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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

引用

3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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SCSE-UNet: A Spatial Channel Squeeze-and-Excitation UNet for Medical image Segmentation 12

SCSE-UNet: A Spatial Channel Squeeze-and-Excitation UNet for...

引用

12th International conference on Information systems and Computing Technology

作者： Liu, xiuli Wu, xiangqiong Chen, Yang Wang, Peng Hunan First Normal Univ Sch Comp Sci Changsha Peoples R China Hunan First Normal Univ Sch Elect Informat Changsha Peoples R China

ISBN: (纸本)9798350379860;9798350379877

As an effective convolutional neural network design, U-Net has shown notable performance in challenges involving the segmentation of medical images. Traditional U-Net models are still limited in their ability to handle complex image features and long-distance relationships, despite the variety of application scenarios and technological advancements. We propose a novel model that incorporates the Spatial Channel Squeeze-and-Excitation (SCSE) module to enhance the model's performance on medical segmentation tasks to further increase the segmentation accuracy and reduce the number of parameters. The proposed model improves the network's capacity to learn important features, especially those about tumor boundaries and regions of interest, by fusing deep convolutional neural networks with SCSE modules. The proposed model achieves higher segmentation accuracy with fewer parameters, according to experimental data, which proves that this approach can provide better segmentation performance and lower processing costs than the original segmentation techniques.

关键词： U-Net medical image segmentation attention mechanism

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Quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

引用

2024 International conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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FOCT: Few-shot Industrial Anomaly Detection with Foreground-aware Online Conditional Transport 24

FOCT: Few-shot Industrial Anomaly Detection with Foreground-...

引用

32nd ACM International conference on Multimedia, MM 2024

作者： Tian, Long Zhao, Hongyi Lu, Ruiying Wang, Rongrong Wu, Yujie Wang, Liming He, xiongpeng Liu, xiyang National Pilot School of Software Engineering Xi'Dian University Xi'an Shi Chang'an Qu China School of Cyber Engineering Xidian University Xi'an Shi Chang'an Qu China National Key Lab. of Radar Signal Processing Xidian University Xi'an Shi Chang'an Qu China National Pilot School of Software Engineering Xi'Dian University Xi'an Chang'an Qu China

ISBN: (纸本)9798400706868

Few-Shot Industrial Anomaly Detection (FS-IAD) has drawn great attention most recently since data efficiency and the ability to design algorithms for fast migration across products have become the main concerns. The difficulty of memory-based IAD in low-data regime primarily lies in inefficient measurement between the memory bank and query images. We address such a pivotal issue from a new perspective of optimal matching between features of image regions. Taking the unbalanced nature of query features into consideration, we adopt Conditional Transport (CT) as a metric to compute the structural distance between representations of the two sets to determine feature relevance. CT distance generates the optimal matching flows between unbalanced structural elements that achieve the minimum matching cost, which can be directly used for IAD since it well reflects the differences of query images compared with the normal memory. Realizing the fact that query images usually come one-by-one or batch-by-batch, we further propose an Online Conditional Transport (OCT) by making full use of the current and historical query images for IAD via simultaneously calibrating the memory bank using the online query images and matching features between the calibrated memory and the current query image. Go one step further, for sparse foreground products, we employ a predominant segment model to implement Foreground-aware OCT (FOCT) for improving the effectiveness and efficiency of OCT by forcing the model to pay more attention to diverse targets rather than redundant backgrounds when calibrating the memory bank. FOCT can improve the diversity of calibrated memory, which is critical for robust FS-IAD in practice. Besides, FOCT is flexible since it can be friendly plugged and played with any pre-trained backbones, such as WRN, and any pre-trained segment models, such as SAM. The effectiveness and efficiency of our model is demonstrated across diverse datasets, including benchmarks of MVTec and

关键词： Anomaly detection

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Indoor Positioning Based on the Integration of IMU and UWB 8

Indoor Positioning Based on the Integration of IMU and UWB

引用

8th International conference on Communication, image and Signal processing, CCISP 2023

作者： Yanming, Liang Jiale, Du Haiyang, Zhao Kui, Xu School of Automation Information and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9798350305838

