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World Environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Shahangian, Seyyed Ahmadreza Tabesh, Massoud Yazdanpanah, Masoud Akbarzadeh, Abbas Raoof, Mohammad Amin Zobeidi, Tahereh Hajibabaei, Mohsen Sitzenfrei, Robert School of Civil Engineering College of Engineering Univ. of Tehran Tehran Iran Water and Wastewater Research Center Water Research Institute Tehran Iran Dept. of Agricultural Extension and Education Agricultural Sciences and Natural Resources Univ. of Khuzestan Mollasani Iran Dept. of Family Youth and Community Sciences Univ. of Florida GainesvilleFL United States Dept. of Civil Engineering Sharif Univ. of Technology Tehran Iran Advancing System Analysis Program International Institute for Applied Systems Analysis Laxenburg Austria Unit of Environmental Engineering Dept. of Infrastructure Engineering Univ. of Innsbruck Innsbruck Austria

ISBN: (数字)9780784484852

ISBN: (纸本)9780784484852

Promoting household adoption of water conservation behaviors (WCBs) has emerged as a vital strategic response to address water scarcity. Nevertheless, it is imperative to recognize that acceptance of residential WCBs relies on households' voluntary engagement, as are interventions designed to encourage households to become involved in such actions. Meanwhile, environmental psychology plays a key role in understanding individuals' WCBs and exploring the psychosocial determinants underlying these behaviors. Furthermore, the spread of technology and behavior among a population are known as a complex adaptive system (CAS), and agent-based modeling (ABM) offers a comprehensive and practical approach to studying CASs. Hence, the main aim of the present research is to introduce a novel, hybrid agent-based framework for simulating the adoption of residential WCBs, in which an extended model of the theory of planned behavior acts as household agents' behavioral rule for adopting water-efficiency behaviors (WEBs). The framework is also grounded in various theories rooted in psychological and social sciences. Moreover, the presented framework integrates ABM and structural equation modeling approaches. Although this study mainly focuses on WEBs, the framework includes a mechanism to update agents' water use based on their adoption level of water curtailment behaviors. Thus, the presented framework allows for simultaneously but separately exploring the preferred level of both types of WCBs among household agents. Finally, total water consumption is systematically calculated based on agents' aggregated behavior. © World Environmental and Water Resources Congress *** rights reserved

关键词： Water conservation

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Predictive Analytics for Learning Performance in First-Year University programming Course 10

Predictive Analytics for Learning Performance in First-Year ...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Kartiwi, Mira Gunawan, Teddy Surya Yusoff, Nelidya Md International Islamic University Malaysia Department of Information Systems Kuala Lumpur53100 Malaysia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

The increasing demand for programming skills has highlighted the need for effective teaching strategies to support student success in programming courses. Despite significant advancements in learning analytics, predictive models explicitly tailored to programming courses remain underexplored. This research aims to develop a machine learning model to predict student performance in programming courses offered within IT programs by analyzing gender, type of activity (readings, coding exercises, assignments), and frequency of access to different activities. Our study utilizes log data of the asynchronous learning activities in the learning management systems of the students enrolled in programming courses. We employ machine learning techniques, decision trees, gradient boosting machines (GBM), and logistic regression to build robust predictive models. In this study, the decision tree model outperformed logistic regression (77.77%) and gradient boosting machine (GBM) (86.57%) by achieving the highest accuracy of 89.09% and excelling in predicting 'Poor' student performance with a recall of 90.67%, establishing it as the most effective model for this predictive analysis. The findings from this research offer actionable insights for educators, enabling early intervention for at-risk students and developing tailored teaching strategies to enhance student performance through strategically provisioning the learning materials in programming courses. This study contributes to the growing knowledge of learning analytics and provides a foundation for future research in predictive modeling for diverse educational contexts. © 2024 IEEE.

关键词： Predictive analytics

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance

arXiv

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arXiv 2024年

作者： Li, Hongkang Zhang, Shuai Zhang, Yihua Wang, Meng Liu, Sijia Chen, Pin-Yu The Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States New Jersey Institute of Technology United States The Dept. of Computer Science and Engineering Michigan State University United States IBM Research United States

Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high average accuracy is accompanied by low accuracy in a minority group. Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive. By formulating the group imbalance problem with the Gaussian Mixture Model, this paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance. Although our theoretical framework is centered on binary classification using a one-hidden-layer neural network, to the best of our knowledge, we provide the first theoretical analysis of the group-level generalization of ERM in addition to the commonly studied average generalization performance. Sample insights of our theoretical results include that when all group-level co-variance is in the medium regime and all mean are close to zero, the learning performance is most desirable in the sense of a small sample complexity, a fast training rate, and a high average and group-level testing accuracy. Moreover, we show that increasing the fraction of the minority group in the training data does not necessarily improve the generalization performance of the minority group. Our theoretical results are validated on both synthetic and empirical datasets, such as CelebA and CIFAR-10 in image classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping

Enhancing Credit Card Fraud Detection through LSTM-Based Seq...

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Networking and Communications (ICNWC), International Conference on

作者： S. Jhansi Ida K. Balasubadra Skandarsini R R Lakshmi Narayanaa T Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India Department of Information Technology RMD Engineering College Thiruvallur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

There is still a serious problem with credit card fraud in the world of financial transactions, requiring innovative solutions to effectively detect and halt illicit behavior. This research presents a unique method of detecting credit card fraud by strategically implementing early pausing in a Long Short-Term Memory (LSTM) neural network. Leveraging the sequential dependencies inherent in transaction data, the LSTM model demonstrates remarkable efficacy in discerning fraudulent from legitimate transactions. The early stopping mechanism ensures that the model’s training halts at the optimal epoch, preventing over fitting and enhancing generalization. Our thorough analysis highlights the model’s versatility by demonstrating a dynamic compromise between recall and accuracy throughout training. Practical implications include enhanced fraud detection accuracy, reduced false alarms, and real-time decision support for financial institutions. The model’s adaptability and generalization make it a valuable asset in the ongoing battle against credit card fraud, contributing to a more secure financial environment. Future research directions may explore additional features and alternative architectures to further advance fraud detection capabilities.

