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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

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Training Set Preparation for Deep Model learning Inpatients with Ischemic Brain Lesions and Gender Identity Disorder 22nd

Training Set Preparation for Deep Model Learning Inpatients ...

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22nd international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Starcevic, Ana Vucinic, Boris Karpiel, Ilona Univ Belgrade Inst Anat Fac Med Lab Multimodal Neuroimaging Belgrade 11000 Serbia Lukasiewicz Res Network Krakow Inst Technol Ctr Biomed Engn Zakopianska Str 73 PL-30418 Krakow Poland

ISBN: (纸本)9783031425073;9783031425080

Increasing number of stress related cerebrovascular insults becomes alerting younger involved as well. Cerebrovascular diseases are the most common cause of functional disabilities, stroke being first, of which 87% are ischemic strokes in the USA. The cause of ischemia likely lies in cerebral small vessel disease, which includes lacunar infarcts, microbleeds, white matter lesions (WML) and enlarged perivascular spaces. The pathophysiology of ischemicWML is unclear, despite the development of radiological markers. The gold standard in the evaluation ofWMLis a volumetric analysis usingmagnetic resonance imaging (MRI). The aim of this study was training set preparation and volumetric analysis of multiple or solitary WML due to ischemic changes in patients with depressions and comparing it with a control group without white matter lesions and ischemic changes. We included 20 participants, 10 with WML and 10 controls as part of the process of applying deep machine learning and preparing a training set for the automated detection of WML. Participants under went 1.5 T 3D-T1v, MPRAGE and 3D FLAIRMRI. Imageswere alignedwith MNI space to normalize their intensity. Manual segmentation was then performed as the gold standard for segmentation in MNI space with ITK-SNAP using T1v and FLAIR images. A summary estimation of all volumes of the image sections was performed. The total volume of all brain lesions was measured without division into hemispheres and their localization. The total volume of the brainwasmeasured and correlated with the same parameters of subjects without noticeable ischemic changes in the brain. We have shown that ischemic lesions of the cerebrum white matter affect reduced total brain volume. The studies will aid in coming up with better treatment guidelines for preventive health care and management of cerebrovascular diseases to address the specific needs of individuals with gender identity disorders or transgenders.

关键词： white matter lesions WML transsexual imaging lesion detection volumetric analysis training MRI

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Evaluation of Centralized, Distributed and Federated learning for IoT Intrusion Detection Systems 11

Evaluation of Centralized, Distributed and Federated Learnin...

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11th IEEE international conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Elnakib, Omar Shaaban, Eman Mahmoud, Mohamed Emara, Karim Ain Shams University Faculty of Computer and Information Sciences Department of Computer Systems Cairo Egypt Tennessee Technological University Electrical and Computer Engineering CookevilleTN United States

ISBN: (纸本)9798350322101

Introducing smart intrusion detection systems (IDSs) in the IoT system was a crucial requirement in the last decades to secure these systems due to the high sensitivity of the sensed data. Smart IDS has often been built based on a machine/deep learning model trained at a centralized node in the cloud. However, the centralized learning approach violates data privacy since IoT nodes are required to share their data to the cloud to train the model. Distributed and federated learning approaches were introduced to solve this issue by keeping data within the local network and letting the edge devices train the model. In this paper, a comparative study among centralized, distributed, and federated learning approaches is presented. First, a deep learning model is proposed to identify traffic behavior using CICIDS2017 dataset. The three learning approaches are then applied with the proposed model to evaluate the effect of each approach on the model accuracy. In the centralized learning approach, an F-Score of 98% can be achieved. Moreover, the dataset is split over ten simulated nodes with various data distribution and entropies to evaluate the distributed and federated learning approaches. In distributed learning, the F-Score ranges between 63% to 93% with an average of 78% over the ten nodes. In the federated learning, the F-Score ranges between 73% to 98% with an average of 89% over the ten nodes. This result confirms that IDSs based on federated learning are a promising solution for both accuracy and preserving data privacy. © 2023 IEEE.

