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Proximal Policy Optimization Algorithm for Integrated Energy System Operation with Adaptive learning Rate Decay Strategy 4

Proximal Policy Optimization Algorithm for Integrated Energy...

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4th international conference on smart Grid and Energy Engineering, SGEE 2023

作者： Wang, Jun Wang, Xin Du, Wei Li, Yuan Tang, Chenghong Liu, Gang Nari Group State Grid Electric Power Research Institute Co. Ltd. Nari Technology Co. Ltd. Nanjing China Nanjing Normal University Nari Group State Grid Electric Power Research Institute Co. Ltd. Nanjing China

ISBN: (纸本)9798350359619

With increasing focus on sustainability and efficiency, Integrated Energy Systems (IES) have gained more attention in the provision of electricity and thermal energy. However, the inherent complexity and uncertainty of IES pose challenges to their optimization and management. this paper proposes a Proximal Policy Optimization (PPO) algorithm for the operation of IES, based on an adaptive learning rate decay strategy, aimed at enhancing the operational efficiency and stability of IES. Initially, a Markov decision process is established to simulate the operation of IES. Subsequently, a PPO algorithm with an adaptive learning rate decay strategy is proposed. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional PPO algorithms in reducing overall operational costs and improving the balance of electricity and thermal energy supply and demand. this research provides an effective optimization method for the efficient and sustainable operation of IES. © 2023 IEEE.

关键词： Markov processes

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Breast Cancer Image Classification Obtained through Dynamic thermography using Deep learning 4

Breast Cancer Image Classification Obtained Through Dynamic ...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Timadius, Ethan Daniel Wongso, Reyner Baihaqi, thirza Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

this paper addresses the vital task of diagnosing breast cancer early and accurately. It acknowledges the difficulty in detecting minor variations in breast cancer patterns through visual examination alone, particularly when analyzing gray scale images. To overcome this challenge, our research concentrates on using image processing models, particularly Convolutional Neural Networks (CNNs), and enhances them with a refined approach to classification. the research methodology encompasses rigorous data collection, preprocessing, and strategic feature selection to enhance model robustness. To address challenges related to data intricacies, the study incorporates transfer learning, refining the model's training process for improved accuracy. the paper not only seeks to elevate diagnostic precision and minimize errors but also aims to provide healthcare professionals with efficient tools for breast cancer detection. Building upon prior research, the study underscores the potential of deep learning methodologies in advancing breast cancer diagnosis. Furthermore, the fine-tuning process involves adapting a pre-trained model to the intricacies of breast cancer patterns, optimizing its performance. the ultimate goal is to contribute to the development of reliable diagnostic tools that empower medical professionals in the fight against breast cancer. the paper concludes by summarizing key findings. Additionally, the model achieved an accuracy of 84%, highlighting its effectiveness in breast cancer detection. the paper also emphasizes the broader significance of leveraging deep learning for enhanced breast cancer detection. © 2024 IEEE.

关键词： Breast Cancer Convolutional Neural Network Deep learning Image Classification thermal Image

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EECon 2024 - Proceedings of the 2024 4th international conference on Electrical Engineering

EECon 2024 - Proceedings of the 2024 4th International Confe...

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4th international conference on Electrical Engineering, EECon 2024

ISBN: (纸本)9798331505950

the proceedings contain 26 papers. the topics discussed include: reinforcement learning based solar power nowcasting with real-time cloud path prediction;technical impacts of high penetration of solar photovoltaic systems in low-voltage radial distribution network – case study;online partial discharge detection & localization of a transformer winding using machine learning;a correlation between eccentricity fault and ac winding losses of permanent magnet synchronous motors;improved maximum power point tracking of standalone solar PV system for faster and accurate convergence;economical sizing of an energy storage system for a community microgrid, through the designing of a smart demand response technology;energy management system for solar PV system to reduce peak demand;development of a model to determine optimum unit capacities and number of turbines for a given mini-hydro site and flow regime;and intelligent battery management algorithm for second-life battery systems using voltage based active cell balancing.

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Research and development of two-factor authentication methods using neural networks 4

Research and development of two-factor authentication method...

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4th IEEE international conference on smart Information Systems and Technologies, SIST 2024

作者： Darbek, Temirkhan Mukasheva, Assel Bissembayev, Alibek Gnatyuk, Sergiy Mukammejanova, Dinargul The Department of Information Systems and Cybersecurity Almaty university of power engineering and telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The School of Information Technology and Engineering Kazakh British Technical University Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering at National Aviation University National Aviation University Kyiv Ukraine The Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (纸本)9798350374865

this article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. the article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. the main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of things. the results of the study represent a significant contribution to the field of digital. © 2024 IEEE.

