soares, António Valente, José Alberto Lencastre, Paulo Oliveira

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This article reviews the Ant Colony Optimization, a nature-inspired algorithm, and its implementation in the Scratch/m-Block programming environment. The Ant Colony Optimization is a part of Swarm Intelligence-based algorithms and is a subset of biological-inspired algorithms. Starting with a problem in which one has a maze and needs to find its path to the center and return to the starting position. This is similar to an ant looking for a path to a food source and returning to its nest. Starting with the implementation of a simple wall follower simulator, the proposed solution uses a dynamic graphical interface that allows young students to observe the ants’ movement while the algorithm optimizes the routes to the maze’s center. Things like interface usability, Data structures, and the conversion of algorithmic language to Scratch syntax were some of the details addressed during this implementation. This gives young students an easier way to understand the computational concepts of sequences, loops, parallelism, data, events, and conditionals, as they are used through all the implemented algorithms. Future work includes the simulation results with real contest mazes and two different pheromone update methods and the comparison with the optimized results of the winners of each one of the editions of the contest. It will also include the creation of a Digital Twin relating the virtual simulator with a real micromouse in a full-size maze. The first test results show that the algorithm found the same optimized solutions that were found by the winners of each one of the editions of the Micromouse contest making this a good solution for maze pathfinding.

Keywords: nature inspired algorithms, scratch, micromouse, problem-solving, computational thinking

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Authors: Mirza Mujtaba Baig

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Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

Procedia PDF Downloads 123

Authors: David Chung

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The development of seventeenth-century French harpsichord music is enigmatic in several respects. Although little is known about the formation of this style before 1650 (we have names of composers, but no surviving music), the style has attained a high degree of refinement and sophistication in the music of the earliest known masters (e.g. Chambonnières, Louis Couperin and D’Anglebert). In fact, how the seventeenth-century musicians acquired the skills of their art remains largely steeped in mystery, as the earliest major treatise on French keyboard pedagogy was not published until 1702 by Saint Lambert. This study fills this lacuna by surveying some twenty recently-rediscovered manuscripts, which offer ample materials for revisiting key issues pertaining to seventeenth-century harpsichord pedagogy. By analyzing the musical contents, the verbal information and explicit notation (such as written-out ornaments and rhythmic effects), this study provides a rich picture of the process of learning at the time, with engaging details of performance nuances often lacking in tutors and treatises. Of even greater significance, that creative skills (such as continuo and ornamentation) were taught alongside fundamental knowledge (solfèges, note values, etc.) at the earliest stage of learning offers fresh challenge for modern pedagogues to rethink how harpsichord pedagogy can be revamped to cater for our own pedagogical and aesthetic needs.

Keywords: French, harpsichord, pedagogy, seventeenth century

Procedia PDF Downloads 261

Authors: Rendani Mercy Makhwathana

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This paper intends to explore the experiences and views of Foundation Phase teachers when teaching English First Additional Language in rural public schools. Teachers all over the world are pillars of any education system. Consequently, any education transformation should start with teachers as critical role players in the education system. As a result, teachers’ experiences and views are worth consideration, for they impact on learners learning and the wellbeing of education in general. An exploratory qualitative approach with the use of phenomenological research design was used in this paper. The population for this paper comprised all Foundation Phase teachers in the district. Purposive sampling technique was used to select a sample of 15 Foundation Phase teachers from five rural-based schools. Data was collected through classroom observation and individual face-to-face interviews. Data were categorised, analysed and interpreted. The findings revealed that from time-to-time teachers experiences one or more challenging situations, learners’ low participation in the classroom to lack of resources. This paper recommends that teachers should be provided with relevant resources and support to effectively teach English First Additional Language.

