Search results for: technology enabled learning

Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Rossella Resi

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This paper describes a project carried out in an Italian school with English learning pupils combining three didactic tools which are attested to be relevant for the success of young learner’s language curriculum: the use of technology, the intralingual and interlingual mediation (according to CEFR) and the cultural dimension. Aim of this project was to test a technological hands-on translation activity like subtitling in a formal teaching context and to exploit its potential as motivational tool for developing listening and writing, translation and cross-cultural skills among language learners. The activities proposed involved the use of professional subtitling software called Aegisub and culture-specific films. The workshop was optional so motivation was entirely based on the pleasure of engaging in the use of a realistic subtitling program and on the challenge of meeting the constraints that a real life/work situation might involve. Twelve pupils in the age between 16 and 18 have attended the afternoon workshop. The workshop was organized in three parts: (i) An introduction where the learners were opened up to the concept and constraints of subtitling and provided with few basic rules on spotting and segmentation. During this session learners had also the time to familiarize with the main software features. (ii) The second part involved three subtitling activities in plenum or in groups. In the first activity the learners experienced the technical dimensions of subtitling. They were provided with a short video segment together with its transcription to be segmented and time-spotted. The second activity involved also oral comprehension. Learners had to understand and transcribe a video segment before subtitling it. The third activity embedded a translation activity of a provided transcription including segmentation and spotting of subtitles. (iii) The workshop ended with a small final project. At this point learners were able to master a short subtitling assignment (transcription, translation, segmenting and spotting) on their own with a similar video interview. The results of these assignments were above expectations since the learners were highly motivated by the authentic and original nature of the assignment. The subtitled videos were evaluated and watched in the regular classroom together with other students who did not take part to the workshop.

Keywords: ICT, L2, language learning, language mediation, subtitling

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Authors: Kornelia Kordiak

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The research is based on a biographical approach and serves as a foundation for understanding individual human destinies through the analysis of the context of life experiences. It focuses on the significance of childhood in shaping family micro-worlds from the perspective of biographical learning. In this case, the family micro-world is interpreted as a complex of beliefs and judgments about elements of the ‘total universe’ based on the individual's experiences. The main aim of the research is to understand the importance of childhood in shaping family micro-worlds from the perspective of reflection on biographical learning. Additionally, it contributes to a deeper understanding of the familial experiences of the studied individuals who form these family micro-worlds and the course of the biographical learning process in the subjects. Biographical research aligns with an interpretative paradigm, where individuals are treated as active interpreters of the world, giving meaning to their experiences and actions based on their own values and beliefs. The research methods used in the project—narrative interview method and analysis of personal documents—enable obtaining a multidimensional perspective on the phenomenon under study. Narrative interviews serve as the main data collection method, allowing researchers to delve into various life contexts of individuals. Analysis of these narratives identifies key moments and life patterns, as well as discovers the significance of childhood in shaping family micro-worlds. Moreover, analysis of personal documents such as diaries or photographs enriches the understanding of the studied phenomena by providing additional contexts and perspectives. The research will be conducted in three phases: preparatory, main, and final. The anticipated schedule includes preparation of research tools, selection of research sample, conducting narrative interviews and analysis of personal documents, as well as analysis and interpretation of collected research material. The narrative interview method and document analysis will be utilized to capture various contexts and interpretations of childhood experiences and family relations. The research will contribute to a better understanding of family dynamics and individual developmental processes. It will allow for the identification and understanding of mechanisms of biographical learning and their significance in shaping identity and family relations. Analysis of adult narratives will enable the identification of factors determining patterns of behavior and attitudes in adult life, which may have significant implications for pedagogical practice.

Keywords: childhood, adulthood, biographical learning, narrative interview, analysis of personal documents, family micro-worlds

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Authors: Romi Dey

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Introduction: One of the most important criteria for technology to sustain and grow is through it’s elaborate and intuitive design methodology and design thinking. Designing for enterprise applications that cater to Just in Time Technology is one of the most challenging and detailed processes any User Experience Designer would come across. Description: The basic principles of Design, when applied to tailor to these technologies, creates an immense challenge and that’s how a set of redefined and revised design principles that can be applied to designing any Just In Time manufacturing solution. Findings: The thorough process of understanding the end user, their existing pain points which they’ve faced in the real world, their responsibilities and expectations, the core needs and last but not the least the demands, creates havoc nurturing of the design methodologies for the Just in Time solutions. With respect to the business aspect, design and design principles play a strong role in any form of innovation. Conclusion: Innovation and knowledge about the latest technologies are the keywords in the manufacturing industry. It becomes crucial for the product development team to be precise in their understanding of the technology and being sure of end users expectation.

