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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40626 条记录，以下是4921-4930 订阅

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A Comprehensive Investigation on Real - Time Object Detection in Deep learning 5

A Comprehensive Investigation on Real - Time Object Detectio...

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5th IEEE international conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Rajeshkumar, M. Arun Raj, L. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305449

Object Detection technique is extremely related among the computer Vision field. Object detection empowers detecting case in the perception of various objects from videos plus imagery as well as videos or pre-recordings video. This will find out the variety of individuality of imagery more willingly than the recognition of mechanism of objects and will provides a logical as well as perceptive of images in efficient manner deeply related to human vision works. Object detection has recognized with significant notice from researchers for the motive of its very close relationship with image detection. Machine learning algorithms interrelated to Convolutional Neural Networks (CNNs) has been currently investigated in a several times of real time object detection applications through the source of various articles. In this study, we have analyzed about various algorithm implemented in the concept of real-time object detection as well as importance and drawbacks of specific algorithm of the existing works in the form of a comparative table. The results of this investigation indicate that the machine learning algorithms are very successful in detecting the real time object. © 2023 IEEE.

关键词： Object detection

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A Max-Min Security Game for Coordinated Backdoor Attacks on Federated learning

A Max-Min Security Game for Coordinated Backdoor Attacks on ...

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2023 IEEE international conference on Big Data, BigData 2023

作者： Wahab, Omar Abdel Avila, Anderson Polytechnique Montréal Department of Computer and Software Engineering Montréal Canada Montréal Canada INRS-UQO Joint Research Unit on Cybersecurity Gatineau Canada

ISBN: (纸本)9798350324457

We address in this paper the challenge of data poisoning attacks on Federated learning. We consider a particularly challenging attack scenario in which a single poisoning attack is coordinated over a set of clients to complicate its detection. In response, the federated learning server assigns a weight to each client's model update with the aim of mitigating the effects of the poisoning on the global model. To address this challenge, we first design a trust mechanism that enables the federated learning server to assess the trustworthiness of each client on the basis of the client's adherence to the federated learning protocol and the quality of data contributed by the client. Capitalizing on the trust mechanism, we model the interactions between the attacker and federated learning server as a security max-min game. The outcome of the game guides the server on the optimal weight assignment strategy over the set of clients' model updates, so as to minimize the effects of the data poisoning on the global model. Simulations conducted on the MNIST and CIFAR-10 datasets suggest that our proposed solution decreases the coordinated attack success rate, as well as the false positive and false negative percentages compared to two baseline solutions. © 2023 IEEE.

关键词： Coordinated Backdoor Attack Federated learning Federated learning Security Game Theory Poisoning Attack

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Research on team Role recognition based on multi-network representation learning

Research on team Role recognition based on multi-network rep...

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2022 international conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Wang, Fa Yi College of Computer Science and Engineeering Shandong University of Science and Technology Huangdao District Qingdao*** China

ISBN: (纸本)9781510663350

Team role research has been a hot research issue in recent years, and a good team background is very important for team role research. A good team is not a simple collection of multiple people but many people with different roles and functions (leadership, group leader, staff), they have close cooperation and strict relationship between superiors and subordinates. Team role research helps us to understand the function and behavior of the members of the team. Compared with the traditional walking method, Mjwalk can walk on multiple independent team networks, showing advantages in multi-network joint learning. The conv_att method proposed in this paper solves the downstream role recognition task and improves the recognition effect. We evaluate the performance of the Mjwalk and conv_att framework with real team data, and improve f1_score and accuracy. © 2023 SPIE.

关键词： Optical engineering

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Modelling of EV penetration for an Indian state using Machine learning Approach 7

Modelling of EV penetration for an Indian state using Machin...