关键词： Intrusion detection

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Framework for Integrating machine learning Methods for Path-Aware Source Routing

Framework for Integrating Machine Learning Methods for Path-...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Al-Najjar, Anees Paraiso, Domingos Kiran, Mariam Dominicini, Cristina Borges, Everson Guimaraes, Rafael Martinello, Magnos Newman, Harvey Oak Ridge National Laboratory United States Federal Institute of Espírito Santo Department of Informatics Brazil California Institute of Technology United States

ISBN: (纸本)9798350355543

Since the advent of software-defined networking (SDN), Traffic Engineering (TE) has been highlighted as one of the key applications that can be achieved through software-controlled protocols (e.g. PCEP and MPLS). Being one of the most complex challenges in networking, TE problems involve difficult decisions such as allocating flows, either via splitting them among multiple paths or by using a reservation system, to minimize congestion. However, creating an optimized solution is cumbersome and difficult as traffic patterns vary and change with network scale, capacity, and demand. AI methods can help alleviate this by finding optimized TE solutions for the best network performance. SDN-based TE tools such as Teal, Hecate and more, use classification techniques or deep reinforcement learning to find optimal network TE solutions that are demonstrated in simulation. Routing control conducted via source routing tools, e.g., PolKA, can help dynamically divert network flows. In this paper, we propose a novel framework that leverages Hecate to practically demonstrate TE on a real network, collaborating with PolKA, a source routing tool. With real-time traffic statistics, Hecate uses this data to compute optimal paths that are then communicated to PolKA to allocate flows. Several contributions are made to show a practical implementation of how this framework is tested using an emulated ecosystem mimicking a real P4 testbed scenario. This work proves valuable for truly engineered self-driving networks helping translate theory to practice. © 2024 IEEE.

关键词： congestion minimization machine learning network optimization segment routing traffic engineering

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Classification of Minority Attacks using machine learning 4

Classification of Minority Attacks using Machine Learning

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international conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022

作者： Kumar, Amit Kumar, Vivek Saini, Ashish Kumari, Amrita Kumar, Vipin Quantum University Roorkee India Haridwar India DCS Haridwar India

ISBN: (纸本)9798350345919

In the digital age, the mass adoption of edge devices or Internet of Things (IoT) devices pose serious challenges to cybersecurity. Today, various new types of attacks including minority attacks are increasing due to the presence of intruders in the network. Furthermore, due to the complex behavior in network or IoT networks, these attacks cannot be detected by traditional algorithms. Therefore, this paper proposes an effective intrusion detection system to detect these attacks in network or IoT networks. machine learning algorithms Decision Trees, Extra Trees, Gradient Boosted Trees, k-Nearest Neighbors and Random Forest classifiers are used to estimate the benchmark dataset CICIDS2017. Furthermore, the RFE (recursive feature elimination technique) is utilized to select the most suitable or optimal set of features for detecting minority attack. © 2022 IEEE.

关键词： CICIDS-2017 Edge computing Internet of Things machine learning

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Few-Shot Domain Adaptation via Prompt-Guided Multi-prototype Alignment Network 20th

Few-Shot Domain Adaptation via Prompt-Guided Multi-prototype...

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20th international conference on Intelligent computing (ICIC)

作者： Li, Yongguang Wang, Shengsheng Fu, Zihao Yin, Ziqi Jilin Univ Coll Software Changchun Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China

