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A novel hybrid deep learning framework for pedestrian detection based on thermal infrared and visible spectrum images

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Multimedia Tools and Applications 2025年 1-27页

作者： Defaoui, Mahassine Koutti, Lahcen El Ansari, Mohamed Lahmyed, Redouan Masmoudi, Lhoussaine LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir80000 Morocco Informatics and Applications Laboratory Faculty of Science My Ismail University Meknes Morocco Conception and Systems Laboratory Faculty of Sciences Mohamed V University Rabat Morocco

For many intelligent systems, designing accurate pedestrian detection approaches is a fundamental task. This paper describes a novel hybrid system to detect pedestrians using both visible and thermal infrared sensors. The proposed method is achieved in two primary stages. The first stage, hypotheses generation (HG), aims to extract the regions that represent suspected pedestrians from the image. It is performed using both thermal and visible images of the same scene. Here, an improved deep learning-based solution with a convolutional neural network named PedTherm-U-net is proposed. It segments the thermal images and maps the segmentation results into the corresponding visible image to extract the ROIs (candidate pedestrians). The second stage, hypotheses verification (HV), classifies the ROIs extracted from the first stage into pedestrians and non-pedestrians. In the HV step, a novel PedVis-ResNet framework to classify the extracted ROIs is introduced. It is a modified version of ResNet model dedicated to classify the visible pedestrian samples. The proposed method has been tested on the publicly accessible OSU Color-Thermal dataset, and the obtained results demonstrate the effectiveness of the proposed method when compared to the recent state of the art pedestrian detection approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Intelligent systems

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IDLat: An Importance-Driven Latent Generation Method for Scientific Data

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IEEE Transactions on Visualization and computer graphics 2023年第1期29卷 679-689页

作者： Shen, Jingyi Li, Haoyu Xu, Jiayi Biswas, Ayan Shen, Han-Wei Department of Computer Science and Engineering The Ohio State University United States Los Alamos National Laboratory United States

Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it difficult to incorporate domain interest to control the size of the latent representations and the quality of the reconstructed data. In this paper, we present a novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications. © 2022 IEEE.

关键词： Data visualization

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Emotion Recognition Using Various Measures and Computational Methods: Review

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 314-329页

作者： Hamrouni, Abir Bendella, Fatima SIMPA Laboratory Department of Computer Science Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf USTOMB Oran Algeria

Emotion recognition has garnered significant attention as a burgeoning research domain, owing to its potential applications across diverse fields such as human-computer interaction, affective gaming, marketing, and human-robot interaction. Accurately interpreting and appropriately responding to human emotions remains a critical challenge in the development of systems. This obstacle necessitates a thorough understanding of emotions to enhance user experiences within such systems. This paper conducts a comprehensive review focusing on advancements in emotion recognition techniques, with an emphasis on leveraging a variety of sensors and computational methods. Our study findings highlight the significant improvement to emotion recognition accuracy when multiple measures and computational methods, rather than a single modality, is used. This article contributes to the field by thoroughly reviewing and comparing diverse measures and computational methods for emotion recognition. The study highlights the pivotal role of employing multiple modalities and advanced machine learning algorithms to achieve superior accuracy and reliability in emotion recognition. Furthermore, this research identifies potential avenues for further investigation and development, such as integrating multimodal data and exploring novel features and fusion techniques. The insights offered in this study provide valuable guidance for researchers and practitioners in the field, facilitating the advancement of technologies that adeptly understand and respond to human emotions. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Adversarial machine learning

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Emergent classical spin liquid phases in an Ising lattice via size effects

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Physical Review B 2024年第2期109卷 L020303-L020303页

作者： Pratik Brahma Sayeef Salahuddin Department of Electrical Engineering and Computer Sciences University of California Berkeley California 94720 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

We show that a classical spin liquid phase can emerge from an ordered magnetic state in the two-dimensional frustrated Shastry-Sutherland Ising lattice due to lateral confinement. Two distinct classical spin liquid states are stabilized: (i) long-range spin-correlated dimers, and (ii) exponentially decaying spin-correlated disordered states, depending on widths of W=3n, 3n+1 or W=3n+2,n being a positive integer. Stabilization of spin liquids in a square-triangular lattice moves beyond the conventional geometric paradigm of kagome, triangular, or tetrahedral arrangements of antiferromagnetic ions, where spin liquids have been discussed conventionally.

关键词： Phase diagrams Spin liquid Classical spin models Lattice types Ising model Transfer matrix calculations

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in Engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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An optimized Parkinson’s disorder identification through evolutionary fast learning network

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 383-400页

作者： Bouslah Ayoub Taleb Nora LRI Laboratory Computer Science DepartmentUniversity of BadjiMokhtar AnnabaAnnabaAlgeria Complex System Engineering Laboratory Computer Science DepartmentUniversity of Badji Mokhtar AnnabaAnnabaAlgeria

Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative ***,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches ***,ML approaches required first to refine their parameters and then to work with the best model *** process often requires an expert user to oversee the performance of the ***,an attention is required towards new approaches for better forecasting ***/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification *** core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN ***-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark *** very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same *** results support that the proposed model can achieve the best performances in terms of accuracy and ***/value-A novel efficient PD detectionmodel is proposed,which is called *** A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection ***,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting *** helps the network to train on complex PD datasets with non-linear features and yields superior result.

