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2023 Future of Educational Innovation-Workshop Series Data in Action, FEIWS 2023

作者： Okoye, Kingsley Islam, Syed Naeem, Usman Hosseini, Samira Institute for Future of Education College of Arts Technologies and Innovation Tecnologico de Monterrey Writing Lab University of East London Monterrey Mexico London United Kingdom College of Arts Technologies and Innovation University of East London School of Architecture Computing and Engineering London United Kingdom

ISBN: (纸本)9781665494168

Today, the growing technological turnaround from ''big data'' to ''big analytics'' has made it essential for process scientists and analysts to leverage more powerful techniques in analyzing and understanding the different types of data and formats, otherwise allied to the notion of''semantic data engineering Technological advancement has provided data mining solutions capable of processing the data/information in formats that are conceptually comprehended by humans and computers in real-Time or realworld settings, and facilitating building of systems and applications that inclusively manage the information or data they contain (machine-understandable systems). For this purpose, this study introduced a ''semantic-based process mining'' technique capable of discovering abstract or useful information or models from event logs of a learning process (educational domain), and then subsequently used to predict users' patterns through the semantically-inclined modeling and exploration of the discovered process models. Technically, the study focused on improving the results/outcomes of the learning process and mining using the set of proposed algorithms and model that incorporates semantic annotation (labeling), semantic representations (ontology), and semantic reasoning (inference modules or reasoner) of the discovered models. In turn, the outcome of the method shows that data processing and models analyses provided by traditional process mining techniques in education can be enhanced by adding ''semantic information'' (properties description) to the discovered models or events about the processes and domain. © 2023 IEEE.

关键词： Semantics

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Deep Convolutional Network Tumor Classification System in Brain MRI

Deep Convolutional Network Tumor Classification System in Br...

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2022 IEEE International Conference on Electronics, computing and Communication Technologies, CONECCT 2022

作者： Vicente, Roderick Y. Tejada, Ryan R. Fernando, Glenn John O. Cimagala, Roselle P. Austria, Yolanda D. Evangelista, Deanne Cameren P. Ifugao State University College of Computing Sciences Ifugao Philippines Bohol Island State University College of Engineering and Architecture Tagbilaran City Philippines Adamson University Department of Computer Engineering Manila Philippines

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Doctors' availability is one of the concerns of people who want to have an appointment with a specialist. Doctors do have a very tight schedule and cannot consistently have appointments, especially for doctors who specialize in brain tumors as they are not that much in numbers here in the Philippines. Brain tumors are a very rare case among individuals and the 10-year survival rate is about 31%, a very low survival rate compared to other diseases. YOLOv3 or a machine learning model using transfer learning and object detection is used as the approach to classify brain tumors. The detection model has an mAP value of 99%, according to the study's findings. The proposed system applies to hospitals, especially those that conduct brain MRIs to classify what type of brain tumor are we dealing with right after the test. The system has a 51% to 99% precision rate per frame © 2022 IEEE.

关键词： Object recognition

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Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

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Multimedia Tools and Applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators

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Journal of Computer Science & Technology 2022年第5期37卷 1200-1220页

作者： Lei Liu Xiu Ma Hua-xiao Liu Guang-li Li Lei Liu College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina

There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech ***,programming productivity hinders the deployment of deep learning *** low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence ***,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive *** this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast *** evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end *** experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.

关键词： deep learning accelerator programming framework domain-specific language

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Coupled THMC model‑based prediction of hydraulic fracture geometry and size under self‑propping phase‑transition fracturing

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International Journal of Coal Science & Technology 2024年第6期11卷 29-53页

作者： Nanlin Zhang Fushen Liu Liangliang Jiang Pinqiang Mo Jingwen Xiao Qi Song Yuhao Luo Computing Center for Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Research Center of Coastal and Urban Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058ZijingangChina Engineering Research Center of Urban Underground Development of Zhejiang Province Hangzhou 310058China Department of Chemical and Petroleum Engineering University of CalgaryCalgaryAB T2N1N4Canada State Key Laboratory of Intelligence Construction and Healthy Operation and Maintenance of Deep Underground Engineering School of Mechanics and Civil EngineeringChina University of Mining and TechnologyXuzhou 221116JiangsuChina Safety Environment and Technology Supervision Research InstitutePetro China Southwest Oil and Gas Field CompanyChengdu 610041China

