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On Multi-image Random Grids by Arbitrary Transformations

Human-Centric Intelligent Systems 2024年第1期4卷 93-110页

作者： Buckley, Neil Nagar, Atulya Kumar Secco, Emanuele Lindo AI Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool UK Robotics Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool UK

Random grids are a method for visual secret sharing, whereby a secret image is encoded into a number of shares, each of which is maximally entropic. There has been growing interest in hiding multiple images in a scheme, such that additional images are revealed by stacking the shares in different ways. This paper proposes a metaheuristic method for generating schemes that allow for a wide range of transformations, or even combinations thereof, to reveal an arbitrary number of secret images. Up to 10 multi-secrets are shown in this paper, as well as hiding multiple secrets in a general access structure. To remove noise from these schemes an algorithm is proposed to extract the information from the noise, and in all cases, relative contrasts are given. In one example, six images are hidden in two shares, such that the mean relative contrast under OR-stacking is 0.152, under XOR-stacking 0.278, and with noise removed, it is 0.74. In an example hiding 10 images in two shares, the values are respectively 0.136, 0.206 and 0.679, respectively.

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Training Variational Autoencoders for Population Synthesis in Public Health with Missing Data

Training Variational Autoencoders for Population Synthesis i...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Blackthorn, Nathan Mahyari, Andrew Arash Srinivasan, Ashok University of West Florida Department of Computer Science PensacolaFL United States PensacolaFL United States University of West Florida Department of Intelligent Systems and Robotics PensacolaFL United States

ISBN: (纸本)9798350362480

The importance of social determinants of health in shaping equitable public health policies is gaining increasing recognition. Emerging data sources, such as mobility and social media data, are becoming key to public health models. However, privacy concerns often limit access to these sensitive data, as even anonymized datasets are vulnerable to deductive disclosure. A promising solution to this challenge is the use of synthetic populations, which not only safeguard privacy but also enable the exploration of various "what-if"scenarios. Most existing studies on synthetic populations assume full access to complete datasets for training. However, in many public health applications, such as census data, this assumption is unrealistic due to incomplete responses, especially regarding sensitive questions like wealth or education. This paper introduces a novel approach for training Variational AutoEncoders (VAEs) with incomplete data, without resorting to missing value imputation. Instead, the VAEs are trained solely on the observed data. Using the 2019 PUMS dataset for Florida, we successfully train VAEs to generate diverse and flexible synthetic populations. By comparing marginal distributions and utilizing t-SNE for analysis, the results highlight the effectiveness of this method in addressing missing data challenges. This work demonstrates the potential of VAEs in generating synthetic populations for health research, even when complete datasets are unavailable, thereby offering a robust solution to advance public health studies while preserving data privacy. © 2024 IEEE.

关键词： Differential privacy

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Modeling and Risk Analysis of Cooperative Adaptive Cruise Control Systems Based on Petri Nets and Distributed Edge Intelligence

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IEEE Internet of Things Journal 2025年第12期12卷 19995-20006页

作者： Yu, Wangyang Cheng, Yumeng Fang, Xianwen Zhai, Xiaojun Wang, Yinglong Jing, Hongyuan Shaanxi Normal University Chang'an District Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Shaanxi Normal University School of Computer Science Chang'an District Xi'an710119 China Anhui University of Science and Technology Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui232001 China University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Beijing Union University College of Robotics Beijing100101 China

Fueled by advancements in intelligent transportation systems, the Internet of Vehicles (IoV) seeks to connect smart vehicles, road infrastructure, and users into a unified network, enhancing traffic efficiency and reducing accident risks. Centralized cloud data collection raises concerns about privacy and communication overhead. To address these, Distributed Edge Intelligence (DEI) reduces transmission costs and improves privacy by implementing machine learning at the network edge. In this context, Cooperative Adaptive Cruise Control (CACC) systems, combined with DEI in the IoV framework, enhance transportation system intelligence through real-time data processing and decentralized decision-making. This paper proposes a modeling and analysis method for CACC systems based on Petri nets. The datasets are automatically generated using tools developed by our team, and machine learning methods are utilized to perform risk prediction analysis on the CACC model. From the perspective of Petri nets synchronization, we propose risk mitigation strategies from a design standpoint. The research results show that the proposed method significantly reduces signal accumulation and enhances synchronization in CACC systems. This improvement provides new theoretical support and technical guidance for the design and implementation of CACC systems, ultimately enhancing their safety and reliability. © 2025 IEEE.