Keywords: the education system, first additional language, foundation phase, intermediate phase, language of learning and teaching, medium of instruction, teacher professional development

Procedia PDF Downloads 98

Authors: Kamaran Fathulla

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Human creativity knows no bounds. More than a millennia ago humans have expressed their knowledge on cave walls and on clay artefacts. Using visuals such as diagrams and paintings have always provided us with a natural and intuitive medium for expressing such creativity. Making sense of human generated visualisation has been influenced by western scientific philosophies which are often reductionist in their nature. Theoretical frameworks such as those delivered by Peirce dominated our views of how to make sense of visualisation where a visual is seen as an emergent property of our thoughts. Others have reduced the richness of human-generated visuals to mere shapes drawn on a piece of paper or on a screen. This paper introduces an alternate framework where the centrality of human functioning is given explicit and richer consideration through the multi aspectual philosophical works of Herman Dooyeweerd. Dooyeweerd's framework of understanding reality was based on fifteen aspects of reality, each having a distinct core meaning. The totality of the aspects formed a ‘rainbow’ like spectrum of meaning. The thesis of this approach is that meaningful human functioning in most cases involves the diversity of all aspects working in synergy and harmony. Illustration of the foundations and applicability of this approach is underpinned in the case of humans use of diagramming for creative purposes, particularly within an educational context. Diagrams play an important role in education. Students and lecturers use diagrams as a powerful tool to aid their thinking. However, research into the role of diagrams used in education continues to reveal difficulties students encounter during both processes of interpretation and construction of diagrams. Their main problems shape up students difficulties with diagrams. The ever-increasing diversity of diagrams' types coupled with the fact that most real-world diagrams often contain a mix of these different types of diagrams such as boxes and lines, bar charts, surfaces, routes, shapes dotted around the drawing area, and so on with each type having its own distinct set of static and dynamic semantics. We argue that the persistence of these problems is grounded in our existing ways of understanding diagrams that are often reductionist in their underpinnings driven by a single perspective or formalism. In this paper, we demonstrate the limitations of these approaches in dealing with the three problems. Consequently, we propose, discuss, and demonstrate the potential of a nonreductionist framework for understanding diagrams based on Symbolic and Spatial Mappings (SySpM) underpinned by Dooyeweerd philosophy. The potential of the framework to account for the meaning of diagrams is demonstrated by applying it to a real-world case study physics diagram.

Keywords: SySpM, drawing style, mapping

Procedia PDF Downloads 241

Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

Procedia PDF Downloads 106

Authors: Cem Bulut, Zeynep B. Kocoglu

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Flipped classroom (FC) method has gained popularity, specifically in higher education, in recent years with the idea that it is possible to use the time spent in classrooms more effectively by simply flipping the passive lecturing parts with the homework exercises. Accordingly, the present study aims to investigate whether using FC method is more effective than the non-flipped method in teaching grammar to English as a Foreign Language (EFL) learners. An experimental research was conducted with the participants of two intact classes having A2 level English courses (N=39 in total) in a vocational school in Kocaeli, Turkey. Results from the post-test indicated that the flipped group achieved higher scores than the non-flipped group did. Additionally, independent samples t-test analysis in SPSS revealed that the difference between two groups was statistically significant. On the other hand, even if the factors that lie beneath this improvement are likely to be attributed to the teaching method, which is also supported by the answers given to the FC perception survey and interview, participants in both groups developed statistically significant positive attitudes towards learning grammar regardless of the method used. In that sense, this result was considered to be related to the level of the course, which was quite low in English level. In sum, the present study provides additional findings to the literature for FC methodology from a different perspective.

