Search results for: interactive learning environments

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: Lucilla Crosta, Anthony Edwards

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Companies and the market place nowadays struggle to find employees with adequate skills in relation to anticipated growth of their businesses. At least half of workers will need to undertake some form of up-skilling process in the next five years in order to remain aligned with the requests of the market . In order to meet these challenges, there is a clear need to explore the potential uses of AI (artificial Intelligence) based tools in assessing transversal skills (critical thinking, communication and soft skills of different types in general) of workers and adult students while empowering them to develop those same skills in a reliable trustworthy way. Companies seek workers with key transversal skills that can make a difference between workers now and in the future. However, critical thinking seems to be the one of the most imprtant skill, bringing unexplored ideas and company growth in business contexts. What employers have been reporting since years now, is that this skill is lacking in the majority of workers and adult students, and this is particularly visible trough their writing. This paper investigates how critical thinking and communication skills are currently developed in Higher Education environments through use of AI tools at postgraduate levels. It analyses the use of a branch of AI namely Machine Learning and Big Data and of Neural Network Analysis. It also examines the potential effect the acquisition of these skills through AI tools and what kind of effects this has on employability This paper will draw information from researchers and studies both at national (Italy & UK) and international level in Higher Education. The issues associated with the development and use of one specific AI tool Edulai, will be examined in details. Finally comparisons will be also made between these tools and the more recent phenomenon of Chat GPT and forthcomings and drawbacks will be analysed.

Keywords: critical thinking, artificial intelligence, higher education, soft skills, chat GPT

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Authors: Nezha Badi

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It has been shown in the literature that Algerian university students suffer from low levels of reading comprehension proficiency, which hinder their overall proficiency in English. This low level is mainly related to the methodology of teaching reading which is employed by the teacher in the classroom (a teacher-centered environment), as well as students’ poor sense of self-efficacy to undertake reading comprehension activities. Arguably, what is needed is an approach necessary for enhancing students’ self-beliefs about their abilities to deal with different reading comprehension activities. This can be done by providing them with opportunities to take responsibility for their own learning (learners’ autonomy). As a result of learning autonomy, learners’ beliefs about their abilities to deal with certain language tasks may increase, and hence, their language learning ability. Therefore, this experimental research study attempts to assess the extent to which an integrated approach combining one particular reading approach known as ‘collaborative strategic reading’ (CSR), and teacher’s attributional feedback (on students’ reading performance and strategy use) can improve the reading comprehension skill and the sense of self-efficacy of EFL Algerian university students. It also seeks to examine students’ main reasons for their successful or unsuccessful achievements in reading comprehension activities, and whether students’ attributions for their reading comprehension outcomes can be modified after exposure to the instruction. To obtain the data, different tools including a reading comprehension test, questionnaires, an observation, an interview, and learning logs were used with 105 second year Algerian EFL university students. The sample of the study was divided into three groups; one control group (with no treatment), one experimental group (CSR group) who received a CSR instruction, and a second intervention group (CSR Plus group) who received teacher’s attribution feedback in addition to the CSR intervention. Students in the CSR Plus group received the same experiment as the CSR group using the same tools, except that they were asked to keep learning logs, for which teacher’s feedback on reading performance and strategy use was provided. The results of this study indicate that the CSR and the attributional feedback intervention was effective in improving students’ reading comprehension proficiency and sense of self-efficacy. However, there was not a significant change in students’ adaptive and maladaptive attributions for their success and failure d from the pre-test to the post-test phase. Analysis of the perception questionnaire, the interview, and the learning logs shows that students have positive perceptions about the CSR and the attributional feedback instruction. Based on the findings, this study, therefore, seeks to provide EFL teachers in general and Algerian EFL university teachers in particular with pedagogical implications on how to teach reading comprehension to their students to help them achieve well and feel more self-efficacious in reading comprehension activities, and in English language learning more generally.

