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A Dynamic Traffic Management System for Itinerary Optimization 11

A Dynamic Traffic Management System for Itinerary Optimizati...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Monzón Catalán, Iván Tomás López, Vicente R. García Fernández, Luís A. Computer Science and Engineering Department Universitat Jaume I Campus del Riu Sec S/N Castelló Spain

ISBN: (纸本)9789897587450

Freight transport is a fundamental pillar of the European economy, accounting for more than 9 % of the European Union’s Gross Domestic Product (GDP). Seaports are critical nodes in this logistics chain, handling more than 67.8% of freight transport in tonne-kilometres. this article presents an intelligent traffic management system designed specifically for road access to the Port of Rotterdam, the largest and busiest port in Europe, with the aim of optimising the flow of vehicles, improving operational efficiency and reducing CO2 emissions. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Freight transportation

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An intelligent Feature Extraction and Multiple learning-Based Classification Techniques for Diagnosis of Breast Cancer 8th

An Intelligent Feature Extraction and Multiple Learning-Base...

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8th international conference on Internet of things and Connected Technologies, ICIoTCT 2023

作者： Patro, Satyabrata Mishra, Jyotirmaya Panda, Bhavani Sankar Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam531162 India

ISBN: (纸本)9789819757855

Breast cancer is often curable if discovered in its early stages. Machine learning algorithms are used to automate disease identification. To increase the likelihood of recognizing the condition at an early stage, an effective classifier for automated diagnosis of breast cancer is needed. this research attempts to introduce a mixed machine learning model for predicting breast cancer. To outperform other strategies, it requires enormous amounts of data. Moreover, the intricate data models increase the training cost. For enhancing the efficiency of basic classifiers, ensemble learning holds great promise. A rapid discrete wavelet transforms using the wrapping approach is then used to extract the characteristics from the Region of Interest (ROI) mammography pictures. In addition to being too high to be categorized, the extracted wavelet coefficients also have incredibly high run-time complexity. the modified whale optimization method, which employs swarm intelligence, has been presented to lower time complexity and select the significant characteristics. Ensemble learning has been applied for the requisite efficiency in the suggested technique. three machine learning (ML) classifiers—the extreme gradient boost classifier (XGB), support vector machine (SVM), and enhanced granular neural network (E-GNN)—form the ensemble voting system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Two-Stage Extended Kalman Filter-Based Approach Against FDI Cyber-Attack in intelligent and Connected Vehicles 11

A Two-Stage Extended Kalman Filter-Based Approach Against FD...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Sun, Bin Yang, Shichun Wang, Yu Lu, Jiayi Cao, Yaoguang School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China

ISBN: (纸本)9789897587450

With the widespread integration of artificial intelligence and telecommunication technologies in vehicles, the challenge of cybersecurity in intelligent and Connected Vehicles (ICVs) has gained significant attention. A typical and high-risk cyber-attack technique involves False data Injection (FDI) into sensors through the network, resulting in deviations in subsequent planning and control algorithm outcomes. Existing approaches suffer from limited robustness, being suitable only for simple models or requiring extensive data for the training model, which limits their practicality. therefore, this paper proposes a method based on a Two-stage Extended Kalman Filter (TSEKF), which not only detects cyber-attacks but also restores the vehicle’s true motion state, thereby enhancing the robustness of vehicle ego state perception. the experimental results demonstrate that the proposed method exhibits strong performance across various motion scenarios, offering an effective solution for the safe operation of ICVs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cyber attacks

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Deep learning Models to Predict Hip Joint Kinetics From Kinematics During Gait 24

Deep Learning Models to Predict Hip Joint Kinetics From Kine...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Hulleck, Abdul Aziz Alshehhi, Aamna Abdullah, Muhammad Katmah, Rateb Khalaf, Kinda El-Rich, Marwan Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798400718274

Hip joint reaction forces serve as crucial indicators of underlying hip disorders such as osteoarthritis, avascular necrosis, and femoro-acetabular impingements. Quantifying hip kinetics traditionally relies on telemeterized implants or non-invasive conventional gait labs, which measure kinematics and external kinetics in controlled laboratory settings. these methods, although non-invasive, require specialized expertise, particularly in the workflows of musculoskeletal modeling. this study aims to develop a sequence-to-sequence (seq2seq) model capable of predicting hip joint reaction forces in three planes using positional data obtained from either marker-based or marker-less motion capture systems. the model was trained and validated using a publicly available database of eighty healthy subjects, employing leave-one-subject-out cross-validation. through optimization of hyperparameters and early stopping techniques, the model achieved RMSE values below 0.5% BW for antero-posterior shear force, 1% BW for compressive forces, and 0.31% BW for medio-lateral shear forces. Normalized RMSE values were less than 0.25% across all components of hip joint reaction forces. In conclusion, this study proposes a deep learning seq2seq model as a viable method for accurately predicting hip joint reaction forces in all three planes. these seq2seq models present a cost-effective alternative to traditional and cumbersome gait lab equipment and current musculoskeletal modeling approaches used in hip kinetics assessment. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Shear flow

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Short Paper: Revealing the Self: Brainwave-Based Human Trait Identification 24

Short Paper: Revealing the Self: Brainwave-Based Human Trait...

