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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是101-110 订阅

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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： artificial intelligence cellular metabolic data mining algorithms pharma logical trajectory

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data Analysis of College Practice Teaching Based on machine learning Algorithm 25

Data Analysis of College Practice Teaching Based on Machine ...

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Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Xinwu Wang Dalian University of Finance and Economics Dalian Liaoning China

ISBN: (纸本)9798400713231

With the rapid development of information technology, especially the wide application of big data and artificial intelligence technology, the field of education is undergoing unprecedented changes. Among them, practical teaching in colleges and universities, as an important part of modern education system, is facing severe challenges. However, the traditional practice teaching mode often has some problems, such as uneven distribution of resources, disjointed teaching content and market demand, and single teaching method, which limit the improvement of practice teaching effect. In this regard, based on the actual needs, this paper will deeply analyze the application feasibility of artificial intelligence and machine learning in the reform of practical teaching in colleges and universities, and put forward a set of data analysis and processing scheme of practical teaching in colleges and universities, aiming at improving the effectiveness and personalized level of practical teaching through the integration, mining and analysis of multi-source data. Practice has proved that machine learning algorithms such as fuzzy C-means (FCM) clustering analysis and frequent pattern growth (FP-growth) correlation analysis are adopted in the scheme, and the data involved in college practice teaching are deeply analyzed, and the feasibility and effectiveness of the model are verified by practical tests, which provides scientific basis and decision support for college practice teaching.

关键词： association rule analysis

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The Analysis and Prediction Mode of Students' Academic Performance and Social Behavior in the Background of Big data 3

The Analysis and Prediction Mode of Students' Academic Perfo...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Qiao, Shaoping Humanities and Design College Chengdu Technological University Sichuan Chengdu China

ISBN: (纸本)9798400712692

The popularization of digital campus brings convenience to the life of teachers and students, and provides rich data for educational data mining (EDM). EDM uses data mining and machine learning techniques to analyze student data, aiming to provide quality services. This paper uses big data to analyze students' behavior, constructs the academic performance prediction model, and provides a reference for decision-making for teaching assistants. The Dis_GAT network based on graph neural network is proposed and compared with traditional methods to prove its effectiveness. By preprocessing and analyzing student data, the behavioral characteristics were extracted, and identifying abnormal students using K-means and isolated forest algorithms to help the counselors fully understand the student situation. Finally, the Dis_GAT network prediction framework is proposed, transforming the academic performance prediction problem into the node classification problem in the graph structure, introduces the concept of graph neural network, improves the graph attention network, and considers the influence of early performance similarity on later academic performance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Academic performance data mining Dis_GAT Social behavior

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Facial Biometrics-Driven Feature Encoding and Deep learning Integration for Secure Password Retrieval

Facial Biometrics-Driven Feature Encoding and Deep Learning ...

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Disruptive Technologies (ICDT), international conference on

作者： Akash Immanuel Solomon A Ashwin M Swaminathan K Dept. of Computer Science St. Joseph's College of Engineering Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The proposed system includes face recognition-based systems designed for secure identity verification and decentralized data storage. The working is supposed to be done in three phases: data capture, training of the machine learning model, and decentralized data storage. Collection in the first phase will include face images of individuals and government-issued identification details, such as Aadhar and PAN. A machine learning model will be trained in the second phase for improving the accuracy in the face recognition process. The third phase ensures secure storage of the recognized faces and associated personal data through a decentralized approach similar to blockchain technology. Security, privacy, and resilience against tampering are further developed in this design. The proposed system will reliably meet the application requirements that involve strong authentication processes and protection of data, therefore making the sectors using these systems include government services and secured personal data management.

关键词： Training Privacy Face recognition Government Memory Passwords Reliability Protection Secure storage Resilience

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Financial Time Series data Analysis Based on data mining 25

Financial Time Series Data Analysis Based on Data Mining

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Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Yang Long School of Business China West Normal University Nanchong Sichuan China

ISBN: (纸本)9798400713231

Based on the in-depth study of data mining, great likelihood estimation, sequential parameter learning and other models, the study designs a sequential Bayesian learning method to estimate the asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) model for the analysis of financial time-series data, aiming to improve its analytical assessment and forecasting performance. The study fully considers the leverage effect, accurately describes the negative correlation between return and volatility, and successfully solves the complex numerical problems in the estimation of stock simulation models. Through comprehensive simulation analysis, the results show that the model can excellently simulate the stock volatility and price trends with remarkable effectiveness, which provides a powerful support for the research and decision-making in the financial market.

关键词： data mining

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862984

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart Computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using machine learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on machine learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on machine learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention mining for Mental Health Diagnosis Using Logistic Regression;machine learning for Accessible and Precise Assessment in Smart Monitoring Systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on data mining and machine learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive learning Techniques;asthma Prediction Using Fuzzification and machine learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of machine learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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Advancing Brain-Computer Interfaces: A Deep learning Approach for Enhanced Neural Signal Processing 3

Advancing Brain-Computer Interfaces: A Deep Learning Approac...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Dharmendra, Kaku Riya Verma, Deepak Kumar Vats, Satvik Marwadi University Department of Computer Engineering Rajkot India Madan Mohan Malaviya University of Technology Department of Computer Science and Engineering U.P. Gorakhpur India

