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Classifying Cognitive Workload Using Machine Learning Techniques and Non-Intrusive Wearable Devices 4

Classifying Cognitive Workload Using Machine Learning Techni...

引用

4th IEEE international conference on Human-Machine systems, ICHMS 2024

作者： Liu, Yunmei Grimaldi, Nicolas S. Basnet, Niosh Wozniak, David Chen, Eric Zahabi, Maryam Kaber, David Ruiz, Jaime University Of Louisville Department Of Industrial Engineering LouisvilleKY United States University Of Florida Department Of Industrial & Systems Engineering GainesvilleFL United States Texas A&m University Wm Michael Barnes '64 Department Of Industrial & Systems Engineering College StationTX United States University Of Florida Department Of Computer & Information Science & Engineering GainesvilleFL United States

ISBN: (纸本)9798350315790

Effective management of cognitive workload is essential to ensure user performance and minimize mental fatigue. This study aimed to evaluate the effectiveness of three machine learning (ML) algorithms, including a Support Vector Machine (SVM), a Random Forest (RF) and Random Fourier Features (RFF) models, for predicting instances of high cognitive workload based on physiological data collected from non-intrusive wearable devices. The study included scenarios to induce high workload. After processing data from 30 human participants, and extracting cognitive workload-related features, we evaluated the ML algorithms in terms of accuracy, Area Under the Curve (AUC), and Average Precision (AP) responses. The RFF model emerged as a top performer, achieving 97% accuracy, 99.5% AUC, and 99% AP, demonstrating the capability to detect high workload scenarios and robustness to unbalanced data in workload predictions. The most significant physiological features in the RFF model included skin conductance level (SCL) change, SCL mean, and percent change in pupil size. Future studies should enhance the present binary classification model to support multi-class or continuous workload predictions and implement real-time analysis using trained algorithms. © 2024 IEEE.

关键词： Wearable technology

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Secured Connected Vehicles for Intelligent Transportation

Secured Connected Vehicles for Intelligent Transportation

引用

2024 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2024

作者： Hegde, Seema B. Venkata Sai Devadutt, P. Chethan Sai, P. Yaswanth, Sanikommu Shashank, T.V. Santhosh, P. Siddaganga Institute Of Technology Department of ECE Tumakuru India Albert Ludwig University of Freiburg Embedded systems engineering Germany

ISBN: (纸本)9798350364040

In recent years, heightened concerns regarding green technology and road safety have driven extensive research and development in Intelligent Transport systems (ITS) for automobiles. This initiative focuses on the implementation of automated vehicle platooning as a promising solution, specifically in investigating control algorithms for platooning behavior studies indoors. The proposed platoon structure involves n vehicles, each equipped with control algorithms for basic path control, longitudinal control, string stability, and platoon converging concepts. Utilizing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications over a wireless network, the vehicles engage in information exchange. Experimental evaluations will be conducted in a controlled laboratory environment using 1/10th scale prototype models, including a leader and follower vehicles, implementing PID controllers and sensor technologies. The primary goal is to develop robust security frameworks, leveraging state-of-the-art encryption, anomaly detection algorithms, and secure communication protocols to safeguard connected vehicles from cyber threats and ensure the integrity of critical communication systems in the transportation infrastructure. © 2024 IEEE.

关键词： Intelligent systems

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A Neural Network Approach for Predicting Air Quality Forecasting in Smart Cities Based on ST-LSTM Technique

A Neural Network Approach for Predicting Air Quality Forecas...

引用

2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Babu, Uppara Raghu Bajpai, Chhavi Arpitha, K. Munjal, Puja Sawadatkar, Sushma Pulugu, Dileep Srinivasa Ramanujan Institute of Technology Department of Civil Engineering Anantapur India Dr A P J Abdul Kalam Technical University Department of Computer Science & Engineering Lucknow India Hyderabad India Jagannath International Management School Department of Information Technology Delhi India Dr D Y Patil Institute of Technology Department of Civil Engineering Pimpri Pune India Malla Reddy College of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350360660

The Internet of Things (IoT) has recently emerged as an intriguing field of study across numerous fields, including commerce, academia, and industry. By utilizing services and apps based on the Internet of Things, "smart cities"make urban living more environmentally friendly. Smart cities that leverage the Internet of Things have the potential to increase efficiency, participation, and knowledge among city stakeholders. Over the last several years, the quantity of data generated by smart city applications that rely on the Internet of Things has grown exponentially. This specific order is required for the execution of model training, feature extraction, and data preprocessing. As part of the preprocessing phase, data is normalized, categorical variables are encoded, and missing values are dealt with. The initial stage of this process is to identify the most crucial components of the air quality index (AQI), which represents the overall air quality, and then choose them. When training ST-LSTM models, the initial step is to retrieve relevant features. A pair of state-of-the-art methods, TCN and LSTM, were surpassed by the suggested methodology. After using the method, accuracy increased by 95.53%. © 2024 IEEE.

