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检索条件"任意字段=5th International Conference on Parallel Processing and Applied Mathematics"

共 3267 条记录，以下是71-80 订阅

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An Image processing-Based Tomato Leaf Disease Prediction Using Deep CNN 5th

An Image Processing-Based Tomato Leaf Disease Prediction Usi...

引用

5th international conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Vanisri, Kesana Raju, K. Srujan Laxmaiah, Bagam Department of CSE CMR Technical Campus Hyderabad India

ISBN: (纸本)9783031751660

Pest outbreaks have a significant impact on plants and crops, important to a reduction in national agricultural productivity. Conventionally, farmers and specialists rely on physical observation, and it can be timeconsuming, more expensive, and prone to errors, to classify diseases in plants. though, by employing leading-edge image processing techniques, an instinctive detection system can be established, providing rapid and precise results. this paper aims to produce a Tomato Leaf Disease identification model with different categorizing leaf images and utilizing deep neural networks. the development of specific plant protection has the potential for substantial growth and improvement, with computer vision applications in the precision agriculture market also showing promising growth. this discussion about outlines all the critical stages for implementing the disease recognition model, initial from the image database group, assessment by agricultural specialists, and applying a deep learning framework to conduct the training of deep CNNs. By using this CNN, we will apply Max pooling layer, Multiple implementations are applied and finally we will get more accuracy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dataset Deep CNN Diseases neural network

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Signal processing and Connectivity Analysis in Calcium Imaging Data: Unveiling Neuronal Network Dynamics 25

Signal Processing and Connectivity Analysis in Calcium Imagi...

引用

Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent Computing

作者： Yuanhang Jia Ruinan Yang Institute of Architecture Tianjin University Tianjin Tianjin China Institute of Biomedical Engineering Sichuan University Chengdu Sichuan China

ISBN: (纸本)9798400712203

Calcium imaging, as a means of high temporal and spatial resolution, has been widely used in neuroscience research to monitor the dynamic activity of neuronal networks. In this paper, a comprehensive analysis process for calcium imaging data is proposed to improve the accuracy and effectiveness of data processing, and the analysis is performed using OASIS. First, Z-score standardization is applied to normalize the signal of each neuron to eliminate the difference in signal amplitude between different neurons. then, we design and implement a high-pass filtering method based on the frequency domain to extract high-frequency components and remove low-frequency noise and background signals by fast Fourier transform (FFT), thus enhancing the signal features of neuronal activity. the method was verified on two sets of calcium imaging data, and the results show that the signal after filtering is clearer and can capture the instantaneous activity and synchronization pattern of neurons more effectively. Finally, the result displays that the highly connected neurons in the primary visual cortex mainly interact with a specific population of neurons, indicating the existence of a specific local network structure. this study provides an effective tool for calcium imaging data analysis in the field of neuroscience and is expected to be widely used in the dynamic study of complex neural networks.

关键词： Calcium imaging

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

引用

Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. the functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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TOIChain™: A Proposal for High Performance Tamper Resistant Transactions Without Scaling Limits

TOIChain™: A Proposal for High Performance Tamper Resistant...

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20th international conference on Foundations of Computer Science, FCS 2024, and 20th international conference on Frontiers in Education, FECS 2024, held as part of the World Congress in Computer Science, Computer Engineering and applied Computing, CSCE 2024

作者： Shi, Justin Y. SMC Labs 630 Freedom Business Drive King of PrussiaPA19406 United States SERC 315 College of Science and Technology Temple University PhiladelphiaPA19122 United States

ISBN: (纸本)9783031859298

this paper proposes a blockchain-based high performance transaction processing system called TOIChain. A new programming paradigm and architecture using Active Content Addressable Networking protocol and Statistic Multiplexed Computing runtime is introduced to overcome the typical service infrastructure scalability challenges. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： parallel processing systems

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A hierarchical model of parallel genetic programming applied to bioinformatic problems 5th

引用

5th international conference on parallel processing and applied mathematics

作者： Frey, J Gras, R Hernandez, P Appel, R CMU Swiss Inst Bioinformat CH-1211 Geneva 4 Switzerland

ISBN: (纸本)3540219463

Genetic Programming (GP), an evolutionary method, can be used to solve difficult problems in various applications. However, three important problems in GP are its tendency to find non-parsimonious solutions (bloat), to converge prematurely and to use a tremendous amount of computing time. In this paper, we present an efficient model of distributed GP to limit these general GP drawbacks. this model uses a multi-objective optimization and a hierarchical communication topology.

