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Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence Algorithms 5th

Last-Mile Delivery Optimization Using Mixed Electric Vehicle...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Hanaa, Bouhia Benhra, Jamal Ed-dehbi, Wissal ESITH Casablanca Morocco Univ Hassan 2 ENSEM Casablanca Morocco

ISBN: (纸本)9783031807749;9783031807756

This paper presents the Electric Vehicle Routing Problem with Drones (EVRPD), a novel strategy aimed at enhancing last-mile delivery through the combined use of Electric Vehicles (EVs) and drones. The primary objective is to reduce energy usage and CO2 emissions while maintaining effective delivery operations. The proposed system employs a diverse fleet where EVs act as mobile bases, and drones handle the final leg of parcel delivery. Both EVs and drones can deliver packages, with drones being utilized for smaller, time-sensitive shipments. The system emphasizes optimal payload distribution to boost energy efficiency and drone endurance. Drones are resupplied on the EVs' rooftops via IoT technology. To address the routing challenge, the solution leverages bio-inspired algorithms, including Ant Colony Optimization (ACO) for EVs and Particle Swarm Optimization (PSO) for drones. This methodology enhances energy efficiency and drone autonomy, ensuring effective parcel delivery. By incorporating these advanced techniques, EVRPD presents a promising approach for a more sustainable and effective urban logistics network.

关键词： EVRPD Last-mile delivery Energy efficiency CO2 reduction EVs Drones Bio-inspired algorithms ACO PSO

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Path Planning of Mobile Robot with Improved RRT Algorithm 5th

Path Planning of Mobile Robot with Improved RRT Algorithm

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Li, Zijian Yang, Zhiqiang Gao, Huanbing Wang, Xueqiu Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Saikesais Biotechnol Co Ltd Jinan Peoples R China

ISBN: (纸本)9789819770038;9789819770045

When applying the RRT algorithm to obstacle avoidance path planning for mobile robots, there are planning efficiency issues such as large search space, long calculation time, and uneven path. This paper proposes an enhanced algorithm that takes map information, starting and ending coordinates as inputs, and outputs an optimized smooth path to address the shortcomings of the original algorithm. This algorithm uses a low-discrepancy Sobol sequence with a probabilistic target bias strategy to optimize the sampling points. Additionally, the concept of target gravity is utilized to adjust the expansion direction of the new nodes, with the gravity coefficient being adaptively adjustable. The original obstacle avoidance path is optimized by pruning optimization removal combined with cubic B-spline curve. In a complex map setting, the improved algorithm demonstrates reductions in running time of 87.71%, 88.59%, and 67.82% compared to RRT, RRT*, and target-biased RRT, respectively. Compared with the other three algorithms, the improved approach reduces the planned path distance by 18.53%, 8.43%, and 15.28%, respectively, demonstrating its efficacy in enhancing path planning efficiency and maintaining path quality.

关键词： Path planning RRT algorithm RRT* algorithm Pruning optimization Target gravity

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DeepMHT: Moving Object Segmentation in the Point Cloud Based on Deep Learning and Multiple Hypothesis Tracking 5th

DeepMHT: Moving Object Segmentation in the Point Cloud Based...

引用

5th China Intelligent Robotics Academic Conference

作者： Chen, Baifan Jin, Yuxuan Zhao, Zishuo Wan, Jiadong Peng, Chengyu Cent South Univ Sch Automat Changsha 410073 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

In autonomous driving, moving object segmentation plays an essential role in improving the accuracy of mapping and path planning. However, accurately and consistently differentiating between objects that are actually moving and those that are potentially moving is still challenging. This paper proposes DeepMHT, a novel approach that combines a multiple hypothesis tracking algorithm (MHT) with deep learning to solve the issue of moving object segmentation of LiDAR point clouds. This paper designs a data association network to extract deep features from the object point cloud and 3D box and fuses these two features to obtain affinity weights. We propose an innovative local hypothesis update model that integrates affinity weights with the weights of MHT to make data associations more accurate and stable. Unlike other moving object segmentation algorithms that calculate differences in the point clouds of adjacent frames, DeepMHT directly estimates the motion state of objects and segments moving objects by their position and velocity, which significantly improves the precision of segmentation. This paper compares DeepMHT with several other moving object segmentation algorithms on the KITTI tracking dataset and finds that DeepMHT is highly accurate.