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7th international conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Chhetri, Abhay Saini, Devender Kumar Upes Department of Electrical Engineering Dehradun India Electrical Cluster Upes Dehradun India

ISBN: (纸本)9798350305005

Rising global warming level and its harmful impact on the environment have imposed a shift towards sustainable transportation systems. Electric vehicles (EVs) offer a promising solution to reduce greenhouse gas emissions and dependence on fossil fuels. This paper presents a model for predicting EV penetration in Uttarakhand, India, considering key parameters such as population, grid availability, altitude, and income. The model is developed using a Machine learning algorithm know as Random Forest technique. To ensure realistic predictions, a dataset of 10,000 data points is generated using the probability density function, out of which the generated data is divided into training and testing data set having 70% and 30% ratio. The model is incorporated with random noise for enhanced robustness. The model assigns weights to the parameters, optimizing them to achieve a coefficient of determination close to 1. After training the model with 7,000 data sets and testing it with approximately 3,000 data sets, an R-square value of around 0.91566 is achieved, indicating a high level of accuracy. At last, using the developed model the total amount of energy required each day for charging is estimated helping the Distribution System Operators to calculate the amount of ancillary service required and the modifications need in the existing grid infrastructure. © 2023 IEEE.

关键词： learning algorithms

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City Road Anomaly Alert for Autonomous Vehicles: Pothole Dimension Estimation with YOLOv5 4th

City Road Anomaly Alert for Autonomous Vehicles: Pothole Dim...

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4th international conference on Image Processing and Capsule Networks, ICIPCN 2023

作者： Kulkarni, Varun Vinod Vishal, S. Mohanty, Monish Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819970926

The problem addressed in this chapter is the pervasive issue of road defects, specifically potholes, on Indian roads. These defects pose significant challenges for both human drivers and autonomous driving technology. For human drivers, potholes can lead to vehicle damage and even pose risks to human life due to accidents caused by sudden manoeuvers to avoid these defects. For autonomous driving technology, the detection and accurate classification of such road defects is a critical task. The solution proposed involves a deep learning model that performs three tasks: classification, pothole detection, and area and distance estimation. The model is trained to detect and classify road defects into three categories. A novel aspect of this work is the estimation of the area and distance of the detected potholes. By calculating the size of the bounding box surrounding the pothole that was spotted in the image, the pothole’s area may be determined. The distance of the pothole is estimated from the bottom of the image, assuming a certain scale between the pixels and actual distance. The results obtained surpass those reported in existing literature, indicating the effectiveness of the proposed methodology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects

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Performance Evaluation of Xception Networks and Short-Time Fourier Transform Spectrograms for Motor Imagery Classification 8

Performance Evaluation of Xception Networks and Short-Time F...

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8th IEEE international conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Yilmaz, Cagatay Murat Yilmaz, Bahar Hatipoglu Karadeniz Technical University Department of Software Engineering Trabzon Turkey Karadeniz Technical University Department of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798350315516

This study seeks to explore the success of the Xception convolutional neural network and short-Time Fourier transform in classifying EEG signals with limited training samples using pre-Trained deep transfer learning. So far, most deep learning research has concentrated on one-dimensional time series inputs. Two-dimensional inputs are another excellent way of giving EEG signals to deep learning. This study used a two-dimensional STFT-based method to transform EEG signals into images and Xception for classification. BCI Competition IV dataset 2b, which includes nine participants, was used to evaluate the performance. This study is the first to report the STFT + Xception approach results for classifying motor imagery signals. It achieved good results from a small number of MIEEG data, averaging over 80% for nine subjects with a very low standard deviation of 2.9% between subjects. Applying data augmentation and training from scratch with more data can show more successful results in the future. © 2023 IEEE.

关键词： Convolutional neural networks

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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A Comparative Analysis for Stroke Risk Prediction Using Machine learning Algorithms and Convolutional Neural Network Model 3

A Comparative Analysis for Stroke Risk Prediction Using Mach...