Keywords: attributions, attributional feedback, collaborative strategic reading, self-efficacy

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Authors: Helen Potkin

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This paper presents a practice-based case study of a project in which the student group designed and planned the curriculum content, classroom activities and assessment briefs in collaboration with the tutor. It focuses on the co-creation of the curriculum within a history and theory module, Researching the Contemporary, which runs for BA (Hons) Fine Art and Art History and for BA (Hons) Art Design History Practice at Kingston University, London. The paper analyses the potential of collaborative approaches to engender students’ investment in their own learning and to encourage reflective and self-conscious understandings of themselves as learners. It also addresses some of the challenges of working in this way, attending to the risks involved and feelings of uncertainty produced in experimental, fluid and open situations of learning. Alongside this, it acknowledges the tensions inherent in adopting such practices within the framework of the institution and within the wider of context of the commodification of higher education in the United Kingdom. The concept underpinning the initiative was to test out co-creation as a creative process and to explore the possibilities of altering the traditional hierarchical relationship between teacher and student in a more active, participatory environment. In other words, the project asked about: what kind of learning could be imagined if we were all in it together? It considered co-creation as producing different ways of being, or becoming, as learners, involving us reconfiguring multiple relationships: to learning, to each other, to research, to the institution and to our emotions. The project provided the opportunity for students to bring their own research and wider interests into the classroom, take ownership of sessions, collaborate with each other and to define the criteria against which they would be assessed. Drawing on students’ reflections on their experience of co-creation alongside theoretical considerations engaging with the processual nature of learning, concepts of equality and the generative qualities of the interrelationships in the classroom, the paper suggests that the dynamic nature of collaborative and participatory modes of engagement have the potential to foster relevant and significant learning experiences. The findings as a result of the project could be quantified in terms of the high level of student engagement in the project, specifically investment in the assessment, alongside the ambition and high quality of the student work produced. However, reflection on the outcomes of the experiment prompts a further set of questions about the nature of positionality in connection to learning, the ways our identities as learners are formed in and through our relationships in the classroom and the potential and productive nature of creative practice in education. Overall, the paper interrogates questions of what it means to work with students to invent and assemble the curriculum and it assesses the benefits and challenges of co-creation. Underpinning it is the argument that, particularly in the current climate of higher education, it is increasingly important to ask what it means to teach and to envisage what kinds of learning can be possible.

Keywords: co-creation, collaboration, learning, participation, risk

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Authors: Hernández Yasmín, Ochoa Alberto, Hurtado Diego

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The aim of this study is developing an intelligent tutoring system for electrical operators training with virtual reality systems at the laboratory center of partials discharges LAPEM. The electrical domain requires efficient and well trained personnel, due to the danger involved in the partials discharges field, qualified electricians are required. This paper presents an overview of the intelligent tutor adaptive learning design and user interface with VR. We propose the develop of constructing a model domain of a subset of partial discharges enables adaptive training through a trainee model which represents the affective and knowledge states of trainees. According to the success of the intelligent tutor system with VR, it is also hypothesized that the trainees will able to learn the electrical domain installations of partial discharges and gain knowledge more efficient and well trained than trainees using traditional methods of teaching without running any risk of being in danger, traditional methods makes training lengthily, costly and dangerously.

Keywords: intelligent tutoring system, artificial intelligence, virtual reality, partials discharges, adaptive learning

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Yuanxiang Miao

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Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

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Authors: Assem El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach using the Healthcare Multi-Agent System. The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are: The Behavioral Pattern Analysis (BPA) modeling methodology. The development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases, Healthcare Multi-Agent System

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Authors: Santri Emilin Pingsaboi Djahimo, Ikhfi Imaniah

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Helping students recognize both their strengths and weaknesses is a beneficial strategy for teachers to be implemented in the classroom, and feedback has been acknowledged as an effective tool to achieve this goal. It will allow teachers to assess the students’ progress, provide targeted support for them, and adjust both teaching and learning strategies. This research has investigated the importance of feedback in English as a Foreign Language (EFL) speaking class in East Nusa Tenggara Province, Indonesia. Through a qualitative study, it has shed light on the crucial roles of feedback in the process of English Language Teaching (ELT), especially, in the context of developing oral communication or speaking skills. Additionally, it has also examined students’ responses to feedback from their teacher by grouping them based on their semester, scores (GPA), and gender. This study, which seeks to provide insights into how feedback practices can be optimized to maximize learning outcomes in the English-speaking classroom, has revealed that these groups of students have different level of needs for feedback, yet all prefer constructive feedback. Looking at the results, it is highly expected that this study can contribute to a deeper understanding of the correlation between feedback and English language learning outcomes, particularly, in terms of speaking proficiency.

