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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Modrzejewski, Mateusz Szachewicz, Jakub Rokita, Przemyslaw Division of Computer Graphics Institute of Computer Science The Faculty of Electronics and Information Technology Warsaw University of Technology Nowowiejska 15/19 Warsaw00-665 Poland

ISBN: (纸本)9783030614003

In this paper we have presented a method for musical genre classification using neural networks. We have used two algorithms (CNN and PRCNN) and two graphical representations: chromograms and spectrograms. We have used a large dataset of music divided into eight genres, with certain overlapping musical features. Key, style-defining elements and the overall character of specific genres are represented in our proposed visual representation and recognized by the networks. We show that the networks have learned to distinguish between genres upon features observable by a human listener and compare the metrics for the network models. Results of the conducted experiments are described and discussed, along with our conclusions and comparison with similar solutions. © 2020, Springer Nature Switzerland AG.

关键词： Music

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Bridging Clinical Gaps: Multi-Dataset Integration for Reliable Multi-Class Lung Disease Classification with DeepCRINet and Occlusion Sensitivity

Bridging Clinical Gaps: Multi-Dataset Integration for Reliab...

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IEEE Symposium on computers and Communications (ISCC)

作者： Naeem Ullah Javed Ali Khan Ivanoe De Falco Giovanna Sannino Department of Electrical Engineering and Information Technology University of Naples "Federico II" Naples Italy Department of Computer Science University of Hertfordshire Hatfield UK Institute for High-Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

This research presents DeepCRINet, a deep learning (DL) model designed for reliable performance across various Chest Radiography Images (CRIs) datasets, in response to the urgent need for quick and accurate lung disease identification utilizing CRIs. Our method builds on earlier research, which frequently used single-source datasets that might not adequately represent the heterogeneity present in clinical situations. Our model’s diagnostic adaptability and real-world dependability are improved by utilizing images from different datasets, which helps us overcome limitations such as dataset bias, robustness, generalizability, and underrepresentation of conditions. With validation on a broad dataset consisting of 14,096 images (from three different datasets), DeepCRINet provides a solution that demonstrates excellent flexibility in recognizing illnesses including TuBerculosis, Pneumonia, COVID-19, and Lung Opacity. Through data augmentation, we improve the dataset, supporting training and testing procedures and confirming the model’s ability to generalize. We used occlusion sensitivity as a kind of explainable AI to openly identify and visually emphasize regions important to proper classification. This ability not only shows that DeepCRINet is analytically better than other DL models and hybrid techniques, but it also improves patient outcomes and diagnosis, which makes it a vital tool for medical professionals like radiologists.

关键词： COVID-19 Training Analytical models Pneumonia Accuracy Tuberculosis Sensitivity analysis Robustness Diagnostic radiography Testing

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A Queueing-theoretic Analysis of the Performance of a Cloud Computing Infrastructure: Accounting for Task Reneging or Dropping

A Queueing-theoretic Analysis of the Performance of a Cloud ...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Kuaban, Godlove Suila Soodan, Bhavneet Singh Kumar, Rakesh Czekalski, Piotr Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Chandigarh University Department of Mathematics Punjab 140413 India Namibia University of Science and Technology Department of Mathematics and Statistics Namibia Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Cloud computing has revolutionized the information technology era. It offers high-speed computing, storage, and ICT resources on demand. One of the significant challenges in cloud computing is the impact of impatient users or request (task) reneging. When a request has been compromised, missed its execution deadline, or depends on other rejected ones, it must be removed from the queue without being processed. The reneging or removal of tasks from queues may trigger the reneging or removal of other tasks that depend of them. We refer to this reneging of requests from the load balancing or computing queues as correlated reneging. We presented the performance analysis of a network of queues that constitute a queueing model of a simplified cloud computing infrastructure. We show the relationship between the load, delay, and probability of buffer saturation (blocking probability). It is seen that at about 80% utilization, a small increase in utilization (due to a small increase in the arrival rate Δλ for a fixed service rate μ) results in a sharp increase in the delay experienced by the tasks, and on the probability of task rejection or blocking. © 2022 IEEE.

