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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Kadu, Renuka Kadu, Akshay Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Nagpur India Yeshwantrao Chavan College of Engineering Department of Electrical Engineering Nagpur India

ISBN: (纸本)9798350381931

Providing Quality of Service (QoS) under a variety of network conditions and security threats has become more difficult as the demand for large-scale wireless networks based on blockchain technology has increased. We address these issues and significantly raise the overall QoS of blockchain-based networks in this paper by presenting an effective incremental learning bioinspired model This work is necessary because large-scale wireless networks must significantly improve their energy efficiency, throughput, delay reduction, and packet delivery ratio. Existing models frequently have difficulty meeting these demands and reducing the effects of different attacks, including Finney, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), Sybil, and Masquerading scenarios. We suggest a novel strategy that combines two optimization techniques to get around these restrictions. In order to create sidechains, we first use Grey Wolf Optimization (GWO), which improves network partitioning and scalability in blockchain-based networks. Our model efficiently distributes the computational load and boosts system performance by dynamically adjusting the sidechain formation. In order to choose the best miner nodes for data mining between network nodes, we integrate Q Learning in the second step. The Q Learning algorithm makes intelligent decisions about the best miner nodes by taking into consideration parameters like throughput, latency, and energy efficiency. This deft choice of miner nodes improves network performance and QoS overall while optimizing data mining process. Through a thorough examination of spatial and temporal parameters, consensus is attained, allowing the system to assess the accuracy and dependability of mined blocks. This guarantees the blockchain network's integrity and security. Results from experiments show how effective our suggested model is. Our model improves energy efficiency by 8.5 percent, delays are cut by 10.4 percent, throughput is increased b

关键词： Blockchain

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Introduction to Quantum Multi-Agent Reinforcement Learning: Concepts and Applications 15

Introduction to Quantum Multi-Agent Reinforcement Learning: ...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Cho, Yeryeong Bin Son, Seok Park, Soohyun Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantum is increasing. Because of efficiency in high action dimensions, quantum computing has advantages in multi-agent fields where the amount of data is huge. At the same time, reinforcement learning (RL) has gained prominence for its potential in unknown environments. Consequently, the integration of quantum computing and multi-agent reinforcement learning (MARL), known as quantum multi-agent reinforcement learning (QMARL), is proposed. This paper introduces the overall concept and applications of quantum computing, MARL, and QMARL. It also shows the current technology trends depending on the application. In conclusion, the paper presents challenges and future research directions for QMARL and shows that QMARL can be utilized in various applications for excellent performance. © 2024 IEEE.

关键词： Quantum computers

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Classification of Sleep Stages Using Single-Channel ECG Signals: A Comparative Analysis of Machine Learning and Deep Learning Methods 14

Classification of Sleep Stages Using Single-Channel ECG Sign...

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Ullah, Ahmad Khan, Muhammad Salman College of Engineering Qatar University Department of Computer Science and Engineering Doha Qatar College of Engineering Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350366556

In this paper, we provide an extensive evaluation of machine learning (ML) and deep learning (DL) methods for automatic sleep stage classification using a single-channel electrocardiogram (ECG) signal. To explore ML methods, we extracted 21 heart rate variability (HRV) features using the R-peak detection. These features were used to train and compare the performance of various models such as support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (K-NN), and Naïve Bayes (NB). Furthermore, we analyzed the DL methods to overcome the manual feature extraction. This method involved feeding raw time-series ECG signal to a 1-dimensional convolutional neural network (CNN) and short-time Fourier transform (STFT) based spectrogram to a 2D CNN. The overall evaluation was performed using 15-subject data each from two publicly available datasets. To process the data for analysis, resampling, bandpass filtering, baseline correction, 30-second interval extraction from whole-night ECG recordings, and z-score normalization techniques were applied. We conducted experiments on one dataset and then replicated the experiments on the other dataset to assess the robustness and generalizability of the methods. Through a comparative analysis, the spectrogram-based 2D CNN emerged as the effective method with the highest test accuracy and k-score of 87.65% and 0.82 respectively. This study contributes to ECG-based sleep classification research, highlighting the effectiveness of deep learning for effective classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Quantum Teleportation: A Premier Quantum Communication Protocol 15

Quantum Teleportation: A Premier Quantum Communication Proto...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Im, Chaemoon Park, Soohyun Kim, Joongheon Korea University Department of Electrical and Computer Engineering Seoul Korea Republic of Sookmyung Women's University Division of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350364637

Communication is one of the fields that adopts quantum characteristics to improve its potential. In particular, quantum teleportation (QT) is considered a fundamental tool for enabling quantum networks. This paper focuses on the concept of QT and how it is applied in conjunction with additional technologies such as entanglement purification, entanglement swapping, and quantum error correction. © 2024 IEEE.