关键词： Genetic programming

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A Multi-Resolution Deep Learning Approach for IoT-Integrated Biomedical Signal processing using CNN-LSTM

A Multi-Resolution Deep Learning Approach for IoT-Integrated...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： K. Priyadharshini Ajisha Mathias R. Santhana Krishnan N Kanthimathi J. Relin Francis Raj P. Stella Rose Malar Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the rapid advancement of Internet of things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. this work proposes an IoT-based health monitoring system integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze physiological signals and detect anomalies effectively. A network of biomedical sensors, including ECG, EMG, EEG, heart rate, blood pressure, SpO2, temperature, accelerometer, gyroscope, and respiratory rate sensors, continuously collects health data. these sensors transmit real-time physiological signals using wireless communication protocols such as Wi-Fi, Bluetooth, LoRa, and NB-IoT, ensuring seamless data acquisition and remote monitoring. To enhance signal quality, the preprocessing stage employs Wavelet Transform (WT) for denoising biomedical signals, mitigating motion artifacts and environmental noise. Discrete Wavelet Transform (DWT) is applied to extract meaningful features while preserving critical health information. Additional preprocessing steps, including feature normalization, segmentation, and feature extraction, improve deep learning model efficiency. the proposed CNN-LSTM hybrid model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in time-series biomedical data. the architecture includes multiple convolutional layers with ReLU activation, batch normalization, max-pooling, bidirectional LSTM layers, dropout regularization, and an attention mechanism. Model training utilizes labeled medical datasets with an Adam optimizer and binary cross-entropy loss function. Performance evaluation considers accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrate improved anomaly detection for conditions such as arrhythmias, respiratory irregularities, and early stroke indicators. Deployment in real-time health monitoring applications facilitates continuous patient assessment, fall detecti

关键词： Deep learning Medical services Feature extraction Real-time systems Convolutional neural networks Biosensors Biomedical monitoring Monitoring Medical diagnostic imaging Anomaly detection

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Program structuring heuristics for parallel systems based on multiple crossbar switches 5th

引用

5th international conference on parallel processing and applied mathematics

作者： Laskowski, E Polish Acad Sci Inst Comp Sci PL-01237 Warsaw Poland

ISBN: (纸本)3540219463

A new graph structuring algorithm for look-ahead reconfigurable multi-processor systems based on multiple crossbar switches is presented. It is based on list scheduling and a new iterative clustering heuristics for graph partitioning. Look-ahead dynamic interprocessor connection reconfiguration is a multi-processor architectural model, which has been proposed to eliminate connection reconfiguration time overheads. It consists in preparing link connections in advance in parallel with program execution. An application program is partitioned into sections, which are executed using redundant communication resources, i.e. crossbar switches. parallel program scheduling in such a kind of environment incorporates graph partitioning problem. the experimental results are presented, which compare the performance of several graph partitioning algorithms for such a kind of environment.

关键词： Application programs

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Minimizing time-dependent total completion time on parallel identical machines

引用

5th international conference on parallel processing and applied mathematics

作者： Gawiejnowicz, S Kurc, W Pankowska, L Adam Mickiewicz Univ Fac Math & Comp Sci PL-61614 Poznan Poland

ISBN: (纸本)3540219463

In the paper a problem of minimizing the total completion time for deteriorating jobs and parallel identical machines is considered. the processing time of each job is a linear function of the starting time of the job. the properties of an optimal schedule are proved and a greedy heuristic for the problem is proposed. Preliminary results of experimental evaluation of the algorithm are given.

关键词： Job shop scheduling

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parallel algorithms for evolutionary history reconstruction 5th

引用

5th international conference on parallel processing and applied mathematics

作者： Blazewicz, J Formanowicz, P Kedziora, P Wojciechowski, P Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Inst Bioorgan Chem PL-61704 Poznan Poland

ISBN: (纸本)3540219463

A phylogenetic tree construction is one of the most important problems in computational biology. From computational point of view it is also one of the most difficult problem because of its intrinsic intractability. Efficient algorithms are known only for some special cases of the problem which are unrealistic from biological point of view. Many algorithms are known for the problem, but since the problem is hard, they are usually heuristics. In this paper we present three exact parallel algorithms for the problem. they have been tested in computational experiment ran on SUN Fire computer.

关键词： parallel algorithms

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High frequency electromagnetic fields analysis with the use of the parallel FDTD method

引用

5th international conference on parallel processing and applied mathematics

作者： Walendziuk, W Forenc, J Jordan, A Bialystok Tech Univ Fac Elect Engn PL-15351 Bialystok Poland Polish Japanese Inst Informat Technol PL-02008 Warsaw Poland

In the paper the parallel algorithms of the Finite-Difference Time-Domain method are presented. those algorithms are based on the space domain decomposition. In the presented work, communications among computation nod... 详细信息

ISBN: (纸本)3540219463

关键词： parallel algorithms

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