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Business Intelligence in e-learning for Higher Education 23

Business Intelligence in e-learning for Higher Education

引用

11th international conference on Computer and Communications Management, ICCCM 2023

作者： Prayogo, Sendy Hidayanto, Muhammad Bambang Lubis, Muharman Information System School of Engineering Telkom University Indonesia

ISBN: (纸本)9798400707735

Business intelligence (BI) is a combination of tools and tactics that allow data to be analyzed and information to be provided to aid decision-making. BI characteristics are widely applied in numerous sectors and produce excellent outcomes in the modern digital transformation era. When BI concepts are integrated into academic processes, they can deliver tremendous benefits. In this project, we are interested in assisting university decision-making in supporting academic matters at Telkom University in the Distance Education (PJJ) study program. We use BI to provide analytics that can aid decision-making for various categories of users (students, lecturers, faculty staff, and other decision-makers). Furthermore, BI can monitor and analyze learning behavior and performance in a collegiate setting. BI can also be used to assess the structure of learning materials' content as well as the effectiveness of students in the learning process. the data we use is the data on the e-learning platform. On the one hand, educators cannot guarantee the effectiveness of the learning process due to a lack of instruments for measuring, assessing, and evaluating learners' performance in learning activities. therefore, providing data that can track and evaluate learners' interactions in e-learning platforms is crucial. this research aims to find relevant data for BI analysis applied to e-learning in higher education from various stakeholder perspectives. the second objective is to visualize student activity data in lectures while using e-learning for study programs that apply asynchronous learning models. Learner activity data in this research will be displayed in the form of a dashboard whose results can be monitored by various stakeholders. For stakeholders such as faculty in higher education, the data collected before, during, and after attending lectures can help in decision-making for multiple parties and the development of learning strategies in the future. © 2023 ACM.

关键词： Decision making

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Automatic Multi-label Classification of Interictal Epileptiform Discharges (IED) Detection Based on Scalp EEG and Transformer 20th

Automatic Multi-label Classification of Interictal Epileptif...

引用

20th international conference on intelligent Computing (ICIC)

作者： Rao, Wenhao Wang, Haochen Zhuang, Kailong Guo, Jiayang Gu, Peipei Zhang, Ling Wang, Xiaolu Jiang, Jun Chen, Duo Nanjing Univ Chinese Med Sch Artificial Intelligence & Informat Technol Nanjing Jiangsu Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen Peoples R China Zhengzhou Univ Light Ind Coll Software Engn Zhengzhou Henan Peoples R China Hubei Univ Sci & Technol Xianning Med Coll Sch Biomed Engn & Med Imaging Xianning Peoples R China Tongji Med Coll Wuhan Childrens Hosp Clin Neuroelectrophysiol Room Wuhan Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Interictal epileptiform discharges (IED) refer to abnormal electroencephalogram (EEG) waveforms that occur between epileptic seizures, which are of great significance for the diagnosis and treatment of epilepsy. Traditionally, the detection of IED requires experienced clinical doctors to visually inspect EEG recordings, a process that is time-consuming, labor-intensive, and subject to expert bias. With the advancement of deep learning technology, computer-aided methods for automatic detection of IED have become possible, providing clinicians with faster and more accurate diagnostic tools. In this paper, we propose an automated IED detection system based on Transformer, which is capable of end-to-end identification of IED from raw EEG data. We evaluated the proposed IED detector on a dataset consisting of EEG recordings from 11 pediatric epilepsy patients collected at Wuhan Children's Hospital. the results show that the average accuracy for the multi-label classification task of different types of IED is 93.47%, and the average F1 score is 93.19%. these findings provide an effective solution for the automated detection of IED and are expected to play an important role in clinical diagnosis.

关键词： Epilepsy EEG IED detection Deep learning Transformer

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A Transfer learning-Based Approach to Unveil Kinase-Specific Phosphorylation Sites of Understudied Kinases 11th

A Transfer Learning-Based Approach to Unveil Kinase-Specific...

引用

11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Huang, Wentao Zhou, Xiaoyan Parisi, Luca Mansour, Youseffi Ma, Renfei Shenzhen MSU BIT Univ Fac Biol Shenzhen Guangdong Peoples R China Tutorantis Dept Comp Sci Edinburgh EH2 4AN Midlothian Scotland Univ Bradford Fac Engn & Informat Bradford BD7 1DP W Yorkshire England

ISBN: (纸本)9783031646355;9783031646362

Phosphorylation plays a pivotal role in regulating protein activities;yet, it is still time-consuming, costly, and human-error prone to identify specific phosphorylation sites for kinases. Existing studies have proposed kinase-specific phosphorylation site prediction tools, primarily focusing on kinases with a sufficient number of experimentally validated phosphorylation sites. Nevertheless, the majority of kinases lack such experimental data or have unknown targets. this study developed and validated a novel transfer learning-driven approach to predict specific phosphorylation sites for understudied kinases. By building upon pre-trained models at the corresponding kinase group levels, custom models have been created by fine-tuning them on layers of closely related kinases. Importantly, the encoding of substrate-specific site locations serves as critical features in the predictive model. the proposed approach achieves a balanced accuracy of 78.3%, 81.9%, and 83.7% for 18, 17, and 13 understudied kinases respectively in the Other, STE, and TKL groups.