Keywords: design thinking, enterprise application, Just in Time, user experience design

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Authors: Slimani Halima

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One of the main problems of degradation of arid steppe rangelands in the southern Mediterranean is the loss of plant diversity and changes in soil properties. During the last decades, these rangelands faced two main driving forces: climate through more or less lasting and recurrent droughts and overgrazing by sheep. In the present work, the preexisting system was an arid steppe with alfa grass (Stipa tenacissima L.) as the dominant plant, which was considered to be the "keystone" species toward the whole ecosystem structure and functioning. Vegetation and soil change was monitored for 45 years along a grazing intensity gradient. Changes in species richness and diversity, in the vegetation and in the soil, enabled to better understand climate fluctuations effects in comparison to overgrazing ones. The aim is to assess the impacts of grazing and climatic variability and change on biodiversity,vegetation and soil over a period of 45 years, based on data from seven reference years.

Keywords: biodiversity, desertification, droughts, el bayadh, overgrazing, soil, steppe

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Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Vijaya Dixit Aasheesh Dixit

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Shipbuilding industry operates in Engineer Procure Construct (EPC) context. Product mix of a shipyard comprises of various types of ships like bulk carriers, tankers, barges, coast guard vessels, sub-marines etc. Each order is unique based on the type of ship and customized requirements, which are engineered into the product right from design stage. Thus, to execute every new project, a shipyard needs to upgrade its production expertise. As a result, over the long run, holistic learning occurs across different types of projects which contributes to the knowledge base of the shipyard. Simultaneously, in the short term, during execution of a project comprising of multiple sister ships, repetition of similar tasks leads to learning at activity level. This research aims to capture above learnings of a shipyard and incorporate learning curve effect in project scheduling and materials procurement to improve project performance. Extant literature provides support for the existence of such learnings in an organization. In shipbuilding, there are sequences of similar activities which are expected to exhibit learning curve behavior. For example, the nearly identical structural sub-blocks which are successively fabricated, erected, and outfitted with piping and electrical systems. Learning curve representation can model not only a decrease in mean completion time of an activity, but also a decrease in uncertainty of activity duration. Sister ships have similar material requirements. The same supplier base supplies materials for all the sister ships within a project. On one hand, this provides an opportunity to reduce transportation cost by batching the order quantities of multiple ships. On the other hand, it increases the inventory holding cost at shipyard and the risk of obsolescence. Further, due to learning curve effect the production scheduled of each consequent ship gets compressed. Thus, the material requirement schedule of every next ship differs from its previous ship. As more and more ships get constructed, compressed production schedules increase the possibility of batching the orders of sister ships. This work aims at integrating materials management with project scheduling of long duration projects for manufacturing of multiple sister ships. It incorporates the learning curve effect on progressively compressing material requirement schedules and addresses the above trade-off of transportation cost and inventory holding and shortage costs while satisfying budget constraints of various stages of the project. The activity durations and lead time of items are not crisp and are available in the form of probabilistic distribution. A Stochastic Mixed Integer Programming (SMIP) model is formulated which is solved using evolutionary algorithm. Its output provides ordering dates of items and degree of order batching for all types of items. Sensitivity analysis determines the threshold number of sister ships required in a project to leverage the advantage of learning curve effect in materials management decisions. This analysis will help materials managers to gain insights about the scenarios: when and to what degree is it beneficial to treat a multiple ship project as an integrated one by batching the order quantities and when and to what degree to practice distinctive procurement for individual ship.

Keywords: learning curve, materials management, shipbuilding, sister ships

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Authors: Proud Arunrangsiwed

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Crisis management strategies could be found in various curriculums, not only in schools of business, but also schools of communication. Young students, such as freshmen and sophomores of undergraduate schools, may not care about learning crisis management strategies. Moreover, crisis management strategies are not a topic art students are familiar with. The current paper discusses a way to adapt entertainment media into a crisis management lesson, and the importance of learning crisis management strategies in the school of animation. Students could learn crisis management strategies by watching movies with content about a crisis and responding to crisis responding. The students should then participate in follow up discussions related to the strategies that were used to address the crisis, as well as their success in solving the crisis.