Keywords: flipped classroom, learning management system, English as a foreign language

Procedia PDF Downloads 125

Authors: Subham Ghosh, Banani Basu, Marami Das

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Background: The development of compact, low-power antenna sensors has resulted in hardware restructuring, allowing for wireless ubiquitous sensing. The antenna sensors can create wireless body-area networks (WBAN) by linking various wireless nodes across the human body. WBAN and IoT applications, such as remote health and fitness monitoring and rehabilitation, are becoming increasingly important. In particular, Parkinson’s disease (PD), a common neurodegenerative disorder, presents clinical features that can be easily misdiagnosed. As a mobility disease, it may greatly benefit from the antenna’s nearfield approach with a variety of activities that can use WBAN and IoT technologies to increase diagnosis accuracy and patient monitoring. Methodology: This study investigates the feasibility of leveraging a single patch antenna mounted (using cloth) on the wrist dorsal to differentiate actual Parkinson's disease (PD) from false PD using a small hardware platform. The semi-flexible antenna operates at the 2.4 GHz ISM band and collects reflection coefficient (Γ) data from patients performing five exercises designed for the classification of PD and other disorders such as essential tremor (ET) or those physiological disorders caused by anxiety or stress. The obtained data is normalized and converted into 2-D representations using the Gabor wavelet transform (GWT). Data augmentation is then used to expand the dataset size. A lightweight deep-learning (DL) model is developed to run on the GPU-enabled NVIDIA Jetson Nano platform. The DL model processes the 2-D images for feature extraction and classification. Findings: The DL model was trained and tested on both the original and augmented datasets, thus doubling the dataset size. To ensure robustness, a 5-fold stratified cross-validation (5-FSCV) method was used. The proposed framework, utilizing a DL model with 1.356 million parameters on the NVIDIA Jetson Nano, achieved optimal performance in terms of accuracy of 88.64%, F1-score of 88.54, and recall of 90.46%, with a latency of 33 seconds per epoch.

Keywords: antenna, deep-learning, GPU-hardware, Parkinson’s disease

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Authors: Jennifer Sturgeon

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Stable relationships provide a predictable and trusting environment and are essential for early development, but high teacher turnover rates in childcare settings make it challenging for infants and toddlers to form stable relationships with their teachers. This can have an adverse effect on development and learning. The qualitative study discussed in this article draws from the experiences of early Head Start teachers and administrators to describe both the impact of teacher turnover and the motivational factors that contribute to teacher retention. A case study approach was used and included classroom observations, a review of exit interviews, and perceptions from focus groups of early Head Start staff in an urban early Head Start childcare center. Emerging from the case study was the discovery that teacher turnover has an impact on the social-emotional development of toddlers, particularly in self-regulation. Additional key findings that emerged include teacher turnover leading to negative effects on learning, a decrease in preschool preparation, and increased chaos in the classroom and center. Motivational factors that contributed to teacher retention included positive leadership, the mission to make a difference, and fair compensation.

Keywords: early childhood, teacher turnover, continuity of care, early head start

Procedia PDF Downloads 73

Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

Procedia PDF Downloads 401

Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Anita Yus

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Several Elementary Schools (SD) ‘favored’ by parents, especially those live in big cities in Indonesia, implicitly demand each child enrolled in the first grade of SD to be able to read, write and calculate. This condition urges the parents to push the teachers in PAUD (Kindergarten) to train their children to read, write, and calculate so they have a set of knowledge. According to Piaget, each child is capable of acquiring knowledge when he is given the opportunity to interact with his environment (things, people, and atmosphere). Teachers can make the interaction occur. There are several learning approaches suitable for the characteristics and needs of child’s growth. This paper talks about a research result conducted to investigate how twelve teachers of early childhood program comprehend the constructivist theory of Piaget, and how they inquire, how the children acquire and construct a number of knowledge through occurred interactions. This is a qualitative research with an observation method followed up by a focus group discussion (FGD). The research result shows that there is a reciprocal interaction between the behaviors of teachers and children affected by the size of the classroom and learning source, teaching experiences, education background, teachers’ attitude and motivation, as well as the way the teachers interpret and support the children’s needs. The teachers involved in this research came up with varied perspective on how knowledge acquired by children at first and how they construct it. This research brings a new perspective in understanding children as scientists.

