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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是71-80 订阅

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6th international conference on Smart Computing and Informatics, SCI 2024

6th International Conference on Smart Computing and Informat...

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6th international conference on Smart Computing and Informatics, SCI 2024

ISBN: (纸本)9789819619801

the proceedings contain 46 papers. the special focus in this conference is on Smart Computing and Informatics. the topics include: Resilient Domain Authentication Framework for Enhancing Digital Identity Security;traffic Sign Detection with pattern recognition Techniques Using Image Processing;exploring Advanced Techniques in Natural Language Processing and machine learning for In-depth Analysis of Insurance Claims;Network Intrusion Detection with SMOTE-ENN and Deep learning Techniques;leveraging Transfer learning to Enhance Location Accuracy in Mapping Services: A Case Study of Google Maps;assessment of Enhanced Email Spam Detection System through machine learning Algorithms;machine learning Methods for Predicting Traffic Congestion Forecasting;hybridization of Computational Intelligence Algorithm for Scheduling of Tasks and Balancing of Load in Cloud Network;MDSV: Mobs Detection by Enhanced Fused Feature Base Deep Neural Network from Surveillance Camera;ioT-Based Solution for Enhanced Tracking of Individuals Living with Dementia;a Novel Task Scheduling Algorithm in Heterogeneous Multi-cloud Environment;Evaluating the Integration and Usage of AI in Higher Education;evaluating the Connectional Benefits of Artificial Intelligence in the Digital Classroom;Influence of AI as an Aspect of Modern Education Era in Present World;Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection;the Advancement and Utilization of Artificial Intelligence and machine learning in the Financial Industry and Its Impact on Macro and Microeconomics;analysis on the Cutting-Edge Approach to Assess Artificial Intelligence’s Educational Consequences in Contemporary Studies;data Analytics in Sales and Marketing: A Comprehensive Methodology for Business Analysts;Wireless Energy Transfer for UAV (Drone) Using machine learning.

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Conditional Probability-Based Feature Embedding for Genomic Sequence data 27th

Conditional Probability-Based Feature Embedding for Genomic ...

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27th international conference on pattern recognition, ICPR 2024

作者： Borah, Parashjyoti Dutta, Aparajita Indian Institute of Information Technology Guwahati Assam Guwahati781015 India National Institute of Technology Silchar Assam Silchar788010 India

ISBN: (纸本)9783031781919

Embedding techniques for categorical attributes play a pivotal role in the performance of machine learning inference, especially for downstream analysis of genomic sequence data. Categorical data do not convey quantitative information to directly influence the model parameters in mapping input to the desired output. Two commonly used state-of-the-art embedding techniques are one-hot embedding and binary embedding, which embed the values of the categorical attributes in vectors of 0s and 1s. One-hot treats values of a particular attribute uniformly, or in other words, it considers uniform probability distribution for the random variable of the categorical attribute. On the other hand, binary embedding assigns some ordinal characteristics to the data without considering the probability distribution. Both of these techniques do not embed the underlying semantics of the genomic features. In this study, we propose two conditional probability–based feature embedding techniques for categorical data that utilize the probability distribution of the data. Furthermore, the proposed embedded vector lengths are based on the number of classes in the training data rather than the unique values an attribute can take. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genome

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A Comparative Analysis of IoT data Using machine learning Algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya516330 India Department of Mathematics Voorhees College Vellore632001 India

ISBN: (纸本)9783031889912

IoT (Internet of things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. this study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Prediction of Chronic Diseases Using Parallel 1D-CNN Feature Extraction and SVM Classification Based on SMOTE 4th

Prediction of Chronic Diseases Using Parallel 1D-CNN Feature...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mesbah, Fatma Zohra Tassadit Ait Abidine, M'hamed Bilal Fergani, Belkacem Univ Sci & Technol Houari Boumediene USTHB Lab Ingn Syst Intelligents & Commun LISIC Fac Elect Engn BP 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9783031821523;9783031821530

