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Spider Monkey Optimization-based image Data Forgery Detection Over Vehicular Cloud Computing 2nd

Spider Monkey Optimization-Based Image Data Forgery Detectio...

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2nd international conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Manasa, M. Chaudhari, Shilpa S. Ramaiah Institute of Technology Bangalore India Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9789819977109

images are commonly shared through various mediums such as newspapers, magazines, internet, and scientific journals. But with the accessibility of software like Photoshop, GIMP, and Coral Draw, it has become increasingly difficult to differentiate between an original and a forged image. Traditional methods for detecting image forgery rely on handcrafted features, but they are limited in their ability to identify specific types of tampering based on certain features in the image. To overcome this obstacle, deep learning methods have been employed. Increased vehicular networking image data faces this challenge. The proposed image forgery detection uses hybrid model of convolution neural networks (CNN), spider monkey optimization technique (SMO), and support vector machines (SVMs). CNN extracts deep semantic features of images transferred as vehicular data over the vehicular cloud using error level analysis (ELA) techniques. SMO chooses the optimal features from deep semantic features. Binary classifications based on SVM provide whether the image is original or forged. The results demonstrate that the proposed method achieves a recognition accuracy of 96%, which is significantly higher than the same model without SMO, which achieved an accuracy of 93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Fault Diagnosis Method based on TCN-LSTM-SE Neural Networks for Distributed PV systems 2

A Fault Diagnosis Method based on TCN-LSTM-SE Neural Network...

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2nd IEEE international conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Wang, Zhao Wang, Chao Ke, Qiwen Zhang, Baoqun Wang, Yaqun Zeng, Shuang Kang, Taifeng Lan, Tian Liu, Zihao Liu, Congwei State Grid Beijing Electric Power Company Beijing China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China North China University of Technology Department of Electrical Engineering Beijing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373646

Distributed photovoltaic power generation grid-connected systems add the variety and complexity of faults to the grid, so the research on fault diagnosis method with high diagnostic accuracy and practicality is of great significance for its safe and stable operation. The traditional modelling, control and detection methods are limited by the construction cost and susceptible to parameter fluctuations, which are difficult to adapt to future development needs. In this paper, we propose a fault diagnosis method based on the TCN-LSTM-SE model for distributed PV grid-connected systems and use it for three-phase unbalance and harmonic voltage prediction and fault diagnosis. Firstly, the SE attention mechanism is used to highlight fault features in the input data, then the TCN is chosen to perform feature extraction while maintaining the signal timing characteristics, the LSTM network finally memorizes the key features to complete the timing modeling. Simulation results show that the method has lower prediction error and higher diagnostic accuracy than mainstream network models, and the fault location and severity can be accurately known. © 2024 IEEE.

关键词： Photovoltaic systems Adaptation models Accuracy Attention mechanisms Neural networks Predictive models Feature extraction Harmonic analysis Timing Long short term memory

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FHRDiff: Leveraging Diffusion Models for Conditional Fetal Heart Rate signal Generation

FHRDiff: Leveraging Diffusion Models for Conditional Fetal H...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaoqing Lu, Yu Ngiam, Kee Yuan Yu, Zichang Furqan, Mohammad Shaheryar Shenzhen Technology University College of Big Data and Internet Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China National University of Singapore Yong Loo Lin School of Medicine Department of Surgery Singapore

ISBN: (纸本)9798350386226

Accurate analysis of Fetal Heart Rate (FHR) signal is often impeded by challenges such as data scarcity and label imbalance, which affect the reliability and robustness of deep learning models. To address these challenges, this study introduces FHRDiff, a novel diffusion model conditioned on Phase-Rectified signal Averaging (PRSA) spectrograms for the creation of synthetic FHR signals. Our model integrates time encoding, condition generation from PRSA spectrograms, and residual blocks with dilated convolutions to effectively manage temporal dynamics and long-range dependencies. Extensive qualitative and quantitative experiments on FHR signal synthesis demonstrate the feasibility and effectiveness of FHRDiff. Compared to Generative Adversarial Networks (GANs) and image-based diffusion model, our method achieves the highest signal fidelity and distribution similarity, with key measures including 0.067 maximum mean deviation (MMD), 0.492 percent root mean square difference (PRD), 1.763 relative entropy (RE), and 0.160 Frechet distance (FD). Expert validation confirms the model's capacity to accurately generate data for normal and abnormal FHR signals based on the paired condition. In addition, an ablation study was conducted to highlight that the spectrogram paired condition can guide the diffusion model to produce synthetics FHR signals with greater diversity compared to unconditional models. The results emphasize diffusion models' potential for broad application in biomedical time series analysis, such as generation, imputation and noise removal. © 2024 IEEE.

