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Software Analysis, Evolution and Reengineering - Companion (SANER-C), IEEE International Conference on

作者： Giuseppe Antonio Pierro Andy Amoordon Dep. of Mathematics and Computer Science University of Cagliari Italy Dep. of Mathematics and Computer Science Université Paris 1 Panthéon-Sorbonne Paris France

ISBN: (数字)9798350351576

ISBN: (纸本)9798350351583

The Ethereum blockchain has confronted challenges related to unconfirmed transactions and high costs for adding transactions to blocks since its inception when it adopted the proof-of-work (PoW) consensus. On the 15th of September 2022, a notable transition occurred within the Ethereum network as it formally embraced a new consensus mechanism known as proof-of-stake (PoS). Numerous studies have investigated the issue of unconfirmed transactions and high fees when Ethereum operated under the PoW consensus. In contrast to other studies, this research examines unconfirmed transactions and the costs associated with mining under the new PoS consensus. In this study, data was gathered over a three-week period, from December 1st to December 21st, 2023. The analysis of this dataset reveals that despite the transition to the new PoS system, both the costs and the number of unconfirmed transactions remain significantly higher than the expectations that were set regarding the adoption of PoS.

关键词： Proof of stake Costs Fluctuations Smart contracts Pricing Proof of Work Stability analysis

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation 20

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Wu, Tung-Ling Kung, Chih-Ming Lin, Guan-Ting Tsai, Kuen-Yu Meng, Guang-Yun Huang, Yu-Che Cheng, Wan-Shu Institute of Transportation at Ministry of Transportation and Communications Taiwan Shin Chin Univeristy Kaohsiung Campus Dep. of Information Technology&Communication Taiwan Providence University Dep. of Computer Science and Information Management Taichung Taiwan National Taiwan Univeristy Department of Electrical Engineering Taipei Taiwan Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics Ntu Taiwan

ISBN: (纸本)9798350365856

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Plant-Leaf Diseases Classification using CNN, CBAM and Vision Transformer 5

Plant-Leaf Diseases Classification using CNN, CBAM and Visio...

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5th International Symposium on Informatics and its Applications, ISIA 2022

作者： Chougui, Abdeldjalil Moussaoui, Achraf Moussaoui, Abdelouahab University of Ferhat Abbes Setif I Dep. of Computer Science Setif Algeria

ISBN: (数字)9781665474733

ISBN: (纸本)9781665474733

Detecting plant diseases is usually difficult without an experts knowledge. In this study we want to propose a new classification model based on deep learning that will be able to classify and identify different plant-leaf diseases with high accuracy that outperforms the state of the art approaches and previous works. Using only training images, CNN can automatically extract features for classification, and achieve high classification performance. We used two datasets in this study, PlantVillage dataset containing 54,303 healthy and unhealthy leaf images divided into 38 categories by species and disease, and Tomato dataset containing 11,000 healthy and unhealthy tomato leaf images with nine diseases to train the models. We propose a deep convolutional neural network architecture, with and without attention mechanism, and we tuned 4 pretrained models that have been trained on large dataset such as MobileNet, VGG-16, VGG-19 and ResNET. We also tuned 2 ViT models, the vit b32 from keras and the base patch 16 from google. Our porposed model obtained an accuracy up to 97.74%. The pretrained models gave an accuracy up to 99.52%. And the ViT models obtained an accuracy up to 99.7%. This study may aid in detecting the plant leaf diseases and improve life conditions to plants which will improve quality of humans life. © 2022 IEEE.

关键词： Convolutional neural networks

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Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

arXiv

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arXiv 2024年

作者： Varotto, Matteo Heinrichs, Florian Schürg, Timo Tomasin, Stefano Valentin, Stefan Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy Dep. of Medical Engineering and Technomathematics FH Aachen University of Applied Sciences Germany

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time. © 2024, CC BY-NC-ND.

关键词： 5G mobile communication systems

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Machine Learning Application to Transmission Quality Assessment in Optical Networks 30

Machine Learning Application to Transmission Quality Assessm...

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30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Kozdrowski, Stanislaw Cichosz, Pawel Sujecki, Slawomir Warsaw University of Technology Institute of Computer Science Warsaw Poland University of Science and Technology Telecommunications and Teleinformatics Dep. Wroclaw Poland

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

This paper presents the application of 2 selected machine learning methods for transmission quality assessment in optical networks. The proposed solution uses real data to train classification models that predict whether a given optical channel meets transmission quality requirements. The training data is extracted directly from the running network through the control plane. Several attribute selection strategies have been analysed. The available attributes are divided into several subsets. The performance of the selected machine learning algorithms for each subset is compared using ROC and PR curves. The results are used to evaluate the predictive performance of the selected machine learning algorithms when applied to the optical channel classification task and to determine how this performance is related to the set of attributes used. © 2022 University of Split, FESB.