Keywords: feedback, English as a foreign language, speaking class, English language teaching

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Adi Gielgun-Katz, Alina S. Rusu

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In the last two decades, education systems worldwide have integrated new pedagogical methods and strategies in lesson plans, such as innovative technologies, social-emotional learning (SEL), gamification, mixed learning, multiple literacies, and many others. Visual language, such as photographs, is known to transcend cultures and languages, and it is commonly used by youth to express positions and affective states in social networks. Therefore, visual language needs more educational attention as a linguistic and communicative component that can create connectedness among the students and their teachers. Nowadays, when SEL is gaining more and more space and meaning in the area of academic improvement in relation to social well-being, and taking and sharing pictures is part of the everyday life of the majority of people, it becomes natural to add the visual language to SEL approach as a reinforcement strategy for connecting education to the contemporary culture and language of the youth. This article presents a program conducted in a high school class in Israel, which combines the five SEL with photography techniques, i.e., Social-Emotional Learning Visual Empowerments (SELVEs) program (experimental group). Another class of students from the same institution represents the control group, which is participating in the SEL program without the photography component. The SEL component of the programs addresses skills such as: troubleshooting, uncertainty, personal strengths and collaboration, accepting others, control of impulses, communication, self-perception, and conflict resolution. The aim of the study is to examine the effects of programs on the level of the five SEL aspects in the two groups of high school students: Self-Awareness, Social Awareness, Self-Management, Responsible Decision Making, and Relationship Skills. The study presents a quantitative assessment of the SEL programs’ impact on the students. The main hypothesis is that the students’ questionnaires' analysis will reveal a better understanding and improvement of the five aspects of the SEL in the group of students involved in the photography-enhanced SEL program.

Keywords: social-emotional learning, photography, education program, adolescents

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: Raveendranath Veeramani, Sunil Jonathan Holla, Parkash Chand, Sunil Chumber

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Gray’s Anatomy for Students has been a well-appreciated book among undergraduate students of anatomy in Asia. However, the current curricular requirements of anatomy require a more focused and organized approach. The editors of the first South Asia edition of Gray’s Anatomy for Students hereby highlight the modifications and importance of this edition. There is an emphasis on active learning by making the clinical relevance of anatomy explicit. Learning anatomy in context has been fostered by the association between anatomists and clinicians in keeping with the emerging integrated curriculum of the 21st century. The language has been simplified to aid students who have studied in the vernacular. The original illustrations have been retained, and few illustrations have been added. There are more figure numbers mentioned in the text to encourage students to refer to the illustrations while learning. The text has been made more student-friendly by adding generalizations, classifications and summaries. There are useful review materials at the beginning of the chapters which include digital resources for self-study. There are updates on imaging techniques to encourage students to appreciate the importance of essential knowledge of the relevant anatomy to interpret images, due emphasis has been laid on dissection. Additional importance has been given to the cranial nerves, by describing their relevant details with several additional illustrations and flowcharts. This new edition includes innovative features such as set inductions, outlines for subchapters and flowcharts to facilitate learning. Set inductions are mostly clinical scenarios to create interest in the need to study anatomy for healthcare professions. The outlines are a modern multimodal facilitating approach towards various topics to empower students to explore content and direct their learning and include learning objectives and material for review. The components of the outline encourage the student to be aware of the need to create solutions to clinical problems. The outlines help students direct their learning to recall facts, demonstrate and analyze relationships, use reason to explain concepts, appreciate the significance of structures and their relationships and apply anatomical knowledge. The 'structures to be identified in a dissection' are given as Level I, II and III which represent the 'must know, desirable to know and nice to know' content respectively. The flowcharts have been added to get an overview of the course of a structure, recapitulate important details about structures, and as an aid to recall. There has been a great effort to balance the need to have content that would enable students to understand concepts as well as get the basic material for the current condensed curriculum.

