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27. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 27th VDE ITG Symposium on Mobile Communication: Technologies and Applications

作者： Stellwagen, Jochen Deegener, Matthias Kuhn, Michael Frankfurt University of Applied Sciences Facility 2: Computer Science and Engineering Frankfurt a.M. Germany Darmstadt University of Applied Sciences Department of Electrical Engineering Darmstadt Germany

ISBN: (纸本)9783800760947

The exchange of information between road users makes it possible to improve the individual perception of traffic situations and thus increase safety in the traffic area. The transmission of safety-critical information requires reliable technologies that meet the requirements of a dynamic network with highly mobile participants. The two technologies, ITS-G5 and LTE-V2X, are currently available for this purpose. Both have individual advantages and disadvantages. This investigation looks at ways to combine ITS-G5 and LTE-V2X to create a hybrid network that will increase transmission reliability and better meet the requirements of security-critical applications. The investigation was carried out with the help of a simulation environment. The results show that a hybrid network can indeed increase the reliability of transmissions, with the number of hybrid participants supporting both technologies being decisive for effect. © 2023 Mobilkommunikation: Technologien und Anwendungen - 27. VDE ITG-Fachtagung. All rights reserved.

关键词： Safety engineering

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Mitigation of Spectrum Sensing Data Falsification Attack (SSDF) in Cognitive Radio Network

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Journal of The Institution of Engineers (India): Series B 2022年第4期103卷 1249-1257页

作者： Banerjee, Subhasish Singh, Tinka Singh, Karam Ratan Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Jote791113 India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati781039 India Department of Basic and Applied Science National Institute of Technology Arunachal Pradesh Arunachal Pradesh Jote791113 India

The most assuring technique to the spectrum scarcity problem is cognitive radio networks (CRN). In CRNs, the individual spectrum sensing by a single cognitive radio (CR) user lessens the reliability of the network. However, as far as cooperative spectrum sensing is concerned, precisely detecting the signal of the principal user (PU) is a major Challenge. Moreover, the cooperative sensing technique is less susceptible to the spectrum sensing data falsification (SSDF) attack. Therefore, to misguide other normal CR users, a malicious user transmits the deceived sensing information in order to make an inaccurate decision of the activity of PU. Thus, to take corrective measures for accurate cooperative spectrum sensing, the identification of the SSDF security attack is very crucial. In this paper, it has been proposed to eliminate the corrupted sensing information while sensing from the malicious SSDF intruders, such that a normal CR user can identify the functioning of PU in distributed cooperative spectrum sensing environment effectively. Finally, based on the simulation results, performance in cooperative sensing has also been carried out. © 2022, The Institution of Engineers (India).

关键词： Cognitive radio

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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Learning Posterior Distributions in Underdetermined Inverse Problems 9th

Learning Posterior Distributions in Underdetermined Inverse...

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9th International Conference on Scale Space and Variational Methods in computer Vision, SSVM 2023

作者： Runkel, Christina Moeller, Michael Schönlieb, Carola-Bibiane Etmann, Christian Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science University of Siegen Siegen Germany

ISBN: (纸本)9783031319747

In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, however, come with the drawback of disregarding prior knowledge of the problem even though it has shown to be beneficial to incorporate this knowledge into the problem-solving process. We thus introduce an unpaired learning approach for learning posterior distributions of underdetermined inverse problems. It combines advantages of deep generative modeling with established ideas of knowledge-driven approaches by incorporating prior information about the inverse problem. We develop a new neural network architecture ’UnDimFlow’ (short for Unequal Dimensionality Flow) consisting of two normalizing flows, one from the data to the latent, and one from the latent to the solution space. Additionally, we incorporate the forward operator to develop an unpaired learning method for the UnDimFlow architecture and propose a tailored point estimator to recover an optimal solution during inference. We evaluate our method on the two underdetermined inverse problems of image inpainting and super-resolution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Inverse problems