Keywords: situational crisis communication theory, crisis response strategies, media effect, unintentional effect

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Authors: Mauro Callejas-Cuervo, Andrea Catherine Alarcon-Aldana, Lorena Paola Castillo-Guerra

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The software industry requires highly-trained professionals, capable of developing the roles integrated in the cycle of software development. That is why a large part of the task is the responsibility of higher education institutions; often through a curriculum established to orientate the academic development of the students. It is so that nowadays there are different models that support proposals for the improvement of the curricula for the area of Software Engineering, such as ACM, IEEE, ABET, Swebok, of which the last stands out, given that it manages and organises the knowledge of Software Engineering and offers a vision of theoretical and practical aspects. Moreover, it has been applied by different universities in the pursuit of achieving coverage in delivering the different topics and increasing the professional quality of future graduates. This research presents the structure of teaching and learning guides from the objectives of training and methodological strategies immersed in the levels of learning of Bloom’s taxonomy with which it is intended to improve the delivery of the topics in the area of Requirements Engineering. Said guides were implemented and validated in a course of Requirements Engineering of the Systems and Computer Engineering programme in the Universidad Pedagógica y Tecnológica de Colombia (Pedagogical and Technological University of Colombia) using a four stage methodology: definition of the evaluation model, implementation of the guides, guide evaluation, and analysis of the results. After the collection and analysis of the data, the results show that in six out of the seven topics proposed in the Swebok guide, the percentage of students who obtained total marks within the 'High grade' level, that is between 4.0 and 4.6 (on a scale of 0.0 to 5.0), was higher than the percentage of students who obtained marks within the 'Acceptable' range of 3.0 to 3.9. In 86% of the topics and the strategies proposed, the teaching and learning guides facilitated the comprehension, analysis, and articulation of the concepts and processes of the students. In addition, they mainly indicate that the guides strengthened the argumentative and interpretative competencies, while the remaining 14% denotes the need to reinforce the strategies regarding the propositive competence, given that it presented the lowest average.

Keywords: pedagogic guide, pedagogic strategies, requirements engineering, Swebok, teaching-learning process

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Authors: Beverley Gallimore

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The Individuals with Disabilities Education Act of 2004 (IDEA) has helped to improve outcomes for students with special education needs. Through IDEA, students with Special Education Needs (SEN) have opportunities for more equitable education within the General Education classroom. However, students with disabilities lack access to instructions that can help them to maximize their fullest learning potential. Recently, educational stakeholders have emphasized Integrated Co-teaching as a tool to increase engagement and learning outcomes for students with disabilities in general education classrooms. As a result of this new approach, general and special education teachers are working collaboratively to teach students with disabilities. However, co-teaching models are not properly designed and structured to effectively benefit students with disabilities. Teachers must be oriented correctly in the co-teaching models if it is to be beneficial for students.

Keywords: CO-teaching, differentiation, equitable, collaborative

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Authors: Wieke Ellen Bouwes

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This study aims to unravel which factors enhance accessibility of video consultations (VCs) for patients with low digital literacy. Thirteen in-depth interviews with patients, hospital employees, eHealth experts, and digital support organizations were held. Patients with low digital literacy received in-home support during real-time video consultations and are observed during the set-up of these consultations. Key findings highlight the importance of patient acceptance, emphasizing video consultations benefits and avoiding standardized courses. The lack of a uniform video consultation system across healthcare providers poses a barrier. Familiarity with support organizations – to support patients in usage of digital tools - among healthcare practitioners enhances accessibility. Moreover, considerations regarding the Dutch General Data Protection Regulation (GDPR) law influence support patients receive. Also, provider readiness to use video consultations influences patient access. Further, alignment between learning styles and support methods seems to determine abilities to learn how to use video consultations. Future research could delve into tailored learning styles and technological solutions for remote access to further explore effectiveness of learning methods.

Keywords: video consultations, digital literacy skills, effectiveness of support, intra- and inter-organizational relationships, patient acceptance of video consultations

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Authors: Namhun Lee, Seong Jin Kim

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The construction industry represents one of the largest industries in the United States, yet it continues to face several occupational health and safety challenges. Many workers on construction sites are suffering from extended exposure to stressful situations such as poor and hazardous work environments and task complexity. Stress can be commonly defined as a feeling of emotional or physical tension, which can easily impact construction safety and result in a higher rate of job-related injuries in the construction industry. Physiological signals transmitted from wearable biosensors can be used to detect excessive stress. Therefore, workers’ stress should be detected and mitigated to prevent any type of serious incident or accident proactively. By doing this, construction productivity, as well as job satisfaction, would also be improved in the construction industry. To establish a foundation in this field of research, a conceptual framework for using wearable technology for construction safety has been developed for continuous and automatic monitoring of worker’s stress. The conceptual framework will serve as a foothold in future studies on the application of wearable technology for construction safety.

