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共 6956 条记录，以下是4551-4560 订阅

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Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data

Machine learning-based prediction of sorghum biomass from UA...

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computing and Communication Systems (I3CS), international conference on

作者： Boubacar Gano Nurzaman Ahmed Nadia Shakoor Donald Danforth Plant Science Center Saint Louis MO USA

Unmanned aerial vehicle (UAV)-based remote sensing applications in plant phenotyping have received attention in modern plant breeding programs that increasingly have the need to automate time-consuming manual measurements of agronomic traits. This paper focuses on the prediction of sorghum biomass using machine learning algorithms such as Linear Regression, KNeighbors Regressor, and the XGBoost Regressor. Results from a field experiment of 344 sorghum genotypes conducted at the Donald Danforth Plant Science Center (Saint Louis, MO, USA) showed accurate prediction models. The K-Neighbors Regression model performed better than the other two models (R 2 =0.65, RMSE =4968.60kg/ha). The developed approach in this study could be used as a decision support tool for sorghum biomass phenotyping in breeding programs.

关键词： Machine learning algorithms Biological system modeling Computational modeling Vegetation mapping Predictive models Autonomous aerial vehicles data models

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DIRS: Dynamic Initial Rate Setting in Congestion Control for Disaggregated Storage Systems

DIRS: Dynamic Initial Rate Setting in Congestion Control for...

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international conference on computing, Networking and Communications (ICNC)

作者： Xiaoqian Zhang Allen Yang Danlin Jia Li Wang Mahsa Bayati Pradeep Subedi Xuebin Yao Bo Sheng Ningfang Mi Department of Computer Science University of Massachusetts Boston Boston USA Department of Electrical and Computer Engineering Northeastern University Boston USA Samsung Semiconductor Inc San Jose CA USA

This paper focuses on network congestion control in a disaggregated storage system. In such a system, the supporting network requires low latency and is extremely sensitive to network congestion. The existing congestion control algorithms for data center networks do not work well in our target system because of the unique characteristics of the network topology and the storage I/O workload. Motivated by the existing issues, we develop a new solution, DIRS, which dynamically sets the initial sending rate for each flow. Our scheme helps improve the effectiveness of the congestion control protocols, especially under heavy I/O traffic. It chooses an appropriate initial rate for a flow and mitigates the congestion from the beginning while not degrading the flow’s network performance.

关键词： data centers Protocols Network topology Heuristic algorithms Low latency communication

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Fortifying Cyber Defenses: A Deep Dive into the Development of an AI-Powered Network Intrusion Detection System 5th

Fortifying Cyber Defenses: A Deep Dive into the Development ...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Rai, Hari Mohan Galymzada, Alin Almas, Konsbayev Nurzhan, Duzbayev Alibek, Makhayev Department of Cybersecurity International Information Technology University Almaty Kazakhstan

ISBN: (纸本)9789819725496

In the ever-evolving world of cybersecurity, it is essential to strengthen defenses against complex threats. This article provides a comprehensive overview of the development and deployment of an AI-powered network intrusion detection system (NIDS) as a fundamental element in improving organizational cyber resilience. Starting with an examination of the challenges posed by traditional intrusion detection systems (IDS), the article highlights the limitations of rule-based approaches in the face of rapidly evolving cyber threats. Emphasizing the need for real-time threat detection, the article introduces AI-powered NIDS as an adaptive solution. A focus on machine-learning algorithms, including supervised and unsupervised techniques, sheds light on the system's ability to automatically distinguish normal network behavior from potential intrusions. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are explored for their transformative impact on accuracy and adaptability in real-time data analysis. The article contextualizes these advancements by tracing the evolution of intrusion detection mechanisms, from rule-based systems to sophisticated AI-driven solutions. It delves into related work, covering machine-learning applications, anomaly detection, and the emergence of ensemble methods and hybrid approaches. Recognizing challenges such as adversarial attacks and interpretability concerns, the article concludes by emphasizing the broader importance of AI-powered NIDS in strengthening digital infrastructures. Serving as a valuable resource for cybersecurity professionals and organizations, this article combines technical depth with practical insights, offering strategic considerations for navigating the complexities of the modern cyber threat landscape. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Optimal Transport in Diffusion Modeling for Conversion Tasks in Audio Domain

Optimal Transport in Diffusion Modeling for Conversion Tasks...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Vadim Popov Amantur Amatov Mikhail Kudinov Vladimir Gogoryan Tasnima Sadekova Ivan Vovk Huawei Noah’s Ark Lab

