Search results for: learning assessment

Authors: Andreas Johansson

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Conceptual understanding needs to be grounded on observation of physical phenomena, experiences or metaphors. Observation of physical phenomena using demonstration experiments has a long tradition within physics education and students need to develop mental models to relate the observations to concepts from scientific theories. This study investigates how live and video experiments involving an acoustic trap to visualize particle-field interaction, field properties and particle properties can help develop students' mental models and how they can be used differently to realize their potential as teaching tools. Initially, they were treated as analogs and the lesson designs were kept identical. With a design-based approach, the experimental and video designs, as well as best practices for a respective teaching tool, were then developed in iterations. Variation theory was used as a theoretical framework to analyze the planned respective realized pattern of variation and invariance in order to explain learning outcomes as measured by a pre-posttest consisting of conceptual multiple-choice questions inspired by the Force Concept Inventory and the Force and Motion Conceptual Evaluation. Interviews with students and teachers were used to inform the design of experiments and videos in each iteration. The lesson designs and the live and video experiments has been developed to help teachers improve student learning and make school physics more interesting by involving experimental setups that usually are out of reach and to bridge the gap between what happens in classrooms and in science research. As students’ conceptual knowledge also rises their interest in physics the aim is to increase their chances of pursuing careers within science, technology, engineering or mathematics.

Keywords: acoustic trap, design-based research, experiments, variation theory

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Franklin Uakhemen Ajogbor

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The issue of the English sentence has remained controversial from Traditional Grammar to modern linguistics. The English sentence occupies the highest rank in the hierarchy of grammatical units. Its consideration is therefore very necessary in learning English as a second language. Unfortunately, divergent views by grammarians on the concept of the English sentence have generated much controversy. There seems not to be a unanimous agreement on what actually constitute a sentence. Some schools of thought believe that a sentence must have a subject and a predicate while some believe that it should not. The types of sentence according to structure are also not devoid of controversy as the views of several linguists have not been properly harmonized. Findings have shown that serious effort and attention have not been paid by previous linguists to clear these ambiguities as it has a negative implication in the learning and teaching of English language. The variations on the concept of the English sentence have become particularly worrisome as a result of the widening patronage of English as a global language. The paper is therefore interested in the investigation of this controversy and suggesting a solution to the problem. In doing this, data was collected from students and scholars that show lack of uniformity in what a sentence is. Using the Systemic Functional Model as theoretical framework, the paper launches into the views held by these various schools of thought with the aim of reconciling these divergent views and also an attempt to open up further research on what actually constitute a sentence.

Keywords: traditional grammar, linguistics, controversy, sentence, grammatical units

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Authors: Azzah Alghamdi

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The Interactive White Boards (IWBs) as one of the innovative educational technologies have been extensively investigated in advanced countries such as the UK, US, and Australia. However, there is a significant lack of research studies, which mainly examine the use of IWBs in Saudi Arabia. Therefore, this study aims to investigate the attitudes of primary teachers towards using IWBs in both the teaching and learning processes. Moreover, it aims to investigate if there is any significant difference between male teachers and females regarding their attitudes towards using this technology. This study concentrated on teachers in primary schools, which participated in Tatweer project in the city of Jeddah, in Saudi Arabia. Mixed methods approach was employed in this study using a designed questionnaire, classroom observations, and a semi-structured interview. 587 teachers (286 men and 301 women) from Tatweer primary schools were completed the questionnaire as well as twenty teachers were interviewed including seven female teachers were observed in their classrooms. The findings of this study indicated that approximately 11% of the teachers within the sample (n=587) had negative attitudes towards the use of IWBs in the teaching and learning processes. However, the majority of them nearly 89% agreed about the benefits of using IWBs in their classrooms. Additionally, all the twenty teachers who were interviewed (including the seven observed female teachers) had positive attitudes towards the use of these technologies. Moreover, 87% of male teachers and 91% of female teachers who completed the questionnaire accepted the usefulness of using IWBs in improving their teaching and students' learning. Thus, this indicates that there was no significant difference between male and female teachers in Tatweer primary schools in terms of their views about using these innovative technologies in their lessons. The findings of the current study will help the Ministry of Education to improve the policies of using IWBs in Saudi Arabia. Indeed, examining teachers’ attitudes towards IWBs is a very important issue because they are the main users in classrooms. Hence, their views should be considered to addressing the powers and boundaries of using IWBs. Moreover, students will feel comfortable to use IWBs if their teachers accept and use them well.