Keywords: constructivist approach, young children as a scientist, teacher practice, teacher education

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Authors: Sima Modirkhamene, Arezoo Khezri

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The purpose of the present investigation was to probe the main motivational strategies Iranian school vs. institute teachers use in virtual and in-person classes to motivate students in learning the English language. Yet another purpose was to understand teachers’ perceptions about any modifications in their use of motivational strategies before and during/after the pandemic. For the purpose of this investigation, a total of 63 EFL teachers (35 female, 28 male) were conveniently sampled from schools and institutes in the cities of Mahabad and Sardasht. Moreover, for the interview phase of the study, 20 percent (n=16) of the sample was selected conveniently. The required data was gathered through a modified questionnaire (Cheng & Dornyei, 2007) consisting of 42 items and a set of semi-structured interviews. The outcomes of a set of non-parametric Mann-Whitney U tests demonstrated that presenting tasks properly in online classes and familiarizing learners with L2- related values in in-person classes came out as the most influential source of motivational strategies practiced by EFL school teachers. Additionally, it was found that proper teacher behavior(showing enthusiasm) in both in-person and virtual classes and presenting tasks properly in in-person classes were overwhelmingly endorsed by EFL institute teachers. The study also portrayed no statistically significant mean difference between school and institute EFL teachers’ overall use of motivational strategies in virtual and in-person classes. The interview results indicated that the strategies of designing tasks through technological aids, provision of videos, gamification techniques, assigning projects, and delivering formative online feedback were held in high regard during/after the pandemic due to the high reliance of teaching on the Internet connection. Meanwhile, the research has indicated that the spread of COVID-19 was the main reason for teachers’ modifications in motivational strategies, in response to the crisis of the pandemic, all educational contexts at all levels resorted to online education as a result their strategies were adapted to the new situation. The findings brought to light through this investigation provided initial evidence of the unintended consequences of the pandemic on teachers’ strategic choices. Therefore, to deliver a better education for the future, the study suggests more concentration on the quality of teaching as well as reframing the status quo of teaching .

Keywords: virtual teaching, motivational teaching strategies, teaching context, online education

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Authors: R. F. Mesquita Brandão

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Act Local, Think Global is the name of a campaign implemented at Superior Institute of Engineering of Porto (ISEP), one of schools of Polytechnic of Porto, with the main objective of increase the sustainability of the campus. ISEP has a campus with 52.000 m2 and more than 7.000 students. The campaign started in 2019 and the results are very clear. In 2019 only 16% of the waste created in the campus was correctly separate for recycling and now almost 50% of waste goes to the correct waste container. Actions to reduce the energy consumption were implemented with significantly results. One of the major problems in the campus are the water leaks. To solve this problem was implemented a methodology for water monitoring during the night, a period of time where consumptions are normally low. If water consumption in the period is higher than a determinate value it may mean a water leak and an alarm is created to the maintenance teams. In terms of energy savings, some measurements were implemented to create savings in energy consumption and in equivalent CO₂ produced. In order to reduce the use of plastics in the campus, was implemented the prohibition of selling 33 cl plastic water bottles and in collaboration with the students association all meals served in the restaurants changed the water plastic bottle for a glass that can be refilled with water in the water dispensers. This measures created a reduction of use of more than 75.000 plastic bottles per year. In parallel was implemented the ISEP water glass bottle to be used in all scientific meetings and events. Has a way of involving all community in sustainability issues was developed and implemented a vertical garden in aquaponic system. In 2019, the first vertical garden without soil was installed inside a large campus building. The system occupies the entire exterior façade (3 floors) of the entrance to ISEP's G building. On each of these floors there is a planter with 42 positions available for plants. Lettuces, strawberries, peppers are examples of some vegetable produced that can be collected by the entire community. Associated to the vertical garden was developed a monitoring system were some parameters of the system are monitored. This project is under development because it will work in a stand-alone energy feeding, with the use of photovoltaic panels for production of energy necessities. All the system was, and still is, developed by students and teachers and is used in class projects of some ISEP courses. These and others measures implemented in the campus, will be more developed in the full paper, as well as all the results obtained, allowed ISEP to be the first Portuguese high school to obtain the certification “Coração Verde” (Green Heart), awarded by LIPOR, a Portuguese company with the mission of transform waste into new resources through the implementation of innovative and circular practices, generating and sharing value.

