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Exercise and Sedentary Activity Recognition Using Late Fusion: Building Adaptable Uncertain Models

Exercise and Sedentary Activity Recognition Using Late Fusio...

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International Conference on Information Fusion

作者： Ezequiel Juarez Garcia Victoria R. Rodrigues Mehrdad Fazli Laura E. Barnes Nicholas J. Napoli Dept. of Electrical and Computer Engineering University of Florida Gainesville FL Dept. of Systems and Information Engineering University of Virginia Charlottesville VA

Wearable smart devices are capable of capturing a variety of information from their users using a multitude of noninvasive sensing modalities. Using features from the raw measurements of wearable devices, sensor fusion enables us to obtain a holistic picture of the users’ context and monitor their activity state with increased accuracy. Human activity recognition using noninvasive sensors allows us to capture the natural behavior of users in their day-to-day lives. This in-the-wild activity recognition, however, poses several key challenges that must be addressed to create effective classification models. The main challenges are class imbalance, uncertainty in classifier decisions, and large feature spaces. To address them, this study further explores a probabilistic sensor fusion method called Naive Adaptive Probabilistic Sensor (NAPS) Fusion. In doing so, we establish the viability of NAPS Fusion for natural human activity recognition using noninvasive sensing modalities. NAPS Fusion handles dimensionality reduction by creating reduced feature sets and mitigates the class imbalance issue through the use of Synthetic Minority Oversampling Technique (SMOTE). Moreover, NAPS Fusion addresses uncertainty in the decisions of classifiers using a Dempster-Shafer theoretic late fusion framework. Our empirical evaluation demonstrates that NAPS Fusion has broad applications beyond its original design for cognitive state detection. It outperforms similar decision level sensor fusion methods (late fusion using averaging, LFA, and late fusion using learned weights, LFL) in the detection of exercise and sedentary activities such as walking, running, lying down, and sitting. We observe improvements of up to 56% in F1 score and up to 59% in precision with NAPS Fusion over the compared methods.

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Analysis of an Urban Air Taxi Inner Loop controller with Noisy IMUs in an Urban Airflow Environment

Analysis of an Urban Air Taxi Inner Loop Controller with Noi...

引用

IEEE Conference on Aerospace

作者： Tariq Maksoud Fidel Khouli Mohamed Atia Dept. of Mechanical and Aerospace Engineering Carleton University Ottawa Ontario Dept. of Computer and Systems Engineering Carleton University Ottawa Ontario

Interest in operating commercial Urban Air Taxis (UAT) around the world has been growing rapidly over the last few years. One of the many challenges in designing aircraft suitable for operating in a turbulent urban airflow environment is to design a robust inner loop flight controller. This study investigates the effect of filtered Angular Random Walk (ARW) error found in Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain stable, wings level, horizontal flight, while not causing noticeable discomfort to passengers and respecting the limits of authority of the aircraft's control surfaces in a representative urban airflow environment. The performance of two controller architectures were investigated: classical Proportional, Integral, Derivative (PID) control scheme as well as Linear Active Disturbance Rejection control (LADRC) control scheme. The conclusion of this study provides recommendations on a minimum threshold of IMU sensor grades and general considersations that would be useful to the controller designer. The findings are demonstrated by observing the vertical acceleration, $n_{z}$ , angular rate setpoint tracking performance, and control surface deflections.

关键词： Robust control PI control Filtering Sensor fusion Stability analysis Noise measurement PD control

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Projection-Based controllers with Inherent Dissipativity Properties

Projection-Based Controllers with Inherent Dissipativity Pro...

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IEEE Conference on Decision and control

作者： Hoang Chu S.J.A.M van den Eijnden W.P.M.H. Heemels Dept. Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology The Netherlands

Projection-based controllers (PBCs) are currently gaining traction in both scientific and engineering communities. In PBCs, the input-output signals of the controller are kept in sector-bounded sets by means of projection. In this paper, we will show how this projection operation can be used to induce useful passivity or general dissipativity properties for broad classes of (unprojected) nonlinear controllers that otherwise would not have these properties. The induced dissipativity properties of PBC will be exploited to guarantee asymptotic stability of negative feedback interconnections of passive nonlinear plants and suitably designed PBC, under mild conditions. Generalizations to so-called $(q,s,r)$ -dissipativity will be presented as well. To illustrate the effectiveness of PBC control design via these passivity-based techniques, a numerical example is provided.

