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International Conference on computer science and Engineering (UBMK)

作者： Uzay Çetin Yunus Emre Gündoğmuş Department Summarify Computer Engineering Galatasaray University Istanbul Türkiye Growth Data Science Summarify Hepsiburada Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

In today's digital world, the real-time generation and analysis of data, particularly on social media platforms, are becoming increasingly important. This study aims to design and evaluate a scalable architecture for a scenario where multiple users simultaneously perform sentiment analysis. Two different architectures were examined in the study: the Basic Architecture and the Proposed Architecture. The Basic Architecture processes and responds to user requests directly using FastAPI and Gunicorn. In contrast, the Proposed Architecture employs FastAPI, Kafka, and Redis to create a more complex and distributed structure. Test results demonstrate that the Proposed Architecture offers higher throughput and lower response times under high demand and user loads. This study presents significant findings on how to establish a scalable and high-performance infrastructure for large language models.

关键词： computer science Sentiment analysis Social networking (online) Large language models Computational modeling computer architecture Throughput Real-time systems Time factors Load modeling

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Intelligent larval zebrafish phenotype recognition via attention mechanism for high-throughput screening

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computers in Biology and Medicine 2025年 188卷 109892-109892页

作者： Wang, Baihua Sun, Qi Liu, Yujia Zhang, Jiheng Li, Gaozheng Wu, Sifang Zheng, Houbing Ye, Jialin Zhou, Meihua Zheng, Haisu Yu, Yongqiang Zhong, Yi Wu, Yuanzi Huang, Da Wang, Biao Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian Fuzhou China Department of Plastic and Cosmetic Surgery The First Affiliated Hospital of Fujian Medical University Fujian Fuzhou China The Center for Big Data Research in Burns and Trauma College of Computer and Data Science/College of Software Fuzhou University Fujian China

Background: Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects is time-consuming and requires specialized knowledge. Method: We proposed a deep network model called RECNet, which combines attention mechanisms and residual structures. In terms of data processing, we applied the mixup data augmentation technique and accumulated a collection of 6805 larval zebrafish phenotype images, mostly generated from our laboratory. Our proposed model was deployed to execute two distinct tasks, including a four-classification of zebrafish phenotypes and a seven-classification involving mixed labels for abnormalities. Results: In the four-class classification task, the RECNet model achieved an accuracy of 0.949, with a mean area under the curve of 0.986 and an F1-score of 0.966. Through interpretable research, attention mechanisms enable the model to focus more accurately on regions of interest. In the mixed-label seven-classification task for anomalies, our model achieved an accuracy of 0.913 and a mean average precision value of 0.847 by employing the weighted loss function (DFBLoss). Furthermore, in a new test dataset, the RECNet model achieved accuracy rates of 0.924 and 0.876 for the two tasks, respectively. Our RECNet model was trained by orders of magnitude larger dataset than previous studies and also showed better accuracy rates. Conclusions: Our method holds promise for diverse applications within zebrafish laboratories and fields such as toxicology, providing indispensable support to scientific research. © 2025 Elsevier Ltd

关键词： Invertebrates

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Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers

arXiv

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arXiv 2023年

作者： Liu, Huafeng Dowdell, Benjamin Engelder, Todd Pulmano, Zarah Osa, Nicolas Barman, Arko Department of Computer Science Rice University United States Data to Knowledge Lab Department of Electrical and Computer Engineering Rice University United States

Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (MRI) scans is one of the most effective methods for the diagnosis and treatment of brain cancers such as glioblastoma. Accurate tumor segmentation in MRI images is often required for treatment planning and risk assessment of treatment methods. Here, we propose a novel pipeline, Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET), which leverages MaskFormer, a vision transformer model, and generates robust tumor segmentation maks. We use an ensemble of nine predictions from three models separately trained on each of the three orthogonal 2D slice directions (axial, sagittal, and coronal) of a 3D brain MRI volume. We train and test our models on the publicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI (mpMRI) scans from 611 subjects. Using Dice coefficient (DC) and 95% Hausdorff distance (HD) for evaluation, our models achieved state-of-the-art results in segmenting all three different tumor regions – tumor core (DC = 0.894, HD = 2.308), whole tumor (DC = 0.891, HD = 3.552), and enhancing tumor (DC = 0.812, HD = 1.608). © 2023, CC BY-SA.

关键词： Tumors

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia computer science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) Artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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MFANEDH: Multimodal Fusion of MRI, PET, CT, and Ultrasound Using Advanced Neural Architectures for Enhanced Early Detection of Heart Cancer

MFANEDH: Multimodal Fusion of MRI, PET, CT, and Ultrasound U...

