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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Belkhyr Safae Boumhidi Jaouad Nikolov Nikola S Computer Science Departement Faculty of Science Dhar EI Mahraz LISAC Laboratory University Sidi Mohamed Ben Abdellah Fez MOROCCO Department of Computer Science and Information Systems University of Limerick Limerick Ireland

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Deep Reinforcement Learning (DRL) has emerged as a pivotal technology in modern transportation systems, offering considerable potential to enhance driving safety and optimize performance. This study underscores the significance of DRL in autonomous driving (AD) and introduces the Advantage Actor-Critic (A2C) approach for lane change decision-making. A2C, representing policy-based methods, is compared with the Prioritized Replay Deep Q-Network (PRDQN), a value-based approach previously used in similar scenarios. Both approaches were evaluated using the CARLA (Car Learning to Act) simulation environment, focusing on static obstacle scenarios. The comparative analysis aims to assess the performance differences between A2C and PRDQN, highlighting the effectiveness of A2C in improving decision-making accuracy, safety, and adaptability. The results demonstrate the superior performance of A2C in improving safety and decision-making within autonomous driving systems.

关键词： Adaptation models Computational modeling Decision making Transportation Focusing Deep reinforcement learning Safety Reliability Collision avoidance Autonomous vehicles

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NORPPA: NOvel Ringed Seal Re-Identification by Pelage Pattern Aggregation

NORPPA: NOvel Ringed Seal Re-Identification by Pelage Patter...

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IEEE Winter Applications and computer vision Workshops (WACVW)

作者： Ekaterina Nepovinnykh Tuomas Eerola Heikki Kälviäinen Ilia Chelak Department of Computational Engineering School of Engineering Sciences Computer Vision and Pattern Recognition Laboratory (CVPRL) Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Computer Science Faculty of Science University of Helsinki Helsinki Finland

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage Pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

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Experimental EEMD analysis of multisource and multicomponent mechanical signals for wet ball mill load

Experimental EEMD analysis of multisource and multicomponent...

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2023 International Workshop on Signal Processing and Machine Learning, WSPML 2023

作者： Zhang, Jian Yu, Gang Tang, Jian Sun, Rongcheng School of Computer Science Nanjing University of Information Science and Technology Nanjing China State and Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing China BGRIMM Technology Group Co. Ltd. Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781510671928

During the process of mineral grinding, the ball mill generates the mechanical signals containing rich information due to different positions, which include mill loads and mill load parameters (MLPs). Actually, as one of the key factors, online MLPs detection is usually exploited to realize the intellectualization and improve the production efficiency of mineral processing plants. In this paper, on the basis of the ensemble empirical mode decomposition technique, the multisource and multicomponent mechanical signals of an experimental ball mill under changing ball, material, and water load conditions are analyzed to obtain different physical sub-signals via the intrinsic mode function. © 2023 SPIE. All rights reserved.

关键词： Empirical mode decomposition

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention 3

Prediction of rainfall-type debris flow in Jiangjiagou based...

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3rd International Conference on computer vision, Image and Deep Learning and International Conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Gao, Yuan Li, Yingna Kunming University of Science and Technology College of Faculty of Information Engineering and Automation Kunming China Key Laboratory of Computer Technology Application of Yunnan Province Kunming China

ISBN: (纸本)9781665459112

Through the neural network model, the rainfall-type debris flow in Jiangjiagou is predicted. Based on seven meteorological factors with time series characteristics, such as daily rainfall, previous effective rainfall, ground temperature, and humidity, which have been screened by features, the Jiangjiagou rainfall debris flow prediction and early warning model based on LSTM-Attention is constructed. The results show that: (1) LSTM-Attention model is 0.02 higher than that of the Attention-LSTM model, and the prediction effect is better. (2) The prediction accuracy of LSTM-Attention model for Jiangjiagou rainfall-type debris flow can reach 0.88, which is 0.05, 0.07, 0.04, 0.25, 0.13 higher than that of LSTM model, RNN model, RNN-Attention model, SVM model, and BP model, respectively. Research results: It provides a new idea and method for the prediction and research of rainfall-type debris flow, which can be referenced by scholars. © 2022 IEEE.