The Self-Propping Phase-transition Fracturing Technology(SPFT)represents a novel and environmentally friendly approach for a cost-effective and efficient development of the world’s abundant unconventional resources,especially in the context of a carbon-constrained sustainable *** involves the coupling of Thermal,Hydraulic,Mechanical,and Chemical(THMC)fields,which makes it challenging to understand the mechanism and path of hydraulic fracture *** study addresses these challenges by developing a set of THMC multifield coupling models based on SPFT parameters and the physical/chemical characteristics of the Phase-transition Fracturing Fluid System(PFFS).An algorithm,integrating the Finite Element Method,Discretized Virtual Internal Bonds,and Element Partition Method(FEM-DVIB-EPM),is proposed and validated through a case *** results demonstrate that the FEM-DVIB-EPM coupling algorithm reduces complexity and enhances solving *** length of the hydraulic fracture increases with the quantity and displacement of PFFS,and excessive displacement may result in uncontrolled fracture *** the parameters considered,a minimal difference in fracture length is observed when the PFFS amount exceeds 130 m^(3),that means the fracture length tends to *** study contributes to understanding the hydraulic fracture propagation mechanism induced by SPFT,offering insights for optimizing hydraulic fracturing technology and treatment parameters.

关键词： Sustainable development,Self Propping phase Transition fracturing technology,Thermal Hydraulic Mechanical Chemical coupling,FEM DVIB EPM,Hydraulic fracture propagation

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Exploring 2D representation and transfer learning techniques for indoor localization

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Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Spatio-Temporal Patterns of Land Use Land Cover Change Associated with the Establishment of UiTM Cawangan Kelantan, Malaysia 1

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4th International Conference on Mathematics, Statistics, and computing Technology, ICMSCT 2023

作者： Wan Yaacob, Wan Fairos Md Nasir, Syerina Azlin Ismail, Nurazleena Wan Omar, Wan Marhaini Adnan, Nor Aizam College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Kelantan Kelantan Kota Bharu Malaysia Faculty of Business and Management Universiti Teknologi MARA Cawangan Kelantan Kelantan Kota Bharu Malaysia Faculty of Architecture Planning and Surveying Universiti Teknologi MARA Selangor Shah Alam Malaysia

ISBN: (数字)9789819734504

ISBN: (纸本)9789819734498

The development of higher learning institutions for the community surrounding is evident. The positive development can be seen from human activities such as farming, land reclamation and industrialization that influence the developed area in terms of land use and land cover changes. These activities have led to urbanization once the university is established. This study examined how the establishment of Universiti Teknologi MARA Cawangan Kelantan, Malaysia as a higher learning institution has influenced land use and land cover changes in Machang, Kelantan. The developed area changes evolution process of the land use situation from 1999 to 2019 were explored. The study adopted GIS and Remote sensing techniques to acquire, analyse and determined changes in Landsat images for 5- and 10-km radius of the year 1999, 2009 and 2019 after the university was established. The land use and land cover classes being used were forest, paddy, mixed agriculture, water bodies, developed area, bare soil, mangrove and cloud. Supervised classification was used to analyse land-use/cover change date generated from GIS and remote sensing. The study revealed that there is a significant influence on land use and land cover changes as a result of the establishment of the university. This is shown from the Landsat images for the years 1999 and 2019 whereby the developed area appeared greener in 1999 as compared to 2019. The study confirmed that land use and land cover changes in 20 years of the university’s establishment provided significant impacts on the environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Landsat

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Mechanistic investigation of highly bendable magnesium alloy sheet fabricated by short-process manufacturing

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Journal of Magnesium and Alloys 2024年第10期12卷 4289-4310页

