Search results for: problem-based learning approach

Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

Procedia PDF Downloads 152

Authors: Shoba Rathilal

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High unemployment rates and a shortage of experienced and qualified employees appear to be a paradox that currently plagues most countries worldwide. In a developing country like South Africa, the rate of unemployment is reported to be approximately 35%, the highest recorded globally. At the same time, a countrywide deficit in experienced and qualified potential employees is reported in South Africa, which is causing fierce rivalry among firms. Employers have reported that graduates are very rarely able to meet the demands of the job as there are gaps in their knowledge and conceptual understanding and other 21st-century competencies, attributes, and dispositions required to successfully negotiate the multiple responsibilities of employees in organizations. In addition, the rates of unemployment and suitability of graduates appear to be skewed by race and social class, the continued effects of a legacy of inequitable educational access. Higher Education in the current technologically advanced and dynamic world needs to serve as an agent of transformation, aspiring to develop graduates to be creative, flexible, critical, and with entrepreneurial acumen. This requires that higher education curricula and pedagogy require a re-envisioning of our selection, sequencing, and pacing of the learning, teaching, and assessment. At a particular Higher education Institution in South Africa, Design Thinking and Project Based learning are being adopted as two approaches that aim to enhance the student experience through the provision of a “distinctive education” that brings together disciplinary knowledge, professional engagement, technology, innovation, and entrepreneurship. Using these methodologies forces the students to solve real-time applied problems using various forms of knowledge and finding innovative solutions that can result in new products and services. The intention is to promote the development of skills for self-directed learning, facilitate the development of self-awareness, and contribute to students being active partners in the application and production of knowledge. These approaches emphasize active and collaborative learning, teamwork, conflict resolution, and problem-solving through effective integration of theory and practice. In principle, both these approaches are extremely impactful. However, at the institution in this study, the implementation of the PBL and DT was not as “smooth” as anticipated. This presentation reports on the analysis of the implementation of these two approaches within higher education curricula at a particular university in South Africa. The study adopts a qualitative case study design. Data were generated through the use of surveys, evaluation feedback at workshops, and content analysis of project reports. Data were analyzed using document analysis, content, and thematic analysis. Initial analysis shows that the forces constraining the implementation of PBL and DT range from the capacity to engage with DT and PBL, both from staff and students, educational contextual realities of higher education institutions, administrative processes, and resources. At the same time, the implementation of DT and PBL was enabled through the allocation of strategic funding and capacity development workshops. These factors, however, could not achieve maximum impact. In addition, the presentation will include recommendations on how DT and PBL could be adapted for differing contexts will be explored.

Keywords: design thinking, project based learning, innovative higher education pedagogy, student and staff capacity development

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Authors: Suzanne Bouma

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In the Netherlands, the assumed causal relationship between employment, economic independence and individual freedom of choice has been extended to the approach of intimate partner violence (IPV). In the interests of combating IPV, it is crucial to further investigate this relationship. Based on a literature review, this article shows that the relationship between economic independence and IPV is highly complex. To unravel this complex relationship, a socio-ecological analysis framework has been applied. First, it is a layered relation, in where employment does not necessarily lead to economic independence, which can be explained by social inequalities. Second, the relation is bidirectional, where women do not by definition have access to their own financial recourses due to tactics of financial control by the intimate partner. This reveals the coexistence of IPV and economic abuse and the extent to which an intimate relationship affects the scope for individual choice. Third, there is a paradoxical relationship in which employment is both a protective and risk factor for IPV. This, in turn, cannot be separated from traditional norms about masculinity and femininity, where men occupy a position of power and derive status from being the breadwinner. These findings imply that not only the approach to IPV but also the labor market policy requires a gender-sensitive approach.