Due to their severity and prevalence, the prediction of chronic diseases (CDs) has become an important area of research, particularly with advances in deep learning. In this paper, we propose a new automated technique for detecting these diseases. A deep network architecture using a parallel unidimensional convolutional neural network (1D-PCNN) is employed to extract deep features. Subsequently, the Support Vector machine (SVM) technique is applied for CD classification. the uniqueness of our framework lies in the design of the 1D-PCNN, which can learn the deep features of the input layer through parallel convolutional layers. As a result, the deep features of each parallel branch are simultaneously extracted before being combined in the fusion layer. Furthermore, in order to improve the efficiency of the proposed model, the Synthetic Minority Oversampling Technique (SMOTE) is used. this strategy manages class imbalance in CD databases. the suggested model is analysed against standard 1D-CNN, 1D-CNN model combined with conventional machine learning methods and other existing state-of-the-art models. the effectiveness of the suggested method was tested using two known databases the Pima Indian Diabetes database (PIDD) and the Cleveland Heart Disease database (CHDD). Results, from these databases indicate that the proposed approach yielded an accuracy rate of 83% and 88% an F score of 73% and 90% and an AUC of 80% and 87% correspondingly. Finally, after applying the SMOTE method, accuracy was improved to 86% and 92%, Fscore to 86% and 93%, and AUC to 86% and 92%, respectively, and outperforms other methods.

关键词： Chronic diseases data classification SMOTE Parallel unidimensional convolutional neural network (1D-PCNN) SVM

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When Uncertainty-Based Active learning May Fail? 27th

When Uncertainty-Based Active Learning May Fail?

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27th international conference on pattern recognition, ICPR 2024

作者： Rahmati, Amir Hossein Fan, Mingzhou Zhou, Ruida Urban, Nathan M. Yoon, Byung-Jun Qian, Xiaoning Texas A&M University College StationTX77843 United States University of California Los AngelesCA90095 United States Brookhaven National Laboratory UptonNY11973 United States

Instead of randomly acquiring training data, Uncertainty-based Active learning60;(UAL) selects pivotal samples from an unlabeled dataset based on the prediction uncertainty and queries their labels so that the labe... 详细信息

ISBN: (纸本)9783031781063

Instead of randomly acquiring training data, Uncertainty-based Active learning (UAL) selects pivotal samples from an unlabeled dataset based on the prediction uncertainty and queries their labels so that the labeling cost for model training can be minimized. As a result, the efficacy of UAL depends on the model capacity as well as the adopted uncertainty-based acquisition function. In this study, our analytical focus is directed toward comprehending how the capacity of the machine learning model may affect UAL efficacy. through theoretical analysis and comprehensive simulation and empirical studies, we demonstrate that UAL can lead to worse performance compared to random sampling when the machine learning model class has low capacity and is unable to cover the underlying ground-truth. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active learning

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Utilization of machine learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 15th

Utilization of Machine Learning Algorithms to Monitor the Gr...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. the early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. the cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. the toxicological study based on fish behavioral data is then summarized. © the Author(s), under exclusive license to Springer Nature

关键词： Deep learning

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Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the data-Scarce RNA Contact Prediction Task 27th

Knowledge from Large-Scale Protein Contact Prediction Model...

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27th international conference on pattern recognition, ICPR 2024

作者： Jian, Yiren Gao, Chongyang Zeng, Chen Zhao, Yunjie Vosoughi, Soroush Dartmouth College HanoverNH03755 United States Northwestern University EvanstonIL60208 United States The George Washington University WashingtonDC20052 United States Central China Normal University Wuhan430079 China

ISBN: (纸本)9783031781919

RNA, whose functionality is largely determined by its structure, plays an important role in many biological activities. the prediction of pairwise structural proximity between each nucleotide of an RNA sequence can characterize the structural information of the RNA. Historically, this problem has been tackled by machine learning models using expert-engineered features and trained on scarce labeled datasets. Here, we find that the knowledge learned by a protein-coevolution Transformer-based language model can be transferred to the RNA contact prediction task. As protein datasets are orders of magnitude larger than those for RNA contact prediction, our findings and the subsequent framework greatly reduce the data scarcity bottleneck. Experiments confirm that RNA contact prediction through transfer learning using a publicly available protein language-model is greatly improved. Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research. the code and data (for inference and training) are available athttps://***/yiren-jian/CoT-RNA-Transfer. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Integrating Fuzzy Approach in Text mining and Summarization

Integrating Fuzzy Approach in Text Mining and Summarization

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： S. Gayathri Abinav Chandar R S Rithicagash J Guna A Department of Artificial Intelligence and Data Science K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

