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2022 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2022

作者： Ma, Tian Fu, Chenhui Guo, Ming Yang, Jiayi Liu, Jia Xi'an University of Science and Technology College of Computer Science and Technology Xi'an Shaanxi China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

Images taken in low-light conditions would have insufficient light intensity and high noise. Many existing methods could not work very well in low-light environments, such as the noise and artifacts in dark conditions will be more obvious when enhanced. Therefore, low-light image enhancement is a challenging task in computer vision. To solve this problem, this paper proposes a lightweight generative adversarial network with dual-attention units to enhance underexposed photos. There is only a simple two-layer convolution in the generator section, and a dual-attention unit is added between the two convolutions to suppress the noise generated during the enhancement process and the deviation of color reduction. Then, non-local correlations of the image are used in the spatial attention module for denoising. Ours low-light image enhancement network is guided by the channel attention module to optimize redundant color features. In addition, the ideas of PatchGAN and Relativistic GAN are combined in the discriminator section to make the discriminator a better measure of the probability of changing from absolute true or false to relative true or false. The experiment results show that, our method could get better enhancement effects on low-illumination image datasets, which has more natural color, better exposure, and less noise and artifacts. © 2022 IEEE.

关键词： Image enhancement

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A Survey on Analyzing Social Media Data and the Data from Past for Emergency Management

A Survey on Analyzing Social Media Data and the Data from Pa...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Gugan, I. Karthikeyan, N. Kavitha, M.S. Karthik, S. Dr. N.G.P. Institute of Technology Department of Computer Science & Engineering Coimbatore India SNS College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350348217

Disaster management is the need of the hour in countries like India, where Information science in still in its developing stage. In order to address this issue, a survey on works related to disaster management in the social media front by various researches is done in this work. The works considered for the survey include different kinds of disasters happened around the world. The modules that help in disaster recovery and response are also surveyed and the techniques used are also deployed. From the deployed issues and the techniques, comparative table of the works being surveyed are tabulated and the future works are also suggested. © 2023 IEEE.

关键词： Data mining

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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science China(Information sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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Smart Patrolling System Using Machine Learning 3

Smart Patrolling System Using Machine Learning

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3rd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Megala, P. Prabhu, R. Devi, V. Yamini, S. Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore641202 India Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi642003 India Hindustan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore641032 India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore641021 India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The implementation of Smart Patrolling System using IoT is to provide a safety to the people those who are present in the area by the help of fixing a Sound Sensor, Ultrasonic sensor, Raspberry pi, Node MCU, Night vision camera, GPS, GSM in street lights. The Smart Patrolling System using IoT continuously monitor the place and it gives the signal to the people those who are living in the place first then the control room. The proposed system can be used in Residential areas for giving better safety to the people. The proposed system gives a very most importance to Women and children safety. By the use of our proposed system several crime attacks can be found immediately and it can be avoided from crucial situations. © 2024 IEEE.

关键词： Sensor nodes

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Revolutionizing Medical Diagnostics: Exploring Creativity in AI for Biomedical Image Analysis

Revolutionizing Medical Diagnostics: Exploring Creativity in...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Krishnamoorthy, P. Swetha, Devulapally Geetha, Pamidimukkala Sai Karunambiga, K. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Thiruthangal Nadar College PG Department of Computer Science Tamil Nadu Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Karpagam Institute of Technology Department of CSE Tamil Nadu Coimbatore India Faculty of Information and Communication Technology Kampar Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by integrating machine learning techniques with a user-friendly web interface. The system takes input images uploaded through the web interface and processes them using advanced machine learning algorithms implemented in Python. The goal is to automate the detection of diseases from these images, enhancing the efficiency and precision of diagnosis. Convolutional Neural Networks (CNNs), a specialized class of deep learning models designed for image analysis, are employed to learn and characterize disease-specific patterns. The proposed approach is evaluated using diverse biomedical images encompassing various diseases and conditions. Extensive experiments demonstrate the system's effectiveness, achieving high accuracy, sensitivity, and specificity in disease detection. Integrating machine learning with a web interface simplifies the diagnostic process and enables remote consultations, making healthcare services more accessible and timelier. In conclusion, this study presents a robust framework for automated biomedical image analysis, demonstrating the potential of machine learning and web technologies in revolutionizing healthcare diagnostics. The system's accuracy and user-friendly interface make it a valuable tool for healthcare professionals, contributing to early disease detection and improved patient care. © 2024 IEEE.

