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2023 5th International Conference on Information science, Electrical, and Automation engineering, ISEAE 2023

作者： Zhou, Fang Gui, Changchun Hu, Xinxiao Xing, Mengdao Zhang, Jiajia School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Electronic Engineering Xidian University Xi'an710071 China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei230088 China

ISBN: (纸本)9781510667440

With the change of observation demands, traditional monostatic synthetic aperture radar(SAR) can no longer meet the requirements such as passive reception and cooperative multimode imaging. Spaceborne/Airborne hybrid bistatic SAR(SA-BSAR) emerges as the times require, and has been widely concerned with its flexibility, concealment. However, as the receiver and transmitter do not share the same frequency oscillation source, the impact of time error on imaging cannot be ignored as the satellite operates for a long time. At the same time, SA-BSAR is in the form of double root sign in the course of slant range. How to obtain two-dimensional(2-D) spectrum is also the key to imaging. This paper proposes a new method to estimate time error based on fractional Fourier transform (FRFT), which simplifies the traditional time error estimation steps. Finally, the Air-Phase method is used to solve the 2-D spectrum to achieve the target imaging. The algorithms proposed in this paper are implemented by FFT without interpolation, which ensures the imaging efficiency. The final experimental results show the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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Recognizing the Possible Advantages of Big Data Insights for Medical Companies

Recognizing the Possible Advantages of Big Data Insights for...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Rainy Sikand Harveer Singh Department of Master of Computer Application Chandigarh School of Business Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

The potential advantages have not yet been completely realized by the medical industry. While a majority of effort is now concentrating on technology, there is an urgent need for a deeper awareness of the strategic aspects of big data. This paper investigates the evolution, organizational layout, and individual functions of big data analytics in order to fill this gap. We were able to identify five big data analytics features from research method of 26 big data adoption examples in universal health care: analytical capacity for modes of care, unprocessed advanced analysis competence, recommender potential, specific number, and connectivity in the information tech (IT) infrastructural, operating, organizational, executive, and strategy sectors, we also delineated the advantages brought about by big data analytics. Additionally, we suggest five ways for healthcare firms thinking about using big data analytics. Our research will enhance hospital companies' efforts to develop more successful document insights approaches by assisting them in better understanding the capability and advantages of big data analytics.

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BIFT: A federated learning System for Connected and Autonomous Vehicles Based on Blockchain

BIFT: A federated learning System for Connected and Autonomo...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Aarti Yadav Amandeep Kaur Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

Algorithms for machine learning(ML) are essential for autonomous driving. The vast majority of history’s automated and connected cars send a significant quantity of movement data collected from many automobiles to a central server for regular training. Nonetheless, the info process has made the safety and confidentiality of information essential. Nowadays, the development of federated learning (FL) for data security has the potential to potential to enhance distributed machine learning’s data privacy. The malicious attackers might still target the training process, though. FL is extremely vulnerable because of its total dependency on the central server. We suggest BIFT as a solution to the aforementioned issue, which consists of two parts: 1) a completely decentralized 2) A blockchain-based machine learning system for CAVs that guarantees privacy. With the use of their own driving data, BIFT’s scattered CAVs may train local machine learning models, which they can subsequently upload to improve the global model. In order to fend off potential attackers, BIFT offers a consensus mechanism called Proof of Federated Learning. We assess BIFT’s performance and demonstrate that it is scalable, reliable, and capable of fending off harmful assaults.

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A Multi-Objective Hyper-Heuristic Improved Configuration of Svm Based on Particle Swarm Optimization for Big Data Cyber Security

A Multi-Objective Hyper-Heuristic Improved Configuration of ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Jyoti Shekhawat Tajpal Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

Big Data Cyber security analytics is a growingly significant field of study and application that uses big data techniques and technology to analyses security event data in order to safeguard networks, computers, and data from illegal access. There have been several reports on Big Data Cybersecurity Analytic Systems in the literature, but an exhaustive and systematic analysis of the research from an architectural viewpoint is lacking. When In this paper, the SVM design process is organized as a bi-objective optimization problem with accuracy and model complexity as two competing goals. The system presents a novel, problem-independent hyper-heuristic framework for bi-objective optimization. It is novel to create a hyper heuristic for this situation. The hyper-heuristic structure proposed comprises of low-level heuristics and a significant level system. In light of the pursuit execution, the significant level system figures out which low-level heuristic ought to be utilized to create another SVM arrangement. Every one of the low-level heuristics look through the SVM design search space really utilizing an alternate arrangement of rules. To estimate the Pareto set of SVM arrangements, the suggested design adaptively combines the benefits of degradation with Pareto-based strategies for dealing with bi-objective enhancement. Microsoft's anomalous intrusion detection and big data malware classification have been used to gauge the efficiency of the suggested methodology.

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Investigation into a Medical Diagnostic System for Chronic Kidney Disease: Preparation and Execution

Investigation into a Medical Diagnostic System for Chronic K...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Arvind Sharma Dalwinder Singh School of Computer Application Lovely Professional University Phagwara Punjab India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab india

This abstract provides a concise overview of a study focused on the development and execution of a medical diagnostic system specifically designed for Chronic Kidney Disease (CKD). The primary objective of this study is to contribute to the advancement of early detection and intervention strategies for chronic kidney disease (CKD), a prevalent and potentially incapacitating condition. The inquiry process encompasses several sequential stages, including the initial planning phase and subsequent execution phase. During this stage, comprehensive literature studies are conducted to examine the various approaches used for diagnosing chronic kidney disease (CKD), as well as the risk factors associated with its development and the biomarkers used for its detection. This literature review aids in identifying the deficiencies within the diagnostic system and highlights the potential utility of advanced techniques such as machine learning and data analytics. The development and implementation of the diagnostic system occur during the execution phase. A comprehensive database is established by the consolidation of data from various sources, including patient records, laboratory findings, and diagnostic imaging. State-of-the-art algorithms and machine learning approaches are utilized to conduct comprehensive data analysis, pattern identification, and the construction of prediction models for the diagnosis of Chronic Kidney Disease (CKD). This article provides a comprehensive examination of various healthcare systems pertaining to the management of chronic kidney disease. Additionally, it presents a systematic review of recent research undertaken by multiple scientists during the past five years.