关键词： Cloud computing

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Significance of Single Interval Discrete Attributes: Case Study on 2-Level Discretisation

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SSRN 2022年

作者： Stańczyk, Urszula Zielosko, Beata Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 Sosnowiec41-200 Poland

Supervised discretisation is widely considered as far more advantageous form of transformation than unsupervised processing of attributes. When evaluating candidate cut-points for splitting the ranges of values of discretised features, the supervised methods help to preserve informative content of a variable, which is useful for classification. After a discretisation process, based on employed criteria, some attributes can be found as irrelevant, with all their values represented in discrete domain by a single interval. In consequence, such variables are removed from considerations, and no knowledge is mined from them. The paper presents research works focused on extended 2-level transformations of attributes values, with combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were divided by unsupervised methods with varying numbers of bins. Resulting variants of data were subjected to selected data mining techniques and performance of chosen classifiers evaluated and compared. The experiments were performed on stylometric task of authorship attribution. © 2022, The Authors. All rights reserved.

关键词： Pattern recognition

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Kvc-Ongoing: Keystroke Verification Challenge

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SSRN 2024年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Acien, Alejandro Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Großmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskóla, Przemys law Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Germany Department of Computer Science Technical University of Darmstadt Germany Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Kazakhstan Regensburg University Of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK – National Research Institute Poland

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects’ age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab. © 2024, The Authors. All rights reserved.

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EEG and HRV-Based Assessment of Neurosurgeons Training for Anxiety Regulation and Stress Monitoring 2

EEG and HRV-Based Assessment of Neurosurgeons Training for A...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Arpaia, Pasquale Carone, Giovanni Castelli, Nicolò D'Errico, Giovanni Gargiulo, Ludovica Maffei, Luigi Mastrati, Giovanna Moccaldi, Nicola Nalin, Marco Perin, Alessandro Picciafuoco, Mauro Puttilli, Cosimo Ramos, Pedro M. Robbio, Rachele University of Naples Federico Ii Department of Electrical Engineering and Information Technology Naples Italy University of Naples Federico Ii Naples Italy I.R.C.C.S. Carlo Besta Neurological Institute Milan Italy Polytechnic University of Turin Department of Applied Science and Technology Turin Italy Instituto Superior Técnico University of Lisbon Department of Electrical and Computer Engineering Lisbon Portugal University of Salento Department of Engineering for Innovation Lecce Italy Ab Medica Spa Milan Italy

ISBN: (纸本)9798350300802

A neurofeedback (NF) -supported training is proposed to enable neurosurgeons to learn how to regulate their emotions. Electroencephalographic (EEG) signal and heart rate (HR) of 5 neurosurgeons were acquired in 4 sessions while performing NF-based emotion regulation (ER). Subjects tried to counteract anxious and stressful states through NF, that is by decreasing the power in the beta band measured in the midline areas of the scalp. The assessment of the neurofeedback effectiveness was based on the use of Heart Rate Variability (HRV) in combination with EEG. Therefore, EEG signal was exploited in real-time to guide the feedback and also in post processing, together with the HRV, to assess the effectiveness of the whole training. As the EEG signal is concerned, the results showed that the power in the beta band decreased within each session when the ER ability was strengthened, as reported in the literature. On the contrary, the HRV did not exhibit the expected increasing trend within each trial. When considering each the session on the whole, thus computing the HRV on a greater time interval, the HRV during the NF training is typically higher than the HRV computed during the rest and negative baseline phases. © 2023 IEEE.

关键词： EEG emotion regulation HRV Neurofeedkack surgery

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Enhanced Lung Cancer Classification and Prediction based on Hybrid Neural Network Approach

Enhanced Lung Cancer Classification and Prediction based on ...