ISBN: (纸本)9798331542375

Brain-to-computer interfaces (BCIs) are a developing technology that has led to a straight link among the human brain and various systems. The complexity and lack of predictability of brain signals make the development of a trustworthy, accurate BCI system. In this study, we look into how advanced deep learning approaches like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Graph Isomorphism Networks (GIN), and Convolutional Neural Network (CNN) can be used to improve neural signal processing performance in BCI. LSTM and GRU are good at discovering the temporally related EEG sequences, while CNNs extract the spatial feature of raw EEG data very well. We model and analyze graph-structured data from functional brain networks using GIN, revealing insights into spatial dynamics and inter-channel connections. Both graph-based and spatiotemporal features are integrated as proposed in the framework, and a deep learning architecture is constructed for signal decoding and classification that allows for a higher signal decoding and classification accuracy. Our experimental results on publicly available EEG datasets demonstrate that this approach outperforms standalone deep learning models and traditional machine learning approaches. It designs its solutions to attain higher accuracy, robustness, and the ability to generalize across various BCI tasks, including cognitive state recognition and motor imagery classification. The result indicates that our model LSTM is 96.92%, CNN is 95.95%, GRU is 95.40%, and GIN is 88.68%. Finally, this study demonstrates how advanced deep learning techniques can address the current limitations of BCI systems, opening the door for more reliable and successful real-world applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Performance Enhancement of DBSCAN Density-Based Clustering Algorithm in data mining

Performance Enhancement of DBSCAN Density-Based Clustering A...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neha Tyagi Rashmi Mishra Sitanshu Kumar Patel Somraj Karki Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to facilitate better data analysis, clustering is a prominent data mining approach which separates data into separate categories based on their similarities and distinctions. It’s a method of unsupervised learning which may generate a model with a training dataset. Throughout this study, the Euclidian distance and EPS (Epsilon) value has been determined using the DBSCAN technique based on the similarities as well as disparity for the input data. Moreover, Euclidian distance is dynamically determined using the back propagation method; a simulation study is carried out that demonstrates both speed and precision improvements. DBSCAN, or spatial clustering based on density of application with noise, is a common clustering technique for geographic datasets that can locate clusters of any shape and automatically detect noise points. To deal with the problem, an improved DBSCAN technique based on the selection of features (FS-DBSCAN) has been suggested. However, the algorithm proved unstable while handling huge dimensional data. Many simulation via real-world datasets testify the algorithm’s efficiency. Comparison of the proposed algorithm to the DBSCAN algorithm demonstrates its benefits. Structures within the collections may be identified by dividing of collections of products into classes and clusters, with a focus on increasing the resemblances of objects within each cluster and lowering the similarities of things belonging to different clusters.

关键词： Backpropagation Training machine learning algorithms Shape Noise Clustering algorithms Spatial databases data mining Object recognition Unsupervised learning

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Human Activity recognition Using Wearable Sensors in IoT Environments: Challenges and Opportunities 3

Human Activity Recognition Using Wearable Sensors in IoT Env...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Singh, B.P. Baxi, Paramjit Parhi, Manoranjan Ahluwalia, Gunveen Sollapur, Shrishail B. Divakaran, Sindu Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Data Sciences Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

One of those applications, Human Activity recognition, uses wearable sensors, made possible by the rapid rise and development of Internet of Things technology. HAR is the automated process of identifying and classifying human activities from sensor data with wearable devices such as smart watches/fabric bands. Given its utility in healthcare, HAR has immense possibilities, especially within IOT environments such as health & fitness, safety, and security. But, at the same time, it introduces new challenges of handling massive real-time sensor data streams and providing privacy for people when their daily routines are reconstructed, as well as developing accurate and reliable algorithms. In addition, human activities are diverse, and the data that sensors collect may also differ, making HAR in IOT very complicated. HAR can also provide various opportunities, e.g., real-time monitoring and analysis of human activities, personalized health/fitness advice, and safe/smart environmental improvements. Implementing artificial intelligence and machine learning models in HAR can also improve performance. HAR based on wearable sensors in an IOT environment holds promise for changing our views of and tracking human activities to a new level that could be applied to many different sectors and improve quality of life. © 2025 IEEE.

关键词： mHealth

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Empirical Study to Select a Place to Stay in NCR Area Using data mining and machine learning 3

Empirical Study to Select a Place to Stay in NCR Area Using ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Jain, Nitin Chauhan, Surendra Singh Goel, Ajay Rastogi, Ashish Kumar Raj, Alok Agrawal, Priyanka Chandigarh University Mohali India Srm University Haryana Sonipat India Bhai Gurdas Institute of Engineering & Technology Punjab Sangrur-148001 India Iilm University Greater Noida India XenonStack Pvt. Ltd. Punjab Mohali India XenonStack Pvt. Ltd. Punjab Mohali India

ISBN: (纸本)9798331542375

From starting of the human civilization, the peoples are traveling to one place to another place for different kinds of works such as business, tourism etc. and they are need accommodation to stay there. Before the 21st century, there was a limited option to search the places for accommodation. In the digital world there are options are increasingly in comparison with the previous time. Now if someone going for a vacation for a short term, he or she can find a hotel or lodge for the same, but when students migrate one place to another or a person transfer to another city or place than he need to find a suitable place to stay for a long time. In this research paper, we try to find the best solution for this kind of problem. Here we discuss the result with some predetermined parameters. These parameters set and trained on the collected data from different type of people from different geolocations. After it, the collected data is fed to the K-Means algorithm. This algorithm is used for the making the clusters. After it by using REST API, the output of K-Means algorithms is shown on a Map. © 2025 IEEE.

关键词： K-means clustering

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