Keywords: IWBs, Saudi teachers’ views, Tatweer schools, teachers' gender

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Authors: Fernanda Mary R. G. Da Silva, Adriana C. F. Mozzambani, Marcelo F. Mello

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Introduction: Sexual assault against children and adolescents is a public health problem with serious consequences on their quality of life, especially for those who develop post-traumatic stress disorder (PTSD). The broad literature in this research area points to greater losses in verbal learning, explicit memory, speed of information processing, attention and executive functioning in PTSD. Objective: To compare the neuropsychological functions of adolescents from 14 to 17 years of age, victims of sexual violence with PTSD with those of healthy controls. Methodology: Application of a neuropsychological battery composed of the following subtests: WASI vocabulary and matrix reasoning; Digit subtests (WISC-IV); verbal auditory learning test RAVLT; Spatial Span subtest of the WMS - III scale; abbreviated version of the Wisconsin test; concentrated attention test - D2; prospective memory subtest of the NEUPSILIN scale; five-digit test - FDT and the Stroop test (Trenerry version) in adolescents with a history of sexual violence in the previous six months, referred to the Prove (Violence Care and Research Program of the Federal University of São Paulo), for further treatment. Results: The results showed a deficit in the word coding process in the RAVLT test, with impairment in A3 (p = 0.004) and A4 (p = 0.016) measures, which compromises the verbal learning process (p = 0.010) and the verbal recognition memory (p = 0.012), seeming to present a worse performance in the acquisition of verbal information that depends on the support of the attentional system. A worse performance was found in list B (p = 0.047), a lower priming effect p = 0.026, that is, lower evocation index of the initial words presented and less perseveration (p = 0.002), repeated words. Therefore, there seems to be a failure in the creation of strategies that help the mnemonic process of retention of the verbal information necessary for learning. Sustained attention was found to be impaired, with greater loss of setting in the Wisconsin test (p = 0.023), a lower rate of correct responses in stage C of the Stroop test (p = 0.023) and, consequently, a higher index of erroneous responses in C of the Stroop test (p = 0.023), besides more type II errors in the D2 test (p = 0.008). A higher incidence of total errors was observed in the reading stage of the FDT test p = 0.002, which suggests fatigue in the execution of the task. Performance is compromised in executive functions in the cognitive flexibility ability, suggesting a higher index of total errors in the alternating step of the FDT test (p = 0.009), as well as a greater number of persevering errors in the Wisconsin test (p = 0.004). Conclusion: The data from this study suggest that sexual violence and PTSD cause significant impairment in the neuropsychological functions of adolescents, evidencing risk to quality of life in stages that are fundamental for the development of learning and cognition.

Keywords: adolescents, neuropsychological functions, PTSD, sexual violence

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Authors: Aishatu Ibrahim Birma, Aminu Tukur, Abdulkarim Abbass Gora

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Machine translation plays a vital role in the Field of Natural Language Processing (NLP), breaking down language barriers and enabling communication across diverse communities. In the context of Hausa, a widely spoken language in West Africa, mainly in Nigeria, effective translation systems are essential for enabling seamless communication and promoting cultural exchange. However, due to the unique linguistic characteristics of Hausa, accurate translation remains a challenging task. The research proposes an approach to improving the machine learning translation of Hausa by integrating Named Entity Recognition (NER) techniques. Named entities, such as person names, locations, organizations, and dates, are critical components of a language's structure and meaning. Incorporating NER into the translation process can enhance the quality and accuracy of translations by preserving the integrity of named entities and also maintaining consistency in translating entities (e.g., proper names), and addressing the cultural references specific to Hausa. The NER will be incorporated into Neural Machine Translation (NMT) for the Hausa to English Translation.

Keywords: machine translation, natural language processing (NLP), named entity recognition (NER), neural machine translation (NMT)

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Christina Van Wingerden

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This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, isolation, learning management system, sense of belonging

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Authors: Y. S. Zhou, C. R. Li, L. L. Tang, C. X. Gao, D. J. Wang, Y. Y. Guo

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Triangular trihedral corner reflector (CR) has been widely used as point target for synthetic aperture radar (SAR) calibration and image quality assessment. The additional “tip” of the triangular plate does not contribute to the reflector’s theoretical RCS and if it interacts with a perfectly reflecting ground plane, it will yield an increase of RCS at the radar bore-sight and decrease the accuracy of SAR calibration and image quality assessment. Regarding this problem, two types of CRs were manufactured. One was the hexagonal trihedral CR. It is a self-illuminating CR with relatively small plate edge length, while large edge length usually introduces unexpected edge diffraction error. The other was the triangular trihedral CR with extended bottom plate which considers the effect of ‘tip’ into the total RCS. In order to assess the performance of the two types of new CRs, flight campaign over the National Calibration and Validation Site for High Resolution Remote Sensors was carried out. Six hexagonal trihedral CRs and two bottom-extended trihedral CRs, as well as several traditional triangular trihedral CRs, were deployed. KOMPSAT-5 X-band SAR image was acquired for the performance analysis of the hexagonal trihedral CRs. C-band airborne SAR images were acquired for the performance analysis of the bottom-extended trihedral CRs. The analysis results showed that the impulse response function of both the hexagonal trihedral CRs and bottom-extended trihedral CRs were much closer to the ideal sinc-function than the traditional triangular trihedral CRs. The flight campaign results validated the advantages of new types of CRs and they might be useful in the future SAR calibration mission.