Keywords: aquaponics, energy efficiency, recycling, sustainability, waste separation

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Authors: Gul Rahim Rahimi

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The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

Procedia PDF Downloads 56

Authors: Maha Rashid Al-Azib, Almuzn Qasem Alqathradi, Amal Munir Alshahrani, Bilqis Mohammed Assiri, Ali Almuflih

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The Occupational Safety and Health Management System in one of the largest Saudi companies has been experiencing in the last 10 years extensive direct and indirect expenses due to lack of proactive leading indicators and safety leadership effective procedures. And since there are no studies that are associated with this department of safety in the company, this research has been conducted. In this study we used a mixed method approach containing a literature review and experts input, then a qualitative questionnaire provided by Institute for Work and Health related to determining the company’s occupational safety and health management system level out from three levels (Compliance - Improvement - Continuous Learning) and the output regarding the company’s level was in Continuous Learning. After that Deming cycle was employed to create a set of proactive leading indicators and analyzed using the SMART method to make sure of its effectiveness and suitability to the company. The objective of this research is to provide a set of proactive indicators to contribute in making an efficient occupational safety and health management system that has less accidents which results in less expenses. Therefore, we provided the company with a prototype of an APP, designed and empowered with our final results to contribute in supporting decisions making processes.

Keywords: proactive leading indicators, OSH MS, safety leadership, accidents reduction

Procedia PDF Downloads 83

Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Stijn Horck

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Background: The value of lessons learned from failure increases when these insights can be put to use by those who did not experience the failure. While learning from others has mostly been researched between individuals or teams within the same environment, transferring knowledge from the person who experienced the failure to an outsider comes with extra challenges. As sense-making of failure is an individual process leading to different learning experiences, the potential of lessons learned from failure is highly variable depending on who is transferring the lessons learned. Using an integrated framework of linguistic aspects related to attributional egotism, this study aims to offer a complete explanation of the challenges in transferring lessons learned from failures that are experienced by others. Method: A case study of a failed foundation established to address the information needs for GPs in times of COVID-19 has been used. An overview of failure causes and lessons learned were made through a preliminary analysis of data collected in two phases with metaphoric examples of failure types. This was followed up by individual narrative interviews with the board members who have all experienced the same events to analyse the individual variance of lessons learned through discourse analysis. This research design uses the researcher-as-instrument approach since the recipient of these lessons learned is the author himself. Results: Thirteen causes were given why the foundation has failed, and nine lessons were formulated. Based on the individually emphasized events, the explanation of the failure events mentioned by all or three respondents consisted of more linguistic aspects related to attributional egotism than failure events mentioned by only one or two. Moreover, the learning events mentioned by all or three respondents involved lessons learned that are based on changed insight, while the lessons expressed by only one or two are more based on direct value. Retrospectively, the lessons expressed as a group in the first data collection phase seem to have captured some but not all of the direct value lessons. Conclusion: Individual variance in expressing lessons learned to outsiders can be reduced using metaphoric or analogical explanations from a third party. In line with the attributional egotism theory, individuals separated from a group that has experienced the same failure are more likely to refer to failure causes of which the chances to be contradicted are the smallest. Lastly, this study contributes to the academic literature by demonstrating that the use of linguistic analysis is suitable for investigating the knowledge transfer from lessons learned after failure.