关键词： Adaptive cruise control

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Comparative analysis of navigation algorithms for mobile robot

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3861-3871页

作者： Heng, Han Ghazali, Mohamad Hazwan Mohd Rahiman, Wan School of Electrical and Electronic Engineering Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Universiti Sains Malaysia Engineering Campus Penang Nibong Tebal14300 Malaysia Daffodil Robotics Lab Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Various applications, including space exploration, transportation, factories, and the military, demand the presence of mobile robots. In those applications, navigation algorithms are essential for enabling mobile robots to operate efficiently and safely in static and dynamic environments. Due to the numerous navigation algorithms available, choosing a suitable and robust one for a specific mobile robot application can be challenging. Therefore, there is a pressing need to conduct a comparative analysis to evaluate the performance and adaptability of different navigation algorithms. This article presents a comparative study of five distinct algorithms in the domain of path planning, which are rapidly random tree (RRT), A*, genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO). The investigation was conducted on three distinct maps, and the strengths and weaknesses of each algorithm are demonstrated. Findings show that increasing the step distance and number of iteration parameters will result in an increase in the planning time. Consequently, the obtained path lengths are also reduced except for the PSO algorithm. Findings show that in the three different environments considered in this paper, the GA exhibits better performance, where a 90% reduction in the planning time is achieved while obtaining the same path length as the A* algorithm, which is the shortest path. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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Unpaired Shadow Removal: Enhancing Attention to Shadow Areas via Dropkey

Unpaired Shadow Removal: Enhancing Attention to Shadow Areas...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Yang, Jaewon Shin, Ukcheol Cho, Donghyeon Chungnam National University Dept. of Electronics Engineering Daejeon Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States Hanyang University Dept. of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798331510756

Shadows appear in image areas where an object obstructs the light path. These areas, having lower values than non-shadow areas, degrade image quality and lead to issues in object recognition and segmentation. Supervised shadow removal depends on datasets with shadow and shadow-free image pairs, sparking increased interest in unpaired techniques. However, unpaired shadow removal faces challenges in accurately focusing on shadow areas without direct supervisory signals from ground truth. Consequently, shadows are not effectively removed, and non-shadow areas may suffer from unintended distortions. In this paper, we introduce a transformer-based network designed to identify shadow areas accurately, leveraging global context through spatial and channel attention mechanisms. Additionally, in the training phase, the transformer network is trained to precisely concentrate on shadow areas using a domain classifier and a dropkey mechanism, which randomly drops features of the keys to enhance focus. Our method is tested across several benchmark datasets for shadow removal, such as ISTD, ISTD+, SRD and WSRD, demonstrating better performance compared to current unpaired approaches. © 2025 IEEE.

关键词： Image segmentation

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Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

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computers, Materials & Continua 2022年第9期72卷 4357-4374页

作者： Noureen Fatima Rashid Jahangir Ghulam Mujtaba Adnan Akhunzada Zahid Hussain Shaikh Faiza Qureshi Center of Excellence for Robotics Artificial Intelligenceand BlockchainDepartment of Computer ScienceSukkur IBA UniversitySukkurPakistan Department of Computer Science COMSATS University Islamabad–Vehari CampusPakistan Faculty of Computing and Informatics University Malaysia SabahKota Kinabalu88400Malaysia Department of Mathematics Sukkur IBA UniversitySukkurPakistan

The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and *** current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)***,this testing method is accurate enough for the diagnosis of ***,it is time-consuming,expensive,expert-dependent,and violates social *** this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural *** multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA *** set of experimental analyses were performed to evaluate the performance of our proposed *** useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification *** set of experimental analyses were performed to evaluate the performance of our proposed *** experimental results showed that our proposed approach outperformed compared to four baseline approaches published *** believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.

