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共 3165 条记录，以下是591-600 订阅

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Mitigation of Straggler in Virtual machine Stack Using Supervised learning Methodology 5

Mitigation of Straggler in Virtual Machine Stack Using Super...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Gaykar, Reshma S. Khanaa, V. Joshi, S.D. Bharath Institute of Higher Education and Research Department of Computer Science Chennai India Information Center Bharath Institute of Higher Education and Research Chennai India Bharati Vidyapeeth University Faculty of Engineering and Technology Pune India

ISBN: (纸本)9781665475242

Hadoop is an inexpensive analytical tool as compared to the other distributed storage in market as it does not need any standalone machines and works on group of commodity hardware. It is a distributed storage system along with this it achieves parallelization of larger data collections. With MapReduce, HDFS (Hadoop distributed file system) provides solution for the system where processing huge datasets is a requirement. Few of the main reasons of stragglers in assorted Hadoop clusters are load inconsistency during storing, resource friction throughout scheduling tasks, hardware downturn due to excessive usage, as well as software configuration issues while managing the cluster. Hadoop's performance lows down in a heterogeneous network due to the technical heterogeneity. We used a supervised machine learning (ML) technique to identify straggler nodes in an eminently distributed network in this article. the suggested technique identifies the proper slow-running job (Straggler) in the network and assign it to other node in the stack to complete the operation with quick succession. Virtual machine (VM) identifier, network bandwidth consumption, number of processors and its load, memory load and other parameters included in the full data set are utilized for recognition. Various feature extraction methodologies have been utilized to develop its training system. the whole data set was processed for heterogenous features on the dataset. We analyzed our approach using our suggested classifier after doing comprehensive empirical work. As out-turn, the system outperforms using typical machine learning models in classification performance. © 2023 IEEE.

关键词： Heterogeneous networks

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sEMG Signal Features Extraction and machine learning Based Gesture recognition for Prosthesis Hand 5

sEMG Signal Features Extraction and Machine Learning Based G...

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5th IEEE international conference of Women in data Science (WiDS-PSU)

作者： Fatayerji, Hala Al Talib, Rabab Alqurashi, Asmaa Qaisar, Saeed Mian Effat Univ Dept Elect & Comp Engn Jeddah 22332 Saudi Arabia

ISBN: (纸本)9781665408127

Amputees around the world barely have any access to top-notch, smarter prosthetics due to the fact that they are either inaccurate or cost-inefficient. One of the more challenging tasks is the accurate detection of gestures and this paper presents a new assembly of segmentation, discrete wavelet decomposition, subbands features extraction, and machine learning algorithms for an automated identification of hand gestures by processing the Surface Electromyogram (sEMG) signals. A comparative analysis of two machine learning algorithms namely, Decision Tree and K-Nearest Neighbor is performed for processing the designed features set. Results confirm that the performance of proposed method is comparable or superior to the counterparts. the applicability is tested by using a publicly available sEMG dataset. Six different hand gestures for a mono male subject are considered. the system secures a highest classification accuracy of 93% for the case of Decision Tree algorithm.

关键词： Surface Electromyogram Signals Discrete Wavelet Transform Features Extraction machine learning Prosthetic Hand Classification

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Big data Analytics and Knowledge Discovery - 25th international conference, DaWaK 2023, Proceedings

Big Data Analytics and Knowledge Discovery - 25th Internatio...

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Big data Analytics and Knowledge Discovery - 25th international conference, DaWaK 2023, Proceedings

ISBN: (纸本)9783031398308

the proceedings contain 36 papers. the special focus in this conference is on Big data Analytics and Knowledge Discovery. the topics include: Discovery of Contrast Itemset with Statistical Background Between Two Continuous Variables;DBGAN: A data Balancing Generative Adversarial Network for Mobility pattern recognition;bitwise Vertical mining of Minimal Rare patterns;inter-item Time Intervals in Sequential patterns;Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record;DAT@Z21: A Comprehensive Multimodal dataset for Rumor Classification in Microblogs;dealing with data Bias in Classification: Can Generated data Ensure Representation and Fairness?;random Hypergraph Model Preserving Two-Mode Clustering Coefficient;a Non-overlapping Community Detection Approach Based on α -Structural Similarity;preventing Technical Errors in data Lake Analyses with Type theory;improving Stochastic Gradient Descent Initializing with data Summarization;feature Analysis of Regional Behavioral Facilitation Information Based on Source Location and Target People in Disaster;exploring Dialog Act recognition in Open Domain Conversational Agents;uniCausal: Unified Benchmark and Repository for Causal Text mining;accounting for Imputation Uncertainty During Neural Network Training;supervised Hybrid Model for Rumor Classification: A Comparative Study of machine and Deep learning Approaches;attention-Based Counterfactual Explanation for Multivariate Time Series;DRUM: A Real Time Detector for Regime Shifts in data Streams via an Unsupervised, Multivariate Framework;hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching;unified Views for Querying Heterogeneous Multi-model Polystores;EXOS: Explaining Outliers in data Streams;RODD: Robust Outlier Detection in data Cubes;data-driven and On-Demand Conceptual Modeling;FLOWER: Viewing data Flow in ER Diagrams;Supporting Big Healthcare data Management and Analytics: the Cloud-Based QFLS Framework;be