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Climate Change-based Urban Geographical Regions Planning: Sustainable Application Using Artificial Intelligence

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 98-107页

作者： Begum, Khaja Shahini Ambala, Srinivas Kumar, Bathina Rajesh Yajid, Mohd Shukri Ab Muniyandy, Elangovan Haldar, Ritwik Department of English Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Management and Science University Selangor Shah Alam Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi 600 062 India

From the level of individual creatures to that of ecosystems, where all species are components of intricate webs of interactions in the natural world, climate change can have a cascade impact. Though their full promise for mitigating climate change is still unrealised, artificial intelligence and machine learning are revolutionising scientific fields. Despite making up a small percentage of the planet’s surface, the enormous population of urban areas has a huge impact on climate change. Nevertheless, our understanding of the consequences of urbanisation on ecosystems and the climate is still lacking. This research proposes a novel technique in urban geographical region planning and climate change detection using a machine learning model. Here, the input has been collected as urban region geographical region changes analysis with climate change dataset and processed for noise removal and normalisation. Then this image has been segmented using a region-based graph cut active contour neural network, and features have been extracted using a deep transfer convolutional Gaussian model. Experimental analysis has been carried out in terms of detection accuracy, random precision, AUC, F1-score, and recall. The proposed model attained detection accuracy of 97%, random precision of 94%, AUC of 96%, F1-score of 95%, and recall of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Active contour Climate change detection Graph cut Machine learning Urban geographical region

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ExACT Explainable Clustering: Unravelling the Intricacies of Cluster Formation 2

ExACT Explainable Clustering: Unravelling the Intricacies of...

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Joint of the 2nd Workshop on Knowledge Diversity and the 2nd Workshop on Cognitive Aspects of Knowledge Representation, KoDis + CAKR 2023

作者： Sabbatini, Federico Calegari, Roberta Department of Pure and Applied Sciences University of Urbino Carlo Bo Italy Department of Computer Science and Engineering Alma Mater Studiorum-University of Bologna Italy

Cluster assignments, in particular the deep clustering ones, are often hard to explain, partially because they depend on all the features of the data in a complicated way, so it is difficult to determine why a particular row of data is classified in a particular bucket. This opaqueness makes their predictions not trustable, as for many predictors based on black boxes. This paper aims to tackle the aforementioned issues by introducing the design and implementation of ExACT, a new explainable clustering algorithm based on the induction of decision trees and performing hypercubic approximations of the input feature space in order to provide output human-interpretable clusters. Furthermore, ExACT is versatile enough to perform explainable classification and regression as well, as demonstrated in this work, proving to be a competitive alternative to existing analogous algorithms. © 2023 CEUR-WS. All rights reserved.

关键词： Decision trees

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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Diabetic Retinopathy Using Deep Learning

Diabetic Retinopathy Using Deep Learning

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Joshi, Shivani Kumar, Rajiv Rai, Praveen Kumar Garg, Sanskar G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering India

ISBN: (纸本)9798350323917

According to a recent survey by WHO, every fourth person is diabetic. It can be due to lifestyle, pollution, heredity or many other reasons. A diabetic person with the time has some threats in life. One of these threats is Retinopathy which is a well-known microvascular consequence of diabetes mellitus that poses a risk to vision is diabetic retinopathy. Worldwide, 93 million people have diabetic retinopathy at this time. Studies on the prevalence of diabetic retinopathy and risk factors are lacking. People who have diabetes may have more chances to develop diabetic retinopathy, an eye disorder that can lead to blindness. The retina's blood vessels are impacted (the light-sensitive layer of tissue in the back of your eye). It's crucial to undergo a thorough dilated eye exam at least once a year if you have diabetes. In this paper, we have used the deep learning method by taking a single snapshot of the human fundus for detecting the stage of diabetic retinopathy. We have also suggested the multistage transfer learning method, which uses comparable datasets with various labelling. With a sensitivity and specificity of 0.99, the proposed method can be used as early diagnosis of diabetic retinopathy on APTOS 2019 dataset for Detecting Blindness (13000 images). We employed a deep learning model Shapley Additive exPlanations (SHAP) for game theoretic strategies to research the diagnosis of diabetic retinopathy. In order to associate optimal credit allocation with local explanations, it makes use of the conventional Shapley values from game theory and their related extensions. To understand the predictions of our model and how to further improve its performance, in this study we will train a base ResNet50 model, evaluate it, and use the SHAP model's explain ability technique. © 2023 IEEE.