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3rd international conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Ferdous, Most. Jannatul Shahriyar, Rifat Bangladesh University of Business and Technology Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

A critical, sometimes fatal medical disease called a stroke happens when the blood flow to a portion of the brain is broken off. In the case of stroke, urgent treatment is very essential. Nowadays, stroke is the main cause of death and impairment globally, according to WHO. In this situation, it will be very helpful if we predict the probability of stroke earlier depending on some most important features. Many researchers use different machine learning algorithms for prediction but very few researchers use stacking methods and CNN. The main contribution of this paper is to develop a stacking classifier of ensemble methods and the CNN model. In this paper, data-set is collected from Kaggle. Stroke data is imbalanced. Random oversampling is used for balancing data-set. Then most important features are find out using feature selection method, then applying different machine learning algorithms such as Logistic Regression, Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbour's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes, Stacking of six algorithms (Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbor's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes) and CNN. Then comparing the performances for predicting the probability of stroke during both the training and testing periods. Results show that the Stacking of six algorithms gives the highest accuracy, which is 99.89% for testing and 100% for training. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of the Performances of Single-mode, Two-mode, and Three-mode Biometric Security Systems Using Deep Structured learning Technique

A Comparative Study of the Performances of Single-mode, Two-...

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2023 international conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Atanda, Oladayo Gbenga Abiodun, Moses Kazeem Awotunde, Joseph Bamidele Adeniyi, Jide Kehinde Adeniyi, Abidemi Emmanuel Landmark University Department of Computer Science Omu-Aran Nigeria Sdg 11 Sustainable Cities and Community University of Ilorin Department of Computer Science Ilorin Nigeria Precious Cornerstone University Department of Computer Science Ibadan Nigeria

ISBN: (纸本)9798350324785

The automatic identification of subjects based on their physiological and behavioural attributes is referred to as biometric recognition. These attributes are specific to each subject and remain unchanged over the course of an individual's lifetime. The single-mode, two-mode biometric recognition systems still suffer problems of high rate of false positives and false negatives. Hence, in this paper multiple instances of three passive biometric modalities were captured and combined to address the drawbacks in two-mode and single-mode biometric systems using Convolution Neural Network_Genetic Algorithm (CNN_GA) which is a deep structured learning strategy. A database with 1026 training samples and 684 probing samples of face, ear, and iris modalities was used to test the system. The full system's design and implementation were completed on MATLAB R2016a programming platform. The system's performance was evaluated on the basis of sensitivity, specificity, precision, system accuracy, and computation time. The outcome demonstrates that the designed three-mode system outperformed the two-mode and single-mode counterparts at various threshold values of 0.20, 0.35, 0.50, and 0.76 in terms of sensitivity, specificity, precision, and system accuracy. As this study's findings demonstrate, combining data from several sources adopting deep structured learning strategy has been able to improve the performance of the systems in terms of sensitivity, specificity, precision, and system accuracy. © 2023 IEEE.

关键词： Deep learning

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Performance Analysis of Transfer learning Methods for Malaria Disease Identification

Performance Analysis of Transfer Learning Methods for Malari...

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2023 international Research conference on Smart Computing and Systems engineering, SCSE 2023

作者： Chandrasekara, E.S.K. Vidanagamachchi, S.M. University of Ruhuna Department of Physics Matara Sri Lanka University of Ruhuna Department of Computer Science Matara Sri Lanka

ISBN: (纸本)9798350341454

Malaria has become a widespread disease and one of the leading causes of many deaths worldwide. Malaria is a blood disease brought on by Plasmodium parasites, which are transmitted by the bite of a female Anopheles mosquito. To diagnose the condition, medical experts analyse thick and thin blood smears. However, their precision is dependent on the quality of the smear and experience in categorising and counting parasitized and uninfected cells. Such an investigation could be complicated and time-consuming for large-scale diagnosis, resulting in poor quality as well. Deep learning (DL) approaches such as Convolutional Neural Networks (CNN) offer highly scalable and improved performance with end-to-end feature extraction and classification in cutting-edge image analysis-based computer-aided-diagnosis (CAD) procedures. Automated malaria screening employing DL approaches could contribute in the development of an effective diagnostic aid. In this study, we assessed the efficacy of VGG16, EfficientNetB3, InceptionV3, and ResNet50 as feature extractors to categorise parasitized and uninfected cells and aid in enhanced malaria disease screening. Our results showed that optimum accuracy of 0.97 is achieved after 40 epochs. Our study demonstrated the successful application of deep learning techniques, specifically ResNet50 and EfficientNetB3, among the analysed models, for malaria disease screening and detection. © 2023 IEEE.

关键词： Diseases

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