Keywords: failure, discourse analysis, knowledge transfer, attributional egotism

Procedia PDF Downloads 119

Authors: Francisca Margarita Ocran

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Underwear, and more particularly bras and panties, are defined as intimate clothing. Strictly speaking, they enhance the place of women in the public or private satchel. Therefore, women's lingerie is a complex garment with a high involvement profile, motivating consumers to buy it not only by its functional utility but also by the multisensory experience it provides them. Customer behavior models are generally based on customer data mining, and each model is designed to answer questions at a specific time. Predicting the customer experience is uncertain and difficult. Thus, knowledge of consumers' tastes in lingerie deserves to be treated as an experiential product, where the dimensions of the experience motivating consumers to buy a lingerie product and to remain faithful to it must be analyzed in detail by the manufacturers and retailers to engage and retain consumers, which is why this research aims to identify the variables that push consumers to choose their lingerie product, based on an in-depth analysis of the types of fabrics used to make lingerie. The data used in this study comes from online purchases. Machine learning approach with the use of Python programming language and Pycaret gives us a precision of 86.34%, 85.98%, and 84.55% for the three algorithms to use concerning the preference of a buyer in front of a range of lingerie. Gradient Boosting, random forest, and K Neighbors were used in this study; they are very promising and rich in the classification of preference in the textile industry.

Keywords: consumer behavior, data mining, lingerie, machine learning, preference

Procedia PDF Downloads 95

Authors: Susan Catapano, Meredith Jones, Carol McNulty

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Analysis of one state’s collaborative effort to support teachers, in both public and private schools, as they moved from face-to-face teaching to remote teaching during the Covid pandemic to identify lessons learned and materials put into place to support teachers and families. Surveys were created, distributed, and analyzed throughout the three months of remote teaching, documents and lesson plans were developed, and training materials were created. All data collected and materials developed were analyzed to identify supports teachers used and needed for successful remote teaching. Researchers found most teachers easily moved to online teaching; however, many families did not have access to technology, so teachers needed to develop non-technology-based access and support for remote teaching. Teachers also reported the need to prepare to teach remotely as part of their teaching training, so they were prepared in the future. Finally, data indicated teachers were able to establish stronger relationships with families than usual as a result of remote teaching. The lessons learned and support developed are part of the state’s ongoing policy for online teaching in the event of disasters and pandemics in the future.

Keywords: remote learning, teacher education, pandemic, families

Procedia PDF Downloads 169

Authors: Daniel J. Patricio Jiménez

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Digital reality demands new ways of thinking, flexibility in learning, acquisition of new competencies, visualizing reality under new approaches, generating open spaces, understanding dimensions in continuous change, etc. We need inclusive growth, where colors are not lacking, where lights do not give a distorted reality, where science is not half-truth. In carrying out this study, the documentary or bibliographic collection has been taken into account, providing a reflective and analytical analysis of current reality. In this context, deductive and inductive methods have been used on different multidisciplinary information sources. Women today and tomorrow are a strategic element in science and arts, which, under the umbrella of sustainability, implies ‘meeting current needs without detriment to future generations’. We must build new scenarios, which qualify ‘the feminine and the masculine’ as an inseparable whole, encouraging cooperative behavior; nothing is exclusive or excluding, and that is where true respect for diversity must be based. We are all part of an ecosystem, which we will make better as long as there is a real balance in terms of gender. It is the time of ‘the lifting of the veil’, in other words, it is the time to discover the pseudonyms, the women who painted, wrote, investigated, recorded advances, etc. However, the current reality demands much more; we must remove doors where they are not needed. Mass processing of data, big data, needs to incorporate algorithms under the perspective of ‘the feminine’. However, most STEM students (science, technology, engineering, and math) are men. Our way of doing science is biased, focused on honors and short-term results to the detriment of sustainability. Historically, the canons of beauty, the way of looking, of perceiving, of feeling, depended on the circumstances and interests of each moment, and women had no voice in this. Parallel to science, there is an under-representation of women in the arts, but not so much in the universities, but when we look at galleries, museums, art dealers, etc., colours impoverish the gaze and once again highlight the gender gap and the silence of the feminine. Art registers sensations by divining the future, science will turn them into reality. The uniqueness of the so-called new normality requires women to be protagonists both in new forms of emotion and thought, and in the experimentation and development of new models. This will result in women playing a decisive role in the so-called "5.0 society" or, in other words, in a more sustainable, more humane world.

