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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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A survey on cyber threat intelligence sharing based on Blockchain

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Advances in Computational Intelligence 2023年第3期3卷 1-9页

作者： El-Kosairy, Ahmed Abdelbaki, Nashwa Aslan, Heba Nile University Cairo Egypt Centre of Informatics Science School of Information Technology and Computer Science Nile University Cairo Egypt Informatics Department Electronics Research Institute Cairo Egypt

In recent years, cyber security attacks have increased massively. This introduces the need to defend against such attacks. Cyber security threat intelligence has recently been introduced to secure systems against security attacks. Cyber security threat intelligence (CTI) should be fast, trustful, and protect the sender's identity to stop these attacks at the right time. Threat intelligence sharing is vitally important since it is considered an effective way to improve threat understanding. This leads to protecting the assets and preventing the attack vectors. However, there is a paradox between the privacy safeguard needs of threat intelligence sharing; the need to produce complete proper threat intelligence feeds to be shared with the community, and other challenges and needs that are not covered in the traditional CTI. This paper aims to study how Blockchain technology can be incorporated with the CTI to solve the current issues and challenges in the traditional CTI. We collected the latest contributions that use Blockchain to overcome the conventional CTI problems and compared them to raise the reader’s awareness about the different methods used. Also, we mentioned the uncovered areas for each paper to offer a wide range of details and information about different areas that need to be investigated. Furthermore, the prospect challenges of integrating the Blockchain and CTI are discussed.

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Optimizing NOMA Transmissions to Advance Federated Learning in Vehicular Networks

arXiv

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arXiv 2024年

作者： Chen, Ziru Ni, Zhou Guan, Peiyuan Wang, Lu Cai, Lin X. Hashemi, Morteza Li, Zongzhi Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Department of Informatics University of Oslo Oslo Norway Department of Civil Engineering Illinois Institute of Technology Chicago United States

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms

arXiv

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arXiv 2024年

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Department of Computer Science and Engineering Uttara University Dhaka Bangladesh National Institute of Technology Silchar India Department of Informatics University of Oslo Oslo Norway Department of Computer Science Maharishi International University Iowa United States Department of Computer Science and Engineering CCN University of Science & Technology Comilla Bangladesh

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024, CC BY.

关键词： Sentiment analysis

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Method of optimizing delay in IoT system using fog calculations 1

Method of optimizing delay in IoT system using fog calculati...

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1st International Workshop on Intelligent and CyberPhysical Systems, ICyberPhyS 2024

作者： Lysenko, Sergii Bondaruk, Oleh Gaj, Piotr Sachenko, Anatoliy Lysenko, Olena Khmelnitskyi National University Instytutska street 11 Khmelnitsky29016 Ukraine Silesian University of Technology ul. Akademicka 2A Gliwice44-100 Poland Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The article presents the improved the method and means of task planning in IoT infrastructure using fog computing. For the purpose of improvement, the task planning method based on the ant colony algorithm (ACO) was chosen. The concept of priority of task execution by fog nodes was introduced. Formulas were also developed to determine the node that will perform calculations with the least time and delay. The environment for conducting DISSECT-CF experimental studies was also considered. Its modules, abstractions and their settings were described. According to the results of experimental studies, it was determined that the use of an improved method of optimizing the IoT infrastructure with the use of fog computing improved the quality of service (QoS) of the system by reducing the delay of processing tasks. © 2023 Copyright for this paper by its authors.

关键词： Fog

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Enhancing Secure Cross-Border Collaboration among Law Enforcement Agencies for Facial Biometric Search

Enhancing Secure Cross-Border Collaboration among Law Enforc...

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Electronic Engineering, Information technology & Education (EEITE), International Conference in

作者： Kyriaki Miniadou Asterios Leonidis Georgios Th. Papadopoulos Constantine Stephanidis Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Heraklion Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Computer Science University of Crete Heraklion Greece

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

Addressing the growing challenge of combating international crime involves the development of secure and efficient methodologies that allow Law Enforcement Agencies to exchange information seamlessly, without being hindered by time-consuming bureaucratic processes. In this context, we present a solution centered on facial biometric search methodologies. Our approach underscores the importance of employing accurate and reliable methods to assess image data similarity, particularly in the domain of facial images, which pose unique challenges due to subtle variations. We propose a comprehensive solution that harnesses hashing techniques and homomorphic encryption. By doing so, our approach ensures secure data exchange while safeguarding confidentiality and integrity. We firmly believe that our approach will substantially improve collaboration in law enforcement efforts and make significant contributions to global security.

关键词： Data privacy Law enforcement Scalability Education Collaboration Fingerprint recognition Regulation

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Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment

Realizing Predictive Maintenance in Production Machinery Thr...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Gabriel Filios Stylianos Karatzas Marios Krousarlis Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Civil Engineering Department University of Patras Greece Department of Computer Science University of Liverpool UK

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed.

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Analysis for optimizer based on spiking-neural oscillator networks with a simple network topology

Analysis for optimizer based on spiking-neural oscillator ne...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Tomoyuki Sasaki Hidehiro Nakano Department of Informatics Shonan Institute of Technology Fujisawa-shi Kanagawa Japan Department of Computer Science Tokyo City University Setagaya-ku Tokyo Japan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are one of the deterministic PSO methods, which are based on dynamics of spiking neural-oscillator networks. In OSNNs, each particle consists of plural spiking oscillators which are coupled with other spiking oscillators by the Ring 1-way network. The Ring 1-way network affects search performances of OSNNs. However, an effectiveness of the Ring 1-way network has not been clarified. In this study, we theoretically analyze the Ring 1-way OSNNs in more detail. In addition, in order to demonstrate analytical results, we conduct numerical simulations and discuss the simulation results.

关键词： Network topology Simulation Numerical simulation Particle swarm optimization Oscillators

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Development of Mutal Recognition of Education Approach in Advanced computer-Oriented Engineering Technologies in the Baltic Region 64

Development of Mutal Recognition of Education Approach in Ad...

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64th IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2023

作者： Kunicina, Nadezhda Rassolkin, Anton Bruzgiene, Rasa Plonis, Darius Caiko, Jelena Riga Technical University Institute of Electrical Engineering and Electronics Feee Riga Latvia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Kaunas University of Technology Faculty of Informatics Department of Computer Sciences Kaunas Lithuania Vilnius Gediminas Technical University Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350317732

The continued improvement and development of study programmes content for mutual recognition of study programmes among the largest technical universities in the Baltic region provides a very good opportunity for regional development as it brings a good advancement in academics and research at the involved universities, allows for more flexible and reliable collaboration with industrial partners, and provides an opportunity to develop new technologies and implement them for regional support. The goal of the development is to promote the involvement of students in the design of adaptive systems by providing them with the necessary set of skills that will allow them to immediately engage in the design of innovative adaptive systems and successfully solve technological challenges that have been identified through the cooperation of educational and research institutions. The adoption of an innovative educational strategy will aid in the creation of new technology and creative solutions even before students begin their studies. Collaboration between academic personnel and industry will help students build their abilities. © 2023 IEEE.

关键词： Students

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COMPLETE VISIBILITY ALGORITHM FOR AUTONOMOUS MOBILE LUMINOUS ROBOTS UNDER AN ASYNCHRONOUS SCHEDULER ON GRID PLANE

arXiv

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arXiv 2023年

作者： Kim, Yonghwan Katayama, Yoshiaki Wada, Koichi Department of Computer Science Nagoya Institute of Technology Aichi Japan Department of Applied Informatics Hosei University Tokyo Japan

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relationship between each robot’s capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane such that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors. © 2023, CC0.

关键词： Visibility

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