Keywords: intimate partner violence, economic independence, literature review, socio-ecological analysis framework

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Cansu Bozer, Saadet İrem Turgut

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Artificial intelligence (AI) has become one of the most important technologies of the digital age and is transforming many sectors, including education. The advantages offered by AI, such as automation, personalised learning, and data analytics, create new opportunities for both teachers and students in education systems. Preschool education plays a fundamental role in the cognitive, social, and emotional development of children. In this period, the foundations of children's creative thinking, problem-solving, and critical thinking skills are laid. Educational technologies, especially artificial intelligence-based applications, are thought to contribute to the development of these skills. For example, artificial intelligence-supported digital learning tools can support learning processes by offering activities that can be customised according to the individual needs of each child. However, the successful use of artificial intelligence-based applications in preschool education can be realised under the guidance of teachers who have the right knowledge about this technology. Therefore, it is of great importance to measure preschool teachers' awareness levels of artificial intelligence and to understand which variables affect this awareness. The aim of this study is to measure preschool teachers' awareness levels of artificial intelligence and to determine which factors are related to this awareness. In line with this purpose, teachers' level of knowledge about artificial intelligence, their thoughts about the role of artificial intelligence in education, and their attitudes towards artificial intelligence will be evaluated. The study will be conducted with 100 teachers working in Turkey using a descriptive survey model. In this context, ‘Artificial Intelligence Awareness Level Scale for Teachers’ developed by Ferikoğlu and Akgün (2022) will be used. The collected data will be analysed using SPSS (Statistical Package for the Social Sciences) software. Descriptive statistics (frequency, percentage, mean, standard deviation) and relationship analyses (correlation and regression analyses) will be used in data analysis. As a result of the study, the level of artificial intelligence awareness of preschool teachers will be determined, and the factors affecting this awareness will be identified. The findings obtained will contribute to the determination of studies that can be done to increase artificial intelligence awareness in preschool education.

Keywords: education, child development, artificial intelligence, preschool teachers

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Authors: Muhammad Haseeb

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It is crucial to have a balanced approach to food's energy and nutritional content in a world with limited resources. The preservation of the environment is vital, and both the agrifood-making and food service sectors will be requested to use fewer resources to produce a wider range of existing foods and develop imaginative foods that are physiologically appropriate for a better sense of good health, have long shelf lives and are conveniently transportable. Delivering healthy diets that satisfy consumer expectations from robust and sustainable agrifood systems is necessary in a world that is changing and where natural resources are running out. Across the whole food supply chain, an integrated multi-sectoral approach is needed to alleviate global food and nutrition insecurity.

Keywords: health, food, nutrition, supply chain

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Authors: Martha Hough RN

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Background: New nursing graduates lack the critical thinking skills required to provide safe nursing care. Critical thinking is essential in providing safe, competent, and skillful nursing interventions. Educational institutions must provide a curriculum that improves nursing students' critical thinking abilities. In addition, the recent pandemic resulted in nursing students who previously received in-person clinical but now most clinical has been converted to remote learning, increasing the use of simulations. Unfolding medium and high-fidelity simulations and Socratic questioning are used in many simulations debriefing sessions. Methodology: Google Scholar was researched with the keywords: critical thinking of nursing students with unfolding simulation, which resulted in 22,000 articles; three were used. A second search was implemented with critical thinking of nursing students Socratic questioning, which resulted in two articles being used. Conclusion: Unfolding simulations increase nursing students' critical thinking, especially during the briefing (pre-briefing and debriefing) phases, where most learning occurs. In addition, the use of Socratic questions during the briefing phases motivates other questions, helps the student analyze and critique their thinking, and assists educators in probing students' thinking, which further increases critical thinking.

Keywords: briefing, critical thinking, Socratic thinking, unfolding simulations

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Musa Auwal Mamman, S. G. Isa

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This study was carried out in order to investigate the effectiveness of GeoGebra software in teaching and learning of linear and quadratic equations amongst senior secondary school students in Fagge Local Government Area, Kano State–Nigeria. Five research items were raised in objectives, research questions and hypotheses respectively. A random sampling method was used in selecting 398 students from a population of 2098 of SS2 students. The experimental group was taught using the GeoGebra software while the control group was taught using the conventional teaching method. The instrument used for the study was the mathematics performance test (MPT) which was administered at the beginning and at the end of the study. The results of the study revealed that students taught with GeoGebra software (experimental group) performed better than students taught with traditional teaching method. The t- test was used to analyze the data obtained from the study.