Keywords: art, digitalization, gender, science

Procedia PDF Downloads 168

Authors: Pravallika Chithuloori, Jin-Man Kim

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Soil liquefaction, triggered by increased porewater pressure, poses a significant threat to infrastructure stability in seismically active regions, and its forecasting remains challenging due to intricate nonlinear interactions. This study uses a dataset of 540 samples that includes seismic parameters and standard penetration test (SPT) results to evaluate liquefaction prediction. SPT N60 values, soil fine content (FC), ground water table (GWT), effective stress of overburden (ESO), peak ground acceleration (PGA), and earthquake magnitude (Mw) are key inputs. A gradient boost classifier (GBC) machine learning (ML) model was utilized to classify liquefaction events. The model’s performance was evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrix analysis, sensitivity analysis, feature importance ranking, and Shapley Additive Explanations (SHAP). According to these evaluations, the most significant variables in predicting liquefaction were PGA, SPT-N60, and GWT. The robustness of the GBC model was further validated through precision-recall curves and k-fold cross-validation, and it achieved an impressive 99.38% prediction accuracy. These results highlight the potential of the GBC technique to advance the reliability of liquefaction forecasting.

Keywords: liquefaction, standard penetration test, gradient boost, machine learning, SHAP

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Authors: Jay Ananth

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The drug discovery process currently requires numerous years of clinical testing as well as money just for a single drug to earn FDA approval. For drugs that even make it this far in the process, there is a very slim chance of receiving FDA approval, resulting in detrimental hurdles to drug accessibility. To minimize these inefficiencies, numerous studies have implemented computational methods, although few computational investigations have focused on a crucial feature of drugs: lipophilicity. Lipophilicity is a physical attribute of a compound that measures its solubility in lipids and is a determinant of drug efficacy. This project leverages Artificial Intelligence to predict the impact of a drug’s lipophilicity on its performance by accounting for factors such as binding affinity and toxicity. The model predicted lipophilicity and binding affinity in the validation set with very high R² scores of 0.921 and 0.788, respectively, while also being applicable to a variety of target receptors. The results expressed a strong positive correlation between lipophilicity and both binding affinity and toxicity. The model helps in both drug development and discovery, providing every pharmaceutical company with recommended lipophilicity levels for drug candidates as well as a rapid assessment of early-stage drugs prior to any testing, eliminating significant amounts of time and resources currently restricting drug accessibility.

Keywords: drug discovery, lipophilicity, ligand-receptor interactions, machine learning, drug development

Procedia PDF Downloads 114

Authors: Thelma Palaoag, Melanie Basa, Jezreel Mark Panilo

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Checking short-answer questions and essays, whether it may be paper or electronic in form, is a tiring and tedious task for teachers. Evaluating a student’s output require wide array of domains. Scoring the work is often a critical task. Several attempts in the past few years to create an automated writing assessment software but only have received negative results from teachers and students alike due to unreliability in scoring, does not provide feedback and others. The study aims to create an application that will be able to check short-answer questions which incorporate information extraction. Information extraction is a subfield of Natural Language Processing (NLP) where a chunk of text (technically known as unstructured text) is being broken down to gather necessary bits of data and/or keywords (structured text) to be further analyzed or rather be utilized by query tools. The proposed system shall be able to extract keywords or phrases from the individual’s answers to match it into a corpora of words (as defined by the instructor), which shall be the basis of evaluation of the individual’s answer. The proposed system shall also enable the teacher to provide feedback and re-evaluate the output of the student for some writing elements in which the computer cannot fully evaluate such as creativity and logic. Teachers can formulate, design, and check short answer questions efficiently by defining keywords or phrases as parameters by assigning weights for checking answers. With the proposed system, teacher’s time in checking and evaluating students output shall be lessened, thus, making the teacher more productive and easier.

Keywords: information extraction, short-answer question, natural language processing, application

Procedia PDF Downloads 431