关键词： Moving object segmentation learning-based data association multiple hypothesis tracking

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Tunisian Sign Language Recognition and Translation Using Deep Learning 4th

Tunisian Sign Language Recognition and Translation Using Dee...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： El Askri, Marah Basly, Hend Bchir, Riadh Zayene, Mohamed Amine Sayadi, Fatma Ezzahra Univ Sousse Natl Engn Sch Sousse Lab Networked Objects Control & Commun Syst NOCCS Eniso Sousse Tunisia Univ Monastir Fac Sci Monastir Monastir Tunisia

ISBN: (纸本)9783031821554;9783031821561

Human activity recognition, a dynamic field within computer vision and machine learning, aims to accurately identify and interpret human actions or activities using video data. This paper delves deeper into the domain by concentrating on Sign Language (SL) as a unique and intricate form of human activity. Sign Language (SL) is a vital communication tool, used by individuals who are deaf or hard of hearing, facilitating their interaction and engagement with others in a visually expressive manner. A lot of research and projects in SL are being done, and it has been observed that most of them are done in American Sign Language (ASL) or Indian Sign Language (ISL), but no such progress has been carried out for the other SLs. Therefore, this paper aims to present a work done in Arabic sign language (ArSL), especially Tunisian SL. The main idea is to recognize Tunisian SL and then translate it into spoken language using artificial intelligence (AI) methods. The purpose of this work is to ease the communication between individuals who are deaf and those who are not. The proposed method uses holistic landmarks with sequence key points for Tunisian sign language and trains it with a Long Short-Term Memory (LSTM) model. The results show that the proposed model can be used for real-time SL estimation, providing a better interpretation method for the deaf community. As a result, our model achieved a validation accuracy of 98% with a cross-validation technique.

关键词： Deep Learning LSTM Sign Language Recognition

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Explainability of Disease Classification from Medical Images Using XGrad-Cam

Explainability of Disease Classification from Medical Images...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Tehsin, Sara Nasir, Inzamam Mashood Damasevicius, Robertas Kaunas Univ Technol Ctr Real Time Comp Syst Kaunas Lithuania

ISBN: (纸本)9783031834349;9783031834356

In this study, a novel approach to disease classification from medical images using a customized convolutional neural network (CNN) and an advanced Explainable AI algorithm, XGrad-Cam, is presented. The proposed CNN model is streamlined with 11 layers, ensuring efficient training and accurate disease classification. XGrad-Cam, an extension of the Grad-Cam algorithm, offers improved visualization by highlighting critical areas contributing to the classification decisions. This model was evaluated on medical images, including chest CT scans for SARS-COVID and brain MRIs for Alzheimer's disease. Through extensive experimentation with both binary and multi-class disease datasets, the model demonstrated high accuracy, precision, recall, sensitivity, specificity, and F-1 scores, notably achieving 90.8% accuracy for SARS-COVID and 91.0% for Alzheimer's disease classification. The utilization of XGrad-Cam enhances the interpretability of the CNN's decisions, providing valuable insights into the disease classification process. This study contributes to the fields of medical image analysis and Explainable AI by offering a highly efficient and interpretable model for disease classification, paving the way for future advancements in automated diagnosis and treatment planning.

关键词： Customized CNN Explainable artificial intelligence Deep learning

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A New Multi-level Knowledge Retrieval Model for Task-Oriented Dialogue 5th

A New Multi-level Knowledge Retrieval Model for Task-Oriente...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Dong, Xuelian Chen, Jiale Weng, Heng Chen, Zili Wang, Fu Lee Hao, Tianyong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China Hong Kong Polytech Univ Coll Profess & Continuing Educ Hong Kong Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China Guangzhou WisTalk Informat Technol Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9789819770069;9789819770076

One of the main challenges in task-oriented dialogue systems is how to retrieve accurate knowledge from external knowledge bases. Existing methods usually retrieve knowledge and entire entity by utilizing dialogue context, while the correlations between dialogue context and entity attributes are overlook, leading suboptimal knowledge retrieval. Therefore, we introduce a Multi-Level knowledge retrieval model for Task-Oriented Dialogue (MLTOD) consisted of an entity retriever, an attribute retriever, a ranker and a response generator. The entity retriever retrieved entities from knowledge bases and the attribute retriever extracts relevant attributes respectively. The ranker dynamically combines the results from the retrievers to select the most relevant knowledge entities. Then the response generator generates final system response based on the ranking result. In addition, this paper introduces a novel multi-level retrieval mechanism. It considers both entity level and attribute level relevance for coarse to fine knowledge retrieval. Experiments on two publicly available datasets show that our MLTOD model outperforms existing state-of-the-art baseline approaches, validating its effectiveness for task-oriented dialogue.

关键词： Knowledge retrieval Task-oriented dialogue Large language model

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Evaluating the Effects XR Has on Users: An Exploratory Study 8th

Evaluating the Effects XR Has on Users: An Exploratory Study

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Chontas, Panagiotis-Efstratios Iftene, Adrian Buraga, Sabin-Corneliu Alexandru Ioan Cuza Univ Fac Comp Sci Iasi Romania

ISBN: (纸本)9783031838446;9783031838453

Recent increases in XR headset sales suggest a growing consumer preference for AR/VR in home and institutional environments. As XR technology becomes mainstream and evolves, it is essential to examine its physical and mental impacts. While recent studies emphasize the positive effects of XR, this paper argues for investigating potential negative outcomes to ensure user well-being and ethical standards. This study involved young adults (ages 18-29) using the Apple Vision Pro headset within a visionOS environment, designed to encompass a range of user interactions. Questionnaires and health tests were conducted to evaluate participants. Findings indicate users' adeptness in interacting with XR technologies, particularly favoring experiences that simulate real-life environments in three dimensions. However, despite their familiarity with technology and extensive screen time, young adults experienced physical side effects early in the testing process. This research underscores the necessity of a balanced understanding of XR's implications to guide its development responsibly.

