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A Novel Framework for Multiagent Knowledge-Based Federated learning Systems 21st

A Novel Framework for Multiagent Knowledge-Based Federated L...

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21st international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2023

作者： Ribeiro, Bruno Gomes, Luis Barbarroxa, Rafael Vale, Zita GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI—Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4200-072 Portugal

ISBN: (纸本)9783031376153

Multiagent systems promote a decentralized and distributed approach that enable the division of complex problems into smaller parts. The use of multiagent systems also enables the representation of physical entities, such as persons, pursuing their own goals in an active and proactive society. Currently developments are promoting the idea of having machine learning models in agents to enable intelligent decisions in agents-side. However, machine learning, required assess to large datasets that cannot be available locally to individual agents, demanding the sharing of data or the use of public available datasets to training models for a given agent. To address this issue, this paper proposes the use of federated learning to enable the existence of a collaborative learning model that respects the data privacy, security, and ownership and can be in compliance with the European General data Protection Regulation (EU GDPR). This paper proposes a novel framework called Python-based framework for agent-based communities powered by federated learning (PEAK FL) that will provide all the necessary tools to build powerful federated learning solutions based on agent communities. This framework provides the users the ability to implement and test hybrid solutions (multiagent-based federated learning systems) in a simple-to-use way, removing the unnecessary boilerplates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 international conference on Advancement in Renewable Energy and intelligent Systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization of Ball Valve Housing Based on Xgboost Regression Model 21

Optimization of Ball Valve Housing Based on Xgboost Regressi...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wenyi, Xu Jiawei, Dai Shun, Gao Xiaoqin, Liang Shuwen, Wang School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

Ball valves play a pivotal role in industrial piping systems, with their design directly influencing system safety, stability, and fluid control efficiency. The thickness of the shell is a crucial parameter for ensuring structural strength and safety. Traditionally, this parameter has been determined using empirical formulas or standard specifications based on nominal pressure, diameter, and material properties. However, these methods are based on idealized assumptions that do not account for complex working conditions such as high pressure and temperature. This can result in reduced accuracy and potential safety risks. To address this issue, this study employs the Extreme Gradient Boosting (XGBoost) regression algorithm, utilizing historical data to train a machine learning model for precise ball valve design predictions. © 2024 IEEE.

关键词： Safety valves

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A Novel Feature Extraction Technique for ECG Arrhythmia Classification Using ML

A Novel Feature Extraction Technique for ECG Arrhythmia Clas...

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2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big data Computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Rahman, Mohammad Mominur Islam, Ashhadul Charni, Skander Bensmail, Halima Hilbel, Thomas Belhaouari, Samir Brahim College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar College of Engineering Qatar University Doha Qatar Doha Qatar Westphalian University of Applied Sciences Department of Electrical Engineering and Applied Sciences Gelsenkirchen Germany

ISBN: (纸本)9798350304602

Feature extraction is the process of transforming raw data into features that are more relevant for machine learning algorithms. The goal of feature extraction is to find a set of features that can be used to accurately predict the target variable. The specific features that are extracted will depend on the specific application. For example, features that are extracted for the purpose of diagnosing arrhythmias will be different from the features that are extracted for the purpose of assessing myocardial infarction. A generalized new algorithm for feature extraction could be helpful for all complex feature extraction data sets. In this paper, we propose a random selection process to generate the required number of new features with the help of existing specific features of the electrocardiogram (ECG) signal. We have named this novel feature extraction method the Random Feature Explorer (RFE). The proposed method was tested and evaluated using Physio Net's MIT-BIH datasets. The results indicate that the suggested method achieved an accuracy of 99.79% in arrhythmia classification. We have made the source code for our proposed method available on GitHub for open access and reproducibility. The code can be accessed at https://***/3NnrH4A © 2023 IEEE.

