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2nd international conference on data science, machine learning and applications, icdsmla 2020

2nd International Conference on Data Science, Machine Learni...

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2nd international conference on data science, machine learning and applications, icdsmla 2020

ISBN: (纸本)9789811636899

The proceedings contain 157 papers. The special focus in this conference is on data science, machine learning and applications. The topics include: Twitter Spam Review Detection Using Hybrid machine learning Techniques;throughput Improvement in Energy Efficient Heterogeneous Wireless Sensor Network;predicting Expected Time of Arrival of Shipments Through Multiple Linear Regression;learning via Long Short-Term Memory (LSTM) Network for Predicting Strains in Railway Bridge Members Under Train Induced Vibration;smart Transportation Using Fog Computing;detection of learning Disability: A Survey;EEG Based Emotion Investigation from Various Brain Region Using Deep learning Algorithm;electric Motor Drive Using Single Input Fuzzy Logic Controller for Husk Extraction in Rice Mill Industry;states Categorization in India Based on Health Security Capacity with machine learning Technique;CovidSORT: Detection of Novel COVID-19 in Chest X-ray Images by Leveraging Deep Transfer learning Models;a Real Time Radio Spectrum Measurement Campaign for machine learning Based Spectrum Inference in Cognitive Radio Network;application of Disruptive Technologies in Intelligent Transportation System;deep learning Models for Rubik’s Cube with Entropy Modelling;performance Analysis of Software Enabled Accelerator Library for Intel Architecture;blind Descent: A Prequel to Gradient Descent;a Survey: Modelling Strategies for Predictive Maintenance;abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep learning and Rule-Based machine learning Approaches;a Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic;Load Forecasting Using LSTM and Its Comparison with Other machine learning Techniques and Their Implementation;application of machine learning to Detect Neuroticism in Individuals Using Handwriting Analysis;preface.

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2020 2nd international conference on Applied machine learning and data science, ICAMLDS2020

2020 2nd International Conference on Applied Machine Learnin...

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2020 2nd international conference on Applied machine learning and data science, ICAMLDS2020

The proceedings contain 104 papers. The topics discussed include: jamming effect evaluation method based on radar behavior recognition;research on application of an improved deep convolutional neural network in handwritten character recognition;analysis of application of machine learning based on bibliometrics in pattern recognition;research on the influencing factors of Chinese medicine cultural identity of international students in Chinese medicine schools;application of SVM model to environmental resource analysis in tourism development;learning recommendation based on hybrid collaborative filtering algorithm;fruit image recognition and classification method based on improved single shot multi-box detector;the construction of a flipped classroom teaching mode of college English based on rainclass and its effectiveness analysis;and research on improved sharpening algorithm based on closed operation and binarization.

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6th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2022, 4th international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2022, 2nd international Workshop on Time Ordered data, ProTime2022, 2nd international Workshop on AI System Engineering: Math, Modelling and Software, AISys2022, 1st international Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st international Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and data science, ARTE2022 held at 33rd international conference on database and Expert Systems applications, DEXA 2022

6th International Workshop on Cyber-Security and Functional ...

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6th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2022, 4th international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2022, 2nd international Workshop on Time Ordered data, ProTime2022, 2nd international Workshop on AI System Engineering: Math, Modelling and Software, AISys2022, 1st international Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st international Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and data science, ARTE2022 held at 33rd international conference on database and Expert Systems applications, DEXA 2022

ISBN: (纸本)9783031143427

The proceedings contain 40 papers. The special focus in this conference is on database and Expert Systems applications. The topics include: Synthetic data in Automatic Number Plate Recognition;An Untold Tale of Scientific Collaboration: SCCH and AC 2 T;on the Creation and Maintenance of a Documentation Generator in an Applied Research Context;towards the Digitalization of Additive Manufacturing;Twenty Years of Successful Translational Research: A Case Study of Three COMET Centers;data Integration, Management, and Quality: From Basic Research to Industrial Application;building a YouTube Channel for science Communication;introduction of Visual Regression Testing in Collaboration Between Industry and Academia;vibration Analysis for Rotatory Elements Wear Detection in Paper Mill machine;applying Time-Inhomogeneous Markov Chains to Math Performance Rating;introducing data science Techniques into a Company Producing Electrical Appliances;a Technology Transfer Portal to Promote Industry-Academia Collaboration in South-Tyrol;fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing;sending Spies as Insurance Against Bitcoin Pool Mining Block Withholding Attacks;risks in DeFi-Lending Protocols - An Exploratory Categorization and Analysis of Interest Rate Differences;battling the Bullwhip Effect with Cryptography;Reporting of Cross-Border Transactions for Tax Purposes via DLT;securing File System Integrity and Version History Via Directory Merkle Trees and Blockchains;taxation of Blockchain Staking Rewards: Propositions Based on a Comparative Legal Analysis;comparison Framework for Blockchain Interoperability Implementations;A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME;towards Strategies for Secure data Transfer of IoT Devices with Limited Resources;application of Validation Obligations to Security Concerns;mode Switching for Secure Edge Devices;a Lifecycle Framework for Semantic Web machine Learn