Keywords: construction safety, occupational stress, stress monitoring, wearable biosensors

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Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

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This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

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Authors: David Lynch, Jake Madden

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There exists a body of literature that reports on the many benefits of partnerships between universities and schools, especially in terms of teaching improvement and school reform. This is because such partnerships can build significant teaching capital, by deepening and expanding the skillsets and mindsets needed to create the connections that support ongoing and embedded teacher professional development and career goals. At the same time, this literature is critical of such initiatives when the partnership outcomes are short- term or one-sided, misaligned to fundamental problems, and not expressly focused on building the desired teaching capabilities. In response to this situation, research conducted by Professor David Lynch and his TeachLab research team, has begun to shed light on the strengths and limitations of school/university partnerships, via the identification of key conceptual elements that appear to act as critical partnership success factors. These elements are theorised as an inter-play between professional knowledge acquisition, readiness, talent management and organisational structure. However, knowledge of how these elements are established, and how they manifest within the school and its teaching workforce as an overall system, remains incomplete. Therefore, research designed to more clearly delineate these elements in relation to their impact on school/university partnerships is thus required. It is within this context that this paper reports on the development and testing of a Professional Learning (PL) model for schools and their teachers that incorporates school-university research partnering within a systematic, whole-of-school PL strategy that is underpinned and structured by a micro-credentialing (MC) regime. MC involves learning a narrow-focused certificate (a micro-credential) in a specific topic area (e.g., 'How to Differentiate Instruction for English as a second language Students') and embedded in the teacher’s day-to-day teaching work. The use of MC is viewed as important to the efficacy and sustainability of teacher PL because it (1) provides an evidence-based framework for teacher learning, (2) has the ability to promote teacher social capital and (3) engender lifelong learning in keeping professional skills current in an embedded and seamless to work manner. The associated research is centred on a primary school in Australia (P-6) that acted as an arena to co-develop, test/investigate and report on outcomes for teacher PL that uses MC to support a whole-of-school partnership with a university.

Keywords: teaching improvement, teacher professional learning, talent management, education partnerships, school-university research

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Authors: Livingstone Makondo

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This ongoing research explores what could be the components of a comprehensive First-Year Student Experience (FYSE) at the Durban University of Technology (DUT) and the preferred implementation modalities. In light of the Siyaphumelela project, this interrogation is premised on the need to glean data for the institution that could be used to ascertain the role of FYSE towards enhancing student success. The research proceeds by examining prevalent models from other South African Universities and beyond in its quest to get at pragmatic comprehensive FYSE programme for DUT. As DUT is a student centered institution and amidst the ever shrinking economy, this research would aid higher education practitioners to ascertain if the hard earned finances are being channelled to a worthy academic venture. This research seeks to get inputs from a) students who participated in FYSE and are now in second and third years at DUT b) students who are currently participating in FYSE c) former and present Tutors d) departmental coordinators e) academics and support staff working with the participating students. This exploratory approach is preferred since 2010 DUT has grappled with how to implement an integrated institution-wide FYSE. This findings of this research could provide the much-needed data to ascertain if the current FYSE package is pivotal towards attainment of DUT Strategic Focus Area 1: Building sustainable student communities of living and learning. The ideal is to have DUT FYSE programme become an institution-wide programme that lays the foundation for consolidated and focused student development programmes for subsequent undergraduate and postgraduate levels of study. Also, armed with data from this research, DUT could develop the capacity and systems to ensure that all students get diverse on-time support to enhance their retention and academic success in their tertiary studies. In essence, the preferred FYSE curriculum woven around DUT graduate attributes should contribute towards the reduction in the first-year students’ dropout rates and subsequently in undergraduate studies. Therefore, this on-going research will feed into Siyaphumelela project and would help position 2018-2020 FYSE initiatives at DUT.