引用

Emerging Trends in Engineering and Medical sciences (ICETEMS), International Conference on

作者： Kiran Khandare Gaurav Divtelwar Charvi Suri Aparna Bondade Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Artificial Intelligence & Data Science Priyadarshini College of Engineering Nagpur India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Heart cancer poses unique diagnostic challenges due to the complexities of the cardiac environment and the subtle nature of tumor manifestations. Timely detection is crucial given the increasing incidence of heart cancer cases. Current imaging technologies, while providing significant advancements, often operate independently, limiting the utilization of diagnostic information. Integrating sense modality such as MRI, PET scans, CT imaging, and ultrasound can overcome these limitations. In response to the fragmented diagnostic viewpoints observed in clinical practice, a multi-modal fusion framework was developed. This framework utilizes deep learning models tailored to each modality's strengths: 3D CNNs for MRI, U-Net combined with ResNet for PET scans, DenseNet for CT imaging, and BiLSTM for ultrasound. By amalgamating these models, the framework effectively leverages the unique features of each modality, resulting in substantial improvements across key diagnostic metrics. The context achieved a 3.5% increase in precision, 2.9% in accuracy, 2.5% in recall, 4.5% in AUC, and 4.9% in specificity. Additionally, it led to an 8.3% reduction in diagnostic delay, thereby enhancing diagnostic heftiness and ensuring timely detection. This research represents a paradigm shift in heart cancer diagnostics, laying the groundwork for integrated imaging platforms and refining patient outcomes and modified treatment strategies.

关键词： Heart Solid modeling Ultrasonic imaging Three-dimensional displays Magnetic resonance imaging Computed tomography Refining Positron emission tomography Cancer Tumors

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The Diameter of Sum Basic Equilibria Games

arXiv

引用

arXiv 2023年

作者： Abiad, Aida Alvarez, Carme Messegué, Arnau Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Belgium Department of Mathematics and Data Science of Vrije Universiteit Brussel Belgium Computer Science Department Polytechnic University of Catalonia Spain

A graph G of order n is said to be a sum basic equilibrium if and only if for every edge uv from G and any node v′ from G, when performing the swap of the edge uv for the edge uv′, the sum of the distances from u to all the other nodes is not strictly reduced. This concept lies in the heart of the so-called network creation games, where the central problem is to understand the structure of the resulting equilibrium graphs, and in particular, how well they globally minimize the diameter. It was shown in [Alon, Demaine, Hajiaghayi, Leighton, SIAM J. Discrete Math. 27(2), 2013] that the diameter of sum basic equilibria is 2O(√log n) in general, and at most 2 for trees. In this paper we show that the upper bound of 2 can be extended to bipartite graphs, and that it also holds for some nonbipartite classes like block graphs and cactus graphs. © 2023, CC BY.

关键词： Graphic methods

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Multi Label Classification Algorithm Based on Conditional Local Correlations among Labels and Ensemble Learning

Multi Label Classification Algorithm Based on Conditional Lo...

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Information Technology (ACIT)

作者： Raed Alazaidah Ola Nasir Wael Hadi Ghassan Samara Essam Al Daoud Mohammad Aljaidi Mohammed Talat Khouj Esraa Abu Elsoud Haneen Alzoubi Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Petra University Amman Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Multi-Label Classification (MLC) is an advanced form of traditional classification, where each data instance is simultaneously associated with multiple labels. The importance of MLC has increased significantly in recent years, due to its wide range of application areas. Modern algorithms, when faced with the challenge of multiple labels, continue to show suboptimal performance, conversely, they show significantly superior performance when processing single-label datasets. This paper proposes a new algorithm to deal with the problem of transforming datasets from multiple labels to single labels by exploiting conditional and local correlations between labels after applying the Apriori predictive algorithm to extract relationships. Six ensemble learning models: Voting Hard, Voting Soft, Voting Weighted, Stacking LR, Stacking RF, and Stacking SVM, along with four classification models: SMO, DecisionStump, Random Forest, and Random Committee, were applied to three datasets. The algorithm showed effective and good performance by comparing the results of the proposed Multi Label Classification algorithm based on Conditional Local Correlations among Labels (MLC-CLCL) with several popular algorithms for dealing with multiple labels.