关键词： Convolutional neural networks

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PPIDM: Privacy-Preserving Inference for Diffusion Model in the Cloud

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhangdong Liu, Zhihuang Luo, Yuanjing Zhou, Tongqing Qin, Jiaohua Cai, Zhiping National University of Defense Technology College of Computer Science and Technology Changsha410073 China Central South University of Forestry and Technology College of Computer Science and Information Technology Changsha410004 China

Cloud environments enhance diffusion model efficiency but introduce privacy risks, including intellectual property theft and data breaches. As AI-generated images gain recognition as copyright-protected works, ensuring their security and intellectual property protection in cloud environments has become a pressing challenge. This paper addresses privacy protection in diffusion model inference under cloud environments, identifying two key characteristics—denoising-encryption antagonism and stepwise generative nature—that create challenges such as incompatibility with traditional encryption, incomplete input parameter representation, and inseparability of the generative process. We propose PPIDM (Privacy-Preserving Inference for Diffusion Models), a framework that balances efficiency and privacy by retaining lightweight text encoding and image decoding on the client while offloading computationally intensive U-Net layers to multiple non-colluding cloud servers. Clientside aggregation reduces computational overhead and enhances security. Experiments show PPIDM offloads 67% of Stable Diffusion computations to the cloud, reduces image leakage by 75%, and maintains high output quality (PSNR = 36.9, FID = 4.56), comparable to standard outputs. PPIDM offers a secure and efficient solution for cloud-based diffusion model inference. © 2025 IEEE. All rights reserved.

关键词： Differential privacy

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ADNet: A neural network model for adversarial example detection based on steganalysis and attention mechanism 3

ADNet: A neural network model for adversarial example detect...

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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： Zhang, Xinrui Wang, Yong Beijing University of Technology College of Computer Science and Technology Beijing China

ISBN: (纸本)9781665459112

The vulnerability of deep learning to adversarial attacks has brought many security risks to its development. However, the currently proposed adversarial attack detection methods are ineffective in defending large-size images. Studies have shown that detection methods based on steganalysis have apparent advantages in defense of large-scale adversarial examples. Still, their detection results are very dependent on the quality of manually extracted features. This paper proposes an end-to-end convolutional neural network (CNN) model based on steganalysis for adversarial examples detection. We found that adversarial attacks tend to embed perturbations in high-texture regions. To focus more on vulnerable areas during feature learning, we use the original image as information to learn the attention distribution, which improves the model's accuracy. And an adversarial loss function is proposed to fine-tune the model so that the normal images and adversarial examples are more separated in the spatial distribution, reducing the probability of misclassifying normal images. Experiments show that this model has a high defense effect against the current state-of-the-art attack methods. © 2022 IEEE.

关键词： Neural network models

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Beyond Precision and Recall: Measuring Search Engine Consistency Using Rank Stability

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IEEE Access 2025年 13卷 92242-92259页

作者： Lakshmi, S. Thinakaran, Rajermani Batri, Krishnan Sowrirajan, R. Murugan, Sivaram Jain University Department of Electronics and Communication Engineering Karnataka Bengaluru India INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Jain University School of Computer Science and Engineering Karnataka Bengaluru India Dr. N.G.P. Arts and Science College Department of Mathematics Tamil Nadu Coimbatore India Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas58000 Turkey

Traditional information retrieval metrics such as precision, recall, and F-measure assess document relevance but fail to capture the stability of search rankings. Search engines frequently update their algorithms, leading to variations in retrieved documents and their rankings across identical queries. This study introduces a novel rank correlation measure that quantifies search result consistency by analyzing rank fluctuations in repeated queries, serving as a proxy for temporally separated searches. The proposed approach distinguishes between overlapping and non-overlapping documents, providing a detailed measure of ranking stability. Empirical evaluations comparing Google and Bing reveal that Google exhibits greater rank fluctuations for non-overlapping documents, whereas both search engines maintain relatively stable rankings for overlapping documents. These findings highlight the importance of rank-based evaluation frameworks in search engine analysis, extending beyond conventional retrieval metrics. By incorporating stability assessments, this research enhances the methodology for evaluating search consistency in dynamic web environments, contributing to process innovation in information retrieval (SDG 9: Industry, Innovation, and Infrastructure) and promoting fair, reliable access to information (SDG 16: Peace, Justice, and Strong Institutions). © 2013 IEEE.

关键词： Online searching

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