关键词： Heart

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Machine Learning based Approach by image Recognition for Detection of Islanding

Machine Learning Based Approach by Image Recognition for Det...

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2024 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2024

作者： Pal, Kumaresh Goswami, Subhadip Benia, Tapas Kumar Bhuyan, Anshuman Manna, Pranendu Kumbhakar, Shyamal National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Techno India University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350364040

Excessive penetration of DG sources poses a significant difficulty known as islanding. The act of islanding has the potential to inflict harm upon both customers and their equipment. As per the IEEE 1547 DG interconnection criteria, it is necessary to identify islanding within a two-second timeframe and then deactivate the DG. The present study introduces a novel methodology for identifying islanding through the utilisation of visual pattern recognition and Machine Learning classification techniques. The Histogram of Oriented Gradient (HOG) is employed as a feature descriptor in order to perform pattern recognition on images, with the objective of identifying both non-islanding and islanding occurrences. Therefore, the spectrogram images are acquired from the time-series signal that exists at the point of common coupling. The provided photos are employed for the purpose of extracting features using the HOG algorithm. These extracted features are subsequently used as input for the k-Nearest Neighbors (KNN) classifier, both for training and testing purposes. The spectrogram image is obtained by using the rate of change of the negative sequence voltage component. The efficacy of the KNN classifier is evaluated using 5,10,20, and 25 -fold cross-validations. The classification findings indicate that the utilisation of the KNN classifier and HOG feature for visual pattern recognition yielded exceptional outcomes, with an accuracy rate of 96.75% and a detection time of 211 ms. © 2024 IEEE.

关键词： image recognition

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Unsupervised Real-Time Flow Data Drift Detection based on Model Logits for internet of Things Network Traffic Classification 17

Unsupervised Real-Time Flow Data Drift Detection Based on Mo...

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IEEE Congress on Cybermatics: 17th IEEE international conference on internet of Things, iThings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Wang, Pan Liu, Minyao Li, Zeyi Wang, Zixuan Chen, Xuejiao Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Nanjing Vocational College of Information Technology School of Communications Nanjing China

ISBN: (纸本)9798350351637

In recent years, numerous internet of Things (IoT) traffic classification techniques based on deep learning have been proposed, demonstrating impressive classification performance. With ubiquitous access to massive heterogeneous IoT endpoints, networks exhibit high levels of dynamism, heterogeneity, and complexity, leading to potential data distribution drift (DDD), which will significantly degrade the performance of IoT network traffic classification. Although numerous scholars have explored various drift detection methods, most studies require real-time stream data labeling. Meanwhile, some unsupervised methods incur high resource consumption and latency when applied to network traffic. To address this issue, we propose an unsupervised real-time drift detection method based on Model Logits (ML-UDD). based on the Logit scores from the deep learning model's traffic classification/inference process, the model's Maximum Logit Score (MLS) will be calculated as the key metric for detection DDD. This method does not need to rely on the data labels, thus it can explore in an unsupervised manner, which is superior to many other supervised methods in terms of expert experience and labor. Furthermore, an adaptive weighted CUSUM-type rapid detection approach is proposed to detect the DDD based on a sliding time window during the traffic flow proceeding. In addition, this method can further locate the 'Drift Flow' where DDD happened, namely Flow Drift Location. This offers a real-time and efficient method to monitor and identify DDD with minimal resource consumption. The experiments have been conducted on two public and private network traffic datasets. The comparative experiments with other SOTA DDD detection methods have demonstrated that the proposed ML-UDD method can surpass these methods in terms of different performance metrics. © 2024 IEEE.

