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2024 International Conference on computer and Applications, ICCA 2024

作者： Amer, Lujina Elias, Catherine M. C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science and Engineering Department Egypt

ISBN: (纸本)9798350367560

Predicting vehicles' motion on highways has become crucial for enhancing road safety and traffic flow. Deep learning, which reached exceptional results in various applications, is now the leading approach for vehicle motion prediction. This work presents a deep learning-based model using Long Short-Term Memory (LSTM) networks to classify vehicle intentions into lane-keeping, left lane-changing, and right lane-changing. Utilizing the PREVENTION dataset, which provides naturalistic driving data, a sequence of centre points, longitudinal distances, lateral distances and yaw angles of a vehicle has been extracted to train the model to predict lane changes effectively. The model consists of three LSTM layers, a dense output layer and a drop out added between layers to prevent overfitting. Experiments focusing on optimizing the model parameters, learning rate, sequence length, and batch size, were conducted to determine their impact on prediction effectiveness. The best-performing model showed significant results in lane changing prediction, achieving an accuracy of 84%, precision of 89%, and recall of 82%. Future work will aim to train the model on more complex features, to account for vehicle inter-dependencies and interactions, and test model on various highway scenarios for a more reliable system. © 2024 IEEE.

关键词： Vehicle safety

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Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

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SN computer science 2024年第4期5卷 1-13页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama Lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data augmentation Industrial data analysis Machine learning Mineral processing

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhou, Hang Liu, Haichao Lu, Hongliang Ma, Jun Ji, Yiding Robotics and Autonomous Systems Thrust Systems Hub Guangzhou China School of Engineering The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong Intelligent Transportation Thrust Systems Hub Guangzhou China

ISBN: (纸本)9781665481090

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark. © 2024 IEEE.

关键词： Deep neural networks

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The EGY-DRiVeS Dataset: Illuminating the Dynamics of Egyptian Road Traffic Behaviors

The EGY-DRiVeS Dataset: Illuminating the Dynamics of Egyptia...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Saadawy, Zeyad H. Elfar, Mohamed A. Aly, Menna M. Adel, Momen Bahy, Mohamed A. Elias, Catherine M. C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science and Engineering Department Egypt

ISBN: (纸本)9798350367560

This paper presents a comprehensive dataset of Egyptian roads integrated with chaotic scenarios captured by EGY-DRiVeS' lab golf car to aid in developing the autonomous driving experience. Using Velodyne 3D laser scanners, C922 Pro HD webcams, and iPhone 11 Pro cameras, a cumulative recording time of 3 hours and 48 minutes was documented. This included 1 hour and 15 minutes of LiDAR recordings, 1 hour and 45 minutes of camera recordings, and 48 minutes and 13 seconds of synced recordings. The dataset encompasses diverse scenarios and objects, ranging from urban environments with dynamic and static objects to rural highways. In-campus roads and intersections are also included, providing real-world scenarios such as vehicular and pedestrian behavior within campus premises. Each part of the dataset is annotated with 2D bounding boxes offering detailed information about vehicles, pedestrians and road objects. The provided data are calibrated, synchronized, and timestamped. Alongside the dataset, data format specifications, annotated data samples, and all supplementary recordings are provided upon request. © 2024 IEEE.

关键词： Network security

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(Information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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On-Edge Driving Maneuvers Detection in Challenging Environments from Smartphone Sensors 10

On-Edge Driving Maneuvers Detection in Challenging Environme...

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Amer, Norhan Elsayed Mahdy, Ahmed El Khamis, Mohamed Gomaa, Walid Shoukry, Amin Faculty of Engineering Alexandria University Department of Computer Science and Engineering Alexandria Egypt Ejada Systems Ltd. Department of Computer Science and Engineering Alexandria Egypt

ISBN: (纸本)9781665464635

Traffic fatalities are increasing in developing countries where there are few investments in road safety. Culture and road conditions also affect driving habits. Therefore, automatic detection and reporting of driver behavior to concerned entities can potentially save lives. In particular, we analyze a driving maneuvers dataset collected from one environment (country) but tested in another environment with aggressive driving habits and irregular road conditions. We also develop an on-edge system with fast response time to serve users on a large scale. Specffically, we propose an approach for detecting aggressive and normal events using random forest classifier. We utilize the accelerometer and gyroscope smartphone readings to classify driving maneuvers events to five types (aggressive acceleration, suddenly break, aggressive turn right, aggressive turn left, and normal). We achieved an accuracy of only 63.4% by training our model on an available dataset collected from a foreigner environment and tested on our environment. The lowest precision value was 54% while the lowest recall was 42%. However, we achieved an accuracy of 98.4% when augmenting an available dataset with data collected with our application. The lowest precision value was 98% while the lowest recall was 90%. From the results, it is shown that the available datasets do not generalize well to different driving habits and road conditions. Finally, an implementation of the random forest model using OpenCV on an Android platform is analyzed. © 2022 IEEE.

关键词： Support vector machines

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An Energy Efficient Hybrid Communication Protocol for Large Area Wireless Sensor Networks 4

An Energy Efficient Hybrid Communication Protocol for Large ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karthigeyan, Shreyas Bhol, Sampark Jain, Akshat Yadav, Karthik Sinha, Akash Padma Priya, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Tamil Nadu Vellore632014 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

Energy conservation is an indispensable aspect of the protocols designed for Wireless Sensor Networks (WSNs). The communication protocols for WSN fall mainly under two categories: centralized and distributed. Centralized protocols are not feasible for real-world WSNs as the network can span across large areas where most of the nodes cannot directly communicate with the base station (BS). The routing issues in such networks can be addressed using multi-hop protocols. However, the process of BS guided clustering in such methods cannot be performed. Distributed clustering methods that minimize the intervention of BS in clustering can provide feasible solutions in such cases, but the energy expenditure is considerably high. This paper, hence, proposes a hybrid clustering based routing protocol for large WSN that employs certain aspects of centralized as well as that of distributed protocols. Experimental evaluation of the proposed protocol in terms of network lifetime gives promising results that clearly indicate the energy efficiency and consistency of the proposed work in different scenarios. The proposed work can prove beneficial in real-life scenarios where the deployed nodes are far away from the BS. © 2025 The Authors. Published by Elsevier B.V.

关键词： Hybrid power

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A Hybrid Approach for Automated GUI Testing Using Quasi-Oppositional Genetic Sparrow Search Algorithm

A Hybrid Approach for Automated GUI Testing Using Quasi-Oppo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jayalakshmi, N. Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Testing of GUI is crucial for assessing software reliability, usability, and functionality;however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) to auto-generate efficient GUI test cases. It combines genetic algorithms, sparrow search dynamics, and quasi-oppositional learning to optimize test suites, minimize redundant test cases, and improve the testing fault. The result obtained for the model OOGSSA is 95.8 % in test coverage, 85.3 % in redundancy, and 92.6 % in fault detection. Furthermore, test generation time was minimized to forty minutes and the overall testing time to seventy-five minutes, which is more efficient than conventional genetic or model-based testing. The outcomes presented above show that OOGSSA is highly efficient and effective in solving the issues of the current GUI testing and improving the overall quality and reliability of software. More enhancements to the algorithm will be done in future work as well as leveraging the algorithm to cover a wider GUI space including mobile and web. © 2024 IEEE.

关键词： Software reliability

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