Keywords: synthetic aperture radar, calibration, corner reflector, KOMPSAT-5

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Authors: B. S. Premalatha, Mereen Rose Babu, Vaishali Prabhu

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Background: Dysphagia must be assessed, either subjectively or objectively, in order to properly address the swallowing difficulty. Providing therapeutic care to patients with dysphagia via tele mode was one approach for providing clinical services during the COVID-19 epidemic. As a result, the teleassessment of dysphagia has increased in India. Aim: This study aimed to identify challenges faced by Indian SLPs while providing teleassessment to individuals with dysphagia during the outbreak of COVID-19 from 2020 to 2021. Method: After receiving approval from the institute's institutional review board and ethics committee, the current study was carried out. The study was cross-sectional in nature and lasted from 2020 to 2021. The study enrolled participants who met the inclusion and exclusion criteria of the study. It was decided to recruit roughly 246 people based on the sample size calculations. The research was done in three stages: questionnaire development and content validation, questionnaire administration. Five speech and hearing professionals' content verified the questionnaire for faults and clarity. Participants received questionnaires via various social media platforms such as e-mail and WhatsApp, which were written in Microsoft Word and then converted to Google Forms. SPSS software was used to examine the data. Results: In light of the obstacles that Indian SLPs encounter, the study's findings were examined. Only 135 people responded. During the COVID-19 lockdowns, 38% of participants said they did not deal with dysphagia patients. After the lockout, 70.4% of SLPs kept working with dysphagia patients, while 29.6% did not. From the beginning of the oromotor examination, the main problems in completing tele evaluation of dysphagia have been highlighted. Around 37.5% of SLPs said they don't undertake the OPME online because of difficulties doing the evaluation, such as the need for repeated instructions from patients and family members and trouble visualizing structures in various positions. The majority of SLPs' online assessments were inefficient and time-consuming. A bigger percentage of SLPs stated that they will not advocate tele evaluation in dysphagia to their colleagues. SLPs' use of dysphagia assessment has decreased as a result of the epidemic. When it came to the amount of food, the majority of people proposed a small amount. Apart from placing the patient for assessment and gaining less cooperation from the family, most SLPs found that Internet speed was a source of concern and a barrier. Hearing impairment and the presence of a tracheostomy in patients with dysphagia proved to be the most difficult conditions to treat online. For patients with NPO, the majority of SLPs did not advise tele-evaluation. In the anterior region of the oral cavity, oral meal residue was more visible. The majority of SLPs reported more anterior than posterior leakage. Even while the majority of SLPs could detect aspiration by coughing, many found it difficult to discern the gurgling tone of speech after swallowing. Conclusion: The current study sheds light on the difficulties that Indian SLPs experience when assessing dysphagia via tele mode, indicating that tele-assessment of dysphagia is still to gain importance in India.

Keywords: dysphagia, teleassessment, challenges, Indian SLP

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Authors: Daniel Angwenyi

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Relationships between protected area managers and adjacent communities, as well as communities' attitudes, views and perceptions of these areas, are critical for the success of conservation efforts. It is, therefore, of utmost importance for protected area managers and administrators understand how local communities view these areas and their management, so that they can build sustainable working relationships. This paper is based on a survey of 375 semi-structured questionnaires administered to household heads, living at distances ranging from the edge of the reserves to 50 km away from the reserve boundary across Great Fish River, Mkambati, Hluleka, and Tsolwana in the Eastern Cape Province, South Africa. The paper provides a longitudinal assessment of households’ knowledge on the role of reserves and how the reserves. In addition to households’ knowledge, the paper also provides an assessment of their attitudes towards the location and management, as well as views on the best way to manage the reserves. For 79% of community members reserves are important as they were seen to conserve biodiversity and valuable ecological systems necessary for sustaining life. Most (75%) respondents indicated that closely located reserves gave them opportunities to learn about nature conservation and to subsidize their incomes through tourism ventures. However, 58% had a problem with reserves’ staff, due to restrictions on resource use, which negatively impacted their livelihoods. Over half (51%) of the households were of the view that sustainable conservation can only be achieved through an integrated approach, where local communities’ and conservation needs are given equal weighting. Thus, it is concluded that reserve management should look at communities as active partners in the running of protected areas if sustainable conservation objectives are to be realised.

Keywords: nature conservation, conservation knowledge, local communities, views, protected areas