Keywords: challenges, comprehensive, dropout, transition

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: Frida C. Jaime Franco, C. Paulina Navarro Núñez, R. Jacob Sánchez Nájera

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The importance of developing multi-language abilities in our global society is noteworthy. However, the necessity, interest, and consciousness of the significance that the development of another language represents, apart from the mother tongue, is not always the same in all contexts as it is in multicultural communities, especially in rural higher education institutions immersed in small communities. Leading opportunities for digital interaction among learners from Mexico and abroad partners represents scaffolding towards, not only language skills development but also intercultural communicative competences (ICC). This study leads us to consider what should be the best approach to work while applying a program of ICC integrated into the practice of EFL. While analyzing the roots of the language, it is possible to obtain the main objective of learning another language, to communicate with a functional purpose, as well as attaching social practices to the learning process, giving a result of functionality and significance to the target language. Hence, the collateral impact that collaborative learning leads to, aims to contribute to a better global understanding as well as a means of self and other cultural awareness through intercultural communication. While communicating through the target language by online collaboration among students in platforms of long-distance communication, language is used as a tool of interaction to broaden students’ perspectives reaching a substantial improvement with the help of their differences. This process should consider the application of the target language in the inquiry of sociocultural information, expecting the learners to integrate communicative skills to handle cultural differentiation at the same time they apply the knowledge of their target language in a real scenario of communication, despite being through virtual resources.

Keywords: collaborative learning, communicative approach, culture, interaction, interculturalism, target language, virtual partnership

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

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The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: educative model, good life, professional social responsibility, values

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Aparajita Mukherjee, D. P. Mukherjee

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This paper critically assessed pollution problems in small and medium enterprises with unique references to foundries and sponge iron industries to survey the adverse impact on human societies and the environment. The objective of this paper was to show how cleaner production concept was implemented in one foundry through improvisation of existing technology in India. Incremental advancement of existing technology minimized environmental issues and resource utilization. This study depicted that poor fiscal help, poor enforcement of government regulations, owners’ attitude and lacking specialized technical workers were the significant hindrances towards cleaner production. The paper explored the possible ways to overcome these hindrances for cleaner production. On a more general level, findings raise important questions regarding the need for a new paradigm for the implementation of cleaner production. Improvisation of existing technology in these enterprises would be cost effective towards sustainable development.

Keywords: SME pollution, ecological crisis, sustainable development, cleaner production, training

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Authors: Hashini E. Mohottala

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We report the combine use of Wikispaces (WikiS) and Group Problem Solving (GPS) sessions conducted in the introductory level physics classes. As a part of this new teaching tool, some essay type problems were posted on the WikiS in weekly basis and students were encouraged to participate in problem solving without providing numerical final answers but the steps. Wikispace is used as a platform for students to meet online and create discussions. Each week students were further evaluated on problem solving skills opening up more opportunity for peer interaction through GPS. Each group was given a different problem to solve and the answers were graded. Students developed a set of skills in decision-making, problem solving, communication, negotiation, critical and independent thinking and teamwork through the combination of WikiS and GPS.

Keywords: group problem solving (GPS), wikispace (WikiS), physics education, learning

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Authors: Ragi Poyyara, V. Vivek, Ashish Khaparde

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The Product Development Process (PDP) in the technology group plays a very important role in the launch of any product. While a manufacturing process encourages the use of certain measures to reduce Health, Safety and Environmental (HSE) risks on the shop floor, the PDP concentrates on the use of Geometric Dimensioning and Tolerancing (GD&T) to develop a flawless design. Furthermore, PDP distributes and coordinates activities between different departments such as marketing, purchasing, and manufacturing. However, it is seldom realized that PDP makes a significant contribution to developing a product that reduces HSE risks by encouraging the Technology group to use effective GD&T. The GD&T is a precise communication tool that uses a set of symbols, rules, and definitions to mathematically define parts to be manufactured. It is a quality assurance method widely used in the oil and gas sector. Traditionally it is used to ensure the interchangeability of a part without affecting its form, fit, and function. Parts that do not meet these requirements are rejected during quality audits. This paper discusses how the Technology group integrates this quality assurance tool into the PDP and how the tool plays a major role in helping the HSE department in its goal towards eliminating HSE incidents. The PDP involves a thorough risk assessment and establishes a method to address those risks during the design stage. An illustration shows how GD&T helped reduce safety risks by ergonomically improving assembling operations. A brief discussion explains how tolerances provided on a part help prevent finger injury. This tool has equipped Technology to produce fixtures, which are used daily in operations as well as manufacturing. By applying GD&T to create good fits, HSE risks are mitigated for operating personnel. Both customers and service providers benefit from reduced safety risks.