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Review on Predicting the Landslide Susceptibility using machine learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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A comparative study of machine learning in diabetes classification: Decision trees, logistic regression, and SVM 2

A comparative study of machine learning in diabetes classifi...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Zhu, Xinglong Liao, Huitong Zhang, Tingting Zhang, Lujuan Li, Xueri Department of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510689053

As the application of computer science in healthcare continues to expand, machine learning techniques have become an important tool for disease diagnosis. In this study, we trained and predicted diabetes datasets by preprocessing, standardising and normalising the data, and then using three machine learning models, aiming to improve the accuracy of diabetes prediction and the value of clinical application. The experimental results show that these machine learning models exhibit a high degree of accuracy and reliability in the field of diabetes diagnosis, among which the SVM performs the best on normalised data, with the highest values of accuracy, precision, recall, F1-score, and AUC evaluation metrics, showing the good performance of the support vector machine in predicting diabetes. The decision tree model performed consistently on raw and standardised data, while the logistic regression model performed best on AUC values. This study not only verifies the effectiveness of machine learning models in diabetes prediction, but also provides clinicians with accurate diagnostic assistance, which is of significant academic and practical significance. © 2025 SPIE.

关键词： Logistic regression

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Enhancing Weather Prediction Accuracy with Gradient Boosting: A machine learning Approach 2

Enhancing Weather Prediction Accuracy with Gradient Boosting...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Jain, Abhinav Sharma, Gargi School of Computer Science and Engineering Lovely Professional University Punjab Phagwara144411 India

ISBN: (纸本)9798331530389

This research focuses on the integration of machine learning (ML) in predicting the climate with the help of daily recorded weather dataset of Seattle [33] which we obtained from the Kaggle. This research focuses on implementing and analyzing multiple ML techniques such as Naive Bayes, K-Nearest Neighbors (K-NN), Support Vector machine (SVM), Gradient Boosting, XGBoost, and Decision Tree. XGBoost able to get to the top position with an accuracy rate of 87.90% which speaks volume of its performance. This integration of ML techniques is useful in understanding the weather prediction which is highly complex due to multiple atmospheric changes. Such modern methods help in recognizing the more complex non-linear features and trends within weather data. machine learning algorithms makes it possible to detect such tendencies from complex datasets over time. ML enhances weather prediction approaches by learning and analyzing patterns from historical data and real time data. This paper highlights the importance of extreme gradient boosting in weather forecasting by providing examples of its successful applications within the interdisciplinary field of meteorology and ML. This study compares different algorithms for best accuracy and also points to the increasing significance of ML in the enhancement of weather forecast models. © 2025 IEEE.

关键词： Adversarial machine learning

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Evaluation of jet energy determination in heavy-ion collisions using machine learning 2

Evaluation of jet energy determination in heavy-ion collisio...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Zhao, Yangguang Wang, Yuhui Zhou, Yujie Bao, Qihan College of Computer Science and Technology Qingdao University Qingdao266071 China College of Food Science Sichuan Agriculture University Chengdu610000 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781510689053

In high-energy physics experiments, the precise reconstruction of the transverse momentum of jets is challenging. The primary obstacle is the interference caused by background noise particles overlapping with the jet and the limitations of the sensors currently being used. A traditional noise reduction method is area-based subtraction, which calculates the mean value of certain events. In previous studies, some noise reduction methods utilizing machine learning techniques have been proposed. In this study, we evaluate the performance of various machine learning models in reconstructing the jet transverse momentum in simulated heavy-ion events, based on Pythia events embedded in a thermal background distribution. The performance of the models at the particle level is compared to that using pseudo-towers of various resolutions in eta, phi to evaluate the impact of the sensor performance limitations. The results demonstrate that as the resolution decreases, the performance of the machine learning models also degrades, but still performs better than the traditional method. This provides a reference for the application of machine learning methods. © 2025 SPIE.