Diffusion models have recently become a popular generative modeling framework in various domains because of their high-quality sampling capabilities. Lately, it has been hypothesized that optimally trained diffusion models supplied with specific differential equation solvers provide a solution to the optimal transport problem between the data distribution and the prior distribution. In this paper, we empirically show that applying the optimal transport point of view on diffusion modeling allows making a good choice of a noise sample the reverse diffusion starts generating from. We consider two audio-related tasks: voice conversion and timbre transfer. In the former, we improve upon the recent state-of-the-art model and demonstrate that the optimal transport helps us to keep the prosody of the source utterances significantly better than the vanilla diffusion-based model does. As for timbre transfer, we propose the novel diffusion model capable of many-to-many timbre transfer performing on par with common algorithms in terms of the overall music quality.

关键词： Signal processing algorithms Differential equations Signal processing Probabilistic logic Mathematical models data models Acoustics

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Towards an Interoperable and Privacy-Respecting Smart City data Ecosystem

Towards an Interoperable and Privacy-Respecting Smart City D...

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IEEE international Workshop on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications

作者： Marco Hecktor Andreas Diepenbrock Sabine Sachweh IDiAL Institute University of Applied Sciences and Arts Dortmund Dortmund Germany

This paper presents an approach for developing an interoperable and privacy-respecting smart city data ecosystem. We propose an architecture that enables companies to share data securely and efficiently, while also respecting the privacy rights of individuals. The architecture employs a public key infrastructure to ensure the authenticity and integrity of data exchange. We also provide an exemplary user workflow to demonstrate the application of the architecture. Future research could focus on extending the privacy mechanisms and implementing rule-based filters for data processing.

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4th international conference on Communication, computing and Electronics Systems, ICCCES 2022

4th International Conference on Communication, Computing and...

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4th international conference on Communication, computing and Electronics Systems, ICCCES 2022

ISBN: (纸本)9789811977527

The proceedings contain 80 papers. The special focus in this conference is on Communication, computing and Electronics Systems. The topics include: Smart Door Unlocking System;An Insight into AI and ICT Towards Sustainable Manufacturing;FPGA Implementation of Efficient 32-Bit 3-Operand Addition Using Kogge–Stone (KS) Parallel Prefix Adder;automatic Mulching Machine;multi-user Hybrid Beamforming for mmWave Systems Using Learning-Aided Link Adaptation;prediction of Disease Using Retinal Image in Deep Learning;plant Health Analyzer Using Convolutional Neural Networks;behaviors of Modern Game Non-playable Characters;Low-Noise Amplifier with Co-designed Microstrip Antenna for 60 GHz Wireless Communications;Impact of High Dimensionality Reduction in Financial datasets of SMEs with Feature Pre-processing in data Mining;generation of Counters and Compressors Using Sorting Network;deep Learning-Based Triphase Community Detection for Multimedia data;Design of a High-Speed and Low-Power AES Architecture;improving Sleep Apnea Screening with Variational Mode Decomposition and Deep Learning Techniques;effect of Selectively-Filled-Ethanol on Dispersion Characteristics of Circular Shaped Hollow Core Photonic Crystal Fiber;a Demand Management Planning System for a Meat Factory Based on the Predicted Market Price Under Indian Market Scenario;Performance Comparison of MCML, PFSCL, and Dynamic CML Gates with Parametric Analysis in 45 nm CMOS Technology;VANET Authentication with Privacy-Preserving Schemes—A Survey;An Effective Protection Approach for Deceive Attacker in AES Attack;Effective EMI Reductıon in Medical Devices and Automotive Power Converters;conformal Antenna with Bow and Arrow Shaped Radiator for Wireless Capsule Endoscopy;gujarati Language Automatic Speech Recognition Using Integrated Feature Extraction and Hybrid Acoustic Model;A CNN-Based Underage Driver Detection System.

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A Deep Disentangled Approach for Interpretable Hyperspectral Unmixing

A Deep Disentangled Approach for Interpretable Hyperspectral...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ricardo Augusto Borsoi Tales Imbiriba Deniz Erdoğmuş CNRS CRAN Vandoeuvre-lès-Nancy Université de Lorraine France Dept. of Electrical & Computer Engineering Northeastern University Boston MA USA

Deep learning-based frameworks have been recently applied to hyperspectral umixing due to their flexibility and powerful representation capabilities. However, such techniques either use black-box models which are not physically interpretable, or fail to address the non-idealities of the unmixing problem. In this paper, we propose a physically interpretable deep learning method for hyperspectral unmixing accounting for nonlinearity and the variability of the endmembers. The proposed method is based on a probabilistic variational deep learning framework which employs semi-supervised disentanglement learning to properly separate the abundances and endmembers. A self-supervised strategy is used to generate labeled training data, and the model is learned end-to-end using stochastic backpropagation. Experimental results on both synthetic and real datasets illustrate the performance of the proposed method compared to state-of-the-art algorithms.

