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检索条件"任意字段=6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023"

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6th international conference on applied machine learning and data analytics, amlda 2023

6th International Conference on Applied Machine Learning and...

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6th international conference on applied machine learning and data analytics, amlda 2023

ISBN: (纸本)9783031554858

the proceedings contain 19 papers. the special focus in this conference is on applied machine learning and data analytics. the topics include: Blockchain - A Secure and Transparent Solution to Detect Counterfeit Products;Process Selection for RPA Projects with MDCM: the Case of Izmir Bakircay University;get It Right: Improving Comprehensibility with Adaptable Speech Expression of a Humanoid Service Robot;Benchmarking ML and DL Models for Mango Leaf Disease Detection: A Comparative Analysis;stock Market Prediction with Artificial Intelligence Techniques in Recession Times;sentiment Analysis of Monkeypox Tweets in Latin America;Intensity-Chromaticity-Luminance (ICL) Based Technique for Face Spoofing Detection;AI Insights: Unleashing Financial Distress Signals;optimizing Portfolio for Highly Funded Industries Within Budget Constraints for the Period of 2023–2024;adaptive Neuro Fuzzy-Based Depression Detection Model for Students in Tertiary Education;cassava Syndrome Scan a Pioneering Deep learning System for Accurate Cassava Leaf Disease Classification;self-regulated and Participatory Automatic Text Simplification;preface;Developers’ Perspective on Trustworthiness of Code Generated by ChatGPT: Insights from Interviews;DESI: Diversification of E-Commerce Recommendations Using Semantic Intelligence;aspect-Based Sentiment Classification of Online Product Reviews Using Hybrid Lexicon-machine learning Approach;forecasting User Payment Behavior Using machine learning.

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5th international conference on applied machine learning and data analytics, amlda 2022

5th International Conference on Applied Machine Learning and...

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5th international conference on applied machine learning and data analytics, amlda 2022

ISBN: (纸本)9783031342219

the proceedings contain 20 papers. the special focus in this conference is on applied machine learning and data analytics. the topics include: RU-Net: A Novel Approach for Gastro-Intestinal Tract Image Segmentation Using Convolutional Neural Network;a Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores;Implementation of YOLOv7 for Pest Detection;online Grocery Shopping: - Key Factors to Understand Shopping Behavior from data analytics Perspective;a Novel Approach: Semantic Web Enabled Subject Searching for Union Catalogue;how People in South America is Facing Monkeypox Outbreak?;multilevel Classification of Satellite Images Using Pretrained AlexNet Architecture;handwriting Recognition for Predicting Gender and Handedness Using Deep learning;retrieval of Weighted Lexicons Based on Supervised learning Method;univariate Feature Fitness Measures for Classification Problems: An Empirical Assessment;performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems;securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading;keratoconus Classification Using Feature Selection and machine learning Approach;Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model;SMDKGG: A Socially Aware Metadata Driven Knowledge Graph Generation for Disaster Tweets;Brain MRI Image Classification Using Deep learning;a Real-Time Face Recognition Attendance Using machine learning;towards Abalone Differentiation through machine learning.

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6th international Workshop on Advanced analytics and learning on Temporal data, AALTD 2021, held at the European conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML-PKDD 2021

6th International Workshop on Advanced Analytics and Learnin...

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6th international Workshop on Advanced analytics and learning on Temporal data, AALTD 2021, held at the European conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML-PKDD 2021

ISBN: (纸本)9783030914448

the proceedings contain 12 papers. the special focus in this conference is on Advanced analytics and learning on Temporal data. the topics include: RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds;from Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information;state Space Approximation of Gaussian Processes for Time Series Forecasting;fast Channel Selection for Scalable Multivariate Time Series Classification;Temporal Phenotyping for Characterisation of Hospital Care Pathways of COVID19 Patients;non-parametric Multivariate Time Series Co-clustering Model applied to Driving-Assistance Systems Validation;TRAMESINO: Traffic Memory System for Intelligent Optimization of Road Traffic Control;detection of Critical Events in Renewable Energy Production Time Series;multimodal Meta-learning for Time Series Regression;cluster-Based Forecasting for Intermittent and Non-intermittent Time Series.