Accurate indoor location information is of utmost importance for the navigation of modern indoor mobile robots. However, achieving accurate positioning solely with ultra-wideband (UWB) technology in indoor environments is challenging due to the susceptibility of UWB measurements to non-line-of-sight (NLOS) effects. To address this issue, this paper proposes an indoor positioning system that integrates an inertial measurement unit (IMU) with UWB. Firstly, an analysis of the errors present in UWB measurement data leads to the development of a two-step calibration method based on least squares (LS) to estimate error parameters. Subsequently, an Extended Kalman Filter (EKF)-based integration localization algorithm for IMU and UWB is proposed to mitigate the impact of NLOS. Through comprehensive simulations, the suggested algorithm's performance is assessed and contrasted with the weighted least squares (WLS) approach. Experimental findings show that the two-step LS-based calibration method estimates error parameters with accuracy. Moreover, the EKF-based IMU and UWB integration algorithm achieves approximately half improvement in localization accuracy compared to WLS, it also demonstrates excellent robustness. Consequently, the suggested indoor positioning system, which integrates IMU and UWB, satisfies the requirements for accuracy for indoor mobile robot navigation. © 2023 IEEE.

关键词： Indoor positioning systems

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A 3D Model Information Hiding Algorithm Based on Region of Interest Blocked Histogram Adaptive Shift 7

A 3D Model Information Hiding Algorithm Based on Region of I...

引用

7th International conference on Advances in image processing, ICAIP 2023

作者： Shuai, Ren xinglong, Yuan Qianqian, Zhang School of Information Engineering Chang’an University Xi’an China

ISBN: (纸本)9798400708275

In order to improve the capacity and robustness of 3D model texture image information hiding, this paper proposes an information hiding algorithm based on the adaptive shift of the block histogram of the region of interest. Firstly, the grid regularization method is applied to redivide the original model to obtain a multi-scale and semi-regular grid, based on the base grid, by adding detailed information of different scales, different levels of meshes are constructed, so as to form a quadtree structure. Secondly, the deviation between the true position and the expected position of each vertex in each level is calculated, so as to construct a wavelet generation tree and extract three-dimensional feature points. According to the correspondence between the texture image and the three-dimensional mesh, the feature points of the texture space are obtained, and then the clustering algorithm is used to divide the region of interest in the texture image. Then, the wavelet transform is performed on the region of interest image, and the quantized wavelet coefficient of the high frequency band of the image is used as the hidden region of the confidential message. Finally, the secret information embedding is realized by adaptive shift of the blockage and wavelet coefficient histogram. Experimental results show that the algorithm can significantly improve the embedding capacity, and has strong robustness to geometric transformation, shearing, simplification and noise attack. © 2023 Copyright held by the owner/author(s).

关键词： Clustering algorithms

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Integer frequency offset algorithm based on DRM system 9

Integer frequency offset algorithm based on DRM system

引用

9th International conference on Computer and Communications, ICCC 2023

作者： Qiao, Yani Li, Bo Cui, Wen Li, Yuji Mao, Ziyu Dong, Lei Sun, Hao Xi'an University of Posts and Telecommunications School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9798350317251

This article proposes an integer frequency offset estimation algorithm based on time pilot for digital radio mondiale (DRM) systems. This algorithm fully utilizes pilot symbols and effectively reduces the difficulty and complexity of the algorithm by creating a difference set between the predetermined pilot symbols and the pilot symbols recovered from the actual signal at the same carrier position. By using mathematical processing to calculate the variance and then sum the two difference sets at the corresponding carrier positions, the reliability of the algorithm is increased while further optimizing and improving the computational efficiency and accuracy of the algorithm. In order to reduce the randomness of algorithm results, the calculation results of multiple sets of symbols are summarized and statistically analyzed. Compared with traditional integer frequency offset estimation algorithms, the algorithm proposed in this paper has higher accuracy in frequency offset estimation. © 2023 IEEE.

