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arXiv 2024年

作者： Follath, Theresa Mickisch, David Hemmerling, Jan Erasmi, Stefan Schwieder, Marcel Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Thünen Institute of Farm Economics Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

Using images acquired by different satellite sensors has shown to improve classification performance in the framework of crop mapping from satellite image time series (SITS). Existing state-of-the-art architectures use self-attention mechanisms to process the temporal dimension and convolutions for the spatial dimension of SITS. Motivated by the success of purely attention-based architectures in crop mapping from single-modal SITS, we introduce several multi-modal multi-temporal transformer-based architectures. Specifically, we investigate the effectiveness of Early Fusion, Cross Attention Fusion and Synchronized Class Token Fusion within the Temporo-Spatial Vision Transformer (TSViT). Experimental results demonstrate significant improvements over state-of-the-art architectures with both convolutional and self-attention components. © 2024, CC BY.

关键词： Remote sensing

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Enhanced Heart Disease Classification: An Ensemble-Based Deep Learning Approach

Enhanced Heart Disease Classification: An Ensemble-Based Dee...

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IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Nivaashini M Sathish Kumar G Uma Maheshwari G Soundariya R S Arun Kumar B Vijaya G Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Artificial Intelligence and Data Science Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

The WHO's assessment indicates a rising prevalence of heart disease, exacerbated by challenges in detection and diagnosis in India due to population growth. Machine Learning (ML) is increasingly used for precise judgments in healthcare, aiming to accurately diagnose heart-related illnesses. This study proposes a Deep Learning (DL) ensemble method, combining ML classifiers to enhance accuracy. The ensemble model outperforms AdaBoost, XGBoost, Random Forest, Decision Tree, SVM, and Logistic Regression with 98.3% accuracy on a UCI dataset. Correlation-based Feature Selection (CFS) for relevant characteristic selection improves the model's accuracy, recall, precision, and f1-score. This research contributes to improved disease prediction and categorization in cardiovascular health, vital for effective healthcare interventions and outcomes.

关键词： Heart Deep learning Support vector machines Accuracy Medical services Signal processing SPICE Regression tree analysis Random forests Diseases

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An Efficient Model based on Machine Learning Algorithms for Virtual Machines Classification in Cloud Computing Environment

An Efficient Model based on Machine Learning Algorithms for ...

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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Abdelhadi Amahrouch Mehdi Bouhamidi Youssef Saadi Said El Kafhali Faculty of Sciences and Techniques Data Science for Sustainable Earth Laboratory Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Industrial and Human Automation Mechanics and Computer Science Polytechnic University Hauts-de-France France Faculty of Sciences and Techniques Computer Networks Modeling and Mobility Laboratory Hassan First University of Settat Settat Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

In cloud computing, virtual machines consolidation (VMC) techniques are commonly used to improve resource utilization and reduce energy consumption. Task scheduling in cloud systems is a crucial aspect of VMC as it involves mapping clients' requirements to the appropriate computing resources such as Virtual Machines (VMs) or Physical Machines (PMs). The cloud provider must ensure that tasks are executed efficiently using the available shared computing resources while maintaining the quality of service (QoS) and minimizing the carbon footprint. Therefore, a good VM migration based on the customer's needs and IT resource capacity is required to maintain the best performance of the cloud system. In this work, we introduce an approach that leverage machine learning-based algorithms for VMs classification based on their latency sensitivity to facilitate subsequent migration into a suitable PM for better VMC. These algorithms categorize VMs into two groups: potentially inter-active machines (exhibiting periodic behavior on a daily scale) and latency-insensitive machines (for example, batch workloads, development, and test workloads). Our model demonstrated robust performance, achieving an accuracy of around 83%, thus establishing itself as the most proficient classifier in this study.

关键词： Cloud computing Machine learning algorithms Sensitivity Processor scheduling Computational modeling Quality of service Virtual machining

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Special issue: Recent developments in causal inference and machine learning

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Behaviormetrika 2022年第2期49卷 275-276页

作者： Shimizu, Shohei Kawano, Shuichi Faculty of Data Science Shiga University 1-1-1 Banba Hikone Shiga 522-8522 Japan Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Tokyo Chofu 182-8585 Japan

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China Kash Institute of Electronics and Information Industry Kash844000 China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Chinese University of Hon g Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of sub-arrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. © 1967-2012 IEEE.

关键词： Budget control

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Advanced Bandwidth Management Techniques Using Wireless Mesh Networks for High-Speed Optical Fiber Communications 3

Advanced Bandwidth Management Techniques Using Wireless Mesh...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yadav, Rakesh Kumar Sharma, Sunil Seth, Kanika Jeyalaxmi, M. Ingle, Yashwant S. Vivek, V. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks. © 2023 IEEE.

