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A Hybrid Feature Selection Framework for Predicting Students Performance

computers, Materials & Continua 2022年第1期70卷 1893-1920页

作者： Maryam Zaffar Manzoor Ahmed Hashmani Raja Habib KS Quraishi Muhammad Irfan Samar Alqhtani Mohammed Hamdi High Performance Cloud Computing(HPC3) Department of Computer and Information SciencesUniversiti Teknologi PETRONAS32610SeriIskandarMalaysia Department of Computer Science and Information Technology University of LahorePakistan Department of Process Engineering Pakistan Institute of Engineering&Applied SciencesNiloreIslamabadPakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia College of Computer Science and Information Systems Najran University61441NajranSaudi Arabia

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic *** are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal *** this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of *** main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic *** key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha

关键词： Educational data mining feature selection hybrid feature selection

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Towards an Analytical Definition of Sufficient Data

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SN computer science 2023年第2期4卷 144页

作者： Byerly, Adam Kalganova, Tatiana Department of Electronic and Electrical Engineering Brunel University London Uxbridge UB8 3PH United Kingdom Department of Computer Science and Information Systems Bradley University Peoria 61615 IL United States

We show that, for each of five datasets of increasing complexity, certain training samples are more informative of class membership than others. These samples can be identified a priori to training by analyzing their position in reduced dimensional space relative to the classes’ centroids. Specifically, we demonstrate that for the datasets studied, samples that are nearer to their classes’ centroids are less informative than those that are furthest from them. For all five datasets studied, we show that there is no statistically significant difference between training on the entire training set and when excluding up to 2% of the data nearest to each class’s centroid. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Class separation Data reduction Dataset severability Dimensional reduction UMAP

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Observer-Based Control of Second-Order Multi-vehicle systems in Bearing-Persistently Exciting Formations 63

Observer-Based Control of Second-Order Multi-vehicle Systems...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Tang, Zhiqi Fidan, Baris Johansson, Karl H. Martensson, Jonas Hamel, Tarek Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-10044 Sweden University of Waterloo Mechanical and Mechatronics Engineering Department ON Canada I3S-UCA Cnrs France

ISBN: (纸本)9798350316339

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers. © 2024 IEEE.

关键词： Acceleration measurement

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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Ensuring the Accuracy of CNN Accelerators Supplied at Ultra-Low Voltage

Ensuring the Accuracy of CNN Accelerators Supplied at Ultra-...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Yamilka Toca-Díaz Rubén Gran Tejero Alejandro Valero Department of Computer Science and Systems Engineering Aragon Institute for Engineering Research - I3A Universidad de Zaragoza Zaragoza Spain

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Underscaling the supply voltage $(V_{dd})$ to ultra-low levels below the safe-operation threshold voltage $(V_{min})$ brings significant energy savings in digital CMOS circuits but introduces reliability challenges due to increased risk of bitcell permanent faults. This work explores the impact of such faults on the accuracy of a CNN inference accelerator supplying on-chip activation memories at ultra-low $V_{dd}$ . By examining fault pat-terns, activation values, and memory usage, this paper proposes two microarchitectural techniques exploiting activation outliers and activation memory underutilization. These approaches are cost-effective, do not require programmer intervention, and are application-independent. Experimental results show that the proposed approaches maintain the original CNN accuracy and achieve energy savings by 2.1 % and 8.2 % compared to the state-of-the-art technique and a conventional accelerator supplied at $V_{min}$ , respectively, with a negligible impact on the system performance (less than 0.25 %).

关键词： Electric potential Accuracy Microarchitecture System performance Energy conservation Threshold voltage System-on-chip Circuit faults Integrated circuit reliability

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Machine acceleration time series prediction for dimensional accuracy of 3D printed parts

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Data science and Management 2024年第3期7卷 218-227页

作者： Jayanta Bhusan Deb Shilpa Chowdhury Soumik Chowdhury Gourab Paul Tonay Pal Jayeeta Deb Sudipta Deb Department of Engineering&Technology Western Carolina UniversityNC28723USA Department of Information Systems and Analysis Lamar UniversityTX77705USA Department of Computer Science The Memorial University of NewfoundlandNLA1B3X5Canada Department of Marketing University of DhakaDhaka1000Bangladesh Department of Management University of DhakaDhaka1000Bangladesh Department of Medicine and Surgery University of ChittagongChittagong4331Bangladesh Institute of Health Economics University of DhakaDhaka1000Bangladesh

This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion *** primary objective was to develop a long short-term memory(LSTM)network capable of assessing this *** conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s *** LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction *** involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM ***,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill *** testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s *** refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of ***,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration *** empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.

关键词： Extrusion-based 3D printing Printer acceleration predictionInfill pattern Root mean square error Dimensional accuracy

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Assessing Driver Engagement Through Machine Learning Classification of Physiological Measures

Assessing Driver Engagement Through Machine Learning Classif...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Lochbihler, Aidan Wallace, Bruce Benthem, Kathleen Van Herdman, Chris Sloan, Will Brightman, Kirsten Goheen, Josh Knoefel, Frank Marshall, Shawn Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Sam 3 Innovation Hub Systems and Computer Engineering Ottawa Canada Carleton University Department of Cognitive Science Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Ottawa Hospital Ottawa Canada

ISBN: (纸本)9781665493840

The assessment to determine if drivers are engaged and supervising autonomous and semi-autonomous vehicles (AV) is becoming an increasingly important task. AVs are becoming more prevalent on the road and adequate driver engagement is a must for safe operation. The full adoption of level 5 vehicles will likely take many years as they do not currently exist for consumer purchase and before this happens the roads will see a mix of level 0-4 vehicles. During this time humans will still be responsible for taking control of the vehicle if a hazardous scenario occurs and the AV does not know how to maneuver around. To have a safe handover from AV to human during these situations the driver must maintain a level of engagement even while the AV is driving. To do this physiological sensors can be used to measure signals such as heart rate and respiration rate, which are known indicators of a driver's engagement. This paper exposes drivers to non-surprise and surprise driving scenarios to assess if attentive drivers can be identified from physiological changes for manual and AV driving. Machine learning (ML) is used to understand the patterns of physiological signals and classify when a driver is engaged during a surprise scenario. Finally, the ML models show a successful ability to classify engaged versus non-engaged drivers with a 73.3% accuracy in manual driving scenarios, 86.7% accuracy in AV, and 70.0% accuracy when the data from both driving scenarios are combined showing the model's ability to generalize. © 2023 IEEE.

关键词： Autonomous vehicles

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) Program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National Laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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