关键词： Air quality

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Adversarial-aware Machine Learning for Enhancing Smart Grid Security 25

Adversarial-aware Machine Learning for Enhancing Smart Grid ...

引用

25th international Middle East Power System conference, MEPCON 2024

作者： Sodiq, Yusuf Ademola Gaber, Tarek Amin, Reham Jimenez-Aranda, Angel Galeela, Mohamed The University of Salford School of Science Engineering & Environment Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt The University of Salford Centre for Sustainable Innovation Salford Business School Manchester United Kingdom TNEI Service LTD Manchester United Kingdom Cairo University Faculty of Engineering Electrical Power Engineering Department Egypt

ISBN: (纸本)9798350379648

Smart grid systems are essential components of modern power infrastructures, where machine learning has found wide-ranging applications, particularly in the development of intrusion detection systems. However, such systems remain vulnerable to adversarial attacks, including data poisoning techniques like label flipping attack. This paper proposes a novel, adversarial-aware machine learning-based intrusion detection system that is robust against label flipping attacks, thereby enhancing the integrity and reliability of the energy distribution network. The proposed solution was evaluated using the ICS cyber-attack dataset from the University of Queensland, with multiple models undergoing progressive testing. Initially, the base model employed three key algorithms: Random Forest, K-Nearest Neighbors, and XGBoost. These models were subjected to simulated label flipping attacks with varying intensities (10%-50%), which caused a significant decline in performance. To counter this, a countermeasure technique was integrated into the system, successfully restoring model accuracy and achieving a consistent accuracy of 75.96% across all attack intensities. Among the algorithms, Random Forest demonstrated the greatest resilience, showing a notable recovery after mitigation. This study underscores the critical role of preventive defense strategies and robust machine learning algorithms in safeguarding smart grids against cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Autonomous Global Threshold Adjustment Algorithm For Energy Detection In Self-reconfigurable Cognitive Radio systems 2

Autonomous Global Threshold Adjustment Algorithm For Energy ...

引用

2nd international conference on Advanced Electrical engineering, ICAEE 2022

作者： Bouhdjeur, Abderrezzaq Azzaz, Mohamed Salah Teguig, Djamel Maali, Abdelmadjid Tanougast, Camel Laboratoire Sen Ecole Militaire Polytechnique Algiers Algeria Laboratoire Télécommunications Ecole Militaire Polytechnique Algiers Algeria Lorraine University Lcoms Metz France

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

This paper suggests a novel autonomous global threshold adjustment algorithm for energy detection (ED) in self-reconfigurable cognitive radio (CR) systems. The proposed method uses the power spectral density (PSD) as an input and generates an L-bin histogram in the first instance. Instead of maximizing the between classes variance as in the classical method, the second step consists of dividing the histogram into two equal parts and finding the maximum difference between histogram bins. Finally, the threshold setting and the distinction between the presence of the signal in the spectral band and the unimodality of the distribution depend on the position of the previously mentioned maximum. The proposed method is validated and compared to the other SATA (self-adaptive threshold adjustment) algorithms on real-life signals. The obtained results are promising and meet the requirements of the IEEE 802.22 standard with a detection rate higher than 90 % and a probability of false alarm lower than 10 %. © 2022 IEEE.

关键词： Cognitive radio

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Exploring the Potential of Machine Learning in Automated Real-Time Data Analysis systems 15

Exploring the Potential of Machine Learning in Automated Rea...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandrakala, P. Meshram, Vishal A. Meena, Mansingh Nithya, M.S. Alam, Intekhab Kumar, V. Mathan Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

This paper explores the current country of gadget learning and its capability to allow real-time records analysis. Automated actual-time statistics analysis structures enable business specialists to reply to situations by extracting complicated patterns faithfully. Gadget learning techniques can assist these units by allowing algorithms to analyze previously amassed information and generalize these consequences to novel scenarios. This paper examines the latest developments in device learning algorithms that will likely be leveraged in automatic actual-time facts evaluation structures. The report also considers challenges associated with data education and the mission's generation selection. ultimately, the paper discusses numerous processes for integrating gadget getting to know into real-time records analytics systems that could permit agencies to take complete advantage of the significant ability of machine mastering. © 2024 IEEE.