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11th international conference on Networking, Systems and Security, NSysS 2024

作者： Islam, Md Mirajul Uddin, Md Nahiyan Hasana, Maoyejatun Pandit, Debojit Rahman, Nafis Mahmud Chellappan, Sriram Azam, Sami Al Islam, A.B.M. Alim Bangladesh University of Engineering and Technology Dhaka Bangladesh Sunbeams Dhaka Bangladesh University of South Florida Tampa United States Charles Darwin University Darwin Australia

ISBN: (纸本)9798400711589

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person’s reaction to an invitation to smoke versus another person’s response to a query about their sleep quality. the identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigoroususer evaluation with an additional 20 participants. the outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification. © 2024 Copyright held by the owner/author(s).

关键词： Brainwaves

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Application Research of intelligent Machinery Design Based on CAD System 14th

Application Research of Intelligent Machinery Design Based o...

引用

14th international conference on Frontier Computing, FC 2024

作者： Wang, Yinghui Guangdong Innovative Technical College Dongguan523960 China

ISBN: (纸本)9789819627974

With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. the system is now able to leverage AI algorithms to optimize the design process and assist designers through pattern recognition, automated design processes, and parameter optimization. Today's intelligent CAD systems can automatically identify design requirements and constraints to provide more accurate design solutions. Moreover, the intelligent CAD system can also carry out complex simulation and analysis, and the application research of intelligent mechanical design based on CAD system shows the great potential of this field, which not only improves the design quality and efficiency, but also opens new possibilities for the future development of the mechanical design industry. According to the experimental data, the success rate of intelligent mechanical design, the adaptation rate of material selection, the accuracy rate of manufacturing process and the cost control scheme of the CAD system combined with manual audit are all above 99.88%, and the error rate is below 0.12%. Moreover, with the continuous progress of technology, the application of intelligent CAD system in the field of mechanical design will be more extensive and in-depth in the near future. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application Design Artificial Intelligence CAD Technology Cost Control Mechanical Design

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7th international Russian Automation conference, RusAutoCon 2024

7th International Russian Automation Conference, RusAutoCon ...

引用

7th international Russian Automation conference, RusAutoCon 2024

ISBN: (纸本)9783031824937

the proceedings contain 37 papers. the special focus in this conference is on Russian Automation. the topics include: Mapping and Path Planning Methods for Highly automated Vehicles in Agriculture;morphological Analysis and Synthesis Features of Technological Processes;a Modern Method of Wireless Control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless Controlling Gauge Device Signal Is Triggered;integration of automated Management Systems for Enterprises’ Transport and Technical Services;development of an Automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural Networks;study of the Performance of Adaptive Sensor Networks for Collecting and Processing thermoelectric data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger Control of Technological Processes of Resource Provision;system for Statistical Assessment of Means of Controlling Engineers’ Qualification for Information Support to Manage the Recruiting Process in Industry;smart Enumeration Technology;comparative Analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement learning with External Teacher for Building Energy Management.

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Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer data 11

Evaluation of Detection Approaches for Road Anomalies Based ...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Pocevicius, Edvinas Paulauskas, Donatas Eglynas, Tomas Jankunas, Valdas Jakovlev, Sergej Jusis, Mindaugas Lizdenis, Dovydas Department of Marine Engineering Klaipeda University H. Manto g. 84 Klaipėda Lithuania Marine Research Institute Klaipeda University H. Manto g. 84 Klaipėda Lithuania Department of Informatics and Statistics Klaipeda University H. Manto g. 84 Klaipėda Lithuania Department of R&D Ltd Inotecha Danes Street 47 Klaipeda Lithuania

ISBN: (纸本)9789897587450

Current container security systems record vibrations and shocks, but their potential for creating smart transportation systems remains underutilized. this study analyzes data collected from a truck and discusses a concept for generating road condition maps from accelerometer data. An experiment was conducted by mounting an accelerometer on a container door to gather acceleration data in various transport conditions. the study focuses on analyzing vertical (Z-axis) accelerations as a primary indicator of road anomalies. the developed concept can be integrated into logistics platforms, enabling vehicle drivers and infrastructure managers to respond to road defects in a timely manner. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Truck transportation

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Prediction of Covid-19 Recovery Chances Using Supervised Machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Short Paper: AI-Driven Disaster Warning System: Integrating Predictive data with LLM for Contextualized Guideline Generation 24

Short Paper: AI-Driven Disaster Warning System: Integrating ...

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11th international conference on Networking, Systems and Security, NSysS 2024

作者： Faiaz, Md. Abrar Nawar, Nowshin Bangladesh University of Engineering and Technology Dhaka Bangladesh University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9798400711589

Early warning systems are the backbone of disaster management, but traditional systems often send static alerts with no personalized advice. this work investigates an AI-powered disaster warning system using predictive analytics and LLMs to design personalized recommendations over efficient response strategies. this system ensures that real-time information on context-specific and geographically relevant preparedness is tailored to the needs of varied populations. It was built using the LangChain framework and features an architectural composition with a predictive layer in disaster monitoring and an advisory layer for proactive safety guidelines. It integrates current weather conditions with advanced machine learning algorithms to enhance disaster prediction and utilizes LLMs in constructing customized actions, hence streamlining emergency messaging with enhanced clarity and relevance. Among the tested models, it turned out that the Gemini Pro LLM is the most effective, consistently generating outputs appropriate in context and accurate. Analysis under different temperature settings demonstrated the ability of the model to maintain high precision on various types of crises. the findings indicate that this approach can transform traditional early warning systems into more adaptive, user-centered frameworks, hence enhancing disaster management practices. © 2024 Copyright held by the owner/author(s).

关键词： Alarm systems

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