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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Radiation Pattern Reshaping of an Open-Circuited Bidirectional Leaky-Wave Antenna

Radiation Pattern Reshaping of an Open-Circuited Bidirection...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Li, Hui-Dong Zhu, Lei University of Macau Taipa Faculty of Science and Technology Department of Electrical and Computer Engineering 999078 China

ISBN: (纸本)9798350336535

In this work, a method of reshaping the radiation pattern of an open-circuited EH1-mode bidirectional leaky-wave antenna is presented. Initially, it is found that an off-broadside radiation pattern is generated from the open-circuited EH1-mode bidirectional leaky-wave antenna. This is due to that several pairs of opposite magnetic currents exist on the antenna aperture. Afterwards, the opposite magnetic currents are reshaped to be with the same directions by introducing the microstrip EH1-mode component properly. Such a way, a single high-gain beam can be generated at broadside. The operation principles of the presented hybrid-mode unit are investigated by analyzing its electric-field distributions. Finally, a prototype containing 4 hybrid-mode units is practically implemented. The simulation and measurement both indicate that the original off-broadside radiation pattern is well reshaped to the desired single high-gain beam at broadside. © 2023 IEEE.

关键词： Electric fields

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Technology Acceptance Readiness Analysis in the Context of Digital Tourism Village using Machine Learning Approach 9

Technology Acceptance Readiness Analysis in the Context of D...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Prawira, Indra Andryani, Nur Afny Catur Yufriadi, Ebnu Bina Nusantara University Faculty of Digital Communication and Hotel & Tourism Mass Communication Program Communication Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Digital tourism village potentially accelerates rural development hence improving a country's economy. The Indonesian government has developed hundreds of tourism villages in recent years but has yet to popularize the development of digital villages. Technology design and development are pivotal factors in tourism villages' digitalization. Therefore, the development digital tourism village must consider village readiness by measuring technology adoption readiness or technology acceptance of the corresponding stakeholders. This paper presents the technology acceptance analysis in the context of a digital tourism village. We use a quantitative analysis approach using the TAM-UTAUT model with data collected from a questionnaire of 101 villagers (n=101) in Pasanggrahan Village, West Java, Indonesia. This research uses machine learning with clustering procedures to analyze the level of technology adoption readiness by implementing K-means Clustering algorithm. In addition, to observe the significance of each influencing factor to the technology adoption readiness level, feature importance analysis approach using Extra tree classifier algorithm is employed. The research finds that attitude toward use, perceived ease of use, and facilitating condition-have a considerable impact on the degree of readiness for technology adoption. On the other hand, the perceived utility and social impact are found to have minimal bearing on the preparedness level for technology adoption. Perceived ease of use and facilitating conditions are the most important factors to afford the acceleration of technology adoption readiness. The research contributes to the development of a digital tourism village and the analysis of technology adoption readiness using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Ensemble Mobile Application for Recommendation of Fertilizer Based on Nutrient Deficiency in Rice Plants Using Transfer Learning Models

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第16期16卷 100-112页

作者： Sobhana, M. Sindhuja, Vallabhaneni Raga Tejaswi, Vasireddy Durgesh Faculty at Department of Computer Science and Engineering V R Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering V R Siddhartha Engineering College Andhra Pradesh Vijayawada India

India is an agricultural country, and farming is the most common occupation among Indians. Rice is a vital crop in the agricultural industry. Productivity has been declining for almost a decade. There are several causes for this, including fragmented land holdings, Indian farmer illiteracy, a lack of decision-making capacity in selecting excellent seeds, manure, and irrigational infrastructure. One of the major reasons for rice crop failure is due to malnutrition. Rice, maybe in particular, lacking in nutrients such as potassium, nitrogen, and phosphorus. Nutrient deficiency detection in crops is necessary to plan further actions to enhance yield. Most studies have relied on the use of transfer learning models for agricultural uses. Ensembling of different transfer learning techniques has the ability to greatly increase the predictive model’s performance. Five transfer learning architectures InceptionV3, Xception, VGG16, Resnet50, and MobileNet are all taken into account, and their different ensemble models are used to perform deficiency detection in rice plants where ensembled models performs better when compared to individual models. The ensembled model i.e. InceptionV3 + Xception has achieved an accuracy of 98% when compared to other models and it can be utilized in real time situations. The mobile application was created as a user-friendly interface to assist farmers. The accurate diagnosis of these nutritional deficiencies like nitrogen, potassium, phosphorus and recommendation of fertilizer to corresponding nutrient deficiency with the help of mobile application could aid farmers in providing correct plant intervention and to keep track of crop growth © 2022, International Journal of Interactive Mobile *** Rights Reserved.

关键词： Crops

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Fabrication of SiC particle dispersed AZ91D magnesium alloy composite by injection molding method and their properties

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第3期74卷 141-146页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Graduate School of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Okayama Soja-shi719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Co. Suwa-shi Nagano Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Soja-shi Japan

AZ91D magnesium alloy chips were coated with SiC powder using a binder, and injection molding was attempted using SiC powder coated magnesium chips as raw material. The metallurgical structure of the injection molded products showed uniform dispersion of SiC particles in the matrix, and there were no significant defects such as voids at the SiC particle-matrix interface. The hardness and 0.2% proof stress of the injection molded products tended to improve with increasing SiC content, while the elongation decreased with increasing SiC content. The Young's modulus of the injection molded products improved with increasing SiC content. It was confirmed that the specific bending rigidity of the injection molded products also improves due to the significant increase in the Young's modulus, although the addition of SiC leads to an increase in density. © 2024 The Japan Institute of Light Metals.