关键词： biometric authentication deep learning digital security identification Neural networks

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End-to-End Network Traffic Examination for Network Intrusion Detection using Feature Embedding learning 4

End-to-End Network Traffic Examination for Network Intrusion...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Arthy, J. Variar, Siddharth D tharakeshwar, N. Ashwatha Narayan, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Ramapuram600089 India

ISBN: (纸本)9798350349900

the rapid evolution of information sharing and portable device technologies has led to a drastic increase in the number of smart devices, which helps in enhancing the level of comfort and intelligence in every household. It also drives the progress and innovation in different fields, including transportation and healthcare industries. Industrial Control Systems help in the development of society as a whole, by controlling and monitoring the flow of communication between the systems in a industrial network. ICSs have also advanced further due to the presence of smart devices. However, this has also led to a alarming increase in network security concerns. Data security is of the highest priority now. Attacks to the networks present in an ICS also lead to very dangerous outcomes, which may sometimes prove to be fatal. Anomaly detection techniques are mainly used in traditional intrusion detection systems. this is done by detecting any abnormal patterns in the network traffic. To develop an effective and flexible IDS, we are going to examine the Deep Neural Network in this paper. the continuous development of network behaviour and rapid increase in attacks call for development of IDS and evaluating many datasets formed over time using static and dynamic methods. this type of research leads to the identification of the most efficient algorithm for identifying any cyber-attacks in the future. the proposed model achieves higher accuracy than the previous machine learning models. © 2024 IEEE.

关键词： Network intrusion

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Hybridization of Self Supervised learning Models for Enhancing Automatic Arabic Speech Recognition 4

Hybridization of Self Supervised Learning Models for Enhanci...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Younis, Hiba Adreese Mohammad, Yusra Faisal University of Mosul College of Computer Science and Mathematics Department of Computer Science Mosul Iraq

ISBN: (纸本)9798350376111

End-to-end systems for automatic recognition of Arabic speech have gained significant importance in the last few years. Arabic is considered as one of the low resource languages that lacks the labeled data required for training large models using supervised learning. To overcome this problem self-supervised learning models appears which can improve the robustness of the system by avoiding several issues with labels resulting from corrupted audio files. the objective of this paper is to assess the effectiveness of various models for Arabic speech recognition and also to compare the base SSL models with hybrid models in terms of word error rate(WER). the methodology includes hybridization of two state of the art models (wav2vec2xlsr, HUBERT) with adapter layer of MMS model. Another hybridization was also implemented by hybridization these SSL models with four layers of transformer encoder. the MMS model was also used for the first time for fine-tuning Arabic language using different hyper parameter values and gave competing results. As optimization algorithm, the SWATS mechanism was being used with these four hybrid models to improve their performance. Results showed that hybrid hubert_transformer model gave the best results on test dataset with 23.83 WER, followed by MMS model, hybrid wave transformer model, hybrid wave_MMS and hybrid hubert_MMS model respectively. © 2024 IEEE.

关键词： Self-supervised learning

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Harnessing Skills for Sustainable development: A Skills Matchmaking System for smart Cities, Green Energy, Blue Economy and Precision Agriculture 4

Harnessing Skills for Sustainable Development: A Skills Matc...

引用

4th international conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Katsis, Marios Papadatos, Panos Rigou, Maria Sirmakessis, Spiros Vossos, Dimitris University of the Peloponnese Department of Electrical and Computer Engineering Patra Greece University of Patras Department of Management Science and Technology Patra Greece

ISBN: (纸本)9798350300918

Current Higher Education Institutions are confronted with the time-pressing challenge to adjust their learning processes and offer skills that expand way beyond the technical knowledge that has been the focus for many decades;they need to grow innovation and entrepreneurial capacity to support value chains and ecosystems across Europe in terms of sustainability. In this work we describe the overall OASIS initiative and focus on one of its core components, the Skills Matchmaking platform setup to offer job orientation or re-orientation on a cross Europe spectrum in sustainability-related domains using OS technologies. the platform allows users to leverage their skills profile to gain insights, explore career possibilities, and make informed decisions regarding their professional journey. the platform not only assists in identifying suitable occupations but also equips users with the necessary information to bridge skill gaps and pursue development opportunities in their chosen sectors. © 2023 IEEE.

关键词： smart city

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A Deep learning for Cardiovascular Diseases Detection on Wearable Devices Data 4

A Deep Learning for Cardiovascular Diseases Detection on Wea...