关键词： Long short-term memory

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Enhanced Explanations for Knowledge-Augmented Clustering using Subgroup Discovery 10

Enhanced Explanations for Knowledge-Augmented Clustering usi...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Szelazek, MacIej Hudson, Dan Bobek, Szymon Nalepa, Grzegorz J. Atzmueller, Martin Agh University Faculty of Applied Computer Science Kraków Poland Institute of Applied Computer Science Jagiellonian University Jagiellonian Human-Centered Artificial Intelligence Laboratory Mark Kac Center for Complex Systems Research Kraków Poland Osnabrück University Semantic Information Systems Group Osnabrück Germany Osnabrück Germany

ISBN: (纸本)9798350345032

Contemporary machine learning techniques are capable of extracting complex structure from data in a way that complements or exceeds manual examination, yet, as is welldocumented, many of these techniques suffer from a lack of interpretability. This paper extends previous work on explainable and interpretable machine learning, in particular on the 'Knowledge-Augmented Clusters (KnAC)' approach, allowing human users to benefit from uninterpretable 'black box' models to extract structure from datasets by clustering and to make this better understandable. One of the key functions of KnAC is to relate expert-annotated clusters to clusters that have been identified by a machine learning method, and then provide a comprehensible explanation, thus clarifying the relationships that KnAC discovered. Our novel contribution in this paper is to examine the usefulness of subgroup discovery as a way to generate comprehensible explanations within KnAC, and to compare this to the existing approach based on the XAI algorithm Anchors through a detailed evaluation. We find that the approach using subgroup discovery performs equally or better in our extensive experimentation testing this on six different datasets. © 2023 IEEE.

关键词： Machine learning

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A Conceptual Deep Learning Model for Real-Time Routin 16

A Conceptual Deep Learning Model for Real-Time Routin

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16th IEEE International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2022

作者： Ikidid, Abdelouafi Fazziki, Abdelaziz El Sadgal, Mohammed Ghazouani, Mohamed El Ichahane, My Youssef Cadi Ayyad University Computer Systems Engineering Laboratory Computer Science Dept Marrakesh Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi Bennour El Jadida Morocco Chouaib Doukkali University Information Technologiy Laboratory El Jadida Morocco

ISBN: (纸本)9781665464956

Urban congestion has been a known problem since the first urban revolution throughout the world. Today's major metropolises are synonymous with traffic congestion and complicated urban circulation. This paper introduces a traffic management system based on agent technology and deep learning model. It acts on the phase layouts constituted of sequences and length to reduce average delay time, maximize intersection throughput, and suggest a routing planning scheme to avoid congested intersection. In the optimization model, lights of each intersection are controlled by a group of agents. The system uptake the ability of the agent to communicate, coordinate and collaborate, to decompose the routing process into decentralized architecture. The proposed model was instantiated in the ANYLOGIC simulator. © 2022 IEEE.

关键词： Traffic congestion

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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PSO Optimization Algorithm for QoS Enhancement in IoT-Enabled WSNs

PSO Optimization Algorithm for QoS Enhancement in IoT-Enable...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Benelhouri, Abdelkader Idrissi-Saba, Hafida Antari, Jilali Laboratory of Metrology and Information Processing Faculty of Science Ibn Zohr University B.P. 8106 Agadir Morocco Laboratory of Computer Systems Engineering Mathematics and Applications Ibn Zohr University Polydisciplinary FacultyB.P. 271 Taroudant83000 Morocco

ISBN: (纸本)9783031262531

Through the recent progress in the field of microelectronics and the emergence of wireless communication technologies, wireless sensor networks (WSNs) have seen the light of day. However, one of the major problems of this type of networks is the energy efficiency. The overage is also a key problem as it determines the ability of a set of sensors to monitor a geographic area efficiently. In order to improve network deployment and throughput, this paper proposes a PSO-based approach to deploy nodes and select cluster heads efficiently. The suggested work has undergone significant QoS experimentation in a variety of scenarios. The results of this algorithm are investigated by implementing the Mod-LEACH algorithm in the data routing process. It is concluded that the PSO based clustering algorithm enhances the network throughput by improving nodes emplacement. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Microelectronics

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Displacement detection method of QR code reference object based on feature points 4

Displacement detection method of QR code reference object ba...

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4th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2022

作者： Yang, Qiuhui Zhang, Baoxiang Ji, Xiaoyu Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China Faculty of Computer and Electronic Information Guangxi University Nanning China Faculty of Information Technology Macau University of Science and Technology China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the continuous development of construction technology and computer technology, in order to meet the needs of concrete quality management, a real-time monitoring information system based on computer vision is established. The identification label is added to the concrete specimen to prevent the exchange of concrete specimens. According to the needs of the application scene, this paper proposes a new feature point extraction algorithm based on Harris. By improving the positioning accuracy of feature points, it can avoid the interference of background noise points or feature points in unclear focus area on feature matching and the calculation of projection matrix;and a few feature points are used to calibrate the camera, which overcomes the problem that the traditional camera calibration method needs to make a calibration board. Experiments show that the algorithm proposed in this paper has good stability to illumination, camera angle and distractors, and is flexible in application. © 2022 IEEE.

关键词： Quality management

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