Keywords: Grays anatomy, South Asia, human anatomy, students anatomy

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Authors: Pedro Martinez-Santos, Víctor Gómez-Escalonilla, Silvia Díaz-Alcaide, Esperanza Montero, Miguel Martín-Loeches

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Groundwater monitoring networks are critical in evaluating the vulnerability of groundwater resources to depletion and contamination, both in space and time. Groundwater monitoring networks typically grow over decades, often in organic fashion, with relatively little overall planning. The groundwater monitoring networks in the Madrid area, Spain, were reviewed for the purpose of identifying gaps and opportunities for improvement. Spatial analysis reveals the presence of various monitoring networks belonging to different institutions, with several hundred observation wells in an area of approximately 4000 km2. This represents several thousand individual data entries, some going back to the early 1970s. Major issues included overlap between the networks, unknown screen depth/vertical distribution for many observation boreholes, uneven time series, uneven monitored species, and potentially suboptimal locations. Results also reveal there is sufficient information to carry out a spatial and temporal analysis of groundwater vulnerability based on machine learning applications. These can contribute to improve the overall planning of monitoring networks’ expansion into the future.

Keywords: groundwater monitoring, observation networks, machine learning, madrid

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Assem El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Intelligent Agent Reservation System (IARS). The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are developing the Behavioral Pattern Analysis (BPA) modeling methodology, and developing an interactive software tool (DECISION) which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, intelligent agent, reservation system, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Gezahegn Bekele Welldgiyorgise

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It is obvious that the economics, politics and social conditions of a country are determined by the quality and standard of its education. Indeed, education plays a vital role in changing the consciousness and awareness of society and transforming it on a large scale. Moreover, education contributes a lot to the advancement of science and technology, information and communication, and above all, it speeds up its progress in no time if it focuses mainly on the qualitative approach to education. Education brings about universal change and transformation and lightens mankind in all dimensions. It creates an educated, enlightened and brightened generation in society. The generation will be sharped, sharpened and well-oriented if it gets modern, sophisticated and standardized education in its field of study. The main goal of education is to produce well-qualified, well-trained and disciplined young offers in a given community. If the youth is well trained and well-mannered, he will certainly be enlightened, problem solvers and solution seekers, researchers, and innovators. In this respect, we have to provide the youth with modern education, a teaching-learning process led by active learning and a participatory approach with a new curriculum preparation for the age of children supported by modern facilities (ICT).In addition to that, the curriculum should have to give attention to mathematics and science lessons that include international experience in a comfortable school and classrooms. Therefore, the generation that will be created through such kinds of the guided education system will make the students active participants, self-confident, researchers and problem solvers, besides that result in changed life standards and a developed country. Similarly, our country, Ethiopia, has aimed to get such change in youth (generation) through modern education, designing a new educational policy and curriculum which was implemented for many years, although the goal of education has not reached the required level. To get the main idea of the article, I should have answered the question of why our country's educational goal had not reached the desired level because it is necessary to lay the foundation for research in finding out problems seen through students learning performance, the first task is selecting primary-school as a sample. Therefore, we selected “Dilla primary school (5-8)” which is a workplace for a teacher and gives me a chance to recognize students’ learning performance to recognize their learning grades (internal and external) and measure performance (achievement) of students easily’.

Keywords: curriculum, performance, innovation, learning

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Authors: Nune Ayvazyan

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Since the proclamation of monolingual foreign-language learning methods (the Berlitz Method in the early 20ᵗʰ century and the like), the dilemma has been to allow or not to allow learners’ mother tongue in the foreign-language learning process. The reason for not allowing learners’ mother tongue is reported to create a situation of immersion where students will only use the target language. It could be argued that this artificial monolingual situation is defective, mainly because there are very few real monolingual situations in the society. This is mainly due to the fact that societies are nowadays increasingly multilingual as plurilingual speakers are the norm rather than an exception. More recently, the use of learners’ mother tongue and translation has been put under the spotlight as valid foreign-language teaching tools. The logic dictates that if learners were permitted to use their mother tongue in the foreign-language learning process, that would not only be natural, but also would give them additional means of participation in class, which could eventually lead to learning. For example, when learners’ metalinguistic skills are poor in the target language, a question they might have could be asked in their mother tongue. Otherwise, that question might be left unasked. Attempts at empirically testing the role of translation as a didactic tool in foreign-language teaching are still very scant. In order to fill this void, this study looks into the interaction patterns between students in two kinds of English-learning classes: one with translation and the other in English only (immersion). The experiment was carried out with 61 students enrolled in a second-year university subject in English grammar in Spain. All the students underwent the two treatments, classes with translation and in English only, in order to see how they interacted under the different conditions. The analysis centered on four categories of interaction: teacher talk, teacher-initiated student interaction, student-initiated student-to-teacher interaction, and student-to-student interaction. Also, pre-experiment and post-experiment questionnaires and individual interviews gathered information about the students’ attitudes to translation. The findings show that translation elicited more student-initiated interaction than did the English-only classes, while the difference in teacher-initiated interactional turns was not statistically significant. Also, student-initiated participation was higher in comprehension-based activities (into L1) as opposed to production-based activities (into L2). As evidenced by the questionnaires, the students’ attitudes to translation were initially positive and mainly did not vary as a result of the experiment.