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Patsy Tan, Dana Baltutis

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This presentation demonstrates the effectiveness of music therapy in co-treatment with speech pathology and occupational therapy as an innovative way when working with children and adolescents with complex individual differences to facilitate communication, emotional, motor and social skills development. Each child with special needs and their carer has an individual profile which encompasses their visual-spatial, auditory, language, learning, mental health, family dynamic, sensory-motor, motor planning and sequencing profiles. The most common issues among children with special needs, especially those diagnosed with Autism Spectrum Disorder, are in the areas of regulation, communication, and social-emotional development. The ability of children living with challenges to communicate and use language and understand verbal and non-verbal information, as well as move their bodies to explore and interact with their environments in social situations, depends on the children being regulated both internally and externally and trusting their communication partners and understanding what is happening in the moment. For carers, it is about understanding the tempo, rhythm, pacing, and timing of their own individual profile, as well as the profile of the child they are interacting with, and how these can sync together. In this study, music therapy is used in co-treatment sessions with a speech pathologist and/or an occupational therapist using the DIRFloortime approach to facilitate the regulation, attention, engagement, reciprocity and social-emotional capacities of children presenting with complex individual differences. Documented changes in 10 domains of children’s development over a 12-month period using the Individual Music Therapy Assessment Profile (IMTAP) were observed. Children were assessed biannually, and results show significant improvements in the social-emotional, musicality and receptive language domains indicating that co-treatment with a music therapist using the DIRFloortime framework is highly effective. This presentation will highlight strategies that facilitate regulation, social-emotional and communication development for children and adolescents with complex individual profiles.

Keywords: communication, shared attention, regulation, social emotional

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Ahmed Inal, Samir Rouidi, Samir Bachouche

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The present study is focused on the distribution and composition of seafloor marine litter associated to trawlable fishing areas along Algerian coast. The sampling was done with a GOC73 bottom trawl during four (04) demersal resource assessment cruises, respectively, in 2016, 2019, 2021 and 2022, carried out on board BELKACEM GRINE R/V. A total of 254 fishing hauls were sampled for the assessment of marine litter. Hauls were performed between 22 and 600 m of depth, the duration was between 30 and 60 min. All sampling was conducted during daylight. After the haul, marine litter was sorted and split from the catch. Then, according to the basis of the MEDITS protocol, litters were sorted into six different categories (plastic, rubber, metal, wood, glass and natural fiber). Thereafter, all marine litter were counted and weighed separately to the nearest 0.5 g. The results shows that the maximums of marine litter densities in the seafloor of the trawling fishing areas along Algerian coast are, respectively, 1996 item/km2 in 2016, 5164 item/km2 in 2019, 2173 item/km2 in 2021 and 7319 item/km2 in 2022. Thus, the plastic is the most abundant litter, it represent, respectively, 46% of marine litter in 2016, 67% in 2019, 69% in 2021 and 74% in 2022. Regarding the weight of the marine litter, it varies between 0.00 and 103 kg in 2016, between 0.04 and 81 kg in 2019, between 0.00 and 68 Kg in 2021 and between 0.00 and 318 kg in 2022. Thus, the maximum rate of marine litter compared to the total catch approximate, respectively, 66% in 2016, 90% in 2019, 65% in 2021 and 91% in 2022. In fact, the average loss in catch is estimated, respectively, at 7.4% in 2016, 8.4% in 2019, 5.7% in 2021 and 6.4% in 2022. However, the bathymetric and geographical variability had a significant impact on both density and weight of marine litter. Marine litter monitoring program is necessary for offering more solution proposals.

Keywords: composition, distribution, seabed, marine litter, algerian coast

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Authors: Jaya Bishnu Pradhan

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Weaving mat is one of the common activities performed in different community generally in the rural part of Nepal. Mat weavers’ practice mathematical ideas and concepts implicitly in order to perform their job. This study is intended to uncover the mathematical ideas embedded in mat weaving that can help teachers and students for the teaching and learning of school geometry. The ethnographic methodology was used to uncover and describe the beliefs, values, understanding, perceptions, and attitudes of the mat weavers towards mathematical ideas and concepts in the process of mat weaving. A total of 4 mat weavers, two mathematics teachers and 12 students from grade level 6-8, who are used to participate in weaving, were selected for the study. The whole process of the mat weaving was observed in a natural setting. The classroom observation and in-depth interview were taken with the participants with the help of interview guidelines and observation checklist. The data obtained from the field were categorized according to the themes regarding mathematical ideas embedded in the weaving activities, and its possibilities in teaching learning of school geometry. In this study, the mathematical activities in different sectors of their lives, their ways of understanding the natural phenomena, and their ethnomathematical knowledge were analyzed with the notions of pluralism. From the field data, it was found that the mat weaver exhibited sophisticated geometrical ideas in the process of construction of frame of mat. They used x-test method for confirming if the mat is rectangular. Mat also provides a good opportunity to understand the space geometry. A rectangular form of mat may be rolled up when it is not in use and can be converted to a cylindrical form, which usually can be used as larder so as to reserve food grains. From the observation of the situations, this cultural experience enables students to calculate volume, curved surface area and total surface area of the cylinder. The possibilities of incorporation of these cultural activities and its pedagogical use were observed in mathematics classroom. It is argued that it is possible to use mat weaving activities in the teaching and learning of school geometry.