关键词： Covid-19 detection long short-term memory feature fusion deep learning audio classification

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An Experimental Study of Keypoint Descriptor Fusion

An Experimental Study of Keypoint Descriptor Fusion

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Pan, Yaling He, Li Guan, Yisheng Zhang, Hong Guangzhou China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Shenzhen China

ISBN: (纸本)9781665481090

Local feature descriptors play a crucial role in computer vision problems, especially robot motion. Existing descriptors are highly accurate, but their performance de-pends on the influence of distracting factors, such as illumi-nation and viewpoint. There is room for further improvement of these descriptors. In this paper, we provide an in-depth analysis of several exciting features of the descriptor fusion model (DFM) we have proposed in our recent work, which uses an autoencoder to combine descriptors and exploit their respective advantages. With this DFM framework, we fur-ther validate that fused descriptors can retain advantageous properties and that our DFM is a generally applicable method with respect to various component descriptors. Specifically, we evaluate multiple combinations of hand-crafted and CNN descriptors concerning their performance on a benchmark dataset with illumination and viewpoint changes to obtain comprehensive experimental results. The results show that the fused descriptors have better matching accuracy than their component descriptors. © 2022 IEEE.

关键词： Benchmarking

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Aug3D: Augmenting large scale outdoor datasets for Generalizable Novel View Synthesis

arXiv

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arXiv 2025年

作者： Rauniyar, Aditya Alama, Omar Yong, Silong Sycara, Katia Scherer, Sebastian Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

Recent photorealistic Novel View Synthesis (NVS) advances have increasingly gained attention. However, these approaches remain constrained to small indoor scenes. While optimization-based NVS models have made attempts to address this, generalizable feed-forward methods—offering significant advantages—remain underexplored. In this work, we train PixelNeRF, a feed-forward NVS model, on the large-scale UrbanScene3D dataset. We propose four training strategies to cluster and train on this dataset, highlighting that performance is hindered by limited view overlap. To address this, we introduce Aug3D, an augmentation technique that leverages reconstructed scenes using traditional Structure-from-Motion (SfM). Aug3D generates well-conditioned novel views through grid and semantic sampling to enhance feed-forward NVS model learning. Our experiments reveal that reducing the number of views per cluster from 20 to 10 improves PSNR by 10%, but the performance remains suboptimal. Aug3D further addresses this by combining the newly generated novel views with the original dataset, demonstrating its effectiveness in improving the model’s ability to predict novel views. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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Deep Learning-Based Classification of Areca Nut Yellow Leaf Disease with ResNet-50 CNN

Deep Learning-Based Classification of Areca Nut Yellow Leaf ...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Veeresha, R.K. Lathish Kumar, N.D. Shetty, Samarth S. Prasad, Shrajan G. Poojary, Swaroop S. Karegoudra, Shilpa Koten, Hasan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India Istanbul Medeniyet University Faculty of Engineering and Pure Sciences Department of Mechanical Engineering Istanbul Turkey

ISBN: (纸本)9798350388602

Detecting healthy arecanut leaves, yellow leaf disease in arecanut, and differentiating these from other types of leaves using deep learning involves designing an advanced neural network model for precise image classification. The model is trained on a dataset comprising images of healthy arecanut leaves, arecanut leaves affected by yellow leaf disease, and various other leaves from different plant species. Convolution Neural Networks (CNNs) are leveraged to extract and analyze intricate patterns in the images, enabling the model to effectively classify each leaf type. Transfer learning techniques might be utilized to improve the model's performance and adaptability. The model's effectiveness is evaluated using accuracy, precision, recall, and F1 score on a dedicated test dataset. This paper explores the methodology and results of employing image detection for the identification of healthy arecanut leaves, yellow leaf disease, and other leaf types, with potential applications in agricultural diagnostics, disease management, and crop monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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