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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Raghavendra, R. Abrol, Monika Tripathi, Ramesh Chandra Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Clinical image segmentation is a challenge that identifies a selected organ or anatomical structure in a given scientific image which can be of radiographic or other modality. Expertise switch is an essential side of clinical image segmentation as medical practitioners may additionally possess information about the patient's anatomy or physiology that could contribute to the segmentation process. Understanding transfer to a deep mastering version, such as a convolution neural community, can allow the model to perform correct segmentation compared to other tactics. In this paper, we explore strategies and packages of expertise switches for scientific photograph segmentation, discuss its blessings and challenges, and eventually advise a singular method based totally on understanding fusion. We increase a two-step framework to fuse photo-degree features and segmentation labels with medical data before segmentation. The proposed approach demonstrates advanced segmentation accuracy in assessing the present strategies... © 2024 IEEE.

关键词： comparison contribute important information segmentation

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Exploring the Potential of Recurrent Neural Networks for Medical Image Segmentation

Exploring the Potential of Recurrent Neural Networks for Med...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Jain, Aaditya Mandal, Sanjeev Kumar Abrol, Monika Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due to the fact they can be skilled to bear in mind patterns over long sequences brand new information. This enables them to perceive structural patterns in an image and carry out sophisticated segmentation obligations together with tumor or organ boundary identification. similarly, RNNs have the ability to contain earlier know-how from different pics and medical data, as well as contextual know-how from external resources such as electronic fitness information. This paper critiques the contemporary in RNNs for medical picture segmentation, outlining the key methods and programs contemporary RNNs inside the field. We discuss both the successes and demanding situations of trendy RNN-based procedures and provide destiny studies directions for the improvement of modern-day extra correct and efficient segmentation equipment. © 2024 IEEE.

关键词： development directions outlining segmentation incorporate

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RAISE:A Resilient Anonymous Information Sharing Environment

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computer Modeling in Engineering & sciences 2023年第12期137卷 2743-2759页

作者： Ning Hu Ling Liu Xin Liu Kaijun Wu Yue Zhao Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Science and Technology on Communication Security Laboratory The 30th Research Institute of China Electronics Technology Group CorporationChengdu610041China College of Computer Engineering and Applied Math Changsha UniversityChangsha410022China

With the widespread application of cloud computing and network virtualization technologies,more and more enterprise applications are directly deployed in the ***,the traditional TCP/IP network transmission model does not fully consider the information security issues caused by the uncontrollable internet *** security communication solutions represented by encrypted virtual private networks(VPN)are facing multiple security *** fact,during the communication process,the user application needs to protect not only the content of the communication but also the behavior of the communication,such as the communication relationship,the communication protocol,and so *** by blockchain and software-defined networking technology,this paper proposes a resilient anonymous information sharing environment,*** RAISE system consists of user agents,a core switching network and a control cluster based on a consortium *** agents are responsible for segmenting,encrypting,and encapsulating user *** core switching network forwards user traffic according to the rules issued by the controller,and the controller dynamically calculates the forwarding rules according to the security *** from onion routing technology,RAISE adopts the controller to replace the onion routing model,which effectively overcomes the uncontrollability of *** dispersed computing model is introduced to replace the TCP/IP pipeline transmission models,which overcomes the problems of anti-tracking and traffic hijacking that cannot be solved by *** propose a blockchain control plane framework,design the desired consensus algorithmand deploy a RAISE systemconsisting of 150 nodes in an internet *** experimental results show that the use of blockchain technology can effectively improve the reliability and security of the control *** maintaining high-performance network transmission,it further provides network communicatio

关键词： Software-defined anonymous communication network blockchain network communication security

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Comparative analysis of distance metrics for intuitionistic fuzzy sets in multi-criteria decision analysis 28th

Comparative analysis of distance metrics for intuitionistic ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Wieckowski, Jakub Niewiadomski, Daniel Kulis, Dominik Salabun, Wojciech National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

This paper presents a comparative analysis of selected distance metrics within the intuitionistic fuzzy environment in Multi-Criteria Decision Analysis (MCDA). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, combined with intuitionistic fuzzy sets (IFS), was employed to investigate how different distance metrics influence decision-making outcomes. The study examines the sensitivity of the obtained recommendations to the variation in the calculation formula used to determine the distance between two IFS. The practical problem of selecting a site for a large-scale rooftop photovoltaic project served as the case study for this comparison. The results indicated that using different distance metrics within the IF-TOPSIS method led to varying preference score ranges and differing rankings, particularly in the middle portions of the rankings. The findings underscore the sensitivity of MCDA methods to the choice of distance metrics used in the calculation process. © 2024 The Authors.