关键词： Electrocardiograms

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Cattle Facial Recognition by Vision Transformer with Bi-Level Routing Attention Mechanism 21

Cattle Facial Recognition by Vision Transformer with Bi-Leve...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yohannes, Gobena Mulugeta Sahlu, Ameneshewa Abush Ashenafi, Asfaw Dibora Ming, Sun School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Cattle face recognition is an emerging application of deep learning to improve animal management in agriculture. This technology enhances breeding, health monitoring, and livestock management by accurately identifying individual cattle. Recent advancements in artificial intelligence have enabled the development of systems for detecting cattle facial images. However, focusing on the most informative image parts remains challenging, impacting recognition accuracy. This paper introduces a Vision Transformer (ViT) with transfer learning and a Bi-former for enhanced cattle face recognition. We analyze CNN algorithms, particularly EfficientNetB0 with transfer learning, and compare them with the proposed model. The Bi-Level Routing Attention mechanism allows the model to focus on various feature levels, improving differentiation among cattle attributes. Trained on a dataset of 200 cattle faces, the model was tested on a validation set to evaluate accuracy. data augmentation addressed dataset imbalance, enhancing image variability. Our model outperformed CNNs, achieving state-of-the-art results with 98.36% accuracy and a 98.90 F1 score, showcasing its effectiveness in cattle face recognition. © 2024 IEEE.

关键词： Contrastive learning

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PolyMerge: A Novel Technique aimed at Dynamic HD Map Updates Leveraging Polylines 21

PolyMerge: A Novel Technique aimed at Dynamic HD Map Updates...

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21st international conference on Advanced Robotics, ICAR 2023

作者： Sayed, Mohamed Perminov, stepan Tsetserukou, Dzmitry Center for Digital Engineering Skolkovo Institute of Science and Technology Intelligent Space Robotics Laboratory Moscow Russia Llc IntegraNT Moscow Russia

ISBN: (纸本)9798350342291

Currently, High-Definition (HD) maps are a pre- requisite for the stable operation of autonomous vehicles. Such maps contain information about all static road objects for the vehicle to consider during navigation, such as road edges, road lanes, crosswalks, and etc. To generate such an HD map, current approaches need to process pre-recorded environment data obtained from onboard sensors. However, recording such a dataset often requires a lot of time and effort. In addition, every time actual road environments are changed, a new dataset should be recorded to generate a relevant HD map. This paper addresses a novel approach that allows to continuously generate or update the HD map using onboard sensor data. When there is no need to pre-record the dataset, updating the HD map can be run in parallel with the main autonomous vehicle navigation pipeline. The proposed approach utilizes the VectorMapNet framework to generate vector road object instances from a sensor data scan. The PolyMerge technique is aimed to merge new instances into previous ones, mitigating detection errors and, therefore, generating or updating the HD map. The performance of the algorithm was confirmed by comparison with ground truth on the NuScenes dataset. Experimental results showed that the mean error for different levels of environment complexity was comparable to the VectorMapNet single instance error. © 2023 IEEE.

关键词： Errors

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stock market prediction under a deep learning approach using Variational Autoencoder, and kernel extreme learning machine 21

Stock market prediction under a deep learning approach using...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Hemant Parida, Ajaya Kumar Kumari, Rina Singh, Aru Ranjan Bandyopadhyay, Anjan Swain, Sujata Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India University of Warwick Warwick Manufacturing Group Coventry United Kingdom

ISBN: (纸本)9798350358230

Forecasting stock market trends is challenging due to its complex and dynamic nature. stock market prediction is important because it helps investors make informed decisions and allows businesses to plan their financial strategies. Prior works introduce several Artificial Intelligence (AI) based works, however, they are not robust in semantical pattern identification from the dataset. In light of the above limitation of existing works, this paper introduces the VAE-KELM-based mechanism. In this work, the convolutional neural network (CNN) technique was implemented to extract features and a variational autoencoder (VAE) for predictions. The goal is to predict the stock price with high accuracy. The technical indicators and historical data were considered input, with the sub-maximum layer being substituted by the kernel-based Extreme learning Machine (KELM). In addition, the application of Autoencoder (AE), Variational Auto Encoder (VAE), Extreme learning Machines (ELM), and full writing from Radial Basis Functions (RBF) is necessary for demonstrating the multiple stock market sources. VAE has been used for future extraction over the extracted feature. ELM mechanism is used for prediction and also this research used historical data and technical indicators to predict stock market trends. Various performance metrics were used to evaluate the proposed model's accuracy, and the results were compared with benchmark methods. The study's results demonstrated the proposed algorithm's effectiveness and superiority of the proposed model compared to other methods. © 2023 IEEE.

关键词： Forecasting

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Organising Personalised learning in the English Language Classroom: Computer engineering and Information Technology Undergraduates Perspectives 15

Organising Personalised Learning in the English Language Cla...

