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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Key Generation using Pseudorandom Number Generator 14

Key Generation using Pseudorandom Number Generator

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sahu, Kamini Awasthi, Lalit K. Kumar, Abhimanyu NIT Uttarakhand Computer Science and Engineering Srinagar India

ISBN: (纸本)9798350335095

Cryptography is crucial for securing data, and the rise of online transaction processing has raised the necessity for security. Cryptography uses various encryption methods to protect sensitive data from threats, which calls for cryptographic keys. Any encryption or decryption method is often built on cryptographic keys. The cryptographic key gives strength to the encryption algorithm. It increases its security, so the generation of keys are essential to data encryption. The significance of any algorithm lies in the fact that the keys are not duplicated so that it is not possible to break. The key is truly random, then perfect secrecy can be obtained. In the paper, we are generating the key using a pseudorandom number generator so that it is complex for any attacker to break the cryptographic key. © 2023 IEEE.

关键词： Sensitive data

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Viric Learning - A Novel Transfer Learning Method 15th

Viric Learning - A Novel Transfer Learning Method

引用

15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Barragan, Alfonso Fontecha, Jesus Gonzalez, Ivan Jonhson, Esperanza Carneros-Prado, David Villa, Laura Univ Castilla La Mancha MAmI Res Grp Paseo Univ 4 Ciudad Real 13071 Spain Spillskolen Hogskolen Innlandet INN Hamar Norway

ISBN: (纸本)9783031486418;9783031486425

Increasingly, information systems include intelligent components to make decisions or perform actions based on data, owing to advances in data science and artificial intelligence. Although advances have been made at the machine level to enable the execution of more complex models, due to the changing context they can quickly become outdated and require a high number of resources to adapt to new conditions, thus transfer learning is being investigated. This discipline seeks to optimize learning tasks of an intelligent model based on the learning from another model. Therefore, a method is proposed that focuses on what all models receive in a generic way: data. The objective is to elaborate a design data register that, in controlled training sequences, transfers only the quality knowledge of the model that from it was created to a new one by means of optimization algorithms. Likewise, it will also obtain clarity on the criteria used by the models to perform their functions, the development of objective mechanisms to obtain traceability of the training data and an optimal method of adaptation to the changing context to which the information systems are subjected.

关键词： Transfer learning Nature Inspired algorithms Neural Network Decision Tree Random Forest Naive Bayes Physical Activity data

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A Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for Image processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

引用

5th international conference on data Science, Machine Learning and Applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. The rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional Neural Networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning Image processing Backpropagation algorithm Convolutional Neural Networks (CNNs) Model structures

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Acconotate: Exploiting Acoustic Changes for Automatic Annotation of Inertial data at the Source 19

Acconotate: Exploiting Acoustic Changes for Automatic Annota...

引用

19th Annual international conference on Distributed computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Chatterjee, Soumyajit Singh, Arun Mitra, Bivas Chakraborty, Sandip Iit Kharagpur Department of Cse India

ISBN: (纸本)9798350346497

Smart infrastructures often intend to provide personalized context-aware services for their residents. These context-aware services, in turn, often rely on sophisticated machine learning algorithms which need vast volumes of costly annotated sensor data. State-of-the-art automated annotation frameworks try to solve this problem by generating annotated sensor data obtained from personal wearables. However, most of these approaches - (a) either need visual data from the environment or (b) can only work for environments with a single resident. This paper discusses the design of a first-of-its-kind framework Acconotate which can automatically generate annotated data from dual resident smart environments without requiring any visual information. Acconotate achieves this by exploiting the typical transitions present in complex human activities first to solve the critical problem of the user-to-activity association and then use that to annotate the sensor stream available from both the users. Rigorous evaluation with two real-life datasets collected in two diverse scenarios shows that Acconotate can successfully generate annotated sensor data over the edge without human intervention. © 2023 IEEE.

关键词： Learning algorithms

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Traffic Sign Detection in the Digital Era: Leveraging Convolutional Neural Networks 4

Traffic Sign Detection in the Digital Era: Leveraging Convol...

引用

4th IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2023

作者： Kothai, G. Anbumozhi, Anna Abirami, R. Kavin, R.A. Karthikeyan, M. Kalyani, T. Gokulpandian, M. TamilNadu Coimbatore India SRM Institute of Science and Technology Department of Data Science and Business TamilNadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies TamilNadu Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Virudhunagar India

ISBN: (纸本)9798350313826

Road safety depends heavily on traffic signs, and as there are more vehicles on the road, the demand for precise traffic sign recognition systems rises. The development of machine-learning algorithms to predict and recognize traffic signs in real-time has been the main focus of research. An overview of the state of advancement in image analysis and classification for recognizing road signs is given in this article. It also goes through their benefits and drawbacks in various traffic situations. Managing various lighting and weather situations, the necessity for real-time processing, and future connection with autonomous driving systems are just a few of the difficulties that are highlighted in the paper. This study seeks to provide guidance to professionals working in the areas of autonomous driving, computer vision, and machine learning who recognize traffic signs. © 2023 IEEE.

关键词： Traffic signs

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ICACS 2023 - Proceedings of the 7th international conference on algorithms, computing and Systems

ICACS 2023 - Proceedings of the 7th International Conference...