关键词： Adversarial machine learning

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Monetary Loan Eligibility Prediction using Logistic Regression Algorithm 2

Monetary Loan Eligibility Prediction using Logistic Regressi...

引用

2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Deepa, M. Pal, Siddhartha Ghusey, Vaishnavi Prashant School of ComputerScience Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

In the banking sector, predicting customer loan eligibility is a crucial task. This paper presents a study of using machine learning algorithms to predict loan eligibility of customers based on credit history, income, and others. The algorithms used in the study are Logistic Regression. Using machine learning algorithms the results are demonstrated the potential of in the banking sector to improve loan decision-making processes and increase efficiency. The study utilized a dataset containing information on various loan applications and their respective approvals. The dataset was pre-processed and various machine learning algorithms were applied to the data to determine the best model for predicting loan approval. The findings of this study can be useful for financial institutions in making informed decisions about loan approvals, reducing the risk of default and improving the overall loan approval process. The model is trained using a dataset containing various demographic and financial parameters of loan applicants. The Logistic regression algorithm is utilized to build a predictive model that determines the eligibility of a loan applicant based on the input features. The system aims to modernize the loan approval process by using predictive algorithms to analyze and evaluate loan applications. The study compares different machine learning algorithms, including logistic regression and support vector machines, and evaluates their performance in terms of accuracy and efficiency. The findings also suggest that the use of machine learning can help banks streamline their loan approval processes. © 2024 IEEE.

关键词： Efficiency

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Blockchain and Machine Learning for Automated Compliance in Regulatory Technology 13

Blockchain and Machine Learning for Automated Compliance in ...

引用

13th IEEE international conference on Communication systems and Network Technologies, CSNT 2024

作者： Jain, Alok Praveena, K. Anandhi, R.J. Kumar, Sandeep Alabdely, Haider Srivastava, Arun Pratap Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P. Bhopal India The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201323 India

ISBN: (纸本)9798350305463

Blockchain and Machine Learning integration has changed the area of Regulatory Technology (RegTech), offering automated compliance solutions for difficult regulatory issues. By combining the benefits of blockchain technology with machine learning algorithms, this article introduces a unique Compliance Assurance Framework utilizing BlockchainEnhanced Machine Learning (CAF-BEML) to improve the effectiveness and trustworthiness of compliance procedures. The Blockchain-Enhanced Compliance Data Verification Algorithm (BCDVA), the Predictive Regulatory Risk Assessment Algorithm (PRRA), and the Secure Compliance Audit Trail Algorithm (SCATA) are the three central algorithms in the proposed framework, and they work together to guarantee data authenticity, forecast potential threats, and generate clear audit trails, all of which contribute to a robust regulatory ecosystem. The suggested method's effectiveness and superiority over current conventional approaches are proved, and its potential to change compliance management in numerous industries is emphasized via this in-depth study and comparison. © 2024 IEEE.

关键词： Risk assessment

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Decentralized Target Localization in Distributed MIMO Radars 16

Decentralized Target Localization in Distributed MIMO Radars

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Qiu, Qinhui Li, Jie Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781510689251

This paper addresses the problem of target estimation in distributed MIMO radars, focusing on the challenge of requiring a robust fusion center to process all the collected data. To overcome this limitation, we propose a solution based on the Alternating Direction Method of Multipliers (ADMM) that eliminates the need for a centralized fusion center. By leveraging the sparsity of target location information within the detection range, the target localization problem is reformulated as a sparse recovery problem, which is then efficiently solved using ADMM. The key innovation of the proposed approach is the transformation of the average value computation, typically required in centralized methods, into a consensus averaging problem, and subsequently into a consensus optimization problem. This transformation allows for fully decentralized processing, where each node only communicates with its neighboring nodes to reach a consensus on the estimation of target location. Simulation results demonstrate that the proposed distributed algorithm achieves performance comparable to that of semi-distributed algorithms while fully exploiting the advantages of decentralization. © 2025 SPIE.

关键词： Consensus algorithm

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th international conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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