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An Overview of the Use of Deep Learning Algorithms to Predict Bankruptcy 7th

An Overview of the Use of Deep Learning Algorithms to Predic...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Pune India Pune411045 India CSE Department Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Computer Engineering & Application’s GLA University Mathura India

ISBN: (纸本)9789819995615

The financial forecasting of different firms in the area of financial status aims to determine whether the company will go bankrupt in the near future or not. This is a critical problem for these companies. Several companies have shown a strong interest in this area, particularly since they are concerned about the future of their companies from a financial perspective and want to determine whether or not they will go out of business. Therefore, in the work that we have done, we have presented three well-known technologies of deep learning in conjunction with ensemble classifiers and boosting ensemble classifiers for the purpose of failure prediction. During our investigation, we used an uneven dataset consisting of businesses from Spain, Poland, and Taiwan. In addition to this, we applied approaches such as oversampling, hybrid balancing, and clustering-based balancing to get rid of the inconsistent data. When taking into account a real-life financial dataset with an appropriate amount of complexity, it was discovered that the MLP-6L model with the SOMTE-ENN balancing approach had the most remarkable performance when measured against the metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Learning algorithms

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Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

Utilizing Mixture Methods for Classifier in NLP: An Essentia...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Ms. Kavita Harveer Singh Department of Master of Computer Application Chandigarh School of Business Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue.

关键词： Visualization Machine learning algorithms Service robots Text categorization Natural languages Transforms Oral communication

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A Facial Expression Recognition Method based on Residual Separable Convolutional Neural Network

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 59-69页

作者： Xu, Xiaoyu Cui, Jianfeng Chen, Xuhui Chen, Chin-Ling College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Software Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Internet of things application technology Xiamen361024 China Xiamen City University of Technology Xiamen361024 China School of Computer and Information Engineering Xiamen City University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

Facial expression recognition is a typical application of artificial intelligence. It has practical value and commercial prospects in application scenarios such as safe driving, medical assistance, online classrooms, and human-computer interaction. Traditional convolutional neural networks have low accuracy in facial expression recognition. To solve this problem, we propose a residual separable convolutional neural network, which combines depth-wise separable convolution with an improved residual network. When transmitting feature information, it fuses facial expression features through skip connection and utilizes global average pooling to reduce dimension, thus preventing gradient disappearance. We conduct experiments based on the data set FER2013. The results show that it can improve the accuracy of facial expression recognition, reduce model parameters and raise the utilization efficiency of the parameters effectively. © The Authors.

关键词： Convolution

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Multiscale Fusion Graph Convolutional Networks for Hyperspectral Image Classification 2

Multiscale Fusion Graph Convolutional Networks for Hyperspec...

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2022 2nd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2022

作者： Wang, Lei Wang, Xili School of Computer Science Shaanxi Normal University Xi’an China School of Mathematics and Computer Application Shangluo University Shangluo China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China

ISBN: (纸本)9783800760268

Graph neural network (GNN) has shown remarkable performance in many fields, such as edge prediction, node classification, etc. However, the traditional graph neural network can only accept the full graph to train the model, which leads to a very high computational complexity. Besides, the lack of multi-scale mechanism limits the actual classification performance. In this paper, we propose a multiscale fusion model based on graph convolutional network (GCN) and convolutional neural network (CNN) for hyperspectral image (HSI) classification tasks. This model can first sample randomly from the full graph to subgraphs of different scales, then trains GCN on sub-graphs of different scales and extract features, and finally integrates the spatial-spectral features extracted by multi-scale CNN classifier and the structural features extracted by multi-scale GCN for end-to-end mini-batch joint training. Experiments on two real HSI data s demonstrate that the proposed method significantly improves the classification performance compared with other comparison methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Graph neural networks

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SC-GNN: A Communication-Efficient Semantic Compression for Distributed Training of GNNs 24

SC-GNN: A Communication-Efficient Semantic Compression for D...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Jihe Wu, Ying Wang, Danghui School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798400706011

Training big graph neural networks (GNNs) in distributed systems is quite time-consuming mainly because of the ubiquitous aggregate operations that involve a large amount of cross-partition communication for collecting embeddings/gradients during the forward and backward propagation. To reduce the volume of the communication, some recent approaches focused on decaying each of connections via sampling, quantifying, or delaying until satisfactory trade-off are obtained between volume and accuracy. However, when applied to popular GNNs, those approaches are found to be bounded by a common volume/accuracy Pareto frontier which shows that the decaying for individual connection cannot further accelerate the aggregate of training. In this work, SC-GNN, a semantic compression of the cross-partition communication, is proposed to concentrate a group of connections as a high-level semantics and transmit to a target partition. Since carrying the overall intent of a group, the semantics can keep transferring the interactions, i.e., embeddings/gradients, between a pair of remote partitions until GNN models converge. In addition, a connection-pattern based differential optimization is proposed to further prune those weak connections, while guaranteeing the training accuracy. The results show that, for multi-field datasets, the compression rate of SC-GNN is 40.8 × higher than SOTA methods and the epoch time is reduced to 31.8% on average. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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