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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor

Defect Recognition based on Radon Transform in Pentacene org...

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Sciuto, Grazia Lo Linde, Sivan Shikler, Rafi Kowol, Pawel Coco, Salvo Capizzi, Giacomo Silesian University of Technology Department of Mechatronics Gliwice Poland Ben-Gurion University of the Negev School of Electrical and Computer Engineering Optoelectronic Organic Semiconductor Devices Laboratory Department of Electrical and Computer Engineering Beer-Sheva Israel Ben-Gurion University of the Negev Ilse Katz Institute for Nanoscale Science and Technology Beer-Sheva Israel University of Catania Department of Electrical Electronics and Informatics Engineering Catania Italy

ISBN: (纸本)9798350348378

In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images. © 2023 IEEE.

关键词： Thin film transistors

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children

Advancing Color Perception: An IoT-driven Pedagogical System...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasandika Balage Nuwan Jayawardene School of Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles.

关键词： Human computer interaction Visualization Accuracy Visual impairment Education Lighting Color Blindness Signal processing Internet of Things

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Comparing the Effectiveness of Generative Adversarial Networks for Clustering Tasks 19

Comparing the Effectiveness of Generative Adversarial Networ...

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19th IEEE International Conference on computer Science and Information Technologies, CSIT 2024

作者： Lytvynenko, Volodymyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution 'Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine' Kyiv Ukraine

ISBN: (纸本)9798331542627

This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silhouette Score, Davies-Bouldin Index, Adjusted Rand Index, and Mutual Information. WGANs demonstrated the best clustering performance, while GANs showed promising results but were outperformed by WGAN. Contrary to expectations, WGAN-GP performed the worst. The research underscores the potential of GANs in clustering and highlights the need for further optimization based on specific datasets. © 2024 IEEE.

关键词： adjusted mutual information Davies-Bouldin index generative adversarial networks adjusted rand index mutual information silhouette index

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Sentiment Analysis of Game Reviews on STEAM using BERT, BiLSTM, and CRF

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLS...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Jalaluddin Al Mursyidy Fadhlurrahman Neng Ayu Herawati Hayyu Rachma Widya Aulya Ira Puspasari Nugraha Priya Utama School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Engineering Department Dinamika University Surabaya Indonesia

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context.

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Wireless spiking neural P systems

Journal of Membrane Computing

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Journal of Membrane Computing 2025年 1-14页

作者： Orellana-Martín, David Cabarle, Francis George C. Paul, Prithwineel Zeng, Xiangxiang Freund, Rudolf Research Group on Natural Computing Department of Computer Science and Artificial Intelligence SCORE lab I3US Universidad de Sevilla Sevilla Spain Department of Computer Science Quezon City Philippines Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science Hunan University Changsha China Faculty of Informatics TU Wien Vienna Austria

Spiking neural P systems (SN P systems) are computing models based on the third generation of neuron models known as spiking neurons. Recent results in neuroscience highlight the importance of extrasynaptic activities of neurons, that is, features and functioning of neurons outside their synapses. Previously it was thought that signals such as neuropeptides only assist neurons, but recently such signals have been given additional importance. Inspired by recent results, we define wireless SN P systems (WSN P systems). In WSN P systems, no synapses exist: regular expressions associated with each neuron are used to decide which spikes it receives. We provide two semantics of how to “interpret” the spikes released by neurons. A specific register machine is simulated to show the different style of programming WSN P systems compared to programming standard SN P systems and other variants. This style emphasizes a trade-off: WSN P systems can be more “flexible” since they are not limited by their synapses for sending spikes; however, losing the useful directed graph structure requires careful design of rules and expressions associated with each neuron. We use linear prime number encodings in constructing the expressions and rules of the neurons to prove that WSN P systems are Turing-complete in both spike semantics.

关键词： Extrasynaptic signaling Membrane computing Natural computing Neuropeptides Spiking neural P systems

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Learning from Multi-Omics Networks to Enhance Disease Prediction: An Optimized Network Embedding and Fusion Approach

Learning from Multi-Omics Networks to Enhance Disease Predic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hussein, Sundous Ramos, Vicente Liu, Weixuan Kechris, Katerina J. Lange, Leslie Bowler, Russell P. Banaei-Kashani, Farnoush University of Colorado Denver Department of Computer Science and Engineering DenverCO United States University of Colorado Anschutz Medical Campus Department of Biostatistics and Informatics AuroraCO United States University of Colorado Anschutz Medical Campus Division of Biomedical Informatics and Personalized Medicine AuroraCO United States Cleveland Clinic Main Campus Genomic Medicine Institute ClevelandOH United States