关键词： image thinning

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image based Action Recognition and Captioning Using Deep Learning 2023

Image Based Action Recognition and Captioning Using Deep Lea...

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15th international conference on Contemporary Computing, IC3 2023

作者： Singh, Ashutosh Gupta, Sarishty Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400700224

image recognition and captioning are two essential tasks in computer vision that have gained significant attention in recent years. With the massive increase in digital images on the internet, there is a growing demand for automated systems that can accurately recognize and describe visual content. image recognition involves classifying an image into predefined categories, whereas image captioning generates a natural language description of the image. These tasks require advanced machine learning algorithms and techniques to analyze and understand visual content, enabling computers to recognize objects, people, and scenes, and generate descriptions that are accurate and relevant. This paper focuses on using machine learning and deep learning algorithms for image recognition and captioning tasks. Specifically, the paper introduces a machine learning approach to image recognition using the SURF algorithm for feature extraction and SVM for classification, comparing the effectiveness of different kernels. The paper then explores the capabilities of deep learning in image captioning, using a pre-trained RESNET50 and VGG16 model for feature extraction and an LSTM-based neural network for generating captions. The paper also compares the effectiveness of the deep learning models by evaluating their accuracy using the BLEU score. The paper comprises two main components: an ML-based image recognition module and a DL-based image captioning module. The ML-based module provides robust image recognition capabilities, while the DL-based module generates high-quality image captions using a neural network-based approach. The paper highlights the importance of these tasks in various industries and concludes that leveraging these techniques can lead to innovative solutions in diverse domains. © 2023 ACM.

关键词： image recognition

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Deep Learning on Knee CT Scans from Osteoarthritis Patients for Joint Space Assessment 16

Deep Learning on Knee CT Scans from Osteoarthritis Patients ...

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Shen, Zijie Laredo, Jean Denis Lomenie, Nicolas Chappard, Christine Univ Paris Cite B3OA UMR CNRS 7052 Inserm U1271 Paris France Univ Paris Cite Lariboisiere Hosp Radiol Dept Paris France Univ Paris Cite LIPADE URP 2517 Paris France

ISBN: (纸本)9781665464956

Computed tomographic radiography (CT) can be potentially used for knee osteoarthritis (OA) diagnosis and follow up. It is possible to quantify knee joint space three-dimensionally. The main purpose of this study was to investigate an accurate, precise, and memory-saving convolutional neural network to accomplish the segmentation of lateral joint space and medial joint space (LJS and MJS) of knee joints. A novel deep learning structure, based on two state-of-the-art networks - RegNet and DeepLabV3P, was constructed for automatic prediction. And a new attention mechanism called "Feature-Location" module was proposed to be added into the models for comparison. The predictions were postprocessed by area opening and alpha shape to get the complete segmentations. From the joint space segmentations, morphological measurements clinically pertinent for knee OA diagnosis and follow up such as the volume, mean thickness, and standard deviation were measured. Finally, compared with the standard U-Net and DeepLabV3P, our new model called "DeepRegY" reduced the memory footprint by more than double. The model also produced the highest Dice coefficients, 0.8378 for LJS and 0.8655 for MJS in the validation set. The morphological parameters showed strong correlations, 0,9356 for volume, 0.99 for mean thickness, and 0.9876 for standard deviation of thickness in the testing set. Bland-Altman analysis showed a mean thickness bias about -0.2 mm which is very low compared to the mean thickness results.

关键词： knee osteoarthritis computed tomography deep learning image segmentation attention mechanisms

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international conference on Artificial Intelligence and Communication technology, ICAICT 2023

International Conference on Artificial Intelligence and Comm...