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Enhancing Parking Online Reservation with a Recommendation System based on User Preferences: A hybrid Approach

Enhancing Parking Online Reservation with a Recommendation S...

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ACS/IEEE International Conference on computer systems and Applications

作者： Serine Guellab Imane Benkhelifa Dept. of Computer Science USTHB University Algiers Algeria Dept. of Computer Systems’Therories and Engineering CERIST Research Center Algiers Algeria

In response to the pressing challenges in parking online reservation platforms, the primary issue this paper addresses is the need for a user-centric parking reservation experience. To tackle this problem, the study aims to develop a recommendation system that enhances user satisfaction and streamlines the parking reservation *** provide personalized parking recommendations, a hybrid multimodal recommendation system is designed, grounded in distance-based recommendation and content-based filtering, and taking into account user preferences and feedback, history behavior and proximity to preferred tourist attractions and points of *** leveraging a rich dataset comprising 1804 parking items, results indicate a notable improvement and more user-centric user experience, as the system suggests parking lots in line with user preferences and points of interest. User feedback mechanisms are seamlessly integrated, facilitating continuous adaptation and refinement based on user convenience and past *** work shows significant potential in enhancing user satisfaction and streamlining the user experience in parking online reservation systems.

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Artificial Intelligence (AI) for Improving Performance at the Cutting Edge of Medical Imaging

Artificial Intelligence (AI) for Improving Performance at th...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Samar M. Nour Dept. Computer and Systems Engineering Faculty of Engineering Badr University in Cairo(BUC) Cairo Egypt

Artificial intelligence (AI) has achieved great strides in recent years, with applications in a variety of areas of study, including healthcare. Consequently, the integration of artificial intelligence (AI) and medical imaging has ushered in a new era in healthcare diagnosis and therapy. Artificial intelligence (AI) has shown impressive potential in enhancing accuracy, efficiency, and diagnostic performance across a range of medical imaging modalities by using the power of deep learning (DL), machine learning (ML), and computer vision. In this paper, we are trying to investigate the connection between artificial intelligence (AI) and medical imaging, concentrating on how AI-driven strategies are improving performance at the cutting edge of medical imaging technologies through the proposed architecture model. Furthermore, the paper also explores the limitations and opportunities that result from incorporating artificial intelligence (AI) into the use of medical imaging. The potential for artificial intelligence (AI) to transform image-guided therapies and its implications for personalized medicine are investigated.

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Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach 19

Selection of Beamforming in 5G MIMO scenarios using Machine ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Silva, Douglas Henrique Augusto Ribeiro, David Ramirez, Miguel Arjona Lopes Rosa, Renata Chaudhary, Sushank Rodriguez, Demostenes Zegarra Federal University of Lavras Dept. of Computer Science MG Lavras Brazil Dept. of Automation Federal University of Lavras MG Lavras Brazil University of São Paulo Dept. of Electronic Systems Engineering São Paulo Brazil Chulalongkorn University Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only. © 2022 IEEE.

关键词： MIMO systems

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

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Silchar Subsection Conference (SILCON), IEEE

作者： Karri Sai Sanjana Reddy Kishor Kumar Reddy C. Srinath Doss Kari Lippert Dept. of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Faculty of Engineering and Technology Botho University Botswana Dept. of Systems Engineering University of South Alabama Alabama USA

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care.