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Analyzing Deep Neural Network Algorithms for recognition of Emotions Using Textual data 1

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5th international conference on Information Systems and Management Science, ISMS 2022

作者： Kumar, Pushpendra Babulal, Kanojia Sindhuben Mahto, Dashrath Khurshid, Zaviya Department of Computer Science and Engineering Central University of Jharkhand Ranchi India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Evaluation of emotion and recognition from textual data is a new and important study in the Natural Language Processing (NLP) field that could provide useful information for various applications. Nowadays people prefer sharing their thoughts, feelings, views, emotions, perceptions, etc. on social media in the form of short articles, e-commerce reviews, posts, and comments. It's important to analyze these textual facts in order to comprehend the nature of people’s thoughts or emotions prevailing on social media. Analyzing emotions from textual data is most challenging for researchers due to text data diversity, and restricted word length. this research article experimented popular deep learning algorithms (LSTM, BiLSTM, GRU, BiGRU) for emotion prediction from textual data extracted from social media. Authors found that performance of experimented models is better than traditional machine learning techniques. Authors adopted Ekman's six primary emotions. the performance of these algorithms is measured on accuracy, recall, precision, and F1-score parameters. the results demonstrate that BiGRU outperforms the other approaches in terms of emotion prediction. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： learning algorithms

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Building Accurate machine learning Models for Predicting the Habitability of Exo-Planets 5

Building Accurate Machine Learning Models for Predicting the...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Raminaidu, Ch. Priyadarshini, V. Swaroop, Ch. Ravi Shankar, R. Shiva Department of CSE Sagi Rama Krishnam Raju Engineering College BhimavaramAndhra Pradesh India

ISBN: (纸本)9781665474672

For the last 20 years, due to the rapid advancement in space technology, mainly advanced telescopes, astronomers, and astrobiologists have been able to find the number of planets outside the solar system, and there is a greatzeal in the scientific community regarding the knowledge of the worlds and possibility of their habitability. NASA has done commendable work in this field;over time, they have found much information regarding extra-terrestrial *** to some studies, there are around 700 quintillion celestial objects in the Universe. Fortunately, with technological advancement, humans can find 5000 plus exo-planets. To find the habitability of the exo-planets depending on various factors such as the planet's size, the temperature on the planet, its position in the habitable zone, the distance of it from the start and other parameters. Some work has been done previously in this field, and we tried to get more accurate and suitable results for the available data. Building a model that could better predict the planet's habitability and help astrobiologists get better results in further experiments on the exoplanets. the data set is taken from the PHL exoplanet Habitability website. For this we used Logistic Regression (LR), which provided S9% accuracy, SVM which provided 98% accuracy, K-fold cross-validation, which provided 94% accuracy, XGBoost which provided 99.6% accuracy;and Stratified K-fold cross-validation, which delivered 99.95% accuracy. Hence, after checking the above models, itis found that Stratified K-fold is the best model for habitability prediction. © 2023 IEEE.

关键词： data mining

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Infrared Small Target Detection Based on Segmented Contrast Feature Enhancement

Infrared Small Target Detection Based on Segmented Contrast ...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yuan Wang Shuxian Liu Hankiz Yilahun Askar Hamdulla College of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In this paper, we explore the importance of infrared small target detection (ISTD) techniques in harsh weather and complex environments, especially in military and civilian domains such as sea rescue and night surveillance. Given the challenges of ISTD such as low signal-to-noise ratio and complex background, we propose an improved YOLOv8 algorithm called YOLOv8-SCFE, which is specially designed with Segmented Contrast Feature Enhancement (SCFE) module to enhance feature contrast. the SCFE module significantly improves the feature differentiation and expressiveness by introducing a contrast enhancement computation in the neck network. Experimental results on two datasets, NUTD-SIRST and Combine-SIRST, show that compared with the baseline algorithm YOLOv8, our proposed algorithm improves the mAP metrics by 4.5% and 3.2%, respectively.

关键词： Measurement Target recognition Surveillance Object detection machine learning Feature extraction pattern recognition Neck Signal to noise ratio Meteorology

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Auto-clustering Pairs Generation Method for Siamese Neural Networks Training 14

Auto-clustering Pairs Generation Method for Siamese Neural N...