Keywords: GeoGebra Software, mathematics performance, random sampling, mathematics teaching

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Authors: V. Geza, J. Vencels, G. Zageris, S. Pavlovs

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One of the most perspective methods to produce SoG-Si is refinement via metallurgical route. The most critical part of this route is refinement from boron and phosphorus. Therefore, a new approach could address this problem. We propose an approach of creating surface waves on silicon melt’s surface in order to enlarge its area and accelerate removal of boron via chemical reactions and evaporation of phosphorus. A two dimensional numerical model is created which includes coupling of electromagnetic and fluid dynamic simulations with free surface dynamics. First results show behaviour similar to experimental results from literature.

Keywords: numerical modelling, silicon refinement, surface waves, VOF method

Procedia PDF Downloads 258

Authors: Xiaoming Jiang, Jinqiao Shi, Qingfeng Tan, Wentao Zhang, Xuebin Wang, Muqian Chen

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Nowadays, big companies such as Google, Microsoft, which have adequate proxy servers, have perfectly implemented their web crawlers for a certain website in parallel. But due to lack of expensive proxy servers, it is still a puzzle for researchers to crawl large amounts of information from a single website in parallel. In this case, it is a good choice for researchers to use free public proxy servers which are crawled from the Internet. In order to improve efficiency of web crawler, the following two issues should be considered primarily: (1) Tasks may fail owing to the instability of free proxy servers; (2) A proxy server will be blocked if it visits a single website frequently. In this paper, we propose Proxisch, an optimization approach of large-scale unstable proxy servers scheduling, which allow anyone with extremely low cost to run a web crawler efficiently. Proxisch is designed to work efficiently by making maximum use of reliable proxy servers. To solve second problem, it establishes a frequency control mechanism which can ensure the visiting frequency of any chosen proxy server below the website’s limit. The results show that our approach performs better than the other scheduling algorithms.

Keywords: proxy server, priority queue, optimization algorithm, distributed web crawling

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: creative problem-based play, educational games, flexible thinking, tertiary education

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

Procedia PDF Downloads 118

Authors: Madhavi-Priya Singh, Liam Lowe, Farouk Arnaout, Ludmilla Pillay, Giordan Perez, Luke Mischker, Steve Costa

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An Emergency Department consultant identified the failure of medical students to complete the task of clerking a patient in its entirety. As six medical students on our first clinical placement, we recognised our own failure and endeavoured to examine why this failure was consistent among all medical students that had been given this task, despite our best motivations as adult learner. Our aim is to understand and investigate the elements which impeded our ability to learn and perform as medical students in the clinical environment, with reference to the prescribed task. We also aim to generate a discussion around the delivery of medical education with potential solutions to these barriers. Six medical students gathered together to have a comprehensive reflective discussion to identify possible factors leading to the failure of the task. First, we thoroughly analysed the delivery of the instructions with reference to the literature to identify potential flaws. We then examined personal, social, ethical, and cultural factors which may have impacted our ability to complete the task in its entirety. Through collation of our shared experiences, with support from discussion in the field of medical education and ethics, we identified two major areas that impacted our ability to complete the set task. First, we experienced an ethical conflict where we believed the inconvenience and potential harm inflicted on patients did not justify the positive impact the patient interaction would have on our medical learning. Second, we identified a lack of confidence stemming from multiple factors, including the conflict between preclinical and clinical learning, perceptions of perfectionism in the culture of medicine, and the influence of upward social comparison. After discussions, we found that the various factors we identified exacerbated the fears and doubts we already had about our own abilities and that of the medical education system. This doubt led us to avoid completing certain aspects of the tasks that were prescribed and further reinforced our vulnerability and perceived incompetence. Exploration of philosophical theories identified the importance of the role of doubt in education. We propose the need for further discussion around incorporating both pedagogic and andragogic teaching styles in clinical medical education and the acceptance of doubt as a driver of our learning. Doubt will continue to permeate our thoughts and actions no matter what. The moral or psychological distress that arises from this is the key motivating factor for our avoidance of tasks. If we accept this doubt and education embraces this doubt, it will no longer linger in the shadows as a negative and restrictive emotion but fuel a brighter dialogue and positive learning experience, ultimately assisting us in achieving our full potential.