关键词： Computational efficiency

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Machine Learning-Based Blind image Quality Assessment: A Review 1

引用

International conference on Data Analytics and Management, ICDAM 2023

作者： Tausif, Mohd Khan, Mohammad Farhan Ansari, Mohammad Samar Khan, Ekram Ghanbari, Mohammed Department of Electronics Engineering Aligarh Muslim University Aligarh202002 India Sir David Bell Building Digby Stuart College University of Roehampton LondonSW15 5PJ United Kingdom Department of Physical Mathematical and Engineering Sciences University of Chester ChesterCH1 4BJ United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

In the era of artificial intelligence, the necessity of a paradigm that can characterise the accuracy, fairness, and consequences of decision-making models is highly desirable. The advancement of IoT-enabled multimedia devices and their increasing usage in complex domains such as intelligent remote sensing and autonomous underwater surveillance require extraction of comprehensive patterns from unstructured data. However, the accuracy of such decision-making models solely depends on the quality of data acquisition, transmission, and processing, which gives rise to the necessity of automated assessment of the quality of image data. Hence, this work reviews the state-of-the-art machine learning-based blind image quality assessment algorithms and discusses their possible limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Machine Perception

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An Adaptive Reversible Data Hiding Scheme using image processing and Deep Learning Techniques 5

An Adaptive Reversible Data Hiding Scheme using Image Proces...

引用

5th International conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Allwyn, Philip Hemalatha, J. Sampathkumar, Nithyanantham Ramaraj, Kottaimalai Swain, Gandharba Sekar, M. AAA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Sivakasi India AAA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Vaddeswaram India AAA College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798331530013

image steganography is the process of hiding data from digital photos without changing how they look on the outside. It functions as a covert mode of communication, giving access to confidential data without raising the suspicions of opponents or unauthorized parties. It provides an extra degree of secrecy and security for digital communications. Prediction-error expansion (PEE) is an extensively used method in the type of reversible data hiding (RDH) that hiding data with intact image quality in the prediction errors. High-accuracy prediction algorithms are typically essential for a PEE-based RDH implementation to be successful. Current PEE-based Reversible Data Hiding (RDH) systems frequently use a single forecasting approach, typically with worldwide optimization, but pay little attention to the local neighborhoods' pixel distribution features. This study aims to analyze the most recent RDH techniques and investigate image processing and deep learning (DL) techniques for reversible data hiding (DH) schemes. RDH is a difficult topic in general which has potential uses in today's digital world. Reversible data hiding techniques properly restore the cover medium while safely transferring secret data (SD). The goal of RDH techniques is to achieve both large capacity and tunable quality. Even though reversible data hiding has been the subject of indepth research recently, there hasn't yet been a thorough literature analysis to identify research gaps and potential future approaches. © 2024 IEEE.

关键词： Data Hiding Data Security image processing Pixel Sorting Reversible Data Hiding Steganography

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Bone Tumor Detection and Classification Using GoogleNet Algorithm

Bone Tumor Detection and Classification Using GoogleNet Algo...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Dharmaraj, N. Suresh Kumar, A. Lakshmikanth, J. Charulatha, R.T. Murugasami, R. Amarnath, M.A. Department of ECE Rajalakshmi Institute of Technology Chennai India Department of ECE M.I.E.T. Engineering College Trichy India Department of CSBS Rajalakshmi Institute of Technology Chennai India Nandha Engineering College Erode India JJ College of Engineering and Technology Trichy India

ISBN: (纸本)9789819613472

Tumors are aberrant tissue growths that can develop in any body organ. Numerous varieties of human tumors have been discovered in recent years, including brain, bone, and lung. image processing is essential in tumor analysis and categorization. Medical image processing is an essential field of study since the results are utilized to improve health issues. When cells within the bone divide uncontrollably, they produce lumps or lumps of aberrant tissue. There are numerous types of bone tumors, each with its own set of characteristics. Bone tumors are classified as noncancerous (benign) or cancerous (malignant). Our project primarily focuses on picture segmentation and classification of skeletal images. Initially, the Fast Non-Native Media (FNLM) filter is used for preprocessing. For the segmentation procedure, a sophisticated algorithm is employed to distinguish malignant nodules from lung pictures. In feature extraction, distinct features are extracted using a gray-level co-occurrence matrix (GLCM). The GoogleNet ranking algorithm is then used to rank the specified features. The suggested classifier is capable of detecting bone cancer with high accuracy. The proposed approach was built in MATLAB, utilizing the dataset from the Bone image database. Various performance measures are assessed and connected with existing classifiers and cutting-edge algorithms. Simulation results show that the devised scheme has a high classification accuracy (99%) compared to previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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