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15th international Scientific and Practical conference on Environment. Technology. Resources, ETR 2024

作者： Malykhin, Oleksandr Aristova, Nataliia Dybkova, Liudmyla Department of Didactics Institute of Pedagogy of NAES of Ukraine Kyiv Ukraine Department of International Relations and Scientific Cooperation Institute of Pedagogy of NAES of Ukraine Kyiv Ukraine Department of Informatics and Systemology Kyiv National Economic University Named after Vadym Hetman Kyiv Ukraine

ISBN: (纸本)9781713899570

The more rapidly the area of information technologies develops, the higher are the requirements for training Computer engineering and Information Technology students. Computer engineering and Information Technology students training should be aimed at equipping them with subject-specific knowledge, on the one hand, and skills of the 21st century including strong communication skills in English, on the other hand. As practice shows good communication skills in English may lead to better job opportunities, more stable and prosperous future. The desire to succeed in mastering communication skills in English is directly related to Computer engineering and Information Technology students’ attitudes towards both English as a discipline and activities used in the English language classroom. Computer engineering and Information Technology students show interest and enjoy learning only when it becomes personally meaningful to them. The research aim is to find out Computer engineering and Information Technology undergraduates’ opinions on organising personalised learning in the English language classroom aimed at developing their communication skills. The researchers used a convenience sampling technique to select the research population. The research population comprised 273 Computer engineering and Information Technology first and second year students from Kyiv National Economic University named after Vadym Hetman and Kyiv National University of Technologies and Design. To collect statistical data on Computer engineering and Information Technology undergraduates’ opinions on organising personalised learning in the English language classroom the team of researchers developed a questionnaire and data collection took place in September-December 2023. © 2024 Oleksandr Malykhin, Nataliia Aristova, Liudmyla Dybkova.

关键词： students

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Disentangling Client Contributions: Improving Federated learning Accuracy in the Presence of Heterogeneous data 21

Disentangling Client Contributions: Improving Federated Lear...

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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： Liu, Chunming Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Zeng, Cheng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

Federated learning (FL) is a promising paradigm that leverages distributed data sources to train machine learning models, thereby offering significant privacy advantages. However, the inherent statistical heterogeneity among clients, characterized by distinct data distributions and varying model performance, poses a substantial challenge. Such heterogeneity adversely affects both the convergence and overall performance of the global model within the FL framework. To address this issue, we introduce a novel FL algorithm, FedVa, which considers both the local data volume and model accuracy to determine client contributions, assigning respective weights. This consideration facilitates greater engagement from key clients during the aggregation process, thereby enhancing their influence on the global model refinement. Experimental results indicate that FedVa surpasses prevailing methods in managing statistical heterogeneity and enhancing global model accuracy, without incurring additional communication costs. Specifically, FedVa achieves a convergence speed 3.9 times faster than FedAvg on the MNIst dataset, and on the CIFAR-10 dataset, it results in a 2.3% increase in final model accuracy compared to FedAvg. Our code is publicly available at https://***/ChunmingLiu23/FedVa. © 2023 IEEE.

关键词： learning systems

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Comparative Analysis of Deep learning Models for Telugu News Text Classification 21

Comparative Analysis of Deep Learning Models for Telugu News...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Settineni, Pooja Veerendra, B. Reddy, Mure Sai Jaideep Samuel, Prithi Sumathi, D. VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India SRM Institute of Science Kattankulanthur Campus Department of Computational Intelligence India

ISBN: (纸本)9798350358230

Text classification is the process of labeling or classifying the text to certain categories based on the content into various text classes. There are many use cases like Natural language processing, sentimental analysis, topic detection, Spam filtering news categorization have been solved using this mechanism. We documented a comparative study of four Deep learning (DL) algorithms to classify and categories the text for two different data sets. These datasets contain the content of Telugu news articles. The four DL models that are used in this works are Long Short-Term Memory (LstM), Gated Recurrent Unit (GRU), bidirectional Gated Recurrent Unit (Bi-GRU) and bidirectional Long Short-Term Memory (Bi-LstM) which are the variants of the recurrent neural networks (RNN) which can process sequential data such as text. The model performance will be compared using the accuracy of it as a metrics. We also have documented pros and cons of each model using the results that they admit. This paper analyzes the results and discuss the advantages and disadvantages of each model. In this paper we first start with the preprocessing techniques used for process the data and further we move to the architecture's explanation for each model. The experimental results are presented using graphs and pie charts. The findings and implications of the study are discussed. Some limitations and future directions of the work are mentioned. © 2023 IEEE.

关键词： Long short-term memory

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