关键词： Training Adaptation models Sequential analysis Credit cards Feature extraction Harmonic analysis Real-time systems

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Change Bit-dept. Procedure for Data Measured Preprocessing before Compression

Change Bit-Depth Procedure for Data Measured Preprocessing b...

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2022 IEEE International Multi-Conference on engineering, computer and Information Sciences, SIBIRCON 2022

作者： Levenets, Alexey V. Bondarenko, Vladislav V. Bahenov, Ruslan I. Belozerov, Oleg I. Belozerova, Svetlana I. Bogatenkov, Sergey A. Pacific National University Dept. Automation and System Engineering Khabarovsk Russia Sholom-Aleichem Priamursky State University Dept. Information Systems and Mathematics Birobidzhan Russia Far Eastern State Transport University Dept. Computer Technology and Computer Graphics Khabarovsk Russia South Ural State University Dept. Information Technology in Economics Chelyabinsk Russia

ISBN: (纸本)9781665464802

The paper proposes to change the bit-dept. of the original data sequence as a preprocessing of the measuring data, which is carried out before the compression procedure. In this case the digit capacity conversion is performed without accounting for the primary data sample boundaries. The simulation was carried out in the Matlab software environment. The findings of the authors' research are presented. They prove to be a promising approach for some types of the studied data at least and several digit capacity values. The analysis of autocorrelation function values showing the data correlation revealed by the conversion is used as an assessment of perspective of the active conversion. In this article the authors review the values of secondary maximums of autocorrelation function. Their occurrence shows the demonstration of the implied correlative correspondence. Such correlations can potentially allow the converted data to be compressed more effectively than the original data using the known compression methods. © 2022 IEEE.

关键词： Data compression

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In-Home Cluttered Environment Gait Analysis using mm-Wave Radar

In-Home Cluttered Environment Gait Analysis using mm-Wave Ra...

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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Hajar Abedi Ahmad Ansariyan Plinio P Morita Jennifer Boger Alexander Wong George Shaker dept. Electrical and Computer Engineering University of Waterloo Waterloo Canada School of Public Health Sciences University of Waterloo Waterloo Canad dept. Systems Design Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350351057

ISBN: (纸本)9798350351064

This paper presents a novel method for human gait analysis in cluttered in-home environments using mm-wave radar systems, leveraging novel signal processing and unsupervised machine learning to extract gait parameters accurately amidst obstacles, multipath, and ghosts. Our approach accurately identifies gait parameters such as stride length and walking speed while walking on a curvy path, outperforming traditional techniques. The findings highlight the potential of mm-wave radar for non-invasive, privacy-preserving gait monitoring, with significant implications for healthcare, elderly care, and ambient assisted living. This work advances human motion analysis technology and opens new avenues for in-home monitoring applications.

关键词： Legged locomotion Microwave technology Radar clutter Medical services Radar signal processing Sensors Biomedical monitoring

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Innovative Portable Quantitative EEG Data Acquisition Based Cyton Biosensing Board using PSD Analysis

Innovative Portable Quantitative EEG Data Acquisition Based ...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Melinda Melinda Prima Dewi Purnamasari Fahmi Fahmi Emerson Sinulingga Muliyadi Muliyadi Imam Ashabul Yamin Dept. electrical and computer engineering Universitas syiah kuala Banda Aceh Indonesia Dept. electrical engineering Universitas Indonesia Depok Indonesia Dept. electrical engineering Universitas sumatera utara Medan Indonesia Dept. electrical engineering (of Affiliation) Universitas sumatera utara Medan Indonesia Doctoral program in engineering dept. electrical engineering Universitas syiah kuala Banda Aceh Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Electroencephalography (EEG) is a non-invasive brain imaging technique essential for neuroscience research and clinical applications. Conventional EEG devices are often expensive and impractical for use outside clinical or laboratory settings. This study reports the experience of acquiring EEG data using the Cyton board biosensing, an open-source and portable biosensor device. Detailed procedures for subject preparation, electrode placement, device configuration, and EEG data acquisition are provided. A Butterworth filter was used to eliminate noise from the EEG signals, with results visualized using the Fast Fourier Transform (FFT). Quantitative EEG (qEEG) analysis was conducted using the Power Spectral Density (PSD) method. The highest PSD values were 428.04 µV2/Hz at 5.9 Hz on channel C4 for subject 1,329.77 µV2/Hz at 5.9 Hz on channel C4 for subject 2, and 385.65 µ V2/Hz at 5.9 Hz on channel Fp1 for subject 3, all indicating increased brain activity in the theta wave range. This study provides insights for researchers and practitioners using the Cyton board for EEG data acquisition and contributes to developing portable, affordable EEG methods.

关键词： Electrodes Brain Sensitivity Fast Fourier transforms Data acquisition Noise Data visualization Information filters Electroencephalography Biosensors

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Learning Adaptive Safety for Multi-Agent systems

arXiv

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arXiv 2023年

作者： Berducci, Luigi Yang, Shuo Mangharam, Rahul Grosu, Radu Institute of Computer Engineering TU Wien Austria Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and a competitive multi-agent racing scenario, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy and flexibility of ASRL, and assess generalization and scalability to out-of-distribution scenarios. Code and supplementary material are public online1 © 2023, CC BY-NC-SA.

关键词： Multi agent systems

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