关键词： Kinase-specific phosphorylation Understudied kinase Transfer learning

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Guided Task Planning Under Complex Constraints 38

Guided Task Planning Under Complex Constraints

引用

38th IEEE international conference on data engineering (ICDE)

作者： Nikookar, Sepideh Sakharkar, Paras Smagh, Baljinder Amer-Yahia, Sihem Roy, Senjuti Basu NJIT Newark NJ 07102 USA Univ Grenoble Alpes CNRS Grenoble France

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Creating a plan, i.e., composing a sequence of items to achieve a task is inherently complex if done manually. this requires not only finding a sequence of relevant items but also understanding user requirements and incorporating them as constraints. For instance, in course planning, items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, items are points of interest (POIs) and constraints represent time and monetary budget, two user-specified requirements. Most importantly, a plan must comply with the ideal interleaving of items to achieve a goal such as enhancing students' skills towards the broader learning goal of an education program, or in the travel scenario, improving the overall user experience. We study the Task Planning Problem (TPP) with the goal of generating a sequence of items that optimizes multiple objectives while satisfying complex constraints. TPP is modeled as a Constrained Markov Decision Process, and we adapt weighted Reinforcement learning to learn a policy that satisfies complex dependencies between items, user requirements, and satisfaction. We present a computational framework RL-Planner for TPP. RL-Planner requires minimal input from domain experts (academic advisors for courses, or travel agents for trips), yet produces personalized plans satisfying all constraints. We run extensive experiments on datasets from university programs and from travel agencies. We compare our solutions with plans drafted by human experts and with fully automated approaches. Our experiments corroborate that existing automated solutions are not suitable to solve TPP and that our plans are highly comparable to expensive handcrafted ones.

关键词： Adaptation models Education Transfer learning Reinforcement learning Markov processes data engineering Probability distribution

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GNMS: A novel method for model stealing based on GAN 11

GNMS: A novel method for model stealing based on GAN

引用

11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Zeng, Moxuan Wang, Yangzhong Zhang, Yangming Niu, Jun Zhang, Yuqing Xidian University School of Computer Science and Technology Xian China Hainan University College of Cyberspace Security Haikou China University of Chinese Academy of Sciences National Computer Network Intrusion Protection Center Beijing China Xidian University School of Cyber Engineering Xian China

ISBN: (纸本)9798350345346

Many well-performing models are currently deployed on the cloud to provide machine learning as a service (MLaaS). However, these models are susceptible to Model Stealing Attacks, where attackers can access the model's functionality, parameters, and internal structure in a black-box. As a result, data-free model stealing methods have gained popularity due to their higher accuracy and not requiring real data. Previous data-free model stealing methods have mainly focused on single scenarios and limited model and dataset variations. In this paper, we introduce a novel generalized network model Stealing method (GNMS), which is suitable for both benchmark and transfer models, achieving high model stealing accuracy across various scenarios. We pre-train generative adversarial network (GAN) using publicly available datasets and efficiently steal model functionality by training a student model with the pre-trained generator and the discriminator. Adversarial samples and the generated image dataset are also used to explore the model's decision boundaries. During the training of the clone model, we train two clone models to minimize the differences with the target model further. We employ a contrastive learning approach to encourage the models to learn meaningful feature representations by distinguishing between similar and dissimilar data points, thereby enhancing the model's accuracy. We achieve a model stealing accuracy of 73.02% and 72.93% on more complex datasets CIFAR100 and Caltech101. Surpass the latest DisGUIDE by 3.55% and 2.61%. © 2023 IEEE.

关键词： Generative adversarial networks

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Using interpretable machine learning for data-driven decision support for infrastructure operation & maintenance 11th

Using interpretable machine learning for data-driven decisio...

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Chatzi, E.N. Abdallah, I. Tatsis, K. Osmani, S. Robles, I. Department of Civil Environmental and Geomatic Engineering ETH Zürich Switzerland RINA Consulting S.p.A. Genova Italy University ofCantabria Santander Spain

ISBN: (纸本)9781032345314

the task of decision support for optimal management of infrastructure systems, including bridges, is a non-trivial one. the advent of measurement and monitoring technologies allows for conveying a wealth of diversified information, including measurements on structural condition, environmental conditions (temperature, humidity, rainfall), geo-information (earth movement), hazards data (e.g. earthquake data), as well as operating loads (e.g. traffic). Despite the generated Big data stream, mere existence of this data in raw form is not meaningful for decision support. Instead, the initial data gathering, which is the task of sensing, needs to be translated into meaningful indicators of performance and condition, or interpreted into understandable events, which pertains to the task of sense-making. As part of Task 4.4 of the FORESEE project, we have delivered a diagnostic and prognostic framework for infrastructure decision support that capitalizes on data extracted from models (simulations) as well as monitoring information. We define an object-oriented framework, relying on graphical model structures (Random Forests and Bayesian Networks), which considers monitored structures as "systems of systems", with each system comprising an assembly of objects. We demonstrate the decision support framework on the illustrative examples of a monitored bridge and a roadway network. © 2022 the Author(s).