Keywords: HSE risks, product development process, geometric dimensioning and tolerances, mechanical engineering

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Authors: Shaibu Baanni Azumah, William Adzawla

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Rice is an important staple crop, with current demand higher than the domestic supply in Ghana. This has led to a high and unfavourable import bill. Therefore, recent policies and interventions in the agricultural sub-sector aim at promoting various improved agricultural technologies in order to improve domestic production and reduce the importation of rice. In this study, we examined the effect of the adoption of Urea Deep Placement (UDP) technology by rice farmers on the position of the production frontier. This involved 200 farmers selected through a multi stage sampling technique in the Northern region of Ghana. A Cobb-Douglas stochastic frontier model was fitted. The result showed that the adoption of UDP technology shifts the output frontier outward and also move the farmers closer to the frontier. Farmers were also operating under diminishing returns to scale which calls for redress. Other factors that significantly influenced rice production were farm size, labour, use of certified seeds and NPK fertilizer. Although there was an opportunity for improvement, the farmers were highly efficient (92%), compared to previous studies. Farmers’ efficiency was improved through increased education, household size, experience, access to credit, and lack of extension service provision by MoFA. The study recommends the revision of Ghana’s agricultural policy to include the UDP technology. Agricultural Extension officers of the Ministry of Food and Agriculture (MoFA) should be trained on the UDP technology to support IFDC’s drive to improve adoption by rice farmers. Rice farmers are also encouraged to expand their farm lands, improve plant population, and also increase the usage of fertilizer to improve yields. Mechanisms through which credit can be made easily accessible and effectively utilised should be identified and promoted.

Keywords: efficiency, rice farmers, stochastic frontier, UDP technology

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Authors: Yassir AbdelRazig, Amine Ghanem

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Construction projects are information intensive in nature and involve many activities that are related to each other. Wireless technologies can be used to improve the accuracy and timeliness of data collected from construction sites and shares it with appropriate parties. Nonetheless, the construction industry tends to be conservative and shows hesitation to adopt new technologies. A main concern for owners, contractors or any person in charge on a job site is the cost of the technology in question. Wireless technologies are not cheap. There are a lot of expenses to be taken into consideration, and a study should be completed to make sure that the importance and savings resulting from the usage of this technology is worth the expenses. This research attempts to assess the effectiveness of using the appropriate wireless technologies based on criteria such as performance, reliability, and risk. The assessment is based on a utility function model that breaks down the selection issue into alternatives attribute. Then the attributes are assigned weights and single attributes are measured. Finally, single attribute are combined to develop one single aggregate utility index for each alternative.

Keywords: analytic hierarchy process, decision theory, utility function, wireless technologies

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Authors: Said Fares, Mary Fares

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It is well documented that introductory computer programming courses are difficult and that failure rates are high. The aim of this project was to reduce the high failure and withdrawal rates in learning to program. This paper presents a number of changes in module organization and instructional delivery system in teaching CS1. Daily out of class help sessions and tutoring services were applied, interactive lectures and laboratories, online resources, and timely feedback were introduced. Five years of data of 563 students in 21 sections was collected and analyzed. The primary results show that the failure and withdrawal rates were cut by more than half. Student surveys indicate a positive evaluation of the modified instructional approach, overall satisfaction with the course and consequently, higher success and retention rates.

Keywords: failure rate, interactive learning, student engagement, CS1

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Authors: Soheila Sadeghi

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In construction projects, predicting project performance metrics accurately is essential for effective management and successful delivery. However, conventional methods often depend on fixed baseline plans, disregarding the evolving nature of project progress and external influences. To address this issue, we introduce a distinct approach based on machine learning to forecast key performance indicators, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category within an urban road reconstruction project. Our proposed model leverages time series forecasting techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance by analyzing historical data and project progress. Additionally, the model incorporates external factors, including weather patterns and resource availability, as features to improve forecast accuracy. By harnessing the predictive capabilities of machine learning, our performance forecasting model enables project managers to proactively identify potential deviations from the baseline plan and take timely corrective measures. To validate the effectiveness of the proposed approach, we conduct a case study on an urban road reconstruction project, comparing the model's predictions with actual project performance data. The outcomes of this research contribute to the advancement of project management practices in the construction industry by providing a data-driven solution for enhancing project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, schedule variance, earned value management

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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