关键词： Blood vessels

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A Blockchain Framework for Investment Authorities to Manage Assets and Funds 5th

A Blockchain Framework for Investment Authorities to Manage ...

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5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Sherimon, P.C. Sherimon, Vinu Thomas, Jeff Jaimon, Kevin Faculty of Computer Studies Arab Open University Muscat Oman Department of IT University of Technology and Applied Sciences Muscat Oman Department of Computer Science and Engineering Saintgits College of Engineering Kerala Pathamuttam Kottayam India

ISBN: (纸本)9783031283239

Investment authorities are broad financial institutions that carefully manage investments on behalf of the national government using a long-term value development approach. To provide a stronger structure or framework for Investment Authorities to govern the distribution of funds to public and private markets, we have started research to create a blockchain-based prototype for managing and tracking numerous finances of such authorities. We have taken the case study of Oman Investment Authority (OIA) of Sultanate of Oman. Oman’s wealth is held in OIA. It is the organization that oversees and utilizes the additional capital generated by oil and gas profits in public and private markets. Unlike other Omani funds, this one focuses primarily on assets outside the Sultanate. The operation of the OIA entails a large number of transactions, necessitating a high level of transparency and administration among the parties involved. Currently, OIA relies on various manuals to achieve its goals, such as the Authorities and Responsibilities manual, the Investment Manual, and the Code of Business Conduct, among others. In this paper, we propose a blockchain-based framework to manage the operations of OIA. Blockchain is a part of the Fourth Industrial Revolution, and it is reshaping every industry. The main components of every blockchain are assets and participants. The funds are the major assets in the proposed study, and the participants are the various fund shareholders/recipients. The blockchain’s transactions are all safe, secure, and immutable, and it is part of a trust less network. The transactions are simple to follow and verify. By replacing intermediary firms with smart contracts, blockchain-based solutions eliminate any middlemen in the fund allocation process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Chest X-Ray-Based Covid-19 Detection Using Preprocessing Techniques and Deep Learning Algorithms 7th

Chest X-Ray-Based Covid-19 Detection Using Preprocessing Tec...

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7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Kurian, Thanu Thangam, S. Raja, A. Saleem Reddy, K. Lokeswar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman

ISBN: (纸本)9789819720880

Covid-19 is a transmissible infection identified in Wuhan, China, in December 2019 where more than million people were killed. When the contamination enters body, it contacts the mucous film that lies in your mouth, eyes, and nose. The virus entering a cell makes new disease parts making use of the cell and spreads infections by attacking nearest cells. Signs could begin one to fourteen days subsequent to presenting to the infection. Majority of them suffer with pneumonia, while a few suffer with extreme side effects like shortness of breath, headache, and fast heartbeat. Medical practitioners can see indications of respiratory infections on a chest X-ray. So, to detect if a person is suffering with Covid-19, the chest images help in the early analysis and the treatment of the patient. This study utilizes multiple datasets containing images of chest X-ray and makes use of preprocessing techniques like image augmentation, resizing, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and trained using Deep learning algorithms like CNN, VGG-16, VGG-19, and AlexNet. CNN model with a maximum pooling layer and a flatten layer outperformed other algorithms when trained on image augmentation and normalization images and achieved accuracy of 0.98. Finally, integrating the best suitable model with web applications like Flask helps to reduce the burden on doctors and minimize the human error in Covid-19 detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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