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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Ramchandani, Monica Sahu, Satya Prakash Dewangan, Deepak Kumar National Institute of Technology Dept. of Information Technology Raipur India Tiet Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665492942

technology development has altered how people live. Intelligent vehicles are being developed as a result of technological advancements in the automobile industry. Pedestrians are the most vulnerable road users, accounting for more than half of all traffic fatalities. Thus, the key thing to take into account is pedestrian detection. In order to safeguard both drivers and pedestrians, an intelligent vehicle system can detect pedestrians in front of moving vehicles and either loudly warn the driver of the risk or automatically slow down the vehicle. The purpose of this study is to examine different sensor-based pedestrian detection techniques, weigh their benefits and drawbacks, and determine how they may be enhanced to make them more useful in real-world situations. Radar, vision sensors, and LiDAR are the mechanisms employed. This study analyses the datasets utilized in the research and identifies numerous state-of-the-art methodologies. © 2023 IEEE.

关键词： Autonomous vehicles

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Adversarial Domain Generalization Defense for Automatic Modulation Classification

Adversarial Domain Generalization Defense for Automatic Modu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Sicheng Liu, Jie Bao, Zliida Yu, Shui Lin, Yun Harbin Engineering University College of Information and Communication Engineering Harbin China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350345384

Automatic modulation classification (AMC) technology plays a vital role in the sixth generation mobile system (6G). However, deep learning (DL) based AMC models possess unexpected vulnerability against adversarial examples, which seriously affects their applications in 6G. To this end, we propose an Adversarial Domain Generalization Defense (ADGD) algorithm to improve the adversarial robustness of DL-based AMC models. Firstly, we sequentially pre-train two classifiers that classify the original signals and the adversarial examples respectively. Secondly, we extract task-relevant features of the original signals and adversarial examples and align them. Finally, we use adversarial training to enhance the adversarial robustness of the models. The comparative experiments with various defense algorithms under white-box and black-box conditions of various attack algorithms demonstrate the outstanding defense performance of the ADGD algorithm. The proposed solution is of great significance to promote the application of AMC technology in 6G. © 2023 IEEE.

关键词： Network security

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 333-353页

作者： Rao, Bosen Zhang, Jiale Wu, Di Zhu, Chengcheng Sun, Xiaobing Chen, Bing Yangzhou University School of Information Engineering Yangzhou225127 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often include sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This article begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the article thoroughly describes and compares the existing defense methods. Finally, the article concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders. © 2020 IEEE.

关键词： Data privacy

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Certifiably Robust Out-Of-Distribution Detection Based on Prediction Uncertainy Weighting

Certifiably Robust Out-Of-Distribution Detection Based on Pr...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Liu, Yanchao Dou, Quansheng Tang, Huanling Zhou, Liyuan Li, Xiujuan College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Computer Science and Technology Kashi University Xinjiang China

ISBN: (纸本)9798350309003

The application of machine learning in safety-critical systems requires a reliable assessment of uncertainy. However, deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. Even if trained to be non-confident on OOD data one can still adversarially manipulate OOD data so that the classifier again assigns high confidence to the manipulated data. In this paper, we improve the existing guaranteed OOD detection GOOD, and propose Certifiably Robust Out-of-distribution Detection based on Prediction Uncertainy Weighting (PUWOOD). Due to the loose boundary of GOOD, it is obviously not robust enough to deal with adversarial OOD close to the boundary of the decision. Therefore, the evaluation and weighting based on prediction uncertainty enable the model to achieve importance ranking in the face of different samples, and make it focus on key samples. Through experiments, we achieve the detection on adversarial OOD data while maintaining good performance on the original classification task and detecting clean data, which proves that our method has better performance in both robustness and accuracy. © 2023 IEEE.

关键词： Forecasting

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Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

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Journal of Safety science and Resilience 2024年第1期5卷 91-101页

作者： Sachin Dhawan Hemanta Kumar Bhuyan Subhendu Kumar Pani Vinayakumar Ravi Rashmi Gupta Arun Rana Alanoud Al Mazroa Dorset College King George BlvdSurreyBritish ColumbiaCanada Department of Information Technology Vignan’s Foundation for ScienceTechnology and Research(Deemed to be University)Andhra PradeshIndia Krupajal Computer Academy Biju Patnaik University of Technology(BPUT)Rourkela 751002India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia NSUT East Campus(Formerly AIACTRDelhi 110031India(Affiliated to Guru Gobind Singh Inderaprastha University))Dwarka 110078India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida 201310Uttar PradeshIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia

The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as ***,this progress has also led to security concerns related to the transmission of confidential ***,safeguarding these data during communication through insecure channels is crucial for obvious *** emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation *** to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification *** simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual *** the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.

关键词： Image enhancement Image processing Steganography Deep learning Fuzzy neural combination

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An Effective Approach to Speech-based Email Assistance for Visually Impaired People 8

An Effective Approach to Speech-based Email Assistance for V...

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8th International Conference on Signal Processing and Communication, ICSC 2022

作者： Sharma, Aditi Ahmed, Visal Sharma, Shivika Jana, Bristi Rani, Kritika Computer Science Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781665454308

In this paper, a new speech based mailing system is proposed for visually impaired. The user will be able to operate the functionalities of Gmail, the most prominent emailing system used around the world, using voice command only. The idea focuses on providing basic functionalities like compose, send, receive, delete and search E-mails in the user's pre-registered Gmail account. This web application accesses Gmail using text to speech and speech to text module to make emailing feasible for users with visual impairments. Unlike existing systems which usually focuses more on Graphical User Interface friendliness of normal user, this system focuses on providing accessibility to blind people. © 2022 IEEE.

关键词： Speech recognition

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Augmented Intelligence Assisted Deep Learning Approach for Multi-Class Skin Cancer Classification 1

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Batool, Amreen Byun, Yung-Cheol Department of Computer Engineering Major of Electronic Engineering Institute of Information Science Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (数字)9783031440977

ISBN: (纸本)9783031440960

Skin cancer is the most common type of cancer in the world. An important aspect of skin cancer diagnosis is detecting lesions as early as possible. Over the past five years, deep learning-based techniques have performed better on image classification tasks. There still needs to be a compromise in analysis accuracy due to image occlusions and imbalanced datasets. The main drawbacks of image occlusion and imbalanced datasets are to need for help to produce generalized models, increased computational overhead, loss of information of occluded regions, etc. Therefore, a robust approach is required to tackle the aforementioned challenges to produce a generalized model for lesion cancer cell classification. This study proposes an augmented-assisted deep learning approach based on DenseNet for multi-class lesion cancer cell classification and detection. In this study, an open-source dataset of lesion cancer cell images is used. Furthermore, data augmentation generates synthetic images to balance the class distribution to avoid over-fitting and poor generalization-related issues. In addition, a robust multi-class classifier based on a DenseNet121 architecture is developed to classify seven skin lesion classes that resize all images to the same size. According to the experimental results, our proposed multi-class classifier achieved an accuracy of 93% and 90.17% using training and validation images set. Similarly, our proposed classifier achieved an accuracy of 90% using a testing (unseen) image set. In the case of skin lesion identification, this research has a positive impact on supporting dramatists in the clinic with more accurate decisions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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