关键词： Support vector machines Radio frequency Correlation Stacking Multi label classification Prediction algorithms Ensemble learning Information technology Random forests

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On Forecast Stability

arXiv

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arXiv 2023年

作者： Godahewa, Rakshitha Bergmeir, Christoph Baz, Zeynep Erkin Zhu, Chengjun Song, Zhangdi García, Salvador Benavides, Dario Department of Data Science and Artificial Intelligence Monash University Australia Department of Computer Science and Artificial Intelligence University of Granada Spain Data Science and Computational Intelligence Andalusian Institute DaSCI Andalucía Spain Meta Inc. CA United States

Forecasts are typically produced in a business context on a regular basis to make downstream decisions. Here, forecasts should not only be as accurate as possible, but also should not change arbitrarily, and be stable in some sense. In this paper, we explore two types of forecast stability that we call vertical stability (for forecasts from different origins for the same target) and horizontal stability (for forecasts from the same origin for different targets). Existing works in the literature are only applicable to certain base models and can only stabilise forecasts vertically. We propose a simple linear-interpolation-based approach to stabilise the forecasts provided by any base model, both vertically and horizontally. Our method makes the trade-off between stability and accuracy explicit, producing forecasts at any point in the spectrum of this trade-off. Using N-BEATS, Pooled Regression, LightGBM, ETS, and ARIMA as the base models, in our evaluation across different error and stability measures on four publicly available datasets, on some datasets, the proposed framework achieves forecasts that are both more accurate and stable than the base forecasts. On the others, we achieve forecasts that are slightly less accurate but much more stable. Copyright © 2023, The Authors. All rights reserved.

关键词： Prediction models

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Cluster Head-Based Wireless Sensor Network Sustainability Algorithm

Cluster Head-Based Wireless Sensor Network Sustainability Al...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Kiran Sree Pokkuluri P. Aswini Rakesh Chandrashekar N Sirisha Manish Kumar K. Adnan Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The growth of a reduced-capacity assembling contract to communicate data from channel heads to BSs (Base Stations) is a main concern in WSNs, and this item outlines individual specific resolutions. In WSN, a multi-level whole of order is beneficial for the data group and destroying. Increasing the network's old age is one of the benefits of hierarchic assembling procedures. The group of the cluster head by way of the use of the centre pattern is the big focus of these algorithms, accompanying the main objective search to embellish the original centroid selection technique. To guarantee the network's staying power, the submitted process takes many determinants into report when selecting a Cluster Head (CH), containing leftover strength, range middle from two points growth, CHs, and the base station. The overall depiction of the imitation is judged distinguished from the depiction of usual algorithms, and effective strength habit is obtained.

关键词： Wireless sensor networks Energy consumption Base stations Protocols Simulation Clustering algorithms Search problems Energy efficiency Sustainable development Standards

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Link/Switch Fault-Tolerant Hamiltonian Path Embedding in BCube Networks for Deadlock-Free Routing

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Lin, Wanling Li, Xiao-Yan Chang, Jou-Ming Xiang, Dong Jia, Xiaohua Fuzhou University College of Computer and Data Science Fuzhou350108 China National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan Tsinghua University School of Software Beijing100084 China City University of Hong Kong Department of Computer Science Hong Kong

BCube stands as a renowned server-centric data center network (DCN), boasting numerous advantages, such as low diameter, high aggregate throughput, and abundant parallel paths. As DCNs expand rapidly, followed by the daily increasing likelihood of failures, fault tolerance has become an important issue in DCNs. Hamiltonian paths constitute a pivotal network topology for parallel and distributed computing, suitable for designing deadlock-free routing algorithms, fault-tolerant routing algorithms, and congestion avoidance. The partitioned edge fault (PEF) model is a recently proposed fault model that exploits the properties of networks to achieve fault tolerance with an exponential scale. In this paper, we explore the existence of Hamiltonian paths in BCube under the PEF model. Since one switch failure will result in multiple faulty links, we also extend the conclusions related to Hamiltonian paths to analyze the fault tolerance of BCube under the PEF model when switch failures occur. Moreover, we provide algorithms to embed a Hamiltonian path between arbitrary two distinct servers into BCube under the PEF model. Experimental analysis and comparisons demonstrate that our approach exhibits exponential enhancements over the other known results, and BCube DCNs possess remarkable fault tolerance in response to both link and switch failures under the PEF model. As a by-product, we obtain a deadlock-free routing based on the constructed Hamiltonian path and assess the routing performance compared to the benchmark routing algorithms. © 2004-2012 IEEE.

关键词： Hamiltonians

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