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Forecasting User Payment Behavior Using machine learning 1

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6th international conference on applied machine learning and data analytics, amlda 2023

作者： Vora, Deepali Choudhary, Rupali Kumar, Aman Kadam, Payal Lavale Pune India College of Engineering Pune India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

the payment prediction model is a software application that uses machine learning algorithms to predict future payments based on historical data. these models are crucial for organizations like credit card companies, banks, and other financial institutions that depend on recurring payments from their clients businesses may determine the possibility that consumers will miss their payments by utilizing payment prediction models, and they can then take action to either avoid or lessen the effects of any prospective non-payment. this system works on real-time data, data goes to different stages including data collection and storage, data pre-processing and cleaning, feature engineering, and model development. the system uses machine learning algorithms such as linear regression, decision trees, or XG boost to predict future payments based on historical payment data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： User interfaces

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Optimizing Portfolio for Highly Funded Industries Within Budget Constraints for the Period of 2023–2024 1

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6th international conference on applied machine learning and data analytics, amlda 2023

作者： Nanjundan, Preethi George, Jossy P. Birari, Abhijeet Geetha, Pamidimukkala Sai Manwali, Manka Bengaluru India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India Graphic Era Hill University Dehradun India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

this research paper aims to analyze and optimize portfolios for the top funded industries based on the budget’23. the study uses a data-driven approach to identify the best investment opportunities within these industries. the methodology involves collecting financial data, conducting market analysis, and using optimization techniques to create an optimal portfolio. the results of the study show that the top funded industries have a high potential for growth, and the optimized portfolios can maximize returns while minimizing risk. the findings can provide valuable insights for investors and fund managers who are seeking to make informed investment decisions in these industries. the study also highlights the importance of considering the budget constraints while optimizing portfolios. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Time series analysis

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Athlete Performance Analysis: machine learning for Predicting Tennis Player Scores 6

Athlete Performance Analysis: Machine Learning for Predictin...

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Zhao, Peizhe Luo, Tianyu Bi, Pengfei Waterford Institute Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350353174

In recent years, machine learning and deep learning have become increasingly prevalent in the field of data classification and regression, with applications in a diverse range of disciplines. Furthermore, the field of sports analytics is also gradually becoming one of the topics of machine learning research. the application of machine learning and deep learning technologies to the analysis of real-time data from sporting events enables a more accurate understanding of the performance and status of athletes. Coaches can then combine these results to determine the current state of the athlete, which allows them to adjust the game strategy accordingly and increase the probability of winning. this study employs the example of tennis, utilising detailed data from the game 'the Championships Wimbledon 2023'. the data was subjected to preliminary processing in order to identify the key factors that influence the players' scores. the data was then subjected to regression and prediction using a variety of machine learning algorithms and multilayer perceptron machines in deep learning. the results demonstrate that LightGBM and NLP are the most effective algorithms for accurately evaluating the current state of athletes with the available data. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning and Deep learning Algorithms for Network data analytics Function in 5G Cellular Networks 6

Machine Learning and Deep Learning Algorithms for Network Da...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Pavani, Ale Kathirvel, A. Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350398496

machine learning and deep learning algorithms have the potential to revolutionize network data analytics in 5G cellular networks. With the increase in the number of connected devices and the explosion of data generated by these devices, traditional methods of network data analytics are becoming increasingly inadequate. However, this function faces several challenges, including managing massive amounts of data, processing data in real-time, addressing privacy concerns, integrating with other network systems, and requiring skilled professionals. Overcoming these challenges will enable the network data analytics function to achieve its objectives and improve the overall performance and reliability of 5G cellular networks. machine learning and deep learning algorithms enable the automatic identification of patterns and insights in large volumes of data, thereby facilitating real-time decision-making and proactive network management. these algorithms can be used for a wide range of network data analytics functions, including network optimization, anomaly detection, prediction of network failures, and dynamic spectrum management. Deep learning algorithms have shown significant promise in processing unstructured network data such as network logs, video, and images. As the 5G network continues to grow and evolve, the use of machine learning and deep learning algorithms in network data analytics is likely to become increasingly essential for ensuring network efficiency, reliability, and security. © 2023 IEEE.

