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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

共 2101 条记录，以下是381-390 订阅

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M-element spread spectrum multi-user detection algorithm based on interference cancellation 3

M-element spread spectrum multi-user detection algorithm bas...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Lv, Jingshun Qin, Pengfei Liu, Kang China Academy of Space Technology Beijing China

ISBN: (纸本)9781665472784

Multiple Access interference (MAI) is one of the main factors affecting the performance of M-element spread spectrum multi-user communication systems. To solve this problem, this paper proposes to use the interference cancellation multi-user detection technology at the receiving end, to reconstruct the signal decision after M element de-amplification, to get the MAI estimated signal of each user, received signal minus MAI estimated signal, so that each user can get more reliable data information, sent to the next level for M element de-amplification decision. Monte-Carlo simulation is carried out in Gaussian channel with m sequence and Gold sequence as spread spectrum codes, respectively. Comparing the simulation results, it can be seen that the algorithm can quickly suppress or even eliminate the influence of MAI on the performance of M element spread spectrum multi-user system, and improve the system capacity. © 2023 IEEE.

关键词： Multiple access interference

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DAAED: A Deep Autoencoder based Adversarial Example detection Technique 3

DAAED: A Deep Autoencoder based Adversarial Example Detectio...

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Kumar, Vivek Kumar, Manish THDC Institute of Hydropower Engineering and Technology Dept. of Computer Science and Engineering New Tehri India

ISBN: (纸本)9798350303360

Deep Learning based intrusion detection systems are susceptible to adversarial examples which are maliciously perturbed data samples that can cause a trained intrusion detection system to misclassify. The original data sample is generally a malicious data sample which is carefully perturbed to an extent that it retains its malicious properties but, due to perturbations, causes the deep learning-based intrusion detection system to misclassify it as benign. This may have disastrous consequences for security critical systems like intrusion detection systems. In this paper, we propose a deep autoencoder based adversarial example detection technique which effectively detects adversarial examples by analyzing the mismatch between the reconstruction error of an adversarial example, and its predicted class by a trained model. Our experimental results on the five different adversarial example generation techniques: FGSM, Deepfool, PGD, BIM and JSMA, depicts the efficiency of our proposed detection technique with upto 97.8% accuracy and very few false positives. The experiments are performed on CICIDS2017 dataset and depict high detection accuracy of adversarial examples. © 2023 IEEE.

关键词： Deep learning

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Edge detection and processing method of plant root image based on joint wavelet transform 3

Edge detection and processing method of plant root image bas...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Zhao, Peng Wang, Lihui Hainan University School of Information and Communication Engineering Haikou China School of Applied Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665472784

The root system of the plant has the function of conserving water source, absorbing nutrients and preventing wind and sand fixation. The outline of the plant root system is an important parameter, which can extract the characteristics of the diameter and length of the plant root system. Therefore, it is very important to find a fast and accurate method for detecting the edge of plant root system. In this paper, the classical edge detection operator method is combined with the image sharpening and enhancement processing method of wavelet transform for edge detection, and Sobel operator and Canny operator are selected for edge detection in spatial domain. The wavelet transform was used for high-pass filtering in the frequency domain to sharpen and enhance the image, and Labelme software was used for image contour annotation. This paper proposed method can extract the edge contour more accurately. © 2023 IEEE.

关键词： Wavelet transforms

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Adaptive Modeling for Real-Time Data Analysis with Machine Learning 3

Adaptive Modeling for Real-Time Data Analysis with Machine L...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mahesh, T.R. Singh, Shweta Srivastav, Vaibhav Kalra, Hitesh Surulivel Rajan, T. Yadav, Gitanjali B. Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making. © 2023 IEEE.

关键词： Forecasting

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A Deep Convolutional Neural Network Approach for Reliable Brain Tumor detection and Segmentation system 3

A Deep Convolutional Neural Network Approach for Reliable Br...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Kalra, Mehar Prateek Sameer, Mustafa Ahmad, Gufran DayalBagh Educational Institute Department of Electrical Engineering Agra India National Institute of Technology Patna Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350338607

Brain tumours are one of the significant causes of cancer-related death globally. The associated mortality and the significant amount of the population affected by brain cancer indicate the need for fast and economical treatment of brain cancer. The standard method of detecting a brain tumour is through MRI scans, which are carried out by expert neurosurgeons. This process requires time, and the accuracy of the detection depends on the expertise of the doctor. But due to the growing population and an increasing number of cancer cases, there is a need for an automatic brain tumour detection and segmentation system. When this system is assisted by deep learning tools and image processing, it results in faster and more accurate predictions. There are various approaches for automatically detecting brain tumours and for segmenting the affected area. In the proposed work, we present an end-to-end approach to building a trustworthy brain tumour detection and segmentation system in a stepwise manner and also present a comparison of time and space parameters of different state-of-the-art artificial neural network architectures to provide options for different scenarios. In our work we achieved a test accuracy of 99.67%. © 2023 IEEE.

关键词： Deep neural networks

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3rd international conference on Machine Vision and Augmented intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on Machine Vision and Augmented intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine Vision and Augmented intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor detection and Analysis from Brain MRI Images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar Signal Processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition system Using Hybrid Deep Learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT system;role of Explainable Artificial intelligence Approaches in Cybersecurity.