Keywords: medical education, clinical education, andragogy, pedagogy

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: Mochammad Thalut

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This essay discusses how educational leadership response the Muslims pupils’ problems and their expectation about education in the UK. As we know, the Muslims community in the country is increasing. However, the debate about educational leadership is still limited to the separation between religion and academic by westerns approach. It is found that there are four major problems of Muslims pupils that need to solve by the educational leader to provide social justice in education. Leader-teacher as an Islamic concept of the educational leader is an alternative approach that can be used by the educational leader to overcome the problems. In the end, it is strongly recommended to bring this issue to the leadership development program in the UK to give all aspiring heads understanding about Muslims expectation about education.

Keywords: Muslim, education, leadership, identity

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Authors: Agnese Natali, Francesco Morelli, Walter Salvatore

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Automated Rack Supported Warehouses (ARSWs) are structures currently designed as steel racks. Even if there are common characteristics, there are differences that don’t allow to adopt the same design approach. Aiming to highlight the factors influencing the design and the behavior of ARSWs, a set of 5 structures designed by 5 European companies specialized in this field is used to perform both a critical analysis of the design approaches and the assessment of the seismic performance, which is used to point out the criticalities and the necessity of new design philosophy.

Keywords: steel racks, automated rack supported warehouse, thin walled cold-formed elements, seismic assessment

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Authors: Joni Agung Sudarmanto

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The concept of design has historically been elusive and characterized by its fluidity, leading to divergent viewpoints on its fundamental nature. Guy Julier views design as inherent in material culture, while Sanders sees it as a collective endeavor focusing on the outcome. Design's dualistic nature, procedural and outcome-oriented, spans various domains, including objects, individuals, and the environment. This comprehensive view of design challenges the notion that design practice is distinct from research, highlighting their shared exploratory nature. The article explores methodological techniques in design research and the three prevalent approaches: "into design," "through design," and "for design." The contradictory meanings of design arise from its etymology and its duality as both process and result, leading to its integrative nature across objects, humans, and the environment. The parallels between design and research activities, underscoring their exploratory and knowledge-generating nature, are situated within creative research, challenging the perception of design practice as separate from research endeavors. The "into design" approach encourages interdisciplinary collaboration, enriching design research with diverse perspectives. The "through design" approach bridges theory and practice, producing more practical outcomes. The "for design" approach supports specific design solutions, providing designers with valuable guidance.

Keywords: dualistic nature of design, integrative perspectives, methodological approaches, design research

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Dalal Tarfaoui, Salah Zkim

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Today, environmental sustainability is everyone’s concern as it contributes in many aspects to a country's development. Morocco is also aware of the increasing threats to its natural resources. Accordingly, many projects and research have been discussed pointing mainly to water security, pollution, desertification, and land degradation, but few studies bothered to dig into the human demeanor to disclose its ecological behavior. Human behavior is accountable for environment deterioration in the first place, but we keep fighting the symptoms instead of limiting the root causes. In the conceptual framework highlighted in the present article, semi-structured interviews have been conducted using a grounded theory approach. Initially this study will serve as a pilot study and a cornerstone to approve a bigger project now in progress. Beyond the existing general ecological measures (GEM), this study has chosen the grounded theory approach to bring out firsthand insights, and probe to which extent an ecological dimension exists in Morocco as a developing country. The discourse of the ecological behavior within the Moroccan context is seen in more realist, social, and community philosophy. The study has revealed an appreciative ecological behavior that is unfortunately repressed by variables beyond people’s control, which would prevent the people’s environmental good intentions to be translated into real ecological actions.