Keywords: foreign language, learning, mother tongue, translation

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Hanan Abufares Elkhimry

Abstract:

Looking for more effective teaching and learning approaches, teaching instructors have been telling trainee teachers to decrease their talking time, but the problem is how best to do this. Doing classroom research, specifically in the area of teacher talking time (TTT), is worthwhile, as it could improve the quality of teaching languages, as the learners are the ones who should be practicing and using the language. This work hopes to ascertain if teachers consider this need in a way that provides the students with the opportunities to increase their production of language. This is a question that is worthwhile answering. As many researchers have found, TTT should be decreased to 30% of classroom talking time and STT should be increased up to 70%. Other researchers agree with this, but add that it should be with awareness of the quality of teacher talking time. Therefore, this study intends to investigate the balance between quantity and quality of teacher talking time in the EFL classroom. For this piece of research and in order to capture the amount of talking in a four classrooms. The amount of talking time was measured. A Checklist was used to assess the quality of the talking time In conclusion, In order to improve the quality of TTT, the results showed that teachers may use more or less than 30% of the classroom talking time and still produce a successful classroom learning experience. As well as, the important factors that can affect TTT is the English level of the students. This was clear in the classroom observations, where the highest TTT recorded was with the lowest English level group.

Keywords: teacher talking time TTT, learning experience, classroom research, effective teaching

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Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Clement Yeboah, Eva Laryea

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A pretest-posttest within subjects experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant, indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant, indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop an interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, computer game-based learning, statistics achievement, statistics anxiety

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Authors: Mohammed Shaath

Abstract:

Aims Technological advancements in teaching and learning have made significant improvements over the past decade and have been incorporated in institutions to aid the learner’s experience. This review aims to assess whether Technology Enhanced Learning (TEL) pedagogy is more effective at improving students’ attitude and knowledge retention in orthodontic training than traditional methods. Methodology The searches comprised Systematic Reviews (SRs) related to the comparison of TEL and traditional teaching methods from the following databases: PubMed, SCOPUS, Medline, and Embase. One researcher performed the screening, data extraction, and analysis and assessed the risk of bias and quality using A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2). Kirkpatrick’s 4-level evaluation model was used to evaluate the educational values. Results A sum of 34 SRs was identified after the removal of duplications and irrelevant SRs; 4 fit the inclusion criteria. On Level 1, students showed positivity to TEL methods, although acknowledging that the harder the platforms to use, the less favourable. Nonetheless, the students still showed high levels of acceptability. Level 2 showed there is no significant overall advantage of increased knowledge when it comes to TEL methods. One SR showed that certain aspects of study within orthodontics deliver a statistical improvement with TEL. Level 3 was the least reported on. Results showed that if left without time restrictions, TEL methods may be advantageous. Level 4 shows that both methods are equally as effective, but TEL has the potential to overtake traditional methods in the future as a form of active, student-centered approach. Conclusion TEL has a high level of acceptability and potential to improve learning in orthodontics. Current reviews have potential to be improved, but the biggest aspect that needs to be addressed is the primary study, which shows a lower level of evidence and heterogeneity in their results. As it stands, the replacement of traditional methods with TEL cannot be fully supported in an evidence-based manner. The potential of TEL methods has been recognized and is already starting to show some evidence of the ability to be more effective in some aspects of learning to cater for a more technology savvy generation.

Keywords: TEL, orthodontic, teaching, traditional

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