关键词： Fuzzy models

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Traffic Sign Recognition Using an Attentive Context Region-Based Detection Framework

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Chinese Journal of Electronics 2021年第6期30卷 1080-1086页

作者： LIU Zhigang DU Juan TIAN Feng WEN Jiazheng School of Computer and Information Technology Northeast Petroleum University Postdoctoral Workstation on Applied Technology Research Institute Northeast Petroleum University Department of Computer Science for Artificial Intelligence University of Nottingham Ningbo

Accurate small traffic sign recognition is more important for the safety of intelligent transportation systems. A recognition framework named attentive context region-based detection framework(AC-RDF) is proposed in this paper. We construct the attentive context feature for the recognition of small traffic signs,which combines the target information and the contextual information by the concatenation operation following a pointwise convolutional layer. The proposed attentive context feature exploits the surrounding information for a given object proposal. Next, we propose a novel attentive loss function to replace the original crossentropy function. It distinguishes hard negative samples from easy positive ones in the total loss, allows the proposed framework to obtain enough training, and further improve the recognition accuracy. The proposed method is evaluated on the challenging Tsinghua-Tencent 100 K dataset. The experimental results indicate that the attentive context region-based detection framework is superior at detecting small traffic signs and achieves stateof-the-art performance compared with other methods.

关键词： attentive context region-based detection framework intelligent transportation systems convolutional neural nets attentive context feature AC-RDF framework attentive loss function traffic signs learning (artificial intelligence) Tsinghua-Tencent 100K dataset object recognition feature extraction traffic sign recognition object detection entropy recognition framework

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Application of COPRAS, PROMETHEE, and EDAS methods in sustainable energy development: A comparative study case 28th

Application of COPRAS, PROMETHEE, and EDAS methods in sustai...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Wieckowski, Jakub Gajewski, Pawel Swaldek, Krzysztof Salabun, Wojciech National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods. © 2024 The Authors.

关键词： Sustainable development goals

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Deriving a Power-Law Model for Aggregating Data with Head Based Clustering

Deriving a Power-Law Model for Aggregating Data with Head Ba...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Solanki, Vipin Rastogi, Ajay Raghavendra, R. Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Head-based Totally Clustering is a technique of grouping facts and factors with similar traits using a rooted tree structure. The research paper Deriving an electricity-law version for Aggregating facts with Head primarily based Clustering outlines an energy-law model for creating clusters of facts points in a head-based totally hierarchy that may be used to efficiently mixture groups of related information. The authors used a topological distance technique to generate the clusters and implemented the method on an objective global dataset of web pages. The proposed model is designed to lessen the complexity of incorporating records from a pre-described tree shape with electricity regulation. The effects confirmed a widespread discount in computational time and aid utilization. Additionally, the approach could increase aggregated facts' accuracy by incorporating numerous information points in a head-based hierarchy. The look gives a similar understanding of the potential application of a strength-regulation model for aggregating statistics with head-primarily based Clustering. © 2024 IEEE.

关键词： aggregating computational Head based incorporating technique

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the Local Binary Pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： Local binary pattern

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A generative adversarial network-based data augmentation approach with transient vibration data 27

A generative adversarial network-based data augmentation app...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Koenig, Timo Cadau, Luca Wagner, Fabian Kley, Markus Institute for Drive Technology University of Applied Sciences Aalen Aalen73430 Germany Faculty for Electronics and Computer Science University of Applied Sciences Aalen Aalen73430 Germany

In order to perform fault classification using machine learning algorithms, sufficient and balanced data is required. The paper presents a novel approach for data augmentation using a Generative Adversarial Network (GAN) with transient, time-dependent vibration data. The proposed approach aims to generate synthetic data in form of spectrograms that closely resemble the characteristics of the real data sets. Synthetic data generation can be used to improve the training performance of neural networks for vibration analysis if not enough or unbalanced real data is available. The authors demonstrate the effectiveness of this approach by training a vibration-based fault detection model using synthetic data and comparing its performance to a model trained on real data only as well as with a t-distributed stochastic neighbour embedding (t-SNE). Real data is acquired on a bearing test rig and measurements are carried out on bearings with four different system states. The results show that the model trained with the synthetic data set outperforms the model trained with real data only, indicating that the synthetic data generated by the proposed approach can improve the training performance and accuracy of the machine learning model. Overall, the paper highlights the potential of GAN-based data augmentation approach via spectrograms for vibration analysis and offers insights into its practical application to bearings. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bearing Condition Monitoring Data Generation Generative Adversarial Network (GAN) Spectrogram

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