关键词： 5G mobile communication systems

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Aspect-Based Sentiment Classification of Online Product Reviews Using Hybrid Lexicon-machine learning Approach 1

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6th international conference on applied machine learning and data analytics, amlda 2023

作者： Asuquo, Daniel Attai, Kingsley Usip, Patience George, Uduak Osang, Francis Department of Computer Science Faculty of Science University of Uyo Uyo Nigeria TETFund Center of Excellence in Computational Intelligence Research UNIUYO Uyo Nigeria Department of Mathematics and Computer Science Ritman University IkotEkpene Ikot Ekpene Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Nowadays, a good number of customers express their experience with online products. these reviews have an important role in customers’ purchase decision process. there may be hundreds or thousands of unstructured and heterogeneous reviews for a popular product. Traditional text processing techniques have limited capability in extracting opinions on customers’ product reviews from huge data over the Internet. Although lexical approaches aim to map words to sentiments by building a lexicon, the process of developing a lexicon with sentiment scores for phrases and sentences becomes tedious and time consuming as data volume increases. Currently, text sentiment analysis requires fast and accurate techniques to decode and quantify the emotion in tweets. this paper presents a hybrid framework based on lexicon and machine learning (ML) algorithms to train previously seen tweets in order to predict the sentiments of some new input tweets into positive, negative, and neutral polarities. Tweepy library was used to extract tweets on Laptop reviews to identify some aspects and classify sentiments towards them into specific polarity. After data pre-processing, the implementation in Python used Natural Language Processing package called TextBlob to assign subjectivity and polarity scores to text. the scores were used by the ML algorithms to analyze and classify sentiments. A dataset of 2226 tweets was used for training and testing Support Vector machine, Random Forest, and Naïve Bayes classifiers. Results indicate that Random Forest classifier outperforms others in the task of classifying sentiments on Laptop reviews with the highest accuracy (96%), precision (97%), and F1-score (96%). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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Handwriting Recognition for Predicting Gender and Handedness Using Deep learning 1

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5th international conference on applied machine learning and data analytics, amlda 2022

作者： Saraswat, Mala Agarwal, Ayushi School of Computer Science Engineering and Technology Bennett University UP Greater Noida India ABES Engineering College UP Ghaziabad India

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

this research focuses on predicting the gender and handedness and also combined prediction through Handwriting Recognition. In this paper we have analyzed various Deep learning approaches using different CNN models. Our proposed approach uses two data sets IAM English dataset and Real-Time dataset that was collected by us for recognizing the handwriting pattern to perform the defined task. Using different Deep learning approaches, experiments were conducted to analyze the performance on the given two datasets for predicting. Gender and handedness. Experiments showed Google Net using CNN is the most efficient for the prediction and recognition with the handwriting samples. For combined prediction the accuracy rate is around 94% with Real-Time dataset and 95% with the IAM dataset. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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data-Driven Approach to Network Intrusion Detection System Using Modified Artificial Bee Colony Algorithm for Nature-Inspired Cybersecurity 1

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6th international conference on applied machine learning and data analytics, amlda 2023

作者： Gupta, V.B. Shandilya, Shishir Kumar Ganguli, Chirag Choudhary, Gaurav School of Data Science and Forecasting Devi Ahilya University Madhya Pradesh Indore India VIT Bhopal University Bhopal India DTU Compute Technical University of Denmark Lyngby Kongens2800 Denmark

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

With ever-evolving cyberspace, adaptive defense is crucial. In this paper, we show the Adaptive Defense Mechanism to identify Anomalous hosts in a network using the Artificial Bees Colonization Algorithm. A self-driven metric has been defined to determine the performance of a network that would detect the behavior of its nodes. this algorithmic metric is inspired by the Nature-Inspired Artificial Bees Colonization Algorithm. the end result is randomly generated using a dimension index that gives the same result on the node’s behavior which is then used to determine the probabilistic parametric fitness of the individual nodes. this helps to determine which nodes are getting affected the most or are nearer to the attack surface. the defense mechanism is based on the Nature Inspired Artificial Bees Colonization Algorithm, which is able to detect the nearest point/s of attack on nodes based on the experimental simulation of attacked nodes. It also shows the impact of the defense mechanism on the various topologies of the nodes as predefined in the testbed implementing a Distributed Denial-of-Service attack on the nodes. the proposed algorithm showcases the nodes that are affected due to the attack, providing the nearest point of the breach, which can provide a comprehensive way of examining the intrusion point. this algorithm outperformed in terms of stability and early identification of the malicious nodes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intrusion detection

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