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International Conference on Communication and Electronics Systems (ICCES)

作者： A. Gopinath P. Gowthaman Lisa Gopal Md. Abul Ala Walid M Manju Priya K Keshav Kumar Department of Electronics and Communication Science DRBCCC Hindu College Pattabiram Chennai Tamilnadu India Department of Electronics Erode Arts and Science College Erode Tamilnadu India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Department of Computer Science Shift II Anna Adarsh College for Women Chennai Tamilnadu India Department of Humanities and Mathematics G. Narayanamma Institute of Technology and Science (for Women) Hyderabad Telangana India

Cancer is the leading killer on a global scale. As the leading cause of cancer-related mortality, lung cancer is among the most prevalent forms of the disease. Uncontrolled cell growth in the lung tissues is the hallmark of lung cancer, a potentially fatal malignancy. This uncontrolled growth can spread to nearby tissues and other sections of the body if the disease is not treated in its early stages. Because there are few or no signs in the early stages of this disease, most cancer cases are typically detected in the later stages, making early detection particularly challenging. Survival rates can be increased via early detection and treatment of lung cancer. And early detection of lung cancer is a crucial step in this direction. Greyscale conversion, image scaling, and noise removal are all part of preprocessing once the input image has been provided. The gaussian filter is used to improve the quality of an image. Following segmentation using the watershed threshold method, features are extracted using area, perimeter, and eccentricity, and the model is trained using an enhanced GAN-mask region-based CNN. In this proposed approached a novel model the GAN-R-CNN model that combines the GAN with the CNN technique.

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

Enhancing IoT Intelligence: A Transformer-based Reinforcemen...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Gaith Rjoub Saidul Islam Jamal Bentahar Mohammed Amin Almaiah Rana Alrawashdeh Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi UAE King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran KSA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decisionmaking efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

关键词： Intelligent automation Navigation Decision making Ecosystems Smart homes Reinforcement learning Transformers

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Innovative Deep Learning Techniques for Solving Complex computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： T. Jayachitra S. Sathya Ramesh P S Jothikumar R Gouri M. S Mohammed Waheeduddin Hussain Department of Electrical Electronics and Communication Engineering Sharda University Greater Noida India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tiruttani India Department of Smart Computing Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Shadan College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Baselios Mathews - II College of Engineering Sasthamcotta Kollam India Department of Information Technology Shadan College of Engineering and Technology Peerancheru Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications.

关键词： Deep learning computer vision Visualization Accuracy Network topology Neural networks Data augmentation Topology Pattern recognition Object recognition

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Atrial Fibrillation Detection Based on Deep Learning Models

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Procedia computer science 2022年 207卷 3752-3760页

作者： Adrian Iftene Alexandru Burlacu Daniela Gifu Faculty of Computer Science “Alexandru Ioan Cuza” University General Berthelot 16 700483 Iasi Romania “Grigore T. Popa” University of Medicine and Pharmacy Universității 16 700115 Iasi Romania Institute of Computer Science Romanian Academy - Iasi branch Bulevardul Carol I 8 700505 Romania

Atrial fibrillation is the most common sustained heart rhythm abnormality in clinical practice that can lead to well-known medical complications associated with increased mortality. The diagnosis of atrial fibrillation can be detected by using a short electrocardiogram recording (ECG). However, because the heartbeat is irregular and does not constantly present on a simple electrocardiogram, a single diagram is not enough for a final and certain diagnosis. To monitorizing one single patient requires hours of monitoring, important costs, and low yield. Our aim is to develop a rapid, inexpensive way to identify patients with atrial fibrillation using neural networks. This study serves to help the clinician with an automatic approach to give a quick and safe diagnosis for each patient population. The experiments show that this approach offers a promising atrial fibrillation classification and outperforms recently published studies that either use extracted features or raw data separately.

关键词： atrial fibrillation detection MIT-bih dataset ECG signals classification model performance

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