作者： Shiwei Xu Peijie Xiao Xiaofan Wu John Holmes Frédéric Mompiou Zhenyu Xiao Chen Jin Tianjiao Li Kuaishe Wang Zhuoran Zeng College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan UniversityChangsha 410082China Suzhou Research Institute of Hunan University Suzhou 215131China College of Engineering Computing and CyberneticAustralian National UniversityACT 2601Australia CEMES-CNRS Universitéde Toulouse29 rue J.MarvigToulouse 31055France Qinghai Salt Lake Teli Magnesium Co. Ltd.Xining 810000China International Joint Laboratory for Light Alloys(Ministry of Education) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China School of Metallurgical Engineering Xi’an University of Architecture&TechnologyXi’an 710055China College of Materials Science and Engineering Hunan UniversityChangsha 410082China

In this study,a commercial magnesium alloy AZ31(Mg-3Al-1Zn-0.3Mn)sheet through a short manufacturing process was found to be ductile and highly formable in *** possessing a strong basal texture,the short-processed sheet without any annealing can be bent at a small radius,only 0.2 times its thickness in the 90°bending ***,it could withstand direct deformation by repeated *** in-situ microstructural characterization reveals that extension twin bands with strain localization appear in the bending *** subsequent flattening,these twin bands underwent detwinning,reducing local strain concentrations and enabling further bending *** outstanding bend formability originated from the significant-type dislocation loops slipping on the prismatic crystal planes within dynamic-recrystallized *** grains underwent a uniform refinement to several microns in the short manufacturing process and exhibited low residual *** active prismatic dislocation slip within refined grains was due to its much lower relative activation stress to basal slip(CRSSprism/CRSSbasal of only∼1.6)owing to the effective grain boundary ***,the prismatic dislocation activity was further enhanced when bypassing Al-Mn nano-particles during motion,leaving debris and loops that facilitated easy multiplication.

关键词： Magnesium Deformation Texture In-situ characterization Dislocation slip.

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Revolutionizing Loan Approval: Harnessing the Power of K-Nearest Neighbors for Predictive Eligibility

Revolutionizing Loan Approval: Harnessing the Power of K-Nea...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Mhd Saeed Sharif Madhav Theeng Tamang Niyas Jaheer Hussain Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London UK College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

A bank has become a basic need of our daily lives. People use banks for several tasks, such as depositing or withdrawing money and borrowing loans. The number of loan applications is rising significantly. It is crucial for the bank and financial sectors to determine loan eligibility and approve loans to suitable applicants only. This study attempts to design a system which can easily predict loan eligibility with the application of several machine learning (ML) algorithms. We employed the five different machine learning algorithms to achieve this research's aim. These algorithms were trained using the pre-processed dataset to create predictive models. F1 score, accuracy, Recall and Precision are used to evaluate the performance of the proposed approaches. The result showed that K-nearest neighbor (KNN) demonstrated outstanding performance, with an accuracy rate of 88.89%. The Random Forest (RF) model had an accuracy of 84.44%, whereas the Support Vector Machine (SVM) and Linear regression (LR) models achieved 82.22% and 80% accuracy, respectively. The Decision Tree (DT) algorithms showed the worst performance among the five algorithms, with an accuracy of 79.88%. The achievement of this research focuses on the possibility of machine learning algorithms as an efficient method for predicting loan eligibility in banks, contributing to the advancement of financial technology by automating and optimizing key banking processes, reducing processing times, and enhancing overall bank efficiency.

关键词： Support vector machines Radio frequency Machine learning algorithms Accuracy Fintech Banking Predictive models Nearest neighbor methods Prediction algorithms Decision trees

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Application of Clustering Algorithms to Enhance Personalized Learning Through Recommendation Model

Application of Clustering Algorithms to Enhance Personalized...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Mhd Saeed Sharif Madhav Theeng Tamang Dyuthi Mani Wael Elmedany Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Kingdom of Bahrain

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Training Technological innovation Recurrent neural networks Collaborative filtering Clustering algorithms Predictive models Prediction algorithms Problem-solving Pupils Recommender systems

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