关键词： Training Accuracy Transfer learning Predictive models Prediction algorithms Genetics Classification algorithms Alzheimer's disease Random forests Medical diagnostic imaging

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Survey of Machine Learning HIDS Techniques 16

Survey of Machine Learning HIDS Techniques

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16th International Conference on computer engineering and systems, ICCES 2021

作者： Soliman, Khaled Sobh, Mohamed Ali Bahaa-Eldin, Ayman M. Ain Shams University Dept. of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9781665408677

The development of cyber-attacks has unprecedented effect on businesses and governments. The recent years have witnessed various number of security breaches against organizations equipped by diverse cybersecurity solutions. These attacks showed the limitations of the current security systems to remediate the advanced threats. This critical challenge poses a vital need to find new techniques to detect the recently developed malwares called Zero-Day Threats. This type of attacks cannot be discovered by signature-based scanning and traditional cybersecurity systems. In addition, they can bypass the network security appliances by concealing malwares in the encrypted channels such as Virtual Private Network (VPN) and Hypertext Transfer Protocol Secure (HTTPS). Therefore, there is a great importance to study the endeavors of developing a Host-based Intrusion Detection System (HIDS) using Artificial Intelligence (AI) techniques to protect computers against sophisticated cyber-attacks. This paper will discuss and compare the recent proposed techniques using Machine Learning at the host level to detect the advanced cybersecurity attacks based on the behavioral impact of these attacks on computers. © 2021 IEEE.

关键词： Cybersecurity

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Realization of a Novel, Lightweight and Secured CoAP Based Monitoring System in Smart Logistics 6

Realization of a Novel, Lightweight and Secured CoAP Based M...

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6th International Multi-Topic ICT Conference, IMTIC 2021

作者： Hasan, Salva Ahmed, Salman Marwat, Safdar Nawaz Khan University of Engineering Technology Dept. of Computer Systems Engg Peshawar Pakistan

ISBN: (纸本)9781665482943

Internet of Things (IoT)-where the physical components are able to communicate with each other and with the internet-is one of the driving forces behind the Fourth Industrial Revolution. Nowadays, IoT applications have become widespread across many industries. However, due to the resource-constrained nature of IoT devices, they cannot be secured using conventional security protocols that are already developed. Hence, lightweight implementations of IoT security are the need of the hour. This paper proposes a secure and tamper-resistant system for monitoring the status of items in a container during transportation and storage using IoT devices. As smart logistics often transport items that are sensitive to environmental changes such as temperature and humidity, the security of data obtained from these sensors is the main focus of this research. To evaluate the performance of the system, a pilot-scale silo has been fabricated. Constrained Application Protocol (CoAP) is used as the communication protocol and a lightweight implementation of Datagram Transport Layer Security (DTLS) protocol has been adapted for the smart container architecture to incorporate cryptographic security. The low power consumption of the lightweight DTLS-secured CoAP implementation will drastically increase the efficiency of the smart logistics architecture without compromising its security. © 2021 IEEE.

关键词： Internet of things

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Corpus of Human-Labeled Indirect Harassment

Corpus of Human-Labeled Indirect Harassment

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International Conference on Emerging Technologies, ICET

作者： Ebtihaj Abdullah Waseem Ullah Khan Safdar Nawaz Khan Marwat Salman Ahmed Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Faculty of Computer Science and Engg GIK Institute of Engineering Sciences & Technology Swabi Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subjects. High quality and high value datasets of such ideas and discussions in textual and formatted form are fundamental to the research aspects of Artificial Intelligence and data-science. In this paper, we present a dataset of indirect harassment. There are approximately 10,700 tweets with binary labels. The labels were assigned by a team of researchers collectively. The dataset contains approximately 19 percent positive indirect harassment labels and 81 percent negative indirect harassment labels. The data is useful for training and running machine and deep learning models to detect indirect and direct harassment. The ease in understanding the data for researchers and other peers is also very crucial. The corpus mentioned in this paper is easy to understand via the use of a binary label. The data is simplistic in nature because there are only two columns with one being the text and the other being the indirect label. This work was necessary because it was a component of a larger project that also needed this kind of dataset.

关键词： Deep learning Training Social networking (online) Oral communication Data science Digital communication Information age Data models

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