关键词： Silicon carbide

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CEGSet: Collection of standardized cause-effect graph specifications 12

CEGSet: Collection of standardized cause-effect graph specif...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Krupalija, Ehlimana Cogo, Emir Becirovic, Seila Prazina, Irfan Pozderac, Damir Besic, Ingmar University of Sarajevo Faculty of Electrical Engineering Department of Computer Science and Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350322910

Cause-effect graphs are a commonly used black-box testing method, and many different algorithms for converting system requirements to cause-effect graph specifications and deriving test case suites have been proposed. However, in order to test the efficiency of black-box testing algorithms on a variety of cause-effect graphs containing different numbers of nodes, logical relations and dependency constraints, a dataset containing a collection of cause-effect graph specifications created by authors of existing papers is necessary. This paper presents CEGSet, the first collection of existing cause-effect graph specifications. The dataset contains a total of 65 graphs collected from the available relevant literature. The specifications were created by using the ETF-RI-CEG graphical software tool and can be used by future authors of papers focusing on the cause-effect graphing technique. The collected graphs can be re-imported in the tool and used for the desired purposes. The collection also includes the specification of system requirements in the form of natural language from which the cause-effect graphs were derived where possible. This will encourage future work on automatizing the process of converting system requirements to cause-effect graph specifications. © 2023 IEEE.

关键词： Black-box testing

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A Compositional Simulation Framework for Testing Adversarial Robustness of Deep Neural Networks

A Compositional Simulation Framework for Testing Adversarial...

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2023 Design and Verification Conference and Exhibition Europe, DVCon Europe 2023

作者： Nader, Youssef Maher Hatab, Mostafa Lotfy Ghaleb, Mazen Mostafa Bakr, Safia Medhat Awaad, Tasneem A. AlGanzouri, Ahmed Abdelsalam, Mohamed El-Kharashi, M. Watheq The Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Department of Electrical and Computer Engineering Faculty of Engineering & Computer Science University of Victoria Victoria Canada Siemens EDA Cairo Egypt

ISBN: (纸本)9783800762064

Deep neural networks (DNN) have reached impressive performance in computer vision, making them a natural choice for object detection problems in automated driving. However, DNNs used for object detection are known to be highly vulnerable to adversarial attacks. Even small changes to the input such as adding a customized noise pattern that remains invisible to the human eye, can stimulate silent prediction errors. In this study, we present a compositional simulation framework for testing the adversarial robustness of DNNs used in object detection. We demonstrate our framework with a comprehensive case study of a speed-sign detector model with two different adversarial attacks. © 2023 Design and Verification Conference and Exhibition Europe, DVCon Europe

关键词： Object detection

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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DeepFake Detection System using Deep Learning 11

DeepFake Detection System using Deep Learning

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Ahmed, Omar Fekry, Sameh Metwally, Abdullah Abdelwahid, Abdelrahman Khattab, Dina Abdulelmagid, Amer Hossieny, Radwa Ain Shams University Faculty of Computer and Information Science Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

Due to the growing usage of DeepFake technology to produce fake photos and videos, the problem of DeepFake identification has become a serious concern in recent years. With the use of this technology, fake material that is difficult to tell apart from real content may be produced. Therefore, the development of accurate DeepFake detection algorithms is essential to identify and prevent the spread of manipulated content. However, current publicly available DeepFake detection datasets suffer from a lack of diversity, with only a few actors appearing in multiple videos. This results in an oversampled training dataset, leading to model overfitting and inadequate performance when tested on new data. The overfitting problem can cause deep neural networks to focus more on facial features than on specific traits of DeepFake content, thereby reducing the model's ability to generalize. To address this issue and broaden the training dataset's variety, we suggest applying data augmentation methods like Face Cutout and Random Erase. The Face Cutout technique randomly removes a rectangular area of an image containing the face, while the Random Erase technique randomly removes a rectangular area of the image. These techniques introduce variations in the images, making it more challenging for the model to overfit and focus on specific traits of DeepFake content. We evaluated our approach using several models and achieved significant accuracy improvements using the EfficientnetV2B0 model and Random Erase augmentation. © 2023 IEEE.

关键词： Deep neural networks

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Transformer-Based Question Answering Model for the Biomedical Domain 5

Transformer-Based Question Answering Model for the Biomedica...

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Haddouche, Ahcene Rabia, Ikram Aid, Aicha Bouira University Faculty of Sciences and Applied Sciences LIM Laboratory Computer Science Department Algeria

ISBN: (纸本)9798350381450

Motivation: Question Answering (QA) is a highly focused topic in the field of Natural Language Processing (NLP). Recent progress in neural network models and the availability of large datasets like SQuAD have played a significant role in improving performance in open domains. However, there remains a need to further effectively implement these systems in more specific domains, especially in the biomedical field, to help medical practitioners provide accurate solutions for inquiries related to medicine and healthcare, including specific subjects such as the COVID-19 disease. Fortunately, recent models, such as transformers, have opened up avenues and modern techniques for developing accurate ***: In this work, we aim to leverage transformer models and Transfer Learning to effectively train models in the biomedical domain. By taking a pre-Trained model for Question Answering tasks and further fine-Tuning it on specific domains, we enhance the system's performance in the biomedical domain. Our ultimate goal is to develop a QA model specifically tailored for COVID-19 ***: We have trained BERT and RoBERTa models on the COVID-QA dataset and achieved competitive results on COVID-19 QA. Our RoBERTa model achieved an Exact Match (EM)/F1 score of 0.38/0.64, respectively, on COVID-QA, indicating successful performance in COVID-19 QA. © 2023 IEEE.

关键词： COVID-19

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