关键词： Communications Convergence Experiments Networks Superior

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EQCNN: Enhanced Remote Sensing Imagery Classification with Circuit-Based Error-Corrected Quantum Convolutional Neural Networks

EQCNN: Enhanced Remote Sensing Imagery Classification with C...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Muhammad Zaman Tanzila Kehkashan Adnan Akhunzada Hashem Alaidaros Mueen Uddin Muhammad Azeem Faculty of Computer Science University of Lahore Sargodha Punjab Pakistan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Data and Cybersecurity College of Computing & IT University of Doha for Science & Technology Doha Qatar Department of Cybersecurity Dar Al-Hekma University Jeddah Saudi Arabia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Quantum information processing is more complex than classical counterpart because of the No-Cloning theorem, decoherence, and issues detecting quantum states. Building a quantum computer without error detection and correction module is challenging. To overcome this challenge, qubit error correction codes are implemented. Inadequate error detection and correction codes have been the downfall of earlier attempts to encode quantum information, leading to disrupted qubit transmission. Qubit information is decohered by circuit noise. Unfortunately, the principal qubit error, phase flip error is not corrected by existing qubit error correction methods. In this study, we proposed Quantum Error Detection and Correction scheme (QEDC) to keep qubit in a coherent state. Used S gates, unitary gates, 10 CNOT gates, and Hadamard gates spanning on 4 physical and 1 logical qubits over the IBM Quantum Experience Machine. We found that the error probability is reduced to 7.91% owing to the efficiency of the quantum information encoding and phase flip control. Furthermore, we proposed Error Corrected Quantum Convolution Neural Network (EQCNN) for Image Classification. EQCNN combat and correct qubit errors that occur during the image processing. EQCNN outperformed over Earth Observatory (EO) dataset, EuroSAT and achieved an impressive results of 99.3% accuracy, 98.9% F1-score, 96.73% precision, and 98.4% recall.

关键词： Quantum system Accuracy Observatories Qubit Neural networks Logic gates Quantum state Convolutional neural networks Remote sensing Image classification

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning University of Adelaide Australia Department of Data Science & AI Faculty of Information Technology Monash University Australia

Annotation scarcity has become a major obstacle for training powerful deep-learning models for medical image segmentation, restricting their deployment in clinical scenarios. To address it, semi-supervised learning by exploiting abundant unlabeled data is highly desirable to boost the model training. However, most existing works still focus on limited medical tasks and underestimate the potential of learning across diverse tasks and multiple datasets. Therefore, in this paper, we introduce a Versatile Semi-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, to capture cross-dataset semantics. Particularly, we create a synthetic task with a cutmix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint. This involves aligning aggregated predictions from various tasks with those from the synthetic task, further guiding the model in accurately segmenting foreground regions during training. We evaluated our VerSemi model on four public benchmarking datasets. Extensive experiments demonstrated that VerSemi can consistently outperform the second-best method by a large margin (e.g., an average 2.69% Dice gain on four datasets), setting new SOTA performance for semi-supervised medical image segmentation. The code will be released. © 2023, CC BY.

关键词： Semantics

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Characterizing Continual Learning Scenarios and Strategies for Audio Analysis

arXiv

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arXiv 2024年

作者： Bhatt, Ruchi Kumari, Pratibha Mahapatra, Dwarikanath Saddik, Abdulmotaleb El Saini, Mukesh The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India The Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Inception Institute of AI Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science University of Ottawa Canada The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India Abu Dhabi United Arab Emirates

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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EpiDoc data Matching for Federated Information Retrieval in the Humanities

EpiDoc Data Matching for Federated Information Retrieval in ...

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Federated Conference on computer science and Information Systems (FedCSIS)

作者： Sylvia Melzer Meike Klettke Franziska Weise Kaja Harter-Uibopuu Ralf Möller Universität Hamburg Centre for the Study of Manuscript Cultures Hamburg Germany University of Luebeck Institute of Information Systems Luebeck Germany Faculty for Computer Science and Data Science University of Regensburg Regensburg Germany

The importance of federated information retrieval (FIR) is growing in humanities research. Unlike traditional centralized information retrieval methods, where searches are conducted within a logically centralised collection of documents, FIR treats each information system as an independent source with its own unique characteristics. Searching these systems together as a centralised source results in lower precision in humanities research, even when the research data itself is structured and stored according to standardised guidelines such as EpiDoc, and requires the need to be able to trace the origin of records to avoid incorrect historical conclusions. Matching of queries against all data sets in each source is proving less effective. A global search index that enables traceable matching of key values deemed relevant would provide a more robust solution here. In this article, we propose a solution that introduces a novel EpiDoc data matching procedure, facilitating traceable FIR across distinct epigraphic sources.

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