Keywords: ethnography, ethnomathematics, geometry, mat weaving, school pedagogy

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Jose Ruiz, Jose Velasquez, Holger Lovon

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Peruvian university buildings are critical structures for which very little research about its seismic vulnerability is available. This paper develops a probabilistic methodology that predicts seismic loss for university buildings with simulated fragility functions. Two university buildings located in the city of Cusco were analyzed. Fragility functions were developed considering seismic and structural parameters uncertainty. The fragility functions were generated with the Latin Hypercube technique, an improved Montecarlo-based method, which optimizes the sampling of structural parameters and provides at least 100 reliable samples for every level of seismic demand. Concrete compressive strength, maximum concrete strain and yield stress of the reinforcing steel were considered as the key structural parameters. The seismic demand is defined by synthetic records which are compatible with the elastic Peruvian design spectrum. Acceleration records are scaled based on the peak ground acceleration on rigid soil (PGA) which goes from 0.05g to 1.00g. A total of 2000 structural models were considered to account for both structural and seismic variability. These functions represent the overall building behavior because they give rational information regarding damage ratios for defined levels of seismic demand. The university buildings show an expected Mean Damage Factor of 8.80% and 19.05%, respectively, for the 0.22g-PGA scenario, which was amplified by the soil type coefficient and resulted in 0.26g-PGA. These ratios were computed considering a seismic demand related to 10% of probability of exceedance in 50 years which is a requirement in the Peruvian seismic code. These results show an acceptable seismic performance for both buildings.

Keywords: fragility functions, university buildings, loss assessment, Montecarlo simulation, latin hypercube

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Authors: Ioannis Andrianakis, Manolis Falelakis, Maria Pavlidou, Konstantinos Papakonstantinou, Ermioni Avramidou, Dimitrios Kalogiannis, Nikolaos Milios, Katerina Bountakidou, Kiriakos Chatzidimitriou, Panagiotis Panagiotopoulos

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Artistic events, such as concerts and performances, are challenging to organize because they involve many people with different skill sets. Small and medium venues often struggle to afford the costs and overheads of booking and hosting remote artists, especially if they lack sponsors or subsidies. This limits the opportunities for both venues and artists, especially those outside of big cities. However, more and more research shows that audiences prefer smaller-scale events and concerts, which benefit local economies and communities. To address this challenge, our project “PerformAnts: Digital Event Manager-Organizer” aims to develop a smart digital tool that automates and optimizes the processes and costs of live shows and tours. By using machine learning, applying best practices and training users through workshops, our platform offers a comprehensive solution for a growing market, enhances the mobility of artists and the accessibility of venues and allows professionals to focus on the creative aspects of concert production.

Keywords: event organization, creative industries, event promotion, machine learning

Procedia PDF Downloads 91

Authors: Mandy W. M. Chan, Samantha Y. N. Shek, Chi K. Yeung, Taro Kono, Henry H. L. Chan

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Fractional radiofrequency devices have shown to improve skin texture such as smoothness, rhytides, brightness as well as atrophic acne scars by increasing dermal thickness, dermal collagen content and dermal fibrillin content. The objective of the study is to assess the efficacy and adverse effects of this device on Asian patients with skin textural changes. In this study, 20 Chinese patients (ranging from 21-60 years old) with irregularities of skin texture, rhytides and acne scars were recruited. Patients received six treatments at 2-4 week intervals. Treatment was initiated with maximum energy tolerated and was adjustable during treatment if patients felt excessive discomfort. A total of two passes were delivered at each session. Physician assessment and standardized photographs were taken at baseline, all treatment visits and at one, two, and six month after final treatment. As a result, 17 patients were recruited and completed the study according to the study protocol. One patient withdrew after the first treatment due to reaction to local anesthesia and two patients were lost to follow-up. At six months follow-up, 71% of the patients were satisfied and 24% were very satisfied, while treatment physician reported various degrees of improvement based on the global assessment scale in 60% of the subjects. Anticipated side effects including erythema, edema, pinpoint bleeding, scabs formation and flare of acne were recorded, but there were no serious adverse effects noted. Conclude up, the use of fractional radiofrequency improves skin texture and appears to be safe in Asian patients. No long-term serious adverse effect was noted.