关键词： Parkinson’s disease(PD) Fast learning network(FLN) Genetic algorithm(GA) Speech and handwriting patterns PD identification system

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Uncertainty-Aware Influence Maximization: Enhancing Propagation in Competitive Social Networks with Subjective Logic

Uncertainty-Aware Influence Maximization: Enhancing Propagat...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Zhang, Qi Kaplan, Lance M. Jøsang, Audun Jeong, Dong Hyun Chen, Feng Cho, Jin-Hee Virginia Tech Department of Computer Science United States Us Devcom Army Research Laboratory United States University of Oslo Department of Informatics Norway University of the District of Columbia Department of Computer Science United States University of Texas Department of Computer Science Dallas United States

ISBN: (纸本)9798350362480

The Competitive Influence Maximization (CIM) problem involves entities competing to maximize influence in online social networks (OSNs). While Deep Reinforcement Learning (DRL) methods have shown promise, most assume binary user opinions and overlook behavioral factors. We introduce DRIM, a novel DRL-based CIM framework using Subjective Logic (SL) to incorporate user preferences and uncertainty, optimizing seed selection to spread true information while countering false information. DRIM's Uncertainty-based Opinion Model (UOM) provides a realistic representation of user opinions. Results demonstrate that UOM maintains over 80% true influence against advanced misinformation, and DRIM outperforms state-of-the-art methods by up to 45% in influence and 77% in speed. DRIM also excels in limited-resource scenarios, networks with 10% invisibility, and when users are inclined to doubt true information. © 2024 IEEE.

关键词： Deep reinforcement learning

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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Towards an Automatic Scoring for Code Assessment in the Universities: A Case Study the Algerian University 1

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13th International Conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Boussaha, Karima Drissi, Samia Department of Mathematics and Computer Science LIAOA Laboratory University of Oum El Bouaghi Oum El Bouaghi Algeria Department of Mathematics and Computer Science LIM Laboratory Mohamed-Cherif Messaadia University Souk Ahras Algeria

ISBN: (数字)9783031605918

ISBN: (纸本)9783031605901

The assessment of learners, whether conducted in traditional classrooms or in the computer Environment for Human Learning (CEHL), has consistently been a subject of ambiguity and lack of clarity for both the evaluated and the evaluator on the one hand, and between the evaluators themselves on the other hand. The approach taken during the validation of the results of the automated assessment tools was to make an analytical comparison with the scores given by the human experts. The goal of this research paper is to compare and contrast two versions of the S_Onto_ALPPWA automated assessment scoring tool with the expert’s scoring. To achieve the underlined goal, we used the correlational research methodology to demonstrate the extent of the correlation between S_Onto_ALPPWA and the manual scores provided by the experts. Our obtained results indicate that, in the majority of instances, the S_Onto_ALPPWA scores are closely aligned with the experts’ scores. In our research, we used Ward’s hierarchical clustering techniques to categorize the experts into three distinct groups: lenient, severe, and moderate experts. This approach was employed to gather relevant information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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Improving Breast Cancer Detection with Pre-trained Models: A CADe and CADx System 6

Improving Breast Cancer Detection with Pre-trained Models: A...

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6th International Hybrid Conference on Informatics and Applied Mathematics, IAM 2023

作者： Lebani, Ali Zakaria Merati, Medjeded Mahmoudi, Said Chahbar, Fatma Computer Science Department IBN Khaldoun University Tiaret Algeria LIM Research Laboratory IBN Khaldoun University Tiaret Algeria Computer Science Department Mons University Mons Belgium

In this research study, we introduce a novel system of two primary modules: (1) computer-Aided Detection (CADe) and (2) computer-Aided Diagnosis (CADx). The CADe module is dedicated to the detection and segmentation of potentially anomalous regions within mammograms through the utilization of a pre-trained YOLOv5-seg model. This approach facilitates the early detection of breast cancer. Subsequently, the CADx module leverages the identified and segmented regions for further analysis, employing the VGG 16 classification model to discern the benign or malignant nature of these regions. To gauge the efficacy of our proposed methodology, extensive experiments were conducted on a substantial dataset procured from the Digital Database for Screening Mammography (DDSM). The CADe system yielded robust results in terms of detection and segmentation assessments, with a mean average precision (mAP) of 88%, a precision rate of 93.93%, and a recall rate of 98.02%. Furthermore, the CADx system demonstrated an accuracy rate of 97%. © 2023 Copyright for this paper by its authors.

关键词： computer aided diagnosis

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