关键词： Quantum communication

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Reinforcement Learning Approach to Velocity and Position Control of Metro Trains 23

Reinforcement Learning Approach to Velocity and Position Con...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Lee, Kyungbae Lee, Seungyeop Kim, Seunghyeon Eun, Yongsoon Dgist Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of

ISBN: (纸本)9788993215274

Velocity and position control for metro trains is typically achieved by classical control methods (PID, etc). Challenges in this control problem include imprecise position sensing, time delay, and external disturbances due to weight changes, curves, and slopes of the rails. In order to achieve acceptable stop position of the trains at each station, the controller design often involves individual gain tuning for each sections in the route, which consumes much time and effort. As a means to reduce the effort, reinforcement learning approach is looked into for train control. Automatic Train Operation (ATO) simulator capable of realistic simulation of train dynamics along the Line 5 in Seoul Metro is used to investigate the feasibility of this approach. Results are discussed from the perspective of practicality. © 2023 ICROS.

关键词： Time delay

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A Natural Approach for Synthetic Short-Form Text Analysis 30

A Natural Approach for Synthetic Short-Form Text Analysis

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Shao, Ruiting Schwarz, Ryan Clifton, Christopher Delp, Edward J. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Department of Computer Science Purdue University West LafayetteIN United States

ISBN: (纸本)9782493814104

Detecting synthetically generated text in the wild has become increasingly difficult with advances in Natural Language Generation techniques and the proliferation of freely available Large Language Models (LLMs). Social media and news sites can be flooded with synthetically generated misinformation via tweets and posts while authentic users can inadvertently spread this text via shares and retweets. Most modern natural language processing techniques designed to detect synthetically generated text focus primarily on long-form content, such as news articles, or incorporate stylometric characteristics and metadata during their analysis. Unfortunately, for short form text like tweets, this information is often unavailable, usually detached from its original source, displayed out of context, and is often too short or informal to yield significant information from stylometry. This paper proposes a method of detecting synthetically generated tweets via a Transformer architecture and incorporating unique style-based features. Additionally, we have created a new dataset consisting of human-generated and Large Language Model generated tweets for 4 topics and another dataset consisting of tweets paraphrased by 3 different paraphrase models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Large datasets

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Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber

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Journal of King Saud University - engineering sciences 2023年第2期35卷 92-100页

作者： Ray, Sourav Rahman, Md Masnun Haque, Mohaiminul Hasan, M. Washif Alam, M. Manjurul Department of Civil and Environmental Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Department of Electrical and Computer Engineering The University of Texas at San Antonio San Antonio United States

Waste management has become a new challenge for the construction industries since rapid urbanization is taking place worldwide. Ceramic waste is one such material which is being originated from construction sites and industries, imposing a significant risk to the environment due to its non-biodegradable nature. With the goal of waste utilization, this study aims to predict the compressive and splitting tensile strength of concrete made with waste Coarse Ceramic aggregate (CCA) and Nylon Fiber (NF) by using two distinct machine learning algorithms, namely, Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). A comprehensive data set for testing and training the models containing 162 records of compressive and splitting tensile strength test results were considered from nine mix proportions. For training the dataset, parameters like cement content, sand content, stone content, ceramic content, nylon fiber content, curing duration, and concrete strength were taken as input variables. The predicted strengths obtained from the SVM and GBM based models are found to be in close agreement with the experimental results. In terms of coefficient of determination (R2), GBM showed significantly better result for both compressive strength (e.g., SVM Overall R2 = 0.879 & GBM Overall R2 = 0.981) and tensile strength (e.g., SVM Overall R2 = 0.706 & GBM Overall R2 = 0.923) prediction. Furthermore, based on the statistical accuracy measures like the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), it has been observed that GBM has yielded much better performance compared to SVM in predicting the mechanical strength of concrete. © 2021 The Authors

关键词： Support vector machines

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Impact of UV content of illumination on acceptability of colour reproduction 12

Impact of UV content of illumination on acceptability of col...