引用

7th international conference on algorithms, computing and Systems, ICACS 2023

ISBN: (纸本)9798400709098

The proceedings contain 24 papers. The topics discussed include: automated algorithm for multi-variate data synthesis with Cholesky decomposition;large matrix multiplication algorithms: analysis and comparison;benchmarking classifiers for loan default prediction using archetypal analysis;enhancing banking security through intelligent models for advanced fraud prevention;innovative predictive modeling of property appraisal: emphasizing coastline proximity as a key factor;improved grey wolf optimization algorithm based on local derivative inertia weights;ensemble deep transfer learning approaches for sales forecasting;an improved teaching-learning-based optimization with adaptive moment estimation for global optimization;preprocessing-based adversarial defense for object detection via feature filtration;and person attributes recognition based on hierarchical classification.

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Exploring Research Opportunities to Apply data Mining Techniques in Software Engineering Lifecycle 1

Exploring Research Opportunities to Apply Data Mining Techni...

引用

1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Anitha Gracy, J. Parthasarathy, S. Bagavathilaksmi, R. Thiagarajar College of Engineering Applied Mathametics and Computational Science Madurai India VELS Institute of Science Technology and Advanced Studies Department of Computer Application/Information Technology Chennai India

ISBN: (纸本)9798350342796

This Software engineering (SE) is the core research area of the software industry, and numerous algorithms and frameworks are proposed every day. Software Engineering is the discipline that keeps up with recent developments in fields and techniques such as data mining, machine learning, natural language processing, and artificial intelligence. It is difficult to manage such a massive amount of data in a software engineering repository if data processing and big data analytics are not available. This paper provides an in-depth study of data mining techniques that to improve the efficiency of the software development lifecycle. The study emphasizes the practical applications and discoveries made by text mining, clustering, classification, and other methods. © 2023 IEEE.

关键词： Classification Clustering data Mining SDLC Software Engineering Text Mining

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Anomaly detection method of power purchase material data based on BIRCH clustering algorithm and time series 3

Anomaly detection method of power purchase material data bas...

引用

3rd international conference on Advanced algorithms and Signal Image processing, AASIP 2023

作者： Guo, Ning Wang, Pengju China Energy Engineering Group Gansu Electric Power Design Institute Co. LTD Lanzhou Gansu730050 China

ISBN: (纸本)9781510668522

At present, the conventional detection methods for abnormal data of power purchase materials mainly use correlation vector machines to extract and reduce the dimensions of data features. Due to less dimensions of data feature extraction, the detection effect is poor. In this regard, a method based on BIRCH clustering algorithm and time series for anomaly detection of power purchase material data is proposed. The data is preprocessed by removing outliers and supplementing missing values, and a time series autoregression model is constructed according to data dimensions to extract the flow characteristics of material data. The determination of abnormal data is realized by using local density threshold. In the experiment, the detection performance of the designed detection method is tested. The final results can prove that the proposed method has a low false detection rate and an ideal detection effect when it is used for abnormal detection of power purchase material data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Clustering algorithms

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Implementing Virtual Network Functions in Named data Networking and Web 3.0

Implementing Virtual Network Functions in Named Data Network...

引用

international conference on computing, Networking and Communications (ICNC)

作者： Fang, Puming Wolf, Tilman Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

ISBN: (纸本)9781665457194

Modern networks use Virtual Network Functions (VNFs) to implement processing functions on network traffic, such as content inspection, load balancing, etc. These network functions can be invoked directly by end systems or transparently by network providers to implement local policies. VNFs have been explored extensively for connection-oriented networks, but the use of VNFs for content-centric/information-centric networks is new. Our prior work has shown that network functions can be applied in the context of Named data Networking (NDN) in principle and that such functionality can provide benefits to implementing network policies. This paper presents results from an actual prototype implementation of Named data Network Functions (NDNF). Our work discusses implementation considerations and a variety of test scenarios that have been evaluated on the widely used ndnSIM simulator. We show that Named data Network Functions can be implemented effectively and invoked by end systems or intermediate nodes to enforce network policies transparently. This technique can be applied to the emerging Web 3.0, which can be based on named data information transfers.

关键词： named data networking virtual network function network protocol simulation Web 3.0

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FedEntropy: Efficient Federated Learning for Non-IID Scenarios Using Maximum Entropy Judgment-based Client Selection 21

FedEntropy: Efficient Federated Learning for Non-IID Scenari...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ling, Zhiwei Yue, Zhihao Xia, Jun Wang, Ting Chen, Mingsong Lian, Xiang East China Normal University MoE Engineering Research Center of SW/HW Co-design Tech. and App. Shanghai China Kent State University Department of Computer Science Kent United States

ISBN: (纸本)9798350329223

Although various techniques have been proposed for Federated Learning (FL) to address its problem of low classification accuracy in non-IID scenarios, most of them neglect both i) distinct data distribution characteristics of heterogeneous devices (i.e., clients), and ii) benefits and hazards of local models for global model aggregation. In this paper, we present FedEntropy, an efficient FL approach with a novel two-stage dynamic client selection scheme that fully takes the above two factors into account. Unlike existing FL methods, FedEntropy firstly selects clients with high potential for benefiting global model aggregation in a coarse manner, and then further filters out inferior clients from such selected clients by using our proposed maximum entropy judgment method. Based on the pre-collected soft labels of the selected clients, FedEntropy only aggregates those local models that can maximize the overall entropy of their soft labels, thus effectively improving global model accuracy while reducing the overall communication overhead. Comprehensive experimental results on well-known benchmarks demonstrate both the superiority of FedEntropy and its compatibility with state-of-the-art FL methods. © 2023 IEEE.

关键词： client selection Federated Learning maximum entropy judgment non-IID data distribution

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