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4th IEEE international conference on smart Information Systems and Technologies, SIST 2024

作者： Baikuvekov, Meirzhan Tursynova, Azhar Yespayev, Galymzhan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan Department of AI and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan Satbayev University Almaty Kazakhstan

ISBN: (纸本)9798350374865

this research paper explores the potential of deep learning techniques in the early detection of cardiovascular diseases using data collected from wearable devices. With the advent of advanced wearable technology, continuous monitoring of physiological parameters, such as heart rate, electrocardiogram (ECG) signals, and physical activity patterns, has become feasible. Leveraging the power of deep neural networks, we investigate the development of predictive models capable of identifying individuals at risk of cardiovascular diseases. the study employs rigorous data preprocessing techniques, encompassing feature extraction and data segmentation, in conjunction with Machine learning algorithms to enhance model accuracy. Additionally, regularization methods, notably dropout, are harnessed to address challenges posed by diverse datasets. the research envisions the incorporation of data derived from everyday activities to comprehensively assess their impact on heart health, ultimately aiming to proactively alert individuals to deteriorating cardiovascular conditions. the findings presented herein encourage further exploration and innovation in the realm of cardiovascular disease detection, fostering advancements that can enhance public health and well-being. © 2024 IEEE.

关键词： Heart

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Eye Disease Classification System Based on Fundus Image Using Transfer learning Convolutional Neural Network Model ResNet50 4

Eye Disease Classification System Based on Fundus Image Usin...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Massie, Gary Jeremi Kaswar, Andi Baso Lamada, Mustari Andayani, Dyah Darma Adiba, Fhatiah State University of Makassar Department of Computer Engineering Makassar Indonesia

ISBN: (纸本)9798350376111

Various eye diseases are common in Indonesia, including cataracts, glaucoma, and diabetes. In the medical world, the symptoms of eye disease are usually identified by taking pictures of the fundus of the retina of the patient's eye and then analyzing the fundus image manually. With the development of times and increasingly advanced technology, it is possible to manage various existing information, including analyzing eye diseases based on fundus images. Various previous studies have been conducted, there are different weaknesses, such as small dataset sizes, limited variations in methods that can be expanded to strengthen the results and generalization of models that have been done before. therefore, this study proposes an Eye Disease Classification System Based on Fundus Images Using Transfer learning Convolutional Neural Network Model ResNet50. this research consists of the stages of collecting retinal fundus image datasets, preprocessing, building architectural models, conducting various tests on existing architectures, and finally classifying eye diseases based on fundus images using transfer learning convolutional neural network model ResNet50. Based on the test scenarios of several transfer learning artificial neural network models, it was found that the ResNet50 architecture model with a combination of the swish - relu activation function and adam optimizer is the best architecture in terms of classifying eye diseases based on fundus drawings. the model was trained using a total of 4000 images, with each class of 1000 images: normal class, cataract, glaucoma, and diabetes. the training results showed an accuracy of 96.31% and a testing accuracy of 90.13% with computation times of 1 hour, 3 minutes, and 24 seconds for 10 epoch. With this research, it is hoped that the creation of technology can help the medical field detect eye diseases early by using an artificial intelligence system combined with digital image processing, so that treatment can be performed e

关键词： Convolutional neural networks

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development and Evaluation of Pre-trained Deep learning Models for Efficient Arabic Sign Language Recognition 4

Development and Evaluation of Pre-trained Deep Learning Mode...

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4th international conference on Emerging smart Technologies and Applications (esmartA)

作者： Mohammed, Ahmed A. A. Esmail, Ezzaldeen Mosleh, Mogeeb A. A. Mohammed, Rehab A. A. Almuhaya, Basheer Taiz Univ Software Engn Dept Taizi Yemen Lovely Profess Univ Sch CSE Phagwara 144411 Punjab India

ISBN: (纸本)9798350354140;9798350354133

Yemeni Sign Language (YSL) is a vital communication means for the deaf- muted Yemen community. the deaf-mute community in Yemen faces communication obstacles due to limited access to several environments such as education, work, and social activities. In addition, there are no datasets available for Yemeni sign language. this study is designed to overcome some of the current limitations of Yemeni Sign Language by extending a new dataset for the Yemeni Sign Language dataset and investigating the efficiency of recent transfer learning approaches for YSL recognition. this study compared the effectiveness of transfer learning techniques in the context of YSL recognition. Deep learning models adopted are AlexNet, ResNet152V2, Swin Transformer, InceptionV3, and Xception. these models were selected due to their performance reported by previous research within a diverse computer vision task. the new dataset was constructed with 24,245 sign images collected manually with specific criteria to obtain 32 different sign labels. A preprocessing for sign images was applied to improve the recognition process of the YSL sign. CNN models are customized using transfer learning and fine-tuning strategies for experimental purposes. Experiment results showed a remarkable accuracy rate for Swin Transformer, AlexNet, InceptionV3, ResNet152V2, and Xception models as 99.0%, 99.0%, 98,0%, 98.0%, and 99.0%, respectively. the study result showed that the Xception model obtained the highest training accuracy rate, and the AlexNet and Swin transformer models obtained the highest testing accuracy rate. In conclusion, the outcomes contribute to developing a robust and accessible YSL recognition system to prompt effective communication and inclusivity for the deaf-muted community in Yemen.

关键词： Yemini Sign Language hand gesture Transfer learning Xception Swin Transformer AlexNet Inceptionv3 Resnet152v2

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