Keywords: Asian, fractional radiogrequency, skin, texture

Procedia PDF Downloads 149

Authors: Hsien-Te Lin

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The purpose of this paper is to introduce the BCFES (building carbon footprint evaluation system), which is a LCA (life cycle assessment) tool developed by the Low Carbon Building Alliance (LCBA) in Taiwan. A qualified BCFES for the building industry should fulfill the function of evaluating carbon footprint throughout all stages in the life cycle of building projects, including the production, transportation and manufacturing of materials, construction, daily energy usage, renovation and demolition. However, many existing BCFESs are too complicated and not very designer-friendly, creating obstacles in the implementation of carbon reduction policies. One of the greatest obstacle is the misapplication of the carbon footprint inventory standards of PAS2050 or ISO14067, which are designed for mass-produced goods rather than building projects. When these product-oriented rules are applied to building projects, one must compute a tremendous amount of data for raw materials and the transportation of construction equipment throughout the construction period based on purchasing lists and construction logs. This verification method is very cumbersome by nature and unhelpful to the promotion of low carbon design. With a view to provide an engineer-oriented BCFE with pre-diagnosis functions, a component input/output (I/O) database system and a scenario simulation method for building energy are proposed herein. Most existing BCFESs base their calculations on a product-oriented carbon database for raw materials like cement, steel, glass, and wood. However, data on raw materials is meaningless for the purpose of encouraging carbon reduction design without a feedback mechanism, because an engineering project is not designed based on raw materials but rather on building components, such as flooring, walls, roofs, ceilings, roads or cabinets. The LCBA Database has been composited from existing carbon footprint databases for raw materials and architectural graphic standards. Project designers can now use the LCBA Database to conduct low carbon design in a much more simple and efficient way. Daily energy usage throughout a building's life cycle, including air conditioning, lighting, and electric equipment, is very difficult for the building designer to predict. A good BCFES should provide a simplified and designer-friendly method to overcome this obstacle in predicting energy consumption. In this paper, the author has developed a simplified tool, the dynamic Energy Use Intensity (EUI) method, to accurately predict energy usage with simple multiplications and additions using EUI data and the designed efficiency levels for the building envelope, AC, lighting and electrical equipment. Remarkably simple to use, it can help designers pre-diagnose hotspots in building carbon footprint and further enhance low carbon designs. The BCFES-LCBA offers the advantages of an engineer-friendly component I/O database, simplified energy prediction methods, pre-diagnosis of carbon hotspots and sensitivity to good low carbon designs, making it an increasingly popular carbon management tool in Taiwan. To date, about thirty projects have been awarded BCFES-LCBA certification and the assessment has become mandatory in some cities.

Keywords: building carbon footprint, life cycle assessment, energy use intensity, building energy

Procedia PDF Downloads 142

Authors: Jae Ni Jang, SoYoon Park, Sukhee Park, Yumin Song, Jae Won Kim, Keum Nae Kang, Young Uk Kim

Abstract:

Objectives: Patellofemoral pain syndrome (PFPS) is a common clinical condition characterized by anterior knee pain. Here, we investigated the quadriceps tendon cross-sectional area (QTCSA) as a novel predictor for the diagnosis of PFPS. By examining the association between the QTCSA and PFPS, we aimed to provide a more valuable diagnostic parameter and more equivocal assessment of the diagnostic potential of PFPS by comparing the QTCSA with the quadriceps tendon thickness (QTT), a traditional measure of quadriceps tendon hypertrophy. Patients and Methods: This retrospective study included 30 patients with PFPS and 30 healthy participants who underwent knee magnetic resonance imaging. T1-weighted turbo spin echo transverse magnetic resonance images were obtained. The QTCSA was measured on the axial-angled phases of the images by drawing outlines, and the QTT was measured at the most hypertrophied quadriceps tendon. Results: The average QTT and QTCSA for patients with PFPS (6.33±0.80 mm and 155.77±36.60 mm², respectively) were significantly greater than those for healthy participants (5.77±0.36 mm and 111.90±24.10 mm2, respectively; both P<0.001). We used a receiver operating characteristic curve to confirm the sensitivities and specificities for both the QTT and QTCSA as predictors of PFPS. The optimal diagnostic cutoff value for QTT was 5.98 mm, with a sensitivity of 66.7%, a specificity of 70.0%, and an area under the curve of 0.75 (0.62–0.88). The optimal diagnostic cutoff value for QTCSA was 121.04 mm², with a sensitivity of 73.3%, a specificity of 70.0%, and an area under the curve of 0.83 (0.74–0.93). Conclusion: The QTCSA was found to be a more reliable diagnostic indicator for PFPS than QTT.

Keywords: patellofemoral pain syndrome, quadriceps muscle, hypertrophy, magnetic resonance imaging

Procedia PDF Downloads 58

Authors: Laura E. Monroe

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As more non-native English-speaking students enroll in English-medium universities, even more faculty will instruct students who are unprepared for the rigors of post-secondary academic writing in English. Many faculty members lack training and knowledge regarding the assessment of non-native English-speaking students’ writing, as well as the ability to provide effective feedback. This quantitative study investigated the possible attitudinal factors, including demographics, which might affect faculty preparedness and grading practices for both native and non-native English-speaking students’ academic writing and plagiarism, as well as the reasons faculty do not deduct points from both populations’ writing errors. Structural equation modeling and SPSS Statistics were employed to analyze the results of a faculty questionnaire disseminated to individuals who had taught non-native English-speaking students in academic subject courses. The findings from this study illustrated that faculty’s native language, years taught, and institution type were significant factors in not deducting points for academic writing errors and plagiarism, and the major reasons for not deducting points for errors were that faculty had too many students to grade, not enough training in assessing student written errors and plagiarism and that the errors and plagiarism would have taken too long to explain. The practical implications gleaned from these results can be applied to most departments in English-medium post-secondary institutions regarding faculty preparedness and training in student academic writing errors and plagiarism, and recommendations for future research are given for similar types of preparation and guidance for post-secondary faculty, regardless of degree path or academic subject.