ISBN: (纸本)9789819755967;9789819755974

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, scarcity of labeled source domain samples in practical scenarios due to high annotation costs or difficulty in sample acquisition poses a challenge. Recent work has proposed Few-Shot Unsupervised Domain Adaptation (FUDA) to address this challenge, achieving promising results through cross-domain self-supervised learning. However, their performance still significantly falls below that of UDA methods utilizing ample labeled source domain samples, primarily due to the scarcity of supervision, which hampers the model's ability to learn feature representations and classifiers that generalize well in the target domain. To address this issue, we introduce the visual-language pre-trained model CLIP as the backbone network and propose a Prompt-Guided Multi-Prototype Alignment framework (PMPA) for FUDA. By learning soft prompts containing diverse semantic information and aligning both source and target domains to multiple sets of class prototypes, PMPA achieves higher performance on the target domain with fewer source domain samples. Extensive experiments on OfficeHome, VisDA-2017, and Mini-DomainNet datasets demonstrate that our approach significantly outperforms previous stateof-the-art FUDAmethods and achieves comparable performance toUDAmethods utilizing ample source domain samples.

关键词： Few-Shot Unsupervised Domain Adaptation Prompt learning Prototype-Based Alignment

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Plant Disease Classification Using Deep learning Methods 2020

Plant Disease Classification Using Deep Learning Methods

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4th international conference on machine learning and soft computing (icmlsc)

作者： Wan, Hu Lu, Zheng Qi, Wang Chen, Yuanyuan SiChuan Univ Dept Comp Sci Chengdu Peoples R China

ISBN: (纸本)9781450376310

Nowadays, the development of agriculture is of great practical significance to all regions and countries. According to statistics, as a large agricultural country, China's annual food loss caused by insufficient pest prevention and control capacity exceeds 30% of the total loss, and its direct economic loss amounts to billions of CNY. The worldwide data is even more huge, so the detection of pests and diseases is particularly important. In the traditional agricultural field, diseases control mainly relies on the accumulated experience of farmers themselves, which is not always stable and requires a long time to form. In this paper we propose a suitable and accurate method for agricultural diseases detection, and finally achieve about 87% accuracy on a relatively large dataset.

关键词： Agriculture Plant Disease Deep learning Convolution Neural Networks

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Dissecting the Problem of Individual Home Power Consumption Prediction using machine learning 8

Dissecting the Problem of Individual Home Power Consumption ...

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Casella, Enrico Sudduth, Eleanor Silvestri, Simone Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

ISBN: (纸本)9781665481526

The growth and widespread diffusion of Internet-of-Things devices and advanced metering infrastructure allows to closely monitor appliances in a user home. Although only few works have focused on the issue of individual home power consumption predictions, recent efforts have unveiled the complexity of this task. As opposed to building-level power predictions, the finer granularity of single home predictions is characterized by the high impact that individual user actions have on the power consumption. As a matter of fact, the current state of the art shows inadequate prediction performance. In this work, we investigate the issue of single home power prediction by analyzing a recent dataset of real power consumption data. We carry out a profound analysis of several processing parameters and environmental parameters that make this task so challenging, thus providing meaningful insights that can guide future research on individual home power consumption predictions. Results show an overall low daily error, and very accurate hourly predictions when less variable usage patterns occur.

关键词： Home appliances Power demand conferences machine learning Complexity theory Task analysis Monitoring

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learning Question Similarity 2020

Learning Question Similarity

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4th international conference on machine learning and soft computing (icmlsc)

作者： Bihani, Pooja Walke, Ashay Fidel Investments Bangalore Karnataka India Accenture Appl Intelligence Bangalore Karnataka India

ISBN: (纸本)9781450376310

Question-answer platforms are trending over the internet nowadays. Clustering questions which ask the same question is a challenging problem faced by such platforms. The authors will discuss and implement techniques that will tell them whether a given pair of questions are asking the same question. They will do this by finding the semantic relationship between the questions. Manhattan Long short-term memory model with Area Under ROC Curve 0.81 is the model which performs the best among all the trained and pre-trained models which they will be discussing in this paper.