关键词： Heavy ions

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Automatic Generation of Training Samples Based on Local Samples Phenological Information for Crop Type Classification 24

Automatic Generation of Training Samples Based on Local Samp...

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2nd international conference on Advances in Artificial Intelligence and applications, AAIA 2024

作者： Liu, Yuhao Xiao, Xingyuan College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798400712883

Reliable crop type classification is essential for ensuring food security. With advancements in computer technology, machine learning has become an indispensable tool in this domain. However, their successful implementation is often constrained by the scarcity of training samples. To address this challenge, this study proposes an effective method for automatically generating training samples using a limited number of localized ground-truth labels. The proposed method involves two steps: (1) automatically generating candidate samples by leveraging the phenological information from a small number of local samples within the study area using Time-Weighted Dynamic Time Warping (TWDTW) technology;(2) Cleaning the candidate samples using temporal ndVI features. We conducted multiple experiments in the study area located at the border of North Dakota and Minnesota. By combining samples collected from CDL data with generated samples, we demonstrated the effectiveness of the proposed method in enhancing classifier performance. The results indicated that, compared to using only the original samples, the average overall accuracy of RF and SVM classifications improved by 7.66% and 4.24%, respectively, when employing the generated training samples. This indicates that the generated training samples exhibit high reliability. This study provides a feasible approach for crop classification in regions with scarce ground data. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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The fusion application of intelligent perception and data analysis in product design innovation 2

The fusion application of intelligent perception and data an...

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2nd international conference on Big data, Computational Intelligence, and applications, BDCIA 2024

作者： Wu, Liting Yu, Dali Art and Design College Guangzhou Vocational College of Technology & Business Guangdong Guangzhou511442 China

ISBN: (纸本)9781510689053

With the rapid development of intelligent perception technology and the continuous advancement of data analysis algorithms, the field of product design has ushered in a golden age of innovation. This study focuses on the integrated application of intelligent perception and data analysis in product design innovation, aiming to explore how to capture user behavior and environmental data through advanced perception technology and optimize the design process by combining data analysis algorithms. In the study, we used machine learning algorithms, and intense learning techniques, to analyze user interaction data and identify key improvement points in the design. Through experimental verification, we found that integrated applications can significantly enhance the user experience and market competitiveness of products. This research underscores the transformative potential of integrating intelligent perception and data analysis in product design, leading to substantial improvements in user engagement and market adaptability. © 2025 SPIE.

关键词： Adversarial machine learning

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Comparison of machine learning Based Anomaly Detection Methods for ADS-B System 2nd

Comparison of Machine Learning Based Anomaly Detection Metho...

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2nd international conference on Information Technologies and Their applications

作者： Cevik, Nursah Akleylek, Sedat HAVELSAN Ankara Turkiye Ondokuz Mayis Univ Computat Sci Samsun Turkiye Univ Tartu Inst Comp Sci Tartu Estonia Istinye Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper introduces an anomaly/intrusion detection system utilizing machine learning techniques for detecting attacks in the Automatic Detection System-Broadcast (ADS-B). Real ADS-B messages between Turkiye's coordinates are collected to train and test machine learning models. After data collection and pre-processing steps, the authors generate the attack datasets by using real ADS-B data to simulate two attack scenarios, which are constant velocity increase/decrease and gradually velocity increase or decrease attacks. The efficacy of fivemachine learning algorithms, including decision trees, extra trees, gaussian naive bayes, k-nearest neighbors, and logistic regression, is evaluated across different attack types. This paper demonstrates that tree-based algorithms consistently exhibit superior performance across a spectrum of attack scenarios. Moreover, the research underscores the significance of anomaly or intrusion detection mechanisms for ADS-B systems, highlights the practical viability of employing tree-based algorithms in air traffic management, and suggests avenues for enhancing safety protocols and mitigating potential risks in the airspace domain.

关键词： ADS-B Anomaly Detection System Intrusion Detection System IDS machine learning Avionics Security Cyber Security

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