ISBN: (纸本)9798350386226

Understanding complex diseases hinges on a profound understanding of intricate biomolecular interactions unfolding within a complex, multidimensional landscape, challenging traditional methods to extract meaningful insights. While multi-omics networks capture the richness of biological data, providing a basis for predicting relationships between biomolecules and various phenotypic traits of complex diseases, their inherent complexity limits their predictive power. To address this challenge, we introduce a novel pipeline that leverages the power of Graph Neural Networks (GNNs) to extract and integrate meaningful information from multi-omics networks. By generating informative node embeddings and seamlessly incorporating them into the original subject-level data, our approach captures both local and global network dependencies, leading to substantial improvements in disease prediction accuracy. The proposed pipeline optimizes the embedding generation process for the specific prediction task, enabling the model to learn task-relevant representations. Through rigorous experimentation, we demonstrate the superior performance of our approach, surpassing existing methods by a substantial margin on nine real-world multi-omics datasets. With remarkable increases in accuracy ranging approximately from 8% to 10% over the best-performing baseline, particularly when the multi-omics networks are moderately dense, striking a balance between capturing complex relationships and avoiding excessive noise. Our findings underscore the potential of GNNs to significantly improve disease prediction by effectively extracting and representing knowledge embedded within multi-omics networks. © 2024 IEEE.

关键词： Graph neural networks

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Public key-based search on encrypted data using blockchain and non-blockchain enabled cloud storage: a comprehensive survey, analysis and future research scopes

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Cluster Computing 2025年第6期28卷 1-43页

作者： Mallick, Debjani Das, Ashok Kumar Varri, Uma Sankararao Park, Youngho Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea Computer Science and Engineering SRM University AP Amaravati India School of Electronic and Electrical Engineering Kyungpook National University Daegu South Korea

Cloud-based storage service has emerged as a promising alternative to managing local storage, offering users additional features, such as storage, backup, and restoration of various resources, including software, applications, and sensitive private information, within a virtual database, while ensuring data confidentiality and security. However, the widespread use of cloud services by individuals and organisations raises concerns regarding user accessibility and data security due to increasing social threats and adversarial attacks. Searchable encryption (SE) has been introduced to address these issues, providing a secure environment for a convenient and effective way of data searching and sharing. SE models have been advanced by integrating blockchain functionalities to tackle certain existing vulnerabilities and enhance user accessibility, data privacy, and integrity. This survey work explores various research works on SE techniques leveraging cloud and blockchain functionalities, discussing and categorizing the diverse approaches based on different criteria. This study also discusses the different enhancements made to SE techniques over the years, including the underlying requirements that led to the inclusion of blockchain functionalities. Moreover, this study provides a comparative analysis of existing survey works done in this area, which highlights a lack of recent literature surveys that thoroughly explore blockchain-based public key encryption with keyword search (PEKS) schemes. Particularly, we delve into the technical aspects of PEKS by classifying, analyzing and comparing different functionalities based on various aspects. The survey concludes with a comparative analysis of existing PEKS solutions and a discussion on identified research gaps, aiming to improve future research on PEKS approaches in this emerging field.

关键词： Searchable encryption Public key cryptosystem Blockchain Cloud storage Security

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Mechanical Ventilator Control System Using Low-Cost Pressure Sensors

Mechanical Ventilator Control System Using Low-Cost Pressure...

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2021 International Conference on computer Science, Information Technology, and Electrical engineering, ICOMITEE 2021

作者： Haq, Endi Sailul Suwardiyanto, Devit Rini, Eka Mistiko Sigit, Riyanto Kristanto, Sepyan Purnama State Polytechnic of Banyuwangi Informatics Engineering Banyuwangi Indonesia Electronic Engineering Polytechnic Institute of Surabaya Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781665401470

The increasingly active cases of people with the COVID- 19 viruses have reached a very worrying level in most countries globally. The availability of ventilators in hospitals is one factor that increases the number of deaths in Indonesia. In this study, we present a control system on a mechanical ventilator using an inexpensive pressure sensor to control pressure, flow rate, and volume during the process of inspiration and expiration. The implemented method uses the venturi meter concept by comparing two air pressure sensors flowed by the Ambu bag. The control system on this ventilator uses a microcontroller and MPX5050DP sensor. The system tries to maintain the PEEP value of 5 cmH2O, and the feedback obtained ranges from 2.7-5.16 cmH2O. At the same time, the expected flowrate value of 55 L/min can be maintained at a value of 53.9 - 59.5 L/min. The tidal volume, which functions as a limiter for inspiration and expiration, is set at a value of 400 ml;the feedback given by the sensor varies between 416 ml - 436 ml. Nevertheless, on the other hand, this system needs to be developed further because there are problems with sensor precision. © 2021 IEEE.

关键词： COVID-19

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Yogi, Kottala Sri Sharma, Avinash Dankan Gowda, V. Saxena, Rini Barua, Tarkeshwar Mohiuddin, Khalid Dept of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune Telangana Hyderabad India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India King Khalid University College of Business Department of Business Informatics PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350382723

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area. © 2024 IEEE.

关键词： Internet of things

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