Keywords: assessment, faculty, non-native English-speaking students, writing

Procedia PDF Downloads 152

Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Leah Li Echiverri, Haoyu Shang, Yue Li

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Wenzhou-Kean University (WKU) is a Sino-US Cooperative University in China. It practices the English Immersion Program (EIP), where all the courses are taught in English. Class discussions and presentations are pervasively interwoven in designing students’ learning experiences. This WKU model has brought positive influences on students and is in some way ahead of traditional college English majors. However, literature to support the perceptions on the positive outcomes of this teaching and learning model remain scarce. The distinctive profile of Chinese-ESL students in an English Medium of Instruction (EMI) environment contributes further to the scarcity of literature compared to existing studies conducted among ESL learners in Western educational settings. Hence, the study investigated the students’ perceptions towards the English Immersion Program and determine how it influences Chinese-ESL students’ academic performance (AP). This research can provide empirical data that would be helpful to educators, teaching practitioners, university administrators, and other researchers in making informed decisions when developing curricular reforms, instructional and pedagogical methods, and university-wide support programs using this educational model. The purpose of the study was to establish the relationship between the English Immersion Program and Academic Performance among Chinese-ESL students enrolled at WKU for the academic year 2020-2021. Course length, immersion location, course type, and instructional design were the constructs of the English immersion program. English language learning, learning efficiency, and class participation were used to measure academic performance. Descriptive-correlational design was used in this cross-sectional research project. A quantitative approach for data analysis was applied to determine the relationship between the English immersion program and Chinese-ESL students’ academic performance. The research was conducted at WKU; a Chinese-American jointly established higher educational institution located in Wenzhou, Zhejiang province. Convenience, random, and snowball sampling of 283 students, a response rate of 10.5%, were applied to represent the WKU student population. The questionnaire was posted through the survey website named Wenjuanxing and shared to QQ or WeChat. Cronbach’s alpha was used to test the reliability of the research instrument. Findings revealed that when professors integrate technology (PowerPoint, videos, and audios) in teaching, students pay more attention. This contributes to the acquisition of more professional knowledge in their major courses. As to course immersion, students perceive WKU as a good place to study, providing them a high degree of confidence to talk with their professors in English. This also contributes to their English fluency and better pronunciation in their communication. In the construct of designing instruction, the use of pictures, video clips, and professors’ non-verbal communication, and demonstration of concern for students encouraged students to be more active in-class participation. Findings on course length and academic performance indicated that students’ perception regarding taking courses during fall and spring terms can moderately contribute to their academic performance. In conclusion, the findings revealed a significantly strong positive relationship between course type, immersion location, instructional design, and academic performance.

Keywords: class participation, English immersion program, English language learning, learning efficiency

Procedia PDF Downloads 177

Authors: G. M. Cappucci, C. Losi, P. Neri, M. Pini, A. M. Ferrari

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Waste treatment and many production processes cause significant emissions of odors, thus typically leading to intense debate. The introduction of odorimetric units and their units of measurement, i.e., U.O. / m3, with the European regulation UE 13725 of 2003 designates the dynamic olfactometry as the official method for odorimetric analysis. Italy has filled the pre-existing legislative gap on the regulation of odorous emissions only recently, by introducing the Legislative Decree n°183 in 2017. The concentration of the odor to which a perceptive response occurs to 50% of the panel corresponds to the odorimetric unit of the sample under examination (1 U.O. / m3) and is equal to the threshold of perceptibility of the substance (O.T.). In particular, the treatment of Municipal Solid Waste (MSW) by Mechanical-Biological Treatment (MBT) plants produces odorous emissions, typically generated by aerobic procedures, potentially leading to significant environmental burdens. The quantification of odorous emissions represents a challenge within a LCA study since primary data are often missing. The aim of this study is to present the preliminary findings of an ongoing study whose aim is to identify and quantify odor emissions from the Tre Monti MBT plant, located in Imola (Bologna, Italy). Particularly, the issues faced with odor emissions in the present work are: i) the identification of the components of the gaseous mixture, whose total quantification in terms of odorimetric units is known, ii) the distribution of the total odorimetric units among the single substances identified and iii) the quantification of the mass emitted for each substance. The environmental analysis was carried out on the basis of the amount of emitted substance. The calculation method IMPact Assessment of Chemical Toxics (IMPACT) 2002+ has been modified since the original one does not take into account indoor emissions. Characterization factors were obtained by adopting a preliminary method in order to calculate indoor human effects. The impact and damage assessments were performed without the identification of new categories, thus in accordance with the categories of the selected calculation method. The results show that the damage associated to odorous emissions is the 0.24% of the total damage, and the most affected damage category is Human Health, mainly as a consequence of ammonia emission (86.06%). In conclusion, this preliminary approach allowed identifying and quantifying the substances responsible for the odour impact, in order to attribute them the relative damage on human health as well as ecosystem quality.