关键词： Deep learning Signal processing algorithms Training data Stochastic processes Self-supervised learning Signal processing Semisupervised learning

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BCDS-SDN: Privacy and Trusted data Sharing Using Blockchain Based on a Software-Defined Network's Edge computing Architecture

BCDS-SDN: Privacy and Trusted Data Sharing Using Blockchain ...

引用

IEEE international conference on Communications (ICC)

作者： Anass Sebbar Mohammed Boulmalf ESIN TICLab International University of Rabat Rabat Morocco

Edge computing offloads the data processing capacity to the user side, provides flexible and efficient computing services for the development of the smart city, and brings many security challenges. To enhance the security of data sharing in edge computing, a secured blockchain shared data in Software Defined Networking System (BCDS-SDN) framework is proposed to ensure secured transactions between edge nodes and the Cloud, control access, and verify trusted edge from malicious nodes. The integration of SDN and blockchain concepts gives us the power to control and verify the identity of edge nodes to protect the infrastructure from many types of attacks, such as MitM, compromised channels, adding malicious nodes, and DDoS attacks. The combination of edge computing and Blockchain in a BCDS-SDN solution provides a vast scale of storage systems, database servers, and authenticated computation to the end in a secure manner. Our findings contribute toward more SDN-based security of data-sharing by providing a testbed-based proof of the efficiency and performance of Blockchain technology in ensuring the security of data-sharing in the edge-computing environment.

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Explainable Predictive Decision Mining for Operational Support 1

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20th international conference on Service-Oriented computing (ICSOC)

作者： Park, Gyunam Kuesters, Aaron Tews, Mara Pitsch, Cameron Schneider, Jonathan van der Aalst, Andwil M. P. Rhein Westfal TH Aachen Proc & Data Sci Grp PADS Aachen Germany Rhein Westfal TH Aachen Aachen Germany

ISBN: (数字)9783031265075

ISBN: (纸本)9783031265068;9783031265075

Several decision points exist in business processes (e.g., whether a purchase order needs a manager's approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than (sic)500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.

关键词： Process mining Decision mining Machine learning Operational support Proactive action

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19th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2023

19th IFIP WG 12.5 International Conference on Artificial Int...

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19th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2023

ISBN: (纸本)9783031341106

The proceedings contain 129 papers. The special focus in this conference is on Artificial Intelligence Applications and Innovations. The topics include: Generating Synthetic Vehicle Speed Records Using LSTM;Measuring the State-Observation-Gap in POMDPs: An Exploration of Observation Confidence and Weighting algorithms;predicting Colour Reflectance with Gradient Boosting and Deep Learning;the Importance of the Current Input in Sequence Modeling;towards Historical Map Analysis Using Deep Learning Techniques;analysis of the Lingering Effects of Covid-19 on Distance Education;exploring the Power of Failed Experiences in Case-Based Reasoning for Improved Decision Making;improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing;a Sharpe Ratio Based Reward Scheme in Deep Reinforcement Learning for Financial Trading;mind the Gap: Addressing Incompleteness Challenge in Case-Based Reasoning Applications;Sentiment Analysis of Tweets on Online Education during COVID-19;image Classification Using Class-Agnostic Object Detection;Ince-PD Model for Parkinson’s Disease Prediction Using MDS-UPDRS I & II and PDQ-8 Score;medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records;Predicting ALzheimer's Disease with AI and Brain Imaging data;Pre-trained Model Robustness Against GAN-Based Poisoning Attack in Medical Imaging Analysis;semi-supervised Brain Tumor Segmentation Using Diffusion Models;A Methodology for Emergency Calls Severity Prediction: From Pre-processing to BERT-Based Classifiers;a Temporal Metric-Based Efficient Approach to Predict Citation Counts of Scientists;algorithmic Forex Trading Using Q-learning;Comparing Vectorization Techniques, Supervised and Unsupervised Classification Methods for Scientific Publication Categorization in the UNESCO Taxonomy;decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling;exploring Pairwise Spatial Relationships for Actions Recognition and

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