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14th international conference on machine Vision (ICMV)

作者： Mokin, Arseniy K. Gayer, Alexander, V Sheshkus, Alexander, V Arlazarov, Vladimir L. Smart Engines Serv LLC Yerevan Armenia RAS Fed Res Ctr Comp Sci & Control Moscow Russia Lomonosov Moscow State Univ Moscow Russia Moscow Inst Phys & Technol Moscow Russia Natl Univ Sci & Technol MISIS Moscow Russia

ISBN: (数字)9781510650459

ISBN: (纸本)9781510650459;9781510650442

In this work, we present the auto-clustering method which can be used for pattern recognition tasks and applied to the training of a metric convolutional neural network. the main idea is that the algorithm creates clusters consisting of classes similar from a network's point of view. the usage of clusters allows the network to pay more attention to classes that are hard to differ. this method improves the generation of pairs during the training process, which is a current problem because the optimal generation of data significantly affects the quality of training. the algorithm works in parallel with the training process and is fully automatic. To evaluate this method we chose the Korean alphabet with the corresponding PHD08 dataset and compared our auto-clustering with random-mining, hard-mining, distance-based mining. Open-source framework Tesseract OCR 4.0.0 was also considered to evaluate the baseline.

关键词： convolutional neural network deep metric learning character recognition

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A Prediction Model of Diabetes Based on Ensemble learning 5

A Prediction Model of Diabetes Based on Ensemble Learning

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Qin, Lei Shanxi University China

ISBN: (纸本)9781450396899

Diabetes is a common disease that seriously endangers human health, mostly in the middle-aged and the elderly. Predicting the incidence rate of diabetes enables doctors to make a scientific treatment plan in advance, which will significantly improve the cure rate and reduce the incidence rate. Based on this situation, this paper proposes a diabetes prediction model based on ensemble learning, which integrates some classical machine learning algorithms, including Logisticregression, Kneigbors, Decisiontree, GaussianNB, and support vector machine (SVM) the first four low correlation algorithms are constructed as basic learners, and then integrated into meta learner SVM to build an integrated learning model. the advantages of the comprehensive model are evaluated from the following aspects: accuracy, precision, recall rate, AUC, and other evaluation indicators. the experiment was carried out on the Pima Indian diabetes data set (PIDD) published by UCI. First, the XGboost algorithm was used to select the optimal features, and then an integrated learning model was constructed to predict. the experimental results show that the accuracy rate of the integrated learning model is 81.63%, the precision rate is 80%, the recall rate is 80%, and the AUC is 84%. the advantages of the model in accuracy, precision, recall, and AUC are verified. the model will effectively help doctors make more accurate diagnoses and predictions of patients' physical conditions and implement more scientific treatment. © 2022 ACM.

关键词： Forecasting

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Fair Latent Representation learning with Adaptive Reweighing 27th

Fair Latent Representation Learning with Adaptive Reweighin...

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27th international conference on pattern recognition, ICPR 2024

作者： Majumdar, Puspita Sharma, Raghav Bhattacharya, Rohit Prajesh, Balraj AI Garage Mastercard Mumbai India

ISBN: (纸本)9783031781094

Artificial intelligence (AI) systems play significant roles in decision making processes, yet concerns persist about potential biases that can lead to unfair outcomes. these biases can arise from two main sources: imbalanced training data distribution and correlations between sensitive attributes (such as race and gender) and the target variable. Conventional model training methods penalize performance for underrepresented groups, resulting in biased outcomes. Further, they may capture features related to sensitive attributes during training, thus exacerbating bias. Biased predictions can have detrimental consequences. To address these concerns, in this research, we propose a novel method for bias mitigation. the proposed method aims to learn fair latent representations by emphasizing task relevant features while suppressing those linked to sensitive attributes. Additionally, we employ an adaptive reweighing technique to balance target class labels during training. the proposed method is evaluated on prominent benchmark datasets and compared with existing algorithms to demonstrate its effectiveness toward bias mitigation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Research on the Method of Constructing Knowledge Graph of Affective Disorder Based on Multi-Source data Fusion 5

Research on the Method of Constructing Knowledge Graph of Af...

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5th international conference on Information Technologies and Electrical Engineering, ICITEE 2022

作者： Wang, Yang Yang, Weihong School of Artificial Intelligence and Big Data Luzhou Vocational and Technical College Sichuan Luzhou646000 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150006 China

ISBN: (纸本)9781450396806

Affective disorder (Depression) has the characteristics of high incidence rate, low recognition rate, high disability and death rate, and has become a serious public health problem that needs to be solved urgently. It is particularly important to use machine learning and big data mining technology to build a complete knowledge graph of affective disorders. In this paper, multi-source data fusion is used to construct the original corpus of the knowledge graph of affective disorders. In the information extraction stage, we designed a machine learning algorithm based on Hidden Markov Model (HMM). the algorithm can extract entities and entity mapping relationships from corpus to form entity database and triplet relationship database. In the knowledge fusion stage, multi-source heterogeneous knowledge fusion algorithm is used to complete entity alignment and relationship deduction. Finally, the open-source graphic database Neo4j is used as the underlying storage structure, so as to realize the visualization of the knowledge graph of affective disorders. © 2022 ACM.

关键词： data fusion

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