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Impact of Deep Feature Synthesis on Deep Learning in Electronic Transaction Fraud detection 3

Impact of Deep Feature Synthesis on Deep Learning in Electro...

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3rd IEEE international conference on Software Engineering and Artificial intelligence, SEAI 2023

作者： Uddin, Mohammed Najim Azad, Salahuddin Hossain, Md Rahat Chugh, Ritesh CQUniversity School of Engineering and Technology Rockhampton Australia CQUniversity School of Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350337952

Electronic transaction fraud has been a severe threat in recent years, causing substantial financial losses and devaluing the reputation of financial institutions. Various machine learning and deep learning models have been cited in the literature to detect electronic transaction fraud effectively. Feature selection is crucial in enhancing the performance of a machine learning model. Automatic feature engineering techniques outweigh the manual feature selection process regarding the time spent and adapting to the changing nature of the dataset. However, to the best of our knowledge, the impact of state-of-the-art feature selection techniques, such as Deep Feature Synthesis (DFS), on the performance of deep learning-based electronic transaction fraud detection has not been thoroughly studied. Our study uses the DFS algorithm to generate features automatically from the labelled credit card transaction dataset collected from open-source sites. The data is fed into a baseline deep learning model, i.e., Convolutional Neural Networks (CNN), in two separate cases- with DFS and without DFS. A comparison of results from both cases shows that CNN with DFS outperforms the standalone CNN significantly in terms of accuracy, precision, recall, and F1 scores. © 2023 IEEE.

关键词： Convolutional neural networks

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HMPFL: Hypernetwork Model based Personalized Federated Learning for Industrial Anomaly detection 3

HMPFL: Hypernetwork Model based Personalized Federated Learn...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Zhang, Zhen Zhang, Weishan Bao, Zhicheng Liu, Qiyuan Liu, Yuru Zhao, Yikang Zhang, Rui Zhu, Wenyin College of Computer Science and Technology China Qingdao Haier Intelligent Technology Rd Co China

ISBN: (纸本)9798350318470

Federated learning allows for collaborative training of distributed deep learning networks, tackling the issue of data silos while ensuring data privacy. However, in the distributed setting of industrial equipment, the collected data is dynamic, non-independent, and identically distributed(i.e., Non-IID), which negatively impacts the performance of federated learning. While the personalized solution can address the Non-IID problem, it falls short in effectively handling the dynamic environment of industrial equipment, which poses a significant challenge to model stability. Model stability enables the model to quickly transfer and share knowledge when dealing with dynamic data. To maintain model stability and generate customized models, we propose a personalized federated learning approach that leverages hypernetwork and model-oriented updates, named HMPFL, which aims to generate stable personalized models for Non-IID dynamic environments. Finally, we evaluate the effectiveness of our framework in addressing industrial equipment anomaly detection tasks using three real industrial air conditioning datasets from different factories. The results demonstrate that our method, surpasses existing personalized federated learning approaches, achieving an average accuracy improvement of 7.00%. © 2023 IEEE.

关键词： Air conditioning

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iCBR Techniques Enabled Cervical Intraepithelial Neoplasia detection system (CINDS) 3

iCBR Techniques Enabled Cervical Intraepithelial Neoplasia D...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kapruwan, Akanksha Goyal, Himanshu Rai Sharma, Sachin Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350319125

Intelligent Case based reasoning (iCBR) is a method that relies on the memory-centered cognitive model and stresses how people learn new skills or make conclusions about unfamiliar situations based on their prior experiences. iCBR is one of many Artificial intelligence tracks that has shown considerable promise in the applications of intelligent systems in a variety of sectors, particularly in the field of healthcare, due to its intrinsic resemblance to human reasoning. The body of information on iCBR systems that has recently been used in the medical industry is reviewed in-depth in this article. We discuss future research directions as well as the iCBR applications in CIN-C. © 2023 IEEE.

关键词： Case based reasoning (CBR) Cervical intraepithelial neoplasia cancer (CIN-C) Squamous intraepithelial lesion (SIL)

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Research on the Motion Model of Chaotic Phenomena Based on Computer Artificial intelligence Simulation 3

Research on the Motion Model of Chaotic Phenomena Based on C...

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3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Lv, Jing Liu, Yue Zheng, Jungang Chen, Shuang Sun, Xiaowei Shenyang Jianzhu University School of Science Shenyang China Shenyang Jianzhu University College of Materials Science and Engineering Shenyang China

ISBN: (纸本)9798350398410

A generalized synchronous chaotic system is constructed by using discrete generalized chaotic synchronization theory and 2D-CS. The improved chaotic system consists of three continuous chaotic systems. The control sequence is used to switch according to certain rules. Through the analysis of phase diagram, bifurcation diagram and maximum Lyapunov exponent, it is found that compared with the subsystem, the system parameter interval, traversal range and maximum Lyapunov exponent increase. Convert the sampled results to IEEE Binary floating-point Arithmetic Standard (IEEE754) format. The final random number is then generated by sequentially truncation, S-box transformation and XOR operation. The result of testing the generated random number sequence shows that the generated random number can pass all the tests. © 2023 IEEE.

关键词： Chaotic systems

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