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12th Colour and Visual Computing Symposium, CVCS 2024

作者： Nussbaum, Peter Gudzenchuk, Anastasiia Green, Phil Faculty of Information Technology and Electrical Engineering Department of Computer Science NTNU Norway Department of Science and Technology Linköping University Sweden

Paper and board for printing commonly contains optical brightening agents that fluoresce in the presence of UV radiation, making the prints appear brighter and bluer. If the relative amount of optical brightening agents (OBAs) is not consistent between proof and print, and the UV content of measurement and the viewing illumination differs, there will be a colorimetric difference between proof and print. Colour management workflows are normally media-relative, and visual adaptation to the media white can conflict with the goals of colorimetric matching. The impact of UV content in measurement and viewing illumination on the visual acceptability of colour reproductions was investigated in a series of psychophysical experiments, using substrates with varying OBA concentrations and a media-relative colour reproduction workflow. The results showed that visual acceptability was largely unaffected by UV content of measurement or viewing illumination, suggesting that visual adaptation to media white discounts the effect of fluorescence. © 2024 Copyright for this paper by its authors.

关键词： Colorimetry

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SAMLFDiag: SAM Generated Latent Segmentation Features for Disease Diagnosis 9

SAMLFDiag: SAM Generated Latent Segmentation Features for Di...

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9th International Workshop on Pattern Recognition

作者： Roy, Tarun Kanti Zaman, Fahim Ahamed Wu, Xiaodong Department of Computer Science The University of Iowa United States Department of Electrical and Computer Engineering The University of Iowa United States Department of Radiation Oncology The University of Iowa United States

ISBN: (数字)9781510685772

ISBN: (纸本)9781510685765

Numerous studies have demonstrated significant correlations between segmented pathological objects in various medical imaging modalities and disease-related pathology. While previous investigations have employed handcrafted features for disease prediction, those approaches neglect the vast potential of leveraging latent features from deep learning (DL) models, which could potentially enhance the overall accuracy of differential diagnosis. Recently, the Segment Anything Model (SAM) has demonstrated remarkable zero-shot segmentation capabilities for natural images and garnered significant attention for its potential applications in medical image segmentation. However, to the best of our knowledge, no studies have explored leveraging the latent features extracted through SAM’s encoder for medical image classification. In this paper, we propose the novel SAMLFDiag method, which harnesses the latent features generated by MedSAM for benign and malignant breast cancer classification. Our proposed model leverages the encoded features from MedSAM’s Vision Transformer (ViT) to maximize the attribute-related information contained within the image features. By exploiting the powerful segmentation capabilities of SAM, our approach aims to extract and utilize the most informative and discriminative features for breast cancer classification. Experiments on a public ultrasound breast cancer dataset were conducted to validate the effectiveness of SAMLFDiag, demonstrating its ability to outperform baseline deep learning models for breast cancer classification. Our work highlights the potential of leveraging state-of-the-art foundation segmentation models for enhancing disease diagnosis through latent feature extraction and zero-shot learning. © 2024 SPIE.

关键词： Ultrasonic imaging

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Leveraging AI for enhanced cybersecurity: a comprehensive review

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Discover Applied sciences 2025年第6期7卷 1-27页

作者： Swetha, Thammisetty Kumaran, U. Meena, V.P. Hameed, Ibrahim A. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India Department of ICT and Natural Sciences Norwegian University of Science and Technology Trondheim7034 Norway

Cybersecurity has become a serious concern in this digital age, with ever-increasing threats aimed at individuals, enterprises, and countries across the globe. AI has been rapidly emerging as an influential technology that possess enormous abilities to enhance data protection measures by automating threat recognition, vulnerability assessment, and incident response. This study focuses on comprehensively examining current literature works dedicated to AI along with its applications in cybersecurity and presents an innovative architecture to deal with challenges that pose enormous risks to worlds community. Explore advanced techniques, including Machine learning (ML) and deep learning (DL), and their implementation in threat intelligence, intrusion recognition, malware analysis, phishing detection, and user authentication. Furthermore, the propounded review deliberates on various limitations such as data scarcity, model interpretability, and vulnerability to adversarial attacks can hinder the effectiveness of existing models in real-world cybersecurity scenarios, leading to reduced accuracy, slower response times, and increased susceptibility to evasion techniques. The future advances, including the evolution of explainable AI, predictive analytics, quantum computing, and real-time management capabilities, are also emphasized. This review paper is intended to offer a systematic understanding of modern AI techniques and prospective solutions in enriching the cybersecurity domain, offering valuable insights for researcher scholars and professionals in diverse fields. © The Author(s) 2025.

关键词： Deep learning

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