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Digital Ecosystem and Emerging Tools for Education, Future of Educational Innovation-Workshop Series Data in Action:

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

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 $\log$ about the processes and domain.

关键词： Analytical models Computational modeling Semantics Knowledge based systems Predictive models Ontologies Data engineering

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Semantic-based process mining: A conceptual model analysis and framework 10th

Semantic-based process mining: A conceptual model analysis a...

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10th International Conference on Innovations in Bio-Inspired computing and Applications, IBICA 2019 and 9th World Congress on Information and Communication Technologies, WICT 2019

作者： Okoye, Kingsley Tecnologico de Monterrey Writing Lab TecLabs Vicerrectoría de Investigación y Transferencia de Tecnología MonterreyNL64849 Mexico School of Architecture Computing and Engineering College of Arts Technologies and Innovation University of East London London United Kingdom

ISBN: (纸本)9783030493387

Semantics has been a major challenge when applying the Process Mining (PM) technique to real-time business processes. In theory, efforts to bridge the semantic gap has spanned the advanced notion of Semantic-based Process Mining (SPM). The SPM devotes its methods to the idea of making use of existing semantic technologies to support the analysis of PM techniques. Technically, the semantic-based process mining is applied through acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how semantically focused process modelling and reasoning methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. Also, the work systematically reviews the current tools and methods that are used to support the outcomes of the process mining, and to this end, propose an SPM-based framework that proves to be more intelligent with a higher level of semantic reasoning aptitudes. In other words, this work provides a process mining approach that uses information (semantics) about different activities that can be found in any given process to generate rules and patterns through the method for annotation, conceptual assertions, and reasoning. Moreover, this is done to determine how the various activities that make up the said processes depend on each other or are performed in reality. In turn, the method is applied to enrich the informative values of the resultant models. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Data mining

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A System to Track the Behaviour or Pattern of Mobile Robot Through RNN Technique

A System to Track the Behaviour or Pattern of Mobile Robot T...

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Advance computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： E.Afreen Banu Senthilnathan Chidambaranathan Naduvathezhath Nessariose Jose Padmaja Kadiri Riyad E. Abed Aqeel Al-Hilali Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Architecture and Design Virtusa New Jersey USA Regent Strategic Solutions Rochester United States Department of AIML School of Computing Mohan Babu University Tirupati Andhra Pradesh India College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The agricultural business has seen a rise in the use of mobile robots because of their ease of navigation in crop fields. Numerous significant issues pertaining to agricultural robot navigation are covered in this paper, such as obstacle detection, route planning, mapping, localization, and controlled guiding. The proposed hybrid recurrent neural network (RNN) model seeks to optimise mobile autonomous robot tracking performance using installed laser-based weeding equipment. The system can handle a range of trajectories, including curves and straight lines, that are observed in agricultural settings by integrating spiral, lateral, and linear speed controls. A comprehensive field test is carried out on a tracked mobile platform to validate the controllers’ functionality in a variety of scenarios, including loose dirt, stones, and humidity.

关键词： Location awareness Recurrent neural networks Spirals Navigation Velocity control Crops Humidity

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Mobility and intrinsic performance of silicon-based nanosheet FETs at 3 nm CMOS and beyond

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Solid-State Electronics 2025年 229卷

作者： Ankit Dixit Ali Rezaei Nikolas Xeni Naveen Kumar Tapas Dutta Ismail Topaloglu Preslav Aleksandrov Asen Asenov Vihar Georgiev James Watt School of Engineering University of Glasgow Scotland UK Institute for Computing Systems Architecture University of Edinburgh Scotland UK

Nanosheet Field-Effect Transistors (NSFETs) have been introduced in the 3 nm CMOS technology due to their advantages over the FinFET technology. In this paper, using our in-house Nano Electronics Simulation Software (NESS), we explore the carrier mobility and the intrinsic performance of NSFETs for different channel orientations. The effective masses for different cross-sections and channel orientations are extracted from the first principal simulations. The mobility and the intrinsic performance are evaluated using the effective mass approximation based non-equilibrium Green’s function (NEGF) simulation module of NESS. The proposed work provides insight into the optimized NSFET design considerations suitable for 3 nm and further technology nodes.

关键词： Nanosheet FET NEGF Scattering NESS

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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Expression of Concern for: Defeating the Credit Card Scams Through Machine Learning Algorithms

Expression of Concern for: Defeating the Credit Card Scams T...

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

作者： Kameron Bains Adebamigbe Fasanmade Jarrad Morden Ali H. Al–Bayatti Mhd Saeed Sharif Ahmed S. Alfakeeh Faculty of Computing Engineering and Media De Montfort University Leicester UK School of Architecture Computing and Engineering UEL University Way Dockland Campus London UK Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

ISBN: (数字)9781665440325

ISBN: (纸本)9781665440332

Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art.

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Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

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IEEE Network 2024年

作者： Chen, Xianfu Wu, Celimuge Shen, Yi Ji, Yusheng Yoshinaga, Tsutomu Ni, Qiang Zarakovitis, Charilaos C. Zhang, Honggang Shenzhen CyberAray Network Technology Co. Ltd China Chinese Academy of Sciences Shanghai Advanced Research Institute China Meta-Networking Research Center China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan Lancaster University School of Computing and Communications Ukraine AXON Logic Greece City University of Macau Faculty of Data Science Macau China China

This article investigates a control system within the context of sixth-generation wireless networks. The remote control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Considering the system dynamics, we formulate the sequential co-design decision-makings of communication and control over a discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potential of our proposed learning framework. Furthermore, we discuss the open issues remaining to make our offline learning framework feasible for real-world implementations and highlight the research directions for future explorations. © 1986-2012 IEEE.

关键词： Markov processes

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction

Impurity explicit optimal Machine learning and statistical p...

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Networking and Communications (ICNWC), International Conference on

作者： Nareshkumar R R. Shalinikumari S. Sri Nandhini Kowsalya Vijay Anand P Ajmal Mohamed S. Gayathri Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Architecture Bharath Institute of Higher Education and Research Selaiyur Chennai Tamilnadu India Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy.

关键词： Measurement Machine learning algorithms Urban areas Sociology Machine learning Predictive models Air pollution

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

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Journal of Computer Science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

A Smart Capacity Enhancement and Estimation Model for Hybrid...

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Disruptive Technologies (ICDT), International Conference on

作者： Senthil Kumar Nageswara Rao Atyam Khadar Nawas K Shashank Awasthi M. Kotteeswaran K. S. Thirunavukkarasu KL School of Architecture Koneru Lakshmanan Education Foundation Guntur Andhra Pradesh India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India School of Computing Science and Engineering Vellore Institute of Technolog Chennai Tamil Nadu India Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India School of Management and Commerce Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards.

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