关键词： Machine learning

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Design and Implementation of Minkowski Feature Selection for Machine Learning Techniques

Design and Implementation of Minkowski Feature Selection for...

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information technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Ekram Hakim Dhiah Al-Shammary Ahmed M. Mahdi Dep. Computer Science College of computer science and information technology University of Al-Qadisiyah Diwaniyah Iraq

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

This paper proposes a design and implementation of a highly efficient feature selection based on Minkowski’s mathematical similarity for machine learning. Moreover, the development of the Minkowski feature selection can be general for intelligent datasets and machine learning. Intelligent optimizers and feature selection represent an advanced stage in machine learning and innovative computer applications by reducing the overall number of features to be included in the classification function process. This paper analyses Minkowski’s selection of best factors for EEG Bonn University data. Minkowski cancels the random movement and systematically limits the number of features and its point-to-point movement in order to promote exploration and reduce the probability of falling into stagnation. The main measures to analyze this research are Accuracy, precision, Recall, and F1score assessment. Moreover, the accuracy of Minkowski’s selection provided better classification results than some of the other approaches (100%)

关键词： Machine learning algorithms Limiting Machine learning computer applications Feature extraction Mathematical models Electroencephalography

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Clinical Informatics System to Understand Lung Cancer Using Transfer Learning-Based computer-Aided Application 7

Clinical Informatics System to Understand Lung Cancer Using ...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Abugabah, Ahed Mehmood, Atif Shahid, Farah College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Zhejiang Normal University Dep. of Computer Science and Technology Jinhua321002 China

ISBN: (纸本)9798350342154

Lung cancer is the second leading cause of death worldwide after cardiovascular disease. Due to late diagnosis, lung cancer patients have an extremely low survival rate when compared to other cancer patients. Hence, early lung cancer detection is critical for patients to undergo early therapies, boosting their chances of surviving or potentially becoming cancer-free. Over the past few years, researchers have developed several deep learning-based computer-aided diagnostic tools to detect lung cancer early. As a consequence, deep learning models are readily susceptible to over-fitting for fewer data samples and can be computationally time-consuming to pick hyper-parameters, which usually leads to a reduction in performance. Our proposed technique came up with a solution to this challenge of lung cancer classification tasks that used a layer-wise transfer learning strategy. In this work, three types of data samples such as Lung adenocarcinoma (Lung a), lung squamous cell carcinoma (Lung scc), and lung benign (Lung b) have been taken and implemented models, namely Group-I, II, III with frozen layers and baseline algorithms as VGG-19. To check the validity of the proposed models on lung samples and attain the best classification accuracy on Lung scc vs. Lung b 97.53%, also discriminate between Lung a vs. Lung b testing accuracy 92.59%. In contrast, the remaining Lung scc vs. Lung a attained 89.30%. Finally, a comparison to other studies demonstrating the suggested model superiority over state-of-the-art models in testing accuracy © 2023 IEEE.

关键词： Diseases

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An Enhanced Subject-Indep.ndent Approach for Hand-Based Micro Activities Recognition

An Enhanced Subject-Independent Approach for Hand-Based Micr...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Florenc Demrozi Fadi Al Machot Dep. of Electrical Engineering and Computer Science University of Stavanger Norway Dep. of Data Science Norwegian University of Life Sciences Norway

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Human activity recognition (HAR) is crucial in various fields, including healthcare, assistive technologies, and human-computer interaction. Recognizing hand-based micro activities presents a unique challenge due to their complexity and variability. In this paper, we propose a novel HAR methodology to identify 24 distinct hand-based Activities of Daily Living (ADLs) using data from a wrist-weared inertial sensor. Our approach, tested on a dataset of 30 participants, employs a unique two- level segmentation and lightweight machine-learning model to handle the complex dynamics of micro-ADLs. The experimental outcomes, showcasing an overall accuracy of 84% and precision, recall, and F1-Scores above 83% underscore the method's efficacy and subject-indep.ndent reliability compared to the existing literature on micro-ADL recognition.

关键词： Human computer interaction Inertial sensors Tiny machine learning Medical services Assistive technologies Complexity theory Human activity recognition

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Comparative Analysis of ML Algorithms for Fraud Detection

Comparative Analysis of ML Algorithms for Fraud Detection

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Smart information Systems and Technologies (SIST), IEEE International Conference on

作者： Gulnara Abitova Miras Abalkanov Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Binghamton NY USA Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.

关键词： Training Logistic regression Machine learning algorithms Accuracy Sensitivity Credit cards Fraud

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