Keywords: life cycle assessment, municipal solid waste, odorous emissions, waste treatment

Procedia PDF Downloads 177

Authors: Md. Shakhawat Hossain, Jimma Hossain, Md. Naushad Ali

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Acute pancreatitis is a dangerous medical emergency in the practice of gastroenterology. Management of acute pancreatitis needs multidisciplinary approach with support starts from emergency to ICU. So, there is a chance of mismanagement in every steps, especially in low resource settings. Other factors such as patient’s financial condition, education, social custom, transport facility, referral system from periphery may also challenge the current guidelines for management. The present study is intended to determine the clinico-pathological profile, severity assessment and challenges of management of acute pancreatitis in a government laid tertiary care hospital to image the real scenario of management in a low resource place. A total 100 patients of acute pancreatitis were studied in this prospective study, held in the Department of Gastroenterology, Rangpur medical college hospital, Bangladesh from July 2017 to July 2018 within one year. Regarding severity, 85 % of the patients were mild, whereas 13 were moderately severe, and 2 had severe acute pancreatitis according to the revised Atlanta criteria. The most common etiologies of acute pancreatitis in our study were gall stone (15%) and biliary sludge (15%), whereas 54% were idiopathic. The most common challenges we faced were delay in hospital admission (59%) and delay in hospital diagnosis (20%). Others are non-adherence of patient party, and lack of investigation facility, physician’s poor knowledge about current guidelines. We were able to give early aggressive fluid to only 18% of patients as per current guideline. Conclusion: Management of acute pancreatitis as per guideline is challenging when optimum facility is lacking. So, modified guidelines for assessment and management of acute pancreatitis should be prepared for low resource setting.

Keywords: acute pancreatitis, challenges of management, severity, prognosis

Procedia PDF Downloads 137

Authors: Durvi Yogesh Vagani

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This research paper explores the effectiveness of Research-informed teaching and Case-based teaching in enhancing the retention and recall of memory during discussions among university students. Additionally, it investigates the impact of using Artificial Intelligence (AI) tools on the quality of research conducted by students and its correlation with better recollection. The study hypothesizes that Case-based teaching will lead to greater recall and storage of information. The research gap in the use of AI in educational settings, particularly with actual participants, is addressed by leveraging a multi-method approach. The hypothesis is that the use of AI, such as ChatGPT and Bard, would lead to better retention and recall of information. Before commencing the study, participants' attention levels and IQ were assessed using the Digit Span Test and the Wechsler Adult Intelligence Scale, respectively, to ensure comparability among participants. Subsequently, participants were divided into four conditions, each group receiving identical information presented in different formats based on their assigned condition. Following this, participants engaged in a group discussion on the given topic. Their responses were then evaluated against a checklist. Finally, participants completed a brief test to measure their recall ability after the discussion. Preliminary findings suggest that students who utilize AI tools for learning demonstrate improved grasping of information and are more likely to integrate relevant information into discussions compared to providing extraneous details. Furthermore, Case-based teaching fosters greater attention and recall during discussions, while Research-informed teaching leads to greater knowledge for application. By addressing the research gap in AI application in education, this study contributes to a deeper understanding of effective teaching methodologies and the role of technology in student learning outcomes. The implication of the present research is to tailor teaching methods based on the subject matter. Case-based teaching facilitates application-based teaching, and research-based teaching can be beneficial for theory-heavy topics. Integrating AI in education. Combining AI with research-based teaching may optimize instructional strategies and deepen learning experiences. This research suggests tailoring teaching methods in psychology based on subject matter. Case-based teaching suits practical subjects, facilitating application, while research-based teaching aids understanding of theory-heavy topics. Integrating AI in education could enhance learning outcomes, offering detailed information tailored to students' needs.

Keywords: artificial intelligence, attention, case-based teaching, memory recall, memory retention, research-informed teaching

Procedia PDF Downloads 37

Authors: Claire Biasco, Thaier Hayajneh

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A smart city is the integration of digital technologies in urban environments to enhance the quality of life. Smart cities capture real-time information from devices, sensors, and network data to analyze and improve city functions such as traffic analysis, public safety, and environmental impacts. Current smart cities face controversy due to their reliance on real-time data tracking and surveillance. Internet of Things (IoT) devices and blockchain technology are converging to reshape smart city infrastructure away from its centralized model. Connecting IoT data to blockchain applications would create a peer-to-peer, decentralized model. Furthermore, blockchain technology powers the ability for IoT device data to shift from the ownership and control of centralized entities to individuals or communities with Decentralized Autonomous Organizations (DAOs). In the context of smart cities, DAOs can govern cyber-physical systems to have a greater influence over how urban services are being provided. This paper will explore how the core components of a smart city now apply to DAOs. We will also analyze different definitions of DAOs to determine their most important aspects in relation to smart cities. Both categorizations will provide a solid foundation to conduct a cybersecurity assessment of DAOs in smart cities. It will identify the benefits and risks of adopting DAOs as they currently operate. The paper will then provide several mitigation methods to combat cybersecurity risks of DAO integrations. Finally, we will give several insights into what challenges will be faced by DAO and blockchain spaces in the coming years before achieving a higher level of maturity.

Keywords: blockchain, IoT, smart city, DAO

Procedia PDF Downloads 127

Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

Procedia PDF Downloads 60