关键词： Sentiment analysis Sentence match Natural Language Processing machine learning Sentence Clustering

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Noisy computing of the Threshold Function 36

Noisy Computing of the Threshold Function

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36th international conference on Algorithmic learning Theory, ALT 2025

作者： Wang, Ziao Ghaddar, Nadim Zhu, Banghua Wang, Lele Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical and Computer Engineering University of Washington United States Department of Electrical and Computer Engineering University of British Columbia Canada

Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwright, 2018;Agarwal et al., 2017;Falahatgar et al., 2017;Heckel et al., 2019;Wang et al., 2024a;Gu and Xu, 2023;Kunisky et al., 2024), noisy searching (Berlekamp, 1964;Horstein, 1963;Burnashev and Zigangirov, 1974;Pelc, 1989;Karp and Kleinberg, 2007), among many others, the goal is to devise algorithms with the minimum number of queries that are robust enough to detect and correct the errors that can happen during the computation. In this work, we consider the noisy computing of the threshold-k function. For n Boolean variables x = (x1, ..., xn) ∈ {0, 1}n, the threshold-k function THk(·) computes whether the number of 1's in x is at least k or not, i.e., (Equation presented) The noisy queries correspond to noisy readings of the bits, where at each time step, the agent queries one of the bits, and with probability p, the wrong value of the bit is returned. It is assumed that the constant p ∈ (0, 1/2) is known to the agent. Our goal is to characterize the optimal query complexity for computing the THk function with error probability at most δ. This model for noisy computation of the THk function has been studied by Feige et al. (1994), where the order of the optimal query complexity is established;however, the exact tight characterization of the optimal number of queries is still open. In this paper, our main contribution is tightening this gap by providing new upper and lower bounds for the computation of the THk function, which simultaneously improve the existing upper and lower bounds. The main result of this paper can be stated as follows: for any 1 ≤ k ≤ n, there exists an algorithm that computes the THk function with an error probability at most δ = o(1), and the algorithm uses at most (Equation presented) queries in expectation. Here we define m (Eq

关键词： Active learning

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Leveraging machine learning to Uncover Key Factors Influencing Satisfaction Among Free Tertiary Education Recipients in the Philippines 26

Leveraging Machine Learning to Uncover Key Factors Influenci...

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26th international conference on Advanced Communications Technology, ICACT 2024

作者： Baragas, John Raymund B. Austero, Lea D. Llovido, Jennifer L. MacEda, Lany L. Abisado, Mideth B. College of Science Bicol University Computer Science and Information Technology Albay Philippines College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9791188428120

In spite of the broad implementation of the Universal Access to Quality Tertiary Education Act (UAQTE) - a groundbreaking legislation benefitting over 2 million students since its enactment in 2017 - a comprehensive evaluation of its outcomes has been notably absent. To bridge this gap, an extensive survey was undertaken among graduating tertiary students in selected regions of the Philippines. This strategically designed survey aimed to pinpoint overlooked aspects of UAQTE and capture firsthand insights from its recipients. The methodology employed to create this survey included focus-group discussions with various stakeholders (i.e., students, parents, faculty) and a pilot test reflecting the target demographic. To facilitate analysis of the results of 1462 responses, five regression machine learning algorithms were then employed to analyze questionnaire data. The decision tree regressor with a root-mean-squared-error of 0.6881 was found to be the best performing model describing the collected questionnaire data. Shapley explanations of the best performing model highlighted the desire of the recipient to pursue international employment as the top predictor of satisfaction in UAQTE among its recipients. Furthermore, insights from employed topic modeling among the open-ended questions in the deployed survey suggested potential inadequacy of UAQTE subsidies, specifically to recipients whose pursued degrees are in the science, technology, engineering, and mathematics courses. This substantial finding promises valuable insights into the effectiveness of the legislation and may inform future policy adjustments to better address the diverse needs of tertiary education in the Philippines. Overall, this research provides a robust framework for assessing the impact of UAQTE and showcases a methodologically sound approach in integrating machine learning and qualitative analysis. © 2024 Global IT Research Institute - GIRI.

关键词： Students

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