News data, which includes both structured and unstructured forms, is a special kind of information. Headlines, content, authors, publishing dates, sources, and places are among the details it includes. Effective analysis of textual data requires the extraction of characteristics due to its various nature. In order to extract features from news data, this study suggests using the Naive Bayes (NB) and RANK SVM approaches, which fully cover language and text-related elements. News stories have attracted a lot of attention from scholars because of their organized and unstructured character. NB and RANK SVM algorithms have been used to investigate a number of studies, such as text summarization to lower data volume, sentiment analysis with emotional dictionaries, text categorization by domain or area depending on user interests, and special character recognition. By improving the precision and effectiveness of news data analysis, this method seeks to advance the general comprehension and use of textual data processing methods.

关键词： Support vector machines Text mining Sentiment analysis machine learning algorithms Accuracy Text recognition Computational modeling Text summarization Feature extraction Bayes methods

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Human Emotion recognition from EEG-brain Signals using Enhanced machine learning Method

Human Emotion Recognition from EEG-brain Signals using Enhan...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Ghulam Ghous Shaheryar Najam Ahmad Jalal Dept. of Electrical & Computer Engineering Riphah International University Islamabad Pakistan Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this study explores the application of advanced machine learning techniques to EEG data for detecting emotions in individuals with cognitive disabilities. Utilizing the SEED-IV dataset, we analyze EEG recordings from 15 participants across multiple sessions to identify emotional states such as happiness, sadness, fear, and neutrality. To ensure data quality, we employ preprocessing techniques including bandpass filtering and downsampling. Our proposed model integrates multi-class Support Vector machine (SVM) with AAFST, an innovative feature selection and transformation mechanism. the effectiveness of this approach is demonstrated by an SVM accuracy of 85%, showcasing its capability to extract subtle emotional responses from EEG data. this research contributes to the growing field of affective computing, emphasizing the advantages of combining machine learning with EEG analysis to enhance the detection and understanding of emotions in individuals with cognitive disabilities.

关键词： Support vector machines Emotion recognition Accuracy Filtering machine learning Feature extraction Electroencephalography Physiology Recording Emotional responses

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Enhanced Emotion recognition from fMRI Using a Multi-model Neural Network Framework 6th

Enhanced Emotion Recognition from fMRI Using a Multi-model N...

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6th international conference on Biomedical and Health Informatics-ICBHI

作者： Lin, Mi-Hsuan Chen, Pin-Han Huang, Rong-Huan Lo, Tzu-Yao Tseng, Li-Yun Tsai, Zi-Xiang Lo, Chun-Yi Zac Chun Yuan Christian Univ Dept Biomed Engn Taoyuan Taiwan

ISBN: (纸本)9783031863226;9783031863233

Functional magnetic resonance imaging (fMRI) has the capability to reflect brain activities, with brain regions such as the amygdala, prefrontal cortex, and hippocampus exhibiting distinct emotional activity patterns. these neural activities interact in complex ways, making interpretation and identification challenging. the objective of this study is to identify participants' emotions using fMRI data from the ICBHI scientific challenge, involving 16 participants in the training set and 4 in the test set. Each participant has undergone 30 trails and reported their emotion classes and levels. the averaged fMRI signals were extracted from 246 regions by Brainnetome atlas, and the regions of interest were selected based on correlations, including emotional related 66 regions. We applied a band-pass filter (sigma = 2) for data preprocessing. there were two models developed, for class and level prediction, respectively. For class prediction, to enhance signal quality and reduce noise in fMRI signals, we employed a nonlinear least squares approach. Specifically, each 25-s signal segment was modeled using a polynomial, and parameters of this polynomial were adjusted to minimize the sum of squares of the differences between observed and predicted values. To test which brain regions' activity patterns are highly correlated with emotions, we trained multiple fully connected neural networks (FCNNs) to select from the previously mentioned 66 brain regions. Ultimately, the signals from brain regions highly correlated with emotions are selected as inputs for subsequent integrated convolutional neural network (CNN) model to distinguish between types of emotions. For level prediction, we utilized long short- term memory (LSTM) models for level prediction to participants' emotions judgement. Finally, the algorithm we proposed achieved a classification accuracy of 56% for identifying emotion classes, 20% for identifying emotional intensity levels. the mean score of 0.6251 was calculat

关键词： neuroscience classification machine learning

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