关键词： Human computer Interaction (HCI) Extended Reality (XR) visionOS Apple Vision Pro Augmented Reality (AR) Virtual Reality (VR) User Interface (UI)

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Load Balanced Transaction Scheduling Using Gaussian Mixture Model-Ant Colony Optimization 4th

Load Balanced Transaction Scheduling Using Gaussian Mixture ...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Gupta, Amisha Sharma, Satish Chander Jyoti, Divya Verma, Ritika Azeem, Mohammad Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 HP India

ISBN: (纸本)9783031837821;9783031837838

In grid computing environment, a balanced transaction scheduling is a NP-hard problem. In this paper, a technique for load-balanced transaction scheduling using Gaussian mixture model-ant colony optimization algorithm is proposed. Gaussian mixture model, which is based on Gaussian density functions, provides clustering characteristics. Our proposed algorithm uses this clustering method to identify the cluster of nodes with less nodes and uses ant colony optimization to find out the appropriate node for the final selection. The proposed algorithm outperforms the existing algorithms.

关键词： Load balancing Transaction Scheduling Gaussian mixture model Ant Colony Optimization

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Target Profile Model to Improve Cybersecurity Capabilities of Educational Institutions Against Cyber-Attacks 4th

Target Profile Model to Improve Cybersecurity Capabilities o...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Cruz, Jeremy La Rosa Toro, Andres Mansilla Lopez, Juan Portugal, Christian Cipriano Univ Peruana Ciencias Aplicadas Lima Peru Cranfield Univ Cranfield Beds England

ISBN: (纸本)9783031840777;9783031840784

This research addresses the limitations of educational institutions in defining the cybersecurity capabilities needed to protect, respond, and recover from the main cybercrime scenarios in the sector. To date, there is evidence of an increase in cyberattacks targeting educational institutions, and there are no models to define the capabilities to reduce risk, so we focused on creating a cybersecurity target profile model based on NIST based on the reality of the education sector. This model will allow the institution to implement a cybersecurity program autonomously, reduce costs, and prioritize critical capabilities for the institution. In this sense, the model has been confirmed through a case study in which the current cybersecurity posture of the institution was calculated and, based on the gaps identified with the proposed target profile, initiatives to improve it were set up. The results show that the implementation of the model improved critical cybersecurity capabilities in educational institutions by 30%, moving capabilities from a low maturity level (Initial) to a defined level. In addition, a 60% reduction in the cost of implementing the cybersecurity program was shown. Thus, the proposed model allows institutions to find gaps and opportunities for improvement, strengthening their cybersecurity management and maturity in this area. It also helps the implementation of a cybersecurity program by reducing costs and providing autonomy to educational institutions.

关键词： Cybersecurity Posture Capabilities Educational Institutions NIST Target Profile

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Human Action Recognition Using Mediapipe Holistic Keypoints: A Deep Learning Approach 4th

Human Action Recognition Using Mediapipe Holistic Keypoints:...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Shandilya, Utkarsh Sharma, Vijeta Mishra, Deepti Cent Univ Haryana Dept Comp Sci & Engn Jaat Haryana India NTNU Norwegian Univ Sci & Technol Educ Technol Lab Dept Comp Sci IDI N-2815 Gjovik Norway

ISBN: (纸本)9783031837951;9783031837968

Human action recognition (HAR) is a crucial field in computer vision with applications ranging from video surveillance to human-computer interaction. This study explores an efficient framework for HAR by leveraging human keypoint extraction using Google's Mediapipe and Long Short-Term Memory (LSTM) networks. The Mediapipe framework provides accurate and real-time human keypoint extraction, significantly reducing the computational complexity associated with raw video data processing. These keypoints, representing skeletal movements, are utilised as input to LSTM networks, which capture the temporal dependencies vital for action classification. The proposed method is evaluated on two benchmark datasets: UCF101 and Kinetics 400. UCF101 contains 13,320 video clips across 101 action classes, while Kinetics 400 features 400 human action categories. The combination of Mediapipe for feature extraction and LSTM for temporal modelling achieves 92.40% and 86.8% accuracy on UCF101 and Kinetics400, respectively. The results demonstrate the effectiveness of keypoint-based approaches for HAR. This study highlights the potential of lightweight, real-time human action recognition frameworks suitable for resource-constrained environments, especially for edgeAI and edge Robotics.

关键词： Human action recognition human-computer interaction keypoint extraction LSTM Mediapipe

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