Keywords: ecological behavior, ecological dimension, variables beyond people’s control, Morocco

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Necati Içer

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Crop-hail insurance plays a vital role in managing risks and reducing the financial consequences of hail damage on crop production. Predicting insurance premium rates with short-term data is a major difficulty in numerous nations because of the unique characteristics of hailstorms. This study aims to suggest a feasible approach for establishing equitable premium rates in crop-hail insurance for nations with short-term insurance data. The primary goal of the rate-making process is to determine premium rates for high and zero loss costs of villages and enhance their credibility. To do this, a technique was created using the author's practical knowledge of crop-hail insurance. With this approach, the rate-making method was developed using a range of temporal and spatial factor combinations with both hypothetical and real data, including extreme cases. This article aims to show how to incorporate the temporal and spatial elements into determining fair premium rates using short-term insurance data. The article ends with a suggestion on the ultimate premium rates for insurance contracts.

Keywords: crop-hail insurance, premium rate, short-term insurance data, spatial and temporal parameters

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Erhan Turgut, Salih Ergün, Abdülkadir Öz

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Terror is a modern war strategy which uses violence as a means of communication in order to achieve political objectives. In today’s security environment narrowing the propaganda field of terrorist organization is the primary goal for the security forces. In this sense, providing and maintaining public support is the most necessary ability for security units. Rather than enemy and threat-oriented approach, homeland security oriented approach is essential to ensure public support. In this study, terror assumed as a homeland security issue and assigning the law enforcement forces with military status is analyzed.

Keywords: terrorism, counter-terrorism, military status law-enforcement, terrorist groups

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Authors: Umar Bello

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Literature has become an invaluable subject which has added a great value and contribution to the teaching of English language and the discovery of many other developed ideas. Literature produces an exhilarating impulse that imprints a lasting picture on the mind of a learner. Many researchers have devised various means and approaches to language Teaching methods which remain unconvinging and which yield little result, but it has remained unconvincing because it has only produced little results. Devicing a method that eliminates monotony and boredome to learners is a good factor that enhances students’ motivation to learning. In this sense, literature and language become unavoidable components that aid intellectual development. This study examines the indispensability of literature as a means of English Language teaching to secondary school classes. The researcher has developed many instructive activities which are believed will help students to improve their study in grammar and vocabulary. The researcher has used quasi-experimental approach using experimental group and control group to find out how literature enhances the students grammar as well as their vocabulary. The findings revealed a positive performance in the experimental group doing better than the control group using simple percentage. The results make it clear that literature allows learners to pay more attention and develop more interest to their studies. In giving a perspicacious linguistic development, literature therefore remains an essential tool for language teaching classrooms, thereby enhancing their grammatical and vocabulary usage.

Keywords: teaching vocabulary, integration, poetry, classroom

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Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez

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This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.

Keywords: sustainability, project-based learning, engineering education, higher education for sustainability

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

Abstract:

Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: Daphne Alroy-Thiberge

Abstract:

Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.

Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care

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Authors: M. Eskandarighadi, C. R. McGann

Abstract:

It is observed from past experiences of earthquakes that local site conditions can significantly affect the strong ground motion characteristics experience at the site. One-dimensional seismic site response analysis is the most common approach for investigating site response. This approach assumes that soil is homogeneous and infinitely extended in the horizontal direction. Therefore, tying side boundaries together is one way to model this behavior, as the wave passage is assumed to be only vertical. However, 1D analysis cannot capture the 2D nature of wave propagation, soil heterogeneity, and 2D soil profile with features such as inclined layer boundaries. In contrast, 2D seismic site response modeling can consider all of the mentioned factors to better understand local site effects on strong ground motions. 2D wave propagation and considering that the soil profile on the two sides of the model may not be identical clarifies the importance of a boundary condition on each side that can minimize the unwanted reflections from the edges of the model and input appropriate loading conditions. Ideally, the model size should be sufficiently large to minimize the wave reflection, however, due to computational limitations, increasing the model size is impractical in some cases. Another approach is to employ free-field boundary conditions that take into account the free-field motion that would exist far from the model domain and apply this to the sides of the model. This research focuses on implementing free-field boundary conditions in OpenSees for 2D site response analysisComparisons are made between 1D models and 2D models with various boundary conditions, and details and limitations of the developed free-field boundary modeling approach are discussed.

Keywords: boundary condition, free-field, opensees, site response analysis, wave propagation

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