关键词： Bayesian networks

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State of Charge Estimation of Lithium Iron Phosphate Battery Using Bidirectional Long Short-Term Memory 11

State of Charge Estimation of Lithium Iron Phosphate Battery...

引用

11th international conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Jeong, Daeung Park, Jongwook Jang, Yohan Bae, Sungwoo Hanyang University Department of Electrical Engineering Seoul04763 Korea Republic of

ISBN: (纸本)9788957083505

Lithium iron phosphate batteries are currently popular in the electric vehicle market due to their high reliability and low price. However, due to the strong non-linearity of lithium iron phosphate open circuit voltage, it is difficult to estimate the state of charge with the traditional method. In this paper, a bidirectional long short-term memory model is used to accurately estimate the state-of-charge of a lithium iron phosphate battery in a usage environment such as an electric vehicle. A lithium iron phosphate battery charge/discharge test applying an electric vehicle driving cycle was preceded, and the state of charge estimation error was confirmed in the bidirectional long short-term memory model through the charge/discharge data. the mean absolute error of the bidirectional long short-term memory model was 1.80%, confirming the best performance among the deep learning models evaluated in this paper. © 2023 the Korean Institute of Power Electronics.

关键词： Lithium compounds

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Determining Positive-Negative Emotions in Male and Female Based on EEG Signals using Machine learning Algorithms 11

Determining Positive-Negative Emotions in Male and Female Ba...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Pamungkas, Yuri Njoto, Edwin Nugroho Medical Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Medicine Surabaya Indonesia

ISBN: (纸本)9798350355314

Emotionsare vital in everyday human life as a controller of behavior, decision-making and as a means to determine product marketing strategy/ market research. All of these things are very dependent on human emotional conditions. In addition, in the development of the computational affective field, brain signal-based emotion recognition (EEG) has become a trending topic of current research. therefore, we attempted to compare positive-negative emotions in men and women based on EEG using a Machine learning algorithm in this study. A total of 20 male and 20 female participants recorded their EEG signals in the frontopolar and frontal areas of the brain. then the EEG data is processed by filtering, removing artifact, and decomposing it into three sub-bands (alpha, beta, and gamma). the extracted signal features are Mean Absolute Deviation and Power Spectral Density. Based on the signal feature analysis results, it is known that the signal feature values (MAD and PSD) for women tend to be higher than for men. Meanwhile, several algorithms are used to classify positive and negative emotions, such as Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Random Forest. Based on the results of classification, the best accuracy rate was 95.8% (on positive emotions for male & female gender), 92.2% (on negative emotions for male & female gender), and 79.8% (on positive-negative emotions for male & female gender) using Random Forest algorithm. © 2024 IEEE.

关键词： Random forests

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Employing Deep Unsupervised learning Method to Identify Testing Scenarios for automated Vehicles 10

Employing Deep Unsupervised Learning Method to Identify Test...

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10th international conference on Dependable Systems and their Applications, DSA 2023

作者： Liu, Shuai Ren, Fan Yang, Guo He, Dingchang Liu, Yonggang State Key Laboratory of Vehicle Nvh and Safety Technology Chongqing401122 China Chongqing University College of Mechanical and Vehicle Engineering Chongqing40030 China Changan Automobile Intelligent Research Institute Chongqing710199 China

ISBN: (纸本)9798350304770

the significance of naturalistic driving data (NDD) lies in its application for assessing autonomous driving systems across diverse driving conditions. this paper introduces an approach to identify testing scenarios for autonomous vehicles using deep unsupervised learning techniques. Initially, a deep regularized autoencoder (DRAE) network is designed to extract feature vectors from the NDD. Next, these feature vectors are then clustered by K-Means clustering (KMC) and seven types of scenarios are obtained. Finally, a comparison is made between the proposed DRAE-KMC approach and other clustering algorithms. the outcome demonstrates that the proposed DRAE-KMC method outperforms other methods. Furthermore, Google Earth is applied to project the identified scenarios. the result illustrates that the projected scenes align with the identified scenarios, which further validates the effectiveness of proposed DRAE-KMC method. © 2023 IEEE.

关键词： Autonomous vehicles

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Research on Integrated Operation Design of Low Orbit Remote Sensing Satellite for intelligent Application 1

引用

11th EAI international conference on MOBILe Wireless MiddleWARE, Operating Systems and Applications, MOBILWARE 2022

作者： Jie, Du Zhuo, Li Liu, Yixin Shi, Mengjie Li, Haihua China Academy of Space Technology 104 Youyi Road Haidian Beijing China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

In this paper, from a remote sensing satellites that orbit earth operation characteristics and application conditions, combined with the elliptic orbit agile imaging and application characteristics of huge amounts of data transmission, and both remote sensing satellites that orbit earth in the next 10 to 20 years on the integration of electronic systems and application requirements of the system software, in view of the existing electronic system, the application of gap is given star to integration of heaven and earth satellite data application interface design, this paper provides a direction for the system software design of the future integrated electronic system on board. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Orbits

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