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international conference on Artificial Intelligence and Communication technology, ICAICT 2023

ISBN: (纸本)9789819966400

The proceedings contain 33 papers. The special focus in this conference is on Artificial Intelligence and Communication technology. The topics include: Optimal Design of Hydrodynamic Journal Bearing based on BP Neural Network Optimized by Improved Particle Swarm Algorithm;the Intelligent Human–Computer Interaction Method for Application Software of Electrical Energy Metering based on Deep Learning Algorithm;Short Text Classification of Invoices based on BERT-TextCNN;design of Fully Automatic Calibration Scheme for Load Box based on Visual Recognition;deepScan: Revolutionizing Garbage Detection and Classification with Deep Learning;Chapter 16 Research on Portable Methane Telemetry System based on TDLAS;Recommendation Algorithm based on Wide&Deep and FM;simulation of E-Commerce Big Data Classification Model based on Artificial Intelligence Algorithm;research and Implementation of Data Feature Extraction technology for Multisource Heterogeneous Data in Electric Distribution Network;extraction and Fusion of Geographic Information from Multi-source Remote Sensing images based on Artificial Intelligence;chapter 20 Design and Optimization of Business Decision Support System based on Deep Learning;performance Evaluation of Container Identification Detection Algorithm;application and Prospect of Deep Learning and Machine Learning technology;chapter 23 Simulation of Intelligent image Processing Model based on Machine Learning Algorithm;Chapter 24 Design and Implementation of a Health Monitoring Management Platform based on IoT and DL;chapter 25 Design of Hospital Equipment Information Management System based on Computer Vision technology;chapter 26 Design and Implementation of an internet of Things-based Real-Time Five-Layer Security Surveillance System;chapter 27 Logistics Security Integrated Communication System Under the Background of 5G Artificial Intelligence.

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A Python-based study of data processing in OCT system 14

A Python-based study of data processing in OCT system

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Optics in Health Care and Biomedical Optics XIV 2024

作者： Zhang, Hongwei Yu, Bin He, Dongsheng Guo, Jian Chen, Kaihong Wu, Shulian Li, Zhifang Key Laboratory of Optoelectronic Science and Technology for Medicine Ministry of Education Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Provincial Key Lab of Photonic Technology Information Photonics Research Center College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350007 China The Internet of Things and Artificial Intelligence College Fujian Polytechnic of Information Technology Fujian Fuzhou China

ISBN: (纸本)9781510682122

Optical Coherence Tomography(OCT) system is a non-contact imaging modality based on low-coherence optical interferometry, used for imaging turbid scattering media. They excel in rendering depth-resolved images of internal structures with micrometer-scale resolution. Previous OCT systems have some defects in image reconstruction, which are limited by complex signal processing and mathematical computation, slow image processing speed, difficult to realize real-time imaging, and high equipment cost. This paper proposes an OCT image reconstruction algorithm acceleration scheme based on the combination of FPGA (Field-Programmable Gate Array) and Python, aiming at accelerating and simplifying the image acquisition and processing of the OCT system through Python, so as to enhance the efficiency of medical diagnosis and biological *** Python as the upper computer control software, provide user-friendly graphical interface, output spectral waveform and then realize the Fourier transform, de-direct current and autocorrelation terms and other algorithmic steps to generate OCT images, to realize the real-time data transmission and *** not only has a powerful data visualization ability, but also has the advantages of simple operation, easy to develop the program to ensure that the system operates efficiently. © 2024 SPIE.

关键词： Python

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Intelligent sensing and adaptive modulation: the key technologies of future communication systems 24

Intelligent sensing and adaptive modulation: the key technol...

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3rd international conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Zhang, Pengqin Zhu, Hongjie Guangzhou Coll Technol & Business Guangzhou 510850 Guangdong Peoples R China

ISBN: (纸本)9798400709777

With the continuous progress of the times and technology, the way of communication between people has evolved into an efficient way of information transmission based on the internet, and the future communication system can only be more efficient and accurate. Therefore, this paper believes that intelligent perception and adaptive modulation can become the main technologies of future communication development, because intelligent perception can efficiently collect information, and adaptive modulation can independently choose the best modulation format, thereby improving bandwidth utilization. In addition, this paper also verifies the idea of this paper through comparative experiments at the end, and the experimental index to measure the performance is the data transmission rate. In the experiments repeated up to 50 times, the average data transmission efficiency of the intelligent communication system using the proposed method is 96.93Mdbs, while that of the ordinary communication system is 66.78Mdbs. It can be seen that there is a big gap between the two, which fully reflects the great improvement of the work efficiency of the communication system brought by the method in this paper.

关键词： Intelligent sensing Adaptive modulation Future communication Data transmission rate signal-to-noise ratio

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